diff --git a/README copy.md b/README copy.md
deleted file mode 100644
index abbf716..0000000
--- a/README copy.md	
+++ /dev/null
@@ -1,227 +0,0 @@
-# FaceAPI
-
-## 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+**.  
-
-Forked from **face-api.js** version **0.22.2** released on March 22nd, 2020  
-
-- <https://github.com/justadudewhohacks/face-api.js>  
-- <https://www.npmjs.com/package/face-api.js>  
-
-Currently based on **`TensorFlow/JS` 2.7.0**  
-If you want to access `TFJS` classes and methods directly, they are exported as `faceapi.tf`
-
-### Why?
-
-Because I needed FaceAPI that does not cause version conflict with newer TFJS 2.0 that I use accross my projects  
-And since original FaceAPI was open-source, I've released this version as well  
-Unfortunately, changes ended up being too large for a simple pull request on original FaceaPI and it ended up being a full-fledged version on its own  
-
-### Differences
-
-- Compatible with `TensorFlow/JS 2.0+`  
-- Compatible with `WebGL`, `CPU` and `WASM` TFJS backends
-- Updated all type castings for TypeScript type checking to `TypeScript 4.1`  
-- Switched bundling from `UMD` to `ESM` + `CommonJS`  
-  This does require separate process for usage in NodeJS vs Browser, but resulting code is much lighter  
-  Fully tree shakable when imported as an `ESM` module  
-  Browser bundle process uses `ESBuild` instead of `Rollup`  
-- Typescript build process now targets `ES2018` and instead of dual ES5/ES6  
-  Resulting code is clean ES2018 JavaScript without polyfills  
-- Removed old tests, docs, examples  
-- Removed old package dependencies (`karma`, `jasmine`, `babel`, etc.)  
-- Updated all package dependencies  
-- Updated TensorFlow/JS dependencies since backends were removed from `@tensorflow/tfjs-core`  
-- Updated mobileNetv1 model due to `batchNorm()` dependency  
-- Added `version` class that returns JSON object with version of FaceAPI as well as linked TFJS  
-- Removed `mtcnn` and `tinyYolov2` models as they were non-functional in latest public version of `Face-API`  
-  *If there is a demand, I can re-implement them back.*  
-
-Which means valid models are **tinyFaceDetector** and **mobileNetv1**  
-
-## Installation
-
-Face-API ships with several pre-build versions of the library:
-- `dist/face-api.js`: IIFE format for client-side Browser execution *with* TFJS pre-bundled
-- `dist/face-api.esm.js`: ESM format for client-side Browser execution *with* TFJS pre-bundled
-- `dist/face-api.esm.nobundle.js`: ESM format for client-side Browser execution *without* TFJS pre-bundled
-- `dist/face-api.node.js`: CommonJS format for server-side NodeJS execution *without* TFJS pre-bundled
-
-Defaults are:
-```json
-{
-  "main": "dist/face-api.cjs",
-  "module": "dist/face-api.esm.js",
-  "browser": "dist/face-api.esm.js",
-}
-```
-
-Reason for additional `nobundle` version is if you want to include a specific version of TFJS and not rely on pre-packaged one  
-`FaceAPI` is compatible with TFJS 2.0+  
-
-Bundled versions are ~1.1MB minified and non-bundled versions are ~169KB non-minified  
-All versions include `sourcemap`
-
-There are several ways to use Face-API: 
-
-### 1. IIFE script
-
-*Recommened for quick tests and backward compatibility with older Browsers that do not support ESM such as IE*
-
-This is simplest way for usage within Browser  
-Simply download `dist/face-api.js`, include it in your `HTML` file & it's ready to use
-
-```html
-<script src="dist/face-api.js"><script>
-``` 
-
-IIFE script bundles TFJS and auto-registers global namespace `faceapi` within Window object which can be accessed directly from a `<script>` tag or from your JS file.  
-
-### 2. ESM module
-
-*Recommended for usage within Browser*
-
-#### 2.1. Direct Import
-
-To use ESM import directly in a Browser, you must import your script (e.g. `index.js`) with a `type="module"`  
-
-```html
-  <script src="./index.js" type="module">
-```
-and then in your `index.js`
-
-```js
-  import * as faceapi from 'dist/face-api.esm.js';
-```
-or to use non-bundled version:
-```js
-  import * as tf from `https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.7.0/tf.es2017.min.js`; // load tfjs directly from CDN link
-  import * as faceapi from 'dist/face-api.nobundle.js';
-```
-
-#### 2.2. With Bundler
-
-Same as above, but expectation is that you've installed `@vladmandic/faceapi` package  
-and that you'll package your script using a bundler such as `webpack`, `rollup` or `esbuild`  
-in which case, you do not need to import a script as module - that depends on your bundler configuration  
-
-```js
-  import * as faceapi from '@vladmandic/face-api';
-```
-or if your bundler doesn't recognize `recommended` type, force usage with:
-```js
-  import * as faceapi from '@vladmandic/face-api/dist/face-api.esm.js';
-```
-or to use non-bundled version
-```js
-  import * as tf from `@tensorflow/tfjs`;
-  import * as faceapi from '@vladmandic/face-api/dist/face-api.nobundle.js';
-```
-
-### 3. NPM module
-
-#### 3.1. Import CommonJS
-
-*Recommended for NodeJS projects*
-
-*Node: Face-API for NodeJS does not bundle TFJS due to binary dependencies that are installed during TFJS installation*
-
-Install with:
-```shell
-  npm install @tensorflow/tfjs-node
-  npm install @vladmandic/face-api 
-```
-And then use with:
-```js
-  const tf = require('@tensorflow/tfjs-node')
-  const faceapi = require('@vladmandic/face-api');
-```
-- if you want to CUDA GPU Accelerated NodeJS, import `tfjs-node-gpu` instead of `tfjs-node`  
-  This requires that system is CUDA enabled and has CUDA libraries already installed.
-- If you want to force CommonJS module instead of relying on `recommended` field:
-```js
-  const faceapi = require('@vladmandic/face-api/dist/face-api.node.js');
-```
-
-### 4. Import Sources
-
-*Recommended for complex NodeJS projects that use TFJS for other purposes and not just FaceaPI*
-
-This way you're importing FaceAPI sources directly and not a bundle, so you have to import `@tensorflow/tfjs` explicitly  
-
-#### 4.1. For Browser with Bundler
-
-##### 4.1.1. For JavaScript projects
-```js
-  import * as tf from '@tensorflow/tfjs';
-  import * as faceapi from '@vladmandic/face-api/build/index.js';
-```
-
-##### 4.1.2. For TypeScript projects
-```js
-  import * as tf from '@tensorflow/tfjs';
-  import * as faceapi from '@vladmandic/face-api/src/index.ts';
-```
-
-#### 4.2. For NodeJS
-
-##### 4.2.1. For JavaScript projects
-```js
-  const tf = require('@tensorflow/tfjs');
-  const faceapi = require('@vladmandic/face-api/build/index.js');
-```
-
-##### 4.1.2. For TypeScript projects
-```js
-  const tf = require('@tensorflow/tfjs');
-  const faceapi = require('@vladmandic/face-api/src/index.ts');
-```
-
-## Weights
-
-Pretrained models and their weights are includes in `./model`.
-
-## Build
-
-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 ESM (standard) and IIFE (minified) bundles as well as sourcemaps in `./dist`
-
-## Credits & Documentation
-
-- Original documentation: [Face-API](https://github.com/justadudewhohacks/face-api.js)  
-- Original model weighs: [Face-API](https://github.com/justadudewhohacks/face-api.js-models)
-- ML API Documentation: [Tensorflow/JS](https://js.tensorflow.org/api/latest/)
-
-## Example
-
-### Browser
-
-Example that uses both models as well as all of the extensions is included in `/example/index.html`  
-Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/>  
-
-### NodeJS
-
-Example is included in `/example/node.js`  
-Note that it does not require any other other 3rd party libraries  
-
-*Note: Photos shown below are taken by me*
-
-![alt text](example/screenshot.png)
diff --git a/README.md b/README.md
index ff982fe..8064cda 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,6 @@ Forked from **face-api.js** version **0.22.2** released on March 22nd, 2020
 - <https://www.npmjs.com/package/face-api.js>  
 
 Currently based on **`TensorFlow/JS` 2.7.0**  
-If you want to access `TFJS` classes and methods directly, they are exported as `faceapi.tf`
 
 ### Why?
 
@@ -21,9 +20,10 @@ Unfortunately, changes ended up being too large for a simple pull request on ori
 ### Differences
 
 - Compatible with `TensorFlow/JS 2.0+`  
-- Compatible with `WebGL`, `CPU` and `WASM` TFJS backends
+- Compatible with `WebGL`, `CPU` and `WASM` TFJS Browser backends
+- Compatible with both `tfjs-node` and `tfjs-node-gpu` TFJS NodeJS backends
 - Updated all type castings for TypeScript type checking to `TypeScript 4.1`  
-- Switched bundling from `UMD` to `ESM` + `CommonJS`  
+- Switched bundling from `UMD` to `ESM` + `CommonJS` with fallback to `IIFE`  
   This does require separate process for usage in NodeJS vs Browser, but resulting code is much lighter  
   Fully tree shakable when imported as an `ESM` module  
   Browser bundle process uses `ESBuild` instead of `Rollup`  
@@ -43,11 +43,11 @@ Which means valid models are **tinyFaceDetector** and **mobileNetv1**
 ## Installation
 
 Face-API ships with several pre-build versions of the library:
-- `dist/face-api.js`: IIFE format for client-side Browser execution
-- `dist/face-api.esm.js`: ESM format for client-side Browser execution with TFJS pre-bundled
-- `dist/face-api.esm.nobundle.js`: ESM format for client-side Browser execution without TFJS and not minified
-- `dist/face-api.node.js`: CommonJS format for server-side NodeJS execution with TFJS pre-bundled
-- `dist/face-api.node.nobundle.js`: CommonJS format for server-side NodeJS execution without TFJS and not minified
+- `dist/face-api.js`: IIFE format for client-side Browser execution *with* TFJS pre-bundled
+- `dist/face-api.esm.js`: ESM format for client-side Browser execution *with* TFJS pre-bundled
+- `dist/face-api.esm-nobundle.js`: ESM format for client-side Browser execution *without* TFJS pre-bundled
+- `dist/face-api.node.js`: CommonJS format for server-side NodeJS execution *without* TFJS pre-bundled
+- `dist/face-api.node-gpu.js`: CommonJS format for server-side NodeJS execution *without* TFJS pre-bundled and optimized for CUDA GPU acceleration
 
 Defaults are:
 ```json
@@ -58,11 +58,15 @@ Defaults are:
 }
 ```
 
-Reason for additional `nobundle` version is if you want to include a specific version of TFJS and not rely on pre-packaged one  
+Bundled `TFJS` can be used directly via export: `faceapi.tf`
+
+Reason for additional `nobundle` version is if you want to include a specific version of TFJS and not rely on  pre-packaged one  
+
 `FaceAPI` is compatible with TFJS 2.0+  
 
-Bundled versions are ~1.1MB minified and non-bundled versions are ~169KB non-minified  
-All versions include `sourcemap`
+All versions include `sourcemap` and `asset manifest`
+
+<br>
 
 There are several ways to use Face-API: 
 
@@ -79,6 +83,8 @@ Simply download `dist/face-api.js`, include it in your `HTML` file & it's ready
 
 IIFE script bundles TFJS and auto-registers global namespace `faceapi` within Window object which can be accessed directly from a `<script>` tag or from your JS file.  
 
+<br>
+
 ### 2. ESM module
 
 *Recommended for usage within Browser*
@@ -97,14 +103,18 @@ and then in your `index.js`
 ```
 or to use non-bundled version:
 ```js
-  import * as tf from `https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.7.0/tf.es2017.min.js`; // load tfjs directly from CDN link
-  import * as faceapi from 'dist/face-api.nobundle.js';
+  import * as tf from `https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.7.0/tf.es2017.min.js`; // load tfjs directly from CDN link or your local system
+  import * as faceapi from 'dist/face-api.esm-nobundle.js';
 ```
 
 #### 2.2. With Bundler
 
-Same as above, but expectation is that you've installed `@vladmandic/faceapi` package  
-and that you'll package your script using a bundler such as `webpack`, `rollup` or `esbuild`  
+Same as above, but expectation is that you've installed `@vladmandic/faceapi` package:
+```shell
+  npm install @vladmandic/face-api 
+```
+
+and that you'll package your application using a bundler such as `webpack`, `rollup` or `esbuild`  
 in which case, you do not need to import a script as module - that depends on your bundler configuration  
 
 ```js
@@ -117,71 +127,46 @@ or if your bundler doesn't recognize `recommended` type, force usage with:
 or to use non-bundled version
 ```js
   import * as tf from `@tensorflow/tfjs`;
-  import * as faceapi from '@vladmandic/face-api/dist/face-api.nobundle.js';
+  import * as faceapi from '@vladmandic/face-api/dist/face-api.esm-nobundle.js';
 ```
 
+<br>
+
 ### 3. NPM module
 
 #### 3.1. Import CommonJS
 
 *Recommended for NodeJS projects*
 
+*Node: Face-API for NodeJS does not bundle TFJS due to binary dependencies that are installed during TFJS installation*
+
 Install with:
 ```shell
+  npm install @tensorflow/tfjs-node
   npm install @vladmandic/face-api 
 ```
 And then use with:
 ```js
+  const tf = require('@tensorflow/tfjs-node')
   const faceapi = require('@vladmandic/face-api');
 ```
-or if you want to force CommonJS module instead of relying on `recommended` field:
+
+If you want to force CommonJS module instead of relying on `recommended` field:
 ```js
   const faceapi = require('@vladmandic/face-api/dist/face-api.node.js');
 ```
-or if you want to use a non-bundled version:
-Install with:
+
+If you want to GPU Accelerated execution in NodeJS, you must have CUDA libraries already installed and working  
+Then install appropriate version of `Face-API`:
+
 ```shell
-  npm install @tensorflow/tfjs
+  npm install @tensorflow/tfjs-node
   npm install @vladmandic/face-api 
 ```
 And then use with:
 ```js
-  const tf = require('@tensorflow/tfjs');
-  const faceapi = require('@vladmandic/face-api/dist/face-api.node.nobundle.js');
-```
-
-### 4. Import Sources
-
-*Recommended for complex NodeJS projects that use TFJS for other purposes and not just FaceaPI*
-
-This way you're importing FaceAPI sources directly and not a bundle, so you have to import `@tensorflow/tfjs` explicitly  
-
-#### 4.1. For Browser with Bundler
-
-##### 4.1.1. For JavaScript projects
-```js
-  import * as tf from '@tensorflow/tfjs';
-  import * as faceapi from '@vladmandic/face-api/build/index.js';
-```
-
-##### 4.1.2. For TypeScript projects
-```js
-  import * as tf from '@tensorflow/tfjs';
-  import * as faceapi from '@vladmandic/face-api/src/index.ts';
-```
-
-#### 4.2. For NodeJS
-
-##### 4.2.1. For JavaScript projects
-```js
-  const tf = require('@tensorflow/tfjs');
-  const faceapi = require('@vladmandic/face-api/build/index.js');
-```
-
-##### 4.1.2. For TypeScript projects
-```js
-  const tf = require('@tensorflow/tfjs');
-  const faceapi = require('@vladmandic/face-api/src/index.ts');
+  const tf = require('@tensorflow/tfjs-node-gpu')
+  const faceapi = require('@vladmandic/face-api/dist/face-api.node-gpu.js'); // this loads face-api version with correct bindings for tfjs-node-gpu
 ```
 
 ## Weights
@@ -208,21 +193,50 @@ npm install
 npm run build
 ```
 
-Which will compile everything in `./src` into `./build` and create both ESM (standard) and IIFE (minified) bundles as well as sourcemaps in `./dist`
+Build process uses script `build.js` that creates optimized build for each target:
 
-## Documentation
+```text
+npm run build
 
-For documentation refer to original project at <https://github.com/justadudewhohacks/face-api.js>  
-For original weighs refer to <https://github.com/justadudewhohacks/face-api.js-models>
-For understanding of ts api code, in js examples below refer to <https://js.tensorflow.org/api/2.7.0/>
+> @vladmandic/face-api@0.8.9 build /home/vlado/dev/face-api
+> rimraf dist/* && node ./build.js
+```
+```json
+2020-12-02 16:31:23 INFO:  @vladmandic/face-api version 0.8.9
+2020-12-02 16:31:23 INFO:  User: vlado Platform: linux Arch: x64 Node: v15.0.1
+2020-12-02 16:31:23 INFO:  Build: file startup all target: es2018
+2020-12-02 16:31:23 STATE:  Build for: node type: tfjs: { imports: 1, importBytes: 39, outputBytes: 1042, outputFiles: 'dist/tfjs.esm.js' }
+2020-12-02 16:31:23 STATE:  Build for: node type: node: { imports: 160, importBytes: 228038, outputBytes: 134190, outputFiles: 'dist/face-api.node.js' }
+2020-12-02 16:31:23 STATE:  Build for: nodeGPU type: tfjs: { imports: 1, importBytes: 43, outputBytes: 1046, outputFiles: 'dist/tfjs.esm.js' }
+2020-12-02 16:31:23 STATE:  Build for: nodeGPU type: node: { imports: 160, importBytes: 228042, outputBytes: 134198, outputFiles: 'dist/face-api.node-gpu.js' }
+2020-12-02 16:31:23 STATE:  Build for: browserNoBundle type: tfjs: { imports: 1, importBytes: 1784, outputBytes: 244, outputFiles: 'dist/tfjs.esm.js' }
+2020-12-02 16:31:23 STATE:  Build for: browserNoBundle type: esm: { imports: 160, importBytes: 227240, outputBytes: 131024, outputFiles: 'dist/face-api.esm-nobundle.js' }
+2020-12-02 16:31:24 STATE:  Build for: browserBundle type: tfjs: { modules: 1045, moduleBytes: 3718721, imports: 7, importBytes: 1784, outputBytes: 1501677, outputFiles: 'dist/tfjs.esm.js' }
+2020-12-02 16:31:24 STATE:  Build for: browserBundle type: iife: { imports: 162, importBytes: 1728673, modules: 576, moduleBytes: 1359851, outputBytes: 1903311, outputFiles: 'dist/face-api.js' }
+2020-12-02 16:31:25 STATE:  Build for: browserBundle type: esm: { imports: 162, importBytes: 1728673, modules: 576, moduleBytes: 1359851, outputBytes: 1900836, outputFiles: 'dist/face-api.esm.js' }
+```
+
+<br>
+
+## Credits & Documentation
+
+- Original project and usage documentation: [Face-API](https://github.com/justadudewhohacks/face-api.js)  
+- Original model weighs: [Face-API](https://github.com/justadudewhohacks/face-api.js-models)
+- ML API Documentation: [Tensorflow/JS](https://js.tensorflow.org/api/latest/)
+
+<br>
 
 ## Example
 
+<br>
+
 ### Browser
 
 Example that uses both models as well as all of the extensions is included in `/example/index.html`  
 Example can be accessed directly using Git pages using URL: <https://vladmandic.github.io/face-api/example/>  
 
+<br>
+
 ### NodeJS
 
 Example is included in `/example/node.js`  
diff --git a/build.js b/build.js
index 55e0722..4ba29e4 100644
--- a/build.js
+++ b/build.js
@@ -39,9 +39,9 @@ const targets = {
     node: {
       platform: 'node',
       format: 'cjs',
-      metafile: 'dist/human.node.json',
-      entryPoints: ['src/human.js'],
-      outfile: 'dist/human.node.js',
+      metafile: 'dist/face-api.node.json',
+      entryPoints: ['src/index.ts'],
+      outfile: 'dist/face-api.node.js',
       external: ['@tensorflow'],
     },
   },
@@ -49,17 +49,17 @@ const targets = {
     tfjs: {
       platform: 'node',
       format: 'cjs',
-      metafile: 'dist/tfjs.esm.json',
       entryPoints: ['src/tfjs/tf-node-gpu.js'],
       outfile: 'dist/tfjs.esm.js',
+      metafile: 'dist/tfjs.esm.json',
       external: ['@tensorflow'],
     },
     node: {
       platform: 'node',
       format: 'cjs',
-      metafile: 'dist/human.node.json',
-      entryPoints: ['src/human.js'],
-      outfile: 'dist/human.node-gpu.js',
+      entryPoints: ['src/index.ts'],
+      outfile: 'dist/face-api.node-gpu.js',
+      metafile: 'dist/face-api.node-gpu.json',
       external: ['@tensorflow'],
     },
   },
@@ -67,17 +67,17 @@ const targets = {
     tfjs: {
       platform: 'browser',
       format: 'esm',
-      metafile: 'dist/tfjs.esm.json',
       entryPoints: ['src/tfjs/tf-browser.js'],
       outfile: 'dist/tfjs.esm.js',
+      metafile: 'dist/tfjs.esm.json',
       external: ['fs', 'buffer', 'util', '@tensorflow'],
     },
     esm: {
       platform: 'browser',
       format: 'esm',
-      metafile: 'dist/human.esm.json',
-      entryPoints: ['src/human.js'],
-      outfile: 'dist/human.esm-nobundle.js',
+      entryPoints: ['src/index.ts'],
+      outfile: 'dist/face-api.esm-nobundle.js',
+      metafile: 'dist/face-api.esm-nobundle.json',
       external: ['fs', 'buffer', 'util', '@tensorflow'],
     },
   },
@@ -85,34 +85,26 @@ const targets = {
     tfjs: {
       platform: 'browser',
       format: 'esm',
-      metafile: 'dist/tfjs.esm.json',
       entryPoints: ['src/tfjs/tf-browser.js'],
       outfile: 'dist/tfjs.esm.js',
+      metafile: 'dist/tfjs.esm.json',
       external: ['fs', 'buffer', 'util'],
     },
     iife: {
       platform: 'browser',
       format: 'iife',
-      globalName: 'Human',
-      metafile: 'dist/human.json',
-      entryPoints: ['src/human.js'],
-      outfile: 'dist/human.js',
+      globalName: 'faceapi',
+      entryPoints: ['src/index.ts'],
+      outfile: 'dist/face-api.js',
+      metafile: 'dist/face-api.json',
       external: ['fs', 'buffer', 'util'],
     },
     esm: {
       platform: 'browser',
       format: 'esm',
-      metafile: 'dist/human.esm.json',
-      entryPoints: ['src/human.js'],
-      outfile: 'dist/human.esm.js',
-      external: ['fs', 'buffer', 'util'],
-    },
-    demo: {
-      platform: 'browser',
-      format: 'esm',
-      metafile: 'dist/demo-browser-index.json',
-      entryPoints: ['demo/browser.js'],
-      outfile: 'dist/demo-browser-index.js',
+      entryPoints: ['src/index.ts'],
+      outfile: 'dist/face-api.esm.js',
+      metafile: 'dist/face-api.esm.json',
       external: ['fs', 'buffer', 'util'],
     },
   },
diff --git a/build/package.json b/build/package.json
deleted file mode 100644
index 7a79361..0000000
--- a/build/package.json
+++ /dev/null
@@ -1,55 +0,0 @@
-{
-    "name": "@vladmandic/face-api",
-    "version": "0.8.8",
-    "description": "JavaScript module for Face Detection and Face Recognition Using Tensorflow/JS",
-    "main": "dist/face-api.node.js",
-    "module": "dist/face-api.esm.js",
-    "browser": "dist/face-api.esm.js",
-    "typings": "build/src/index.d.ts",
-    "engines": {
-        "node": ">=12.0.0"
-    },
-    "scripts": {
-        "start": "node --trace-warnings example/node.js",
-        "build-esm": "esbuild --bundle --format=esm --target=es2018 --platform=browser --minify --sourcemap --outfile=./dist/face-api.esm.js --log-level=error --tsconfig=./tsconfig.json --external:util --external:string_decoder --external:fs src/index.ts",
-        "build-esm-nobundle": "esbuild --bundle --format=esm --target=es2018 --platform=browser --sourcemap --outfile=./dist/face-api.esm.nobundle.js --log-level=error --tsconfig=./tsconfig.json --external:@tensorflow --external:util --external:string_decoder --external:fs --global-name=faceapi src/index.ts",
-        "build-iife": "esbuild --bundle --format=iife --target=es2018 --platform=browser --minify --sourcemap --outfile=./dist/face-api.js --log-level=error --tsconfig=./tsconfig.json --external:util --external:string_decoder --external:fs --global-name=faceapi src/index.ts",
-        "build-node": "esbuild --bundle --format=cjs --target=es2018 --platform=node --minify --sourcemap --outfile=./dist/face-api.node.js --log-level=error --tsconfig=./tsconfig.json src/index.ts",
-        "build-node-nobundle": "esbuild --bundle --format=cjs --target=es2018 --platform=node --sourcemap --outfile=./dist/face-api.node.nobundle.js --external:@tensorflow --log-level=error --tsconfig=./tsconfig.json src/index.ts",
-        "build": "rimraf build/* dist/* && tsc && npm run build-iife && npm run build-esm && npm run build-esm-nobundle && npm run build-node && npm run build-node-nobundle && ls -l dist/"
-    },
-    "keywords": [
-        "tensorflow",
-        "tf",
-        "tfjs",
-        "face",
-        "face-api",
-        "face-detection",
-        "age-gender"
-    ],
-    "repository": {
-        "type": "git",
-        "url": "git+https://github.com/vladmandic/face-api.git"
-    },
-    "publishConfig": {
-        "registry": "https://registry.npmjs.org/"
-    },
-    "author": "Vladimir Mandic <mandic00@live.com>",
-    "license": "MIT",
-    "bugs": {
-        "url": "https://github.com/vladmandic/face-api/issues"
-    },
-    "homepage": "https://github.com/vladmandic/face-api#readme",
-    "Dependencies": {},
-    "devDependencies": {
-        "@tensorflow/tfjs": "^2.7.0",
-        "@tensorflow/tfjs-node": "^2.7.0",
-        "@types/node": "^14.14.10",
-        "esbuild": "^0.8.15",
-        "rimraf": "^3.0.2",
-        "ts-node": "^9.0.0",
-        "tslib": "^2.0.3",
-        "typescript": "^4.1.2"
-    },
-    "dependencies": {}
-}
diff --git a/build/src/NeuralNetwork.d.ts b/build/src/NeuralNetwork.d.ts
deleted file mode 100644
index 08f6d25..0000000
--- a/build/src/NeuralNetwork.d.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from './common';
-export declare abstract class NeuralNetwork<TNetParams> {
-    protected _name: string;
-    protected _params: TNetParams | undefined;
-    protected _paramMappings: ParamMapping[];
-    constructor(_name: string);
-    get params(): TNetParams | undefined;
-    get paramMappings(): ParamMapping[];
-    get isLoaded(): boolean;
-    getParamFromPath(paramPath: string): tf.Tensor;
-    reassignParamFromPath(paramPath: string, tensor: tf.Tensor): void;
-    getParamList(): {
-        path: string;
-        tensor: any;
-    }[];
-    getTrainableParams(): {
-        path: string;
-        tensor: any;
-    }[];
-    getFrozenParams(): {
-        path: string;
-        tensor: any;
-    }[];
-    variable(): void;
-    freeze(): void;
-    dispose(throwOnRedispose?: boolean): void;
-    serializeParams(): Float32Array;
-    load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
-    loadFromUri(uri: string | undefined): Promise<void>;
-    loadFromDisk(filePath: string | undefined): Promise<void>;
-    loadFromWeightMap(weightMap: tf.NamedTensorMap): void;
-    extractWeights(weights: Float32Array): void;
-    private traversePropertyPath;
-    protected abstract getDefaultModelName(): string;
-    protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: TNetParams;
-        paramMappings: ParamMapping[];
-    };
-    protected abstract extractParams(weights: Float32Array): {
-        params: TNetParams;
-        paramMappings: ParamMapping[];
-    };
-}
diff --git a/build/src/NeuralNetwork.js b/build/src/NeuralNetwork.js
deleted file mode 100644
index a475731..0000000
--- a/build/src/NeuralNetwork.js
+++ /dev/null
@@ -1,122 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { getModelUris } from './common/getModelUris';
-import { loadWeightMap } from './dom';
-import { env } from './env';
-export class NeuralNetwork {
-    constructor(_name) {
-        this._name = _name;
-        this._params = undefined;
-        this._paramMappings = [];
-        /*
-        try {
-          const testTensor = tf.tensor([0]);
-          testTensor.toFloat();
-        } catch (err) {
-          throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);
-        }
-        */
-    }
-    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, tensor) {
-        const { obj, objProp } = this.traversePropertyPath(paramPath);
-        obj[objProp].dispose();
-        obj[objProp] = tensor;
-    }
-    getParamList() {
-        return this._paramMappings.map(({ paramPath }) => ({
-            path: paramPath,
-            tensor: this.getParamFromPath(paramPath)
-        }));
-    }
-    getTrainableParams() {
-        return this.getParamList().filter(param => param.tensor instanceof tf.Variable);
-    }
-    getFrozenParams() {
-        return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable));
-    }
-    variable() {
-        this.getFrozenParams().forEach(({ path, tensor }) => {
-            this.reassignParamFromPath(path, tensor.variable());
-        });
-    }
-    freeze() {
-        this.getTrainableParams().forEach(({ path, tensor: variable }) => {
-            const tensor = tf.tensor(variable.dataSync());
-            variable.dispose();
-            this.reassignParamFromPath(path, tensor);
-        });
-    }
-    dispose(throwOnRedispose = true) {
-        this.getParamList().forEach(param => {
-            if (throwOnRedispose && param.tensor.isDisposed) {
-                throw new Error(`param tensor has already been disposed for path ${param.path}`);
-            }
-            param.tensor.dispose();
-        });
-        this._params = undefined;
-    }
-    serializeParams() {
-        return new Float32Array(this.getParamList()
-            .map(({ tensor }) => Array.from(tensor.dataSync()))
-            .reduce((flat, arr) => flat.concat(arr)));
-    }
-    async load(weightsOrUrl) {
-        if (weightsOrUrl instanceof Float32Array) {
-            this.extractWeights(weightsOrUrl);
-            return;
-        }
-        await this.loadFromUri(weightsOrUrl);
-    }
-    async loadFromUri(uri) {
-        if (uri && typeof uri !== 'string') {
-            throw new Error(`${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(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);
-        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, objProp) => {
-            if (!res.nextObj.hasOwnProperty(objProp)) {
-                throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`);
-            }
-            return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] };
-        }, { nextObj: this.params });
-        const { obj, objProp } = result;
-        if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {
-            throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);
-        }
-        return { obj, objProp };
-    }
-}
-//# sourceMappingURL=NeuralNetwork.js.map
\ No newline at end of file
diff --git a/build/src/NeuralNetwork.js.map b/build/src/NeuralNetwork.js.map
deleted file mode 100644
index bd4073d..0000000
--- a/build/src/NeuralNetwork.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/Platform.d.ts b/build/src/Platform.d.ts
deleted file mode 100644
index a9db786..0000000
--- a/build/src/Platform.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-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;
-}
diff --git a/build/src/Platform.js b/build/src/Platform.js
deleted file mode 100644
index 6256f3e..0000000
--- a/build/src/Platform.js
+++ /dev/null
@@ -1,21 +0,0 @@
-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
deleted file mode 100644
index c9ff9aa..0000000
--- a/build/src/Platform.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ageGenderNet/AgeGenderNet.d.ts b/build/src/ageGenderNet/AgeGenderNet.d.ts
deleted file mode 100644
index f37d87e..0000000
--- a/build/src/ageGenderNet/AgeGenderNet.d.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { TinyXception } from '../xception/TinyXception';
-import { AgeAndGenderPrediction, NetOutput, NetParams } from './types';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { NetInput, TNetInput } from '../dom';
-export declare class AgeGenderNet extends NeuralNetwork<NetParams> {
-    private _faceFeatureExtractor;
-    constructor(faceFeatureExtractor?: TinyXception);
-    get faceFeatureExtractor(): TinyXception;
-    runNet(input: NetInput | tf.Tensor4D): NetOutput;
-    forwardInput(input: NetInput | tf.Tensor4D): NetOutput;
-    forward(input: TNetInput): Promise<NetOutput>;
-    predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]>;
-    protected getDefaultModelName(): string;
-    dispose(throwOnRedispose?: boolean): void;
-    loadClassifierParams(weights: Float32Array): void;
-    extractClassifierParams(weights: Float32Array): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParams(weights: Float32Array): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-}
diff --git a/build/src/ageGenderNet/AgeGenderNet.js b/build/src/ageGenderNet/AgeGenderNet.js
deleted file mode 100644
index 3c89557..0000000
--- a/build/src/ageGenderNet/AgeGenderNet.js
+++ /dev/null
@@ -1,93 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
-import { seperateWeightMaps } from '../faceProcessor/util';
-import { TinyXception } from '../xception/TinyXception';
-import { extractParams } from './extractParams';
-import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
-import { Gender } from './types';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { NetInput, toNetInput } from '../dom';
-export 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 tf.tidy(() => {
-            const bottleneckFeatures = input instanceof NetInput
-                ? this.faceFeatureExtractor.forwardInput(input)
-                : input;
-            const pooled = tf.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 tf.tidy(() => {
-            const { age, gender } = this.runNet(input);
-            return { age, gender: tf.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 = tf.unstack(out.age);
-        const genders = tf.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 extractParams(weights);
-    }
-    extractParamsFromWeigthMap(weightMap) {
-        const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);
-        this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
-        return extractParamsFromWeigthMap(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);
-    }
-}
-//# sourceMappingURL=AgeGenderNet.js.map
\ No newline at end of file
diff --git a/build/src/ageGenderNet/AgeGenderNet.js.map b/build/src/ageGenderNet/AgeGenderNet.js.map
deleted file mode 100644
index 21e0505..0000000
--- a/build/src/ageGenderNet/AgeGenderNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ageGenderNet/extractParams.d.ts b/build/src/ageGenderNet/extractParams.d.ts
deleted file mode 100644
index f4e4b30..0000000
--- a/build/src/ageGenderNet/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from '../common';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/ageGenderNet/extractParams.js b/build/src/ageGenderNet/extractParams.js
deleted file mode 100644
index c3201fe..0000000
--- a/build/src/ageGenderNet/extractParams.js
+++ /dev/null
@@ -1,16 +0,0 @@
-import { extractFCParamsFactory, extractWeightsFactory } from '../common';
-export function extractParams(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 } }
-    };
-}
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/src/ageGenderNet/extractParams.js.map b/build/src/ageGenderNet/extractParams.js.map
deleted file mode 100644
index 17c6c1e..0000000
--- a/build/src/ageGenderNet/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../../src/ageGenderNet/extractParams.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,sBAAsB,EAAE,qBAAqB,EAAgB,MAAM,WAAW,CAAC;AAGxF,MAAM,UAAU,aAAa,CAAC,OAAqB;IAEjD,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,cAAc,EACd,mBAAmB,EACpB,GAAG,qBAAqB,CAAC,OAAO,CAAC,CAAA;IAElC,MAAM,eAAe,GAAG,sBAAsB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAE7E,MAAM,GAAG,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,QAAQ,CAAC,CAAA;IAC7C,MAAM,MAAM,GAAG,eAAe,CAAC,GAAG,EAAE,CAAC,EAAE,WAAW,CAAC,CAAA;IAEnD,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,kCAAkC,mBAAmB,EAAE,CAAC,MAAM,EAAE,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa;QACb,MAAM,EAAE,EAAE,EAAE,EAAE,EAAE,GAAG,EAAE,MAAM,EAAE,EAAE;KAChC,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts b/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts
deleted file mode 100644
index aa24371..0000000
--- a/build/src/ageGenderNet/extractParamsFromWeigthMap.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from '../common';
-import { NetParams } from './types';
-export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/ageGenderNet/extractParamsFromWeigthMap.js b/build/src/ageGenderNet/extractParamsFromWeigthMap.js
deleted file mode 100644
index 58e0d84..0000000
--- a/build/src/ageGenderNet/extractParamsFromWeigthMap.js
+++ /dev/null
@@ -1,19 +0,0 @@
-import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';
-export function extractParamsFromWeigthMap(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 };
-}
-//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
diff --git a/build/src/ageGenderNet/extractParamsFromWeigthMap.js.map b/build/src/ageGenderNet/extractParamsFromWeigthMap.js.map
deleted file mode 100644
index 226ad4a..0000000
--- a/build/src/ageGenderNet/extractParamsFromWeigthMap.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ageGenderNet/index.d.ts b/build/src/ageGenderNet/index.d.ts
deleted file mode 100644
index 922b2de..0000000
--- a/build/src/ageGenderNet/index.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-export * from './AgeGenderNet';
-export * from './types';
diff --git a/build/src/ageGenderNet/index.js b/build/src/ageGenderNet/index.js
deleted file mode 100644
index 6ef6393..0000000
--- a/build/src/ageGenderNet/index.js
+++ /dev/null
@@ -1,3 +0,0 @@
-export * from './AgeGenderNet';
-export * from './types';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/ageGenderNet/index.js.map b/build/src/ageGenderNet/index.js.map
deleted file mode 100644
index 89040d9..0000000
--- a/build/src/ageGenderNet/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/ageGenderNet/index.ts"],"names":[],"mappings":"AAAA,cAAc,gBAAgB,CAAC;AAC/B,cAAc,SAAS,CAAC"}
\ No newline at end of file
diff --git a/build/src/ageGenderNet/types.d.ts b/build/src/ageGenderNet/types.d.ts
deleted file mode 100644
index 50aa699..0000000
--- a/build/src/ageGenderNet/types.d.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { FCParams } from '../common';
-export declare type AgeAndGenderPrediction = {
-    age: number;
-    gender: Gender;
-    genderProbability: number;
-};
-export declare enum Gender {
-    FEMALE = "female",
-    MALE = "male"
-}
-export declare type NetOutput = {
-    age: tf.Tensor1D;
-    gender: tf.Tensor2D;
-};
-export declare type NetParams = {
-    fc: {
-        age: FCParams;
-        gender: FCParams;
-    };
-};
diff --git a/build/src/ageGenderNet/types.js b/build/src/ageGenderNet/types.js
deleted file mode 100644
index 6808b5c..0000000
--- a/build/src/ageGenderNet/types.js
+++ /dev/null
@@ -1,6 +0,0 @@
-export var Gender;
-(function (Gender) {
-    Gender["FEMALE"] = "female";
-    Gender["MALE"] = "male";
-})(Gender || (Gender = {}));
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/ageGenderNet/types.js.map b/build/src/ageGenderNet/types.js.map
deleted file mode 100644
index 28b1c32..0000000
--- a/build/src/ageGenderNet/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/ageGenderNet/types.ts"],"names":[],"mappings":"AAUA,MAAM,CAAN,IAAY,MAGX;AAHD,WAAY,MAAM;IAChB,2BAAiB,CAAA;IACjB,uBAAa,CAAA;AACf,CAAC,EAHW,MAAM,KAAN,MAAM,QAGjB"}
\ No newline at end of file
diff --git a/build/src/classes/BoundingBox.d.ts b/build/src/classes/BoundingBox.d.ts
deleted file mode 100644
index 7c5f096..0000000
--- a/build/src/classes/BoundingBox.d.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import { Box } from './Box';
-export interface IBoundingBox {
-    left: number;
-    top: number;
-    right: number;
-    bottom: number;
-}
-export declare class BoundingBox extends Box<BoundingBox> implements IBoundingBox {
-    constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions?: boolean);
-}
diff --git a/build/src/classes/BoundingBox.js b/build/src/classes/BoundingBox.js
deleted file mode 100644
index c7d5175..0000000
--- a/build/src/classes/BoundingBox.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { Box } from './Box';
-export class BoundingBox extends Box {
-    constructor(left, top, right, bottom, allowNegativeDimensions = false) {
-        super({ left, top, right, bottom }, allowNegativeDimensions);
-    }
-}
-//# sourceMappingURL=BoundingBox.js.map
\ No newline at end of file
diff --git a/build/src/classes/BoundingBox.js.map b/build/src/classes/BoundingBox.js.map
deleted file mode 100644
index b975386..0000000
--- a/build/src/classes/BoundingBox.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"BoundingBox.js","sourceRoot":"","sources":["../../../src/classes/BoundingBox.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,OAAO,CAAC;AAS5B,MAAM,OAAO,WAAY,SAAQ,GAAgB;IAC/C,YAAY,IAAY,EAAE,GAAW,EAAE,KAAa,EAAE,MAAc,EAAE,0BAAmC,KAAK;QAC5G,KAAK,CAAC,EAAE,IAAI,EAAE,GAAG,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,uBAAuB,CAAC,CAAA;IAC9D,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/classes/Box.d.ts b/build/src/classes/Box.d.ts
deleted file mode 100644
index 950f56a..0000000
--- a/build/src/classes/Box.d.ts
+++ /dev/null
@@ -1,46 +0,0 @@
-import { IBoundingBox } from './BoundingBox';
-import { IDimensions } from './Dimensions';
-import { Point } from './Point';
-import { IRect } from './Rect';
-export declare class Box<BoxType = any> implements IBoundingBox, IRect {
-    static isRect(rect: any): boolean;
-    static assertIsValidBox(box: any, callee: string, allowNegativeDimensions?: boolean): void;
-    private _x;
-    private _y;
-    private _width;
-    private _height;
-    constructor(_box: IBoundingBox | IRect, allowNegativeDimensions?: boolean);
-    get x(): number;
-    get y(): number;
-    get width(): number;
-    get height(): number;
-    get left(): number;
-    get top(): number;
-    get right(): number;
-    get bottom(): number;
-    get area(): number;
-    get topLeft(): Point;
-    get topRight(): Point;
-    get bottomLeft(): Point;
-    get bottomRight(): Point;
-    round(): Box<BoxType>;
-    floor(): Box<BoxType>;
-    toSquare(): Box<BoxType>;
-    rescale(s: IDimensions | number): Box<BoxType>;
-    pad(padX: number, padY: number): Box<BoxType>;
-    clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType>;
-    shift(sx: number, sy: number): Box<BoxType>;
-    padAtBorders(imageHeight: number, imageWidth: number): {
-        dy: number;
-        edy: number;
-        dx: number;
-        edx: number;
-        y: number;
-        ey: number;
-        x: number;
-        ex: number;
-        w: number;
-        h: number;
-    };
-    calibrate(region: Box): Box<any>;
-}
diff --git a/build/src/classes/Box.js b/build/src/classes/Box.js
deleted file mode 100644
index df4affa..0000000
--- a/build/src/classes/Box.js
+++ /dev/null
@@ -1,140 +0,0 @@
-import { isDimensions, isValidNumber } from '../utils';
-import { Point } from './Point';
-export 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();
-    }
-}
-//# sourceMappingURL=Box.js.map
\ No newline at end of file
diff --git a/build/src/classes/Box.js.map b/build/src/classes/Box.js.map
deleted file mode 100644
index e8357b0..0000000
--- a/build/src/classes/Box.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/classes/Dimensions.d.ts b/build/src/classes/Dimensions.d.ts
deleted file mode 100644
index fe169cc..0000000
--- a/build/src/classes/Dimensions.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-export interface IDimensions {
-    width: number;
-    height: number;
-}
-export declare class Dimensions implements IDimensions {
-    private _width;
-    private _height;
-    constructor(width: number, height: number);
-    get width(): number;
-    get height(): number;
-    reverse(): Dimensions;
-}
diff --git a/build/src/classes/Dimensions.js b/build/src/classes/Dimensions.js
deleted file mode 100644
index 02482c8..0000000
--- a/build/src/classes/Dimensions.js
+++ /dev/null
@@ -1,16 +0,0 @@
-import { isValidNumber } from '../utils';
-export 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);
-    }
-}
-//# sourceMappingURL=Dimensions.js.map
\ No newline at end of file
diff --git a/build/src/classes/Dimensions.js.map b/build/src/classes/Dimensions.js.map
deleted file mode 100644
index 4391a32..0000000
--- a/build/src/classes/Dimensions.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"Dimensions.js","sourceRoot":"","sources":["../../../src/classes/Dimensions.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,aAAa,EAAE,MAAM,UAAU,CAAC;AAOzC,MAAM,OAAO,UAAU;IAKrB,YAAY,KAAa,EAAE,MAAc;QACvC,IAAI,CAAC,aAAa,CAAC,KAAK,CAAC,IAAI,CAAC,aAAa,CAAC,MAAM,CAAC,EAAE;YACnD,MAAM,IAAI,KAAK,CAAC,wFAAwF,IAAI,CAAC,SAAS,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,CAAC,EAAE,CAAC,CAAA;SAC7I;QAED,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;IACvB,CAAC;IAED,IAAW,KAAK,KAAa,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;IACjD,IAAW,MAAM,KAAa,OAAO,IAAI,CAAC,OAAO,CAAA,CAAC,CAAC;IAE5C,OAAO;QACZ,OAAO,IAAI,UAAU,CAAC,CAAC,GAAG,IAAI,CAAC,KAAK,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,CAAC,CAAA;IACxD,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/classes/FaceDetection.d.ts b/build/src/classes/FaceDetection.d.ts
deleted file mode 100644
index 5377318..0000000
--- a/build/src/classes/FaceDetection.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { Box } from './Box';
-import { IDimensions } from './Dimensions';
-import { ObjectDetection } from './ObjectDetection';
-import { Rect } from './Rect';
-export interface IFaceDetecion {
-    score: number;
-    box: Box;
-}
-export declare class FaceDetection extends ObjectDetection implements IFaceDetecion {
-    constructor(score: number, relativeBox: Rect, imageDims: IDimensions);
-    forSize(width: number, height: number): FaceDetection;
-}
diff --git a/build/src/classes/FaceDetection.js b/build/src/classes/FaceDetection.js
deleted file mode 100644
index e685cdf..0000000
--- a/build/src/classes/FaceDetection.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import { ObjectDetection } from './ObjectDetection';
-export 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);
-    }
-}
-//# sourceMappingURL=FaceDetection.js.map
\ No newline at end of file
diff --git a/build/src/classes/FaceDetection.js.map b/build/src/classes/FaceDetection.js.map
deleted file mode 100644
index efc40c9..0000000
--- a/build/src/classes/FaceDetection.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceDetection.js","sourceRoot":"","sources":["../../../src/classes/FaceDetection.ts"],"names":[],"mappings":"AAEA,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AAQpD,MAAM,OAAO,aAAc,SAAQ,eAAe;IAChD,YACE,KAAa,EACb,WAAiB,EACjB,SAAsB;QAEtB,KAAK,CAAC,KAAK,EAAE,KAAK,EAAE,EAAE,EAAE,WAAW,EAAE,SAAS,CAAC,CAAA;IACjD,CAAC;IAEM,OAAO,CAAC,KAAa,EAAE,MAAc;QAC1C,MAAM,EAAE,KAAK,EAAE,WAAW,EAAE,SAAS,EAAE,GAAG,KAAK,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,CAAA;QACtE,OAAO,IAAI,aAAa,CAAC,KAAK,EAAE,WAAW,EAAE,SAAS,CAAC,CAAA;IACzD,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/classes/FaceLandmarks.d.ts b/build/src/classes/FaceLandmarks.d.ts
deleted file mode 100644
index ead3ca9..0000000
--- a/build/src/classes/FaceLandmarks.d.ts
+++ /dev/null
@@ -1,42 +0,0 @@
-import { IBoundingBox } from './BoundingBox';
-import { Box } from './Box';
-import { Dimensions, IDimensions } from './Dimensions';
-import { FaceDetection } from './FaceDetection';
-import { Point } from './Point';
-import { IRect } from './Rect';
-export interface IFaceLandmarks {
-    positions: Point[];
-    shift: Point;
-}
-export declare class FaceLandmarks implements IFaceLandmarks {
-    protected _shift: Point;
-    protected _positions: Point[];
-    protected _imgDims: Dimensions;
-    constructor(relativeFaceLandmarkPositions: Point[], imgDims: IDimensions, shift?: Point);
-    get shift(): Point;
-    get imageWidth(): number;
-    get imageHeight(): number;
-    get positions(): Point[];
-    get relativePositions(): Point[];
-    forSize<T extends FaceLandmarks>(width: number, height: number): T;
-    shiftBy<T extends FaceLandmarks>(x: number, y: number): T;
-    shiftByPoint<T extends FaceLandmarks>(pt: Point): T;
-    /**
-     * Aligns the face landmarks after face detection from the relative positions of the faces
-     * bounding box, or it's current shift. This function should be used to align the face images
-     * after face detection has been performed, before they are passed to the face recognition net.
-     * This will make the computed face descriptor more accurate.
-     *
-     * @param detection (optional) The bounding box of the face or the face detection result. If
-     * no argument was passed the position of the face landmarks are assumed to be relative to
-     * it's current shift.
-     * @returns The bounding box of the aligned face.
-     */
-    align(detection?: FaceDetection | IRect | IBoundingBox | null, options?: {
-        useDlibAlignment?: boolean;
-        minBoxPadding?: number;
-    }): Box;
-    private alignDlib;
-    private alignMinBbox;
-    protected getRefPointsForAlignment(): Point[];
-}
diff --git a/build/src/classes/FaceLandmarks.js b/build/src/classes/FaceLandmarks.js
deleted file mode 100644
index cb83e12..0000000
--- a/build/src/classes/FaceLandmarks.js
+++ /dev/null
@@ -1,79 +0,0 @@
-import { minBbox } from '../ops';
-import { getCenterPoint } from '../utils';
-import { Box } from './Box';
-import { Dimensions } from './Dimensions';
-import { FaceDetection } from './FaceDetection';
-import { Point } from './Point';
-import { Rect } from './Rect';
-// face alignment constants
-const relX = 0.5;
-const relY = 0.43;
-const relScale = 0.45;
-export 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);
-    }
-    /**
-     * Aligns the face landmarks after face detection from the relative positions of the faces
-     * bounding box, or it's current shift. This function should be used to align the face images
-     * after face detection has been performed, before they are passed to the face recognition net.
-     * This will make the computed face descriptor more accurate.
-     *
-     * @param detection (optional) The bounding box of the face or the face detection result. If
-     * no argument was passed the position of the face landmarks are assumed to be relative to
-     * it's current shift.
-     * @returns The bounding box of the aligned face.
-     */
-    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);
-        // TODO: pad in case rectangle is out of image bounds
-        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');
-    }
-}
-//# sourceMappingURL=FaceLandmarks.js.map
\ No newline at end of file
diff --git a/build/src/classes/FaceLandmarks.js.map b/build/src/classes/FaceLandmarks.js.map
deleted file mode 100644
index 5433350..0000000
--- a/build/src/classes/FaceLandmarks.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/classes/FaceLandmarks5.d.ts b/build/src/classes/FaceLandmarks5.d.ts
deleted file mode 100644
index e514bcf..0000000
--- a/build/src/classes/FaceLandmarks5.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import { FaceLandmarks } from './FaceLandmarks';
-import { Point } from './Point';
-export declare class FaceLandmarks5 extends FaceLandmarks {
-    protected getRefPointsForAlignment(): Point[];
-}
diff --git a/build/src/classes/FaceLandmarks5.js b/build/src/classes/FaceLandmarks5.js
deleted file mode 100644
index 4f4d6f0..0000000
--- a/build/src/classes/FaceLandmarks5.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import { getCenterPoint } from '../utils';
-import { FaceLandmarks } from './FaceLandmarks';
-export class FaceLandmarks5 extends FaceLandmarks {
-    getRefPointsForAlignment() {
-        const pts = this.positions;
-        return [
-            pts[0],
-            pts[1],
-            getCenterPoint([pts[3], pts[4]])
-        ];
-    }
-}
-//# sourceMappingURL=FaceLandmarks5.js.map
\ No newline at end of file
diff --git a/build/src/classes/FaceLandmarks5.js.map b/build/src/classes/FaceLandmarks5.js.map
deleted file mode 100644
index 8975d93..0000000
--- a/build/src/classes/FaceLandmarks5.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/classes/FaceLandmarks68.d.ts b/build/src/classes/FaceLandmarks68.d.ts
deleted file mode 100644
index dce3ba5..0000000
--- a/build/src/classes/FaceLandmarks68.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { FaceLandmarks } from './FaceLandmarks';
-import { Point } from './Point';
-export declare class FaceLandmarks68 extends FaceLandmarks {
-    getJawOutline(): Point[];
-    getLeftEyeBrow(): Point[];
-    getRightEyeBrow(): Point[];
-    getNose(): Point[];
-    getLeftEye(): Point[];
-    getRightEye(): Point[];
-    getMouth(): Point[];
-    protected getRefPointsForAlignment(): Point[];
-}
diff --git a/build/src/classes/FaceLandmarks68.js b/build/src/classes/FaceLandmarks68.js
deleted file mode 100644
index b802995..0000000
--- a/build/src/classes/FaceLandmarks68.js
+++ /dev/null
@@ -1,33 +0,0 @@
-import { getCenterPoint } from '../utils';
-import { FaceLandmarks } from './FaceLandmarks';
-export 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);
-    }
-}
-//# sourceMappingURL=FaceLandmarks68.js.map
\ No newline at end of file
diff --git a/build/src/classes/FaceLandmarks68.js.map b/build/src/classes/FaceLandmarks68.js.map
deleted file mode 100644
index f2bc3dc..0000000
--- a/build/src/classes/FaceLandmarks68.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmarks68.js","sourceRoot":"","sources":["../../../src/classes/FaceLandmarks68.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,cAAc,EAAE,MAAM,UAAU,CAAC;AAC1C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAGhD,MAAM,OAAO,eAAgB,SAAQ,aAAa;IACzC,aAAa;QAClB,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,EAAE,EAAE,CAAC,CAAA;IACpC,CAAC;IAEM,cAAc;QACnB,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,eAAe;QACpB,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,OAAO;QACZ,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,UAAU;QACf,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,WAAW;QAChB,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAEM,QAAQ;QACb,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,CAAA;IACrC,CAAC;IAES,wBAAwB;QAChC,OAAO;YACL,IAAI,CAAC,UAAU,EAAE;YACjB,IAAI,CAAC,WAAW,EAAE;YAClB,IAAI,CAAC,QAAQ,EAAE;SAChB,CAAC,GAAG,CAAC,cAAc,CAAC,CAAA;IACvB,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/classes/FaceMatch.d.ts b/build/src/classes/FaceMatch.d.ts
deleted file mode 100644
index 01e4a27..0000000
--- a/build/src/classes/FaceMatch.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-export interface IFaceMatch {
-    label: string;
-    distance: number;
-}
-export declare class FaceMatch implements IFaceMatch {
-    private _label;
-    private _distance;
-    constructor(label: string, distance: number);
-    get label(): string;
-    get distance(): number;
-    toString(withDistance?: boolean): string;
-}
diff --git a/build/src/classes/FaceMatch.js b/build/src/classes/FaceMatch.js
deleted file mode 100644
index 1a3cf1f..0000000
--- a/build/src/classes/FaceMatch.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import { round } from '../utils';
-export 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)})` : ''}`;
-    }
-}
-//# sourceMappingURL=FaceMatch.js.map
\ No newline at end of file
diff --git a/build/src/classes/FaceMatch.js.map b/build/src/classes/FaceMatch.js.map
deleted file mode 100644
index 1b4ec66..0000000
--- a/build/src/classes/FaceMatch.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceMatch.js","sourceRoot":"","sources":["../../../src/classes/FaceMatch.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AAOjC,MAAM,OAAO,SAAS;IAIpB,YAAY,KAAa,EAAE,QAAgB;QACzC,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,SAAS,GAAG,QAAQ,CAAA;IAC3B,CAAC;IAED,IAAW,KAAK,KAAa,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;IACjD,IAAW,QAAQ,KAAa,OAAO,IAAI,CAAC,SAAS,CAAA,CAAC,CAAC;IAEhD,QAAQ,CAAC,eAAwB,IAAI;QAC1C,OAAO,GAAG,IAAI,CAAC,KAAK,GAAG,YAAY,CAAC,CAAC,CAAC,KAAK,KAAK,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAA;IAC3E,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/classes/LabeledBox.d.ts b/build/src/classes/LabeledBox.d.ts
deleted file mode 100644
index 6cf39c0..0000000
--- a/build/src/classes/LabeledBox.d.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-import { IBoundingBox } from './BoundingBox';
-import { Box } from './Box';
-import { IRect } from './Rect';
-export declare class LabeledBox extends Box<LabeledBox> {
-    static assertIsValidLabeledBox(box: any, callee: string): void;
-    private _label;
-    constructor(box: IBoundingBox | IRect | any, label: number);
-    get label(): number;
-}
diff --git a/build/src/classes/LabeledBox.js b/build/src/classes/LabeledBox.js
deleted file mode 100644
index 49f2579..0000000
--- a/build/src/classes/LabeledBox.js
+++ /dev/null
@@ -1,16 +0,0 @@
-import { isValidNumber } from '../utils';
-import { Box } from './Box';
-export 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; }
-}
-//# sourceMappingURL=LabeledBox.js.map
\ No newline at end of file
diff --git a/build/src/classes/LabeledBox.js.map b/build/src/classes/LabeledBox.js.map
deleted file mode 100644
index 1471dba..0000000
--- a/build/src/classes/LabeledBox.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"LabeledBox.js","sourceRoot":"","sources":["../../../src/classes/LabeledBox.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,aAAa,EAAE,MAAM,UAAU,CAAC;AAEzC,OAAO,EAAE,GAAG,EAAE,MAAM,OAAO,CAAC;AAG5B,MAAM,OAAO,UAAW,SAAQ,GAAe;IAY7C,YAAY,GAA+B,EAAE,KAAa;QACxD,KAAK,CAAC,GAAG,CAAC,CAAA;QACV,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;IACrB,CAAC;IAbM,MAAM,CAAC,uBAAuB,CAAC,GAAQ,EAAE,MAAc;QAC5D,GAAG,CAAC,gBAAgB,CAAC,GAAG,EAAE,MAAM,CAAC,CAAA;QAEjC,IAAI,CAAC,aAAa,CAAC,GAAG,CAAC,KAAK,CAAC,EAAE;YAC7B,MAAM,IAAI,KAAK,CAAC,GAAG,MAAM,+BAA+B,GAAG,CAAC,KAAK,kBAAkB,CAAC,CAAA;SACrF;IACH,CAAC;IASD,IAAW,KAAK,KAAa,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;CAElD"}
\ No newline at end of file
diff --git a/build/src/classes/LabeledFaceDescriptors.d.ts b/build/src/classes/LabeledFaceDescriptors.d.ts
deleted file mode 100644
index 6015429..0000000
--- a/build/src/classes/LabeledFaceDescriptors.d.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-export declare class LabeledFaceDescriptors {
-    private _label;
-    private _descriptors;
-    constructor(label: string, descriptors: Float32Array[]);
-    get label(): string;
-    get descriptors(): Float32Array[];
-    toJSON(): any;
-    static fromJSON(json: any): LabeledFaceDescriptors;
-}
diff --git a/build/src/classes/LabeledFaceDescriptors.js b/build/src/classes/LabeledFaceDescriptors.js
deleted file mode 100644
index c41093f..0000000
--- a/build/src/classes/LabeledFaceDescriptors.js
+++ /dev/null
@@ -1,27 +0,0 @@
-export 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);
-    }
-}
-//# sourceMappingURL=LabeledFaceDescriptors.js.map
\ No newline at end of file
diff --git a/build/src/classes/LabeledFaceDescriptors.js.map b/build/src/classes/LabeledFaceDescriptors.js.map
deleted file mode 100644
index a0a6cb0..0000000
--- a/build/src/classes/LabeledFaceDescriptors.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"LabeledFaceDescriptors.js","sourceRoot":"","sources":["../../../src/classes/LabeledFaceDescriptors.ts"],"names":[],"mappings":"AAAA,MAAM,OAAO,sBAAsB;IAIjC,YAAY,KAAa,EAAE,WAA2B;QACpD,IAAI,CAAC,CAAC,OAAO,KAAK,KAAK,QAAQ,CAAC,EAAE;YAChC,MAAM,IAAI,KAAK,CAAC,oEAAoE,CAAC,CAAA;SACtF;QAED,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,IAAI,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC,CAAC,IAAI,YAAY,YAAY,CAAC,CAAC,EAAE;YAC5F,MAAM,IAAI,KAAK,CAAC,0FAA0F,CAAC,CAAA;SAC5G;QAED,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,YAAY,GAAG,WAAW,CAAA;IACjC,CAAC;IAED,IAAW,KAAK,KAAa,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;IACjD,IAAW,WAAW,KAAqB,OAAO,IAAI,CAAC,YAAY,CAAA,CAAC,CAAC;IAE9D,MAAM;QACX,OAAO;YACL,KAAK,EAAE,IAAI,CAAC,KAAK;YACjB,WAAW,EAAE,IAAI,CAAC,WAAW,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;SACxD,CAAC;IACJ,CAAC;IAEM,MAAM,CAAC,QAAQ,CAAC,IAAS;QAC9B,MAAM,WAAW,GAAG,IAAI,CAAC,WAAW,CAAC,GAAG,CAAC,CAAC,CAAM,EAAE,EAAE;YAClD,OAAO,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC;QAC7B,CAAC,CAAC,CAAC;QACH,OAAO,IAAI,sBAAsB,CAAC,IAAI,CAAC,KAAK,EAAE,WAAW,CAAC,CAAC;IAC7D,CAAC;CAEF"}
\ No newline at end of file
diff --git a/build/src/classes/ObjectDetection.d.ts b/build/src/classes/ObjectDetection.d.ts
deleted file mode 100644
index feae090..0000000
--- a/build/src/classes/ObjectDetection.d.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-import { Box } from './Box';
-import { Dimensions, IDimensions } from './Dimensions';
-import { IRect } from './Rect';
-export declare class ObjectDetection {
-    private _score;
-    private _classScore;
-    private _className;
-    private _box;
-    private _imageDims;
-    constructor(score: number, classScore: number, className: string, relativeBox: IRect, imageDims: IDimensions);
-    get score(): number;
-    get classScore(): number;
-    get className(): string;
-    get box(): Box;
-    get imageDims(): Dimensions;
-    get imageWidth(): number;
-    get imageHeight(): number;
-    get relativeBox(): Box;
-    forSize(width: number, height: number): ObjectDetection;
-}
diff --git a/build/src/classes/ObjectDetection.js b/build/src/classes/ObjectDetection.js
deleted file mode 100644
index b4af12d..0000000
--- a/build/src/classes/ObjectDetection.js
+++ /dev/null
@@ -1,23 +0,0 @@
-import { Box } from './Box';
-import { Dimensions } from './Dimensions';
-export 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 });
-    }
-}
-//# sourceMappingURL=ObjectDetection.js.map
\ No newline at end of file
diff --git a/build/src/classes/ObjectDetection.js.map b/build/src/classes/ObjectDetection.js.map
deleted file mode 100644
index e0b95a9..0000000
--- a/build/src/classes/ObjectDetection.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"ObjectDetection.js","sourceRoot":"","sources":["../../../src/classes/ObjectDetection.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,OAAO,CAAC;AAC5B,OAAO,EAAE,UAAU,EAAe,MAAM,cAAc,CAAC;AAGvD,MAAM,OAAO,eAAe;IAO1B,YACE,KAAa,EACb,UAAkB,EAClB,SAAiB,EACjB,WAAkB,EAClB,SAAsB;QAEtB,IAAI,CAAC,UAAU,GAAG,IAAI,UAAU,CAAC,SAAS,CAAC,KAAK,EAAE,SAAS,CAAC,MAAM,CAAC,CAAA;QACnE,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,WAAW,GAAG,UAAU,CAAA;QAC7B,IAAI,CAAC,UAAU,GAAG,SAAS,CAAA;QAC3B,IAAI,CAAC,IAAI,GAAG,IAAI,GAAG,CAAC,WAAW,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,UAAU,CAAC,CAAA;IAC3D,CAAC;IAED,IAAW,KAAK,KAAa,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;IACjD,IAAW,UAAU,KAAa,OAAO,IAAI,CAAC,WAAW,CAAA,CAAC,CAAC;IAC3D,IAAW,SAAS,KAAa,OAAO,IAAI,CAAC,UAAU,CAAA,CAAC,CAAC;IACzD,IAAW,GAAG,KAAU,OAAO,IAAI,CAAC,IAAI,CAAA,CAAC,CAAC;IAC1C,IAAW,SAAS,KAAiB,OAAO,IAAI,CAAC,UAAU,CAAA,CAAC,CAAC;IAC7D,IAAW,UAAU,KAAa,OAAO,IAAI,CAAC,SAAS,CAAC,KAAK,CAAA,CAAC,CAAC;IAC/D,IAAW,WAAW,KAAa,OAAO,IAAI,CAAC,SAAS,CAAC,MAAM,CAAA,CAAC,CAAC;IACjE,IAAW,WAAW,KAAU,OAAO,IAAI,GAAG,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,SAAS,CAAC,OAAO,EAAE,CAAC,CAAA,CAAC,CAAC;IAEtF,OAAO,CAAC,KAAa,EAAE,MAAc;QAC1C,OAAO,IAAI,eAAe,CACxB,IAAI,CAAC,KAAK,EACV,IAAI,CAAC,UAAU,EACf,IAAI,CAAC,SAAS,EACd,IAAI,CAAC,WAAW,EAChB,EAAE,KAAK,EAAE,MAAM,EAAC,CACjB,CAAA;IACH,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/classes/Point.d.ts b/build/src/classes/Point.d.ts
deleted file mode 100644
index 2c330fb..0000000
--- a/build/src/classes/Point.d.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-export interface IPoint {
-    x: number;
-    y: number;
-}
-export declare class Point implements IPoint {
-    private _x;
-    private _y;
-    constructor(x: number, y: number);
-    get x(): number;
-    get y(): number;
-    add(pt: IPoint): Point;
-    sub(pt: IPoint): Point;
-    mul(pt: IPoint): Point;
-    div(pt: IPoint): Point;
-    abs(): Point;
-    magnitude(): number;
-    floor(): Point;
-}
diff --git a/build/src/classes/Point.js b/build/src/classes/Point.js
deleted file mode 100644
index 1cd1ac0..0000000
--- a/build/src/classes/Point.js
+++ /dev/null
@@ -1,30 +0,0 @@
-export 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));
-    }
-}
-//# sourceMappingURL=Point.js.map
\ No newline at end of file
diff --git a/build/src/classes/Point.js.map b/build/src/classes/Point.js.map
deleted file mode 100644
index a8e3925..0000000
--- a/build/src/classes/Point.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"Point.js","sourceRoot":"","sources":["../../../src/classes/Point.ts"],"names":[],"mappings":"AAKA,MAAM,OAAO,KAAK;IAIhB,YAAY,CAAS,EAAE,CAAS;QAC9B,IAAI,CAAC,EAAE,GAAG,CAAC,CAAA;QACX,IAAI,CAAC,EAAE,GAAG,CAAC,CAAA;IACb,CAAC;IAED,IAAI,CAAC,KAAa,OAAO,IAAI,CAAC,EAAE,CAAA,CAAC,CAAC;IAClC,IAAI,CAAC,KAAa,OAAO,IAAI,CAAC,EAAE,CAAA,CAAC,CAAC;IAE3B,GAAG,CAAC,EAAU;QACnB,OAAO,IAAI,KAAK,CAAC,IAAI,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,CAAA;IAChD,CAAC;IAEM,GAAG,CAAC,EAAU;QACnB,OAAO,IAAI,KAAK,CAAC,IAAI,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,CAAA;IAChD,CAAC;IAEM,GAAG,CAAC,EAAU;QACnB,OAAO,IAAI,KAAK,CAAC,IAAI,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,CAAA;IAChD,CAAC;IAEM,GAAG,CAAC,EAAU;QACnB,OAAO,IAAI,KAAK,CAAC,IAAI,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,CAAA;IAChD,CAAC;IAEM,GAAG;QACR,OAAO,IAAI,KAAK,CAAC,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAA;IACtD,CAAC;IAEM,SAAS;QACd,OAAO,IAAI,CAAC,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC7D,CAAC;IAEM,KAAK;QACV,OAAO,IAAI,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1D,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/classes/PredictedBox.d.ts b/build/src/classes/PredictedBox.d.ts
deleted file mode 100644
index 8a72117..0000000
--- a/build/src/classes/PredictedBox.d.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-import { IBoundingBox } from './BoundingBox';
-import { LabeledBox } from './LabeledBox';
-import { IRect } from './Rect';
-export declare class PredictedBox extends LabeledBox {
-    static assertIsValidPredictedBox(box: any, callee: string): void;
-    private _score;
-    private _classScore;
-    constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number);
-    get score(): number;
-    get classScore(): number;
-}
diff --git a/build/src/classes/PredictedBox.js b/build/src/classes/PredictedBox.js
deleted file mode 100644
index ad33c9c..0000000
--- a/build/src/classes/PredictedBox.js
+++ /dev/null
@@ -1,19 +0,0 @@
-import { isValidProbablitiy } from '../utils';
-import { LabeledBox } from './LabeledBox';
-export 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; }
-}
-//# sourceMappingURL=PredictedBox.js.map
\ No newline at end of file
diff --git a/build/src/classes/PredictedBox.js.map b/build/src/classes/PredictedBox.js.map
deleted file mode 100644
index eaa4f40..0000000
--- a/build/src/classes/PredictedBox.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"PredictedBox.js","sourceRoot":"","sources":["../../../src/classes/PredictedBox.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,kBAAkB,EAAE,MAAM,UAAU,CAAC;AAE9C,OAAO,EAAE,UAAU,EAAE,MAAM,cAAc,CAAC;AAG1C,MAAM,OAAO,YAAa,SAAQ,UAAU;IAgB1C,YAAY,GAA+B,EAAE,KAAa,EAAE,KAAa,EAAE,UAAkB;QAC3F,KAAK,CAAC,GAAG,EAAE,KAAK,CAAC,CAAA;QACjB,IAAI,CAAC,MAAM,GAAG,KAAK,CAAA;QACnB,IAAI,CAAC,WAAW,GAAG,UAAU,CAAA;IAC/B,CAAC;IAlBM,MAAM,CAAC,yBAAyB,CAAC,GAAQ,EAAE,MAAc;QAC9D,UAAU,CAAC,uBAAuB,CAAC,GAAG,EAAE,MAAM,CAAC,CAAA;QAE/C,IACE,CAAC,kBAAkB,CAAC,GAAG,CAAC,KAAK,CAAC;eAC3B,CAAC,kBAAkB,CAAC,GAAG,CAAC,UAAU,CAAC,EACtC;YACA,MAAM,IAAI,KAAK,CAAC,GAAG,MAAM,iCAAiC,GAAG,CAAC,KAAK,UAAU,GAAG,CAAC,UAAU,iCAAiC,CAAC,CAAA;SAC9H;IACH,CAAC;IAWD,IAAW,KAAK,KAAa,OAAO,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC;IACjD,IAAW,UAAU,KAAa,OAAO,IAAI,CAAC,WAAW,CAAA,CAAC,CAAC;CAE5D"}
\ No newline at end of file
diff --git a/build/src/classes/Rect.d.ts b/build/src/classes/Rect.d.ts
deleted file mode 100644
index c88c89a..0000000
--- a/build/src/classes/Rect.d.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import { Box } from './Box';
-export interface IRect {
-    x: number;
-    y: number;
-    width: number;
-    height: number;
-}
-export declare class Rect extends Box<Rect> implements IRect {
-    constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions?: boolean);
-}
diff --git a/build/src/classes/Rect.js b/build/src/classes/Rect.js
deleted file mode 100644
index 711acfd..0000000
--- a/build/src/classes/Rect.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { Box } from './Box';
-export class Rect extends Box {
-    constructor(x, y, width, height, allowNegativeDimensions = false) {
-        super({ x, y, width, height }, allowNegativeDimensions);
-    }
-}
-//# sourceMappingURL=Rect.js.map
\ No newline at end of file
diff --git a/build/src/classes/Rect.js.map b/build/src/classes/Rect.js.map
deleted file mode 100644
index 517bc0c..0000000
--- a/build/src/classes/Rect.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"Rect.js","sourceRoot":"","sources":["../../../src/classes/Rect.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,OAAO,CAAC;AAS5B,MAAM,OAAO,IAAK,SAAQ,GAAS;IACjC,YAAY,CAAS,EAAE,CAAS,EAAE,KAAa,EAAE,MAAc,EAAE,0BAAmC,KAAK;QACvG,KAAK,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,uBAAuB,CAAC,CAAA;IACzD,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/classes/index.d.ts b/build/src/classes/index.d.ts
deleted file mode 100644
index b66b71e..0000000
--- a/build/src/classes/index.d.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-export * from './BoundingBox';
-export * from './Box';
-export * from './Dimensions';
-export * from './FaceDetection';
-export * from './FaceLandmarks';
-export * from './FaceLandmarks5';
-export * from './FaceLandmarks68';
-export * from './FaceMatch';
-export * from './LabeledBox';
-export * from './LabeledFaceDescriptors';
-export * from './ObjectDetection';
-export * from './Point';
-export * from './PredictedBox';
-export * from './Rect';
diff --git a/build/src/classes/index.js b/build/src/classes/index.js
deleted file mode 100644
index a6d936b..0000000
--- a/build/src/classes/index.js
+++ /dev/null
@@ -1,15 +0,0 @@
-export * from './BoundingBox';
-export * from './Box';
-export * from './Dimensions';
-export * from './FaceDetection';
-export * from './FaceLandmarks';
-export * from './FaceLandmarks5';
-export * from './FaceLandmarks68';
-export * from './FaceMatch';
-export * from './LabeledBox';
-export * from './LabeledFaceDescriptors';
-export * from './ObjectDetection';
-export * from './Point';
-export * from './PredictedBox';
-export * from './Rect';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/classes/index.js.map b/build/src/classes/index.js.map
deleted file mode 100644
index e9b2c23..0000000
--- a/build/src/classes/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/classes/index.ts"],"names":[],"mappings":"AAAA,cAAc,eAAe,CAAA;AAC7B,cAAc,OAAO,CAAA;AACrB,cAAc,cAAc,CAAA;AAC5B,cAAc,iBAAiB,CAAC;AAChC,cAAc,iBAAiB,CAAC;AAChC,cAAc,kBAAkB,CAAC;AACjC,cAAc,mBAAmB,CAAC;AAClC,cAAc,aAAa,CAAC;AAC5B,cAAc,cAAc,CAAA;AAC5B,cAAc,0BAA0B,CAAC;AACzC,cAAc,mBAAmB,CAAA;AACjC,cAAc,SAAS,CAAA;AACvB,cAAc,gBAAgB,CAAA;AAC9B,cAAc,QAAQ,CAAA"}
\ No newline at end of file
diff --git a/build/src/common/convLayer.d.ts b/build/src/common/convLayer.d.ts
deleted file mode 100644
index e7638d1..0000000
--- a/build/src/common/convLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ConvParams } from './types';
-export declare function convLayer(x: tf.Tensor4D, params: ConvParams, padding?: 'valid' | 'same', withRelu?: boolean): tf.Tensor4D;
diff --git a/build/src/common/convLayer.js b/build/src/common/convLayer.js
deleted file mode 100644
index 371216c..0000000
--- a/build/src/common/convLayer.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function convLayer(x, params, padding = 'same', withRelu = false) {
-    return tf.tidy(() => {
-        const out = tf.add(tf.conv2d(x, params.filters, [1, 1], padding), params.bias);
-        return withRelu ? tf.relu(out) : out;
-    });
-}
-//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
diff --git a/build/src/common/convLayer.js.map b/build/src/common/convLayer.js.map
deleted file mode 100644
index fd232ff..0000000
--- a/build/src/common/convLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../../src/common/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAIzD,MAAM,UAAU,SAAS,CACvB,CAAc,EACd,MAAkB,EAClB,UAA4B,MAAM,EAClC,WAAoB,KAAK;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAChB,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,EAC7C,MAAM,CAAC,IAAI,CACG,CAAA;QAEhB,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;IACtC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/depthwiseSeparableConv.d.ts b/build/src/common/depthwiseSeparableConv.d.ts
deleted file mode 100644
index eb5f3cf..0000000
--- a/build/src/common/depthwiseSeparableConv.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { SeparableConvParams } from './types';
-export declare function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams, stride: [number, number]): tf.Tensor4D;
diff --git a/build/src/common/depthwiseSeparableConv.js b/build/src/common/depthwiseSeparableConv.js
deleted file mode 100644
index 218f145..0000000
--- a/build/src/common/depthwiseSeparableConv.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function depthwiseSeparableConv(x, params, stride) {
-    return tf.tidy(() => {
-        let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
-        out = tf.add(out, params.bias);
-        return out;
-    });
-}
-//# sourceMappingURL=depthwiseSeparableConv.js.map
\ No newline at end of file
diff --git a/build/src/common/depthwiseSeparableConv.js.map b/build/src/common/depthwiseSeparableConv.js.map
deleted file mode 100644
index e681f1d..0000000
--- a/build/src/common/depthwiseSeparableConv.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"depthwiseSeparableConv.js","sourceRoot":"","sources":["../../../src/common/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAIzD,MAAM,UAAU,sBAAsB,CACpC,CAAc,EACd,MAA2B,EAC3B,MAAwB;IAExB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,CAAC,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,EAAE,MAAM,CAAC,CAAA;QACjG,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;QAC9B,OAAO,GAAG,CAAA;IACZ,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/disposeUnusedWeightTensors.d.ts b/build/src/common/disposeUnusedWeightTensors.d.ts
deleted file mode 100644
index 927401b..0000000
--- a/build/src/common/disposeUnusedWeightTensors.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { ParamMapping } from './types';
-export declare function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]): void;
diff --git a/build/src/common/disposeUnusedWeightTensors.js b/build/src/common/disposeUnusedWeightTensors.js
deleted file mode 100644
index 08a5c61..0000000
--- a/build/src/common/disposeUnusedWeightTensors.js
+++ /dev/null
@@ -1,8 +0,0 @@
-export function disposeUnusedWeightTensors(weightMap, paramMappings) {
-    Object.keys(weightMap).forEach(path => {
-        if (!paramMappings.some(pm => pm.originalPath === path)) {
-            weightMap[path].dispose();
-        }
-    });
-}
-//# sourceMappingURL=disposeUnusedWeightTensors.js.map
\ No newline at end of file
diff --git a/build/src/common/disposeUnusedWeightTensors.js.map b/build/src/common/disposeUnusedWeightTensors.js.map
deleted file mode 100644
index c0e87e6..0000000
--- a/build/src/common/disposeUnusedWeightTensors.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"disposeUnusedWeightTensors.js","sourceRoot":"","sources":["../../../src/common/disposeUnusedWeightTensors.ts"],"names":[],"mappings":"AAEA,MAAM,UAAU,0BAA0B,CAAC,SAAc,EAAE,aAA6B;IACtF,MAAM,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,EAAE;QACpC,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,YAAY,KAAK,IAAI,CAAC,EAAE;YACvD,SAAS,CAAC,IAAI,CAAC,CAAC,OAAO,EAAE,CAAA;SAC1B;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/extractConvParamsFactory.d.ts b/build/src/common/extractConvParamsFactory.d.ts
deleted file mode 100644
index 03908f7..0000000
--- a/build/src/common/extractConvParamsFactory.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';
-export declare function extractConvParamsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): (channelsIn: number, channelsOut: number, filterSize: number, mappedPrefix: string) => ConvParams;
diff --git a/build/src/common/extractConvParamsFactory.js b/build/src/common/extractConvParamsFactory.js
deleted file mode 100644
index 07b7c7e..0000000
--- a/build/src/common/extractConvParamsFactory.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function extractConvParamsFactory(extractWeights, paramMappings) {
-    return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
-        const filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-        const bias = tf.tensor1d(extractWeights(channelsOut));
-        paramMappings.push({ paramPath: `${mappedPrefix}/filters` }, { paramPath: `${mappedPrefix}/bias` });
-        return { filters, bias };
-    };
-}
-//# sourceMappingURL=extractConvParamsFactory.js.map
\ No newline at end of file
diff --git a/build/src/common/extractConvParamsFactory.js.map b/build/src/common/extractConvParamsFactory.js.map
deleted file mode 100644
index d861cbe..0000000
--- a/build/src/common/extractConvParamsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractConvParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractConvParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAIzD,MAAM,UAAU,wBAAwB,CACtC,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UACL,UAAkB,EAClB,WAAmB,EACnB,UAAkB,EAClB,YAAoB;QAGpB,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACzB,cAAc,CAAC,UAAU,GAAG,WAAW,GAAG,UAAU,GAAG,UAAU,CAAC,EAClE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,WAAW,CAAC,CAClD,CAAA;QACD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,EACxC,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO,EAAE,OAAO,EAAE,IAAI,EAAE,CAAA;IAC1B,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/extractFCParamsFactory.d.ts b/build/src/common/extractFCParamsFactory.d.ts
deleted file mode 100644
index 173afc4..0000000
--- a/build/src/common/extractFCParamsFactory.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { ExtractWeightsFunction, FCParams, ParamMapping } from './types';
-export declare function extractFCParamsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): (channelsIn: number, channelsOut: number, mappedPrefix: string) => FCParams;
diff --git a/build/src/common/extractFCParamsFactory.js b/build/src/common/extractFCParamsFactory.js
deleted file mode 100644
index 5d0ed49..0000000
--- a/build/src/common/extractFCParamsFactory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function extractFCParamsFactory(extractWeights, paramMappings) {
-    return function (channelsIn, channelsOut, mappedPrefix) {
-        const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-        const fc_bias = tf.tensor1d(extractWeights(channelsOut));
-        paramMappings.push({ paramPath: `${mappedPrefix}/weights` }, { paramPath: `${mappedPrefix}/bias` });
-        return {
-            weights: fc_weights,
-            bias: fc_bias
-        };
-    };
-}
-//# sourceMappingURL=extractFCParamsFactory.js.map
\ No newline at end of file
diff --git a/build/src/common/extractFCParamsFactory.js.map b/build/src/common/extractFCParamsFactory.js.map
deleted file mode 100644
index 6abbfce..0000000
--- a/build/src/common/extractFCParamsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractFCParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractFCParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAKzD,MAAM,UAAU,sBAAsB,CACpC,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UACL,UAAkB,EAClB,WAAmB,EACnB,YAAoB;QAGpB,MAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QACnG,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAExD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,EACxC,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO;YACL,OAAO,EAAE,UAAU;YACnB,IAAI,EAAE,OAAO;SACd,CAAA;IACH,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/extractSeparableConvParamsFactory.d.ts b/build/src/common/extractSeparableConvParamsFactory.d.ts
deleted file mode 100644
index 1e20f3b..0000000
--- a/build/src/common/extractSeparableConvParamsFactory.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';
-export declare function extractSeparableConvParamsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): (channelsIn: number, channelsOut: number, mappedPrefix: string) => SeparableConvParams;
-export declare function loadSeparableConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T): (prefix: string) => SeparableConvParams;
diff --git a/build/src/common/extractSeparableConvParamsFactory.js b/build/src/common/extractSeparableConvParamsFactory.js
deleted file mode 100644
index 258d9e9..0000000
--- a/build/src/common/extractSeparableConvParamsFactory.js
+++ /dev/null
@@ -1,20 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { SeparableConvParams } from './types';
-export function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
-    return function (channelsIn, channelsOut, mappedPrefix) {
-        const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-        const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-        const bias = tf.tensor1d(extractWeights(channelsOut));
-        paramMappings.push({ paramPath: `${mappedPrefix}/depthwise_filter` }, { paramPath: `${mappedPrefix}/pointwise_filter` }, { paramPath: `${mappedPrefix}/bias` });
-        return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-    };
-}
-export 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);
-    };
-}
-//# sourceMappingURL=extractSeparableConvParamsFactory.js.map
\ No newline at end of file
diff --git a/build/src/common/extractSeparableConvParamsFactory.js.map b/build/src/common/extractSeparableConvParamsFactory.js.map
deleted file mode 100644
index 3b917eb..0000000
--- a/build/src/common/extractSeparableConvParamsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractSeparableConvParamsFactory.js","sourceRoot":"","sources":["../../../src/common/extractSeparableConvParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAwC,mBAAmB,EAAE,MAAM,SAAS,CAAC;AAEpF,MAAM,UAAU,iCAAiC,CAC/C,cAAsC,EACtC,aAA6B;IAG7B,OAAO,UAAS,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAC3E,MAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,GAAG,UAAU,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,CAAC,CAAC,CAAC,CAAA;QAC/F,MAAM,gBAAgB,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,UAAU,GAAG,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC,CAAA;QAC/G,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,CAAA;QAErD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,mBAAmB,EAAE,EACjD,EAAE,SAAS,EAAE,GAAG,YAAY,mBAAmB,EAAE,EACjD,EAAE,SAAS,EAAE,GAAG,YAAY,OAAO,EAAE,CACtC,CAAA;QAED,OAAO,IAAI,mBAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC,CAAA;AAEH,CAAC;AAED,MAAM,UAAU,8BAA8B,CAC5C,kBAAqE;IAGrE,OAAO,UAAU,MAAc;QAC7B,MAAM,gBAAgB,GAAG,kBAAkB,CAAc,GAAG,MAAM,mBAAmB,EAAE,CAAC,CAAC,CAAA;QACzF,MAAM,gBAAgB,GAAG,kBAAkB,CAAc,GAAG,MAAM,mBAAmB,EAAE,CAAC,CAAC,CAAA;QACzF,MAAM,IAAI,GAAG,kBAAkB,CAAc,GAAG,MAAM,OAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,IAAI,mBAAmB,CAC5B,gBAAgB,EAChB,gBAAgB,EAChB,IAAI,CACL,CAAA;IACH,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/extractWeightEntryFactory.d.ts b/build/src/common/extractWeightEntryFactory.d.ts
deleted file mode 100644
index b532003..0000000
--- a/build/src/common/extractWeightEntryFactory.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { ParamMapping } from './types';
-export declare function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]): <T>(originalPath: string, paramRank: number, mappedPath?: string | undefined) => T;
diff --git a/build/src/common/extractWeightEntryFactory.js b/build/src/common/extractWeightEntryFactory.js
deleted file mode 100644
index 6a87e33..0000000
--- a/build/src/common/extractWeightEntryFactory.js
+++ /dev/null
@@ -1,12 +0,0 @@
-import { isTensor } from '../utils';
-export function extractWeightEntryFactory(weightMap, paramMappings) {
-    return function (originalPath, paramRank, mappedPath) {
-        const tensor = weightMap[originalPath];
-        if (!isTensor(tensor, paramRank)) {
-            throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`);
-        }
-        paramMappings.push({ originalPath, paramPath: mappedPath || originalPath });
-        return tensor;
-    };
-}
-//# sourceMappingURL=extractWeightEntryFactory.js.map
\ No newline at end of file
diff --git a/build/src/common/extractWeightEntryFactory.js.map b/build/src/common/extractWeightEntryFactory.js.map
deleted file mode 100644
index 85d6f5e..0000000
--- a/build/src/common/extractWeightEntryFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractWeightEntryFactory.js","sourceRoot":"","sources":["../../../src/common/extractWeightEntryFactory.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,QAAQ,EAAE,MAAM,UAAU,CAAC;AAGpC,MAAM,UAAU,yBAAyB,CAAC,SAAc,EAAE,aAA6B;IAErF,OAAO,UAAa,YAAoB,EAAE,SAAiB,EAAE,UAAmB;QAC9E,MAAM,MAAM,GAAG,SAAS,CAAC,YAAY,CAAC,CAAA;QAEtC,IAAI,CAAC,QAAQ,CAAC,MAAM,EAAE,SAAS,CAAC,EAAE;YAChC,MAAM,IAAI,KAAK,CAAC,sBAAsB,YAAY,mBAAmB,SAAS,mBAAmB,MAAM,EAAE,CAAC,CAAA;SAC3G;QAED,aAAa,CAAC,IAAI,CAChB,EAAE,YAAY,EAAE,SAAS,EAAE,UAAU,IAAI,YAAY,EAAE,CACxD,CAAA;QAED,OAAO,MAAM,CAAA;IACf,CAAC,CAAA;AAEH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/extractWeightsFactory.d.ts b/build/src/common/extractWeightsFactory.d.ts
deleted file mode 100644
index baf2ef9..0000000
--- a/build/src/common/extractWeightsFactory.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export declare function extractWeightsFactory(weights: Float32Array): {
-    extractWeights: (numWeights: number) => Float32Array;
-    getRemainingWeights: () => Float32Array;
-};
diff --git a/build/src/common/extractWeightsFactory.js b/build/src/common/extractWeightsFactory.js
deleted file mode 100644
index 0bd2890..0000000
--- a/build/src/common/extractWeightsFactory.js
+++ /dev/null
@@ -1,16 +0,0 @@
-export 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
-    };
-}
-//# sourceMappingURL=extractWeightsFactory.js.map
\ No newline at end of file
diff --git a/build/src/common/extractWeightsFactory.js.map b/build/src/common/extractWeightsFactory.js.map
deleted file mode 100644
index b599a03..0000000
--- a/build/src/common/extractWeightsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractWeightsFactory.js","sourceRoot":"","sources":["../../../src/common/extractWeightsFactory.ts"],"names":[],"mappings":"AAAA,MAAM,UAAU,qBAAqB,CAAC,OAAqB;IACzD,IAAI,gBAAgB,GAAG,OAAO,CAAA;IAE9B,SAAS,cAAc,CAAC,UAAkB;QACxC,MAAM,GAAG,GAAG,gBAAgB,CAAC,KAAK,CAAC,CAAC,EAAE,UAAU,CAAC,CAAA;QACjD,gBAAgB,GAAG,gBAAgB,CAAC,KAAK,CAAC,UAAU,CAAC,CAAA;QACrD,OAAO,GAAG,CAAA;IACZ,CAAC;IAED,SAAS,mBAAmB;QAC1B,OAAO,gBAAgB,CAAA;IACzB,CAAC;IAED,OAAO;QACL,cAAc;QACd,mBAAmB;KACpB,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/fullyConnectedLayer.d.ts b/build/src/common/fullyConnectedLayer.d.ts
deleted file mode 100644
index 241f277..0000000
--- a/build/src/common/fullyConnectedLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { FCParams } from './types';
-export declare function fullyConnectedLayer(x: tf.Tensor2D, params: FCParams): tf.Tensor2D;
diff --git a/build/src/common/fullyConnectedLayer.js b/build/src/common/fullyConnectedLayer.js
deleted file mode 100644
index daa9ea2..0000000
--- a/build/src/common/fullyConnectedLayer.js
+++ /dev/null
@@ -1,5 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function fullyConnectedLayer(x, params) {
-    return tf.tidy(() => tf.add(tf.matMul(x, params.weights), params.bias));
-}
-//# sourceMappingURL=fullyConnectedLayer.js.map
\ No newline at end of file
diff --git a/build/src/common/fullyConnectedLayer.js.map b/build/src/common/fullyConnectedLayer.js.map
deleted file mode 100644
index cf1b5ec..0000000
--- a/build/src/common/fullyConnectedLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"fullyConnectedLayer.js","sourceRoot":"","sources":["../../../src/common/fullyConnectedLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAIzD,MAAM,UAAU,mBAAmB,CACjC,CAAc,EACd,MAAgB;IAEhB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAClB,EAAE,CAAC,GAAG,CACJ,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAC5B,MAAM,CAAC,IAAI,CACZ,CACF,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/getModelUris.d.ts b/build/src/common/getModelUris.d.ts
deleted file mode 100644
index 9a5f64a..0000000
--- a/build/src/common/getModelUris.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export declare function getModelUris(uri: string | undefined, defaultModelName: string): {
-    modelBaseUri: string;
-    manifestUri: string;
-};
diff --git a/build/src/common/getModelUris.js b/build/src/common/getModelUris.js
deleted file mode 100644
index f532647..0000000
--- a/build/src/common/getModelUris.js
+++ /dev/null
@@ -1,28 +0,0 @@
-export 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}`
-    };
-}
-//# sourceMappingURL=getModelUris.js.map
\ No newline at end of file
diff --git a/build/src/common/getModelUris.js.map b/build/src/common/getModelUris.js.map
deleted file mode 100644
index 5d84ce5..0000000
--- a/build/src/common/getModelUris.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"getModelUris.js","sourceRoot":"","sources":["../../../src/common/getModelUris.ts"],"names":[],"mappings":"AAAA,MAAM,UAAU,YAAY,CAAC,GAAuB,EAAE,gBAAwB;IAC5E,MAAM,uBAAuB,GAAG,GAAG,gBAAgB,wBAAwB,CAAA;IAE3E,IAAI,CAAC,GAAG,EAAE;QACR,OAAO;YACL,YAAY,EAAE,EAAE;YAChB,WAAW,EAAE,uBAAuB;SACrC,CAAA;KACF;IAED,IAAI,GAAG,KAAK,GAAG,EAAE;QACf,OAAO;YACL,YAAY,EAAE,GAAG;YACjB,WAAW,EAAE,IAAI,uBAAuB,EAAE;SAC3C,CAAA;KACF;IACD,MAAM,QAAQ,GAAG,GAAG,CAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC;IACtG,GAAG,GAAG,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC;IAEhC,MAAM,KAAK,GAAG,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAE3C,MAAM,YAAY,GAAG,GAAG,CAAC,QAAQ,CAAC,OAAO,CAAC;QACxC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC;QACzB,CAAC,CAAC,uBAAuB,CAAA;IAE3B,IAAI,YAAY,GAAG,QAAQ,GAAG,CAAC,GAAG,CAAC,QAAQ,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAC1G,YAAY,GAAG,GAAG,CAAC,UAAU,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,IAAI,YAAY,EAAE,CAAC,CAAC,CAAC,YAAY,CAAA;IAEtE,OAAO;QACL,YAAY;QACZ,WAAW,EAAE,YAAY,KAAK,GAAG,CAAC,CAAC,CAAC,IAAI,YAAY,EAAE,CAAC,CAAC,CAAC,GAAG,YAAY,IAAI,YAAY,EAAE;KAC3F,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/index.d.ts b/build/src/common/index.d.ts
deleted file mode 100644
index fc307a0..0000000
--- a/build/src/common/index.d.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-export * from './convLayer';
-export * from './depthwiseSeparableConv';
-export * from './disposeUnusedWeightTensors';
-export * from './extractConvParamsFactory';
-export * from './extractFCParamsFactory';
-export * from './extractSeparableConvParamsFactory';
-export * from './extractWeightEntryFactory';
-export * from './extractWeightsFactory';
-export * from './getModelUris';
-export * from './types';
diff --git a/build/src/common/index.js b/build/src/common/index.js
deleted file mode 100644
index fc1aa0f..0000000
--- a/build/src/common/index.js
+++ /dev/null
@@ -1,11 +0,0 @@
-export * from './convLayer';
-export * from './depthwiseSeparableConv';
-export * from './disposeUnusedWeightTensors';
-export * from './extractConvParamsFactory';
-export * from './extractFCParamsFactory';
-export * from './extractSeparableConvParamsFactory';
-export * from './extractWeightEntryFactory';
-export * from './extractWeightsFactory';
-export * from './getModelUris';
-export * from './types';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/common/index.js.map b/build/src/common/index.js.map
deleted file mode 100644
index caf2411..0000000
--- a/build/src/common/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/common/index.ts"],"names":[],"mappings":"AAAA,cAAc,aAAa,CAAA;AAC3B,cAAc,0BAA0B,CAAA;AACxC,cAAc,8BAA8B,CAAA;AAC5C,cAAc,4BAA4B,CAAA;AAC1C,cAAc,0BAA0B,CAAA;AACxC,cAAc,qCAAqC,CAAA;AACnD,cAAc,6BAA6B,CAAA;AAC3C,cAAc,yBAAyB,CAAA;AACvC,cAAc,gBAAgB,CAAA;AAC9B,cAAc,SAAS,CAAA"}
\ No newline at end of file
diff --git a/build/src/common/loadConvParamsFactory.d.ts b/build/src/common/loadConvParamsFactory.d.ts
deleted file mode 100644
index c139c7b..0000000
--- a/build/src/common/loadConvParamsFactory.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { ConvParams } from './types';
-export declare function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T): (prefix: string) => ConvParams;
diff --git a/build/src/common/loadConvParamsFactory.js b/build/src/common/loadConvParamsFactory.js
deleted file mode 100644
index 88f1b7a..0000000
--- a/build/src/common/loadConvParamsFactory.js
+++ /dev/null
@@ -1,8 +0,0 @@
-export function loadConvParamsFactory(extractWeightEntry) {
-    return function (prefix) {
-        const filters = extractWeightEntry(`${prefix}/filters`, 4);
-        const bias = extractWeightEntry(`${prefix}/bias`, 1);
-        return { filters, bias };
-    };
-}
-//# sourceMappingURL=loadConvParamsFactory.js.map
\ No newline at end of file
diff --git a/build/src/common/loadConvParamsFactory.js.map b/build/src/common/loadConvParamsFactory.js.map
deleted file mode 100644
index b941bf7..0000000
--- a/build/src/common/loadConvParamsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"loadConvParamsFactory.js","sourceRoot":"","sources":["../../../src/common/loadConvParamsFactory.ts"],"names":[],"mappings":"AAIA,MAAM,UAAU,qBAAqB,CAAC,kBAAqE;IACzG,OAAO,UAAS,MAAc;QAC5B,MAAM,OAAO,GAAG,kBAAkB,CAAc,GAAG,MAAM,UAAU,EAAE,CAAC,CAAC,CAAA;QACvE,MAAM,IAAI,GAAG,kBAAkB,CAAc,GAAG,MAAM,OAAO,EAAE,CAAC,CAAC,CAAA;QAEjE,OAAO,EAAE,OAAO,EAAE,IAAI,EAAE,CAAA;IAC1B,CAAC,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/common/types.d.ts b/build/src/common/types.d.ts
deleted file mode 100644
index a1a341c..0000000
--- a/build/src/common/types.d.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export declare type ExtractWeightsFunction = (numWeights: number) => Float32Array;
-export declare type ParamMapping = {
-    originalPath?: string;
-    paramPath: string;
-};
-export declare type ConvParams = {
-    filters: tf.Tensor4D;
-    bias: tf.Tensor1D;
-};
-export declare type FCParams = {
-    weights: tf.Tensor2D;
-    bias: tf.Tensor1D;
-};
-export declare class SeparableConvParams {
-    depthwise_filter: tf.Tensor4D;
-    pointwise_filter: tf.Tensor4D;
-    bias: tf.Tensor1D;
-    constructor(depthwise_filter: tf.Tensor4D, pointwise_filter: tf.Tensor4D, bias: tf.Tensor1D);
-}
diff --git a/build/src/common/types.js b/build/src/common/types.js
deleted file mode 100644
index b1cb624..0000000
--- a/build/src/common/types.js
+++ /dev/null
@@ -1,8 +0,0 @@
-export class SeparableConvParams {
-    constructor(depthwise_filter, pointwise_filter, bias) {
-        this.depthwise_filter = depthwise_filter;
-        this.pointwise_filter = pointwise_filter;
-        this.bias = bias;
-    }
-}
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/common/types.js.map b/build/src/common/types.js.map
deleted file mode 100644
index b7a01a5..0000000
--- a/build/src/common/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/common/types.ts"],"names":[],"mappings":"AAmBA,MAAM,OAAO,mBAAmB;IAC9B,YACS,gBAA6B,EAC7B,gBAA6B,EAC7B,IAAiB;QAFjB,qBAAgB,GAAhB,gBAAgB,CAAa;QAC7B,qBAAgB,GAAhB,gBAAgB,CAAa;QAC7B,SAAI,GAAJ,IAAI,CAAa;IACvB,CAAC;CACL"}
\ No newline at end of file
diff --git a/build/src/dom/NetInput.d.ts b/build/src/dom/NetInput.d.ts
deleted file mode 100644
index 64d83ba..0000000
--- a/build/src/dom/NetInput.d.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { Dimensions } from '../classes/Dimensions';
-import { TResolvedNetInput } from './types';
-export declare class NetInput {
-    private _imageTensors;
-    private _canvases;
-    private _batchSize;
-    private _treatAsBatchInput;
-    private _inputDimensions;
-    private _inputSize;
-    constructor(inputs: Array<TResolvedNetInput>, treatAsBatchInput?: boolean);
-    get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D>;
-    get canvases(): HTMLCanvasElement[];
-    get isBatchInput(): boolean;
-    get batchSize(): number;
-    get inputDimensions(): number[][];
-    get inputSize(): number | undefined;
-    get reshapedInputDimensions(): Dimensions[];
-    getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement;
-    getInputDimensions(batchIdx: number): number[];
-    getInputHeight(batchIdx: number): number;
-    getInputWidth(batchIdx: number): number;
-    getReshapedInputDimensions(batchIdx: number): Dimensions;
-    /**
-     * Create a batch tensor from all input canvases and tensors
-     * with size [batchSize, inputSize, inputSize, 3].
-     *
-     * @param inputSize Height and width of the tensor.
-     * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
-     * both sides of the minor dimension oof the image.
-     * @returns The batch tensor.
-     */
-    toBatchTensor(inputSize: number, isCenterInputs?: boolean): tf.Tensor4D;
-}
diff --git a/build/src/dom/NetInput.js b/build/src/dom/NetInput.js
deleted file mode 100644
index 3a648a7..0000000
--- a/build/src/dom/NetInput.js
+++ /dev/null
@@ -1,115 +0,0 @@
-import * as tf from '@tensorflow/tfjs-core';
-import { env } from '../env';
-import { padToSquare } from '../ops/padToSquare';
-import { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';
-import { createCanvasFromMedia } from './createCanvas';
-import { imageToSquare } from './imageToSquare';
-export 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);
-    }
-    /**
-     * Create a batch tensor from all input canvases and tensors
-     * with size [batchSize, inputSize, inputSize, 3].
-     *
-     * @param inputSize Height and width of the tensor.
-     * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
-     * both sides of the minor dimension oof the image.
-     * @returns The batch tensor.
-     */
-    toBatchTensor(inputSize, isCenterInputs = true) {
-        this._inputSize = inputSize;
-        return tf.tidy(() => {
-            const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {
-                const input = this.getInput(batchIdx);
-                if (input instanceof tf.Tensor) {
-                    // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.
-                    let imgTensor = isTensor4D(input) ? input : input.expandDims();
-                    // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.
-                    imgTensor = padToSquare(imgTensor, isCenterInputs);
-                    if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
-                        imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);
-                    }
-                    return imgTensor.as3D(inputSize, inputSize, 3);
-                }
-                if (input instanceof env.getEnv().Canvas) {
-                    return tf.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 = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)
-            const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3);
-            // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);
-            return batchTensor;
-        });
-    }
-}
-//# sourceMappingURL=NetInput.js.map
\ No newline at end of file
diff --git a/build/src/dom/NetInput.js.map b/build/src/dom/NetInput.js.map
deleted file mode 100644
index ae5bc74..0000000
--- a/build/src/dom/NetInput.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/dom/awaitMediaLoaded.d.ts b/build/src/dom/awaitMediaLoaded.d.ts
deleted file mode 100644
index d8eca6a..0000000
--- a/build/src/dom/awaitMediaLoaded.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement): Promise<unknown>;
diff --git a/build/src/dom/awaitMediaLoaded.js b/build/src/dom/awaitMediaLoaded.js
deleted file mode 100644
index 7e58daf..0000000
--- a/build/src/dom/awaitMediaLoaded.js
+++ /dev/null
@@ -1,26 +0,0 @@
-import { env } from '../env';
-import { isMediaLoaded } from './isMediaLoaded';
-export 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);
-    });
-}
-//# sourceMappingURL=awaitMediaLoaded.js.map
\ No newline at end of file
diff --git a/build/src/dom/awaitMediaLoaded.js.map b/build/src/dom/awaitMediaLoaded.js.map
deleted file mode 100644
index 28b51cf..0000000
--- a/build/src/dom/awaitMediaLoaded.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"awaitMediaLoaded.js","sourceRoot":"","sources":["../../../src/dom/awaitMediaLoaded.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAEhD,MAAM,UAAU,gBAAgB,CAAC,KAA8D;IAE7F,OAAO,IAAI,OAAO,CAAC,CAAC,OAAO,EAAE,MAAM,EAAE,EAAE;QACrC,IAAI,KAAK,YAAY,GAAG,CAAC,MAAM,EAAE,CAAC,MAAM,IAAI,aAAa,CAAC,KAAK,CAAC,EAAE;YAChE,OAAO,OAAO,CAAC,IAAI,CAAC,CAAA;SACrB;QAED,SAAS,MAAM,CAAC,CAAQ;YACtB,IAAI,CAAC,CAAC,CAAC,aAAa;gBAAE,OAAM;YAC5B,CAAC,CAAC,aAAa,CAAC,mBAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,CAAA;YACnD,CAAC,CAAC,aAAa,CAAC,mBAAmB,CAAC,OAAO,EAAE,OAAO,CAAC,CAAA;YACrD,OAAO,CAAC,CAAC,CAAC,CAAA;QACZ,CAAC;QAED,SAAS,OAAO,CAAC,CAAQ;YACvB,IAAI,CAAC,CAAC,CAAC,aAAa;gBAAE,OAAM;YAC5B,CAAC,CAAC,aAAa,CAAC,mBAAmB,CAAC,MAAM,EAAE,MAAM,CAAC,CAAA;YACnD,CAAC,CAAC,aAAa,CAAC,mBAAmB,CAAC,OAAO,EAAE,OAAO,CAAC,CAAA;YACrD,MAAM,CAAC,CAAC,CAAC,CAAA;QACX,CAAC;QAED,KAAK,CAAC,gBAAgB,CAAC,MAAM,EAAE,MAAM,CAAC,CAAA;QACtC,KAAK,CAAC,gBAAgB,CAAC,OAAO,EAAE,OAAO,CAAC,CAAA;IAC1C,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/bufferToImage.d.ts b/build/src/dom/bufferToImage.d.ts
deleted file mode 100644
index f153ac7..0000000
--- a/build/src/dom/bufferToImage.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function bufferToImage(buf: Blob): Promise<HTMLImageElement>;
diff --git a/build/src/dom/bufferToImage.js b/build/src/dom/bufferToImage.js
deleted file mode 100644
index 1395413..0000000
--- a/build/src/dom/bufferToImage.js
+++ /dev/null
@@ -1,21 +0,0 @@
-import { env } from '../env';
-export 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);
-    });
-}
-//# sourceMappingURL=bufferToImage.js.map
\ No newline at end of file
diff --git a/build/src/dom/bufferToImage.js.map b/build/src/dom/bufferToImage.js.map
deleted file mode 100644
index b8122a1..0000000
--- a/build/src/dom/bufferToImage.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"bufferToImage.js","sourceRoot":"","sources":["../../../src/dom/bufferToImage.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAE7B,MAAM,UAAU,aAAa,CAAC,GAAS;IACrC,OAAO,IAAI,OAAO,CAAC,CAAC,OAAO,EAAE,MAAM,EAAE,EAAE;QACrC,IAAI,CAAC,CAAC,GAAG,YAAY,IAAI,CAAC,EAAE;YAC1B,OAAO,MAAM,CAAC,kDAAkD,CAAC,CAAA;SAClE;QAED,MAAM,MAAM,GAAG,IAAI,UAAU,EAAE,CAAA;QAC/B,MAAM,CAAC,MAAM,GAAG,GAAG,EAAE;YACnB,IAAI,OAAO,MAAM,CAAC,MAAM,KAAK,QAAQ,EAAE;gBACrC,OAAO,MAAM,CAAC,kEAAkE,CAAC,CAAA;aAClF;YAED,MAAM,GAAG,GAAG,GAAG,CAAC,MAAM,EAAE,CAAC,kBAAkB,EAAE,CAAA;YAC7C,GAAG,CAAC,MAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,CAAC,CAAA;YAC/B,GAAG,CAAC,OAAO,GAAG,MAAM,CAAA;YACpB,GAAG,CAAC,GAAG,GAAG,MAAM,CAAC,MAAM,CAAA;QACzB,CAAC,CAAA;QACD,MAAM,CAAC,OAAO,GAAG,MAAM,CAAA;QACvB,MAAM,CAAC,aAAa,CAAC,GAAG,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/createCanvas.d.ts b/build/src/dom/createCanvas.d.ts
deleted file mode 100644
index 73ea4d5..0000000
--- a/build/src/dom/createCanvas.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import { IDimensions } from '../classes/Dimensions';
-export declare function createCanvas({ width, height }: IDimensions): HTMLCanvasElement;
-export declare function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement;
diff --git a/build/src/dom/createCanvas.js b/build/src/dom/createCanvas.js
deleted file mode 100644
index e656020..0000000
--- a/build/src/dom/createCanvas.js
+++ /dev/null
@@ -1,27 +0,0 @@
-import { env } from '../env';
-import { getContext2dOrThrow } from './getContext2dOrThrow';
-import { getMediaDimensions } from './getMediaDimensions';
-import { isMediaLoaded } from './isMediaLoaded';
-export function createCanvas({ width, height }) {
-    const { createCanvasElement } = env.getEnv();
-    const canvas = createCanvasElement();
-    canvas.width = width;
-    canvas.height = height;
-    return canvas;
-}
-export function createCanvasFromMedia(media, dims) {
-    const { ImageData } = env.getEnv();
-    if (!(media instanceof ImageData) && !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 ImageData) {
-        getContext2dOrThrow(canvas).putImageData(media, 0, 0);
-    }
-    else {
-        getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
-    }
-    return canvas;
-}
-//# sourceMappingURL=createCanvas.js.map
\ No newline at end of file
diff --git a/build/src/dom/createCanvas.js.map b/build/src/dom/createCanvas.js.map
deleted file mode 100644
index 43fd9df..0000000
--- a/build/src/dom/createCanvas.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"createCanvas.js","sourceRoot":"","sources":["../../../src/dom/createCanvas.ts"],"names":[],"mappings":"AACA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,mBAAmB,EAAE,MAAM,uBAAuB,CAAC;AAC5D,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAC1D,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAEhD,MAAM,UAAU,YAAY,CAAC,EAAE,KAAK,EAAE,MAAM,EAAe;IAEzD,MAAM,EAAE,mBAAmB,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;IAC5C,MAAM,MAAM,GAAG,mBAAmB,EAAE,CAAA;IACpC,MAAM,CAAC,KAAK,GAAG,KAAK,CAAA;IACpB,MAAM,CAAC,MAAM,GAAG,MAAM,CAAA;IACtB,OAAO,MAAM,CAAA;AACf,CAAC;AAED,MAAM,UAAU,qBAAqB,CAAC,KAAsD,EAAE,IAAkB;IAE9G,MAAM,EAAE,SAAS,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;IAElC,IAAI,CAAC,CAAC,KAAK,YAAY,SAAS,CAAC,IAAI,CAAC,aAAa,CAAC,KAAK,CAAC,EAAE;QAC1D,MAAM,IAAI,KAAK,CAAC,4DAA4D,CAAC,CAAA;KAC9E;IAED,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,IAAI,IAAI,kBAAkB,CAAC,KAAK,CAAC,CAAA;IAC3D,MAAM,MAAM,GAAG,YAAY,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,CAAC,CAAA;IAE9C,IAAI,KAAK,YAAY,SAAS,EAAE;QAC9B,mBAAmB,CAAC,MAAM,CAAC,CAAC,YAAY,CAAC,KAAK,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;KACtD;SAAM;QACL,mBAAmB,CAAC,MAAM,CAAC,CAAC,SAAS,CAAC,KAAK,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;KAClE;IACD,OAAO,MAAM,CAAA;AACf,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/extractFaceTensors.d.ts b/build/src/dom/extractFaceTensors.d.ts
deleted file mode 100644
index 523b452..0000000
--- a/build/src/dom/extractFaceTensors.d.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { Rect } from '../classes';
-import { FaceDetection } from '../classes/FaceDetection';
-/**
- * Extracts the tensors of the image regions containing the detected faces.
- * Useful if you want to compute the face descriptors for the face images.
- * Using this method is faster then extracting a canvas for each face and
- * converting them to tensors individually.
- *
- * @param imageTensor The image tensor that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns Tensors of the corresponding image region for each detected face.
- */
-export declare function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor3D[]>;
diff --git a/build/src/dom/extractFaceTensors.js b/build/src/dom/extractFaceTensors.js
deleted file mode 100644
index b097f31..0000000
--- a/build/src/dom/extractFaceTensors.js
+++ /dev/null
@@ -1,31 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { FaceDetection } from '../classes/FaceDetection';
-import { isTensor3D, isTensor4D } from '../utils';
-/**
- * Extracts the tensors of the image regions containing the detected faces.
- * Useful if you want to compute the face descriptors for the face images.
- * Using this method is faster then extracting a canvas for each face and
- * converting them to tensors individually.
- *
- * @param imageTensor The image tensor that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns Tensors of the corresponding image region for each detected face.
- */
-export 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 tf.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 }) => tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]));
-        return faceTensors;
-    });
-}
-//# sourceMappingURL=extractFaceTensors.js.map
\ No newline at end of file
diff --git a/build/src/dom/extractFaceTensors.js.map b/build/src/dom/extractFaceTensors.js.map
deleted file mode 100644
index 75668af..0000000
--- a/build/src/dom/extractFaceTensors.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAGzD,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAE,UAAU,EAAE,UAAU,EAAE,MAAM,UAAU,CAAC;AAElD;;;;;;;;;GASG;AACH,MAAM,CAAC,KAAK,UAAU,kBAAkB,CACtC,WAAsC,EACtC,UAAuC;IAGvC,IAAI,CAAC,UAAU,CAAC,WAAW,CAAC,IAAI,CAAC,UAAU,CAAC,WAAW,CAAC,EAAE;QACxD,MAAM,IAAI,KAAK,CAAC,2DAA2D,CAAC,CAAA;KAC7E;IAED,IAAI,UAAU,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;QACvD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;KACpE;IAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,GAAG,WAAW,CAAC,KAAK,CAAC,KAAK,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAEnG,MAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,aAAa;YACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,GAAG;YACtC,CAAC,CAAC,GAAG,CACR;aACE,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,CAAA;QAE1D,MAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,EAAE,CACxD,EAAE,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC,CACxG,CAAA;QAED,OAAO,WAAW,CAAA;IACpB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/extractFaces.d.ts b/build/src/dom/extractFaces.d.ts
deleted file mode 100644
index 562e7da..0000000
--- a/build/src/dom/extractFaces.d.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-import { FaceDetection } from '../classes/FaceDetection';
-import { Rect } from '../classes/Rect';
-import { TNetInput } from './types';
-/**
- * Extracts the image regions containing the detected faces.
- *
- * @param input The image that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns The Canvases of the corresponding image region for each detected face.
- */
-export declare function extractFaces(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<HTMLCanvasElement[]>;
diff --git a/build/src/dom/extractFaces.js b/build/src/dom/extractFaces.js
deleted file mode 100644
index 818fd0a..0000000
--- a/build/src/dom/extractFaces.js
+++ /dev/null
@@ -1,39 +0,0 @@
-import { FaceDetection } from '../classes/FaceDetection';
-import { env } from '../env';
-import { createCanvas } from './createCanvas';
-import { getContext2dOrThrow } from './getContext2dOrThrow';
-import { imageTensorToCanvas } from './imageTensorToCanvas';
-import { toNetInput } from './toNetInput';
-/**
- * Extracts the image regions containing the detected faces.
- *
- * @param input The image that face detection has been performed on.
- * @param detections The face detection results or face bounding boxes for that image.
- * @returns The Canvases of the corresponding image region for each detected face.
- */
-export 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;
-    });
-}
-//# sourceMappingURL=extractFaces.js.map
\ No newline at end of file
diff --git a/build/src/dom/extractFaces.js.map b/build/src/dom/extractFaces.js.map
deleted file mode 100644
index 0410db1..0000000
--- a/build/src/dom/extractFaces.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractFaces.js","sourceRoot":"","sources":["../../../src/dom/extractFaces.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAEzD,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,YAAY,EAAE,MAAM,gBAAgB,CAAC;AAC9C,OAAO,EAAE,mBAAmB,EAAE,MAAM,uBAAuB,CAAC;AAC5D,OAAO,EAAE,mBAAmB,EAAE,MAAM,uBAAuB,CAAC;AAC5D,OAAO,EAAE,UAAU,EAAE,MAAM,cAAc,CAAC;AAG1C;;;;;;GAMG;AACH,MAAM,CAAC,KAAK,UAAU,YAAY,CAChC,KAAgB,EAChB,UAAuC;IAGvC,MAAM,EAAE,MAAM,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;IAE/B,IAAI,MAAM,GAAG,KAA0B,CAAA;IAEvC,IAAI,CAAC,CAAC,KAAK,YAAY,MAAM,CAAC,EAAE;QAC9B,MAAM,QAAQ,GAAG,MAAM,UAAU,CAAC,KAAK,CAAC,CAAA;QAExC,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;YAC1B,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;SAC9D;QAED,MAAM,cAAc,GAAG,QAAQ,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA;QAC3C,MAAM,GAAG,cAAc,YAAY,MAAM;YACvC,CAAC,CAAC,cAAc;YAChB,CAAC,CAAC,MAAM,mBAAmB,CAAC,cAAc,CAAC,CAAA;KAC9C;IAED,MAAM,GAAG,GAAG,mBAAmB,CAAC,MAAM,CAAC,CAAA;IACvC,MAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,aAAa;QACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC,GAAG,CAAC,KAAK,EAAE;QACtD,CAAC,CAAC,GAAG,CACR;SACE,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,kBAAkB,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC,CAAA;IAElE,OAAO,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,EAAE,EAAE;QAC3C,MAAM,OAAO,GAAG,YAAY,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,CAAC,CAAA;QAC/C,mBAAmB,CAAC,OAAO,CAAC;aACzB,YAAY,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;QAC5D,OAAO,OAAO,CAAA;IAChB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/fetchImage.d.ts b/build/src/dom/fetchImage.d.ts
deleted file mode 100644
index f072b3e..0000000
--- a/build/src/dom/fetchImage.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function fetchImage(uri: string): Promise<HTMLImageElement>;
diff --git a/build/src/dom/fetchImage.js b/build/src/dom/fetchImage.js
deleted file mode 100644
index 5caac3a..0000000
--- a/build/src/dom/fetchImage.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import { bufferToImage } from './bufferToImage';
-import { fetchOrThrow } from './fetchOrThrow';
-export 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);
-}
-//# sourceMappingURL=fetchImage.js.map
\ No newline at end of file
diff --git a/build/src/dom/fetchImage.js.map b/build/src/dom/fetchImage.js.map
deleted file mode 100644
index 3b901e8..0000000
--- a/build/src/dom/fetchImage.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"fetchImage.js","sourceRoot":"","sources":["../../../src/dom/fetchImage.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,YAAY,EAAE,MAAM,gBAAgB,CAAC;AAE9C,MAAM,CAAC,KAAK,UAAU,UAAU,CAAC,GAAW;IAC1C,MAAM,GAAG,GAAG,MAAM,YAAY,CAAC,GAAG,CAAC,CAAA;IACnC,MAAM,IAAI,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,IAAI,EAAE,CAAA;IAE/B,IAAI,CAAC,IAAI,CAAC,IAAI,CAAC,UAAU,CAAC,QAAQ,CAAC,EAAE;QACnC,MAAM,IAAI,KAAK,CAAC,wEAAwE,IAAI,CAAC,IAAI,cAAc,GAAG,CAAC,GAAG,EAAE,CAAC,CAAA;KAC1H;IACD,OAAO,aAAa,CAAC,IAAI,CAAC,CAAA;AAC5B,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/fetchJson.d.ts b/build/src/dom/fetchJson.d.ts
deleted file mode 100644
index bdb5a0b..0000000
--- a/build/src/dom/fetchJson.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function fetchJson<T>(uri: string): Promise<T>;
diff --git a/build/src/dom/fetchJson.js b/build/src/dom/fetchJson.js
deleted file mode 100644
index 215338b..0000000
--- a/build/src/dom/fetchJson.js
+++ /dev/null
@@ -1,5 +0,0 @@
-import { fetchOrThrow } from './fetchOrThrow';
-export async function fetchJson(uri) {
-    return (await fetchOrThrow(uri)).json();
-}
-//# sourceMappingURL=fetchJson.js.map
\ No newline at end of file
diff --git a/build/src/dom/fetchJson.js.map b/build/src/dom/fetchJson.js.map
deleted file mode 100644
index bc127ff..0000000
--- a/build/src/dom/fetchJson.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"fetchJson.js","sourceRoot":"","sources":["../../../src/dom/fetchJson.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,YAAY,EAAE,MAAM,gBAAgB,CAAC;AAE9C,MAAM,CAAC,KAAK,UAAU,SAAS,CAAI,GAAW;IAC5C,OAAO,CAAC,MAAM,YAAY,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,EAAE,CAAA;AACzC,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/fetchNetWeights.d.ts b/build/src/dom/fetchNetWeights.d.ts
deleted file mode 100644
index f0875fe..0000000
--- a/build/src/dom/fetchNetWeights.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function fetchNetWeights(uri: string): Promise<Float32Array>;
diff --git a/build/src/dom/fetchNetWeights.js b/build/src/dom/fetchNetWeights.js
deleted file mode 100644
index a1bf5ee..0000000
--- a/build/src/dom/fetchNetWeights.js
+++ /dev/null
@@ -1,5 +0,0 @@
-import { fetchOrThrow } from './fetchOrThrow';
-export async function fetchNetWeights(uri) {
-    return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());
-}
-//# sourceMappingURL=fetchNetWeights.js.map
\ No newline at end of file
diff --git a/build/src/dom/fetchNetWeights.js.map b/build/src/dom/fetchNetWeights.js.map
deleted file mode 100644
index 3293978..0000000
--- a/build/src/dom/fetchNetWeights.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"fetchNetWeights.js","sourceRoot":"","sources":["../../../src/dom/fetchNetWeights.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,YAAY,EAAE,MAAM,gBAAgB,CAAC;AAE9C,MAAM,CAAC,KAAK,UAAU,eAAe,CAAC,GAAW;IAC/C,OAAO,IAAI,YAAY,CAAC,MAAM,CAAC,MAAM,YAAY,CAAC,GAAG,CAAC,CAAC,CAAC,WAAW,EAAE,CAAC,CAAA;AACxE,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/fetchOrThrow.d.ts b/build/src/dom/fetchOrThrow.d.ts
deleted file mode 100644
index a73c618..0000000
--- a/build/src/dom/fetchOrThrow.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function fetchOrThrow(url: string, init?: RequestInit): Promise<Response>;
diff --git a/build/src/dom/fetchOrThrow.js b/build/src/dom/fetchOrThrow.js
deleted file mode 100644
index f00015b..0000000
--- a/build/src/dom/fetchOrThrow.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import { env } from '../env';
-export 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;
-}
-//# sourceMappingURL=fetchOrThrow.js.map
\ No newline at end of file
diff --git a/build/src/dom/fetchOrThrow.js.map b/build/src/dom/fetchOrThrow.js.map
deleted file mode 100644
index 8158300..0000000
--- a/build/src/dom/fetchOrThrow.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"fetchOrThrow.js","sourceRoot":"","sources":["../../../src/dom/fetchOrThrow.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAE7B,MAAM,CAAC,KAAK,UAAU,YAAY,CAChC,GAAW,EACX,IAAkB;IAGlB,MAAM,KAAK,GAAG,GAAG,CAAC,MAAM,EAAE,CAAC,KAAK,CAAA;IAChC,MAAM,GAAG,GAAG,MAAM,KAAK,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;IAClC,IAAI,CAAC,CAAC,GAAG,CAAC,MAAM,GAAG,GAAG,CAAC,EAAE;QACvB,MAAM,IAAI,KAAK,CAAC,qBAAqB,GAAG,CAAC,MAAM,KAAK,GAAG,CAAC,UAAU,eAAe,GAAG,CAAC,GAAG,EAAE,CAAC,CAAA;KAC5F;IACD,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/getContext2dOrThrow.d.ts b/build/src/dom/getContext2dOrThrow.d.ts
deleted file mode 100644
index d9dcd4a..0000000
--- a/build/src/dom/getContext2dOrThrow.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D;
diff --git a/build/src/dom/getContext2dOrThrow.js b/build/src/dom/getContext2dOrThrow.js
deleted file mode 100644
index c7b04f6..0000000
--- a/build/src/dom/getContext2dOrThrow.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import { env } from '../env';
-import { resolveInput } from './resolveInput';
-export function getContext2dOrThrow(canvasArg) {
-    const { Canvas, CanvasRenderingContext2D } = env.getEnv();
-    if (canvasArg instanceof CanvasRenderingContext2D) {
-        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;
-}
-//# sourceMappingURL=getContext2dOrThrow.js.map
\ No newline at end of file
diff --git a/build/src/dom/getContext2dOrThrow.js.map b/build/src/dom/getContext2dOrThrow.js.map
deleted file mode 100644
index 56ac26c..0000000
--- a/build/src/dom/getContext2dOrThrow.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"getContext2dOrThrow.js","sourceRoot":"","sources":["../../../src/dom/getContext2dOrThrow.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,YAAY,EAAE,MAAM,gBAAgB,CAAC;AAE9C,MAAM,UAAU,mBAAmB,CAAC,SAAgE;IAElG,MAAM,EAAE,MAAM,EAAE,wBAAwB,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;IAEzD,IAAI,SAAS,YAAY,wBAAwB,EAAE;QACjD,OAAO,SAAS,CAAA;KACjB;IAED,MAAM,MAAM,GAAG,YAAY,CAAC,SAAS,CAAC,CAAA;IAEtC,IAAI,CAAC,CAAC,MAAM,YAAY,MAAM,CAAC,EAAE;QAC/B,MAAM,IAAI,KAAK,CAAC,gEAAgE,CAAC,CAAA;KAClF;IAED,MAAM,GAAG,GAAG,MAAM,CAAC,UAAU,CAAC,IAAI,CAAC,CAAA;IACnC,IAAI,CAAC,GAAG,EAAE;QACR,MAAM,IAAI,KAAK,CAAC,8CAA8C,CAAC,CAAA;KAChE;IAED,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/getMediaDimensions.d.ts b/build/src/dom/getMediaDimensions.d.ts
deleted file mode 100644
index a946f38..0000000
--- a/build/src/dom/getMediaDimensions.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Dimensions, IDimensions } from '../classes/Dimensions';
-export declare function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions;
diff --git a/build/src/dom/getMediaDimensions.js b/build/src/dom/getMediaDimensions.js
deleted file mode 100644
index fb8def5..0000000
--- a/build/src/dom/getMediaDimensions.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import { Dimensions } from '../classes/Dimensions';
-import { env } from '../env';
-export 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);
-}
-//# sourceMappingURL=getMediaDimensions.js.map
\ No newline at end of file
diff --git a/build/src/dom/getMediaDimensions.js.map b/build/src/dom/getMediaDimensions.js.map
deleted file mode 100644
index aad1880..0000000
--- a/build/src/dom/getMediaDimensions.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"getMediaDimensions.js","sourceRoot":"","sources":["../../../src/dom/getMediaDimensions.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,UAAU,EAAe,MAAM,uBAAuB,CAAC;AAChE,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAE7B,MAAM,UAAU,kBAAkB,CAAC,KAA4E;IAE7G,MAAM,EAAE,KAAK,EAAE,KAAK,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;IAErC,IAAI,KAAK,YAAY,KAAK,EAAE;QAC1B,OAAO,IAAI,UAAU,CAAC,KAAK,CAAC,YAAY,EAAE,KAAK,CAAC,aAAa,CAAC,CAAA;KAC/D;IACD,IAAI,KAAK,YAAY,KAAK,EAAE;QAC1B,OAAO,IAAI,UAAU,CAAC,KAAK,CAAC,UAAU,EAAE,KAAK,CAAC,WAAW,CAAC,CAAA;KAC3D;IACD,OAAO,IAAI,UAAU,CAAC,KAAK,CAAC,KAAK,EAAE,KAAK,CAAC,MAAM,CAAC,CAAA;AAClD,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/imageTensorToCanvas.d.ts b/build/src/dom/imageTensorToCanvas.d.ts
deleted file mode 100644
index b40f152..0000000
--- a/build/src/dom/imageTensorToCanvas.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export declare function imageTensorToCanvas(imgTensor: tf.Tensor, canvas?: HTMLCanvasElement): Promise<HTMLCanvasElement>;
diff --git a/build/src/dom/imageTensorToCanvas.js b/build/src/dom/imageTensorToCanvas.js
deleted file mode 100644
index 598db64..0000000
--- a/build/src/dom/imageTensorToCanvas.js
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { env } from '../env';
-import { isTensor4D } from '../utils';
-export async function imageTensorToCanvas(imgTensor, canvas) {
-    const targetCanvas = canvas || env.getEnv().createCanvasElement();
-    const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0);
-    const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt());
-    await tf.browser.toPixels(imgTensor3D, targetCanvas);
-    imgTensor3D.dispose();
-    return targetCanvas;
-}
-//# sourceMappingURL=imageTensorToCanvas.js.map
\ No newline at end of file
diff --git a/build/src/dom/imageTensorToCanvas.js.map b/build/src/dom/imageTensorToCanvas.js.map
deleted file mode 100644
index 7fb20cc..0000000
--- a/build/src/dom/imageTensorToCanvas.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"imageTensorToCanvas.js","sourceRoot":"","sources":["../../../src/dom/imageTensorToCanvas.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,UAAU,EAAE,MAAM,UAAU,CAAC;AAEtC,MAAM,CAAC,KAAK,UAAU,mBAAmB,CACvC,SAAoB,EACpB,MAA0B;IAG1B,MAAM,YAAY,GAAG,MAAM,IAAI,GAAG,CAAC,MAAM,EAAE,CAAC,mBAAmB,EAAE,CAAA;IAEjE,MAAM,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACzF,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC,KAAK,EAAE,CAAC,CAAA;IACrF,MAAM,EAAE,CAAC,OAAO,CAAC,QAAQ,CAAC,WAAW,EAAE,YAAY,CAAC,CAAA;IAEpD,WAAW,CAAC,OAAO,EAAE,CAAA;IAErB,OAAO,YAAY,CAAA;AACrB,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/imageToSquare.d.ts b/build/src/dom/imageToSquare.d.ts
deleted file mode 100644
index 7276c53..0000000
--- a/build/src/dom/imageToSquare.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage?: boolean): HTMLCanvasElement;
diff --git a/build/src/dom/imageToSquare.js b/build/src/dom/imageToSquare.js
deleted file mode 100644
index 79cdf2b..0000000
--- a/build/src/dom/imageToSquare.js
+++ /dev/null
@@ -1,22 +0,0 @@
-import { env } from '../env';
-import { createCanvas, createCanvasFromMedia } from './createCanvas';
-import { getContext2dOrThrow } from './getContext2dOrThrow';
-import { getMediaDimensions } from './getMediaDimensions';
-export 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 scale = inputSize / Math.max(dims.height, dims.width);
-    const width = scale * dims.width;
-    const height = scale * 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;
-}
-//# sourceMappingURL=imageToSquare.js.map
\ No newline at end of file
diff --git a/build/src/dom/imageToSquare.js.map b/build/src/dom/imageToSquare.js.map
deleted file mode 100644
index fff0b45..0000000
--- a/build/src/dom/imageToSquare.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"imageToSquare.js","sourceRoot":"","sources":["../../../src/dom/imageToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAC7B,OAAO,EAAE,YAAY,EAAE,qBAAqB,EAAE,MAAM,gBAAgB,CAAC;AACrE,OAAO,EAAE,mBAAmB,EAAE,MAAM,uBAAuB,CAAC;AAC5D,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAE1D,MAAM,UAAU,aAAa,CAAC,KAA2C,EAAE,SAAiB,EAAE,cAAuB,KAAK;IAExH,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;IAEtC,IAAI,CAAC,CAAC,KAAK,YAAY,KAAK,IAAI,KAAK,YAAY,MAAM,CAAC,EAAE;QACxD,MAAM,IAAI,KAAK,CAAC,0EAA0E,CAAC,CAAA;KAC5F;IAED,MAAM,IAAI,GAAG,kBAAkB,CAAC,KAAK,CAAC,CAAA;IACtC,MAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,MAAM,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC3D,MAAM,KAAK,GAAG,KAAK,GAAG,IAAI,CAAC,KAAK,CAAA;IAChC,MAAM,MAAM,GAAG,KAAK,GAAG,IAAI,CAAC,MAAM,CAAA;IAElC,MAAM,YAAY,GAAG,YAAY,CAAC,EAAE,KAAK,EAAE,SAAS,EAAE,MAAM,EAAE,SAAS,EAAE,CAAC,CAAA;IAC1E,MAAM,WAAW,GAAG,KAAK,YAAY,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,qBAAqB,CAAC,KAAK,CAAC,CAAA;IAElF,MAAM,MAAM,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,GAAG,CAAC,CAAA;IAC3C,MAAM,EAAE,GAAG,WAAW,IAAI,KAAK,GAAG,MAAM,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IACrD,MAAM,EAAE,GAAG,WAAW,IAAI,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IACrD,mBAAmB,CAAC,YAAY,CAAC,CAAC,SAAS,CAAC,WAAW,EAAE,EAAE,EAAE,EAAE,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;IAE/E,OAAO,YAAY,CAAA;AACrB,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/index.d.ts b/build/src/dom/index.d.ts
deleted file mode 100644
index 10e5aff..0000000
--- a/build/src/dom/index.d.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-export * from './awaitMediaLoaded';
-export * from './bufferToImage';
-export * from './createCanvas';
-export * from './extractFaces';
-export * from './extractFaceTensors';
-export * from './fetchImage';
-export * from './fetchJson';
-export * from './fetchNetWeights';
-export * from './fetchOrThrow';
-export * from './getContext2dOrThrow';
-export * from './getMediaDimensions';
-export * from './imageTensorToCanvas';
-export * from './imageToSquare';
-export * from './isMediaElement';
-export * from './isMediaLoaded';
-export * from './loadWeightMap';
-export * from './matchDimensions';
-export * from './NetInput';
-export * from './resolveInput';
-export * from './toNetInput';
-export * from './types';
diff --git a/build/src/dom/index.js b/build/src/dom/index.js
deleted file mode 100644
index 4ba64a7..0000000
--- a/build/src/dom/index.js
+++ /dev/null
@@ -1,22 +0,0 @@
-export * from './awaitMediaLoaded';
-export * from './bufferToImage';
-export * from './createCanvas';
-export * from './extractFaces';
-export * from './extractFaceTensors';
-export * from './fetchImage';
-export * from './fetchJson';
-export * from './fetchNetWeights';
-export * from './fetchOrThrow';
-export * from './getContext2dOrThrow';
-export * from './getMediaDimensions';
-export * from './imageTensorToCanvas';
-export * from './imageToSquare';
-export * from './isMediaElement';
-export * from './isMediaLoaded';
-export * from './loadWeightMap';
-export * from './matchDimensions';
-export * from './NetInput';
-export * from './resolveInput';
-export * from './toNetInput';
-export * from './types';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/dom/index.js.map b/build/src/dom/index.js.map
deleted file mode 100644
index fcd6398..0000000
--- a/build/src/dom/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/dom/index.ts"],"names":[],"mappings":"AAAA,cAAc,oBAAoB,CAAA;AAClC,cAAc,iBAAiB,CAAA;AAC/B,cAAc,gBAAgB,CAAA;AAC9B,cAAc,gBAAgB,CAAA;AAC9B,cAAc,sBAAsB,CAAA;AACpC,cAAc,cAAc,CAAA;AAC5B,cAAc,aAAa,CAAA;AAC3B,cAAc,mBAAmB,CAAA;AACjC,cAAc,gBAAgB,CAAA;AAC9B,cAAc,uBAAuB,CAAA;AACrC,cAAc,sBAAsB,CAAA;AACpC,cAAc,uBAAuB,CAAA;AACrC,cAAc,iBAAiB,CAAA;AAC/B,cAAc,kBAAkB,CAAA;AAChC,cAAc,iBAAiB,CAAA;AAC/B,cAAc,iBAAiB,CAAA;AAC/B,cAAc,mBAAmB,CAAA;AACjC,cAAc,YAAY,CAAA;AAC1B,cAAc,gBAAgB,CAAA;AAC9B,cAAc,cAAc,CAAA;AAC5B,cAAc,SAAS,CAAA"}
\ No newline at end of file
diff --git a/build/src/dom/isMediaElement.d.ts b/build/src/dom/isMediaElement.d.ts
deleted file mode 100644
index 1d359c2..0000000
--- a/build/src/dom/isMediaElement.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function isMediaElement(input: any): boolean;
diff --git a/build/src/dom/isMediaElement.js b/build/src/dom/isMediaElement.js
deleted file mode 100644
index f0305c0..0000000
--- a/build/src/dom/isMediaElement.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import { env } from '../env';
-export function isMediaElement(input) {
-    const { Image, Canvas, Video } = env.getEnv();
-    return input instanceof Image
-        || input instanceof Canvas
-        || input instanceof Video;
-}
-//# sourceMappingURL=isMediaElement.js.map
\ No newline at end of file
diff --git a/build/src/dom/isMediaElement.js.map b/build/src/dom/isMediaElement.js.map
deleted file mode 100644
index 68a2bc6..0000000
--- a/build/src/dom/isMediaElement.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"isMediaElement.js","sourceRoot":"","sources":["../../../src/dom/isMediaElement.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAE7B,MAAM,UAAU,cAAc,CAAC,KAAU;IAEvC,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,KAAK,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;IAE7C,OAAO,KAAK,YAAY,KAAK;WACxB,KAAK,YAAY,MAAM;WACvB,KAAK,YAAY,KAAK,CAAA;AAC7B,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/isMediaLoaded.d.ts b/build/src/dom/isMediaLoaded.d.ts
deleted file mode 100644
index 211e01e..0000000
--- a/build/src/dom/isMediaLoaded.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement): boolean;
diff --git a/build/src/dom/isMediaLoaded.js b/build/src/dom/isMediaLoaded.js
deleted file mode 100644
index 266a8e9..0000000
--- a/build/src/dom/isMediaLoaded.js
+++ /dev/null
@@ -1,7 +0,0 @@
-import { env } from '../env';
-export function isMediaLoaded(media) {
-    const { Image, Video } = env.getEnv();
-    return (media instanceof Image && media.complete)
-        || (media instanceof Video && media.readyState >= 3);
-}
-//# sourceMappingURL=isMediaLoaded.js.map
\ No newline at end of file
diff --git a/build/src/dom/isMediaLoaded.js.map b/build/src/dom/isMediaLoaded.js.map
deleted file mode 100644
index 3479c6e..0000000
--- a/build/src/dom/isMediaLoaded.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"isMediaLoaded.js","sourceRoot":"","sources":["../../../src/dom/isMediaLoaded.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAE7B,MAAM,UAAU,aAAa,CAAC,KAA0C;IAEtE,MAAM,EAAE,KAAK,EAAE,KAAK,EAAE,GAAG,GAAG,CAAC,MAAM,EAAE,CAAA;IAErC,OAAO,CAAC,KAAK,YAAY,KAAK,IAAI,KAAK,CAAC,QAAQ,CAAC;WAC5C,CAAC,KAAK,YAAY,KAAK,IAAI,KAAK,CAAC,UAAU,IAAI,CAAC,CAAC,CAAA;AACxD,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/loadWeightMap.d.ts b/build/src/dom/loadWeightMap.d.ts
deleted file mode 100644
index 69ef237..0000000
--- a/build/src/dom/loadWeightMap.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export declare function loadWeightMap(uri: string | undefined, defaultModelName: string): Promise<tf.NamedTensorMap>;
diff --git a/build/src/dom/loadWeightMap.js b/build/src/dom/loadWeightMap.js
deleted file mode 100644
index 2b0d921..0000000
--- a/build/src/dom/loadWeightMap.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { getModelUris } from '../common/getModelUris';
-import { fetchJson } from './fetchJson';
-export async function loadWeightMap(uri, defaultModelName) {
-    const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName);
-    let manifest = await fetchJson(manifestUri);
-    // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];
-    return tf.io.loadWeights(manifest, modelBaseUri);
-}
-//# sourceMappingURL=loadWeightMap.js.map
\ No newline at end of file
diff --git a/build/src/dom/loadWeightMap.js.map b/build/src/dom/loadWeightMap.js.map
deleted file mode 100644
index d71ef3d..0000000
--- a/build/src/dom/loadWeightMap.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"loadWeightMap.js","sourceRoot":"","sources":["../../../src/dom/loadWeightMap.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,YAAY,EAAE,MAAM,wBAAwB,CAAC;AACtD,OAAO,EAAE,SAAS,EAAE,MAAM,aAAa,CAAC;AAExC,MAAM,CAAC,KAAK,UAAU,aAAa,CACjC,GAAuB,EACvB,gBAAwB;IAExB,MAAM,EAAE,WAAW,EAAE,YAAY,EAAE,GAAG,YAAY,CAAC,GAAG,EAAE,gBAAgB,CAAC,CAAA;IACzE,IAAI,QAAQ,GAAG,MAAM,SAAS,CAA8B,WAAW,CAAC,CAAA;IACxE,2EAA2E;IAC3E,OAAO,EAAE,CAAC,EAAE,CAAC,WAAW,CAAC,QAAQ,EAAE,YAAY,CAAC,CAAA;AAClD,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/matchDimensions.d.ts b/build/src/dom/matchDimensions.d.ts
deleted file mode 100644
index 9fab19a..0000000
--- a/build/src/dom/matchDimensions.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import { IDimensions } from '../classes';
-export declare function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions?: boolean): {
-    width: number;
-    height: number;
-};
diff --git a/build/src/dom/matchDimensions.js b/build/src/dom/matchDimensions.js
deleted file mode 100644
index aa32108..0000000
--- a/build/src/dom/matchDimensions.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import { getMediaDimensions } from './getMediaDimensions';
-export function matchDimensions(input, reference, useMediaDimensions = false) {
-    const { width, height } = useMediaDimensions
-        ? getMediaDimensions(reference)
-        : reference;
-    input.width = width;
-    input.height = height;
-    return { width, height };
-}
-//# sourceMappingURL=matchDimensions.js.map
\ No newline at end of file
diff --git a/build/src/dom/matchDimensions.js.map b/build/src/dom/matchDimensions.js.map
deleted file mode 100644
index 223c45d..0000000
--- a/build/src/dom/matchDimensions.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"matchDimensions.js","sourceRoot":"","sources":["../../../src/dom/matchDimensions.ts"],"names":[],"mappings":"AACA,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAE1D,MAAM,UAAU,eAAe,CAAC,KAAkB,EAAE,SAAsB,EAAE,qBAA8B,KAAK;IAC7G,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,kBAAkB;QAC1C,CAAC,CAAC,kBAAkB,CAAC,SAAS,CAAC;QAC/B,CAAC,CAAC,SAAS,CAAA;IACb,KAAK,CAAC,KAAK,GAAG,KAAK,CAAA;IACnB,KAAK,CAAC,MAAM,GAAG,MAAM,CAAA;IACrB,OAAO,EAAE,KAAK,EAAE,MAAM,EAAE,CAAA;AAC1B,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/resolveInput.d.ts b/build/src/dom/resolveInput.d.ts
deleted file mode 100644
index 2fa6a8a..0000000
--- a/build/src/dom/resolveInput.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function resolveInput(arg: string | any): any;
diff --git a/build/src/dom/resolveInput.js b/build/src/dom/resolveInput.js
deleted file mode 100644
index 37b844d..0000000
--- a/build/src/dom/resolveInput.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import { env } from '../env';
-export function resolveInput(arg) {
-    if (!env.isNodejs() && typeof arg === 'string') {
-        return document.getElementById(arg);
-    }
-    return arg;
-}
-//# sourceMappingURL=resolveInput.js.map
\ No newline at end of file
diff --git a/build/src/dom/resolveInput.js.map b/build/src/dom/resolveInput.js.map
deleted file mode 100644
index 67e6bb2..0000000
--- a/build/src/dom/resolveInput.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"resolveInput.js","sourceRoot":"","sources":["../../../src/dom/resolveInput.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAE,MAAM,QAAQ,CAAC;AAE7B,MAAM,UAAU,YAAY,CAAC,GAAiB;IAC5C,IAAI,CAAC,GAAG,CAAC,QAAQ,EAAE,IAAI,OAAO,GAAG,KAAK,QAAQ,EAAE;QAC9C,OAAO,QAAQ,CAAC,cAAc,CAAC,GAAG,CAAC,CAAA;KACpC;IACD,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/toNetInput.d.ts b/build/src/dom/toNetInput.d.ts
deleted file mode 100644
index b361487..0000000
--- a/build/src/dom/toNetInput.d.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import { NetInput } from './NetInput';
-import { TNetInput } from './types';
-/**
- * Validates the input to make sure, they are valid net inputs and awaits all media elements
- * to be finished loading.
- *
- * @param input The input, which can be a media element or an array of different media elements.
- * @returns A NetInput instance, which can be passed into one of the neural networks.
- */
-export declare function toNetInput(inputs: TNetInput): Promise<NetInput>;
diff --git a/build/src/dom/toNetInput.js b/build/src/dom/toNetInput.js
deleted file mode 100644
index e96792b..0000000
--- a/build/src/dom/toNetInput.js
+++ /dev/null
@@ -1,44 +0,0 @@
-import { isTensor3D, isTensor4D } from '../utils';
-import { awaitMediaLoaded } from './awaitMediaLoaded';
-import { isMediaElement } from './isMediaElement';
-import { NetInput } from './NetInput';
-import { resolveInput } from './resolveInput';
-/**
- * Validates the input to make sure, they are valid net inputs and awaits all media elements
- * to be finished loading.
- *
- * @param input The input, which can be a media element or an array of different media elements.
- * @returns A NetInput instance, which can be passed into one of the neural networks.
- */
-export 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)) {
-            // if tf.Tensor4D is passed in the input array, the batch size has to be 1
-            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`);
-            }
-        }
-    });
-    // wait for all media elements being loaded
-    await Promise.all(inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input)));
-    return new NetInput(inputArray, Array.isArray(inputs));
-}
-//# sourceMappingURL=toNetInput.js.map
\ No newline at end of file
diff --git a/build/src/dom/toNetInput.js.map b/build/src/dom/toNetInput.js.map
deleted file mode 100644
index f5ab6d9..0000000
--- a/build/src/dom/toNetInput.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"toNetInput.js","sourceRoot":"","sources":["../../../src/dom/toNetInput.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,UAAU,EAAE,UAAU,EAAE,MAAM,UAAU,CAAC;AAClD,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AACtD,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,QAAQ,EAAE,MAAM,YAAY,CAAC;AACtC,OAAO,EAAE,YAAY,EAAE,MAAM,gBAAgB,CAAC;AAG9C;;;;;;GAMG;AACH,MAAM,CAAC,KAAK,UAAU,UAAU,CAAC,MAAiB;IAChD,IAAI,MAAM,YAAY,QAAQ,EAAE;QAC9B,OAAO,MAAM,CAAA;KACd;IAED,IAAI,aAAa,GAAG,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC;QACrC,CAAC,CAAC,MAAM;QACR,CAAC,CAAC,CAAC,MAAM,CAAC,CAAA;IAEd,IAAI,CAAC,aAAa,CAAC,MAAM,EAAE;QACzB,MAAM,IAAI,KAAK,CAAC,0CAA0C,CAAC,CAAA;KAC5D;IAED,MAAM,UAAU,GAAG,CAAC,GAAW,EAAE,EAAE,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,mBAAmB,GAAG,GAAG,CAAC,CAAC,CAAC,EAAE,CAAA;IAE1F,MAAM,UAAU,GAAG,aAAa,CAAC,GAAG,CAAC,YAAY,CAAC,CAAA;IAElD,UAAU,CAAC,OAAO,CAAC,CAAC,KAAK,EAAE,CAAC,EAAE,EAAE;QAC9B,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,IAAI,CAAC,UAAU,CAAC,KAAK,CAAC,IAAI,CAAC,UAAU,CAAC,KAAK,CAAC,EAAE;YAEtE,IAAI,OAAO,aAAa,CAAC,CAAC,CAAC,KAAK,QAAQ,EAAE;gBACxC,MAAM,IAAI,KAAK,CAAC,eAAe,UAAU,CAAC,CAAC,CAAC,oEAAoE,aAAa,CAAC,CAAC,CAAC,EAAE,CAAC,CAAA;aACpI;YAED,MAAM,IAAI,KAAK,CAAC,eAAe,UAAU,CAAC,CAAC,CAAC,6HAA6H,CAAC,CAAA;SAC3K;QAED,IAAI,UAAU,CAAC,KAAK,CAAC,EAAE;YACrB,0EAA0E;YAC1E,MAAM,SAAS,GAAG,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;YAChC,IAAI,SAAS,KAAK,CAAC,EAAE;gBACnB,MAAM,IAAI,KAAK,CAAC,eAAe,UAAU,CAAC,CAAC,CAAC,+BAA+B,SAAS,2CAA2C,CAAC,CAAA;aACjI;SACF;IACH,CAAC,CAAC,CAAA;IAEF,2CAA2C;IAC3C,MAAM,OAAO,CAAC,GAAG,CACf,UAAU,CAAC,GAAG,CAAC,KAAK,CAAC,EAAE,CAAC,cAAc,CAAC,KAAK,CAAC,IAAI,gBAAgB,CAAC,KAAK,CAAC,CAAC,CAC1E,CAAA;IAED,OAAO,IAAI,QAAQ,CAAC,UAAU,EAAE,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC,CAAA;AACxD,CAAC"}
\ No newline at end of file
diff --git a/build/src/dom/types.d.ts b/build/src/dom/types.d.ts
deleted file mode 100644
index 2456e33..0000000
--- a/build/src/dom/types.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { NetInput } from './NetInput';
-export declare type TMediaElement = HTMLImageElement | HTMLVideoElement | HTMLCanvasElement;
-export declare type TResolvedNetInput = TMediaElement | tf.Tensor3D | tf.Tensor4D;
-export declare type TNetInputArg = string | TResolvedNetInput;
-export declare type TNetInput = TNetInputArg | Array<TNetInputArg> | NetInput | tf.Tensor4D;
diff --git a/build/src/dom/types.js b/build/src/dom/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/dom/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/dom/types.js.map b/build/src/dom/types.js.map
deleted file mode 100644
index 81849c0..0000000
--- a/build/src/dom/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/dom/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/src/draw/DrawBox.d.ts b/build/src/draw/DrawBox.d.ts
deleted file mode 100644
index 6fd97be..0000000
--- a/build/src/draw/DrawBox.d.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-import { Box, IBoundingBox, IRect } from '../classes';
-import { DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';
-export interface IDrawBoxOptions {
-    boxColor?: string;
-    lineWidth?: number;
-    drawLabelOptions?: IDrawTextFieldOptions;
-    label?: string;
-}
-export declare class DrawBoxOptions {
-    boxColor: string;
-    lineWidth: number;
-    drawLabelOptions: DrawTextFieldOptions;
-    label?: string;
-    constructor(options?: IDrawBoxOptions);
-}
-export declare class DrawBox {
-    box: Box;
-    options: DrawBoxOptions;
-    constructor(box: IBoundingBox | IRect, options?: IDrawBoxOptions);
-    draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void;
-}
diff --git a/build/src/draw/DrawBox.js b/build/src/draw/DrawBox.js
deleted file mode 100644
index ca6f256..0000000
--- a/build/src/draw/DrawBox.js
+++ /dev/null
@@ -1,35 +0,0 @@
-import { Box } from '../classes';
-import { getContext2dOrThrow } from '../dom/getContext2dOrThrow';
-import { AnchorPosition, DrawTextField, DrawTextFieldOptions } from './DrawTextField';
-export 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));
-    }
-}
-export 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);
-        }
-    }
-}
-//# sourceMappingURL=DrawBox.js.map
\ No newline at end of file
diff --git a/build/src/draw/DrawBox.js.map b/build/src/draw/DrawBox.js.map
deleted file mode 100644
index 75b0cef..0000000
--- a/build/src/draw/DrawBox.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"DrawBox.js","sourceRoot":"","sources":["../../../src/draw/DrawBox.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAuB,MAAM,YAAY,CAAC;AACtD,OAAO,EAAE,mBAAmB,EAAE,MAAM,4BAA4B,CAAC;AACjE,OAAO,EAAE,cAAc,EAAE,aAAa,EAAE,oBAAoB,EAAyB,MAAM,iBAAiB,CAAC;AAS7G,MAAM,OAAO,cAAc;IAMzB,YAAY,UAA2B,EAAE;QACvC,MAAM,EAAE,QAAQ,EAAE,SAAS,EAAE,KAAK,EAAE,gBAAgB,EAAE,GAAG,OAAO,CAAA;QAChE,IAAI,CAAC,QAAQ,GAAG,QAAQ,IAAI,oBAAoB,CAAA;QAChD,IAAI,CAAC,SAAS,GAAG,SAAS,IAAI,CAAC,CAAA;QAC/B,IAAI,CAAC,KAAK,GAAG,KAAK,CAAA;QAElB,MAAM,uBAAuB,GAAG;YAC9B,cAAc,EAAE,cAAc,CAAC,WAAW;YAC1C,eAAe,EAAE,IAAI,CAAC,QAAQ;SAC/B,CAAA;QACD,IAAI,CAAC,gBAAgB,GAAG,IAAI,oBAAoB,CAAC,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,uBAAuB,EAAE,gBAAgB,CAAC,CAAC,CAAA;IAChH,CAAC;CACF;AAED,MAAM,OAAO,OAAO;IAIlB,YACE,GAAyB,EACzB,UAA2B,EAAE;QAE7B,IAAI,CAAC,GAAG,GAAG,IAAI,GAAG,CAAC,GAAG,CAAC,CAAA;QACvB,IAAI,CAAC,OAAO,GAAG,IAAI,cAAc,CAAC,OAAO,CAAC,CAAA;IAC5C,CAAC;IAED,IAAI,CAAC,SAAgE;QACnE,MAAM,GAAG,GAAG,mBAAmB,CAAC,SAAS,CAAC,CAAA;QAE1C,MAAM,EAAE,QAAQ,EAAE,SAAS,EAAE,GAAG,IAAI,CAAC,OAAO,CAAA;QAE5C,MAAM,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,IAAI,CAAC,GAAG,CAAA;QACxC,GAAG,CAAC,WAAW,GAAG,QAAQ,CAAA;QAC1B,GAAG,CAAC,SAAS,GAAG,SAAS,CAAA;QACzB,GAAG,CAAC,UAAU,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;QAEnC,MAAM,EAAE,KAAK,EAAE,GAAG,IAAI,CAAC,OAAO,CAAA;QAC9B,IAAI,KAAK,EAAE;YACT,IAAI,aAAa,CAAC,CAAC,KAAK,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,GAAG,CAAC,SAAS,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,IAAI,CAAC,OAAO,CAAC,gBAAgB,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAA;SACzG;IACH,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/draw/DrawFaceLandmarks.d.ts b/build/src/draw/DrawFaceLandmarks.d.ts
deleted file mode 100644
index 4833c4b..0000000
--- a/build/src/draw/DrawFaceLandmarks.d.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-import { FaceLandmarks } from '../classes/FaceLandmarks';
-import { WithFaceDetection } from '../factories/WithFaceDetection';
-import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
-export interface IDrawFaceLandmarksOptions {
-    drawLines?: boolean;
-    drawPoints?: boolean;
-    lineWidth?: number;
-    pointSize?: number;
-    lineColor?: string;
-    pointColor?: string;
-}
-export declare class DrawFaceLandmarksOptions {
-    drawLines: boolean;
-    drawPoints: boolean;
-    lineWidth: number;
-    pointSize: number;
-    lineColor: string;
-    pointColor: string;
-    constructor(options?: IDrawFaceLandmarksOptions);
-}
-export declare class DrawFaceLandmarks {
-    faceLandmarks: FaceLandmarks;
-    options: DrawFaceLandmarksOptions;
-    constructor(faceLandmarks: FaceLandmarks, options?: IDrawFaceLandmarksOptions);
-    draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void;
-}
-export declare type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>;
-export declare function drawFaceLandmarks(canvasArg: string | HTMLCanvasElement, faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>): void;
diff --git a/build/src/draw/DrawFaceLandmarks.js b/build/src/draw/DrawFaceLandmarks.js
deleted file mode 100644
index 9aa0e35..0000000
--- a/build/src/draw/DrawFaceLandmarks.js
+++ /dev/null
@@ -1,60 +0,0 @@
-import { FaceLandmarks } from '../classes/FaceLandmarks';
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-import { getContext2dOrThrow } from '../dom/getContext2dOrThrow';
-import { isWithFaceLandmarks } from '../factories/WithFaceLandmarks';
-import { drawContour } from './drawContour';
-export 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)';
-    }
-}
-export 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);
-        }
-    }
-}
-export function drawFaceLandmarks(canvasArg, faceLandmarks) {
-    const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
-    faceLandmarksArray.forEach(f => {
-        const landmarks = f instanceof FaceLandmarks
-            ? f
-            : (isWithFaceLandmarks(f) ? f.landmarks : undefined);
-        if (!landmarks) {
-            throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');
-        }
-        new DrawFaceLandmarks(landmarks).draw(canvasArg);
-    });
-}
-//# sourceMappingURL=DrawFaceLandmarks.js.map
\ No newline at end of file
diff --git a/build/src/draw/DrawFaceLandmarks.js.map b/build/src/draw/DrawFaceLandmarks.js.map
deleted file mode 100644
index 229c80d..0000000
--- a/build/src/draw/DrawFaceLandmarks.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/draw/DrawTextField.d.ts b/build/src/draw/DrawTextField.d.ts
deleted file mode 100644
index abed043..0000000
--- a/build/src/draw/DrawTextField.d.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-import { IDimensions, IPoint } from '../classes';
-export declare enum AnchorPosition {
-    TOP_LEFT = "TOP_LEFT",
-    TOP_RIGHT = "TOP_RIGHT",
-    BOTTOM_LEFT = "BOTTOM_LEFT",
-    BOTTOM_RIGHT = "BOTTOM_RIGHT"
-}
-export interface IDrawTextFieldOptions {
-    anchorPosition?: AnchorPosition;
-    backgroundColor?: string;
-    fontColor?: string;
-    fontSize?: number;
-    fontStyle?: string;
-    padding?: number;
-}
-export declare class DrawTextFieldOptions implements IDrawTextFieldOptions {
-    anchorPosition: AnchorPosition;
-    backgroundColor: string;
-    fontColor: string;
-    fontSize: number;
-    fontStyle: string;
-    padding: number;
-    constructor(options?: IDrawTextFieldOptions);
-}
-export declare class DrawTextField {
-    text: string[];
-    anchor: IPoint;
-    options: DrawTextFieldOptions;
-    constructor(text: string | string[] | DrawTextField, anchor: IPoint, options?: IDrawTextFieldOptions);
-    measureWidth(ctx: CanvasRenderingContext2D): number;
-    measureHeight(): number;
-    getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint;
-    draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): void;
-}
diff --git a/build/src/draw/DrawTextField.js b/build/src/draw/DrawTextField.js
deleted file mode 100644
index 8e6024b..0000000
--- a/build/src/draw/DrawTextField.js
+++ /dev/null
@@ -1,72 +0,0 @@
-import { getContext2dOrThrow } from '../dom/getContext2dOrThrow';
-import { resolveInput } from '../dom/resolveInput';
-export var AnchorPosition;
-(function (AnchorPosition) {
-    AnchorPosition["TOP_LEFT"] = "TOP_LEFT";
-    AnchorPosition["TOP_RIGHT"] = "TOP_RIGHT";
-    AnchorPosition["BOTTOM_LEFT"] = "BOTTOM_LEFT";
-    AnchorPosition["BOTTOM_RIGHT"] = "BOTTOM_RIGHT";
-})(AnchorPosition || (AnchorPosition = {}));
-export 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;
-    }
-}
-export 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;
-        // adjust anchor if text box exceeds canvas borders
-        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);
-        });
-    }
-}
-//# sourceMappingURL=DrawTextField.js.map
\ No newline at end of file
diff --git a/build/src/draw/DrawTextField.js.map b/build/src/draw/DrawTextField.js.map
deleted file mode 100644
index df53cad..0000000
--- a/build/src/draw/DrawTextField.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"DrawTextField.js","sourceRoot":"","sources":["../../../src/draw/DrawTextField.ts"],"names":[],"mappings":"AACA,OAAO,EAAE,mBAAmB,EAAE,MAAM,4BAA4B,CAAC;AACjE,OAAO,EAAE,YAAY,EAAE,MAAM,qBAAqB,CAAC;AAEnD,MAAM,CAAN,IAAY,cAKX;AALD,WAAY,cAAc;IACxB,uCAAqB,CAAA;IACrB,yCAAuB,CAAA;IACvB,6CAA2B,CAAA;IAC3B,+CAA6B,CAAA;AAC/B,CAAC,EALW,cAAc,KAAd,cAAc,QAKzB;AAWD,MAAM,OAAO,oBAAoB;IAQ/B,YAAY,UAAiC,EAAE;QAC7C,MAAM,EAAE,cAAc,EAAE,eAAe,EAAE,SAAS,EAAE,QAAQ,EAAE,SAAS,EAAE,OAAO,EAAE,GAAG,OAAO,CAAA;QAC5F,IAAI,CAAC,cAAc,GAAG,cAAc,IAAI,cAAc,CAAC,QAAQ,CAAA;QAC/D,IAAI,CAAC,eAAe,GAAG,eAAe,IAAI,oBAAoB,CAAA;QAC9D,IAAI,CAAC,SAAS,GAAG,SAAS,IAAI,wBAAwB,CAAA;QACtD,IAAI,CAAC,QAAQ,GAAG,QAAQ,IAAI,EAAE,CAAA;QAC9B,IAAI,CAAC,SAAS,GAAG,SAAS,IAAI,SAAS,CAAA;QACvC,IAAI,CAAC,OAAO,GAAG,OAAO,IAAI,CAAC,CAAA;IAC7B,CAAC;CACF;AAED,MAAM,OAAO,aAAa;IAKxB,YACE,IAAuC,EACvC,MAAc,EACd,UAAiC,EAAE;QAEnC,IAAI,CAAC,IAAI,GAAG,OAAO,IAAI,KAAK,QAAQ;YAClC,CAAC,CAAC,CAAC,IAAI,CAAC;YACR,CAAC,CAAC,CAAC,IAAI,YAAY,aAAa,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,CAAC,CAAA;QACtD,IAAI,CAAC,MAAM,GAAG,MAAM,CAAA;QACpB,IAAI,CAAC,OAAO,GAAG,IAAI,oBAAoB,CAAC,OAAO,CAAC,CAAA;IAClD,CAAC;IAED,YAAY,CAAC,GAA6B;QACxC,MAAM,EAAE,OAAO,EAAE,GAAG,IAAI,CAAC,OAAO,CAAA;QAChC,OAAO,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,MAAM,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,GAAG,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,OAAO,CAAC,CAAA;IAC9G,CAAC;IAED,aAAa;QACX,MAAM,EAAE,QAAQ,EAAE,OAAO,EAAE,GAAG,IAAI,CAAC,OAAO,CAAA;QAC1C,OAAO,IAAI,CAAC,IAAI,CAAC,MAAM,GAAG,QAAQ,GAAG,CAAC,CAAC,GAAG,OAAO,CAAC,CAAA;IACpD,CAAC;IAED,YAAY,CAAC,GAA6B,EAAE,UAAwB;QAClE,MAAM,EAAE,cAAc,EAAE,GAAG,IAAI,CAAC,OAAO,CAAA;QACvC,MAAM,WAAW,GAAG,cAAc,KAAK,cAAc,CAAC,YAAY,IAAI,cAAc,KAAK,cAAc,CAAC,SAAS,CAAA;QACjH,MAAM,UAAU,GAAG,cAAc,KAAK,cAAc,CAAC,WAAW,IAAI,cAAc,KAAK,cAAc,CAAC,YAAY,CAAA;QAElH,MAAM,cAAc,GAAG,IAAI,CAAC,YAAY,CAAC,GAAG,CAAC,CAAA;QAC7C,MAAM,eAAe,GAAG,IAAI,CAAC,aAAa,EAAE,CAAA;QAC5C,MAAM,CAAC,GAAG,CAAC,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,GAAG,cAAc,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA;QACxE,MAAM,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,GAAG,eAAe,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,CAAA;QAEtE,mDAAmD;QACnD,IAAI,UAAU,EAAE;YACd,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,UAAU,CAAA;YACpC,MAAM,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,GAAG,cAAc,CAAC,EAAE,CAAC,CAAC,CAAA;YAC7D,MAAM,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,GAAG,eAAe,CAAC,EAAE,CAAC,CAAC,CAAA;YAC/D,OAAO,EAAE,CAAC,EAAE,IAAI,EAAE,CAAC,EAAE,IAAI,EAAE,CAAA;SAC5B;QACD,OAAO,EAAE,CAAC,EAAE,CAAC,EAAE,CAAA;IACjB,CAAC;IAED,IAAI,CAAC,SAAgE;QACnE,MAAM,MAAM,GAAG,YAAY,CAAC,SAAS,CAAC,CAAA;QACtC,MAAM,GAAG,GAAG,mBAAmB,CAAC,MAAM,CAAC,CAAA;QAEvC,MAAM,EAAE,eAAe,EAAE,SAAS,EAAE,QAAQ,EAAE,SAAS,EAAE,OAAO,EAAE,GAAG,IAAI,CAAC,OAAO,CAAA;QAEjF,GAAG,CAAC,IAAI,GAAG,GAAG,QAAQ,MAAM,SAAS,EAAE,CAAA;QACvC,MAAM,YAAY,GAAG,IAAI,CAAC,YAAY,CAAC,GAAG,CAAC,CAAA;QAC3C,MAAM,UAAU,GAAG,IAAI,CAAC,aAAa,EAAE,CAAA;QAEvC,GAAG,CAAC,SAAS,GAAG,eAAe,CAAA;QAC/B,MAAM,SAAS,GAAG,IAAI,CAAC,YAAY,CAAC,GAAG,EAAE,MAAM,CAAC,CAAA;QAChD,GAAG,CAAC,QAAQ,CAAC,SAAS,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,EAAE,YAAY,EAAE,UAAU,CAAC,CAAA;QAEhE,GAAG,CAAC,SAAS,GAAG,SAAS,CAAC;QAC1B,IAAI,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,QAAQ,EAAE,CAAC,EAAE,EAAE;YAChC,MAAM,CAAC,GAAG,OAAO,GAAG,SAAS,CAAC,CAAC,CAAA;YAC/B,MAAM,CAAC,GAAG,OAAO,GAAG,SAAS,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,QAAQ,CAAC,CAAA;YACtD,GAAG,CAAC,QAAQ,CAAC,QAAQ,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;QAC9B,CAAC,CAAC,CAAA;IACJ,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/draw/drawContour.d.ts b/build/src/draw/drawContour.d.ts
deleted file mode 100644
index 209c87d..0000000
--- a/build/src/draw/drawContour.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Point } from '../classes';
-export declare function drawContour(ctx: CanvasRenderingContext2D, points: Point[], isClosed?: boolean): void;
diff --git a/build/src/draw/drawContour.js b/build/src/draw/drawContour.js
deleted file mode 100644
index 2278a13..0000000
--- a/build/src/draw/drawContour.js
+++ /dev/null
@@ -1,19 +0,0 @@
-export 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();
-}
-//# sourceMappingURL=drawContour.js.map
\ No newline at end of file
diff --git a/build/src/draw/drawContour.js.map b/build/src/draw/drawContour.js.map
deleted file mode 100644
index 9a97700..0000000
--- a/build/src/draw/drawContour.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"drawContour.js","sourceRoot":"","sources":["../../../src/draw/drawContour.ts"],"names":[],"mappings":"AAEA,MAAM,UAAU,WAAW,CACzB,GAA6B,EAC7B,MAAe,EACf,WAAoB,KAAK;IAEzB,GAAG,CAAC,SAAS,EAAE,CAAA;IAEf,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,OAAO,EAAE,EAAE;QAC5C,MAAM,IAAI,GAAG,MAAM,CAAC,OAAO,CAAC,CAAA;QAC5B,GAAG,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAA;QAC1B,GAAG,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAA;IAClB,CAAC,CAAC,CAAA;IAEF,IAAI,QAAQ,EAAE;QACZ,MAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC,MAAM,GAAG,CAAC,CAAC,CAAA;QACtC,MAAM,EAAE,GAAG,MAAM,CAAC,CAAC,CAAC,CAAA;QACpB,IAAI,CAAC,IAAI,IAAI,CAAC,EAAE,EAAE;YAChB,OAAM;SACP;QAED,GAAG,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAA;QAC1B,GAAG,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAA;KACvB;IAED,GAAG,CAAC,MAAM,EAAE,CAAA;AACd,CAAC"}
\ No newline at end of file
diff --git a/build/src/draw/drawDetections.d.ts b/build/src/draw/drawDetections.d.ts
deleted file mode 100644
index 9e94d7e..0000000
--- a/build/src/draw/drawDetections.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import { IBoundingBox, IRect } from '../classes';
-import { FaceDetection } from '../classes/FaceDetection';
-import { WithFaceDetection } from '../factories/WithFaceDetection';
-export declare type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>;
-export declare function drawDetections(canvasArg: string | HTMLCanvasElement, detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>): void;
diff --git a/build/src/draw/drawDetections.js b/build/src/draw/drawDetections.js
deleted file mode 100644
index d56ab27..0000000
--- a/build/src/draw/drawDetections.js
+++ /dev/null
@@ -1,19 +0,0 @@
-import { Box } from '../classes';
-import { FaceDetection } from '../classes/FaceDetection';
-import { isWithFaceDetection } from '../factories/WithFaceDetection';
-import { round } from '../utils';
-import { DrawBox } from './DrawBox';
-export 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 : undefined);
-        const box = det instanceof FaceDetection
-            ? det.box
-            : (isWithFaceDetection(det) ? det.detection.box : new Box(det));
-        const label = score ? `${round(score)}` : undefined;
-        new DrawBox(box, { label }).draw(canvasArg);
-    });
-}
-//# sourceMappingURL=drawDetections.js.map
\ No newline at end of file
diff --git a/build/src/draw/drawDetections.js.map b/build/src/draw/drawDetections.js.map
deleted file mode 100644
index 43b14fe..0000000
--- a/build/src/draw/drawDetections.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"drawDetections.js","sourceRoot":"","sources":["../../../src/draw/drawDetections.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,GAAG,EAAuB,MAAM,YAAY,CAAC;AACtD,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAE,mBAAmB,EAAqB,MAAM,gCAAgC,CAAC;AACxF,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AACjC,OAAO,EAAE,OAAO,EAAE,MAAM,WAAW,CAAC;AAIpC,MAAM,UAAU,cAAc,CAC5B,SAAqC,EACrC,UAA8D;IAE9D,MAAM,eAAe,GAAG,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAA;IAE7E,eAAe,CAAC,OAAO,CAAC,GAAG,CAAC,EAAE;QAC5B,MAAM,KAAK,GAAG,GAAG,YAAY,aAAa;YACxC,CAAC,CAAC,GAAG,CAAC,KAAK;YACX,CAAC,CAAC,CAAC,mBAAmB,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,SAAS,CAAC,CAAA;QAEhE,MAAM,GAAG,GAAG,GAAG,YAAY,aAAa;YACtC,CAAC,CAAC,GAAG,CAAC,GAAG;YACT,CAAC,CAAC,CAAC,mBAAmB,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,SAAS,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,GAAG,CAAC,GAAG,CAAC,CAAC,CAAA;QAEjE,MAAM,KAAK,GAAG,KAAK,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,KAAK,CAAC,EAAE,CAAC,CAAC,CAAC,SAAS,CAAA;QACnD,IAAI,OAAO,CAAC,GAAG,EAAE,EAAE,KAAK,EAAE,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAA;IAC7C,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/draw/drawFaceExpressions.d.ts b/build/src/draw/drawFaceExpressions.d.ts
deleted file mode 100644
index f96cc10..0000000
--- a/build/src/draw/drawFaceExpressions.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import { IPoint } from '../classes';
-import { FaceExpressions } from '../faceExpressionNet';
-import { WithFaceExpressions } from '../factories/WithFaceExpressions';
-export declare type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>;
-export declare function drawFaceExpressions(canvasArg: string | HTMLCanvasElement, faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>, minConfidence?: number, textFieldAnchor?: IPoint): void;
diff --git a/build/src/draw/drawFaceExpressions.js b/build/src/draw/drawFaceExpressions.js
deleted file mode 100644
index 3292c22..0000000
--- a/build/src/draw/drawFaceExpressions.js
+++ /dev/null
@@ -1,25 +0,0 @@
-import { Point } from '../classes';
-import { FaceExpressions } from '../faceExpressionNet';
-import { isWithFaceDetection } from '../factories/WithFaceDetection';
-import { isWithFaceExpressions } from '../factories/WithFaceExpressions';
-import { round } from '../utils';
-import { DrawTextField } from './DrawTextField';
-export 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 : undefined);
-        if (!expr) {
-            throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');
-        }
-        const sorted = expr.asSortedArray();
-        const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence);
-        const anchor = isWithFaceDetection(e)
-            ? e.detection.box.bottomLeft
-            : (textFieldAnchor || new Point(0, 0));
-        const drawTextField = new DrawTextField(resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`), anchor);
-        drawTextField.draw(canvasArg);
-    });
-}
-//# sourceMappingURL=drawFaceExpressions.js.map
\ No newline at end of file
diff --git a/build/src/draw/drawFaceExpressions.js.map b/build/src/draw/drawFaceExpressions.js.map
deleted file mode 100644
index dd4dd39..0000000
--- a/build/src/draw/drawFaceExpressions.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"drawFaceExpressions.js","sourceRoot":"","sources":["../../../src/draw/drawFaceExpressions.ts"],"names":[],"mappings":"AAAA,OAAO,EAAU,KAAK,EAAE,MAAM,YAAY,CAAC;AAC3C,OAAO,EAAE,eAAe,EAAE,MAAM,sBAAsB,CAAC;AACvD,OAAO,EAAE,mBAAmB,EAAE,MAAM,gCAAgC,CAAC;AACrE,OAAO,EAAE,qBAAqB,EAAuB,MAAM,kCAAkC,CAAC;AAC9F,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AACjC,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAIhD,MAAM,UAAU,mBAAmB,CACjC,SAAqC,EACrC,eAA2E,EAC3E,aAAa,GAAG,GAAG,EACnB,eAAwB;IAExB,MAAM,oBAAoB,GAAG,KAAK,CAAC,OAAO,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,CAAA;IAEjG,oBAAoB,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE;QAC/B,MAAM,IAAI,GAAG,CAAC,YAAY,eAAe;YACvC,CAAC,CAAC,CAAC;YACH,CAAC,CAAC,CAAC,qBAAqB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,SAAS,CAAC,CAAA;QAC1D,IAAI,CAAC,IAAI,EAAE;YACT,MAAM,IAAI,KAAK,CAAC,iHAAiH,CAAC,CAAA;SACnI;QAED,MAAM,MAAM,GAAG,IAAI,CAAC,aAAa,EAAE,CAAA;QACnC,MAAM,gBAAgB,GAAG,MAAM,CAAC,MAAM,CAAC,IAAI,CAAC,EAAE,CAAC,IAAI,CAAC,WAAW,GAAG,aAAa,CAAC,CAAA;QAEhF,MAAM,MAAM,GAAG,mBAAmB,CAAC,CAAC,CAAC;YACnC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,UAAU;YAC5B,CAAC,CAAC,CAAC,eAAe,IAAI,IAAI,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExC,MAAM,aAAa,GAAG,IAAI,aAAa,CACrC,gBAAgB,CAAC,GAAG,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,UAAU,KAAK,KAAK,CAAC,IAAI,CAAC,WAAW,CAAC,GAAG,CAAC,EAC/E,MAAM,CACP,CAAA;QACD,aAAa,CAAC,IAAI,CAAC,SAAS,CAAC,CAAA;IAC/B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/draw/index.d.ts b/build/src/draw/index.d.ts
deleted file mode 100644
index 520d940..0000000
--- a/build/src/draw/index.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-export * from './drawContour';
-export * from './drawDetections';
-export * from './drawFaceExpressions';
-export * from './DrawBox';
-export * from './DrawFaceLandmarks';
-export * from './DrawTextField';
diff --git a/build/src/draw/index.js b/build/src/draw/index.js
deleted file mode 100644
index 6e3f6d8..0000000
--- a/build/src/draw/index.js
+++ /dev/null
@@ -1,7 +0,0 @@
-export * from './drawContour';
-export * from './drawDetections';
-export * from './drawFaceExpressions';
-export * from './DrawBox';
-export * from './DrawFaceLandmarks';
-export * from './DrawTextField';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/draw/index.js.map b/build/src/draw/index.js.map
deleted file mode 100644
index 696792a..0000000
--- a/build/src/draw/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/draw/index.ts"],"names":[],"mappings":"AAAA,cAAc,eAAe,CAAA;AAC7B,cAAc,kBAAkB,CAAA;AAChC,cAAc,uBAAuB,CAAA;AACrC,cAAc,WAAW,CAAA;AACzB,cAAc,qBAAqB,CAAA;AACnC,cAAc,iBAAiB,CAAA"}
\ No newline at end of file
diff --git a/build/src/env/createBrowserEnv.d.ts b/build/src/env/createBrowserEnv.d.ts
deleted file mode 100644
index f7f144c..0000000
--- a/build/src/env/createBrowserEnv.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Environment } from './types';
-export declare function createBrowserEnv(): Environment;
diff --git a/build/src/env/createBrowserEnv.js b/build/src/env/createBrowserEnv.js
deleted file mode 100644
index 4243f3e..0000000
--- a/build/src/env/createBrowserEnv.js
+++ /dev/null
@@ -1,20 +0,0 @@
-export 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: CanvasRenderingContext2D,
-        Image: HTMLImageElement,
-        ImageData: ImageData,
-        Video: HTMLVideoElement,
-        createCanvasElement: () => document.createElement('canvas'),
-        createImageElement: () => document.createElement('img'),
-        fetch,
-        readFile
-    };
-}
-//# sourceMappingURL=createBrowserEnv.js.map
\ No newline at end of file
diff --git a/build/src/env/createBrowserEnv.js.map b/build/src/env/createBrowserEnv.js.map
deleted file mode 100644
index f0d1171..0000000
--- a/build/src/env/createBrowserEnv.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"createBrowserEnv.js","sourceRoot":"","sources":["../../../src/env/createBrowserEnv.ts"],"names":[],"mappings":"AAEA,MAAM,UAAU,gBAAgB;IAE9B,MAAM,KAAK,GAAG,MAAM,CAAC,OAAO,CAAC,IAAI;QAC/B,MAAM,IAAI,KAAK,CAAC,8DAA8D,CAAC,CAAA;IACjF,CAAC,CAAA;IAED,MAAM,QAAQ,GAAG;QACf,MAAM,IAAI,KAAK,CAAC,6DAA6D,CAAC,CAAA;IAChF,CAAC,CAAA;IAED,OAAO;QACL,MAAM,EAAE,iBAAiB;QACzB,wBAAwB,EAAE,wBAAwB;QAClD,KAAK,EAAE,gBAAgB;QACvB,SAAS,EAAE,SAAS;QACpB,KAAK,EAAE,gBAAgB;QACvB,mBAAmB,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;QAC3D,kBAAkB,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,aAAa,CAAC,KAAK,CAAC;QACvD,KAAK;QACL,QAAQ;KACT,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/env/createFileSystem.d.ts b/build/src/env/createFileSystem.d.ts
deleted file mode 100644
index 9e53e28..0000000
--- a/build/src/env/createFileSystem.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { FileSystem } from './types';
-export declare function createFileSystem(fs?: any): FileSystem;
diff --git a/build/src/env/createFileSystem.js b/build/src/env/createFileSystem.js
deleted file mode 100644
index 08efdf5..0000000
--- a/build/src/env/createFileSystem.js
+++ /dev/null
@@ -1,26 +0,0 @@
-export 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
-    };
-}
-//# sourceMappingURL=createFileSystem.js.map
\ No newline at end of file
diff --git a/build/src/env/createFileSystem.js.map b/build/src/env/createFileSystem.js.map
deleted file mode 100644
index 94a8102..0000000
--- a/build/src/env/createFileSystem.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"createFileSystem.js","sourceRoot":"","sources":["../../../src/env/createFileSystem.ts"],"names":[],"mappings":"AAEA,MAAM,UAAU,gBAAgB,CAAC,EAAQ;IAEvC,IAAI,cAAc,GAAG,EAAE,CAAA;IAEvB,IAAI,CAAC,EAAE,EAAE;QACP,IAAI;YACF,EAAE,GAAG,OAAO,CAAC,IAAI,CAAC,CAAA;SACnB;QAAC,OAAO,GAAG,EAAE;YACZ,cAAc,GAAG,GAAG,CAAC,QAAQ,EAAE,CAAA;SAChC;KACF;IAED,MAAM,QAAQ,GAAG,EAAE;QACjB,CAAC,CAAC,UAAS,QAAgB;YACzB,OAAO,IAAI,OAAO,CAAS,CAAC,GAAG,EAAE,GAAG,EAAE,EAAE;gBACtC,EAAE,CAAC,QAAQ,CAAC,QAAQ,EAAE,UAAS,GAAQ,EAAE,MAAc;oBACrD,OAAO,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,MAAM,CAAC,CAAA;gBACrC,CAAC,CAAC,CAAA;YACJ,CAAC,CAAC,CAAA;QACJ,CAAC;QACD,CAAC,CAAC;YACA,MAAM,IAAI,KAAK,CAAC,qEAAqE,cAAc,EAAE,CAAC,CAAA;QACxG,CAAC,CAAA;IAEH,OAAO;QACL,QAAQ;KACT,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/env/createNodejsEnv.d.ts b/build/src/env/createNodejsEnv.d.ts
deleted file mode 100644
index a64159b..0000000
--- a/build/src/env/createNodejsEnv.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Environment } from './types';
-export declare function createNodejsEnv(): Environment;
diff --git a/build/src/env/createNodejsEnv.js b/build/src/env/createNodejsEnv.js
deleted file mode 100644
index 02a362d..0000000
--- a/build/src/env/createNodejsEnv.js
+++ /dev/null
@@ -1,38 +0,0 @@
-import { createFileSystem } from './createFileSystem';
-export 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
-    };
-}
-//# sourceMappingURL=createNodejsEnv.js.map
\ No newline at end of file
diff --git a/build/src/env/createNodejsEnv.js.map b/build/src/env/createNodejsEnv.js.map
deleted file mode 100644
index 9f0719b..0000000
--- a/build/src/env/createNodejsEnv.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"createNodejsEnv.js","sourceRoot":"","sources":["../../../src/env/createNodejsEnv.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AAGtD,MAAM,UAAU,eAAe;IAE7B,MAAM,MAAM,GAAG,MAAM,CAAC,QAAQ,CAAC,IAAI,MAAM,CAAC,mBAAmB,CAAC,CAAA;IAC9D,MAAM,KAAK,GAAG,MAAM,CAAC,OAAO,CAAC,IAAI,MAAM,CAAC,kBAAkB,CAAC,CAAA;IAE3D,MAAM,mBAAmB,GAAG;QAC1B,IAAI,MAAM,EAAE;YACV,OAAO,IAAI,MAAM,EAAE,CAAA;SACpB;QACD,MAAM,IAAI,KAAK,CAAC,4EAA4E,CAAC,CAAA;IAC/F,CAAC,CAAA;IAED,MAAM,kBAAkB,GAAG;QACzB,IAAI,KAAK,EAAE;YACT,OAAO,IAAI,KAAK,EAAE,CAAA;SACnB;QACD,MAAM,IAAI,KAAK,CAAC,0EAA0E,CAAC,CAAA;IAC7F,CAAC,CAAA;IAED,MAAM,KAAK,GAAG,MAAM,CAAC,OAAO,CAAC,IAAI;QAC/B,MAAM,IAAI,KAAK,CAAC,6DAA6D,CAAC,CAAA;IAChF,CAAC,CAAA;IAED,MAAM,UAAU,GAAG,gBAAgB,EAAE,CAAA;IAErC,OAAO;QACL,MAAM,EAAE,MAAM,IAAI;SAAQ;QAC1B,wBAAwB,EAAE,MAAM,CAAC,0BAA0B,CAAC,IAAI;SAAQ;QACxE,KAAK,EAAE,KAAK,IAAI;SAAQ;QACxB,SAAS,EAAE,MAAM,CAAC,WAAW,CAAC,IAAI;SAAQ;QAC1C,KAAK,EAAE,MAAM,CAAC,kBAAkB,CAAC,IAAI;SAAQ;QAC7C,mBAAmB;QACnB,kBAAkB;QAClB,KAAK;QACL,GAAG,UAAU;KACd,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/env/index.d.ts b/build/src/env/index.d.ts
deleted file mode 100644
index 525a8ab..0000000
--- a/build/src/env/index.d.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-import { createBrowserEnv } from './createBrowserEnv';
-import { createFileSystem } from './createFileSystem';
-import { createNodejsEnv } from './createNodejsEnv';
-import { isBrowser } from './isBrowser';
-import { isNodejs } from './isNodejs';
-import { Environment } from './types';
-declare function getEnv(): Environment;
-declare function setEnv(env: Environment): void;
-declare function initialize(): void;
-declare function monkeyPatch(env: Partial<Environment>): void;
-export declare const env: {
-    getEnv: typeof getEnv;
-    setEnv: typeof setEnv;
-    initialize: typeof initialize;
-    createBrowserEnv: typeof createBrowserEnv;
-    createFileSystem: typeof createFileSystem;
-    createNodejsEnv: typeof createNodejsEnv;
-    monkeyPatch: typeof monkeyPatch;
-    isBrowser: typeof isBrowser;
-    isNodejs: typeof isNodejs;
-};
-export * from './types';
diff --git a/build/src/env/index.js b/build/src/env/index.js
deleted file mode 100644
index 4a53eb1..0000000
--- a/build/src/env/index.js
+++ /dev/null
@@ -1,56 +0,0 @@
-import { createBrowserEnv } from './createBrowserEnv';
-import { createFileSystem } from './createFileSystem';
-import { createNodejsEnv } from './createNodejsEnv';
-import { isBrowser } from './isBrowser';
-import { isNodejs } from './isNodejs';
-let environment;
-function getEnv() {
-    if (!environment) {
-        throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');
-    }
-    return environment;
-}
-function setEnv(env) {
-    environment = env;
-}
-function initialize() {
-    // check for isBrowser() first to prevent electron renderer process
-    // to be initialized with wrong environment due to isNodejs() returning true
-    if (isBrowser()) {
-        return setEnv(createBrowserEnv());
-    }
-    if (isNodejs()) {
-        return setEnv(createNodejsEnv());
-    }
-}
-function monkeyPatch(env) {
-    if (!environment) {
-        initialize();
-    }
-    if (!environment) {
-        throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');
-    }
-    const { Canvas = environment.Canvas, Image = environment.Image } = env;
-    environment.Canvas = Canvas;
-    environment.Image = Image;
-    environment.createCanvasElement = env.createCanvasElement || (() => new Canvas());
-    environment.createImageElement = env.createImageElement || (() => new Image());
-    environment.ImageData = env.ImageData || environment.ImageData;
-    environment.Video = env.Video || environment.Video;
-    environment.fetch = env.fetch || environment.fetch;
-    environment.readFile = env.readFile || environment.readFile;
-}
-export const env = {
-    getEnv,
-    setEnv,
-    initialize,
-    createBrowserEnv,
-    createFileSystem,
-    createNodejsEnv,
-    monkeyPatch,
-    isBrowser,
-    isNodejs
-};
-initialize();
-export * from './types';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/env/index.js.map b/build/src/env/index.js.map
deleted file mode 100644
index 76619a5..0000000
--- a/build/src/env/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/env/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AACtD,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AACtD,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AACpD,OAAO,EAAE,SAAS,EAAE,MAAM,aAAa,CAAC;AACxC,OAAO,EAAE,QAAQ,EAAE,MAAM,YAAY,CAAC;AAGtC,IAAI,WAA+B,CAAA;AAEnC,SAAS,MAAM;IACb,IAAI,CAAC,WAAW,EAAE;QAChB,MAAM,IAAI,KAAK,CAAC,uEAAuE,CAAC,CAAA;KACzF;IACD,OAAO,WAAW,CAAA;AACpB,CAAC;AAED,SAAS,MAAM,CAAC,GAAgB;IAC9B,WAAW,GAAG,GAAG,CAAA;AACnB,CAAC;AAED,SAAS,UAAU;IACjB,mEAAmE;IACnE,4EAA4E;IAC5E,IAAI,SAAS,EAAE,EAAE;QACf,OAAO,MAAM,CAAC,gBAAgB,EAAE,CAAC,CAAA;KAClC;IACD,IAAI,QAAQ,EAAE,EAAE;QACd,OAAO,MAAM,CAAC,eAAe,EAAE,CAAC,CAAA;KACjC;AACH,CAAC;AAED,SAAS,WAAW,CAAC,GAAyB;IAC5C,IAAI,CAAC,WAAW,EAAE;QAChB,UAAU,EAAE,CAAA;KACb;IAED,IAAI,CAAC,WAAW,EAAE;QAChB,MAAM,IAAI,KAAK,CAAC,4EAA4E,CAAC,CAAA;KAC9F;IAED,MAAM,EAAE,MAAM,GAAG,WAAW,CAAC,MAAM,EAAE,KAAK,GAAG,WAAW,CAAC,KAAK,EAAE,GAAG,GAAG,CAAA;IACtE,WAAW,CAAC,MAAM,GAAG,MAAM,CAAA;IAC3B,WAAW,CAAC,KAAK,GAAG,KAAK,CAAA;IACzB,WAAW,CAAC,mBAAmB,GAAG,GAAG,CAAC,mBAAmB,IAAI,CAAC,GAAG,EAAE,CAAC,IAAI,MAAM,EAAE,CAAC,CAAA;IACjF,WAAW,CAAC,kBAAkB,GAAG,GAAG,CAAC,kBAAkB,IAAI,CAAC,GAAG,EAAE,CAAC,IAAI,KAAK,EAAE,CAAC,CAAA;IAE9E,WAAW,CAAC,SAAS,GAAG,GAAG,CAAC,SAAS,IAAI,WAAW,CAAC,SAAS,CAAA;IAC9D,WAAW,CAAC,KAAK,GAAG,GAAG,CAAC,KAAK,IAAI,WAAW,CAAC,KAAK,CAAA;IAClD,WAAW,CAAC,KAAK,GAAG,GAAG,CAAC,KAAK,IAAI,WAAW,CAAC,KAAK,CAAA;IAClD,WAAW,CAAC,QAAQ,GAAG,GAAG,CAAC,QAAQ,IAAI,WAAW,CAAC,QAAQ,CAAA;AAC7D,CAAC;AAED,MAAM,CAAC,MAAM,GAAG,GAAG;IACjB,MAAM;IACN,MAAM;IACN,UAAU;IACV,gBAAgB;IAChB,gBAAgB;IAChB,eAAe;IACf,WAAW;IACX,SAAS;IACT,QAAQ;CACT,CAAA;AAED,UAAU,EAAE,CAAA;AAEZ,cAAc,SAAS,CAAA"}
\ No newline at end of file
diff --git a/build/src/env/isBrowser.d.ts b/build/src/env/isBrowser.d.ts
deleted file mode 100644
index adcd129..0000000
--- a/build/src/env/isBrowser.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function isBrowser(): boolean;
diff --git a/build/src/env/isBrowser.js b/build/src/env/isBrowser.js
deleted file mode 100644
index d64a57b..0000000
--- a/build/src/env/isBrowser.js
+++ /dev/null
@@ -1,10 +0,0 @@
-export function isBrowser() {
-    return typeof window === 'object'
-        && typeof document !== 'undefined'
-        && typeof HTMLImageElement !== 'undefined'
-        && typeof HTMLCanvasElement !== 'undefined'
-        && typeof HTMLVideoElement !== 'undefined'
-        && typeof ImageData !== 'undefined'
-        && typeof CanvasRenderingContext2D !== 'undefined';
-}
-//# sourceMappingURL=isBrowser.js.map
\ No newline at end of file
diff --git a/build/src/env/isBrowser.js.map b/build/src/env/isBrowser.js.map
deleted file mode 100644
index 731714e..0000000
--- a/build/src/env/isBrowser.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"isBrowser.js","sourceRoot":"","sources":["../../../src/env/isBrowser.ts"],"names":[],"mappings":"AAAA,MAAM,UAAU,SAAS;IACvB,OAAO,OAAO,MAAM,KAAK,QAAQ;WAC5B,OAAO,QAAQ,KAAK,WAAW;WAC/B,OAAO,gBAAgB,KAAK,WAAW;WACvC,OAAO,iBAAiB,KAAK,WAAW;WACxC,OAAO,gBAAgB,KAAK,WAAW;WACvC,OAAO,SAAS,KAAK,WAAW;WAChC,OAAO,wBAAwB,KAAK,WAAW,CAAA;AACtD,CAAC"}
\ No newline at end of file
diff --git a/build/src/env/isNodejs.d.ts b/build/src/env/isNodejs.d.ts
deleted file mode 100644
index 6c90f35..0000000
--- a/build/src/env/isNodejs.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function isNodejs(): boolean;
diff --git a/build/src/env/isNodejs.js b/build/src/env/isNodejs.js
deleted file mode 100644
index 60117fd..0000000
--- a/build/src/env/isNodejs.js
+++ /dev/null
@@ -1,9 +0,0 @@
-export function isNodejs() {
-    return typeof global === 'object'
-        && typeof require === 'function'
-        && typeof module !== 'undefined'
-        // issues with gatsby.js: module.exports is undefined
-        // && !!module.exports
-        && typeof process !== 'undefined' && !!process.version;
-}
-//# sourceMappingURL=isNodejs.js.map
\ No newline at end of file
diff --git a/build/src/env/isNodejs.js.map b/build/src/env/isNodejs.js.map
deleted file mode 100644
index 4fe9a1d..0000000
--- a/build/src/env/isNodejs.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"isNodejs.js","sourceRoot":"","sources":["../../../src/env/isNodejs.ts"],"names":[],"mappings":"AAAA,MAAM,UAAU,QAAQ;IACtB,OAAO,OAAO,MAAM,KAAK,QAAQ;WAC5B,OAAO,OAAO,KAAK,UAAU;WAC7B,OAAO,MAAM,KAAK,WAAW;QAChC,qDAAqD;QACrD,sBAAsB;WACnB,OAAO,OAAO,KAAK,WAAW,IAAI,CAAC,CAAC,OAAO,CAAC,OAAO,CAAA;AAC1D,CAAC"}
\ No newline at end of file
diff --git a/build/src/env/types.d.ts b/build/src/env/types.d.ts
deleted file mode 100644
index 5337ab3..0000000
--- a/build/src/env/types.d.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-/// <reference types="node" />
-export declare type FileSystem = {
-    readFile: (filePath: string) => Promise<Buffer>;
-};
-export declare type Environment = FileSystem & {
-    Canvas: typeof HTMLCanvasElement;
-    CanvasRenderingContext2D: typeof CanvasRenderingContext2D;
-    Image: typeof HTMLImageElement;
-    ImageData: typeof ImageData;
-    Video: typeof HTMLVideoElement;
-    createCanvasElement: () => HTMLCanvasElement;
-    createImageElement: () => HTMLImageElement;
-    fetch: (url: string, init?: RequestInit) => Promise<Response>;
-};
diff --git a/build/src/env/types.js b/build/src/env/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/env/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/env/types.js.map b/build/src/env/types.js.map
deleted file mode 100644
index 7d4d788..0000000
--- a/build/src/env/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/env/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/src/euclideanDistance.d.ts b/build/src/euclideanDistance.d.ts
deleted file mode 100644
index d555de2..0000000
--- a/build/src/euclideanDistance.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array): number;
diff --git a/build/src/euclideanDistance.js b/build/src/euclideanDistance.js
deleted file mode 100644
index 04e3ca0..0000000
--- a/build/src/euclideanDistance.js
+++ /dev/null
@@ -1,10 +0,0 @@
-export 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));
-}
-//# sourceMappingURL=euclideanDistance.js.map
\ No newline at end of file
diff --git a/build/src/euclideanDistance.js.map b/build/src/euclideanDistance.js.map
deleted file mode 100644
index 6d7dac8..0000000
--- a/build/src/euclideanDistance.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"euclideanDistance.js","sourceRoot":"","sources":["../../src/euclideanDistance.ts"],"names":[],"mappings":"AAAA,MAAM,UAAU,iBAAiB,CAAC,IAA6B,EAAE,IAA6B;IAC5F,IAAI,IAAI,CAAC,MAAM,KAAK,IAAI,CAAC,MAAM;QAC7B,MAAM,IAAI,KAAK,CAAC,gDAAgD,CAAC,CAAA;IAEnE,MAAM,KAAK,GAAG,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;IAC9B,MAAM,KAAK,GAAG,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;IAE9B,OAAO,IAAI,CAAC,IAAI,CACd,KAAK;SACF,GAAG,CAAC,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,CAAC,GAAG,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC;SAC/B,MAAM,CAAC,CAAC,GAAG,EAAE,IAAI,EAAE,EAAE,CAAC,GAAG,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CACrD,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceExpressionNet/FaceExpressionNet.d.ts b/build/src/faceExpressionNet/FaceExpressionNet.d.ts
deleted file mode 100644
index dca7ebb..0000000
--- a/build/src/faceExpressionNet/FaceExpressionNet.d.ts
+++ /dev/null
@@ -1,15 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { NetInput, TNetInput } from '../dom';
-import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
-import { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
-import { FaceProcessor } from '../faceProcessor/FaceProcessor';
-import { FaceExpressions } from './FaceExpressions';
-export declare class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {
-    constructor(faceFeatureExtractor?: FaceFeatureExtractor);
-    forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D;
-    forward(input: TNetInput): Promise<tf.Tensor2D>;
-    predictExpressions(input: TNetInput): Promise<FaceExpressions | FaceExpressions[]>;
-    protected getDefaultModelName(): string;
-    protected getClassifierChannelsIn(): number;
-    protected getClassifierChannelsOut(): number;
-}
diff --git a/build/src/faceExpressionNet/FaceExpressionNet.js b/build/src/faceExpressionNet/FaceExpressionNet.js
deleted file mode 100644
index c9711b4..0000000
--- a/build/src/faceExpressionNet/FaceExpressionNet.js
+++ /dev/null
@@ -1,41 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { toNetInput } from '../dom';
-import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
-import { FaceProcessor } from '../faceProcessor/FaceProcessor';
-import { FaceExpressions } from './FaceExpressions';
-export class FaceExpressionNet extends FaceProcessor {
-    constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
-        super('FaceExpressionNet', faceFeatureExtractor);
-    }
-    forwardInput(input) {
-        return tf.tidy(() => tf.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(tf.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;
-    }
-}
-//# sourceMappingURL=FaceExpressionNet.js.map
\ No newline at end of file
diff --git a/build/src/faceExpressionNet/FaceExpressionNet.js.map b/build/src/faceExpressionNet/FaceExpressionNet.js.map
deleted file mode 100644
index e92e7cc..0000000
--- a/build/src/faceExpressionNet/FaceExpressionNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceExpressionNet.js","sourceRoot":"","sources":["../../../src/faceExpressionNet/FaceExpressionNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,oBAAoB,EAAE,MAAM,8CAA8C,CAAC;AAEpF,OAAO,EAAE,aAAa,EAAE,MAAM,gCAAgC,CAAC;AAC/D,OAAO,EAAE,eAAe,EAAE,MAAM,mBAAmB,CAAC;AAEpD,MAAM,OAAO,iBAAkB,SAAQ,aAAyC;IAE9E,YAAY,uBAA6C,IAAI,oBAAoB,EAAE;QACjF,KAAK,CAAC,mBAAmB,EAAE,oBAAoB,CAAC,CAAA;IAClD,CAAC;IAEM,YAAY,CAAC,KAA6B;QAC/C,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IACtD,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAEM,KAAK,CAAC,kBAAkB,CAAC,KAAgB;QAC9C,MAAM,QAAQ,GAAG,MAAM,UAAU,CAAC,KAAK,CAAC,CAAA;QACxC,MAAM,GAAG,GAAG,MAAM,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAA;QAC7C,MAAM,mBAAmB,GAAG,MAAM,OAAO,CAAC,GAAG,CAAC,EAAE,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,KAAK,EAAC,CAAC,EAAC,EAAE;YAC1E,MAAM,IAAI,GAAG,MAAM,CAAC,CAAC,IAAI,EAAE,CAAA;YAC3B,CAAC,CAAC,OAAO,EAAE,CAAA;YACX,OAAO,IAAI,CAAA;QACb,CAAC,CAAC,CAAC,CAAA;QACH,GAAG,CAAC,OAAO,EAAE,CAAA;QAEb,MAAM,kBAAkB,GAAG,mBAAmB;aAC3C,GAAG,CAAC,YAAY,CAAC,EAAE,CAAC,IAAI,eAAe,CAAC,YAA4B,CAAC,CAAC,CAAA;QAEzE,OAAO,QAAQ,CAAC,YAAY;YAC1B,CAAC,CAAC,kBAAkB;YACpB,CAAC,CAAC,kBAAkB,CAAC,CAAC,CAAC,CAAA;IAC3B,CAAC;IAES,mBAAmB;QAC3B,OAAO,uBAAuB,CAAA;IAChC,CAAC;IAES,uBAAuB;QAC/B,OAAO,GAAG,CAAA;IACZ,CAAC;IAES,wBAAwB;QAChC,OAAO,CAAC,CAAA;IACV,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceExpressionNet/FaceExpressions.d.ts b/build/src/faceExpressionNet/FaceExpressions.d.ts
deleted file mode 100644
index 4e01ae3..0000000
--- a/build/src/faceExpressionNet/FaceExpressions.d.ts
+++ /dev/null
@@ -1,15 +0,0 @@
-export declare const FACE_EXPRESSION_LABELS: string[];
-export declare class FaceExpressions {
-    neutral: number;
-    happy: number;
-    sad: number;
-    angry: number;
-    fearful: number;
-    disgusted: number;
-    surprised: number;
-    constructor(probabilities: number[] | Float32Array);
-    asSortedArray(): {
-        expression: string;
-        probability: number;
-    }[];
-}
diff --git a/build/src/faceExpressionNet/FaceExpressions.js b/build/src/faceExpressionNet/FaceExpressions.js
deleted file mode 100644
index 604c0fa..0000000
--- a/build/src/faceExpressionNet/FaceExpressions.js
+++ /dev/null
@@ -1,17 +0,0 @@
-export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];
-export 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);
-    }
-}
-//# sourceMappingURL=FaceExpressions.js.map
\ No newline at end of file
diff --git a/build/src/faceExpressionNet/FaceExpressions.js.map b/build/src/faceExpressionNet/FaceExpressions.js.map
deleted file mode 100644
index a27df59..0000000
--- a/build/src/faceExpressionNet/FaceExpressions.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceExpressions.js","sourceRoot":"","sources":["../../../src/faceExpressionNet/FaceExpressions.ts"],"names":[],"mappings":"AAAA,MAAM,CAAC,MAAM,sBAAsB,GAAG,CAAC,SAAS,EAAE,OAAO,EAAE,KAAK,EAAE,OAAO,EAAE,SAAS,EAAE,WAAW,EAAE,WAAW,CAAC,CAAA;AAE/G,MAAM,OAAO,eAAe;IAS1B,YAAY,aAAsC;QAChD,IAAI,aAAa,CAAC,MAAM,KAAK,CAAC,EAAE;YAC9B,MAAM,IAAI,KAAK,CAAC,8EAA8E,aAAa,CAAC,MAAM,EAAE,CAAC,CAAA;SACtH;QAED,sBAAsB,CAAC,OAAO,CAAC,CAAC,UAAU,EAAE,GAAG,EAAE,EAAE;YACjD,IAAI,CAAC,UAAU,CAAC,GAAG,aAAa,CAAC,GAAG,CAAC,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAED,aAAa;QACX,OAAO,sBAAsB;aAC1B,GAAG,CAAC,UAAU,CAAC,EAAE,CAAC,CAAC,EAAE,UAAU,EAAE,WAAW,EAAE,IAAI,CAAC,UAAU,CAAW,EAAE,CAAC,CAAC;aAC5E,IAAI,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,GAAG,EAAE,CAAC,WAAW,CAAC,CAAA;IACtD,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceExpressionNet/index.d.ts b/build/src/faceExpressionNet/index.d.ts
deleted file mode 100644
index d79c683..0000000
--- a/build/src/faceExpressionNet/index.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-export * from './FaceExpressionNet';
-export * from './FaceExpressions';
diff --git a/build/src/faceExpressionNet/index.js b/build/src/faceExpressionNet/index.js
deleted file mode 100644
index 40ccccb..0000000
--- a/build/src/faceExpressionNet/index.js
+++ /dev/null
@@ -1,3 +0,0 @@
-export * from './FaceExpressionNet';
-export * from './FaceExpressions';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/faceExpressionNet/index.js.map b/build/src/faceExpressionNet/index.js.map
deleted file mode 100644
index 4ac1bfc..0000000
--- a/build/src/faceExpressionNet/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceExpressionNet/index.ts"],"names":[],"mappings":"AAAA,cAAc,qBAAqB,CAAC;AACpC,cAAc,mBAAmB,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts b/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts
deleted file mode 100644
index d523fd2..0000000
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.d.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { NetInput, TNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';
-export declare class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {
-    constructor();
-    forwardInput(input: NetInput): tf.Tensor4D;
-    forward(input: TNetInput): Promise<tf.Tensor4D>;
-    protected getDefaultModelName(): string;
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: FaceFeatureExtractorParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParams(weights: Float32Array): {
-        params: FaceFeatureExtractorParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-}
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js
deleted file mode 100644
index c4551a6..0000000
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js
+++ /dev/null
@@ -1,42 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { toNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { normalize } from '../ops';
-import { denseBlock4 } from './denseBlock';
-import { extractParams } from './extractParams';
-import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
-export class FaceFeatureExtractor extends NeuralNetwork {
-    constructor() {
-        super('FaceFeatureExtractor');
-    }
-    forwardInput(input) {
-        const { params } = this;
-        if (!params) {
-            throw new Error('FaceFeatureExtractor - load model before inference');
-        }
-        return tf.tidy(() => {
-            const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
-            const meanRgb = [122.782, 117.001, 104.298];
-            const normalized = normalize(batchTensor, meanRgb).div(tf.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 = tf.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);
-    }
-}
-//# sourceMappingURL=FaceFeatureExtractor.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map b/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
deleted file mode 100644
index 5aa3c9c..0000000
--- a/build/src/faceFeatureExtractor/FaceFeatureExtractor.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/FaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,MAAM,OAAO,oBAAqB,SAAQ,aAAyC;IAEjF;QACE,KAAK,CAAC,sBAAsB,CAAC,CAAA;IAC/B,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,oDAAoD,CAAC,CAAA;SACtE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YACvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAE9C,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,8BAA8B,CAAA;IACvC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts
deleted file mode 100644
index 0c8d95a..0000000
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.d.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { NetInput, TNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';
-export declare class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {
-    constructor();
-    forwardInput(input: NetInput): tf.Tensor4D;
-    forward(input: TNetInput): Promise<tf.Tensor4D>;
-    protected getDefaultModelName(): string;
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: TinyFaceFeatureExtractorParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParams(weights: Float32Array): {
-        params: TinyFaceFeatureExtractorParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-}
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
deleted file mode 100644
index b43da07..0000000
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js
+++ /dev/null
@@ -1,41 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { toNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { normalize } from '../ops';
-import { denseBlock3 } from './denseBlock';
-import { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';
-import { extractParamsTiny } from './extractParamsTiny';
-export class TinyFaceFeatureExtractor extends NeuralNetwork {
-    constructor() {
-        super('TinyFaceFeatureExtractor');
-    }
-    forwardInput(input) {
-        const { params } = this;
-        if (!params) {
-            throw new Error('TinyFaceFeatureExtractor - load model before inference');
-        }
-        return tf.tidy(() => {
-            const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
-            const meanRgb = [122.782, 117.001, 104.298];
-            const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));
-            let out = denseBlock3(normalized, params.dense0, true);
-            out = denseBlock3(out, params.dense1);
-            out = denseBlock3(out, params.dense2);
-            out = tf.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);
-    }
-}
-//# sourceMappingURL=TinyFaceFeatureExtractor.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map b/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
deleted file mode 100644
index e56255a..0000000
--- a/build/src/faceFeatureExtractor/TinyFaceFeatureExtractor.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"TinyFaceFeatureExtractor.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,WAAW,EAAE,MAAM,cAAc,CAAC;AAC3C,OAAO,EAAE,8BAA8B,EAAE,MAAM,kCAAkC,CAAC;AAClF,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,OAAO,wBAAyB,SAAQ,aAA6C;IAEzF;QACE,KAAK,CAAC,0BAA0B,CAAC,CAAA;IACnC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAA;SAC1E;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YACvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,WAAW,CAAC,UAAU,EAAE,MAAM,CAAC,MAAM,EAAE,IAAI,CAAC,CAAA;YACtD,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;YACrC,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;YAEhD,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,mCAAmC,CAAA;IAC5C,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,8BAA8B,CAAC,SAAS,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,iBAAiB,CAAC,OAAO,CAAC,CAAA;IACnC,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/denseBlock.d.ts b/build/src/faceFeatureExtractor/denseBlock.d.ts
deleted file mode 100644
index d87c1f5..0000000
--- a/build/src/faceFeatureExtractor/denseBlock.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { DenseBlock3Params, DenseBlock4Params } from './types';
-export declare function denseBlock3(x: tf.Tensor4D, denseBlockParams: DenseBlock3Params, isFirstLayer?: boolean): tf.Tensor4D;
-export declare function denseBlock4(x: tf.Tensor4D, denseBlockParams: DenseBlock4Params, isFirstLayer?: boolean, isScaleDown?: boolean): tf.Tensor4D;
diff --git a/build/src/faceFeatureExtractor/denseBlock.js b/build/src/faceFeatureExtractor/denseBlock.js
deleted file mode 100644
index 6d4a5ef..0000000
--- a/build/src/faceFeatureExtractor/denseBlock.js
+++ /dev/null
@@ -1,27 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';
-export function denseBlock3(x, denseBlockParams, isFirstLayer = false) {
-    return tf.tidy(() => {
-        const out1 = tf.relu(isFirstLayer
-            ? tf.add(tf.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 = tf.relu(tf.add(out1, out2));
-        const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-        return tf.relu(tf.add(out1, tf.add(out2, out3)));
-    });
-}
-export function denseBlock4(x, denseBlockParams, isFirstLayer = false, isScaleDown = true) {
-    return tf.tidy(() => {
-        const out1 = tf.relu(isFirstLayer
-            ? tf.add(tf.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 = tf.relu(tf.add(out1, out2));
-        const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
-        const in4 = tf.relu(tf.add(out1, tf.add(out2, out3)));
-        const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
-        return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4))));
-    });
-}
-//# sourceMappingURL=denseBlock.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/denseBlock.js.map b/build/src/faceFeatureExtractor/denseBlock.js.map
deleted file mode 100644
index abbc0b8..0000000
--- a/build/src/faceFeatureExtractor/denseBlock.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"denseBlock.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/denseBlock.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAGzD,OAAO,EAAE,sBAAsB,EAAE,MAAM,kCAAkC,CAAC;AAG1E,MAAM,UAAU,WAAW,CACzB,CAAc,EACd,gBAAmC,EACnC,eAAwB,KAAK;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAClB,YAAY;YACV,CAAC,CAAC,EAAE,CAAC,GAAG,CACN,EAAE,CAAC,MAAM,CAAC,CAAC,EAAG,gBAAgB,CAAC,KAAoB,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,EAC5E,gBAAgB,CAAC,KAAK,CAAC,IAAI,CAC5B;YACD,CAAC,CAAC,sBAAsB,CAAC,CAAC,EAAE,gBAAgB,CAAC,KAA4B,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CACtE,CAAA;QAChB,MAAM,IAAI,GAAG,sBAAsB,CAAC,IAAI,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEzE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAgB,CAAA;QACtD,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAgB,CAAA;IACjE,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,MAAM,UAAU,WAAW,CACzB,CAAc,EACd,gBAAmC,EACnC,eAAwB,KAAK,EAC7B,cAAuB,IAAI;IAE3B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAClB,YAAY;YACV,CAAC,CAAC,EAAE,CAAC,GAAG,CACN,EAAE,CAAC,MAAM,CAAC,CAAC,EAAG,gBAAgB,CAAC,KAAoB,CAAC,OAAO,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,EACnG,gBAAgB,CAAC,KAAK,CAAC,IAAI,CAC5B;YACD,CAAC,CAAC,sBAAsB,CAAC,CAAC,EAAE,gBAAgB,CAAC,KAA4B,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAC7F,CAAA;QAChB,MAAM,IAAI,GAAG,sBAAsB,CAAC,IAAI,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAEzE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAgB,CAAA;QACtD,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,MAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAgB,CAAA;QACpE,MAAM,IAAI,GAAG,sBAAsB,CAAC,GAAG,EAAE,gBAAgB,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAExE,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAC,CAAgB,CAAA;IAC/E,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParams.d.ts b/build/src/faceFeatureExtractor/extractParams.d.ts
deleted file mode 100644
index 4b4b30f..0000000
--- a/build/src/faceFeatureExtractor/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from '../common';
-import { FaceFeatureExtractorParams } from './types';
-export declare function extractParams(weights: Float32Array): {
-    params: FaceFeatureExtractorParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/faceFeatureExtractor/extractParams.js b/build/src/faceFeatureExtractor/extractParams.js
deleted file mode 100644
index 053e843..0000000
--- a/build/src/faceFeatureExtractor/extractParams.js
+++ /dev/null
@@ -1,19 +0,0 @@
-import { extractWeightsFactory } from '../common';
-import { extractorsFactory } from './extractorsFactory';
-export 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 }
-    };
-}
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParams.js.map b/build/src/faceFeatureExtractor/extractParams.js.map
deleted file mode 100644
index ea12d4d..0000000
--- a/build/src/faceFeatureExtractor/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/extractParams.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,qBAAqB,EAAgB,MAAM,WAAW,CAAC;AAChE,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAIxD,MAAM,UAAU,aAAa,CAAC,OAAqB;IAEjD,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,cAAc,EACd,mBAAmB,EACpB,GAAG,qBAAqB,CAAC,OAAO,CAAC,CAAA;IAElC,MAAM,EACJ,wBAAwB,EACzB,GAAG,iBAAiB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEpD,MAAM,MAAM,GAAG,wBAAwB,CAAC,CAAC,EAAE,EAAE,EAAE,QAAQ,EAAE,IAAI,CAAC,CAAA;IAC9D,MAAM,MAAM,GAAG,wBAAwB,CAAC,EAAE,EAAE,EAAE,EAAE,QAAQ,CAAC,CAAA;IACzD,MAAM,MAAM,GAAG,wBAAwB,CAAC,EAAE,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAA;IAC1D,MAAM,MAAM,GAAG,wBAAwB,CAAC,GAAG,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAA;IAE3D,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,kCAAkC,mBAAmB,EAAE,CAAC,MAAM,EAAE,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa;QACb,MAAM,EAAE,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE;KAC3C,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts b/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts
deleted file mode 100644
index bbcfdf8..0000000
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from '../common';
-import { FaceFeatureExtractorParams } from './types';
-export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-    params: FaceFeatureExtractorParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js b/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js
deleted file mode 100644
index 7f6e4b8..0000000
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js
+++ /dev/null
@@ -1,15 +0,0 @@
-import { disposeUnusedWeightTensors } from '../common';
-import { loadParamsFactory } from './loadParamsFactory';
-export 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 };
-}
-//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js.map b/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js.map
deleted file mode 100644
index 0da0744..0000000
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMap.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParamsFromWeigthMap.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAEA,OAAO,EAAE,0BAA0B,EAAgB,MAAM,WAAW,CAAC;AACrE,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,UAAU,0BAA0B,CACxC,SAA4B;IAG5B,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,wBAAwB,EACzB,GAAG,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE/C,MAAM,MAAM,GAAG;QACb,MAAM,EAAE,wBAAwB,CAAC,QAAQ,EAAE,IAAI,CAAC;QAChD,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;QAC1C,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;QAC1C,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;KAC3C,CAAA;IAED,0BAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAEpD,OAAO,EAAE,MAAM,EAAE,aAAa,EAAE,CAAA;AAClC,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts b/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts
deleted file mode 100644
index 92cb5f2..0000000
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from '../common';
-import { TinyFaceFeatureExtractorParams } from './types';
-export declare function extractParamsFromWeigthMapTiny(weightMap: tf.NamedTensorMap): {
-    params: TinyFaceFeatureExtractorParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js b/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js
deleted file mode 100644
index 2f873cf..0000000
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js
+++ /dev/null
@@ -1,14 +0,0 @@
-import { disposeUnusedWeightTensors } from '../common';
-import { loadParamsFactory } from './loadParamsFactory';
-export 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 };
-}
-//# sourceMappingURL=extractParamsFromWeigthMapTiny.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js.map b/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js.map
deleted file mode 100644
index 599c2e8..0000000
--- a/build/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParamsFromWeigthMapTiny.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts"],"names":[],"mappings":"AAEA,OAAO,EAAE,0BAA0B,EAAgB,MAAM,WAAW,CAAC;AACrE,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAGxD,MAAM,UAAU,8BAA8B,CAC5C,SAA4B;IAG5B,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,wBAAwB,EACzB,GAAG,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE/C,MAAM,MAAM,GAAG;QACb,MAAM,EAAE,wBAAwB,CAAC,QAAQ,EAAE,IAAI,CAAC;QAChD,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;QAC1C,MAAM,EAAE,wBAAwB,CAAC,QAAQ,CAAC;KAC3C,CAAA;IAED,0BAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAEpD,OAAO,EAAE,MAAM,EAAE,aAAa,EAAE,CAAA;AAClC,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParamsTiny.d.ts b/build/src/faceFeatureExtractor/extractParamsTiny.d.ts
deleted file mode 100644
index 583fc92..0000000
--- a/build/src/faceFeatureExtractor/extractParamsTiny.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from '../common';
-import { TinyFaceFeatureExtractorParams } from './types';
-export declare function extractParamsTiny(weights: Float32Array): {
-    params: TinyFaceFeatureExtractorParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/faceFeatureExtractor/extractParamsTiny.js b/build/src/faceFeatureExtractor/extractParamsTiny.js
deleted file mode 100644
index 61e5a6e..0000000
--- a/build/src/faceFeatureExtractor/extractParamsTiny.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import { extractWeightsFactory } from '../common';
-import { extractorsFactory } from './extractorsFactory';
-export 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 }
-    };
-}
-//# sourceMappingURL=extractParamsTiny.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractParamsTiny.js.map b/build/src/faceFeatureExtractor/extractParamsTiny.js.map
deleted file mode 100644
index f2f16f9..0000000
--- a/build/src/faceFeatureExtractor/extractParamsTiny.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParamsTiny.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/extractParamsTiny.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,qBAAqB,EAAgB,MAAM,WAAW,CAAC;AAChE,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAKxD,MAAM,UAAU,iBAAiB,CAAC,OAAqB;IAErD,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,cAAc,EACd,mBAAmB,EACpB,GAAG,qBAAqB,CAAC,OAAO,CAAC,CAAA;IAElC,MAAM,EACJ,wBAAwB,EACzB,GAAG,iBAAiB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEpD,MAAM,MAAM,GAAG,wBAAwB,CAAC,CAAC,EAAE,EAAE,EAAE,QAAQ,EAAE,IAAI,CAAC,CAAA;IAC9D,MAAM,MAAM,GAAG,wBAAwB,CAAC,EAAE,EAAE,EAAE,EAAE,QAAQ,CAAC,CAAA;IACzD,MAAM,MAAM,GAAG,wBAAwB,CAAC,EAAE,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAA;IAE1D,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,kCAAkC,mBAAmB,EAAE,CAAC,MAAM,EAAE,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa;QACb,MAAM,EAAE,EAAE,MAAM,EAAE,MAAM,EAAE,MAAM,EAAE;KACnC,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractorsFactory.d.ts b/build/src/faceFeatureExtractor/extractorsFactory.d.ts
deleted file mode 100644
index 93ca270..0000000
--- a/build/src/faceFeatureExtractor/extractorsFactory.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ExtractWeightsFunction, ParamMapping } from '../common';
-import { DenseBlock3Params, DenseBlock4Params } from './types';
-export declare function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]): {
-    extractDenseBlock3Params: (channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer?: boolean) => DenseBlock3Params;
-    extractDenseBlock4Params: (channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer?: boolean) => DenseBlock4Params;
-};
diff --git a/build/src/faceFeatureExtractor/extractorsFactory.js b/build/src/faceFeatureExtractor/extractorsFactory.js
deleted file mode 100644
index 721392d..0000000
--- a/build/src/faceFeatureExtractor/extractorsFactory.js
+++ /dev/null
@@ -1,23 +0,0 @@
-import { extractConvParamsFactory, extractSeparableConvParamsFactory, } from '../common';
-export 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 conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`);
-        return { conv0, conv1, conv2 };
-    }
-    function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-        const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer);
-        const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`);
-        return { conv0, conv1, conv2, conv3 };
-    }
-    return {
-        extractDenseBlock3Params,
-        extractDenseBlock4Params
-    };
-}
-//# sourceMappingURL=extractorsFactory.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/extractorsFactory.js.map b/build/src/faceFeatureExtractor/extractorsFactory.js.map
deleted file mode 100644
index f6940db..0000000
--- a/build/src/faceFeatureExtractor/extractorsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/index.d.ts b/build/src/faceFeatureExtractor/index.d.ts
deleted file mode 100644
index 99061f8..0000000
--- a/build/src/faceFeatureExtractor/index.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-export * from './FaceFeatureExtractor';
-export * from './TinyFaceFeatureExtractor';
diff --git a/build/src/faceFeatureExtractor/index.js b/build/src/faceFeatureExtractor/index.js
deleted file mode 100644
index 149df2a..0000000
--- a/build/src/faceFeatureExtractor/index.js
+++ /dev/null
@@ -1,3 +0,0 @@
-export * from './FaceFeatureExtractor';
-export * from './TinyFaceFeatureExtractor';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/index.js.map b/build/src/faceFeatureExtractor/index.js.map
deleted file mode 100644
index 431d20b..0000000
--- a/build/src/faceFeatureExtractor/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/index.ts"],"names":[],"mappings":"AAAA,cAAc,wBAAwB,CAAC;AACvC,cAAc,4BAA4B,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/loadParamsFactory.d.ts b/build/src/faceFeatureExtractor/loadParamsFactory.d.ts
deleted file mode 100644
index 4e89f77..0000000
--- a/build/src/faceFeatureExtractor/loadParamsFactory.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from '../common';
-import { DenseBlock3Params, DenseBlock4Params } from './types';
-export declare function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]): {
-    extractDenseBlock3Params: (prefix: string, isFirstLayer?: boolean) => DenseBlock3Params;
-    extractDenseBlock4Params: (prefix: string, isFirstLayer?: boolean) => DenseBlock4Params;
-};
diff --git a/build/src/faceFeatureExtractor/loadParamsFactory.js b/build/src/faceFeatureExtractor/loadParamsFactory.js
deleted file mode 100644
index 06706eb..0000000
--- a/build/src/faceFeatureExtractor/loadParamsFactory.js
+++ /dev/null
@@ -1,29 +0,0 @@
-import { extractWeightEntryFactory, loadSeparableConvParamsFactory } from '../common';
-import { loadConvParamsFactory } from '../common/loadConvParamsFactory';
-export 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(`${prefix}/conv1`);
-        const conv2 = extractSeparableConvParams(`${prefix}/conv2`);
-        return { conv0, conv1, conv2 };
-    }
-    function extractDenseBlock4Params(prefix, isFirstLayer = false) {
-        const conv0 = isFirstLayer
-            ? extractConvParams(`${prefix}/conv0`)
-            : extractSeparableConvParams(`${prefix}/conv0`);
-        const conv1 = extractSeparableConvParams(`${prefix}/conv1`);
-        const conv2 = extractSeparableConvParams(`${prefix}/conv2`);
-        const conv3 = extractSeparableConvParams(`${prefix}/conv3`);
-        return { conv0, conv1, conv2, conv3 };
-    }
-    return {
-        extractDenseBlock3Params,
-        extractDenseBlock4Params
-    };
-}
-//# sourceMappingURL=loadParamsFactory.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/loadParamsFactory.js.map b/build/src/faceFeatureExtractor/loadParamsFactory.js.map
deleted file mode 100644
index b46c078..0000000
--- a/build/src/faceFeatureExtractor/loadParamsFactory.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"loadParamsFactory.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/loadParamsFactory.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,yBAAyB,EAAE,8BAA8B,EAAgB,MAAM,WAAW,CAAC;AACpG,OAAO,EAAE,qBAAqB,EAAE,MAAM,iCAAiC,CAAC;AAGxE,MAAM,UAAU,iBAAiB,CAAC,SAAc,EAAE,aAA6B;IAE7E,MAAM,kBAAkB,GAAG,yBAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,MAAM,iBAAiB,GAAG,qBAAqB,CAAC,kBAAkB,CAAC,CAAA;IACnE,MAAM,0BAA0B,GAAG,8BAA8B,CAAC,kBAAkB,CAAC,CAAA;IAErF,SAAS,wBAAwB,CAAC,MAAc,EAAE,eAAwB,KAAK;QAC7E,MAAM,KAAK,GAAG,YAAY;YACxB,CAAC,CAAC,iBAAiB,CAAC,GAAG,MAAM,QAAQ,CAAC;YACtC,CAAC,CAAC,0BAA0B,CAAC,GAAG,MAAM,QAAQ,CAAC,CAAA;QACjD,MAAM,KAAK,GAAG,0BAA0B,CAAC,GAAG,MAAM,QAAQ,CAAC,CAAA;QAC3D,MAAM,KAAK,GAAG,0BAA0B,CAAC,GAAG,MAAM,QAAQ,CAAC,CAAA;QAE3D,OAAO,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,CAAA;IAChC,CAAC;IAED,SAAS,wBAAwB,CAAC,MAAc,EAAE,eAAwB,KAAK;QAC7E,MAAM,KAAK,GAAG,YAAY;YACxB,CAAC,CAAC,iBAAiB,CAAC,GAAG,MAAM,QAAQ,CAAC;YACtC,CAAC,CAAC,0BAA0B,CAAC,GAAG,MAAM,QAAQ,CAAC,CAAA;QACjD,MAAM,KAAK,GAAG,0BAA0B,CAAC,GAAG,MAAM,QAAQ,CAAC,CAAA;QAC3D,MAAM,KAAK,GAAG,0BAA0B,CAAC,GAAG,MAAM,QAAQ,CAAC,CAAA;QAC3D,MAAM,KAAK,GAAG,0BAA0B,CAAC,GAAG,MAAM,QAAQ,CAAC,CAAA;QAE3D,OAAO,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,CAAA;IACvC,CAAC;IAED,OAAO;QACL,wBAAwB;QACxB,wBAAwB;KACzB,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/types.d.ts b/build/src/faceFeatureExtractor/types.d.ts
deleted file mode 100644
index 0fb6c33..0000000
--- a/build/src/faceFeatureExtractor/types.d.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { NetInput, TNetInput } from '..';
-import { ConvParams, SeparableConvParams } from '../common';
-import { NeuralNetwork } from '../NeuralNetwork';
-export declare type ConvWithBatchNormParams = BatchNormParams & {
-    filter: tf.Tensor4D;
-};
-export declare type BatchNormParams = {
-    mean: tf.Tensor1D;
-    variance: tf.Tensor1D;
-    scale: tf.Tensor1D;
-    offset: tf.Tensor1D;
-};
-export declare type SeparableConvWithBatchNormParams = {
-    depthwise: ConvWithBatchNormParams;
-    pointwise: ConvWithBatchNormParams;
-};
-export declare type DenseBlock3Params = {
-    conv0: SeparableConvParams | ConvParams;
-    conv1: SeparableConvParams;
-    conv2: SeparableConvParams;
-};
-export declare type DenseBlock4Params = DenseBlock3Params & {
-    conv3: SeparableConvParams;
-};
-export declare type TinyFaceFeatureExtractorParams = {
-    dense0: DenseBlock3Params;
-    dense1: DenseBlock3Params;
-    dense2: DenseBlock3Params;
-};
-export declare type FaceFeatureExtractorParams = {
-    dense0: DenseBlock4Params;
-    dense1: DenseBlock4Params;
-    dense2: DenseBlock4Params;
-    dense3: DenseBlock4Params;
-};
-export interface IFaceFeatureExtractor<TNetParams extends TinyFaceFeatureExtractorParams | FaceFeatureExtractorParams> extends NeuralNetwork<TNetParams> {
-    forwardInput(input: NetInput): tf.Tensor4D;
-    forward(input: TNetInput): Promise<tf.Tensor4D>;
-}
diff --git a/build/src/faceFeatureExtractor/types.js b/build/src/faceFeatureExtractor/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/faceFeatureExtractor/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/faceFeatureExtractor/types.js.map b/build/src/faceFeatureExtractor/types.js.map
deleted file mode 100644
index d4dd37d..0000000
--- a/build/src/faceFeatureExtractor/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/faceFeatureExtractor/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68Net.d.ts b/build/src/faceLandmarkNet/FaceLandmark68Net.d.ts
deleted file mode 100644
index ba213c9..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68Net.d.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
-import { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
-import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
-export declare class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {
-    constructor(faceFeatureExtractor?: FaceFeatureExtractor);
-    protected getDefaultModelName(): string;
-    protected getClassifierChannelsIn(): number;
-}
diff --git a/build/src/faceLandmarkNet/FaceLandmark68Net.js b/build/src/faceLandmarkNet/FaceLandmark68Net.js
deleted file mode 100644
index 6d4c73d..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68Net.js
+++ /dev/null
@@ -1,14 +0,0 @@
-import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
-import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
-export class FaceLandmark68Net extends FaceLandmark68NetBase {
-    constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
-        super('FaceLandmark68Net', faceFeatureExtractor);
-    }
-    getDefaultModelName() {
-        return 'face_landmark_68_model';
-    }
-    getClassifierChannelsIn() {
-        return 256;
-    }
-}
-//# sourceMappingURL=FaceLandmark68Net.js.map
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68Net.js.map b/build/src/faceLandmarkNet/FaceLandmark68Net.js.map
deleted file mode 100644
index d084878..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68Net.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmark68Net.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68Net.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,oBAAoB,EAAE,MAAM,8CAA8C,CAAC;AAEpF,OAAO,EAAE,qBAAqB,EAAE,MAAM,yBAAyB,CAAC;AAEhE,MAAM,OAAO,iBAAkB,SAAQ,qBAAiD;IAEtF,YAAY,uBAA6C,IAAI,oBAAoB,EAAE;QACjF,KAAK,CAAC,mBAAmB,EAAE,oBAAoB,CAAC,CAAA;IAClD,CAAC;IAES,mBAAmB;QAC3B,OAAO,wBAAwB,CAAA;IACjC,CAAC;IAES,uBAAuB;QAC/B,OAAO,GAAG,CAAA;IACZ,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts b/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts
deleted file mode 100644
index 71d7a10..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.d.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { IDimensions } from '../classes';
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-import { NetInput, TNetInput } from '../dom';
-import { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
-import { FaceProcessor } from '../faceProcessor/FaceProcessor';
-export declare abstract class FaceLandmark68NetBase<TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams> extends FaceProcessor<TExtractorParams> {
-    postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D;
-    forwardInput(input: NetInput): tf.Tensor2D;
-    forward(input: TNetInput): Promise<tf.Tensor2D>;
-    detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
-    protected getClassifierChannelsOut(): number;
-}
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js
deleted file mode 100644
index fa494c6..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js
+++ /dev/null
@@ -1,60 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { Point } from '../classes';
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-import { toNetInput } from '../dom';
-import { FaceProcessor } from '../faceProcessor/FaceProcessor';
-import { isEven } from '../utils';
-export class FaceLandmark68NetBase extends FaceProcessor {
-    postProcess(output, inputSize, originalDimensions) {
-        const inputDimensions = originalDimensions.map(({ width, height }) => {
-            const scale = inputSize / Math.max(height, width);
-            return {
-                width: width * scale,
-                height: height * scale
-            };
-        });
-        const batchSize = inputDimensions.length;
-        return tf.tidy(() => {
-            const createInterleavedTensor = (fillX, fillY) => tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 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(tf.fill([batchSize, 136], inputSize, 'float32'))
-                .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx)))))
-                .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) => createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height))));
-            return landmarkTensors;
-        });
-    }
-    forwardInput(input) {
-        return tf.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 = tf.tidy(() => tf.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;
-    }
-}
-//# sourceMappingURL=FaceLandmark68NetBase.js.map
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map b/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
deleted file mode 100644
index 7f353bd..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68NetBase.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68TinyNet.d.ts b/build/src/faceLandmarkNet/FaceLandmark68TinyNet.d.ts
deleted file mode 100644
index 5811187..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68TinyNet.d.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';
-import { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
-import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
-export declare class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {
-    constructor(faceFeatureExtractor?: TinyFaceFeatureExtractor);
-    protected getDefaultModelName(): string;
-    protected getClassifierChannelsIn(): number;
-}
diff --git a/build/src/faceLandmarkNet/FaceLandmark68TinyNet.js b/build/src/faceLandmarkNet/FaceLandmark68TinyNet.js
deleted file mode 100644
index 5f58220..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68TinyNet.js
+++ /dev/null
@@ -1,14 +0,0 @@
-import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';
-import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
-export class FaceLandmark68TinyNet extends FaceLandmark68NetBase {
-    constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) {
-        super('FaceLandmark68TinyNet', faceFeatureExtractor);
-    }
-    getDefaultModelName() {
-        return 'face_landmark_68_tiny_model';
-    }
-    getClassifierChannelsIn() {
-        return 128;
-    }
-}
-//# sourceMappingURL=FaceLandmark68TinyNet.js.map
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/FaceLandmark68TinyNet.js.map b/build/src/faceLandmarkNet/FaceLandmark68TinyNet.js.map
deleted file mode 100644
index b759a1c..0000000
--- a/build/src/faceLandmarkNet/FaceLandmark68TinyNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceLandmark68TinyNet.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/FaceLandmark68TinyNet.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,wBAAwB,EAAE,MAAM,kDAAkD,CAAC;AAE5F,OAAO,EAAE,qBAAqB,EAAE,MAAM,yBAAyB,CAAC;AAEhE,MAAM,OAAO,qBAAsB,SAAQ,qBAAqD;IAE9F,YAAY,uBAAiD,IAAI,wBAAwB,EAAE;QACzF,KAAK,CAAC,uBAAuB,EAAE,oBAAoB,CAAC,CAAA;IACtD,CAAC;IAES,mBAAmB;QAC3B,OAAO,6BAA6B,CAAA;IACtC,CAAC;IAES,uBAAuB;QAC/B,OAAO,GAAG,CAAA;IACZ,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/index.d.ts b/build/src/faceLandmarkNet/index.d.ts
deleted file mode 100644
index b49c3e8..0000000
--- a/build/src/faceLandmarkNet/index.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import { FaceLandmark68Net } from './FaceLandmark68Net';
-export * from './FaceLandmark68Net';
-export * from './FaceLandmark68TinyNet';
-export declare class FaceLandmarkNet extends FaceLandmark68Net {
-}
diff --git a/build/src/faceLandmarkNet/index.js b/build/src/faceLandmarkNet/index.js
deleted file mode 100644
index 154ed93..0000000
--- a/build/src/faceLandmarkNet/index.js
+++ /dev/null
@@ -1,6 +0,0 @@
-import { FaceLandmark68Net } from './FaceLandmark68Net';
-export * from './FaceLandmark68Net';
-export * from './FaceLandmark68TinyNet';
-export class FaceLandmarkNet extends FaceLandmark68Net {
-}
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/faceLandmarkNet/index.js.map b/build/src/faceLandmarkNet/index.js.map
deleted file mode 100644
index 20a6599..0000000
--- a/build/src/faceLandmarkNet/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceLandmarkNet/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,iBAAiB,EAAE,MAAM,qBAAqB,CAAC;AAExD,cAAc,qBAAqB,CAAC;AACpC,cAAc,yBAAyB,CAAC;AAExC,MAAM,OAAO,eAAgB,SAAQ,iBAAiB;CAAG"}
\ No newline at end of file
diff --git a/build/src/faceProcessor/FaceProcessor.d.ts b/build/src/faceProcessor/FaceProcessor.d.ts
deleted file mode 100644
index 995f986..0000000
--- a/build/src/faceProcessor/FaceProcessor.d.ts
+++ /dev/null
@@ -1,28 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { NetInput } from '../dom';
-import { FaceFeatureExtractorParams, IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { NetParams } from './types';
-export declare abstract class FaceProcessor<TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams> extends NeuralNetwork<NetParams> {
-    protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>;
-    constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>);
-    get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams>;
-    protected abstract getDefaultModelName(): string;
-    protected abstract getClassifierChannelsIn(): number;
-    protected abstract getClassifierChannelsOut(): number;
-    runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D;
-    dispose(throwOnRedispose?: boolean): void;
-    loadClassifierParams(weights: Float32Array): void;
-    extractClassifierParams(weights: Float32Array): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParams(weights: Float32Array): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-}
diff --git a/build/src/faceProcessor/FaceProcessor.js b/build/src/faceProcessor/FaceProcessor.js
deleted file mode 100644
index a03d2aa..0000000
--- a/build/src/faceProcessor/FaceProcessor.js
+++ /dev/null
@@ -1,55 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
-import { NetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { extractParams } from './extractParams';
-import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
-import { seperateWeightMaps } from './util';
-export 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 tf.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 extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());
-    }
-    extractParamsFromWeigthMap(weightMap) {
-        const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap);
-        this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
-        return extractParamsFromWeigthMap(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);
-    }
-}
-//# sourceMappingURL=FaceProcessor.js.map
\ No newline at end of file
diff --git a/build/src/faceProcessor/FaceProcessor.js.map b/build/src/faceProcessor/FaceProcessor.js.map
deleted file mode 100644
index 08fcd11..0000000
--- a/build/src/faceProcessor/FaceProcessor.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceProcessor.js","sourceRoot":"","sources":["../../../src/faceProcessor/FaceProcessor.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,mBAAmB,EAAE,MAAM,+BAA+B,CAAC;AACpE,OAAO,EAAE,QAAQ,EAAE,MAAM,QAAQ,CAAC;AAMlC,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAE1E,OAAO,EAAE,kBAAkB,EAAE,MAAM,QAAQ,CAAC;AAE5C,MAAM,OAAgB,aAGpB,SAAQ,aAAwB;IAIhC,YAAY,KAAa,EAAE,oBAA6D;QACtF,KAAK,CAAC,KAAK,CAAC,CAAA;QACZ,IAAI,CAAC,qBAAqB,GAAG,oBAAoB,CAAA;IACnD,CAAC;IAED,IAAW,oBAAoB;QAC7B,OAAO,IAAI,CAAC,qBAAqB,CAAA;IACnC,CAAC;IAMM,MAAM,CAAC,KAA6B;QAEzC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,GAAG,IAAI,CAAC,KAAK,gCAAgC,CAAC,CAAA;SAC/D;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,kBAAkB,GAAG,KAAK,YAAY,QAAQ;gBAClD,CAAC,CAAC,IAAI,CAAC,oBAAoB,CAAC,YAAY,CAAC,KAAK,CAAC;gBAC/C,CAAC,CAAC,KAAK,CAAA;YACT,OAAO,mBAAmB,CAAC,kBAAkB,CAAC,IAAI,CAAC,kBAAkB,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;QACjG,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,OAAO,CAAC,mBAA4B,IAAI;QAC7C,IAAI,CAAC,oBAAoB,CAAC,OAAO,CAAC,gBAAgB,CAAC,CAAA;QACnD,KAAK,CAAC,OAAO,CAAC,gBAAgB,CAAC,CAAA;IACjC,CAAC;IAEM,oBAAoB,CAAC,OAAqB;QAC/C,MAAM,EAAE,MAAM,EAAE,aAAa,EAAE,GAAG,IAAI,CAAC,uBAAuB,CAAC,OAAO,CAAC,CAAA;QACvE,IAAI,CAAC,OAAO,GAAG,MAAM,CAAA;QACrB,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;IACrC,CAAC;IAEM,uBAAuB,CAAC,OAAqB;QAClD,OAAO,aAAa,CAAC,OAAO,EAAE,IAAI,CAAC,uBAAuB,EAAE,EAAE,IAAI,CAAC,wBAAwB,EAAE,CAAC,CAAA;IAChG,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAE/D,MAAM,EAAE,mBAAmB,EAAE,aAAa,EAAE,GAAG,kBAAkB,CAAC,SAAS,CAAC,CAAA;QAE5E,IAAI,CAAC,oBAAoB,CAAC,iBAAiB,CAAC,mBAAmB,CAAC,CAAA;QAEhE,OAAO,0BAA0B,CAAC,aAAa,CAAC,CAAA;IAClD,CAAC;IAES,aAAa,CAAC,OAAqB;QAE3C,MAAM,GAAG,GAAG,IAAI,CAAC,uBAAuB,EAAE,CAAA;QAC1C,MAAM,IAAI,GAAG,IAAI,CAAC,wBAAwB,EAAE,CAAA;QAC5C,MAAM,oBAAoB,GAAG,CAAC,IAAI,GAAG,GAAG,CAAE,GAAG,IAAI,CAAA;QAEjD,MAAM,uBAAuB,GAAG,OAAO,CAAC,KAAK,CAAC,CAAC,EAAE,OAAO,CAAC,MAAM,GAAG,oBAAoB,CAAC,CAAA;QACvF,MAAM,iBAAiB,GAAG,OAAO,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,GAAG,oBAAoB,CAAC,CAAA;QAE9E,IAAI,CAAC,oBAAoB,CAAC,cAAc,CAAC,uBAAuB,CAAC,CAAA;QACjE,OAAO,IAAI,CAAC,uBAAuB,CAAC,iBAAiB,CAAC,CAAA;IACxD,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceProcessor/extractParams.d.ts b/build/src/faceProcessor/extractParams.d.ts
deleted file mode 100644
index 9fc55e1..0000000
--- a/build/src/faceProcessor/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from '../common';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/faceProcessor/extractParams.js b/build/src/faceProcessor/extractParams.js
deleted file mode 100644
index 76eeb97..0000000
--- a/build/src/faceProcessor/extractParams.js
+++ /dev/null
@@ -1,15 +0,0 @@
-import { extractFCParamsFactory, extractWeightsFactory } from '../common';
-export function extractParams(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 }
-    };
-}
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/src/faceProcessor/extractParams.js.map b/build/src/faceProcessor/extractParams.js.map
deleted file mode 100644
index 078140f..0000000
--- a/build/src/faceProcessor/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../../src/faceProcessor/extractParams.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,sBAAsB,EAAE,qBAAqB,EAAgB,MAAM,WAAW,CAAC;AAGxF,MAAM,UAAU,aAAa,CAAC,OAAqB,EAAE,UAAkB,EAAE,WAAmB;IAE1F,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,cAAc,EACd,mBAAmB,EACpB,GAAG,qBAAqB,CAAC,OAAO,CAAC,CAAA;IAElC,MAAM,eAAe,GAAG,sBAAsB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAE7E,MAAM,EAAE,GAAG,eAAe,CAAC,UAAU,EAAE,WAAW,EAAE,IAAI,CAAC,CAAA;IAEzD,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,kCAAkC,mBAAmB,EAAE,CAAC,MAAM,EAAE,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa;QACb,MAAM,EAAE,EAAE,EAAE,EAAE;KACf,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts b/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts
deleted file mode 100644
index aa24371..0000000
--- a/build/src/faceProcessor/extractParamsFromWeigthMap.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from '../common';
-import { NetParams } from './types';
-export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/faceProcessor/extractParamsFromWeigthMap.js b/build/src/faceProcessor/extractParamsFromWeigthMap.js
deleted file mode 100644
index 67c620e..0000000
--- a/build/src/faceProcessor/extractParamsFromWeigthMap.js
+++ /dev/null
@@ -1,16 +0,0 @@
-import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';
-export function extractParamsFromWeigthMap(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 };
-}
-//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
diff --git a/build/src/faceProcessor/extractParamsFromWeigthMap.js.map b/build/src/faceProcessor/extractParamsFromWeigthMap.js.map
deleted file mode 100644
index 3383d82..0000000
--- a/build/src/faceProcessor/extractParamsFromWeigthMap.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParamsFromWeigthMap.js","sourceRoot":"","sources":["../../../src/faceProcessor/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAEA,OAAO,EAAE,0BAA0B,EAAE,yBAAyB,EAA0B,MAAM,WAAW,CAAC;AAG1G,MAAM,UAAU,0BAA0B,CACxC,SAA4B;IAG5B,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,kBAAkB,GAAG,yBAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,SAAS,eAAe,CAAC,MAAc;QACrC,MAAM,OAAO,GAAG,kBAAkB,CAAc,GAAG,MAAM,UAAU,EAAE,CAAC,CAAC,CAAA;QACvE,MAAM,IAAI,GAAG,kBAAkB,CAAc,GAAG,MAAM,OAAO,EAAE,CAAC,CAAC,CAAA;QACjE,OAAO,EAAE,OAAO,EAAE,IAAI,EAAE,CAAA;IAC1B,CAAC;IAED,MAAM,MAAM,GAAG;QACb,EAAE,EAAE,eAAe,CAAC,IAAI,CAAC;KAC1B,CAAA;IAED,0BAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAEpD,OAAO,EAAE,MAAM,EAAE,aAAa,EAAE,CAAA;AAClC,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceProcessor/index.d.ts b/build/src/faceProcessor/index.d.ts
deleted file mode 100644
index 9e98e7d..0000000
--- a/build/src/faceProcessor/index.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export * from './FaceProcessor';
diff --git a/build/src/faceProcessor/index.js b/build/src/faceProcessor/index.js
deleted file mode 100644
index 43ae5c6..0000000
--- a/build/src/faceProcessor/index.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export * from './FaceProcessor';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/faceProcessor/index.js.map b/build/src/faceProcessor/index.js.map
deleted file mode 100644
index 41e3704..0000000
--- a/build/src/faceProcessor/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceProcessor/index.ts"],"names":[],"mappings":"AAAA,cAAc,iBAAiB,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceProcessor/types.d.ts b/build/src/faceProcessor/types.d.ts
deleted file mode 100644
index 2e6085a..0000000
--- a/build/src/faceProcessor/types.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import { FCParams } from '../common';
-export declare type NetParams = {
-    fc: FCParams;
-};
diff --git a/build/src/faceProcessor/types.js b/build/src/faceProcessor/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/faceProcessor/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/faceProcessor/types.js.map b/build/src/faceProcessor/types.js.map
deleted file mode 100644
index 42a7693..0000000
--- a/build/src/faceProcessor/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/faceProcessor/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/src/faceProcessor/util.d.ts b/build/src/faceProcessor/util.d.ts
deleted file mode 100644
index 42af311..0000000
--- a/build/src/faceProcessor/util.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export declare function seperateWeightMaps(weightMap: tf.NamedTensorMap): {
-    featureExtractorMap: any;
-    classifierMap: any;
-};
diff --git a/build/src/faceProcessor/util.js b/build/src/faceProcessor/util.js
deleted file mode 100644
index f400d03..0000000
--- a/build/src/faceProcessor/util.js
+++ /dev/null
@@ -1,10 +0,0 @@
-export 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 };
-}
-//# sourceMappingURL=util.js.map
\ No newline at end of file
diff --git a/build/src/faceProcessor/util.js.map b/build/src/faceProcessor/util.js.map
deleted file mode 100644
index 3ba9927..0000000
--- a/build/src/faceProcessor/util.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"util.js","sourceRoot":"","sources":["../../../src/faceProcessor/util.ts"],"names":[],"mappings":"AAEA,MAAM,UAAU,kBAAkB,CAAC,SAA4B;IAE7D,MAAM,mBAAmB,GAAsB,EAAE,CAAA;IACjD,MAAM,aAAa,GAAsB,EAAE,CAAA;IAE3C,MAAM,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,EAAE;QACnC,MAAM,GAAG,GAAG,GAAG,CAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,mBAAmB,CAAA;QACtE,GAAG,CAAC,GAAG,CAAC,GAAG,SAAS,CAAC,GAAG,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;IAEF,OAAO,EAAE,mBAAmB,EAAE,aAAa,EAAE,CAAA;AAE/C,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts b/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts
deleted file mode 100644
index 56b70de..0000000
--- a/build/src/faceRecognitionNet/FaceRecognitionNet.d.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { NetInput, TNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { NetParams } from './types';
-export declare class FaceRecognitionNet extends NeuralNetwork<NetParams> {
-    constructor();
-    forwardInput(input: NetInput): tf.Tensor2D;
-    forward(input: TNetInput): Promise<tf.Tensor2D>;
-    computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
-    protected getDefaultModelName(): string;
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParams(weights: Float32Array): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-}
diff --git a/build/src/faceRecognitionNet/FaceRecognitionNet.js b/build/src/faceRecognitionNet/FaceRecognitionNet.js
deleted file mode 100644
index 8ef4091..0000000
--- a/build/src/faceRecognitionNet/FaceRecognitionNet.js
+++ /dev/null
@@ -1,66 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { toNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { normalize } from '../ops';
-import { convDown } from './convLayer';
-import { extractParams } from './extractParams';
-import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
-import { residual, residualDown } from './residualLayer';
-export class FaceRecognitionNet extends NeuralNetwork {
-    constructor() {
-        super('FaceRecognitionNet');
-    }
-    forwardInput(input) {
-        const { params } = this;
-        if (!params) {
-            throw new Error('FaceRecognitionNet - load model before inference');
-        }
-        return tf.tidy(() => {
-            // const batchTensor = input.toBatchTensor(150, true).toFloat()
-            const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');
-            const meanRgb = [122.782, 117.001, 104.298];
-            const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
-            let out = convDown(normalized, params.conv32_down);
-            out = tf.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 = tf.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 = tf.tidy(() => tf.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 extractParamsFromWeigthMap(weightMap);
-    }
-    extractParams(weights) {
-        return extractParams(weights);
-    }
-}
-//# sourceMappingURL=FaceRecognitionNet.js.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/FaceRecognitionNet.js.map b/build/src/faceRecognitionNet/FaceRecognitionNet.js.map
deleted file mode 100644
index bf1e4b3..0000000
--- a/build/src/faceRecognitionNet/FaceRecognitionNet.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"FaceRecognitionNet.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,QAAQ,EAAE,MAAM,aAAa,CAAC;AACvC,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAC1E,OAAO,EAAE,QAAQ,EAAE,YAAY,EAAE,MAAM,iBAAiB,CAAC;AAIzD,MAAM,OAAO,kBAAmB,SAAQ,aAAwB;IAE9D;QACE,KAAK,CAAC,oBAAoB,CAAC,CAAA;IAC7B,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;SACpE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,+DAA+D;YAC/D,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YAEvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,QAAQ,CAAC,UAAU,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAClD,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;YAEpC,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,YAAY,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAC3C,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,YAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YAErC,GAAG,GAAG,YAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,QAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,YAAY,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,CAAC,CAAA;YAEhD,MAAM,SAAS,GAAG,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CAAA;YACjD,MAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;YAEtD,OAAO,cAAc,CAAA;QACvB,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAEM,KAAK,CAAC,qBAAqB,CAAC,KAAgB;QACjD,MAAM,QAAQ,GAAG,MAAM,UAAU,CAAC,KAAK,CAAC,CAAA;QAExC,MAAM,qBAAqB,GAAG,EAAE,CAAC,IAAI,CACnC,GAAG,EAAE,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,CAC9C,CAAA;QAED,MAAM,uBAAuB,GAAG,MAAM,OAAO,CAAC,GAAG,CAAC,qBAAqB,CAAC,GAAG,CACzE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,IAAI,EAAE,CACd,CAAmB,CAAA;QAEpB,qBAAqB,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC,CAAA;QAE/C,OAAO,QAAQ,CAAC,YAAY;YAC1B,CAAC,CAAC,uBAAuB;YACzB,CAAC,CAAC,uBAAuB,CAAC,CAAC,CAAC,CAAA;IAChC,CAAC;IAES,mBAAmB;QAC3B,OAAO,wBAAwB,CAAA;IACjC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/convLayer.d.ts b/build/src/faceRecognitionNet/convLayer.d.ts
deleted file mode 100644
index ae04839..0000000
--- a/build/src/faceRecognitionNet/convLayer.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ConvLayerParams } from './types';
-export declare function conv(x: tf.Tensor4D, params: ConvLayerParams): any;
-export declare function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams): any;
-export declare function convDown(x: tf.Tensor4D, params: ConvLayerParams): any;
diff --git a/build/src/faceRecognitionNet/convLayer.js b/build/src/faceRecognitionNet/convLayer.js
deleted file mode 100644
index 524a1bd..0000000
--- a/build/src/faceRecognitionNet/convLayer.js
+++ /dev/null
@@ -1,19 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { scale } from './scaleLayer';
-function convLayer(x, params, strides, withRelu, padding = 'same') {
-    const { filters, bias } = params.conv;
-    let out = tf.conv2d(x, filters, strides, padding);
-    out = tf.add(out, bias);
-    out = scale(out, params.scale);
-    return withRelu ? tf.relu(out) : out;
-}
-export function conv(x, params) {
-    return convLayer(x, params, [1, 1], true);
-}
-export function convNoRelu(x, params) {
-    return convLayer(x, params, [1, 1], false);
-}
-export function convDown(x, params) {
-    return convLayer(x, params, [2, 2], true, 'valid');
-}
-//# sourceMappingURL=convLayer.js.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/convLayer.js.map b/build/src/faceRecognitionNet/convLayer.js.map
deleted file mode 100644
index 2fc0b09..0000000
--- a/build/src/faceRecognitionNet/convLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"convLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/convLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,KAAK,EAAE,MAAM,cAAc,CAAC;AAIrC,SAAS,SAAS,CAChB,CAAc,EACd,MAAuB,EACvB,OAAyB,EACzB,QAAiB,EACjB,UAA4B,MAAM;IAElC,MAAM,EAAE,OAAO,EAAE,IAAI,EAAE,GAAG,MAAM,CAAC,IAAI,CAAA;IAErC,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;IACjD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;IACvB,GAAG,GAAG,KAAK,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC9B,OAAO,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAA;AACtC,CAAC;AAED,MAAM,UAAU,IAAI,CAAC,CAAc,EAAE,MAAuB;IAC1D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAA;AAC3C,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,CAAc,EAAE,MAAuB;IAChE,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAA;AAC5C,CAAC;AAED,MAAM,UAAU,QAAQ,CAAC,CAAc,EAAE,MAAuB;IAC9D,OAAO,SAAS,CAAC,CAAC,EAAE,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,IAAI,EAAE,OAAO,CAAC,CAAA;AACpD,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/extractParams.d.ts b/build/src/faceRecognitionNet/extractParams.d.ts
deleted file mode 100644
index f4e4b30..0000000
--- a/build/src/faceRecognitionNet/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from '../common';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/faceRecognitionNet/extractParams.js b/build/src/faceRecognitionNet/extractParams.js
deleted file mode 100644
index 58b8b83..0000000
--- a/build/src/faceRecognitionNet/extractParams.js
+++ /dev/null
@@ -1,87 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { extractWeightsFactory } from '../common';
-import { isFloat } from '../utils';
-function extractorsFactory(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: ${filterSize}`);
-        }
-        return tf.tidy(() => tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]));
-    }
-    function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
-        const filters = extractFilterValues(numFilterValues, numFilters, filterSize);
-        const bias = tf.tensor1d(extractWeights(numFilters));
-        paramMappings.push({ paramPath: `${mappedPrefix}/filters` }, { paramPath: `${mappedPrefix}/bias` });
-        return { filters, bias };
-    }
-    function extractScaleLayerParams(numWeights, mappedPrefix) {
-        const weights = tf.tensor1d(extractWeights(numWeights));
-        const biases = tf.tensor1d(extractWeights(numWeights));
-        paramMappings.push({ paramPath: `${mappedPrefix}/weights` }, { paramPath: `${mappedPrefix}/biases` });
-        return {
-            weights,
-            biases
-        };
-    }
-    function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
-        const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`);
-        const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`);
-        return { conv, scale };
-    }
-    function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown = false) {
-        const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`);
-        const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`);
-        return { conv1, conv2 };
-    }
-    return {
-        extractConvLayerParams,
-        extractResidualLayerParams
-    };
-}
-export function extractParams(weights) {
-    const { extractWeights, getRemainingWeights } = extractWeightsFactory(weights);
-    const paramMappings = [];
-    const { extractConvLayerParams, extractResidualLayerParams } = extractorsFactory(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 = tf.tidy(() => tf.transpose(tf.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 };
-}
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/extractParams.js.map b/build/src/faceRecognitionNet/extractParams.js.map
deleted file mode 100644
index 34377fc..0000000
--- a/build/src/faceRecognitionNet/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts b/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts
deleted file mode 100644
index aa24371..0000000
--- a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from '../common';
-import { NetParams } from './types';
-export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.js b/build/src/faceRecognitionNet/extractParamsFromWeigthMap.js
deleted file mode 100644
index 8394d3d..0000000
--- a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.js
+++ /dev/null
@@ -1,71 +0,0 @@
-import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';
-import { isTensor2D } from '../utils';
-function extractorsFactory(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(`${prefix}/conv/bias`, 1);
-        const scale = extractScaleLayerParams(prefix);
-        return { conv: { filters, bias }, scale };
-    }
-    function extractResidualLayerParams(prefix) {
-        return {
-            conv1: extractConvLayerParams(`${prefix}/conv1`),
-            conv2: extractConvLayerParams(`${prefix}/conv2`)
-        };
-    }
-    return {
-        extractConvLayerParams,
-        extractResidualLayerParams
-    };
-}
-export function extractParamsFromWeigthMap(weightMap) {
-    const paramMappings = [];
-    const { extractConvLayerParams, extractResidualLayerParams } = extractorsFactory(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 };
-}
-//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.js.map b/build/src/faceRecognitionNet/extractParamsFromWeigthMap.js.map
deleted file mode 100644
index ecc9578..0000000
--- a/build/src/faceRecognitionNet/extractParamsFromWeigthMap.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/index.d.ts b/build/src/faceRecognitionNet/index.d.ts
deleted file mode 100644
index 487d6af..0000000
--- a/build/src/faceRecognitionNet/index.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import { FaceRecognitionNet } from './FaceRecognitionNet';
-export * from './FaceRecognitionNet';
-export declare function createFaceRecognitionNet(weights: Float32Array): FaceRecognitionNet;
diff --git a/build/src/faceRecognitionNet/index.js b/build/src/faceRecognitionNet/index.js
deleted file mode 100644
index 20f2421..0000000
--- a/build/src/faceRecognitionNet/index.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import { FaceRecognitionNet } from './FaceRecognitionNet';
-export * from './FaceRecognitionNet';
-export function createFaceRecognitionNet(weights) {
-    const net = new FaceRecognitionNet();
-    net.extractWeights(weights);
-    return net;
-}
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/index.js.map b/build/src/faceRecognitionNet/index.js.map
deleted file mode 100644
index 6a41920..0000000
--- a/build/src/faceRecognitionNet/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAE1D,cAAc,sBAAsB,CAAC;AAErC,MAAM,UAAU,wBAAwB,CAAC,OAAqB;IAC5D,MAAM,GAAG,GAAG,IAAI,kBAAkB,EAAE,CAAA;IACpC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/residualLayer.d.ts b/build/src/faceRecognitionNet/residualLayer.d.ts
deleted file mode 100644
index fb24180..0000000
--- a/build/src/faceRecognitionNet/residualLayer.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ResidualLayerParams } from './types';
-export declare function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
-export declare function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D;
diff --git a/build/src/faceRecognitionNet/residualLayer.js b/build/src/faceRecognitionNet/residualLayer.js
deleted file mode 100644
index 9ff4b40..0000000
--- a/build/src/faceRecognitionNet/residualLayer.js
+++ /dev/null
@@ -1,32 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { conv, convDown, convNoRelu } from './convLayer';
-export function residual(x, params) {
-    let out = conv(x, params.conv1);
-    out = convNoRelu(out, params.conv2);
-    out = tf.add(out, x);
-    out = tf.relu(out);
-    return out;
-}
-export function residualDown(x, params) {
-    let out = convDown(x, params.conv1);
-    out = convNoRelu(out, params.conv2);
-    let pooled = tf.avgPool(x, 2, 2, 'valid');
-    const zeros = tf.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 = tf.zeros(padShapeX);
-        out = tf.concat([out, zerosW], 1);
-        const padShapeY = [...out.shape];
-        padShapeY[2] = 1;
-        const zerosH = tf.zeros(padShapeY);
-        out = tf.concat([out, zerosH], 2);
-    }
-    pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;
-    out = tf.add(pooled, out);
-    out = tf.relu(out);
-    return out;
-}
-//# sourceMappingURL=residualLayer.js.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/residualLayer.js.map b/build/src/faceRecognitionNet/residualLayer.js.map
deleted file mode 100644
index dde7336..0000000
--- a/build/src/faceRecognitionNet/residualLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"residualLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/residualLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,IAAI,EAAE,QAAQ,EAAE,UAAU,EAAE,MAAM,aAAa,CAAC;AAGzD,MAAM,UAAU,QAAQ,CAAC,CAAc,EAAE,MAA2B;IAClE,IAAI,GAAG,GAAG,IAAI,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAC/B,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IACnC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACpB,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAClB,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,UAAU,YAAY,CAAC,CAAc,EAAE,MAA2B;IACtE,IAAI,GAAG,GAAG,QAAQ,CAAC,CAAC,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IACnC,GAAG,GAAG,UAAU,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;IAEnC,IAAI,MAAM,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAgB,CAAA;IACxD,MAAM,KAAK,GAAG,EAAE,CAAC,KAAK,CAAa,MAAM,CAAC,KAAK,CAAC,CAAA;IAChD,MAAM,KAAK,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAC9C,MAAM,aAAa,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,IAAI,MAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAE1F,IAAI,aAAa,EAAE;QACjB,MAAM,SAAS,GAAG,CAAC,GAAG,GAAG,CAAC,KAAK,CAAqC,CAAA;QACpE,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;QAChB,MAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAa,SAAS,CAAC,CAAA;QAC9C,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,CAAC,CAAA;QAEjC,MAAM,SAAS,GAAG,CAAC,GAAG,GAAG,CAAC,KAAK,CAAqC,CAAA;QACpE,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;QAChB,MAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAa,SAAS,CAAC,CAAA;QAC9C,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,CAAC,CAAA;KAClC;IAED,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;IACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAgB,CAAA;IAExC,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;IAClB,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/scaleLayer.d.ts b/build/src/faceRecognitionNet/scaleLayer.d.ts
deleted file mode 100644
index 6f2fa85..0000000
--- a/build/src/faceRecognitionNet/scaleLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ScaleLayerParams } from './types';
-export declare function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D;
diff --git a/build/src/faceRecognitionNet/scaleLayer.js b/build/src/faceRecognitionNet/scaleLayer.js
deleted file mode 100644
index 2dd6bf5..0000000
--- a/build/src/faceRecognitionNet/scaleLayer.js
+++ /dev/null
@@ -1,5 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function scale(x, params) {
-    return tf.add(tf.mul(x, params.weights), params.biases);
-}
-//# sourceMappingURL=scaleLayer.js.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/scaleLayer.js.map b/build/src/faceRecognitionNet/scaleLayer.js.map
deleted file mode 100644
index fa42252..0000000
--- a/build/src/faceRecognitionNet/scaleLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"scaleLayer.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/scaleLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAIzD,MAAM,UAAU,KAAK,CAAC,CAAc,EAAE,MAAwB;IAC5D,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,EAAE,MAAM,CAAC,MAAM,CAAC,CAAA;AACzD,CAAC"}
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/types.d.ts b/build/src/faceRecognitionNet/types.d.ts
deleted file mode 100644
index 5f1e2ef..0000000
--- a/build/src/faceRecognitionNet/types.d.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ConvParams } from '../common';
-export declare type ScaleLayerParams = {
-    weights: tf.Tensor1D;
-    biases: tf.Tensor1D;
-};
-export declare type ResidualLayerParams = {
-    conv1: ConvLayerParams;
-    conv2: ConvLayerParams;
-};
-export declare type ConvLayerParams = {
-    conv: ConvParams;
-    scale: ScaleLayerParams;
-};
-export declare type NetParams = {
-    conv32_down: ConvLayerParams;
-    conv32_1: ResidualLayerParams;
-    conv32_2: ResidualLayerParams;
-    conv32_3: ResidualLayerParams;
-    conv64_down: ResidualLayerParams;
-    conv64_1: ResidualLayerParams;
-    conv64_2: ResidualLayerParams;
-    conv64_3: ResidualLayerParams;
-    conv128_down: ResidualLayerParams;
-    conv128_1: ResidualLayerParams;
-    conv128_2: ResidualLayerParams;
-    conv256_down: ResidualLayerParams;
-    conv256_1: ResidualLayerParams;
-    conv256_2: ResidualLayerParams;
-    conv256_down_out: ResidualLayerParams;
-    fc: tf.Tensor2D;
-};
diff --git a/build/src/faceRecognitionNet/types.js b/build/src/faceRecognitionNet/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/faceRecognitionNet/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/faceRecognitionNet/types.js.map b/build/src/faceRecognitionNet/types.js.map
deleted file mode 100644
index 3e89976..0000000
--- a/build/src/faceRecognitionNet/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/faceRecognitionNet/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/src/factories/WithAge.d.ts b/build/src/factories/WithAge.d.ts
deleted file mode 100644
index b924852..0000000
--- a/build/src/factories/WithAge.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-export declare type WithAge<TSource> = TSource & {
-    age: number;
-};
-export declare function isWithAge(obj: any): obj is WithAge<{}>;
-export declare function extendWithAge<TSource>(sourceObj: TSource, age: number): WithAge<TSource>;
diff --git a/build/src/factories/WithAge.js b/build/src/factories/WithAge.js
deleted file mode 100644
index 0cf40e3..0000000
--- a/build/src/factories/WithAge.js
+++ /dev/null
@@ -1,8 +0,0 @@
-export function isWithAge(obj) {
-    return typeof obj['age'] === 'number';
-}
-export function extendWithAge(sourceObj, age) {
-    const extension = { age };
-    return Object.assign({}, sourceObj, extension);
-}
-//# sourceMappingURL=WithAge.js.map
\ No newline at end of file
diff --git a/build/src/factories/WithAge.js.map b/build/src/factories/WithAge.js.map
deleted file mode 100644
index 852484b..0000000
--- a/build/src/factories/WithAge.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"WithAge.js","sourceRoot":"","sources":["../../../src/factories/WithAge.ts"],"names":[],"mappings":"AAIA,MAAM,UAAU,SAAS,CAAC,GAAQ;IAChC,OAAO,OAAO,GAAG,CAAC,KAAK,CAAC,KAAK,QAAQ,CAAA;AACvC,CAAC;AAED,MAAM,UAAU,aAAa,CAG3B,SAAkB,EAClB,GAAW;IAGX,MAAM,SAAS,GAAG,EAAE,GAAG,EAAE,CAAA;IACzB,OAAO,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,SAAS,EAAE,SAAS,CAAC,CAAA;AAChD,CAAC"}
\ No newline at end of file
diff --git a/build/src/factories/WithFaceDescriptor.d.ts b/build/src/factories/WithFaceDescriptor.d.ts
deleted file mode 100644
index 45175ba..0000000
--- a/build/src/factories/WithFaceDescriptor.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export declare type WithFaceDescriptor<TSource> = TSource & {
-    descriptor: Float32Array;
-};
-export declare function extendWithFaceDescriptor<TSource>(sourceObj: TSource, descriptor: Float32Array): WithFaceDescriptor<TSource>;
diff --git a/build/src/factories/WithFaceDescriptor.js b/build/src/factories/WithFaceDescriptor.js
deleted file mode 100644
index db68278..0000000
--- a/build/src/factories/WithFaceDescriptor.js
+++ /dev/null
@@ -1,5 +0,0 @@
-export function extendWithFaceDescriptor(sourceObj, descriptor) {
-    const extension = { descriptor };
-    return Object.assign({}, sourceObj, extension);
-}
-//# sourceMappingURL=WithFaceDescriptor.js.map
\ No newline at end of file
diff --git a/build/src/factories/WithFaceDescriptor.js.map b/build/src/factories/WithFaceDescriptor.js.map
deleted file mode 100644
index ff7e1db..0000000
--- a/build/src/factories/WithFaceDescriptor.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"WithFaceDescriptor.js","sourceRoot":"","sources":["../../../src/factories/WithFaceDescriptor.ts"],"names":[],"mappings":"AAIA,MAAM,UAAU,wBAAwB,CAGtC,SAAkB,EAClB,UAAwB;IAGxB,MAAM,SAAS,GAAG,EAAE,UAAU,EAAE,CAAA;IAChC,OAAO,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,SAAS,EAAE,SAAS,CAAC,CAAA;AAChD,CAAC"}
\ No newline at end of file
diff --git a/build/src/factories/WithFaceDetection.d.ts b/build/src/factories/WithFaceDetection.d.ts
deleted file mode 100644
index 718ea71..0000000
--- a/build/src/factories/WithFaceDetection.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { FaceDetection } from '../classes/FaceDetection';
-export declare type WithFaceDetection<TSource> = TSource & {
-    detection: FaceDetection;
-};
-export declare function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}>;
-export declare function extendWithFaceDetection<TSource>(sourceObj: TSource, detection: FaceDetection): WithFaceDetection<TSource>;
diff --git a/build/src/factories/WithFaceDetection.js b/build/src/factories/WithFaceDetection.js
deleted file mode 100644
index fc28fb5..0000000
--- a/build/src/factories/WithFaceDetection.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import { FaceDetection } from '../classes/FaceDetection';
-export function isWithFaceDetection(obj) {
-    return obj['detection'] instanceof FaceDetection;
-}
-export function extendWithFaceDetection(sourceObj, detection) {
-    const extension = { detection };
-    return Object.assign({}, sourceObj, extension);
-}
-//# sourceMappingURL=WithFaceDetection.js.map
\ No newline at end of file
diff --git a/build/src/factories/WithFaceDetection.js.map b/build/src/factories/WithFaceDetection.js.map
deleted file mode 100644
index a195cb7..0000000
--- a/build/src/factories/WithFaceDetection.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"WithFaceDetection.js","sourceRoot":"","sources":["../../../src/factories/WithFaceDetection.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAMzD,MAAM,UAAU,mBAAmB,CAAC,GAAQ;IAC1C,OAAO,GAAG,CAAC,WAAW,CAAC,YAAY,aAAa,CAAA;AAClD,CAAC;AAED,MAAM,UAAU,uBAAuB,CAGrC,SAAkB,EAClB,SAAwB;IAGxB,MAAM,SAAS,GAAG,EAAE,SAAS,EAAE,CAAA;IAC/B,OAAO,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,SAAS,EAAE,SAAS,CAAC,CAAA;AAChD,CAAC"}
\ No newline at end of file
diff --git a/build/src/factories/WithFaceExpressions.d.ts b/build/src/factories/WithFaceExpressions.d.ts
deleted file mode 100644
index f7960b2..0000000
--- a/build/src/factories/WithFaceExpressions.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';
-export declare type WithFaceExpressions<TSource> = TSource & {
-    expressions: FaceExpressions;
-};
-export declare function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}>;
-export declare function extendWithFaceExpressions<TSource>(sourceObj: TSource, expressions: FaceExpressions): WithFaceExpressions<TSource>;
diff --git a/build/src/factories/WithFaceExpressions.js b/build/src/factories/WithFaceExpressions.js
deleted file mode 100644
index 933e101..0000000
--- a/build/src/factories/WithFaceExpressions.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';
-export function isWithFaceExpressions(obj) {
-    return obj['expressions'] instanceof FaceExpressions;
-}
-export function extendWithFaceExpressions(sourceObj, expressions) {
-    const extension = { expressions };
-    return Object.assign({}, sourceObj, extension);
-}
-//# sourceMappingURL=WithFaceExpressions.js.map
\ No newline at end of file
diff --git a/build/src/factories/WithFaceExpressions.js.map b/build/src/factories/WithFaceExpressions.js.map
deleted file mode 100644
index 24288cc..0000000
--- a/build/src/factories/WithFaceExpressions.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"WithFaceExpressions.js","sourceRoot":"","sources":["../../../src/factories/WithFaceExpressions.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,eAAe,EAAE,MAAM,sCAAsC,CAAC;AAMvE,MAAM,UAAU,qBAAqB,CAAC,GAAQ;IAC5C,OAAO,GAAG,CAAC,aAAa,CAAC,YAAY,eAAe,CAAA;AACtD,CAAC;AAED,MAAM,UAAU,yBAAyB,CAGvC,SAAkB,EAClB,WAA4B;IAG5B,MAAM,SAAS,GAAG,EAAE,WAAW,EAAE,CAAA;IACjC,OAAO,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,SAAS,EAAE,SAAS,CAAC,CAAA;AAChD,CAAC"}
\ No newline at end of file
diff --git a/build/src/factories/WithFaceLandmarks.d.ts b/build/src/factories/WithFaceLandmarks.d.ts
deleted file mode 100644
index 99b9315..0000000
--- a/build/src/factories/WithFaceLandmarks.d.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-import { FaceDetection } from '../classes/FaceDetection';
-import { FaceLandmarks } from '../classes/FaceLandmarks';
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-import { WithFaceDetection } from './WithFaceDetection';
-export declare type WithFaceLandmarks<TSource extends WithFaceDetection<{}>, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68> = TSource & {
-    landmarks: TFaceLandmarks;
-    unshiftedLandmarks: TFaceLandmarks;
-    alignedRect: FaceDetection;
-};
-export declare function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks>;
-export declare function extendWithFaceLandmarks<TSource extends WithFaceDetection<{}>, TFaceLandmarks extends FaceLandmarks = FaceLandmarks68>(sourceObj: TSource, unshiftedLandmarks: TFaceLandmarks): WithFaceLandmarks<TSource, TFaceLandmarks>;
diff --git a/build/src/factories/WithFaceLandmarks.js b/build/src/factories/WithFaceLandmarks.js
deleted file mode 100644
index 2df14d1..0000000
--- a/build/src/factories/WithFaceLandmarks.js
+++ /dev/null
@@ -1,23 +0,0 @@
-import { FaceDetection } from '../classes/FaceDetection';
-import { FaceLandmarks } from '../classes/FaceLandmarks';
-import { isWithFaceDetection } from './WithFaceDetection';
-export function isWithFaceLandmarks(obj) {
-    return isWithFaceDetection(obj)
-        && obj['landmarks'] instanceof FaceLandmarks
-        && obj['unshiftedLandmarks'] instanceof FaceLandmarks
-        && obj['alignedRect'] instanceof FaceDetection;
-}
-export 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);
-}
-//# sourceMappingURL=WithFaceLandmarks.js.map
\ No newline at end of file
diff --git a/build/src/factories/WithFaceLandmarks.js.map b/build/src/factories/WithFaceLandmarks.js.map
deleted file mode 100644
index e95843b..0000000
--- a/build/src/factories/WithFaceLandmarks.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"WithFaceLandmarks.js","sourceRoot":"","sources":["../../../src/factories/WithFaceLandmarks.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,0BAA0B,CAAC;AAEzD,OAAO,EAAE,mBAAmB,EAAqB,MAAM,qBAAqB,CAAC;AAW7E,MAAM,UAAU,mBAAmB,CAAC,GAAQ;IAC1C,OAAO,mBAAmB,CAAC,GAAG,CAAC;WAC1B,GAAG,CAAC,WAAW,CAAC,YAAY,aAAa;WACzC,GAAG,CAAC,oBAAoB,CAAC,YAAY,aAAa;WAClD,GAAG,CAAC,aAAa,CAAC,YAAY,aAAa,CAAA;AAClD,CAAC;AAED,MAAM,UAAU,uBAAuB,CAIrC,SAAkB,EAClB,kBAAkC;IAGlC,MAAM,EAAE,GAAG,EAAE,KAAK,EAAE,GAAG,SAAS,CAAC,SAAS,CAAA;IAC1C,MAAM,SAAS,GAAG,kBAAkB,CAAC,OAAO,CAAiB,KAAK,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAA;IAE9E,MAAM,IAAI,GAAG,SAAS,CAAC,KAAK,EAAE,CAAA;IAC9B,MAAM,EAAE,SAAS,EAAE,GAAG,SAAS,CAAC,SAAS,CAAA;IACzC,MAAM,WAAW,GAAG,IAAI,aAAa,CAAC,SAAS,CAAC,SAAS,CAAC,KAAK,EAAE,IAAI,CAAC,OAAO,CAAC,SAAS,CAAC,OAAO,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;IAE9G,MAAM,SAAS,GAAG;QAChB,SAAS;QACT,kBAAkB;QAClB,WAAW;KACZ,CAAA;IAED,OAAO,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,SAAS,EAAE,SAAS,CAAC,CAAA;AAChD,CAAC"}
\ No newline at end of file
diff --git a/build/src/factories/WithGender.d.ts b/build/src/factories/WithGender.d.ts
deleted file mode 100644
index 6f26c11..0000000
--- a/build/src/factories/WithGender.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { Gender } from '../ageGenderNet/types';
-export declare type WithGender<TSource> = TSource & {
-    gender: Gender;
-    genderProbability: number;
-};
-export declare function isWithGender(obj: any): obj is WithGender<{}>;
-export declare function extendWithGender<TSource>(sourceObj: TSource, gender: Gender, genderProbability: number): WithGender<TSource>;
diff --git a/build/src/factories/WithGender.js b/build/src/factories/WithGender.js
deleted file mode 100644
index d6535be..0000000
--- a/build/src/factories/WithGender.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import { Gender } from '../ageGenderNet/types';
-import { isValidProbablitiy } from '../utils';
-export function isWithGender(obj) {
-    return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)
-        && isValidProbablitiy(obj['genderProbability']);
-}
-export function extendWithGender(sourceObj, gender, genderProbability) {
-    const extension = { gender, genderProbability };
-    return Object.assign({}, sourceObj, extension);
-}
-//# sourceMappingURL=WithGender.js.map
\ No newline at end of file
diff --git a/build/src/factories/WithGender.js.map b/build/src/factories/WithGender.js.map
deleted file mode 100644
index b1e9cb1..0000000
--- a/build/src/factories/WithGender.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"WithGender.js","sourceRoot":"","sources":["../../../src/factories/WithGender.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,MAAM,EAAE,MAAM,uBAAuB,CAAC;AAC/C,OAAO,EAAE,kBAAkB,EAAE,MAAM,UAAU,CAAC;AAO9C,MAAM,UAAU,YAAY,CAAC,GAAQ;IACnC,OAAO,CAAC,GAAG,CAAC,QAAQ,CAAC,KAAK,MAAM,CAAC,IAAI,IAAI,GAAG,CAAC,QAAQ,CAAC,KAAK,MAAM,CAAC,MAAM,CAAC;WACpE,kBAAkB,CAAC,GAAG,CAAC,mBAAmB,CAAC,CAAC,CAAA;AACnD,CAAC;AAED,MAAM,UAAU,gBAAgB,CAG9B,SAAkB,EAClB,MAAc,EACd,iBAAyB;IAGzB,MAAM,SAAS,GAAG,EAAE,MAAM,EAAE,iBAAiB,EAAE,CAAA;IAC/C,OAAO,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,SAAS,EAAE,SAAS,CAAC,CAAA;AAChD,CAAC"}
\ No newline at end of file
diff --git a/build/src/factories/index.d.ts b/build/src/factories/index.d.ts
deleted file mode 100644
index c5094ab..0000000
--- a/build/src/factories/index.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-export * from './WithFaceDescriptor';
-export * from './WithFaceDetection';
-export * from './WithFaceExpressions';
-export * from './WithFaceLandmarks';
-export * from './WithAge';
-export * from './WithGender';
diff --git a/build/src/factories/index.js b/build/src/factories/index.js
deleted file mode 100644
index 0143670..0000000
--- a/build/src/factories/index.js
+++ /dev/null
@@ -1,7 +0,0 @@
-export * from './WithFaceDescriptor';
-export * from './WithFaceDetection';
-export * from './WithFaceExpressions';
-export * from './WithFaceLandmarks';
-export * from './WithAge';
-export * from './WithGender';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/factories/index.js.map b/build/src/factories/index.js.map
deleted file mode 100644
index 2e055c9..0000000
--- a/build/src/factories/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/factories/index.ts"],"names":[],"mappings":"AAAA,cAAc,sBAAsB,CAAA;AACpC,cAAc,qBAAqB,CAAA;AACnC,cAAc,uBAAuB,CAAA;AACrC,cAAc,qBAAqB,CAAA;AACnC,cAAc,WAAW,CAAA;AACzB,cAAc,cAAc,CAAA"}
\ No newline at end of file
diff --git a/build/src/globalApi/ComposableTask.d.ts b/build/src/globalApi/ComposableTask.d.ts
deleted file mode 100644
index b7203e3..0000000
--- a/build/src/globalApi/ComposableTask.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-export declare class ComposableTask<T> {
-    then(onfulfilled: (value: T) => T | PromiseLike<T>): Promise<T>;
-    run(): Promise<T>;
-}
diff --git a/build/src/globalApi/ComposableTask.js b/build/src/globalApi/ComposableTask.js
deleted file mode 100644
index 7dde649..0000000
--- a/build/src/globalApi/ComposableTask.js
+++ /dev/null
@@ -1,9 +0,0 @@
-export class ComposableTask {
-    async then(onfulfilled) {
-        return onfulfilled(await this.run());
-    }
-    async run() {
-        throw new Error('ComposableTask - run is not implemented');
-    }
-}
-//# sourceMappingURL=ComposableTask.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/ComposableTask.js.map b/build/src/globalApi/ComposableTask.js.map
deleted file mode 100644
index 809a1b9..0000000
--- a/build/src/globalApi/ComposableTask.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"ComposableTask.js","sourceRoot":"","sources":["../../../src/globalApi/ComposableTask.ts"],"names":[],"mappings":"AAAA,MAAM,OAAO,cAAc;IAElB,KAAK,CAAC,IAAI,CACf,WAA6C;QAE7C,OAAO,WAAW,CAAC,MAAM,IAAI,CAAC,GAAG,EAAE,CAAC,CAAA;IACtC,CAAC;IAEM,KAAK,CAAC,GAAG;QACd,MAAM,IAAI,KAAK,CAAC,yCAAyC,CAAC,CAAA;IAC5D,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/globalApi/ComputeFaceDescriptorsTasks.d.ts b/build/src/globalApi/ComputeFaceDescriptorsTasks.d.ts
deleted file mode 100644
index a17900b..0000000
--- a/build/src/globalApi/ComputeFaceDescriptorsTasks.d.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-import { TNetInput } from '../dom';
-import { WithFaceDescriptor } from '../factories/WithFaceDescriptor';
-import { WithFaceDetection } from '../factories/WithFaceDetection';
-import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
-import { ComposableTask } from './ComposableTask';
-import { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';
-import { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';
-export declare class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
-    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>;
-    protected input: TNetInput;
-    constructor(parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>, input: TNetInput);
-}
-export declare class ComputeAllFaceDescriptorsTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {
-    run(): Promise<WithFaceDescriptor<TSource>[]>;
-    withFaceExpressions(): PredictAllFaceExpressionsWithFaceAlignmentTask<WithFaceDescriptor<TSource>>;
-    withAgeAndGender(): PredictAllAgeAndGenderWithFaceAlignmentTask<WithFaceDescriptor<TSource>>;
-}
-export declare class ComputeSingleFaceDescriptorTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {
-    run(): Promise<WithFaceDescriptor<TSource> | undefined>;
-    withFaceExpressions(): PredictSingleFaceExpressionsWithFaceAlignmentTask<WithFaceDescriptor<TSource>>;
-    withAgeAndGender(): PredictSingleAgeAndGenderWithFaceAlignmentTask<WithFaceDescriptor<TSource>>;
-}
diff --git a/build/src/globalApi/ComputeFaceDescriptorsTasks.js b/build/src/globalApi/ComputeFaceDescriptorsTasks.js
deleted file mode 100644
index 45b9e48..0000000
--- a/build/src/globalApi/ComputeFaceDescriptorsTasks.js
+++ /dev/null
@@ -1,43 +0,0 @@
-import { extendWithFaceDescriptor } from '../factories/WithFaceDescriptor';
-import { ComposableTask } from './ComposableTask';
-import { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';
-import { nets } from './nets';
-import { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask, } from './PredictAgeAndGenderTask';
-import { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask, } from './PredictFaceExpressionsTask';
-export class ComputeFaceDescriptorsTaskBase extends ComposableTask {
-    constructor(parentTask, input) {
-        super();
-        this.parentTask = parentTask;
-        this.input = input;
-    }
-}
-export 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);
-    }
-}
-export 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, parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true }));
-        return extendWithFaceDescriptor(parentResult, descriptor);
-    }
-    withFaceExpressions() {
-        return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withAgeAndGender() {
-        return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-}
-//# sourceMappingURL=ComputeFaceDescriptorsTasks.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/ComputeFaceDescriptorsTasks.js.map b/build/src/globalApi/ComputeFaceDescriptorsTasks.js.map
deleted file mode 100644
index dec589e..0000000
--- a/build/src/globalApi/ComputeFaceDescriptorsTasks.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"ComputeFaceDescriptorsTasks.js","sourceRoot":"","sources":["../../../src/globalApi/ComputeFaceDescriptorsTasks.ts"],"names":[],"mappings":"AACA,OAAO,EAAE,wBAAwB,EAAsB,MAAM,iCAAiC,CAAC;AAG/F,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,gCAAgC,EAAE,iCAAiC,EAAE,MAAM,iCAAiC,CAAC;AACtH,OAAO,EAAE,IAAI,EAAE,MAAM,QAAQ,CAAC;AAC9B,OAAO,EACL,2CAA2C,EAC3C,8CAA8C,GAC/C,MAAM,2BAA2B,CAAC;AACnC,OAAO,EACL,8CAA8C,EAC9C,iDAAiD,GAClD,MAAM,8BAA8B,CAAC;AAEtC,MAAM,OAAO,8BAAuD,SAAQ,cAAuB;IACjG,YACY,UAAkE,EAClE,KAAgB;QAE1B,KAAK,EAAE,CAAA;QAHG,eAAU,GAAV,UAAU,CAAwD;QAClE,UAAK,GAAL,KAAK,CAAW;IAG5B,CAAC;CACF;AAED,MAAM,OAAO,6BAEX,SAAQ,8BAAwE;IAEzE,KAAK,CAAC,GAAG;QAEd,MAAM,aAAa,GAAG,MAAM,IAAI,CAAC,UAAU,CAAA;QAE3C,MAAM,WAAW,GAAG,MAAM,gCAAgC,CACxD,aAAa,EACb,IAAI,CAAC,KAAK,EACV,KAAK,CAAC,EAAE,CAAC,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,IAAI,CAAC,EAAE,CACpC,IAAI,CAAC,kBAAkB,CAAC,qBAAqB,CAAC,IAAI,CAA0B,CAC7E,CAAC,EACF,IAAI,EACJ,YAAY,CAAC,EAAE,CAAC,YAAY,CAAC,SAAS,CAAC,KAAK,CAAC,IAAI,EAAE,EAAE,gBAAgB,EAAE,IAAI,EAAE,CAAC,CAC/E,CAAA;QAED,OAAO,WAAW,CAAC,GAAG,CAAC,CAAC,UAAU,EAAE,CAAC,EAAE,EAAE,CAAC,wBAAwB,CAAU,aAAa,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAA;IAC5G,CAAC;IAED,mBAAmB;QACjB,OAAO,IAAI,8CAA8C,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC7E,CAAC;IAED,gBAAgB;QACd,OAAO,IAAI,2CAA2C,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC1E,CAAC;CACF;AAED,MAAM,OAAO,+BAEX,SAAQ,8BAA4F;IAE7F,KAAK,CAAC,GAAG;QAEd,MAAM,YAAY,GAAG,MAAM,IAAI,CAAC,UAAU,CAAA;QAC1C,IAAI,CAAC,YAAY,EAAE;YACjB,OAAM;SACP;QACD,MAAM,UAAU,GAAG,MAAM,iCAAiC,CACxD,YAAY,EACZ,IAAI,CAAC,KAAK,EACV,IAAI,CAAC,EAAE,CAAC,IAAI,CAAC,kBAAkB,CAAC,qBAAqB,CAAC,IAAI,CAA0B,EACpF,IAAI,EACJ,YAAY,CAAC,EAAE,CAAC,YAAY,CAAC,SAAS,CAAC,KAAK,CAAC,IAAI,EAAE,EAAE,gBAAgB,EAAE,IAAI,EAAE,CAAC,CAC/E,CAAA;QAED,OAAO,wBAAwB,CAAC,YAAY,EAAE,UAAU,CAAC,CAAA;IAC3D,CAAC;IAED,mBAAmB;QACjB,OAAO,IAAI,iDAAiD,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAChF,CAAC;IAED,gBAAgB;QACd,OAAO,IAAI,8CAA8C,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC7E,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/globalApi/DetectFaceLandmarksTasks.d.ts b/build/src/globalApi/DetectFaceLandmarksTasks.d.ts
deleted file mode 100644
index 53b6fd8..0000000
--- a/build/src/globalApi/DetectFaceLandmarksTasks.d.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-import { TNetInput } from '../dom';
-import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
-import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
-import { WithFaceDetection } from '../factories/WithFaceDetection';
-import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
-import { ComposableTask } from './ComposableTask';
-import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
-import { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';
-import { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';
-export declare class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
-    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>;
-    protected input: TNetInput;
-    protected useTinyLandmarkNet: boolean;
-    constructor(parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>, input: TNetInput, useTinyLandmarkNet: boolean);
-    protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet;
-}
-export declare class DetectAllFaceLandmarksTask<TSource extends WithFaceDetection<{}>> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {
-    run(): Promise<WithFaceLandmarks<TSource>[]>;
-    withFaceExpressions(): PredictAllFaceExpressionsWithFaceAlignmentTask<WithFaceLandmarks<TSource, FaceLandmarks68>>;
-    withAgeAndGender(): PredictAllAgeAndGenderWithFaceAlignmentTask<WithFaceLandmarks<TSource, FaceLandmarks68>>;
-    withFaceDescriptors(): ComputeAllFaceDescriptorsTask<WithFaceLandmarks<TSource, FaceLandmarks68>>;
-}
-export declare class DetectSingleFaceLandmarksTask<TSource extends WithFaceDetection<{}>> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {
-    run(): Promise<WithFaceLandmarks<TSource> | undefined>;
-    withFaceExpressions(): PredictSingleFaceExpressionsWithFaceAlignmentTask<WithFaceLandmarks<TSource, FaceLandmarks68>>;
-    withAgeAndGender(): PredictSingleAgeAndGenderWithFaceAlignmentTask<WithFaceLandmarks<TSource, FaceLandmarks68>>;
-    withFaceDescriptor(): ComputeSingleFaceDescriptorTask<WithFaceLandmarks<TSource, FaceLandmarks68>>;
-}
diff --git a/build/src/globalApi/DetectFaceLandmarksTasks.js b/build/src/globalApi/DetectFaceLandmarksTasks.js
deleted file mode 100644
index d1a2af8..0000000
--- a/build/src/globalApi/DetectFaceLandmarksTasks.js
+++ /dev/null
@@ -1,67 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { extractFaces, extractFaceTensors } from '../dom';
-import { extendWithFaceLandmarks } from '../factories/WithFaceLandmarks';
-import { ComposableTask } from './ComposableTask';
-import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
-import { nets } from './nets';
-import { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask, } from './PredictAgeAndGenderTask';
-import { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask, } from './PredictFaceExpressionsTask';
-export 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;
-    }
-}
-export class DetectAllFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
-    async run() {
-        const parentResults = await this.parentTask;
-        const detections = parentResults.map(res => res.detection);
-        const faces = this.input instanceof tf.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 tf.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);
-    }
-}
-export class DetectSingleFaceLandmarksTask extends DetectFaceLandmarksTaskBase {
-    async run() {
-        const parentResult = await this.parentTask;
-        if (!parentResult) {
-            return;
-        }
-        const { detection } = parentResult;
-        const faces = this.input instanceof tf.Tensor
-            ? await extractFaceTensors(this.input, [detection])
-            : await extractFaces(this.input, [detection]);
-        const landmarks = await this.landmarkNet.detectLandmarks(faces[0]);
-        faces.forEach(f => f instanceof tf.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);
-    }
-}
-//# sourceMappingURL=DetectFaceLandmarksTasks.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/DetectFaceLandmarksTasks.js.map b/build/src/globalApi/DetectFaceLandmarksTasks.js.map
deleted file mode 100644
index ab29e89..0000000
--- a/build/src/globalApi/DetectFaceLandmarksTasks.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/globalApi/DetectFacesTasks.d.ts b/build/src/globalApi/DetectFacesTasks.d.ts
deleted file mode 100644
index 1da11c5..0000000
--- a/build/src/globalApi/DetectFacesTasks.d.ts
+++ /dev/null
@@ -1,38 +0,0 @@
-import { FaceDetection } from '../classes/FaceDetection';
-import { TNetInput } from '../dom';
-import { ComposableTask } from './ComposableTask';
-import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
-import { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';
-import { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';
-import { FaceDetectionOptions } from './types';
-export declare class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {
-    protected input: TNetInput;
-    protected options: FaceDetectionOptions;
-    constructor(input: TNetInput, options?: FaceDetectionOptions);
-}
-export declare class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {
-    run(): Promise<FaceDetection[]>;
-    private runAndExtendWithFaceDetections;
-    withFaceLandmarks(useTinyLandmarkNet?: boolean): DetectAllFaceLandmarksTask<{
-        detection: FaceDetection;
-    }>;
-    withFaceExpressions(): PredictAllFaceExpressionsTask<{
-        detection: FaceDetection;
-    }>;
-    withAgeAndGender(): PredictAllAgeAndGenderTask<{
-        detection: FaceDetection;
-    }>;
-}
-export declare class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {
-    run(): Promise<FaceDetection | undefined>;
-    private runAndExtendWithFaceDetection;
-    withFaceLandmarks(useTinyLandmarkNet?: boolean): DetectSingleFaceLandmarksTask<{
-        detection: FaceDetection;
-    }>;
-    withFaceExpressions(): PredictSingleFaceExpressionsTask<{
-        detection: FaceDetection;
-    }>;
-    withAgeAndGender(): PredictSingleAgeAndGenderTask<{
-        detection: FaceDetection;
-    }>;
-}
diff --git a/build/src/globalApi/DetectFacesTasks.js b/build/src/globalApi/DetectFacesTasks.js
deleted file mode 100644
index f10628e..0000000
--- a/build/src/globalApi/DetectFacesTasks.js
+++ /dev/null
@@ -1,75 +0,0 @@
-import { extendWithFaceDetection } from '../factories/WithFaceDetection';
-import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
-import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
-import { TinyYolov2Options } from '../tinyYolov2';
-import { ComposableTask } from './ComposableTask';
-import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
-import { nets } from './nets';
-import { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';
-import { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';
-export class DetectFacesTaskBase extends ComposableTask {
-    constructor(input, options = new SsdMobilenetv1Options()) {
-        super();
-        this.input = input;
-        this.options = options;
-    }
-}
-export class DetectAllFacesTask extends DetectFacesTaskBase {
-    async run() {
-        const { input, options } = this;
-        const faceDetectionFunction = options instanceof TinyFaceDetectorOptions
-            ? (input) => nets.tinyFaceDetector.locateFaces(input, options)
-            : (options instanceof SsdMobilenetv1Options
-                ? (input) => nets.ssdMobilenetv1.locateFaces(input, options)
-                : (options instanceof TinyYolov2Options
-                    ? (input) => nets.tinyYolov2.locateFaces(input, 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);
-    }
-}
-export 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) : undefined);
-        });
-    }
-    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);
-    }
-}
-//# sourceMappingURL=DetectFacesTasks.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/DetectFacesTasks.js.map b/build/src/globalApi/DetectFacesTasks.js.map
deleted file mode 100644
index fbf0d98..0000000
--- a/build/src/globalApi/DetectFacesTasks.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/globalApi/FaceMatcher.d.ts b/build/src/globalApi/FaceMatcher.d.ts
deleted file mode 100644
index 4479ca0..0000000
--- a/build/src/globalApi/FaceMatcher.d.ts
+++ /dev/null
@@ -1,15 +0,0 @@
-import { FaceMatch } from '../classes/FaceMatch';
-import { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';
-import { WithFaceDescriptor } from '../factories';
-export declare class FaceMatcher {
-    private _labeledDescriptors;
-    private _distanceThreshold;
-    constructor(inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>, distanceThreshold?: number);
-    get labeledDescriptors(): LabeledFaceDescriptors[];
-    get distanceThreshold(): number;
-    computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number;
-    matchDescriptor(queryDescriptor: Float32Array): FaceMatch;
-    findBestMatch(queryDescriptor: Float32Array): FaceMatch;
-    toJSON(): any;
-    static fromJSON(json: any): FaceMatcher;
-}
diff --git a/build/src/globalApi/FaceMatcher.js b/build/src/globalApi/FaceMatcher.js
deleted file mode 100644
index b8e654b..0000000
--- a/build/src/globalApi/FaceMatcher.js
+++ /dev/null
@@ -1,57 +0,0 @@
-import { FaceMatch } from '../classes/FaceMatch';
-import { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';
-import { euclideanDistance } from '../euclideanDistance';
-export 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);
-    }
-}
-//# sourceMappingURL=FaceMatcher.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/FaceMatcher.js.map b/build/src/globalApi/FaceMatcher.js.map
deleted file mode 100644
index 44faf8d..0000000
--- a/build/src/globalApi/FaceMatcher.js.map
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/build/src/globalApi/PredictAgeAndGenderTask.d.ts b/build/src/globalApi/PredictAgeAndGenderTask.d.ts
deleted file mode 100644
index 5d8266c..0000000
--- a/build/src/globalApi/PredictAgeAndGenderTask.d.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-import { TNetInput } from '../dom';
-import { WithAge } from '../factories/WithAge';
-import { WithFaceDetection } from '../factories/WithFaceDetection';
-import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
-import { WithGender } from '../factories/WithGender';
-import { ComposableTask } from './ComposableTask';
-import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
-import { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask } from './PredictFaceExpressionsTask';
-export declare class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
-    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>;
-    protected input: TNetInput;
-    protected extractedFaces?: any[] | undefined;
-    constructor(parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>, input: TNetInput, extractedFaces?: any[] | undefined);
-}
-export declare class PredictAllAgeAndGenderTask<TSource extends WithFaceDetection<{}>> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {
-    run(): Promise<WithAge<WithGender<TSource>>[]>;
-    withFaceExpressions(): PredictAllFaceExpressionsTask<WithAge<WithGender<TSource>>>;
-}
-export declare class PredictSingleAgeAndGenderTask<TSource extends WithFaceDetection<{}>> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {
-    run(): Promise<WithAge<WithGender<TSource>> | undefined>;
-    withFaceExpressions(): PredictSingleFaceExpressionsTask<WithAge<WithGender<TSource>>>;
-}
-export declare class PredictAllAgeAndGenderWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictAllAgeAndGenderTask<TSource> {
-    withFaceExpressions(): PredictAllFaceExpressionsWithFaceAlignmentTask<WithAge<WithGender<TSource>>>;
-    withFaceDescriptors(): ComputeAllFaceDescriptorsTask<WithAge<WithGender<TSource>>>;
-}
-export declare class PredictSingleAgeAndGenderWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictSingleAgeAndGenderTask<TSource> {
-    withFaceExpressions(): PredictSingleFaceExpressionsWithFaceAlignmentTask<WithAge<WithGender<TSource>>>;
-    withFaceDescriptor(): ComputeSingleFaceDescriptorTask<WithAge<WithGender<TSource>>>;
-}
diff --git a/build/src/globalApi/PredictAgeAndGenderTask.js b/build/src/globalApi/PredictAgeAndGenderTask.js
deleted file mode 100644
index 5a17aa8..0000000
--- a/build/src/globalApi/PredictAgeAndGenderTask.js
+++ /dev/null
@@ -1,58 +0,0 @@
-import { extendWithAge } from '../factories/WithAge';
-import { extendWithGender } from '../factories/WithGender';
-import { ComposableTask } from './ComposableTask';
-import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
-import { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';
-import { nets } from './nets';
-import { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask, } from './PredictFaceExpressionsTask';
-export class PredictAgeAndGenderTaskBase extends ComposableTask {
-    constructor(parentTask, input, extractedFaces) {
-        super();
-        this.parentTask = parentTask;
-        this.input = input;
-        this.extractedFaces = extractedFaces;
-    }
-}
-export 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);
-    }
-}
-export 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);
-    }
-}
-export class PredictAllAgeAndGenderWithFaceAlignmentTask extends PredictAllAgeAndGenderTask {
-    withFaceExpressions() {
-        return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptors() {
-        return new ComputeAllFaceDescriptorsTask(this, this.input);
-    }
-}
-export class PredictSingleAgeAndGenderWithFaceAlignmentTask extends PredictSingleAgeAndGenderTask {
-    withFaceExpressions() {
-        return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptor() {
-        return new ComputeSingleFaceDescriptorTask(this, this.input);
-    }
-}
-//# sourceMappingURL=PredictAgeAndGenderTask.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/PredictAgeAndGenderTask.js.map b/build/src/globalApi/PredictAgeAndGenderTask.js.map
deleted file mode 100644
index a69d9e3..0000000
--- a/build/src/globalApi/PredictAgeAndGenderTask.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/globalApi/PredictFaceExpressionsTask.d.ts b/build/src/globalApi/PredictFaceExpressionsTask.d.ts
deleted file mode 100644
index 7bd27c3..0000000
--- a/build/src/globalApi/PredictFaceExpressionsTask.d.ts
+++ /dev/null
@@ -1,29 +0,0 @@
-import { TNetInput } from '../dom';
-import { WithFaceDetection } from '../factories/WithFaceDetection';
-import { WithFaceExpressions } from '../factories/WithFaceExpressions';
-import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
-import { ComposableTask } from './ComposableTask';
-import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
-import { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask } from './PredictAgeAndGenderTask';
-export declare class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
-    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>;
-    protected input: TNetInput;
-    protected extractedFaces?: any[] | undefined;
-    constructor(parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>, input: TNetInput, extractedFaces?: any[] | undefined);
-}
-export declare class PredictAllFaceExpressionsTask<TSource extends WithFaceDetection<{}>> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {
-    run(): Promise<WithFaceExpressions<TSource>[]>;
-    withAgeAndGender(): PredictAllAgeAndGenderTask<WithFaceExpressions<TSource>>;
-}
-export declare class PredictSingleFaceExpressionsTask<TSource extends WithFaceDetection<{}>> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {
-    run(): Promise<WithFaceExpressions<TSource> | undefined>;
-    withAgeAndGender(): PredictSingleAgeAndGenderTask<WithFaceExpressions<TSource>>;
-}
-export declare class PredictAllFaceExpressionsWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictAllFaceExpressionsTask<TSource> {
-    withAgeAndGender(): PredictAllAgeAndGenderWithFaceAlignmentTask<WithFaceExpressions<TSource>>;
-    withFaceDescriptors(): ComputeAllFaceDescriptorsTask<WithFaceExpressions<TSource>>;
-}
-export declare class PredictSingleFaceExpressionsWithFaceAlignmentTask<TSource extends WithFaceLandmarks<WithFaceDetection<{}>>> extends PredictSingleFaceExpressionsTask<TSource> {
-    withAgeAndGender(): PredictSingleAgeAndGenderWithFaceAlignmentTask<WithFaceExpressions<TSource>>;
-    withFaceDescriptor(): ComputeSingleFaceDescriptorTask<WithFaceExpressions<TSource>>;
-}
diff --git a/build/src/globalApi/PredictFaceExpressionsTask.js b/build/src/globalApi/PredictFaceExpressionsTask.js
deleted file mode 100644
index 7c528af..0000000
--- a/build/src/globalApi/PredictFaceExpressionsTask.js
+++ /dev/null
@@ -1,54 +0,0 @@
-import { extendWithFaceExpressions } from '../factories/WithFaceExpressions';
-import { ComposableTask } from './ComposableTask';
-import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
-import { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';
-import { nets } from './nets';
-import { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask, } from './PredictAgeAndGenderTask';
-export class PredictFaceExpressionsTaskBase extends ComposableTask {
-    constructor(parentTask, input, extractedFaces) {
-        super();
-        this.parentTask = parentTask;
-        this.input = input;
-        this.extractedFaces = extractedFaces;
-    }
-}
-export 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);
-    }
-}
-export 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);
-    }
-}
-export class PredictAllFaceExpressionsWithFaceAlignmentTask extends PredictAllFaceExpressionsTask {
-    withAgeAndGender() {
-        return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptors() {
-        return new ComputeAllFaceDescriptorsTask(this, this.input);
-    }
-}
-export class PredictSingleFaceExpressionsWithFaceAlignmentTask extends PredictSingleFaceExpressionsTask {
-    withAgeAndGender() {
-        return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-    }
-    withFaceDescriptor() {
-        return new ComputeSingleFaceDescriptorTask(this, this.input);
-    }
-}
-//# sourceMappingURL=PredictFaceExpressionsTask.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/PredictFaceExpressionsTask.js.map b/build/src/globalApi/PredictFaceExpressionsTask.js.map
deleted file mode 100644
index e30b298..0000000
--- a/build/src/globalApi/PredictFaceExpressionsTask.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"PredictFaceExpressionsTask.js","sourceRoot":"","sources":["../../../src/globalApi/PredictFaceExpressionsTask.ts"],"names":[],"mappings":"AAKA,OAAO,EAAE,yBAAyB,EAAuB,MAAM,kCAAkC,CAAC;AAElG,OAAO,EAAE,cAAc,EAAE,MAAM,kBAAkB,CAAC;AAClD,OAAO,EAAE,6BAA6B,EAAE,+BAA+B,EAAE,MAAM,+BAA+B,CAAC;AAC/G,OAAO,EAAE,gCAAgC,EAAE,iCAAiC,EAAE,MAAM,iCAAiC,CAAC;AACtH,OAAO,EAAE,IAAI,EAAE,MAAM,QAAQ,CAAC;AAC9B,OAAO,EACL,0BAA0B,EAC1B,2CAA2C,EAC3C,6BAA6B,EAC7B,8CAA8C,GAC/C,MAAM,2BAA2B,CAAC;AAEnC,MAAM,OAAO,8BAAuD,SAAQ,cAAuB;IACjG,YACY,UAAkE,EAClE,KAAgB,EAChB,cAAuD;QAEjE,KAAK,EAAE,CAAA;QAJG,eAAU,GAAV,UAAU,CAAwD;QAClE,UAAK,GAAL,KAAK,CAAW;QAChB,mBAAc,GAAd,cAAc,CAAyC;IAGnE,CAAC;CACF;AAED,MAAM,OAAO,6BAEX,SAAQ,8BAAyE;IAE1E,KAAK,CAAC,GAAG;QAEd,MAAM,aAAa,GAAG,MAAM,IAAI,CAAC,UAAU,CAAA;QAE3C,MAAM,qBAAqB,GAAG,MAAM,gCAAgC,CAClE,aAAa,EACb,IAAI,CAAC,KAAK,EACV,KAAK,EAAC,KAAK,EAAC,EAAE,CAAC,MAAM,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CACxC,IAAI,CAAC,EAAE,CAAC,IAAI,CAAC,iBAAiB,CAAC,kBAAkB,CAAC,IAAI,CAA6B,CACpF,CAAC,EACF,IAAI,CAAC,cAAc,CACpB,CAAA;QAED,OAAO,aAAa,CAAC,GAAG,CACtB,CAAC,YAAY,EAAE,CAAC,EAAE,EAAE,CAAC,yBAAyB,CAAU,YAAY,EAAE,qBAAqB,CAAC,CAAC,CAAC,CAAC,CAChG,CAAA;IACH,CAAC;IAED,gBAAgB;QACd,OAAO,IAAI,0BAA0B,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IACzD,CAAC;CACF;AAED,MAAM,OAAO,gCAEV,SAAQ,8BAA6F;IAE/F,KAAK,CAAC,GAAG;QAEd,MAAM,YAAY,GAAG,MAAM,IAAI,CAAC,UAAU,CAAA;QAC1C,IAAI,CAAC,YAAY,EAAE;YACjB,OAAM;SACP;QAED,MAAM,eAAe,GAAG,MAAM,iCAAiC,CAC7D,YAAY,EACZ,IAAI,CAAC,KAAK,EACV,IAAI,CAAC,EAAE,CAAC,IAAI,CAAC,iBAAiB,CAAC,kBAAkB,CAAC,IAAI,CAA6B,EACnF,IAAI,CAAC,cAAc,CACpB,CAAA;QAED,OAAO,yBAAyB,CAAC,YAAY,EAAE,eAAe,CAAC,CAAA;IACjE,CAAC;IAED,gBAAgB;QACd,OAAO,IAAI,6BAA6B,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC5D,CAAC;CACF;AAED,MAAM,OAAO,8CAEX,SAAQ,6BAAsC;IAE9C,gBAAgB;QACd,OAAO,IAAI,2CAA2C,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC1E,CAAC;IAED,mBAAmB;QACjB,OAAO,IAAI,6BAA6B,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC5D,CAAC;CACF;AAED,MAAM,OAAO,iDAEX,SAAQ,gCAAyC;IAEjD,gBAAgB;QACd,OAAO,IAAI,8CAA8C,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC7E,CAAC;IAED,kBAAkB;QAChB,OAAO,IAAI,+BAA+B,CAAC,IAAI,EAAE,IAAI,CAAC,KAAK,CAAC,CAAA;IAC9D,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/globalApi/allFaces.d.ts b/build/src/globalApi/allFaces.d.ts
deleted file mode 100644
index 4240267..0000000
--- a/build/src/globalApi/allFaces.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { TNetInput } from '../dom';
-import { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';
-import { ITinyYolov2Options } from '../tinyYolov2';
-export declare function allFacesSsdMobilenetv1(input: TNetInput, minConfidence?: number): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]>;
-export declare function allFacesTinyYolov2(input: TNetInput, forwardParams?: ITinyYolov2Options): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]>;
-export declare const allFaces: typeof allFacesSsdMobilenetv1;
diff --git a/build/src/globalApi/allFaces.js b/build/src/globalApi/allFaces.js
deleted file mode 100644
index 556c384..0000000
--- a/build/src/globalApi/allFaces.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import { SsdMobilenetv1Options } from '../ssdMobilenetv1';
-import { TinyYolov2Options } from '../tinyYolov2';
-import { detectAllFaces } from './detectFaces';
-// export allFaces API for backward compatibility
-export 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();
-}
-export 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();
-}
-export const allFaces = allFacesSsdMobilenetv1;
-//# sourceMappingURL=allFaces.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/allFaces.js.map b/build/src/globalApi/allFaces.js.map
deleted file mode 100644
index 595bd27..0000000
--- a/build/src/globalApi/allFaces.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"allFaces.js","sourceRoot":"","sources":["../../../src/globalApi/allFaces.ts"],"names":[],"mappings":"AAEA,OAAO,EAAE,qBAAqB,EAAE,MAAM,mBAAmB,CAAC;AAC1D,OAAO,EAAsB,iBAAiB,EAAE,MAAM,eAAe,CAAC;AACtE,OAAO,EAAE,cAAc,EAAE,MAAM,eAAe,CAAC;AAE/C,iDAAiD;AAEjD,MAAM,CAAC,KAAK,UAAU,sBAAsB,CAC1C,KAAgB,EAChB,aAAsB;IAEtB,OAAO,CAAC,IAAI,CAAC,+FAA+F,CAAC,CAAA;IAC7G,OAAO,MAAM,cAAc,CAAC,KAAK,EAAE,IAAI,qBAAqB,CAAC,aAAa,CAAC,CAAC,CAAC,EAAE,aAAa,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC;SAClG,iBAAiB,EAAE;SACnB,mBAAmB,EAAE,CAAA;AAC1B,CAAC;AAED,MAAM,CAAC,KAAK,UAAU,kBAAkB,CACtC,KAAgB,EAChB,gBAAoC,EAAE;IAEtC,OAAO,CAAC,IAAI,CAAC,2FAA2F,CAAC,CAAA;IACzG,OAAO,MAAM,cAAc,CAAC,KAAK,EAAE,IAAI,iBAAiB,CAAC,aAAa,CAAC,CAAC;SACrE,iBAAiB,EAAE;SACnB,mBAAmB,EAAE,CAAA;AAC1B,CAAC;AAED,MAAM,CAAC,MAAM,QAAQ,GAAG,sBAAsB,CAAA"}
\ No newline at end of file
diff --git a/build/src/globalApi/detectFaces.d.ts b/build/src/globalApi/detectFaces.d.ts
deleted file mode 100644
index c5a3169..0000000
--- a/build/src/globalApi/detectFaces.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import { TNetInput } from '../dom';
-import { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';
-import { FaceDetectionOptions } from './types';
-export declare function detectSingleFace(input: TNetInput, options?: FaceDetectionOptions): DetectSingleFaceTask;
-export declare function detectAllFaces(input: TNetInput, options?: FaceDetectionOptions): DetectAllFacesTask;
diff --git a/build/src/globalApi/detectFaces.js b/build/src/globalApi/detectFaces.js
deleted file mode 100644
index 94ae9c2..0000000
--- a/build/src/globalApi/detectFaces.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
-import { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';
-export function detectSingleFace(input, options = new SsdMobilenetv1Options()) {
-    return new DetectSingleFaceTask(input, options);
-}
-export function detectAllFaces(input, options = new SsdMobilenetv1Options()) {
-    return new DetectAllFacesTask(input, options);
-}
-//# sourceMappingURL=detectFaces.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/detectFaces.js.map b/build/src/globalApi/detectFaces.js.map
deleted file mode 100644
index 8e0d5bf..0000000
--- a/build/src/globalApi/detectFaces.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"detectFaces.js","sourceRoot":"","sources":["../../../src/globalApi/detectFaces.ts"],"names":[],"mappings":"AACA,OAAO,EAAE,qBAAqB,EAAE,MAAM,yCAAyC,CAAC;AAChF,OAAO,EAAE,kBAAkB,EAAE,oBAAoB,EAAE,MAAM,oBAAoB,CAAC;AAG9E,MAAM,UAAU,gBAAgB,CAC9B,KAAgB,EAChB,UAAgC,IAAI,qBAAqB,EAAE;IAE3D,OAAO,IAAI,oBAAoB,CAAC,KAAK,EAAE,OAAO,CAAC,CAAA;AACjD,CAAC;AAED,MAAM,UAAU,cAAc,CAC5B,KAAgB,EAChB,UAAgC,IAAI,qBAAqB,EAAE;IAE3D,OAAO,IAAI,kBAAkB,CAAC,KAAK,EAAE,OAAO,CAAC,CAAA;AAC/C,CAAC"}
\ No newline at end of file
diff --git a/build/src/globalApi/extractFacesAndComputeResults.d.ts b/build/src/globalApi/extractFacesAndComputeResults.d.ts
deleted file mode 100644
index 45eea0d..0000000
--- a/build/src/globalApi/extractFacesAndComputeResults.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { FaceDetection } from '../classes/FaceDetection';
-import { TNetInput } from '../dom';
-import { WithFaceDetection } from '../factories/WithFaceDetection';
-import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
-export declare function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(parentResults: TSource[], input: TNetInput, computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>, extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null, getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection): Promise<TResult>;
-export declare function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(parentResult: TSource, input: TNetInput, computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>, extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null, getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection): Promise<TResult>;
diff --git a/build/src/globalApi/extractFacesAndComputeResults.js b/build/src/globalApi/extractFacesAndComputeResults.js
deleted file mode 100644
index 9453a85..0000000
--- a/build/src/globalApi/extractFacesAndComputeResults.js
+++ /dev/null
@@ -1,18 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { extractFaces, extractFaceTensors } from '../dom';
-import { isWithFaceLandmarks } from '../factories/WithFaceLandmarks';
-export 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 tf.Tensor
-        ? await extractFaceTensors(input, faceBoxes)
-        : await extractFaces(input, faceBoxes));
-    const results = await computeResults(faces);
-    faces.forEach(f => f instanceof tf.Tensor && f.dispose());
-    return results;
-}
-export async function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {
-    return extractAllFacesAndComputeResults([parentResult], input, async (faces) => computeResult(faces[0]), extractedFaces, getRectForAlignment);
-}
-//# sourceMappingURL=extractFacesAndComputeResults.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/extractFacesAndComputeResults.js.map b/build/src/globalApi/extractFacesAndComputeResults.js.map
deleted file mode 100644
index be51436..0000000
--- a/build/src/globalApi/extractFacesAndComputeResults.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractFacesAndComputeResults.js","sourceRoot":"","sources":["../../../src/globalApi/extractFacesAndComputeResults.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAGzD,OAAO,EAAE,YAAY,EAAE,kBAAkB,EAAa,MAAM,QAAQ,CAAC;AAErE,OAAO,EAAE,mBAAmB,EAAqB,MAAM,gCAAgC,CAAC;AAExF,MAAM,CAAC,KAAK,UAAU,gCAAgC,CACpD,aAAwB,EACxB,KAAgB,EAChB,cAAmF,EACnF,cAA8D,EAC9D,sBAAwF,CAAC,EAAE,WAAW,EAAE,EAAE,EAAE,CAAC,WAAW;IAExH,MAAM,SAAS,GAAG,aAAa,CAAC,GAAG,CAAC,YAAY,CAAC,EAAE,CACjD,mBAAmB,CAAC,YAAY,CAAC;QAC/B,CAAC,CAAC,mBAAmB,CAAC,YAAY,CAAC;QACnC,CAAC,CAAC,YAAY,CAAC,SAAS,CAC3B,CAAA;IACD,MAAM,KAAK,GAA2C,cAAc,IAAI,CACtE,KAAK,YAAY,EAAE,CAAC,MAAM;QACxB,CAAC,CAAC,MAAM,kBAAkB,CAAC,KAAK,EAAE,SAAS,CAAC;QAC5C,CAAC,CAAC,MAAM,YAAY,CAAC,KAAK,EAAE,SAAS,CAAC,CACzC,CAAA;IAED,MAAM,OAAO,GAAG,MAAM,cAAc,CAAC,KAAK,CAAC,CAAA;IAE3C,KAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,YAAY,EAAE,CAAC,MAAM,IAAI,CAAC,CAAC,OAAO,EAAE,CAAC,CAAA;IAEzD,OAAO,OAAO,CAAA;AAChB,CAAC;AAED,MAAM,CAAC,KAAK,UAAU,iCAAiC,CACrD,YAAqB,EACrB,KAAgB,EAChB,aAA0E,EAC1E,cAA8D,EAC9D,mBAAsF;IAEtF,OAAO,gCAAgC,CACrC,CAAC,YAAY,CAAC,EACd,KAAK,EACL,KAAK,EAAC,KAAK,EAAC,EAAE,CAAC,aAAa,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,EACtC,cAAc,EACd,mBAAmB,CACpB,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/globalApi/index.d.ts b/build/src/globalApi/index.d.ts
deleted file mode 100644
index de0e0c0..0000000
--- a/build/src/globalApi/index.d.ts
+++ /dev/null
@@ -1,9 +0,0 @@
-export * from './allFaces';
-export * from './ComposableTask';
-export * from './ComputeFaceDescriptorsTasks';
-export * from './detectFaces';
-export * from './DetectFacesTasks';
-export * from './DetectFaceLandmarksTasks';
-export * from './FaceMatcher';
-export * from './nets';
-export * from './types';
diff --git a/build/src/globalApi/index.js b/build/src/globalApi/index.js
deleted file mode 100644
index e9c24a1..0000000
--- a/build/src/globalApi/index.js
+++ /dev/null
@@ -1,10 +0,0 @@
-export * from './allFaces';
-export * from './ComposableTask';
-export * from './ComputeFaceDescriptorsTasks';
-export * from './detectFaces';
-export * from './DetectFacesTasks';
-export * from './DetectFaceLandmarksTasks';
-export * from './FaceMatcher';
-export * from './nets';
-export * from './types';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/index.js.map b/build/src/globalApi/index.js.map
deleted file mode 100644
index 0376116..0000000
--- a/build/src/globalApi/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/globalApi/index.ts"],"names":[],"mappings":"AAAA,cAAc,YAAY,CAAA;AAC1B,cAAc,kBAAkB,CAAA;AAChC,cAAc,+BAA+B,CAAA;AAC7C,cAAc,eAAe,CAAA;AAC7B,cAAc,oBAAoB,CAAA;AAClC,cAAc,4BAA4B,CAAA;AAC1C,cAAc,eAAe,CAAA;AAC7B,cAAc,QAAQ,CAAA;AACtB,cAAc,SAAS,CAAA"}
\ No newline at end of file
diff --git a/build/src/globalApi/nets.d.ts b/build/src/globalApi/nets.d.ts
deleted file mode 100644
index e747259..0000000
--- a/build/src/globalApi/nets.d.ts
+++ /dev/null
@@ -1,105 +0,0 @@
-import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';
-import { AgeAndGenderPrediction } from '../ageGenderNet/types';
-import { FaceDetection } from '../classes/FaceDetection';
-import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
-import { TNetInput } from '../dom';
-import { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';
-import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';
-import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
-import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
-import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
-import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
-import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
-import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
-import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
-import { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';
-export declare const nets: {
-    ssdMobilenetv1: SsdMobilenetv1;
-    tinyFaceDetector: TinyFaceDetector;
-    tinyYolov2: TinyYolov2;
-    faceLandmark68Net: FaceLandmark68Net;
-    faceLandmark68TinyNet: FaceLandmark68TinyNet;
-    faceRecognitionNet: FaceRecognitionNet;
-    faceExpressionNet: FaceExpressionNet;
-    ageGenderNet: AgeGenderNet;
-};
-/**
- * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
- *
- * @param input The input image.
- * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
- * @returns Bounding box of each face with score.
- */
-export declare const ssdMobilenetv1: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
-/**
- * Attempts to detect all faces in an image using the Tiny Face Detector.
- *
- * @param input The input image.
- * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
- * @returns Bounding box of each face with score.
- */
-export declare const tinyFaceDetector: (input: TNetInput, options: TinyFaceDetectorOptions) => Promise<FaceDetection[]>;
-/**
- * Attempts to detect all faces in an image using the Tiny Yolov2 Network.
- *
- * @param input The input image.
- * @param options (optional, default: see TinyYolov2Options constructor for default parameters).
- * @returns Bounding box of each face with score.
- */
-export declare const tinyYolov2: (input: TNetInput, options: ITinyYolov2Options) => Promise<FaceDetection[]>;
-/**
- * Detects the 68 point face landmark positions of the face shown in an image.
- *
- * @param inputs The face image extracted from the bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns 68 point face landmarks or array thereof in case of batch input.
- */
-export declare const detectFaceLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
-/**
- * Detects the 68 point face landmark positions of the face shown in an image
- * using a tinier version of the 68 point face landmark model, which is slightly
- * faster at inference, but also slightly less accurate.
- *
- * @param inputs The face image extracted from the bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns 68 point face landmarks or array thereof in case of batch input.
- */
-export declare const detectFaceLandmarksTiny: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
-/**
- * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
- * which uniquely represents the features of that persons face. The computed face descriptor can
- * be used to measure the similarity between faces, by computing the euclidean distance of two
- * face descriptors.
- *
- * @param inputs The face image extracted from the aligned bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns Face descriptor with 128 entries or array thereof in case of batch input.
- */
-export declare const computeFaceDescriptor: (input: TNetInput) => Promise<Float32Array | Float32Array[]>;
-/**
- * Recognizes the facial expressions from a face image.
- *
- * @param inputs The face image extracted from the bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.
- */
-export declare const recognizeFaceExpressions: (input: TNetInput) => Promise<FaceExpressions | FaceExpressions[]>;
-/**
- * Predicts age and gender from a face image.
- *
- * @param inputs The face image extracted from the bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.
- */
-export declare const predictAgeAndGender: (input: TNetInput) => Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]>;
-export declare const loadSsdMobilenetv1Model: (url: string) => Promise<void>;
-export declare const loadTinyFaceDetectorModel: (url: string) => Promise<void>;
-export declare const loadTinyYolov2Model: (url: string) => Promise<void>;
-export declare const loadFaceLandmarkModel: (url: string) => Promise<void>;
-export declare const loadFaceLandmarkTinyModel: (url: string) => Promise<void>;
-export declare const loadFaceRecognitionModel: (url: string) => Promise<void>;
-export declare const loadFaceExpressionModel: (url: string) => Promise<void>;
-export declare const loadAgeGenderModel: (url: string) => Promise<void>;
-export declare const loadFaceDetectionModel: (url: string) => Promise<void>;
-export declare const locateFaces: (input: TNetInput, options: SsdMobilenetv1Options) => Promise<FaceDetection[]>;
-export declare const detectLandmarks: (input: TNetInput) => Promise<FaceLandmarks68 | FaceLandmarks68[]>;
diff --git a/build/src/globalApi/nets.js b/build/src/globalApi/nets.js
deleted file mode 100644
index c39b26b..0000000
--- a/build/src/globalApi/nets.js
+++ /dev/null
@@ -1,100 +0,0 @@
-import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';
-import { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';
-import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
-import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
-import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
-import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
-import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
-import { TinyYolov2 } from '../tinyYolov2';
-export 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()
-};
-/**
- * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
- *
- * @param input The input image.
- * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
- * @returns Bounding box of each face with score.
- */
-export const ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options);
-/**
- * Attempts to detect all faces in an image using the Tiny Face Detector.
- *
- * @param input The input image.
- * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
- * @returns Bounding box of each face with score.
- */
-export const tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options);
-/**
- * Attempts to detect all faces in an image using the Tiny Yolov2 Network.
- *
- * @param input The input image.
- * @param options (optional, default: see TinyYolov2Options constructor for default parameters).
- * @returns Bounding box of each face with score.
- */
-export const tinyYolov2 = (input, options) => nets.tinyYolov2.locateFaces(input, options);
-/**
- * Detects the 68 point face landmark positions of the face shown in an image.
- *
- * @param inputs The face image extracted from the bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns 68 point face landmarks or array thereof in case of batch input.
- */
-export const detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input);
-/**
- * Detects the 68 point face landmark positions of the face shown in an image
- * using a tinier version of the 68 point face landmark model, which is slightly
- * faster at inference, but also slightly less accurate.
- *
- * @param inputs The face image extracted from the bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns 68 point face landmarks or array thereof in case of batch input.
- */
-export const detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input);
-/**
- * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
- * which uniquely represents the features of that persons face. The computed face descriptor can
- * be used to measure the similarity between faces, by computing the euclidean distance of two
- * face descriptors.
- *
- * @param inputs The face image extracted from the aligned bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns Face descriptor with 128 entries or array thereof in case of batch input.
- */
-export const computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input);
-/**
- * Recognizes the facial expressions from a face image.
- *
- * @param inputs The face image extracted from the bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.
- */
-export const recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input);
-/**
- * Predicts age and gender from a face image.
- *
- * @param inputs The face image extracted from the bounding box of a face. Can
- * also be an array of input images, which will be batch processed.
- * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.
- */
-export const predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input);
-export const loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url);
-export const loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url);
-export const loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url);
-export const loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url);
-export const loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url);
-export const loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url);
-export const loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url);
-export const loadAgeGenderModel = (url) => nets.ageGenderNet.load(url);
-// backward compatibility
-export const loadFaceDetectionModel = loadSsdMobilenetv1Model;
-export const locateFaces = ssdMobilenetv1;
-export const detectLandmarks = detectFaceLandmarks;
-//# sourceMappingURL=nets.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/nets.js.map b/build/src/globalApi/nets.js.map
deleted file mode 100644
index 850ab45..0000000
--- a/build/src/globalApi/nets.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/globalApi/types.d.ts b/build/src/globalApi/types.d.ts
deleted file mode 100644
index 16ef018..0000000
--- a/build/src/globalApi/types.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { FaceDetection } from '../classes/FaceDetection';
-import { TNetInput } from '../dom';
-import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
-import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
-import { TinyYolov2Options } from '../tinyYolov2';
-export declare type FaceDetectionOptions = TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options;
-export declare type FaceDetectionFunction = (input: TNetInput) => Promise<FaceDetection[]>;
diff --git a/build/src/globalApi/types.js b/build/src/globalApi/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/globalApi/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/globalApi/types.js.map b/build/src/globalApi/types.js.map
deleted file mode 100644
index e079214..0000000
--- a/build/src/globalApi/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/globalApi/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/src/index.d.ts b/build/src/index.d.ts
deleted file mode 100644
index 2e77187..0000000
--- a/build/src/index.d.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import * as draw from './draw';
-import * as utils from './utils';
-export { tf, draw, utils };
-export * from './ageGenderNet/index';
-export * from './classes/index';
-export * from './dom/index';
-export * from './env/index';
-export * from './faceExpressionNet/index';
-export * from './faceLandmarkNet/index';
-export * from './faceRecognitionNet/index';
-export * from './factories/index';
-export * from './globalApi/index';
-export * from './ops/index';
-export * from './ssdMobilenetv1/index';
-export * from './tinyFaceDetector/index';
-export * from './tinyYolov2/index';
-export * from './euclideanDistance';
-export * from './NeuralNetwork';
-export * from './resizeResults';
-export declare const version: {
-    faceapi: string;
-    node: boolean;
-    browser: boolean;
-};
diff --git a/build/src/index.js b/build/src/index.js
deleted file mode 100644
index ef45a5a..0000000
--- a/build/src/index.js
+++ /dev/null
@@ -1,35 +0,0 @@
-// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');
-// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');
-// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');
-import * as draw from './draw';
-import * as utils from './utils';
-export { tf, draw, utils };
-export * from './ageGenderNet/index';
-export * from './classes/index';
-export * from './dom/index';
-export * from './env/index';
-export * from './faceExpressionNet/index';
-export * from './faceLandmarkNet/index';
-export * from './faceRecognitionNet/index';
-export * from './factories/index';
-export * from './globalApi/index';
-export * from './ops/index';
-export * from './ssdMobilenetv1/index';
-export * from './tinyFaceDetector/index';
-export * from './tinyYolov2/index';
-export * from './euclideanDistance';
-export * from './NeuralNetwork';
-export * from './resizeResults';
-import * as pkg from '../package.json';
-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);
-// tf.setBackend('cpu');
-// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';
-// export {MathBackendCPU};
-// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/index.js.map b/build/src/index.js.map
deleted file mode 100644
index ba2b1ec..0000000
--- a/build/src/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ops/index.d.ts b/build/src/ops/index.d.ts
deleted file mode 100644
index 825d345..0000000
--- a/build/src/ops/index.d.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-export * from './iou';
-export * from './minBbox';
-export * from './nonMaxSuppression';
-export * from './normalize';
-export * from './padToSquare';
-export * from './shuffleArray';
-export declare function sigmoid(x: number): number;
-export declare function inverseSigmoid(x: number): number;
diff --git a/build/src/ops/index.js b/build/src/ops/index.js
deleted file mode 100644
index 5b45757..0000000
--- a/build/src/ops/index.js
+++ /dev/null
@@ -1,13 +0,0 @@
-export * from './iou';
-export * from './minBbox';
-export * from './nonMaxSuppression';
-export * from './normalize';
-export * from './padToSquare';
-export * from './shuffleArray';
-export function sigmoid(x) {
-    return 1 / (1 + Math.exp(-x));
-}
-export function inverseSigmoid(x) {
-    return Math.log(x / (1 - x));
-}
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/ops/index.js.map b/build/src/ops/index.js.map
deleted file mode 100644
index 0fe23bd..0000000
--- a/build/src/ops/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ops/iou.d.ts b/build/src/ops/iou.d.ts
deleted file mode 100644
index 0e800d7..0000000
--- a/build/src/ops/iou.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Box } from '../classes/Box';
-export declare function iou(box1: Box, box2: Box, isIOU?: boolean): number;
diff --git a/build/src/ops/iou.js b/build/src/ops/iou.js
deleted file mode 100644
index bcbec9d..0000000
--- a/build/src/ops/iou.js
+++ /dev/null
@@ -1,9 +0,0 @@
-export function iou(box1, box2, isIOU = true) {
-    const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));
-    const height = Math.max(0.0, 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);
-}
-//# sourceMappingURL=iou.js.map
\ No newline at end of file
diff --git a/build/src/ops/iou.js.map b/build/src/ops/iou.js.map
deleted file mode 100644
index c449aa5..0000000
--- a/build/src/ops/iou.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"iou.js","sourceRoot":"","sources":["../../../src/ops/iou.ts"],"names":[],"mappings":"AAEA,MAAM,UAAU,GAAG,CAAC,IAAS,EAAE,IAAS,EAAE,QAAiB,IAAI;IAC7D,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,KAAK,EAAE,IAAI,CAAC,KAAK,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,IAAI,EAAE,IAAI,CAAC,IAAI,CAAC,CAAC,CAAA;IAC9F,MAAM,MAAM,GAAG,IAAI,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,MAAM,EAAE,IAAI,CAAC,MAAM,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,EAAE,IAAI,CAAC,GAAG,CAAC,CAAC,CAAA;IAC/F,MAAM,YAAY,GAAG,KAAK,GAAG,MAAM,CAAA;IAEnC,OAAO,KAAK;QACV,CAAC,CAAC,YAAY,GAAG,CAAC,IAAI,CAAC,IAAI,GAAG,IAAI,CAAC,IAAI,GAAG,YAAY,CAAC;QACvD,CAAC,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,IAAI,EAAE,IAAI,CAAC,IAAI,CAAC,CAAA;AACnD,CAAC"}
\ No newline at end of file
diff --git a/build/src/ops/minBbox.d.ts b/build/src/ops/minBbox.d.ts
deleted file mode 100644
index 37e1358..0000000
--- a/build/src/ops/minBbox.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { BoundingBox, IPoint } from '../classes';
-export declare function minBbox(pts: IPoint[]): BoundingBox;
diff --git a/build/src/ops/minBbox.js b/build/src/ops/minBbox.js
deleted file mode 100644
index e7915e3..0000000
--- a/build/src/ops/minBbox.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import { BoundingBox } from '../classes';
-export 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);
-}
-//# sourceMappingURL=minBbox.js.map
\ No newline at end of file
diff --git a/build/src/ops/minBbox.js.map b/build/src/ops/minBbox.js.map
deleted file mode 100644
index d923388..0000000
--- a/build/src/ops/minBbox.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"minBbox.js","sourceRoot":"","sources":["../../../src/ops/minBbox.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,WAAW,EAAU,MAAM,YAAY,CAAC;AAEjD,MAAM,UAAU,OAAO,CAAC,GAAa;IACnC,MAAM,EAAE,GAAG,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC9B,MAAM,EAAE,GAAG,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC9B,MAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,EAAE,QAAQ,CAAC,CAAA;IAC/D,MAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC,GAAG,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,EAAE,QAAQ,CAAC,CAAA;IAC/D,MAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,CAAC,GAAG,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACxD,MAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,CAAC,EAAE,EAAE,CAAC,GAAG,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IAExD,OAAO,IAAI,WAAW,CAAC,IAAI,EAAE,IAAI,EAAE,IAAI,EAAE,IAAI,CAAC,CAAA;AAChD,CAAC"}
\ No newline at end of file
diff --git a/build/src/ops/nonMaxSuppression.d.ts b/build/src/ops/nonMaxSuppression.d.ts
deleted file mode 100644
index bba434a..0000000
--- a/build/src/ops/nonMaxSuppression.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { Box } from '../classes/Box';
-export declare function nonMaxSuppression(boxes: Box[], scores: number[], iouThreshold: number, isIOU?: boolean): number[];
diff --git a/build/src/ops/nonMaxSuppression.js b/build/src/ops/nonMaxSuppression.js
deleted file mode 100644
index 05ded75..0000000
--- a/build/src/ops/nonMaxSuppression.js
+++ /dev/null
@@ -1,23 +0,0 @@
-import { iou } from './iou';
-export 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;
-}
-//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file
diff --git a/build/src/ops/nonMaxSuppression.js.map b/build/src/ops/nonMaxSuppression.js.map
deleted file mode 100644
index 008360c..0000000
--- a/build/src/ops/nonMaxSuppression.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"nonMaxSuppression.js","sourceRoot":"","sources":["../../../src/ops/nonMaxSuppression.ts"],"names":[],"mappings":"AACA,OAAO,EAAE,GAAG,EAAE,MAAM,OAAO,CAAC;AAE5B,MAAM,UAAU,iBAAiB,CAC/B,KAAY,EACZ,MAAgB,EAChB,YAAoB,EACpB,QAAiB,IAAI;IAGrB,IAAI,oBAAoB,GAAG,MAAM;SAC9B,GAAG,CAAC,CAAC,KAAK,EAAE,QAAQ,EAAE,EAAE,CAAC,CAAC,EAAE,KAAK,EAAE,QAAQ,EAAE,CAAC,CAAC;SAC/C,IAAI,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,KAAK,GAAG,EAAE,CAAC,KAAK,CAAC;SACrC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAA;IAEvB,MAAM,IAAI,GAAa,EAAE,CAAA;IAEzB,OAAM,oBAAoB,CAAC,MAAM,GAAG,CAAC,EAAE;QACrC,MAAM,IAAI,GAAG,oBAAoB,CAAC,GAAG,EAAY,CAAA;QACjD,IAAI,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;QAEf,MAAM,OAAO,GAAG,oBAAoB,CAAA;QAEpC,MAAM,OAAO,GAAa,EAAE,CAAA;QAC5B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YACvC,MAAM,GAAG,GAAG,OAAO,CAAC,CAAC,CAAC,CAAA;YAEtB,MAAM,OAAO,GAAG,KAAK,CAAC,IAAI,CAAC,CAAA;YAC3B,MAAM,MAAM,GAAG,KAAK,CAAC,GAAG,CAAC,CAAA;YAEzB,OAAO,CAAC,IAAI,CAAC,GAAG,CAAC,OAAO,EAAE,MAAM,EAAE,KAAK,CAAC,CAAC,CAAA;SAC1C;QAED,oBAAoB,GAAG,oBAAoB,CAAC,MAAM,CAChD,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,IAAI,YAAY,CACrC,CAAA;KACF;IAED,OAAO,IAAI,CAAA;AAEb,CAAC"}
\ No newline at end of file
diff --git a/build/src/ops/normalize.d.ts b/build/src/ops/normalize.d.ts
deleted file mode 100644
index 6bfd3ea..0000000
--- a/build/src/ops/normalize.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export declare function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D;
diff --git a/build/src/ops/normalize.js b/build/src/ops/normalize.js
deleted file mode 100644
index 2f94096..0000000
--- a/build/src/ops/normalize.js
+++ /dev/null
@@ -1,12 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function normalize(x, meanRgb) {
-    return tf.tidy(() => {
-        const [r, g, b] = meanRgb;
-        const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32');
-        const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32');
-        const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32');
-        const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
-        return tf.sub(x, avg_rgb);
-    });
-}
-//# sourceMappingURL=normalize.js.map
\ No newline at end of file
diff --git a/build/src/ops/normalize.js.map b/build/src/ops/normalize.js.map
deleted file mode 100644
index c197d3f..0000000
--- a/build/src/ops/normalize.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../../src/ops/normalize.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,MAAM,UAAU,SAAS,CAAC,CAAc,EAAE,OAAiB;IACzD,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,OAAO,CAAA;QACzB,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAChE,MAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;IAC3B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ops/padToSquare.d.ts b/build/src/ops/padToSquare.d.ts
deleted file mode 100644
index 671026f..0000000
--- a/build/src/ops/padToSquare.d.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-/**
- * Pads the smaller dimension of an image tensor with zeros, such that width === height.
- *
- * @param imgTensor The image tensor.
- * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
- * both sides of the minor dimension oof the image.
- * @returns The padded tensor with width === height.
- */
-export declare function padToSquare(imgTensor: tf.Tensor4D, isCenterImage?: boolean): tf.Tensor4D;
diff --git a/build/src/ops/padToSquare.js b/build/src/ops/padToSquare.js
deleted file mode 100644
index f544b9f..0000000
--- a/build/src/ops/padToSquare.js
+++ /dev/null
@@ -1,40 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-/**
- * Pads the smaller dimension of an image tensor with zeros, such that width === height.
- *
- * @param imgTensor The image tensor.
- * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
- * both sides of the minor dimension oof the image.
- * @returns The padded tensor with width === height.
- */
-export function padToSquare(imgTensor, isCenterImage = false) {
-    return tf.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 = (paddingAmount) => {
-            const paddingTensorShape = imgTensor.shape.slice();
-            paddingTensorShape[paddingAxis] = paddingAmount;
-            return tf.fill(paddingTensorShape, 0, 'float32');
-        };
-        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: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]
-            .map((t) => tf.cast(t, 'float32'));
-        return tf.concat(tensorsToStack, paddingAxis);
-    });
-}
-//# sourceMappingURL=padToSquare.js.map
\ No newline at end of file
diff --git a/build/src/ops/padToSquare.js.map b/build/src/ops/padToSquare.js.map
deleted file mode 100644
index 184bd74..0000000
--- a/build/src/ops/padToSquare.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"padToSquare.js","sourceRoot":"","sources":["../../../src/ops/padToSquare.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD;;;;;;;GAOG;AACH,MAAM,UAAU,WAAW,CACzB,SAAsB,EACtB,gBAAyB,KAAK;IAE9B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,CAAC,MAAM,EAAE,KAAK,CAAC,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAChD,IAAI,MAAM,KAAK,KAAK,EAAE;YACpB,OAAO,SAAS,CAAA;SACjB;QAED,MAAM,OAAO,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,GAAG,KAAK,CAAC,CAAA;QACxC,MAAM,aAAa,GAAG,IAAI,CAAC,KAAK,CAAC,OAAO,GAAG,CAAC,aAAa,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACrE,MAAM,WAAW,GAAG,MAAM,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QAE1C,MAAM,mBAAmB,GAAG,CAAC,aAAqB,EAAa,EAAE;YAC/D,MAAM,kBAAkB,GAAG,SAAS,CAAC,KAAK,CAAC,KAAK,EAAE,CAAA;YAClD,kBAAkB,CAAC,WAAW,CAAC,GAAG,aAAa,CAAA;YAC/C,OAAO,EAAE,CAAC,IAAI,CAAC,kBAAkB,EAAE,CAAC,EAAE,SAAS,CAAC,CAAA;QAClD,CAAC,CAAA;QAED,MAAM,mBAAmB,GAAG,mBAAmB,CAAC,aAAa,CAAC,CAAA;QAC9D,MAAM,sBAAsB,GAAG,OAAO,GAAI,mBAAmB,CAAC,KAAK,CAAC,WAAW,CAAY,CAAA;QAE3F,MAAM,oBAAoB,GAAG,aAAa,IAAI,sBAAsB;YAClE,CAAC,CAAC,mBAAmB,CAAC,sBAAsB,CAAC;YAC7C,CAAC,CAAC,IAAI,CAAA;QAER,MAAM,cAAc,GAAG;YACrB,oBAAoB;YACpB,SAAS;YACT,mBAAmB;SACpB;aACE,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;YACjB,uDAAuD;aACtD,GAAG,CAAC,CAAC,CAAY,EAAE,EAAE,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,SAAS,CAAC,CAAkB,CAAA;QAChE,OAAO,EAAE,CAAC,MAAM,CAAC,cAAc,EAAE,WAAW,CAAC,CAAA;IAE/C,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ops/shuffleArray.d.ts b/build/src/ops/shuffleArray.d.ts
deleted file mode 100644
index 2783fec..0000000
--- a/build/src/ops/shuffleArray.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export declare function shuffleArray(inputArray: any[]): any[];
diff --git a/build/src/ops/shuffleArray.js b/build/src/ops/shuffleArray.js
deleted file mode 100644
index e9baaa0..0000000
--- a/build/src/ops/shuffleArray.js
+++ /dev/null
@@ -1,11 +0,0 @@
-export 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;
-}
-//# sourceMappingURL=shuffleArray.js.map
\ No newline at end of file
diff --git a/build/src/ops/shuffleArray.js.map b/build/src/ops/shuffleArray.js.map
deleted file mode 100644
index 163eedc..0000000
--- a/build/src/ops/shuffleArray.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"shuffleArray.js","sourceRoot":"","sources":["../../../src/ops/shuffleArray.ts"],"names":[],"mappings":"AAAA,MAAM,UAAU,YAAY,CAAC,UAAiB;IAC5C,MAAM,KAAK,GAAG,UAAU,CAAC,KAAK,EAAE,CAAA;IAChC,KAAK,IAAI,CAAC,GAAG,KAAK,CAAC,MAAM,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;QACvC,MAAM,CAAC,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,MAAM,EAAE,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;QAC7C,MAAM,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC,CAAA;QAClB,KAAK,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC,CAAA;QACnB,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA;KACf;IACD,OAAO,KAAK,CAAA;AACd,CAAC"}
\ No newline at end of file
diff --git a/build/src/resizeResults.d.ts b/build/src/resizeResults.d.ts
deleted file mode 100644
index 3910d16..0000000
--- a/build/src/resizeResults.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { IDimensions } from './classes';
-export declare function resizeResults<T>(results: T, dimensions: IDimensions): T;
diff --git a/build/src/resizeResults.js b/build/src/resizeResults.js
deleted file mode 100644
index 93cd9eb..0000000
--- a/build/src/resizeResults.js
+++ /dev/null
@@ -1,28 +0,0 @@
-import { Dimensions } from './classes';
-import { FaceDetection } from './classes/FaceDetection';
-import { FaceLandmarks } from './classes/FaceLandmarks';
-import { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';
-import { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';
-export 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 })) as any as T
-        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;
-}
-//# sourceMappingURL=resizeResults.js.map
\ No newline at end of file
diff --git a/build/src/resizeResults.js.map b/build/src/resizeResults.js.map
deleted file mode 100644
index 091f9f8..0000000
--- a/build/src/resizeResults.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"resizeResults.js","sourceRoot":"","sources":["../../src/resizeResults.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,UAAU,EAAe,MAAM,WAAW,CAAC;AACpD,OAAO,EAAE,aAAa,EAAE,MAAM,yBAAyB,CAAC;AACxD,OAAO,EAAE,aAAa,EAAE,MAAM,yBAAyB,CAAC;AACxD,OAAO,EAAE,uBAAuB,EAAE,mBAAmB,EAAE,MAAM,+BAA+B,CAAC;AAC7F,OAAO,EAAE,uBAAuB,EAAE,mBAAmB,EAAE,MAAM,+BAA+B,CAAC;AAE7F,MAAM,UAAU,aAAa,CAAI,OAAU,EAAE,UAAuB;IAElE,MAAM,EAAE,KAAK,EAAE,MAAM,EAAE,GAAG,IAAI,UAAU,CAAC,UAAU,CAAC,KAAK,EAAE,UAAU,CAAC,MAAM,CAAC,CAAA;IAE7E,IAAI,KAAK,IAAI,CAAC,IAAI,MAAM,IAAI,CAAC,EAAE;QAC7B,MAAM,IAAI,KAAK,CAAC,uCAAuC,IAAI,CAAC,SAAS,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,CAAC,EAAE,CAAC,CAAA;KAC5F;IAED,IAAI,KAAK,CAAC,OAAO,CAAC,OAAO,CAAC,EAAE;QAC1B,+EAA+E;QAC/E,OAAQ,OAAsB,CAAC,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,aAAa,CAAC,GAAG,EAAE,EAAE,KAAK,EAAE,MAAM,EAAiB,CAAC,CAAa,CAAA;KAC5G;IAED,IAAI,mBAAmB,CAAC,OAAO,CAAC,EAAE;QAChC,MAAM,gBAAgB,GAAG,OAAO,CAAC,SAAS,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,CAAA;QACjE,MAAM,gBAAgB,GAAG,OAAO,CAAC,kBAAkB,CAAC,OAAO,CAAC,gBAAgB,CAAC,GAAG,CAAC,KAAK,EAAE,gBAAgB,CAAC,GAAG,CAAC,MAAM,CAAC,CAAA;QACpH,OAAO,uBAAuB,CAAC,uBAAuB,CAAC,OAAO,EAAE,gBAAgB,CAAC,EAAE,gBAAgB,CAAC,CAAA;KACrG;IAED,IAAI,mBAAmB,CAAC,OAAO,CAAC,EAAE;QAChC,OAAO,uBAAuB,CAAC,OAAO,EAAE,OAAO,CAAC,SAAS,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAA;KAClF;IAED,IAAI,OAAO,YAAY,aAAa,IAAI,OAAO,YAAY,aAAa,EAAE;QACxE,OAAQ,OAAe,CAAC,OAAO,CAAC,KAAK,EAAE,MAAM,CAAC,CAAA;KAC/C;IAED,OAAO,OAAO,CAAA;AAChB,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts b/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts
deleted file mode 100644
index d82e572..0000000
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1.d.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { FaceDetection } from '../classes/FaceDetection';
-import { NetInput, TNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { ISsdMobilenetv1Options } from './SsdMobilenetv1Options';
-import { NetParams } from './types';
-export declare class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
-    constructor();
-    forwardInput(input: NetInput): any;
-    forward(input: TNetInput): Promise<any>;
-    locateFaces(input: TNetInput, options?: ISsdMobilenetv1Options): Promise<FaceDetection[]>;
-    protected getDefaultModelName(): string;
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParams(weights: Float32Array): {
-        params: NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-}
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1.js b/build/src/ssdMobilenetv1/SsdMobilenetv1.js
deleted file mode 100644
index 606898f..0000000
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1.js
+++ /dev/null
@@ -1,83 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { Rect } from '../classes';
-import { FaceDetection } from '../classes/FaceDetection';
-import { toNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { extractParams } from './extractParams';
-import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
-import { mobileNetV1 } from './mobileNetV1';
-import { nonMaxSuppression } from './nonMaxSuppression';
-import { outputLayer } from './outputLayer';
-import { predictionLayer } from './predictionLayer';
-import { SsdMobilenetv1Options } from './SsdMobilenetv1Options';
-export class SsdMobilenetv1 extends NeuralNetwork {
-    constructor() {
-        super('SsdMobilenetv1');
-    }
-    forwardInput(input) {
-        const { params } = this;
-        if (!params) {
-            throw new Error('SsdMobilenetv1 - load model before inference');
-        }
-        return tf.tidy(() => {
-            // const batchTensor = input.toBatchTensor(512, false).toFloat()
-            const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');
-            const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.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);
-        // TODO batches
-        const boxes = _boxes[0];
-        const scores = _scores[0];
-        for (let i = 1; i < _boxes.length; i++) {
-            _boxes[i].dispose();
-            _scores[i].dispose();
-        }
-        // TODO find a better way to filter by minConfidence
-        const scoresData = Array.from(await scores.data());
-        const iouThreshold = 0.5;
-        const indices = nonMaxSuppression(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(1.0, boxesData[idx][2])
-            ].map(val => val * padY);
-            const [left, right] = [
-                Math.max(0, boxesData[idx][1]),
-                Math.min(1.0, 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 extractParamsFromWeigthMap(weightMap);
-    }
-    extractParams(weights) {
-        return extractParams(weights);
-    }
-}
-//# sourceMappingURL=SsdMobilenetv1.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1.js.map b/build/src/ssdMobilenetv1/SsdMobilenetv1.js.map
deleted file mode 100644
index ddd8aa7..0000000
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1Options.d.ts b/build/src/ssdMobilenetv1/SsdMobilenetv1Options.d.ts
deleted file mode 100644
index 0317a9b..0000000
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1Options.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-export interface ISsdMobilenetv1Options {
-    minConfidence?: number;
-    maxResults?: number;
-}
-export declare class SsdMobilenetv1Options {
-    protected _name: string;
-    private _minConfidence;
-    private _maxResults;
-    constructor({ minConfidence, maxResults }?: ISsdMobilenetv1Options);
-    get minConfidence(): number;
-    get maxResults(): number;
-}
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1Options.js b/build/src/ssdMobilenetv1/SsdMobilenetv1Options.js
deleted file mode 100644
index da74d09..0000000
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1Options.js
+++ /dev/null
@@ -1,16 +0,0 @@
-export 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; }
-}
-//# sourceMappingURL=SsdMobilenetv1Options.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/SsdMobilenetv1Options.js.map b/build/src/ssdMobilenetv1/SsdMobilenetv1Options.js.map
deleted file mode 100644
index 767eddf..0000000
--- a/build/src/ssdMobilenetv1/SsdMobilenetv1Options.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"SsdMobilenetv1Options.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/SsdMobilenetv1Options.ts"],"names":[],"mappings":"AAKA,MAAM,OAAO,qBAAqB;IAMhC,YAAY,EAAE,aAAa,EAAE,UAAU,KAA6B,EAAE;QAL5D,UAAK,GAAW,uBAAuB,CAAA;QAM/C,IAAI,CAAC,cAAc,GAAG,aAAa,IAAI,GAAG,CAAA;QAC1C,IAAI,CAAC,WAAW,GAAG,UAAU,IAAI,GAAG,CAAA;QAEpC,IAAI,OAAO,IAAI,CAAC,cAAc,KAAK,QAAQ,IAAI,IAAI,CAAC,cAAc,IAAI,CAAC,IAAI,IAAI,CAAC,cAAc,IAAI,CAAC,EAAE;YACnG,MAAM,IAAI,KAAK,CAAC,GAAG,IAAI,CAAC,KAAK,0DAA0D,CAAC,CAAA;SACzF;QAED,IAAI,OAAO,IAAI,CAAC,WAAW,KAAK,QAAQ,EAAE;YACxC,MAAM,IAAI,KAAK,CAAC,GAAG,IAAI,CAAC,KAAK,uCAAuC,CAAC,CAAA;SACtE;IACH,CAAC;IAED,IAAI,aAAa,KAAa,OAAO,IAAI,CAAC,cAAc,CAAA,CAAC,CAAC;IAC1D,IAAI,UAAU,KAAa,OAAO,IAAI,CAAC,WAAW,CAAA,CAAC,CAAC;CACrD"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts b/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts
deleted file mode 100644
index d25ab62..0000000
--- a/build/src/ssdMobilenetv1/boxPredictionLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { BoxPredictionParams } from './types';
-export declare function boxPredictionLayer(x: tf.Tensor4D, params: BoxPredictionParams): any;
diff --git a/build/src/ssdMobilenetv1/boxPredictionLayer.js b/build/src/ssdMobilenetv1/boxPredictionLayer.js
deleted file mode 100644
index 9e4e698..0000000
--- a/build/src/ssdMobilenetv1/boxPredictionLayer.js
+++ /dev/null
@@ -1,14 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { convLayer } from '../common';
-export function boxPredictionLayer(x, params) {
-    return tf.tidy(() => {
-        const batchSize = x.shape[0];
-        const boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
-        const classPrediction = tf.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
-        return {
-            boxPredictionEncoding,
-            classPrediction
-        };
-    });
-}
-//# sourceMappingURL=boxPredictionLayer.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/boxPredictionLayer.js.map b/build/src/ssdMobilenetv1/boxPredictionLayer.js.map
deleted file mode 100644
index 4b924ce..0000000
--- a/build/src/ssdMobilenetv1/boxPredictionLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/extractParams.d.ts b/build/src/ssdMobilenetv1/extractParams.d.ts
deleted file mode 100644
index f4e4b30..0000000
--- a/build/src/ssdMobilenetv1/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from '../common';
-import { NetParams } from './types';
-export declare function extractParams(weights: Float32Array): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/ssdMobilenetv1/extractParams.js b/build/src/ssdMobilenetv1/extractParams.js
deleted file mode 100644
index 889a5a4..0000000
--- a/build/src/ssdMobilenetv1/extractParams.js
+++ /dev/null
@@ -1,159 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { extractWeightsFactory } from '../common';
-function extractorsFactory(extractWeights, paramMappings) {
-    function extractDepthwiseConvParams(numChannels, mappedPrefix) {
-        const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
-        const batch_norm_scale = tf.tensor1d(extractWeights(numChannels));
-        const batch_norm_offset = tf.tensor1d(extractWeights(numChannels));
-        const batch_norm_mean = tf.tensor1d(extractWeights(numChannels));
-        const batch_norm_variance = tf.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 = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
-        const bias = tf.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
-    };
-}
-export function extractParams(weights) {
-    const paramMappings = [];
-    const { extractWeights, getRemainingWeights } = extractWeightsFactory(weights);
-    const { extractMobilenetV1Params, extractPredictionLayerParams } = extractorsFactory(extractWeights, paramMappings);
-    const mobilenetv1 = extractMobilenetV1Params();
-    const prediction_layer = extractPredictionLayerParams();
-    const extra_dim = tf.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: ${getRemainingWeights().length}`);
-    }
-    return {
-        params: {
-            mobilenetv1,
-            prediction_layer,
-            output_layer
-        },
-        paramMappings
-    };
-}
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/extractParams.js.map b/build/src/ssdMobilenetv1/extractParams.js.map
deleted file mode 100644
index cf147b7..0000000
--- a/build/src/ssdMobilenetv1/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts b/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts
deleted file mode 100644
index aa24371..0000000
--- a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from '../common';
-import { NetParams } from './types';
-export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-    params: NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js b/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js
deleted file mode 100644
index 8e32ed4..0000000
--- a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js
+++ /dev/null
@@ -1,100 +0,0 @@
-import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';
-import { isTensor3D } from '../utils';
-function extractorsFactory(weightMap, paramMappings) {
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
-        const filters = extractWeightEntry(`${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
-    };
-}
-export function extractParamsFromWeigthMap(weightMap) {
-    const paramMappings = [];
-    const { extractMobilenetV1Params, extractPredictionLayerParams } = extractorsFactory(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 };
-}
-//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js.map b/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js.map
deleted file mode 100644
index 33fa2a6..0000000
--- a/build/src/ssdMobilenetv1/extractParamsFromWeigthMap.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/index.d.ts b/build/src/ssdMobilenetv1/index.d.ts
deleted file mode 100644
index b83f479..0000000
--- a/build/src/ssdMobilenetv1/index.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { SsdMobilenetv1 } from './SsdMobilenetv1';
-export * from './SsdMobilenetv1';
-export * from './SsdMobilenetv1Options';
-export declare function createSsdMobilenetv1(weights: Float32Array): SsdMobilenetv1;
-export declare function createFaceDetectionNet(weights: Float32Array): SsdMobilenetv1;
-export declare class FaceDetectionNet extends SsdMobilenetv1 {
-}
diff --git a/build/src/ssdMobilenetv1/index.js b/build/src/ssdMobilenetv1/index.js
deleted file mode 100644
index 7ca7b0d..0000000
--- a/build/src/ssdMobilenetv1/index.js
+++ /dev/null
@@ -1,15 +0,0 @@
-import { SsdMobilenetv1 } from './SsdMobilenetv1';
-export * from './SsdMobilenetv1';
-export * from './SsdMobilenetv1Options';
-export function createSsdMobilenetv1(weights) {
-    const net = new SsdMobilenetv1();
-    net.extractWeights(weights);
-    return net;
-}
-export function createFaceDetectionNet(weights) {
-    return createSsdMobilenetv1(weights);
-}
-// alias for backward compatibily
-export class FaceDetectionNet extends SsdMobilenetv1 {
-}
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/index.js.map b/build/src/ssdMobilenetv1/index.js.map
deleted file mode 100644
index 3f044f2..0000000
--- a/build/src/ssdMobilenetv1/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/mobileNetV1.d.ts b/build/src/ssdMobilenetv1/mobileNetV1.d.ts
deleted file mode 100644
index 2baa387..0000000
--- a/build/src/ssdMobilenetv1/mobileNetV1.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { MobileNetV1 } from './types';
-export declare function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params): any;
diff --git a/build/src/ssdMobilenetv1/mobileNetV1.js b/build/src/ssdMobilenetv1/mobileNetV1.js
deleted file mode 100644
index 2efc141..0000000
--- a/build/src/ssdMobilenetv1/mobileNetV1.js
+++ /dev/null
@@ -1,51 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { pointwiseConvLayer } from './pointwiseConvLayer';
-const epsilon = 0.0010000000474974513;
-function depthwiseConvLayer(x, params, strides) {
-    return tf.tidy(() => {
-        let out = tf.depthwiseConv2d(x, params.filters, strides, 'same');
-        out = tf.batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);
-        return tf.clipByValue(out, 0, 6);
-    });
-}
-function getStridesForLayerIdx(layerIdx) {
-    return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1];
-}
-export function mobileNetV1(x, params) {
-    return tf.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: conv11
-        };
-    });
-}
-//# sourceMappingURL=mobileNetV1.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/mobileNetV1.js.map b/build/src/ssdMobilenetv1/mobileNetV1.js.map
deleted file mode 100644
index c05dd79..0000000
--- a/build/src/ssdMobilenetv1/mobileNetV1.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/nonMaxSuppression.d.ts b/build/src/ssdMobilenetv1/nonMaxSuppression.d.ts
deleted file mode 100644
index 506f66e..0000000
--- a/build/src/ssdMobilenetv1/nonMaxSuppression.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs';
-export declare function nonMaxSuppression(boxes: tf.Tensor2D, scores: number[], maxOutputSize: number, iouThreshold: number, scoreThreshold: number): number[];
diff --git a/build/src/ssdMobilenetv1/nonMaxSuppression.js b/build/src/ssdMobilenetv1/nonMaxSuppression.js
deleted file mode 100644
index e705ac5..0000000
--- a/build/src/ssdMobilenetv1/nonMaxSuppression.js
+++ /dev/null
@@ -1,54 +0,0 @@
-export function nonMaxSuppression(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 iou = IOU(boxes, c.boxIndex, selected[j]);
-            if (iou === 0.0) {
-                continue;
-            }
-            c.score *= suppressFunc(iou);
-            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 0.0;
-    }
-    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, 0.0) *
-        Math.max(intersectionXmax - intersectionXmin, 0.0);
-    return intersectionArea / (areaI + areaJ - intersectionArea);
-}
-//# sourceMappingURL=nonMaxSuppression.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/nonMaxSuppression.js.map b/build/src/ssdMobilenetv1/nonMaxSuppression.js.map
deleted file mode 100644
index 37cf30d..0000000
--- a/build/src/ssdMobilenetv1/nonMaxSuppression.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"nonMaxSuppression.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/nonMaxSuppression.ts"],"names":[],"mappings":"AAEA,MAAM,UAAU,iBAAiB,CAC/B,KAAkB,EAClB,MAAgB,EAChB,aAAqB,EACrB,YAAoB,EACpB,cAAsB;IAGtB,MAAM,QAAQ,GAAG,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;IAC/B,MAAM,UAAU,GAAG,IAAI,CAAC,GAAG,CACzB,aAAa,EACb,QAAQ,CACT,CAAA;IAED,MAAM,UAAU,GAAG,MAAM;SACtB,GAAG,CAAC,CAAC,KAAK,EAAE,QAAQ,EAAE,EAAE,CAAC,CAAC,EAAE,KAAK,EAAE,QAAQ,EAAE,CAAC,CAAC;SAC/C,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,KAAK,GAAG,cAAc,CAAC;SACrC,IAAI,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,KAAK,GAAG,EAAE,CAAC,KAAK,CAAC,CAAA;IAExC,MAAM,YAAY,GAAG,CAAC,CAAS,EAAE,EAAE,CAAC,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IAE7D,MAAM,QAAQ,GAAa,EAAE,CAAA;IAE7B,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE;QACrB,IAAI,QAAQ,CAAC,MAAM,IAAI,UAAU,EAAE;YACjC,OAAM;SACP;QACD,MAAM,aAAa,GAAG,CAAC,CAAC,KAAK,CAAA;QAE7B,KAAK,IAAI,CAAC,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,EAAE,CAAC,EAAE;YAC7C,MAAM,GAAG,GAAG,GAAG,CAAC,KAAK,EAAE,CAAC,CAAC,QAAQ,EAAE,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/C,IAAI,GAAG,KAAK,GAAG,EAAE;gBACf,SAAQ;aACT;YACD,CAAC,CAAC,KAAK,IAAI,YAAY,CAAC,GAAG,CAAC,CAAA;YAC5B,IAAI,CAAC,CAAC,KAAK,IAAI,cAAc,EAAE;gBAC7B,MAAK;aACN;SACF;QAED,IAAI,aAAa,KAAK,CAAC,CAAC,KAAK,EAAE;YAC7B,QAAQ,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAA;SAC1B;IACH,CAAC,CAAC,CAAA;IAEF,OAAO,QAAQ,CAAA;AACjB,CAAC;AAED,SAAS,GAAG,CAAC,KAAkB,EAAE,CAAS,EAAE,CAAS;IACnD,MAAM,SAAS,GAAG,KAAK,CAAC,SAAS,EAAE,CAAA;IACnC,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACxD,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACxD,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACxD,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACxD,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACxD,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACxD,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACxD,MAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IACxD,MAAM,KAAK,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,CAAA;IAC/C,MAAM,KAAK,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,CAAA;IAC/C,IAAI,KAAK,IAAI,CAAC,IAAI,KAAK,IAAI,CAAC,EAAE;QAC5B,OAAO,GAAG,CAAA;KACX;IACD,MAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAA;IAC/C,MAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAA;IAC/C,MAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAA;IAC/C,MAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAA;IAC/C,MAAM,gBAAgB,GAClB,IAAI,CAAC,GAAG,CAAC,gBAAgB,GAAG,gBAAgB,EAAE,GAAG,CAAC;QAClD,IAAI,CAAC,GAAG,CAAC,gBAAgB,GAAG,gBAAgB,EAAE,GAAG,CAAC,CAAA;IACtD,OAAO,gBAAgB,GAAG,CAAC,KAAK,GAAG,KAAK,GAAG,gBAAgB,CAAC,CAAA;AAC9D,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/outputLayer.d.ts b/build/src/ssdMobilenetv1/outputLayer.d.ts
deleted file mode 100644
index 7f428c3..0000000
--- a/build/src/ssdMobilenetv1/outputLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { OutputLayerParams } from './types';
-export declare function outputLayer(boxPredictions: tf.Tensor4D, classPredictions: tf.Tensor4D, params: OutputLayerParams): any;
diff --git a/build/src/ssdMobilenetv1/outputLayer.js b/build/src/ssdMobilenetv1/outputLayer.js
deleted file mode 100644
index 4a42dff..0000000
--- a/build/src/ssdMobilenetv1/outputLayer.js
+++ /dev/null
@@ -1,47 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-function getCenterCoordinatesAndSizesLayer(x) {
-    const vec = tf.unstack(tf.transpose(x, [1, 0]));
-    const sizes = [
-        tf.sub(vec[2], vec[0]),
-        tf.sub(vec[3], vec[1])
-    ];
-    const centers = [
-        tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
-        tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
-    ];
-    return {
-        sizes,
-        centers
-    };
-}
-function decodeBoxesLayer(x0, x1) {
-    const { sizes, centers } = getCenterCoordinatesAndSizesLayer(x0);
-    const vec = tf.unstack(tf.transpose(x1, [1, 0]));
-    const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));
-    const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);
-    const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));
-    const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);
-    return tf.transpose(tf.stack([
-        tf.sub(add0_out, div0_out),
-        tf.sub(add1_out, div1_out),
-        tf.add(add0_out, div0_out),
-        tf.add(add1_out, div1_out)
-    ]), [1, 0]);
-}
-export function outputLayer(boxPredictions, classPredictions, params) {
-    return tf.tidy(() => {
-        const batchSize = boxPredictions.shape[0];
-        let boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));
-        boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
-        const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
-        let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
-        scores = tf.reshape(scores, [batchSize, scores.shape[1]]);
-        const boxesByBatch = tf.unstack(boxes);
-        const scoresByBatch = tf.unstack(scores);
-        return {
-            boxes: boxesByBatch,
-            scores: scoresByBatch
-        };
-    });
-}
-//# sourceMappingURL=outputLayer.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/outputLayer.js.map b/build/src/ssdMobilenetv1/outputLayer.js.map
deleted file mode 100644
index 94870df..0000000
--- a/build/src/ssdMobilenetv1/outputLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"outputLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/outputLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAKzD,SAAS,iCAAiC,CAAC,CAAc;IACvD,MAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAE/C,MAAM,KAAK,GAAG;QACZ,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;QACtB,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;KACvB,CAAA;IAED,MAAM,OAAO,GAAG;QACd,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QAC9C,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;KAC/C,CAAA;IAED,OAAO;QACL,KAAK;QACL,OAAO;KACR,CAAA;AACH,CAAC;AAED,SAAS,gBAAgB,CAAC,EAAe,EAAE,EAAe;IACxD,MAAM,EACJ,KAAK,EACL,OAAO,EACR,GAAG,iCAAiC,CAAC,EAAE,CAAC,CAAA;IAEzC,MAAM,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,SAAS,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAEhD,MAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IAC7F,MAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA;IAEpF,MAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA;IAC7F,MAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA;IAEpF,OAAO,EAAE,CAAC,SAAS,CACjB,EAAE,CAAC,KAAK,CAAC;QACP,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;QAC1B,EAAE,CAAC,GAAG,CAAC,QAAQ,EAAE,QAAQ,CAAC;KAC3B,CAAC,EACF,CAAC,CAAC,EAAE,CAAC,CAAC,CACP,CAAA;AACH,CAAC;AAED,MAAM,UAAU,WAAW,CACzB,cAA2B,EAC3B,gBAA6B,EAC7B,MAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,SAAS,GAAG,cAAc,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA;QAEzC,IAAI,KAAK,GAAG,gBAAgB,CAC1B,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,IAAI,CAAC,MAAM,CAAC,SAAS,EAAE,CAAC,SAAS,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,EAChF,EAAE,CAAC,OAAO,CAAC,cAAc,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CACnD,CAAA;QACD,KAAK,GAAG,EAAE,CAAC,OAAO,CAChB,KAAK,EACL,CAAC,SAAS,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,EAAE,CAAC,CAAC,CAC7C,CAAA;QAED,MAAM,gBAAgB,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,KAAK,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;QACxF,IAAI,MAAM,GAAG,EAAE,CAAC,KAAK,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAc,CAAA;QAE5E,MAAM,GAAG,EAAE,CAAC,OAAO,CACjB,MAAM,EACN,CAAC,SAAS,EAAE,MAAM,CAAC,KAAK,CAAC,CAAC,CAAW,CAAC,CACvC,CAAA;QAED,MAAM,YAAY,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,CAAkB,CAAA;QACvD,MAAM,aAAa,GAAG,EAAE,CAAC,OAAO,CAAC,MAAM,CAAkB,CAAA;QAEzD,OAAO;YACL,KAAK,EAAE,YAAY;YACnB,MAAM,EAAE,aAAa;SACtB,CAAA;IAEH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts b/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts
deleted file mode 100644
index d55bca0..0000000
--- a/build/src/ssdMobilenetv1/pointwiseConvLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { PointwiseConvParams } from './types';
-export declare function pointwiseConvLayer(x: tf.Tensor4D, params: PointwiseConvParams, strides: [number, number]): any;
diff --git a/build/src/ssdMobilenetv1/pointwiseConvLayer.js b/build/src/ssdMobilenetv1/pointwiseConvLayer.js
deleted file mode 100644
index d7b7808..0000000
--- a/build/src/ssdMobilenetv1/pointwiseConvLayer.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function pointwiseConvLayer(x, params, strides) {
-    return tf.tidy(() => {
-        let out = tf.conv2d(x, params.filters, strides, 'same');
-        out = tf.add(out, params.batch_norm_offset);
-        return tf.clipByValue(out, 0, 6);
-    });
-}
-//# sourceMappingURL=pointwiseConvLayer.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/pointwiseConvLayer.js.map b/build/src/ssdMobilenetv1/pointwiseConvLayer.js.map
deleted file mode 100644
index 1e76766..0000000
--- a/build/src/ssdMobilenetv1/pointwiseConvLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"pointwiseConvLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/pointwiseConvLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAIzD,MAAM,UAAU,kBAAkB,CAChC,CAAc,EACd,MAA2B,EAC3B,OAAyB;IAEzB,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,IAAI,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;QACvD,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,iBAAiB,CAAC,CAAA;QAC3C,OAAO,EAAE,CAAC,WAAW,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;IAElC,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/predictionLayer.d.ts b/build/src/ssdMobilenetv1/predictionLayer.d.ts
deleted file mode 100644
index 38ce3d3..0000000
--- a/build/src/ssdMobilenetv1/predictionLayer.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { PredictionLayerParams } from './types';
-export declare function predictionLayer(x: tf.Tensor4D, conv11: tf.Tensor4D, params: PredictionLayerParams): any;
diff --git a/build/src/ssdMobilenetv1/predictionLayer.js b/build/src/ssdMobilenetv1/predictionLayer.js
deleted file mode 100644
index c69ad81..0000000
--- a/build/src/ssdMobilenetv1/predictionLayer.js
+++ /dev/null
@@ -1,42 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { boxPredictionLayer } from './boxPredictionLayer';
-import { pointwiseConvLayer } from './pointwiseConvLayer';
-export function predictionLayer(x, conv11, params) {
-    return tf.tidy(() => {
-        const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
-        const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
-        const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
-        const conv3 = pointwiseConvLayer(conv2, 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 = tf.concat([
-            boxPrediction0.boxPredictionEncoding,
-            boxPrediction1.boxPredictionEncoding,
-            boxPrediction2.boxPredictionEncoding,
-            boxPrediction3.boxPredictionEncoding,
-            boxPrediction4.boxPredictionEncoding,
-            boxPrediction5.boxPredictionEncoding
-        ], 1);
-        const classPredictions = tf.concat([
-            boxPrediction0.classPrediction,
-            boxPrediction1.classPrediction,
-            boxPrediction2.classPrediction,
-            boxPrediction3.classPrediction,
-            boxPrediction4.classPrediction,
-            boxPrediction5.classPrediction
-        ], 1);
-        return {
-            boxPredictions,
-            classPredictions
-        };
-    });
-}
-//# sourceMappingURL=predictionLayer.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/predictionLayer.js.map b/build/src/ssdMobilenetv1/predictionLayer.js.map
deleted file mode 100644
index 58f59f5..0000000
--- a/build/src/ssdMobilenetv1/predictionLayer.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"predictionLayer.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/predictionLayer.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAC1D,OAAO,EAAE,kBAAkB,EAAE,MAAM,sBAAsB,CAAC;AAG1D,MAAM,UAAU,eAAe,CAC7B,CAAc,EACd,MAAmB,EACnB,MAA6B;IAE7B,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAElB,MAAM,KAAK,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC1D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAC9D,MAAM,KAAK,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;QAE9D,MAAM,cAAc,GAAG,kBAAkB,CAAC,MAAM,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACzE,MAAM,cAAc,GAAG,kBAAkB,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACpE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QACxE,MAAM,cAAc,GAAG,kBAAkB,CAAC,KAAK,EAAE,MAAM,CAAC,eAAe,CAAC,CAAA;QAExE,MAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC;YAC/B,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;YACpC,cAAc,CAAC,qBAAqB;SACrC,EAAE,CAAC,CAAgB,CAAA;QAEpB,MAAM,gBAAgB,GAAG,EAAE,CAAC,MAAM,CAAC;YACjC,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;YAC9B,cAAc,CAAC,eAAe;SAC/B,EAAE,CAAC,CAAgB,CAAA;QAEpB,OAAO;YACL,cAAc;YACd,gBAAgB;SACjB,CAAA;IACH,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/types.d.ts b/build/src/ssdMobilenetv1/types.d.ts
deleted file mode 100644
index 87efddd..0000000
--- a/build/src/ssdMobilenetv1/types.d.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ConvParams } from '../common';
-export declare type PointwiseConvParams = {
-    filters: tf.Tensor4D;
-    batch_norm_offset: tf.Tensor1D;
-};
-export declare namespace MobileNetV1 {
-    type DepthwiseConvParams = {
-        filters: tf.Tensor4D;
-        batch_norm_scale: tf.Tensor1D;
-        batch_norm_offset: tf.Tensor1D;
-        batch_norm_mean: tf.Tensor1D;
-        batch_norm_variance: tf.Tensor1D;
-    };
-    type ConvPairParams = {
-        depthwise_conv: DepthwiseConvParams;
-        pointwise_conv: PointwiseConvParams;
-    };
-    type Params = {
-        conv_0: PointwiseConvParams;
-        conv_1: ConvPairParams;
-        conv_2: ConvPairParams;
-        conv_3: ConvPairParams;
-        conv_4: ConvPairParams;
-        conv_5: ConvPairParams;
-        conv_6: ConvPairParams;
-        conv_7: ConvPairParams;
-        conv_8: ConvPairParams;
-        conv_9: ConvPairParams;
-        conv_10: ConvPairParams;
-        conv_11: ConvPairParams;
-        conv_12: ConvPairParams;
-        conv_13: ConvPairParams;
-    };
-}
-export declare type BoxPredictionParams = {
-    box_encoding_predictor: ConvParams;
-    class_predictor: ConvParams;
-};
-export declare type PredictionLayerParams = {
-    conv_0: PointwiseConvParams;
-    conv_1: PointwiseConvParams;
-    conv_2: PointwiseConvParams;
-    conv_3: PointwiseConvParams;
-    conv_4: PointwiseConvParams;
-    conv_5: PointwiseConvParams;
-    conv_6: PointwiseConvParams;
-    conv_7: PointwiseConvParams;
-    box_predictor_0: BoxPredictionParams;
-    box_predictor_1: BoxPredictionParams;
-    box_predictor_2: BoxPredictionParams;
-    box_predictor_3: BoxPredictionParams;
-    box_predictor_4: BoxPredictionParams;
-    box_predictor_5: BoxPredictionParams;
-};
-export declare type OutputLayerParams = {
-    extra_dim: tf.Tensor3D;
-};
-export declare type NetParams = {
-    mobilenetv1: MobileNetV1.Params;
-    prediction_layer: PredictionLayerParams;
-    output_layer: OutputLayerParams;
-};
diff --git a/build/src/ssdMobilenetv1/types.js b/build/src/ssdMobilenetv1/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/ssdMobilenetv1/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/ssdMobilenetv1/types.js.map b/build/src/ssdMobilenetv1/types.js.map
deleted file mode 100644
index 977d82a..0000000
--- a/build/src/ssdMobilenetv1/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/ssdMobilenetv1/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/TinyFaceDetector.d.ts b/build/src/tinyFaceDetector/TinyFaceDetector.d.ts
deleted file mode 100644
index 3a423c9..0000000
--- a/build/src/tinyFaceDetector/TinyFaceDetector.d.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { FaceDetection, Point } from '../classes';
-import { ParamMapping } from '../common';
-import { TNetInput } from '../dom';
-import { ITinyYolov2Options } from '../tinyYolov2';
-import { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';
-import { TinyYolov2NetParams } from '../tinyYolov2/types';
-export declare class TinyFaceDetector extends TinyYolov2Base {
-    constructor();
-    get anchors(): Point[];
-    locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]>;
-    protected getDefaultModelName(): string;
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: TinyYolov2NetParams;
-        paramMappings: ParamMapping[];
-    };
-}
diff --git a/build/src/tinyFaceDetector/TinyFaceDetector.js b/build/src/tinyFaceDetector/TinyFaceDetector.js
deleted file mode 100644
index 9024d31..0000000
--- a/build/src/tinyFaceDetector/TinyFaceDetector.js
+++ /dev/null
@@ -1,31 +0,0 @@
-import { FaceDetection } from '../classes';
-import { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';
-import { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';
-export class TinyFaceDetector extends TinyYolov2Base {
-    constructor() {
-        const config = {
-            withSeparableConvs: true,
-            iouThreshold: IOU_THRESHOLD,
-            classes: ['face'],
-            anchors: BOX_ANCHORS,
-            meanRgb: MEAN_RGB,
-            isFirstLayerConv2d: true,
-            filterSizes: [3, 16, 32, 64, 128, 256, 512]
-        };
-        super(config);
-    }
-    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);
-    }
-}
-//# sourceMappingURL=TinyFaceDetector.js.map
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/TinyFaceDetector.js.map b/build/src/tinyFaceDetector/TinyFaceDetector.js.map
deleted file mode 100644
index ec66b24..0000000
--- a/build/src/tinyFaceDetector/TinyFaceDetector.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"TinyFaceDetector.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/TinyFaceDetector.ts"],"names":[],"mappings":"AAEA,OAAO,EAAE,aAAa,EAAS,MAAM,YAAY,CAAC;AAIlD,OAAO,EAAE,cAAc,EAAE,MAAM,8BAA8B,CAAC;AAE9D,OAAO,EAAE,WAAW,EAAE,aAAa,EAAE,QAAQ,EAAE,MAAM,SAAS,CAAC;AAE/D,MAAM,OAAO,gBAAiB,SAAQ,cAAc;IAElD;QACE,MAAM,MAAM,GAAG;YACb,kBAAkB,EAAE,IAAI;YACxB,YAAY,EAAE,aAAa;YAC3B,OAAO,EAAE,CAAC,MAAM,CAAC;YACjB,OAAO,EAAE,WAAW;YACpB,OAAO,EAAE,QAAQ;YACjB,kBAAkB,EAAE,IAAI;YACxB,WAAW,EAAE,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC;SAC5C,CAAA;QAED,KAAK,CAAC,MAAM,CAAC,CAAA;IACf,CAAC;IAED,IAAW,OAAO;QAChB,OAAO,IAAI,CAAC,MAAM,CAAC,OAAO,CAAA;IAC5B,CAAC;IAEM,KAAK,CAAC,WAAW,CAAC,KAAgB,EAAE,aAAiC;QAC1E,MAAM,gBAAgB,GAAG,MAAM,IAAI,CAAC,MAAM,CAAC,KAAK,EAAE,aAAa,CAAC,CAAA;QAChE,OAAO,gBAAgB,CAAC,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,IAAI,aAAa,CAAC,GAAG,CAAC,KAAK,EAAE,GAAG,CAAC,WAAW,EAAE,EAAE,KAAK,EAAE,GAAG,CAAC,UAAU,EAAE,MAAM,EAAE,GAAG,CAAC,WAAW,EAAE,CAAC,CAAC,CAAA;IACvI,CAAC;IAES,mBAAmB;QAC3B,OAAO,0BAA0B,CAAA;IACnC,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,KAAK,CAAC,0BAA0B,CAAC,SAAS,CAAC,CAAA;IACpD,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/TinyFaceDetectorOptions.d.ts b/build/src/tinyFaceDetector/TinyFaceDetectorOptions.d.ts
deleted file mode 100644
index 8730ed7..0000000
--- a/build/src/tinyFaceDetector/TinyFaceDetectorOptions.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';
-export interface ITinyFaceDetectorOptions extends ITinyYolov2Options {
-}
-export declare class TinyFaceDetectorOptions extends TinyYolov2Options {
-    protected _name: string;
-}
diff --git a/build/src/tinyFaceDetector/TinyFaceDetectorOptions.js b/build/src/tinyFaceDetector/TinyFaceDetectorOptions.js
deleted file mode 100644
index 53d6172..0000000
--- a/build/src/tinyFaceDetector/TinyFaceDetectorOptions.js
+++ /dev/null
@@ -1,8 +0,0 @@
-import { TinyYolov2Options } from '../tinyYolov2';
-export class TinyFaceDetectorOptions extends TinyYolov2Options {
-    constructor() {
-        super(...arguments);
-        this._name = 'TinyFaceDetectorOptions';
-    }
-}
-//# sourceMappingURL=TinyFaceDetectorOptions.js.map
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/TinyFaceDetectorOptions.js.map b/build/src/tinyFaceDetector/TinyFaceDetectorOptions.js.map
deleted file mode 100644
index 77d9f63..0000000
--- a/build/src/tinyFaceDetector/TinyFaceDetectorOptions.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"TinyFaceDetectorOptions.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/TinyFaceDetectorOptions.ts"],"names":[],"mappings":"AAAA,OAAO,EAAsB,iBAAiB,EAAE,MAAM,eAAe,CAAC;AAItE,MAAM,OAAO,uBAAwB,SAAQ,iBAAiB;IAA9D;;QACY,UAAK,GAAW,yBAAyB,CAAA;IACrD,CAAC;CAAA"}
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/const.d.ts b/build/src/tinyFaceDetector/const.d.ts
deleted file mode 100644
index 2efa870..0000000
--- a/build/src/tinyFaceDetector/const.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import { Point } from '../classes';
-export declare const IOU_THRESHOLD = 0.4;
-export declare const BOX_ANCHORS: Point[];
-export declare const MEAN_RGB: [number, number, number];
diff --git a/build/src/tinyFaceDetector/const.js b/build/src/tinyFaceDetector/const.js
deleted file mode 100644
index f1cbe87..0000000
--- a/build/src/tinyFaceDetector/const.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import { Point } from '../classes';
-export const IOU_THRESHOLD = 0.4;
-export const BOX_ANCHORS = [
-    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)
-];
-export const MEAN_RGB = [117.001, 114.697, 97.404];
-//# sourceMappingURL=const.js.map
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/const.js.map b/build/src/tinyFaceDetector/const.js.map
deleted file mode 100644
index 369850e..0000000
--- a/build/src/tinyFaceDetector/const.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"const.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/const.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,KAAK,EAAE,MAAM,YAAY,CAAA;AAElC,MAAM,CAAC,MAAM,aAAa,GAAG,GAAG,CAAA;AAEhC,MAAM,CAAC,MAAM,WAAW,GAAG;IACzB,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;CAC9B,CAAA;AAED,MAAM,CAAC,MAAM,QAAQ,GAA6B,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA"}
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/index.d.ts b/build/src/tinyFaceDetector/index.d.ts
deleted file mode 100644
index cde93ca..0000000
--- a/build/src/tinyFaceDetector/index.d.ts
+++ /dev/null
@@ -1,4 +0,0 @@
-import { TinyFaceDetector } from './TinyFaceDetector';
-export * from './TinyFaceDetector';
-export * from './TinyFaceDetectorOptions';
-export declare function createTinyFaceDetector(weights: Float32Array): TinyFaceDetector;
diff --git a/build/src/tinyFaceDetector/index.js b/build/src/tinyFaceDetector/index.js
deleted file mode 100644
index 0c0bd91..0000000
--- a/build/src/tinyFaceDetector/index.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import { TinyFaceDetector } from './TinyFaceDetector';
-export * from './TinyFaceDetector';
-export * from './TinyFaceDetectorOptions';
-export function createTinyFaceDetector(weights) {
-    const net = new TinyFaceDetector();
-    net.extractWeights(weights);
-    return net;
-}
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/tinyFaceDetector/index.js.map b/build/src/tinyFaceDetector/index.js.map
deleted file mode 100644
index 549dfee..0000000
--- a/build/src/tinyFaceDetector/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/tinyFaceDetector/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,gBAAgB,EAAE,MAAM,oBAAoB,CAAC;AAEtD,cAAc,oBAAoB,CAAC;AACnC,cAAc,2BAA2B,CAAC;AAE1C,MAAM,UAAU,sBAAsB,CAAC,OAAqB;IAC1D,MAAM,GAAG,GAAG,IAAI,gBAAgB,EAAE,CAAA;IAClC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/TinyYolov2.d.ts b/build/src/tinyYolov2/TinyYolov2.d.ts
deleted file mode 100644
index bde0e74..0000000
--- a/build/src/tinyYolov2/TinyYolov2.d.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { FaceDetection, Point } from '../classes';
-import { ParamMapping } from '../common/types';
-import { TNetInput } from '../dom/types';
-import { TinyYolov2Base } from './TinyYolov2Base';
-import { ITinyYolov2Options } from './TinyYolov2Options';
-import { TinyYolov2NetParams } from './types';
-export declare class TinyYolov2 extends TinyYolov2Base {
-    constructor(withSeparableConvs?: boolean);
-    get withSeparableConvs(): boolean;
-    get anchors(): Point[];
-    locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]>;
-    protected getDefaultModelName(): string;
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: TinyYolov2NetParams;
-        paramMappings: ParamMapping[];
-    };
-}
diff --git a/build/src/tinyYolov2/TinyYolov2.js b/build/src/tinyYolov2/TinyYolov2.js
deleted file mode 100644
index a69ea6d..0000000
--- a/build/src/tinyYolov2/TinyYolov2.js
+++ /dev/null
@@ -1,38 +0,0 @@
-import { FaceDetection } from '../classes';
-import { BOX_ANCHORS, BOX_ANCHORS_SEPARABLE, DEFAULT_MODEL_NAME, DEFAULT_MODEL_NAME_SEPARABLE_CONV, IOU_THRESHOLD, MEAN_RGB_SEPARABLE, } from './const';
-import { TinyYolov2Base } from './TinyYolov2Base';
-export class TinyYolov2 extends TinyYolov2Base {
-    constructor(withSeparableConvs = true) {
-        const config = Object.assign({}, {
-            withSeparableConvs,
-            iouThreshold: IOU_THRESHOLD,
-            classes: ['face']
-        }, withSeparableConvs
-            ? {
-                anchors: BOX_ANCHORS_SEPARABLE,
-                meanRgb: MEAN_RGB_SEPARABLE
-            }
-            : {
-                anchors: BOX_ANCHORS,
-                withClassScores: true
-            });
-        super(config);
-    }
-    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);
-    }
-}
-//# sourceMappingURL=TinyYolov2.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/TinyYolov2.js.map b/build/src/tinyYolov2/TinyYolov2.js.map
deleted file mode 100644
index 4c95c57..0000000
--- a/build/src/tinyYolov2/TinyYolov2.js.map
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/build/src/tinyYolov2/TinyYolov2Base.d.ts b/build/src/tinyYolov2/TinyYolov2Base.d.ts
deleted file mode 100644
index ee26aa4..0000000
--- a/build/src/tinyYolov2/TinyYolov2Base.d.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { Dimensions } from '../classes/Dimensions';
-import { ObjectDetection } from '../classes/ObjectDetection';
-import { NetInput } from '../dom/NetInput';
-import { TNetInput } from '../dom/types';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { TinyYolov2Config } from './config';
-import { ITinyYolov2Options } from './TinyYolov2Options';
-import { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';
-export declare class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {
-    static DEFAULT_FILTER_SIZES: number[];
-    private _config;
-    constructor(config: TinyYolov2Config);
-    get config(): TinyYolov2Config;
-    get withClassScores(): boolean;
-    get boxEncodingSize(): number;
-    runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D;
-    runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D;
-    forwardInput(input: NetInput, inputSize: number): tf.Tensor4D;
-    forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D>;
-    detect(input: TNetInput, forwardParams?: ITinyYolov2Options): Promise<ObjectDetection[]>;
-    protected getDefaultModelName(): string;
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: TinyYolov2NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParams(weights: Float32Array): {
-        params: TinyYolov2NetParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractBoxes(outputTensor: tf.Tensor4D, inputBlobDimensions: Dimensions, scoreThreshold?: number): Promise<any>;
-    private extractPredictedClass;
-}
diff --git a/build/src/tinyYolov2/TinyYolov2Base.js b/build/src/tinyYolov2/TinyYolov2Base.js
deleted file mode 100644
index 32c2f0f..0000000
--- a/build/src/tinyYolov2/TinyYolov2Base.js
+++ /dev/null
@@ -1,187 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { BoundingBox } from '../classes/BoundingBox';
-import { ObjectDetection } from '../classes/ObjectDetection';
-import { convLayer } from '../common';
-import { toNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { sigmoid } from '../ops';
-import { nonMaxSuppression } from '../ops/nonMaxSuppression';
-import { normalize } from '../ops/normalize';
-import { validateConfig } from './config';
-import { convWithBatchNorm } from './convWithBatchNorm';
-import { depthwiseSeparableConv } from './depthwiseSeparableConv';
-import { extractParams } from './extractParams';
-import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
-import { leaky } from './leaky';
-import { TinyYolov2Options } from './TinyYolov2Options';
-export class TinyYolov2Base extends NeuralNetwork {
-    constructor(config) {
-        super('TinyYolov2');
-        validateConfig(config);
-        this._config = config;
-    }
-    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 = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = convWithBatchNorm(out, params.conv1);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = convWithBatchNorm(out, params.conv2);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = convWithBatchNorm(out, params.conv3);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = convWithBatchNorm(out, params.conv4);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = convWithBatchNorm(out, params.conv5);
-        out = tf.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))
-            : depthwiseSeparableConv(x, params.conv0);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = depthwiseSeparableConv(out, params.conv1);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = depthwiseSeparableConv(out, params.conv2);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = depthwiseSeparableConv(out, params.conv3);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = depthwiseSeparableConv(out, params.conv4);
-        out = tf.maxPool(out, [2, 2], [2, 2], 'same');
-        out = depthwiseSeparableConv(out, params.conv5);
-        out = tf.maxPool(out, [2, 2], [1, 1], 'same');
-        out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;
-        out = params.conv7 ? depthwiseSeparableConv(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 tf.tidy(() => {
-            // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()
-            let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');
-            batchTensor = this.config.meanRgb
-                ? normalize(batchTensor, this.config.meanRgb)
-                : batchTensor;
-            batchTensor = batchTensor.div(tf.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 = tf.tidy(() => tf.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 extractParamsFromWeigthMap(weightMap, this.config);
-    }
-    extractParams(weights) {
-        const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;
-        const numFilters = filterSizes ? filterSizes.length : undefined;
-        if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
-            throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`);
-        }
-        return extractParams(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] = tf.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
-                ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)
-                : tf.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 width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;
-                        const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;
-                        const x = (ctX - (width / 2));
-                        const y = (ctY - (height / 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 + width, y + height),
-                            score: 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
-];
-//# sourceMappingURL=TinyYolov2Base.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/TinyYolov2Base.js.map b/build/src/tinyYolov2/TinyYolov2Base.js.map
deleted file mode 100644
index ebce6ed..0000000
--- a/build/src/tinyYolov2/TinyYolov2Base.js.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/build/src/tinyYolov2/TinyYolov2Options.d.ts b/build/src/tinyYolov2/TinyYolov2Options.d.ts
deleted file mode 100644
index ee5c17f..0000000
--- a/build/src/tinyYolov2/TinyYolov2Options.d.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-export declare enum TinyYolov2SizeType {
-    XS = 224,
-    SM = 320,
-    MD = 416,
-    LG = 608
-}
-export interface ITinyYolov2Options {
-    inputSize?: number;
-    scoreThreshold?: number;
-}
-export declare class TinyYolov2Options {
-    protected _name: string;
-    private _inputSize;
-    private _scoreThreshold;
-    constructor({ inputSize, scoreThreshold }?: ITinyYolov2Options);
-    get inputSize(): number;
-    get scoreThreshold(): number;
-}
diff --git a/build/src/tinyYolov2/TinyYolov2Options.js b/build/src/tinyYolov2/TinyYolov2Options.js
deleted file mode 100644
index 679a167..0000000
--- a/build/src/tinyYolov2/TinyYolov2Options.js
+++ /dev/null
@@ -1,23 +0,0 @@
-export var TinyYolov2SizeType;
-(function (TinyYolov2SizeType) {
-    TinyYolov2SizeType[TinyYolov2SizeType["XS"] = 224] = "XS";
-    TinyYolov2SizeType[TinyYolov2SizeType["SM"] = 320] = "SM";
-    TinyYolov2SizeType[TinyYolov2SizeType["MD"] = 416] = "MD";
-    TinyYolov2SizeType[TinyYolov2SizeType["LG"] = 608] = "LG";
-})(TinyYolov2SizeType || (TinyYolov2SizeType = {}));
-export 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; }
-}
-//# sourceMappingURL=TinyYolov2Options.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/TinyYolov2Options.js.map b/build/src/tinyYolov2/TinyYolov2Options.js.map
deleted file mode 100644
index 78604bd..0000000
--- a/build/src/tinyYolov2/TinyYolov2Options.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"TinyYolov2Options.js","sourceRoot":"","sources":["../../../src/tinyYolov2/TinyYolov2Options.ts"],"names":[],"mappings":"AAAA,MAAM,CAAN,IAAY,kBAKX;AALD,WAAY,kBAAkB;IAC5B,yDAAQ,CAAA;IACR,yDAAQ,CAAA;IACR,yDAAQ,CAAA;IACR,yDAAQ,CAAA;AACV,CAAC,EALW,kBAAkB,KAAlB,kBAAkB,QAK7B;AAOD,MAAM,OAAO,iBAAiB;IAM5B,YAAY,EAAE,SAAS,EAAE,cAAc,KAAyB,EAAE;QALxD,UAAK,GAAW,mBAAmB,CAAA;QAM3C,IAAI,CAAC,UAAU,GAAG,SAAS,IAAI,GAAG,CAAA;QAClC,IAAI,CAAC,eAAe,GAAG,cAAc,IAAI,GAAG,CAAA;QAE5C,IAAI,OAAO,IAAI,CAAC,UAAU,KAAK,QAAQ,IAAI,IAAI,CAAC,UAAU,GAAG,EAAE,KAAK,CAAC,EAAE;YACrE,MAAM,IAAI,KAAK,CAAC,GAAG,IAAI,CAAC,KAAK,sDAAsD,CAAC,CAAA;SACrF;QAED,IAAI,OAAO,IAAI,CAAC,eAAe,KAAK,QAAQ,IAAI,IAAI,CAAC,eAAe,IAAI,CAAC,IAAI,IAAI,CAAC,eAAe,IAAI,CAAC,EAAE;YACtG,MAAM,IAAI,KAAK,CAAC,GAAG,IAAI,CAAC,KAAK,2DAA2D,CAAC,CAAA;SAC1F;IACH,CAAC;IAED,IAAI,SAAS,KAAa,OAAO,IAAI,CAAC,UAAU,CAAA,CAAC,CAAC;IAClD,IAAI,cAAc,KAAa,OAAO,IAAI,CAAC,eAAe,CAAA,CAAC,CAAC;CAC7D"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/config.d.ts b/build/src/tinyYolov2/config.d.ts
deleted file mode 100644
index 4158ad0..0000000
--- a/build/src/tinyYolov2/config.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { Point } from '../classes/Point';
-export declare type TinyYolov2Config = {
-    withSeparableConvs: boolean;
-    iouThreshold: number;
-    anchors: Point[];
-    classes: string[];
-    meanRgb?: [number, number, number];
-    withClassScores?: boolean;
-    filterSizes?: number[];
-    isFirstLayerConv2d?: boolean;
-};
-export declare function validateConfig(config: any): void;
diff --git a/build/src/tinyYolov2/config.js b/build/src/tinyYolov2/config.js
deleted file mode 100644
index d492168..0000000
--- a/build/src/tinyYolov2/config.js
+++ /dev/null
@@ -1,28 +0,0 @@
-const isNumber = (arg) => typeof arg === 'number';
-export function validateConfig(config) {
-    if (!config) {
-        throw new Error(`invalid config: ${config}`);
-    }
-    if (typeof config.withSeparableConvs !== 'boolean') {
-        throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`);
-    }
-    if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {
-        throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`);
-    }
-    if (!Array.isArray(config.classes)
-        || !config.classes.length
-        || !config.classes.every((c) => typeof c === 'string')) {
-        throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`);
-    }
-    if (!Array.isArray(config.anchors)
-        || !config.anchors.length
-        || !config.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(config.anchors)}`);
-    }
-    if (config.meanRgb && (!Array.isArray(config.meanRgb)
-        || config.meanRgb.length !== 3
-        || !config.meanRgb.every(isNumber))) {
-        throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`);
-    }
-}
-//# sourceMappingURL=config.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/config.js.map b/build/src/tinyYolov2/config.js.map
deleted file mode 100644
index 75dfb0e..0000000
--- a/build/src/tinyYolov2/config.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"config.js","sourceRoot":"","sources":["../../../src/tinyYolov2/config.ts"],"names":[],"mappings":"AAaA,MAAM,QAAQ,GAAG,CAAC,GAAQ,EAAE,EAAE,CAAC,OAAO,GAAG,KAAK,QAAQ,CAAA;AAEtD,MAAM,UAAU,cAAc,CAAC,MAAW;IACxC,IAAI,CAAC,MAAM,EAAE;QACX,MAAM,IAAI,KAAK,CAAC,mBAAmB,MAAM,EAAE,CAAC,CAAA;KAC7C;IAED,IAAI,OAAO,MAAM,CAAC,kBAAkB,KAAK,SAAS,EAAE;QAClD,MAAM,IAAI,KAAK,CAAC,wDAAwD,MAAM,CAAC,kBAAkB,EAAE,CAAC,CAAA;KACrG;IAED,IAAI,CAAC,QAAQ,CAAC,MAAM,CAAC,YAAY,CAAC,IAAI,MAAM,CAAC,YAAY,GAAG,CAAC,IAAI,MAAM,CAAC,YAAY,GAAG,GAAG,EAAE;QAC1F,MAAM,IAAI,KAAK,CAAC,gEAAgE,MAAM,CAAC,YAAY,EAAE,CAAC,CAAA;KACvG;IAED,IACE,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,OAAO,CAAC;WAC3B,CAAC,MAAM,CAAC,OAAO,CAAC,MAAM;WACtB,CAAC,MAAM,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAM,EAAE,EAAE,CAAC,OAAO,CAAC,KAAK,QAAQ,CAAC,EAC3D;QAEA,MAAM,IAAI,KAAK,CAAC,kEAAkE,IAAI,CAAC,SAAS,CAAC,MAAM,CAAC,OAAO,CAAC,EAAE,CAAC,CAAA;KACpH;IAED,IACE,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,OAAO,CAAC;WAC3B,CAAC,MAAM,CAAC,OAAO,CAAC,MAAM;WACtB,CAAC,MAAM,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAM,EAAE,EAAE,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAM,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAC7F;QAEA,MAAM,IAAI,KAAK,CAAC,wEAAwE,IAAI,CAAC,SAAS,CAAC,MAAM,CAAC,OAAO,CAAC,EAAE,CAAC,CAAA;KAC1H;IAED,IAAI,MAAM,CAAC,OAAO,IAAI,CACpB,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,OAAO,CAAC;WAC3B,MAAM,CAAC,OAAO,CAAC,MAAM,KAAK,CAAC;WAC3B,CAAC,MAAM,CAAC,OAAO,CAAC,KAAK,CAAC,QAAQ,CAAC,CACnC,EAAE;QAED,MAAM,IAAI,KAAK,CAAC,8EAA8E,IAAI,CAAC,SAAS,CAAC,MAAM,CAAC,OAAO,CAAC,EAAE,CAAC,CAAA;KAChI;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/const.d.ts b/build/src/tinyYolov2/const.d.ts
deleted file mode 100644
index 9f3fd32..0000000
--- a/build/src/tinyYolov2/const.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { Point } from '../classes';
-export declare const IOU_THRESHOLD = 0.4;
-export declare const BOX_ANCHORS: Point[];
-export declare const BOX_ANCHORS_SEPARABLE: Point[];
-export declare const MEAN_RGB_SEPARABLE: [number, number, number];
-export declare const DEFAULT_MODEL_NAME = "tiny_yolov2_model";
-export declare const DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
diff --git a/build/src/tinyYolov2/const.js b/build/src/tinyYolov2/const.js
deleted file mode 100644
index 75ac107..0000000
--- a/build/src/tinyYolov2/const.js
+++ /dev/null
@@ -1,20 +0,0 @@
-import { Point } from '../classes';
-export const IOU_THRESHOLD = 0.4;
-export 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)
-];
-export 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)
-];
-export const MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
-export const DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
-export const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
-//# sourceMappingURL=const.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/const.js.map b/build/src/tinyYolov2/const.js.map
deleted file mode 100644
index 5e25381..0000000
--- a/build/src/tinyYolov2/const.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"const.js","sourceRoot":"","sources":["../../../src/tinyYolov2/const.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AAEnC,MAAM,CAAC,MAAM,aAAa,GAAG,GAAG,CAAA;AAEhC,MAAM,CAAC,MAAM,WAAW,GAAG;IACzB,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,OAAO,EAAE,OAAO,CAAC;IAC3B,IAAI,KAAK,CAAC,OAAO,EAAE,OAAO,CAAC;IAC3B,IAAI,KAAK,CAAC,MAAM,EAAE,OAAO,CAAC;IAC1B,IAAI,KAAK,CAAC,OAAO,EAAE,OAAO,CAAC;CAC5B,CAAA;AAED,MAAM,CAAC,MAAM,qBAAqB,GAAG;IACnC,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;IAC7B,IAAI,KAAK,CAAC,QAAQ,EAAE,QAAQ,CAAC;CAC9B,CAAA;AAED,MAAM,CAAC,MAAM,kBAAkB,GAA6B,CAAC,OAAO,EAAE,OAAO,EAAE,MAAM,CAAC,CAAA;AAEtF,MAAM,CAAC,MAAM,kBAAkB,GAAG,mBAAmB,CAAA;AACrD,MAAM,CAAC,MAAM,iCAAiC,GAAG,kCAAkC,CAAA"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/convWithBatchNorm.d.ts b/build/src/tinyYolov2/convWithBatchNorm.d.ts
deleted file mode 100644
index 68c3026..0000000
--- a/build/src/tinyYolov2/convWithBatchNorm.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ConvWithBatchNorm } from './types';
-export declare function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D;
diff --git a/build/src/tinyYolov2/convWithBatchNorm.js b/build/src/tinyYolov2/convWithBatchNorm.js
deleted file mode 100644
index 15fd44f..0000000
--- a/build/src/tinyYolov2/convWithBatchNorm.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { leaky } from './leaky';
-export function convWithBatchNorm(x, params) {
-    return tf.tidy(() => {
-        let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-        out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');
-        out = tf.sub(out, params.bn.sub);
-        out = tf.mul(out, params.bn.truediv);
-        out = tf.add(out, params.conv.bias);
-        return leaky(out);
-    });
-}
-//# sourceMappingURL=convWithBatchNorm.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/convWithBatchNorm.js.map b/build/src/tinyYolov2/convWithBatchNorm.js.map
deleted file mode 100644
index 83d67b4..0000000
--- a/build/src/tinyYolov2/convWithBatchNorm.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"convWithBatchNorm.js","sourceRoot":"","sources":["../../../src/tinyYolov2/convWithBatchNorm.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAGhC,MAAM,UAAU,iBAAiB,CAAC,CAAc,EAAE,MAAyB;IACzE,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAgB,CAAA;QAEpE,GAAG,GAAG,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;QAC1D,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,GAAG,CAAC,CAAA;QAChC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,EAAE,CAAC,OAAO,CAAC,CAAA;QACpC,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,CAAA;QAEnC,OAAO,KAAK,CAAC,GAAG,CAAC,CAAA;IACnB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/depthwiseSeparableConv.d.ts b/build/src/tinyYolov2/depthwiseSeparableConv.d.ts
deleted file mode 100644
index 20a2a47..0000000
--- a/build/src/tinyYolov2/depthwiseSeparableConv.d.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { SeparableConvParams } from '../common/types';
-export declare function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D;
diff --git a/build/src/tinyYolov2/depthwiseSeparableConv.js b/build/src/tinyYolov2/depthwiseSeparableConv.js
deleted file mode 100644
index 8d3d69a..0000000
--- a/build/src/tinyYolov2/depthwiseSeparableConv.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { leaky } from './leaky';
-export function depthwiseSeparableConv(x, params) {
-    return tf.tidy(() => {
-        let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-        out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
-        out = tf.add(out, params.bias);
-        return leaky(out);
-    });
-}
-//# sourceMappingURL=depthwiseSeparableConv.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/depthwiseSeparableConv.js.map b/build/src/tinyYolov2/depthwiseSeparableConv.js.map
deleted file mode 100644
index 2f4b526..0000000
--- a/build/src/tinyYolov2/depthwiseSeparableConv.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"depthwiseSeparableConv.js","sourceRoot":"","sources":["../../../src/tinyYolov2/depthwiseSeparableConv.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAGzD,OAAO,EAAE,KAAK,EAAE,MAAM,SAAS,CAAC;AAEhC,MAAM,UAAU,sBAAsB,CAAC,CAAc,EAAE,MAA2B;IAChF,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,IAAI,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAgB,CAAA;QAEpE,GAAG,GAAG,EAAE,CAAC,eAAe,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,EAAE,MAAM,CAAC,gBAAgB,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAA;QAChG,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;QAE9B,OAAO,KAAK,CAAC,GAAG,CAAC,CAAA;IACnB,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/extractParams.d.ts b/build/src/tinyYolov2/extractParams.d.ts
deleted file mode 100644
index 2722193..0000000
--- a/build/src/tinyYolov2/extractParams.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { ParamMapping } from '../common/types';
-import { TinyYolov2Config } from './config';
-import { TinyYolov2NetParams } from './types';
-export declare function extractParams(weights: Float32Array, config: TinyYolov2Config, boxEncodingSize: number, filterSizes: number[]): {
-    params: TinyYolov2NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/tinyYolov2/extractParams.js b/build/src/tinyYolov2/extractParams.js
deleted file mode 100644
index b65be0c..0000000
--- a/build/src/tinyYolov2/extractParams.js
+++ /dev/null
@@ -1,63 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { extractConvParamsFactory } from '../common';
-import { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';
-import { extractWeightsFactory } from '../common/extractWeightsFactory';
-function extractorsFactory(extractWeights, paramMappings) {
-    const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-    function extractBatchNormParams(size, mappedPrefix) {
-        const sub = tf.tensor1d(extractWeights(size));
-        const truediv = tf.tensor1d(extractWeights(size));
-        paramMappings.push({ paramPath: `${mappedPrefix}/sub` }, { paramPath: `${mappedPrefix}/truediv` });
-        return { sub, truediv };
-    }
-    function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
-        const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`);
-        const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`);
-        return { conv, bn };
-    }
-    const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-    return {
-        extractConvParams,
-        extractConvWithBatchNormParams,
-        extractSeparableConvParams
-    };
-}
-export function extractParams(weights, config, boxEncodingSize, filterSizes) {
-    const { extractWeights, getRemainingWeights } = extractWeightsFactory(weights);
-    const paramMappings = [];
-    const { extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams } = extractorsFactory(extractWeights, paramMappings);
-    let params;
-    if (config.withSeparableConvs) {
-        const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes;
-        const conv0 = config.isFirstLayerConv2d
-            ? extractConvParams(s0, s1, 3, 'conv0')
-            : extractSeparableConvParams(s0, s1, 'conv0');
-        const conv1 = extractSeparableConvParams(s1, s2, 'conv1');
-        const conv2 = 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') : undefined;
-        const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;
-        const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');
-        params = { conv0, conv1, conv2, 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 conv2 = 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, conv3, conv4, conv5, conv6, conv7, conv8 };
-    }
-    if (getRemainingWeights().length !== 0) {
-        throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);
-    }
-    return { params, paramMappings };
-}
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/extractParams.js.map b/build/src/tinyYolov2/extractParams.js.map
deleted file mode 100644
index d22a1ce..0000000
--- a/build/src/tinyYolov2/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../../src/tinyYolov2/extractParams.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,wBAAwB,EAAE,MAAM,WAAW,CAAC;AACrD,OAAO,EAAE,iCAAiC,EAAE,MAAM,6CAA6C,CAAC;AAChG,OAAO,EAAE,qBAAqB,EAAE,MAAM,iCAAiC,CAAC;AAKxE,SAAS,iBAAiB,CAAC,cAAsC,EAAE,aAA6B;IAE9F,MAAM,iBAAiB,GAAG,wBAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEjF,SAAS,sBAAsB,CAAC,IAAY,EAAE,YAAoB;QAEhE,MAAM,GAAG,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAC7C,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAA;QAEjD,aAAa,CAAC,IAAI,CAChB,EAAE,SAAS,EAAE,GAAG,YAAY,MAAM,EAAE,EACpC,EAAE,SAAS,EAAE,GAAG,YAAY,UAAU,EAAE,CACzC,CAAA;QAED,OAAO,EAAE,GAAG,EAAE,OAAO,EAAE,CAAA;IACzB,CAAC;IAED,SAAS,8BAA8B,CAAC,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAEnG,MAAM,IAAI,GAAG,iBAAiB,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,EAAE,GAAG,YAAY,OAAO,CAAC,CAAA;QAClF,MAAM,EAAE,GAAG,sBAAsB,CAAC,WAAW,EAAE,GAAG,YAAY,KAAK,CAAC,CAAA;QAEpE,OAAO,EAAE,IAAI,EAAE,EAAE,EAAE,CAAA;IACrB,CAAC;IACD,MAAM,0BAA0B,GAAG,iCAAiC,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEnG,OAAO;QACL,iBAAiB;QACjB,8BAA8B;QAC9B,0BAA0B;KAC3B,CAAA;AAEH,CAAC;AAED,MAAM,UAAU,aAAa,CAC3B,OAAqB,EACrB,MAAwB,EACxB,eAAuB,EACvB,WAAqB;IAGrB,MAAM,EACJ,cAAc,EACd,mBAAmB,EACpB,GAAG,qBAAqB,CAAC,OAAO,CAAC,CAAA;IAElC,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,iBAAiB,EACjB,8BAA8B,EAC9B,0BAA0B,EAC3B,GAAG,iBAAiB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEpD,IAAI,MAA2B,CAAA;IAE/B,IAAI,MAAM,CAAC,kBAAkB,EAAE;QAC7B,MAAM,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,GAAG,WAAW,CAAA;QAExD,MAAM,KAAK,GAAG,MAAM,CAAC,kBAAkB;YACrC,CAAC,CAAC,iBAAiB,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,OAAO,CAAC;YACvC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC/C,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QACzD,MAAM,KAAK,GAAG,EAAE,CAAC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA;QAC1E,MAAM,KAAK,GAAG,EAAE,CAAC,CAAC,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA;QAC1E,MAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,IAAI,EAAE,IAAI,EAAE,EAAE,CAAC,GAAG,eAAe,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;QAChF,MAAM,GAAG,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,CAAA;KAC3E;SAAM;QACL,MAAM,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,GAAG,WAAW,CAAA;QACxD,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAE,CAAA;QAC9D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,8BAA8B,CAAC,EAAE,EAAE,EAAE,EAAE,OAAO,CAAC,CAAA;QAC7D,MAAM,KAAK,GAAG,iBAAiB,CAAC,EAAE,EAAE,CAAC,GAAG,eAAe,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;QACpE,MAAM,GAAG,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,CAAA;KAC3E;IAED,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,kCAAkC,mBAAmB,EAAE,CAAC,MAAM,EAAE,CAAC,CAAA;KAClF;IAGD,OAAO,EAAE,MAAM,EAAE,aAAa,EAAE,CAAA;AAClC,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts b/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts
deleted file mode 100644
index 1e27ba1..0000000
--- a/build/src/tinyYolov2/extractParamsFromWeigthMap.d.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from '../common/types';
-import { TinyYolov2Config } from './config';
-import { TinyYolov2NetParams } from './types';
-export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap, config: TinyYolov2Config): {
-    params: TinyYolov2NetParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/tinyYolov2/extractParamsFromWeigthMap.js b/build/src/tinyYolov2/extractParamsFromWeigthMap.js
deleted file mode 100644
index 30e2194..0000000
--- a/build/src/tinyYolov2/extractParamsFromWeigthMap.js
+++ /dev/null
@@ -1,62 +0,0 @@
-import { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';
-import { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';
-import { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';
-function extractorsFactory(weightMap, paramMappings) {
-    const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-    function extractBatchNormParams(prefix) {
-        const sub = extractWeightEntry(`${prefix}/sub`, 1);
-        const truediv = extractWeightEntry(`${prefix}/truediv`, 1);
-        return { sub, truediv };
-    }
-    function extractConvParams(prefix) {
-        const filters = extractWeightEntry(`${prefix}/filters`, 4);
-        const bias = extractWeightEntry(`${prefix}/bias`, 1);
-        return { filters, bias };
-    }
-    function extractConvWithBatchNormParams(prefix) {
-        const conv = extractConvParams(`${prefix}/conv`);
-        const bn = extractBatchNormParams(`${prefix}/bn`);
-        return { conv, bn };
-    }
-    const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-    return {
-        extractConvParams,
-        extractConvWithBatchNormParams,
-        extractSeparableConvParams
-    };
-}
-export function extractParamsFromWeigthMap(weightMap, config) {
-    const paramMappings = [];
-    const { extractConvParams, extractConvWithBatchNormParams, extractSeparableConvParams } = extractorsFactory(weightMap, paramMappings);
-    let params;
-    if (config.withSeparableConvs) {
-        const numFilters = (config.filterSizes && config.filterSizes.length || 9);
-        params = {
-            conv0: config.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') : undefined,
-            conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,
-            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 };
-}
-//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/extractParamsFromWeigthMap.js.map b/build/src/tinyYolov2/extractParamsFromWeigthMap.js.map
deleted file mode 100644
index d3a3262..0000000
--- a/build/src/tinyYolov2/extractParamsFromWeigthMap.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParamsFromWeigthMap.js","sourceRoot":"","sources":["../../../src/tinyYolov2/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAGA,OAAO,EAAE,0BAA0B,EAAE,MAAM,sCAAsC,CAAC;AAClF,OAAO,EAAE,8BAA8B,EAAE,MAAM,6CAA6C,CAAC;AAC7F,OAAO,EAAE,yBAAyB,EAAE,MAAM,qCAAqC,CAAC;AAKhF,SAAS,iBAAiB,CAAC,SAAc,EAAE,aAA6B;IAEtE,MAAM,kBAAkB,GAAG,yBAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,SAAS,sBAAsB,CAAC,MAAc;QAC5C,MAAM,GAAG,GAAG,kBAAkB,CAAc,GAAG,MAAM,MAAM,EAAE,CAAC,CAAC,CAAA;QAC/D,MAAM,OAAO,GAAG,kBAAkB,CAAc,GAAG,MAAM,UAAU,EAAE,CAAC,CAAC,CAAA;QACvE,OAAO,EAAE,GAAG,EAAE,OAAO,EAAE,CAAA;IACzB,CAAC;IAED,SAAS,iBAAiB,CAAC,MAAc;QACvC,MAAM,OAAO,GAAG,kBAAkB,CAAc,GAAG,MAAM,UAAU,EAAE,CAAC,CAAC,CAAA;QACvE,MAAM,IAAI,GAAG,kBAAkB,CAAc,GAAG,MAAM,OAAO,EAAE,CAAC,CAAC,CAAA;QACjE,OAAO,EAAE,OAAO,EAAE,IAAI,EAAE,CAAA;IAC1B,CAAC;IAED,SAAS,8BAA8B,CAAC,MAAc;QACpD,MAAM,IAAI,GAAG,iBAAiB,CAAC,GAAG,MAAM,OAAO,CAAC,CAAA;QAChD,MAAM,EAAE,GAAG,sBAAsB,CAAC,GAAG,MAAM,KAAK,CAAC,CAAA;QACjD,OAAO,EAAE,IAAI,EAAE,EAAE,EAAE,CAAA;IACrB,CAAC;IAED,MAAM,0BAA0B,GAAG,8BAA8B,CAAC,kBAAkB,CAAC,CAAA;IAErF,OAAO;QACL,iBAAiB;QACjB,8BAA8B;QAC9B,0BAA0B;KAC3B,CAAA;AAEH,CAAC;AAED,MAAM,UAAU,0BAA0B,CACxC,SAA4B,EAC5B,MAAwB;IAGxB,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,iBAAiB,EACjB,8BAA8B,EAC9B,0BAA0B,EAC3B,GAAG,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE/C,IAAI,MAA2B,CAAA;IAE/B,IAAI,MAAM,CAAC,kBAAkB,EAAE;QAC7B,MAAM,UAAU,GAAG,CAAC,MAAM,CAAC,WAAW,IAAI,MAAM,CAAC,WAAW,CAAC,MAAM,IAAI,CAAC,CAAC,CAAA;QACzE,MAAM,GAAG;YACP,KAAK,EAAE,MAAM,CAAC,kBAAkB,CAAC,CAAC,CAAC,iBAAiB,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,0BAA0B,CAAC,OAAO,CAAC;YACnG,KAAK,EAAE,0BAA0B,CAAC,OAAO,CAAC;YAC1C,KAAK,EAAE,0BAA0B,CAAC,OAAO,CAAC;YAC1C,KAAK,EAAE,0BAA0B,CAAC,OAAO,CAAC;YAC1C,KAAK,EAAE,0BAA0B,CAAC,OAAO,CAAC;YAC1C,KAAK,EAAE,0BAA0B,CAAC,OAAO,CAAC;YAC1C,KAAK,EAAE,UAAU,GAAG,CAAC,CAAC,CAAC,CAAC,0BAA0B,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS;YACvE,KAAK,EAAE,UAAU,GAAG,CAAC,CAAC,CAAC,CAAC,0BAA0B,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS;YACvE,KAAK,EAAE,iBAAiB,CAAC,OAAO,CAAC;SAClC,CAAA;KACF;SAAM;QACL,MAAM,GAAG;YACP,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;YAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;YAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;YAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;YAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;YAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;YAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;YAC9C,KAAK,EAAE,8BAA8B,CAAC,OAAO,CAAC;YAC9C,KAAK,EAAE,iBAAiB,CAAC,OAAO,CAAC;SAClC,CAAA;KACF;IAED,0BAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAEpD,OAAO,EAAE,MAAM,EAAE,aAAa,EAAE,CAAA;AAClC,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/index.d.ts b/build/src/tinyYolov2/index.d.ts
deleted file mode 100644
index bfef96a..0000000
--- a/build/src/tinyYolov2/index.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { TinyYolov2 } from './TinyYolov2';
-export * from './TinyYolov2Options';
-export * from './config';
-export * from './types';
-export { TinyYolov2 };
-export declare function createTinyYolov2(weights: Float32Array, withSeparableConvs?: boolean): TinyYolov2;
diff --git a/build/src/tinyYolov2/index.js b/build/src/tinyYolov2/index.js
deleted file mode 100644
index 6714ce2..0000000
--- a/build/src/tinyYolov2/index.js
+++ /dev/null
@@ -1,11 +0,0 @@
-import { TinyYolov2 } from './TinyYolov2';
-export * from './TinyYolov2Options';
-export * from './config';
-export * from './types';
-export { TinyYolov2 };
-export function createTinyYolov2(weights, withSeparableConvs = true) {
-    const net = new TinyYolov2(withSeparableConvs);
-    net.extractWeights(weights);
-    return net;
-}
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/index.js.map b/build/src/tinyYolov2/index.js.map
deleted file mode 100644
index 7c42c70..0000000
--- a/build/src/tinyYolov2/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/tinyYolov2/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,UAAU,EAAE,MAAM,cAAc,CAAC;AAE1C,cAAc,qBAAqB,CAAC;AAEpC,cAAc,UAAU,CAAA;AACxB,cAAc,SAAS,CAAA;AAEvB,OAAO,EAAE,UAAU,EAAE,CAAA;AAErB,MAAM,UAAU,gBAAgB,CAAC,OAAqB,EAAE,qBAA8B,IAAI;IACxF,MAAM,GAAG,GAAG,IAAI,UAAU,CAAC,kBAAkB,CAAC,CAAA;IAC9C,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/leaky.d.ts b/build/src/tinyYolov2/leaky.d.ts
deleted file mode 100644
index 5e3edb7..0000000
--- a/build/src/tinyYolov2/leaky.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export declare function leaky(x: tf.Tensor4D): tf.Tensor4D;
diff --git a/build/src/tinyYolov2/leaky.js b/build/src/tinyYolov2/leaky.js
deleted file mode 100644
index cd714ea..0000000
--- a/build/src/tinyYolov2/leaky.js
+++ /dev/null
@@ -1,9 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-export function leaky(x) {
-    return tf.tidy(() => {
-        const min = tf.mul(x, tf.scalar(0.10000000149011612));
-        return tf.add(tf.relu(tf.sub(x, min)), min);
-        //return tf.maximum(x, min)
-    });
-}
-//# sourceMappingURL=leaky.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/leaky.js.map b/build/src/tinyYolov2/leaky.js.map
deleted file mode 100644
index a3e59bc..0000000
--- a/build/src/tinyYolov2/leaky.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"leaky.js","sourceRoot":"","sources":["../../../src/tinyYolov2/leaky.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,MAAM,UAAU,KAAK,CAAC,CAAc;IAClC,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;QAClB,MAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,mBAAmB,CAAC,CAAC,CAAA;QACrD,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAA;QAC3C,2BAA2B;IAC7B,CAAC,CAAC,CAAA;AACJ,CAAC"}
\ No newline at end of file
diff --git a/build/src/tinyYolov2/types.d.ts b/build/src/tinyYolov2/types.d.ts
deleted file mode 100644
index 10eea7c..0000000
--- a/build/src/tinyYolov2/types.d.ts
+++ /dev/null
@@ -1,34 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ConvParams } from '../common';
-import { SeparableConvParams } from '../common/types';
-export declare type BatchNorm = {
-    sub: tf.Tensor1D;
-    truediv: tf.Tensor1D;
-};
-export declare type ConvWithBatchNorm = {
-    conv: ConvParams;
-    bn: BatchNorm;
-};
-export declare type MobilenetParams = {
-    conv0: SeparableConvParams | ConvParams;
-    conv1: SeparableConvParams;
-    conv2: SeparableConvParams;
-    conv3: SeparableConvParams;
-    conv4: SeparableConvParams;
-    conv5: SeparableConvParams;
-    conv6?: SeparableConvParams;
-    conv7?: SeparableConvParams;
-    conv8: ConvParams;
-};
-export declare type DefaultTinyYolov2NetParams = {
-    conv0: ConvWithBatchNorm;
-    conv1: ConvWithBatchNorm;
-    conv2: ConvWithBatchNorm;
-    conv3: ConvWithBatchNorm;
-    conv4: ConvWithBatchNorm;
-    conv5: ConvWithBatchNorm;
-    conv6: ConvWithBatchNorm;
-    conv7: ConvWithBatchNorm;
-    conv8: ConvParams;
-};
-export declare type TinyYolov2NetParams = DefaultTinyYolov2NetParams | MobilenetParams;
diff --git a/build/src/tinyYolov2/types.js b/build/src/tinyYolov2/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/tinyYolov2/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/tinyYolov2/types.js.map b/build/src/tinyYolov2/types.js.map
deleted file mode 100644
index 4b1403d..0000000
--- a/build/src/tinyYolov2/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/tinyYolov2/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/build/src/utils/index.d.ts b/build/src/utils/index.d.ts
deleted file mode 100644
index bbfd105..0000000
--- a/build/src/utils/index.d.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { Point } from '../classes';
-import { Dimensions, IDimensions } from '../classes/Dimensions';
-export declare function isTensor(tensor: any, dim: number): boolean;
-export declare function isTensor1D(tensor: any): tensor is tf.Tensor1D;
-export declare function isTensor2D(tensor: any): tensor is tf.Tensor2D;
-export declare function isTensor3D(tensor: any): tensor is tf.Tensor3D;
-export declare function isTensor4D(tensor: any): tensor is tf.Tensor4D;
-export declare function isFloat(num: number): boolean;
-export declare function isEven(num: number): boolean;
-export declare function round(num: number, prec?: number): number;
-export declare function isDimensions(obj: any): boolean;
-export declare function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number): Dimensions;
-export declare function getCenterPoint(pts: Point[]): Point;
-export declare function range(num: number, start: number, step: number): number[];
-export declare function isValidNumber(num: any): boolean;
-export declare function isValidProbablitiy(num: any): boolean;
diff --git a/build/src/utils/index.js b/build/src/utils/index.js
deleted file mode 100644
index 163bd8e..0000000
--- a/build/src/utils/index.js
+++ /dev/null
@@ -1,49 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { Point } from '../classes';
-import { Dimensions } from '../classes/Dimensions';
-export function isTensor(tensor, dim) {
-    return tensor instanceof tf.Tensor && tensor.shape.length === dim;
-}
-export function isTensor1D(tensor) {
-    return isTensor(tensor, 1);
-}
-export function isTensor2D(tensor) {
-    return isTensor(tensor, 2);
-}
-export function isTensor3D(tensor) {
-    return isTensor(tensor, 3);
-}
-export function isTensor4D(tensor) {
-    return isTensor(tensor, 4);
-}
-export function isFloat(num) {
-    return num % 1 !== 0;
-}
-export function isEven(num) {
-    return num % 2 === 0;
-}
-export function round(num, prec = 2) {
-    const f = Math.pow(10, prec);
-    return Math.floor(num * f) / f;
-}
-export function isDimensions(obj) {
-    return obj && obj.width && obj.height;
-}
-export function computeReshapedDimensions({ width, height }, inputSize) {
-    const scale = inputSize / Math.max(height, width);
-    return new Dimensions(Math.round(width * scale), Math.round(height * scale));
-}
-export function getCenterPoint(pts) {
-    return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))
-        .div(new Point(pts.length, pts.length));
-}
-export function range(num, start, step) {
-    return Array(num).fill(0).map((_, i) => start + (i * step));
-}
-export function isValidNumber(num) {
-    return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
-}
-export function isValidProbablitiy(num) {
-    return isValidNumber(num) && 0 <= num && num <= 1.0;
-}
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/utils/index.js.map b/build/src/utils/index.js.map
deleted file mode 100644
index f8d54b9..0000000
--- a/build/src/utils/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/utils/index.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAE,KAAK,EAAE,MAAM,YAAY,CAAC;AACnC,OAAO,EAAE,UAAU,EAAe,MAAM,uBAAuB,CAAC;AAEhE,MAAM,UAAU,QAAQ,CAAC,MAAW,EAAE,GAAW;IAC/C,OAAO,MAAM,YAAY,EAAE,CAAC,MAAM,IAAI,MAAM,CAAC,KAAK,CAAC,MAAM,KAAK,GAAG,CAAA;AACnE,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,MAAW;IACpC,OAAO,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA;AAC5B,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,MAAW;IACpC,OAAO,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA;AAC5B,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,MAAW;IACpC,OAAO,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA;AAC5B,CAAC;AAED,MAAM,UAAU,UAAU,CAAC,MAAW;IACpC,OAAO,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA;AAC5B,CAAC;AAED,MAAM,UAAU,OAAO,CAAC,GAAW;IACjC,OAAO,GAAG,GAAG,CAAC,KAAK,CAAC,CAAA;AACtB,CAAC;AAED,MAAM,UAAU,MAAM,CAAC,GAAW;IAChC,OAAO,GAAG,GAAG,CAAC,KAAK,CAAC,CAAA;AACtB,CAAC;AAED,MAAM,UAAU,KAAK,CAAC,GAAW,EAAE,OAAe,CAAC;IACjD,MAAM,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,EAAE,EAAE,IAAI,CAAC,CAAA;IAC5B,OAAO,IAAI,CAAC,KAAK,CAAC,GAAG,GAAG,CAAC,CAAC,GAAG,CAAC,CAAA;AAChC,CAAC;AAED,MAAM,UAAU,YAAY,CAAC,GAAQ;IACnC,OAAO,GAAG,IAAI,GAAG,CAAC,KAAK,IAAI,GAAG,CAAC,MAAM,CAAA;AACvC,CAAC;AAED,MAAM,UAAU,yBAAyB,CAAC,EAAE,KAAK,EAAE,MAAM,EAAe,EAAE,SAAiB;IACzF,MAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;IACjD,OAAO,IAAI,UAAU,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,GAAG,KAAK,CAAC,EAAE,IAAI,CAAC,KAAK,CAAC,MAAM,GAAG,KAAK,CAAC,CAAC,CAAA;AAC9E,CAAC;AAED,MAAM,UAAU,cAAc,CAAC,GAAY;IACzC,OAAO,GAAG,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,EAAE,EAAE,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,IAAI,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;SACzD,GAAG,CAAC,IAAI,KAAK,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAC,MAAM,CAAC,CAAC,CAAA;AAC3C,CAAC;AAED,MAAM,UAAU,KAAK,CAAC,GAAW,EAAE,KAAa,EAAE,IAAY;IAC5D,OAAO,KAAK,CAAC,GAAG,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,KAAK,GAAG,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAA;AAC7D,CAAC;AAED,MAAM,UAAU,aAAa,CAAC,GAAQ;IACpC,OAAO,CAAC,CAAC,GAAG,IAAI,GAAG,KAAK,QAAQ,IAAI,GAAG,KAAK,CAAC,QAAQ,IAAI,CAAC,KAAK,CAAC,GAAG,CAAC,IAAI,GAAG,KAAK,CAAC,CAAA;AACnF,CAAC;AAED,MAAM,UAAU,kBAAkB,CAAC,GAAQ;IACzC,OAAO,aAAa,CAAC,GAAG,CAAC,IAAI,CAAC,IAAI,GAAG,IAAI,GAAG,IAAI,GAAG,CAAA;AACrD,CAAC"}
\ No newline at end of file
diff --git a/build/src/xception/TinyXception.d.ts b/build/src/xception/TinyXception.d.ts
deleted file mode 100644
index ef2381a..0000000
--- a/build/src/xception/TinyXception.d.ts
+++ /dev/null
@@ -1,19 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { NetInput, TNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { TinyXceptionParams } from './types';
-export declare class TinyXception extends NeuralNetwork<TinyXceptionParams> {
-    private _numMainBlocks;
-    constructor(numMainBlocks: number);
-    forwardInput(input: NetInput): tf.Tensor4D;
-    forward(input: TNetInput): Promise<tf.Tensor4D>;
-    protected getDefaultModelName(): string;
-    protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): {
-        params: TinyXceptionParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-    protected extractParams(weights: Float32Array): {
-        params: TinyXceptionParams;
-        paramMappings: import("../common").ParamMapping[];
-    };
-}
diff --git a/build/src/xception/TinyXception.js b/build/src/xception/TinyXception.js
deleted file mode 100644
index 3a6ce2e..0000000
--- a/build/src/xception/TinyXception.js
+++ /dev/null
@@ -1,65 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { depthwiseSeparableConv } from '../common';
-import { toNetInput } from '../dom';
-import { NeuralNetwork } from '../NeuralNetwork';
-import { normalize } from '../ops';
-import { range } from '../utils';
-import { extractParams } from './extractParams';
-import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
-function conv(x, params, stride) {
-    return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);
-}
-function reductionBlock(x, params, isActivateInput = true) {
-    let out = isActivateInput ? tf.relu(x) : x;
-    out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);
-    out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);
-    out = tf.maxPool(out, [3, 3], [2, 2], 'same');
-    out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));
-    return out;
-}
-function mainBlock(x, params) {
-    let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);
-    out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);
-    out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);
-    out = tf.add(out, x);
-    return out;
-}
-export 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 tf.tidy(() => {
-            const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');
-            const meanRgb = [122.782, 117.001, 104.298];
-            const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));
-            let out = tf.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 = tf.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 extractParamsFromWeigthMap(weightMap, this._numMainBlocks);
-    }
-    extractParams(weights) {
-        return extractParams(weights, this._numMainBlocks);
-    }
-}
-//# sourceMappingURL=TinyXception.js.map
\ No newline at end of file
diff --git a/build/src/xception/TinyXception.js.map b/build/src/xception/TinyXception.js.map
deleted file mode 100644
index d7d1e30..0000000
--- a/build/src/xception/TinyXception.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"TinyXception.js","sourceRoot":"","sources":["../../../src/xception/TinyXception.ts"],"names":[],"mappings":"AAAA,OAAO,KAAK,EAAE,MAAM,oCAAoC,CAAC;AAEzD,OAAO,EAAc,sBAAsB,EAAE,MAAM,WAAW,CAAC;AAC/D,OAAO,EAAuB,UAAU,EAAE,MAAM,QAAQ,CAAC;AACzD,OAAO,EAAE,aAAa,EAAE,MAAM,kBAAkB,CAAC;AACjD,OAAO,EAAE,SAAS,EAAE,MAAM,QAAQ,CAAC;AACnC,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AACjC,OAAO,EAAE,aAAa,EAAE,MAAM,iBAAiB,CAAC;AAChD,OAAO,EAAE,0BAA0B,EAAE,MAAM,8BAA8B,CAAC;AAG1E,SAAS,IAAI,CAAC,CAAc,EAAE,MAAkB,EAAE,MAAwB;IACxE,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,EAAE,MAAM,EAAE,MAAM,CAAC,EAAE,MAAM,CAAC,IAAI,CAAC,CAAA;AAC1E,CAAC;AAED,SAAS,cAAc,CAAC,CAAc,EAAE,MAA4B,EAAE,kBAA2B,IAAI;IACnG,IAAI,GAAG,GAAG,eAAe,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1C,GAAG,GAAG,sBAAsB,CAAC,GAAG,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IACjE,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAG,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC3E,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,CAAC,CAAA;IAC7C,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC,EAAG,MAAM,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;IAC1D,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,SAAS,SAAS,CAAC,CAAc,EAAE,MAAuB;IACxD,IAAI,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC5E,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC1E,GAAG,GAAG,sBAAsB,CAAC,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,MAAM,CAAC,eAAe,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;IAC1E,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,CAAC,CAAA;IACpB,OAAO,GAAG,CAAA;AACZ,CAAC;AAED,MAAM,OAAO,YAAa,SAAQ,aAAiC;IAIjE,YAAY,aAAqB;QAC/B,KAAK,CAAC,cAAc,CAAC,CAAA;QACrB,IAAI,CAAC,cAAc,GAAG,aAAa,CAAA;IACrC,CAAC;IAEM,YAAY,CAAC,KAAe;QAEjC,MAAM,EAAE,MAAM,EAAE,GAAG,IAAI,CAAA;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;SAC9D;QAED,OAAO,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE;YAClB,MAAM,WAAW,GAAG,EAAE,CAAC,IAAI,CAAC,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,EAAE,SAAS,CAAC,CAAC;YACvE,MAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,MAAM,UAAU,GAAG,SAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,IAAI,CAAC,UAAU,EAAE,MAAM,CAAC,UAAU,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACtE,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,iBAAiB,EAAE,KAAK,CAAC,CAAA;YACrE,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,UAAU,CAAC,iBAAiB,CAAC,CAAA;YAE9D,KAAK,CAAC,IAAI,CAAC,cAAc,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,GAAG,EAAE,EAAE;gBAC/C,GAAG,GAAG,SAAS,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,cAAc,GAAG,EAAE,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAC,CAAA;YAEF,GAAG,GAAG,cAAc,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,eAAe,CAAC,CAAA;YAC3D,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,sBAAsB,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,cAAc,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA;YACnF,OAAO,GAAG,CAAA;QACZ,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,KAAK,CAAC,OAAO,CAAC,KAAgB;QACnC,OAAO,IAAI,CAAC,YAAY,CAAC,MAAM,UAAU,CAAC,KAAK,CAAC,CAAC,CAAA;IACnD,CAAC;IAES,mBAAmB;QAC3B,OAAO,qBAAqB,CAAA;IAC9B,CAAC;IAES,0BAA0B,CAAC,SAA4B;QAC/D,OAAO,0BAA0B,CAAC,SAAS,EAAE,IAAI,CAAC,cAAc,CAAC,CAAA;IACnE,CAAC;IAES,aAAa,CAAC,OAAqB;QAC3C,OAAO,aAAa,CAAC,OAAO,EAAE,IAAI,CAAC,cAAc,CAAC,CAAA;IACpD,CAAC;CACF"}
\ No newline at end of file
diff --git a/build/src/xception/extractParams.d.ts b/build/src/xception/extractParams.d.ts
deleted file mode 100644
index 886c5ca..0000000
--- a/build/src/xception/extractParams.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import { ParamMapping } from '../common/types';
-import { TinyXceptionParams } from './types';
-export declare function extractParams(weights: Float32Array, numMainBlocks: number): {
-    params: TinyXceptionParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/xception/extractParams.js b/build/src/xception/extractParams.js
deleted file mode 100644
index 66772a5..0000000
--- a/build/src/xception/extractParams.js
+++ /dev/null
@@ -1,55 +0,0 @@
-import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';
-import { range } from '../utils';
-function extractorsFactory(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
-    };
-}
-export function extractParams(weights, numMainBlocks) {
-    const paramMappings = [];
-    const { extractWeights, getRemainingWeights } = extractWeightsFactory(weights);
-    const { extractConvParams, extractSeparableConvParams, extractReductionBlockParams, extractMainBlockParams } = extractorsFactory(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 }
-    };
-}
-//# sourceMappingURL=extractParams.js.map
\ No newline at end of file
diff --git a/build/src/xception/extractParams.js.map b/build/src/xception/extractParams.js.map
deleted file mode 100644
index b97542f..0000000
--- a/build/src/xception/extractParams.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParams.js","sourceRoot":"","sources":["../../../src/xception/extractParams.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,wBAAwB,EAAE,iCAAiC,EAAE,qBAAqB,EAAE,MAAM,WAAW,CAAC;AAE/G,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AAGjC,SAAS,iBAAiB,CAAC,cAAsC,EAAE,aAA6B;IAE9F,MAAM,iBAAiB,GAAG,wBAAwB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IACjF,MAAM,0BAA0B,GAAG,iCAAiC,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEnG,SAAS,2BAA2B,CAAC,UAAkB,EAAE,WAAmB,EAAE,YAAoB;QAEhG,MAAM,eAAe,GAAG,0BAA0B,CAAC,UAAU,EAAE,WAAW,EAAE,GAAG,YAAY,kBAAkB,CAAC,CAAA;QAC9G,MAAM,eAAe,GAAG,0BAA0B,CAAC,WAAW,EAAE,WAAW,EAAE,GAAG,YAAY,kBAAkB,CAAC,CAAA;QAC/G,MAAM,cAAc,GAAG,iBAAiB,CAAC,UAAU,EAAE,WAAW,EAAE,CAAC,EAAE,GAAG,YAAY,iBAAiB,CAAC,CAAA;QAEtG,OAAO,EAAE,eAAe,EAAE,eAAe,EAAE,cAAc,EAAE,CAAA;IAC7D,CAAC;IAED,SAAS,sBAAsB,CAAC,QAAgB,EAAE,YAAoB;QAEpE,MAAM,eAAe,GAAG,0BAA0B,CAAC,QAAQ,EAAE,QAAQ,EAAE,GAAG,YAAY,kBAAkB,CAAC,CAAA;QACzG,MAAM,eAAe,GAAG,0BAA0B,CAAC,QAAQ,EAAE,QAAQ,EAAE,GAAG,YAAY,kBAAkB,CAAC,CAAA;QACzG,MAAM,eAAe,GAAG,0BAA0B,CAAC,QAAQ,EAAE,QAAQ,EAAE,GAAG,YAAY,kBAAkB,CAAC,CAAA;QAEzG,OAAO,EAAE,eAAe,EAAE,eAAe,EAAE,eAAe,EAAE,CAAA;IAC9D,CAAC;IAED,OAAO;QACL,iBAAiB;QACjB,0BAA0B;QAC1B,2BAA2B;QAC3B,sBAAsB;KACvB,CAAA;AAEH,CAAC;AAED,MAAM,UAAU,aAAa,CAAC,OAAqB,EAAE,aAAqB;IAExE,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,cAAc,EACd,mBAAmB,EACpB,GAAG,qBAAqB,CAAC,OAAO,CAAC,CAAA;IAElC,MAAM,EACJ,iBAAiB,EACjB,0BAA0B,EAC1B,2BAA2B,EAC3B,sBAAsB,EACvB,GAAG,iBAAiB,CAAC,cAAc,EAAE,aAAa,CAAC,CAAA;IAEpD,MAAM,kBAAkB,GAAG,iBAAiB,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,oBAAoB,CAAC,CAAA;IAC5E,MAAM,4BAA4B,GAAG,2BAA2B,CAAC,EAAE,EAAE,EAAE,EAAE,8BAA8B,CAAC,CAAA;IACxG,MAAM,4BAA4B,GAAG,2BAA2B,CAAC,EAAE,EAAE,GAAG,EAAE,8BAA8B,CAAC,CAAA;IAEzG,MAAM,UAAU,GAAG;QACjB,OAAO,EAAE,kBAAkB;QAC3B,iBAAiB,EAAE,4BAA4B;QAC/C,iBAAiB,EAAE,4BAA4B;KAChD,CAAA;IAED,MAAM,WAAW,GAAG,EAAE,CAAA;IACtB,KAAK,CAAC,aAAa,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,GAAG,EAAE,EAAE;QACzC,WAAW,CAAC,cAAc,GAAG,EAAE,CAAC,GAAG,sBAAsB,CAAC,GAAG,EAAE,0BAA0B,GAAG,EAAE,CAAC,CAAA;IACjG,CAAC,CAAC,CAAA;IAEF,MAAM,yBAAyB,GAAG,2BAA2B,CAAC,GAAG,EAAE,GAAG,EAAE,2BAA2B,CAAC,CAAA;IACpG,MAAM,wBAAwB,GAAG,0BAA0B,CAAC,GAAG,EAAE,GAAG,EAAE,0BAA0B,CAAC,CAAA;IAEjG,MAAM,SAAS,GAAG;QAChB,eAAe,EAAE,yBAAyB;QAC1C,cAAc,EAAE,wBAAwB;KACzC,CAAA;IAED,IAAI,mBAAmB,EAAE,CAAC,MAAM,KAAK,CAAC,EAAE;QACtC,MAAM,IAAI,KAAK,CAAC,kCAAkC,mBAAmB,EAAE,CAAC,MAAM,EAAE,CAAC,CAAA;KAClF;IAED,OAAO;QACL,aAAa;QACb,MAAM,EAAE,EAAE,UAAU,EAAE,WAAW,EAAE,SAAS,EAAE;KAC/C,CAAA;AACH,CAAC"}
\ No newline at end of file
diff --git a/build/src/xception/extractParamsFromWeigthMap.d.ts b/build/src/xception/extractParamsFromWeigthMap.d.ts
deleted file mode 100644
index ce8a282..0000000
--- a/build/src/xception/extractParamsFromWeigthMap.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import { ParamMapping } from '../common';
-import { TinyXceptionParams } from './types';
-export declare function extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap, numMainBlocks: number): {
-    params: TinyXceptionParams;
-    paramMappings: ParamMapping[];
-};
diff --git a/build/src/xception/extractParamsFromWeigthMap.js b/build/src/xception/extractParamsFromWeigthMap.js
deleted file mode 100644
index 5bc6512..0000000
--- a/build/src/xception/extractParamsFromWeigthMap.js
+++ /dev/null
@@ -1,51 +0,0 @@
-import { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, } from '../common';
-import { loadConvParamsFactory } from '../common/loadConvParamsFactory';
-import { range } from '../utils';
-function loadParamsFactory(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(`${mappedPrefix}/separable_conv2`);
-        return { separable_conv0, separable_conv1, separable_conv2 };
-    }
-    return {
-        extractConvParams,
-        extractSeparableConvParams,
-        extractReductionBlockParams,
-        extractMainBlockParams
-    };
-}
-export function extractParamsFromWeigthMap(weightMap, numMainBlocks) {
-    const paramMappings = [];
-    const { extractConvParams, extractSeparableConvParams, extractReductionBlockParams, extractMainBlockParams } = loadParamsFactory(weightMap, paramMappings);
-    const entry_flow_conv_in = extractConvParams('entry_flow/conv_in');
-    const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');
-    const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');
-    const entry_flow = {
-        conv_in: entry_flow_conv_in,
-        reduction_block_0: entry_flow_reduction_block_0,
-        reduction_block_1: entry_flow_reduction_block_1
-    };
-    const middle_flow = {};
-    range(numMainBlocks, 0, 1).forEach((idx) => {
-        middle_flow[`main_block_${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 };
-}
-//# sourceMappingURL=extractParamsFromWeigthMap.js.map
\ No newline at end of file
diff --git a/build/src/xception/extractParamsFromWeigthMap.js.map b/build/src/xception/extractParamsFromWeigthMap.js.map
deleted file mode 100644
index 4fabe2b..0000000
--- a/build/src/xception/extractParamsFromWeigthMap.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"extractParamsFromWeigthMap.js","sourceRoot":"","sources":["../../../src/xception/extractParamsFromWeigthMap.ts"],"names":[],"mappings":"AAEA,OAAO,EACL,0BAA0B,EAC1B,yBAAyB,EACzB,8BAA8B,GAE/B,MAAM,WAAW,CAAC;AACnB,OAAO,EAAE,qBAAqB,EAAE,MAAM,iCAAiC,CAAC;AACxE,OAAO,EAAE,KAAK,EAAE,MAAM,UAAU,CAAC;AAGjC,SAAS,iBAAiB,CAAC,SAAc,EAAE,aAA6B;IAEtE,MAAM,kBAAkB,GAAG,yBAAyB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE9E,MAAM,iBAAiB,GAAG,qBAAqB,CAAC,kBAAkB,CAAC,CAAA;IACnE,MAAM,0BAA0B,GAAG,8BAA8B,CAAC,kBAAkB,CAAC,CAAA;IAErF,SAAS,2BAA2B,CAAC,YAAoB;QAEvD,MAAM,eAAe,GAAG,0BAA0B,CAAC,GAAG,YAAY,kBAAkB,CAAC,CAAA;QACrF,MAAM,eAAe,GAAG,0BAA0B,CAAC,GAAG,YAAY,kBAAkB,CAAC,CAAA;QACrF,MAAM,cAAc,GAAG,iBAAiB,CAAC,GAAG,YAAY,iBAAiB,CAAC,CAAA;QAE1E,OAAO,EAAE,eAAe,EAAE,eAAe,EAAE,cAAc,EAAE,CAAA;IAC7D,CAAC;IAED,SAAS,sBAAsB,CAAC,YAAoB;QAElD,MAAM,eAAe,GAAG,0BAA0B,CAAC,GAAG,YAAY,kBAAkB,CAAC,CAAA;QACrF,MAAM,eAAe,GAAG,0BAA0B,CAAC,GAAG,YAAY,kBAAkB,CAAC,CAAA;QACrF,MAAM,eAAe,GAAG,0BAA0B,CAAC,GAAG,YAAY,kBAAkB,CAAC,CAAA;QAErF,OAAO,EAAE,eAAe,EAAE,eAAe,EAAE,eAAe,EAAE,CAAA;IAC9D,CAAC;IAED,OAAO;QACL,iBAAiB;QACjB,0BAA0B;QAC1B,2BAA2B;QAC3B,sBAAsB;KACvB,CAAA;AACH,CAAC;AAED,MAAM,UAAU,0BAA0B,CACxC,SAA4B,EAC5B,aAAqB;IAGrB,MAAM,aAAa,GAAmB,EAAE,CAAA;IAExC,MAAM,EACJ,iBAAiB,EACjB,0BAA0B,EAC1B,2BAA2B,EAC3B,sBAAsB,EACvB,GAAG,iBAAiB,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAE/C,MAAM,kBAAkB,GAAG,iBAAiB,CAAC,oBAAoB,CAAC,CAAA;IAClE,MAAM,4BAA4B,GAAG,2BAA2B,CAAC,8BAA8B,CAAC,CAAA;IAChG,MAAM,4BAA4B,GAAG,2BAA2B,CAAC,8BAA8B,CAAC,CAAA;IAEhG,MAAM,UAAU,GAAG;QACjB,OAAO,EAAE,kBAAkB;QAC3B,iBAAiB,EAAE,4BAA4B;QAC/C,iBAAiB,EAAE,4BAA4B;KAChD,CAAA;IAED,MAAM,WAAW,GAAG,EAAE,CAAA;IACtB,KAAK,CAAC,aAAa,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,GAAG,EAAE,EAAE;QACzC,WAAW,CAAC,cAAc,GAAG,EAAE,CAAC,GAAG,sBAAsB,CAAC,0BAA0B,GAAG,EAAE,CAAC,CAAA;IAC5F,CAAC,CAAC,CAAA;IAEF,MAAM,yBAAyB,GAAG,2BAA2B,CAAC,2BAA2B,CAAC,CAAA;IAC1F,MAAM,wBAAwB,GAAG,0BAA0B,CAAC,0BAA0B,CAAC,CAAA;IAEvF,MAAM,SAAS,GAAG;QAChB,eAAe,EAAE,yBAAyB;QAC1C,cAAc,EAAE,wBAAwB;KACzC,CAAA;IAED,0BAA0B,CAAC,SAAS,EAAE,aAAa,CAAC,CAAA;IAEpD,OAAO,EAAE,MAAM,EAAE,EAAE,UAAU,EAAE,WAAW,EAAE,SAAS,EAAE,EAAE,aAAa,EAAE,CAAA;AAC1E,CAAC"}
\ No newline at end of file
diff --git a/build/src/xception/index.d.ts b/build/src/xception/index.d.ts
deleted file mode 100644
index 021d0de..0000000
--- a/build/src/xception/index.d.ts
+++ /dev/null
@@ -1 +0,0 @@
-export * from './TinyXception';
diff --git a/build/src/xception/index.js b/build/src/xception/index.js
deleted file mode 100644
index 0fc246d..0000000
--- a/build/src/xception/index.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export * from './TinyXception';
-//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/build/src/xception/index.js.map b/build/src/xception/index.js.map
deleted file mode 100644
index b3f3477..0000000
--- a/build/src/xception/index.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../../src/xception/index.ts"],"names":[],"mappings":"AAAA,cAAc,gBAAgB,CAAC"}
\ No newline at end of file
diff --git a/build/src/xception/types.d.ts b/build/src/xception/types.d.ts
deleted file mode 100644
index 0d8bddb..0000000
--- a/build/src/xception/types.d.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-import { ConvParams, SeparableConvParams } from '../common';
-export declare type ReductionBlockParams = {
-    separable_conv0: SeparableConvParams;
-    separable_conv1: SeparableConvParams;
-    expansion_conv: ConvParams;
-};
-export declare type MainBlockParams = {
-    separable_conv0: SeparableConvParams;
-    separable_conv1: SeparableConvParams;
-    separable_conv2: SeparableConvParams;
-};
-export declare type TinyXceptionParams = {
-    entry_flow: {
-        conv_in: ConvParams;
-        reduction_block_0: ReductionBlockParams;
-        reduction_block_1: ReductionBlockParams;
-    };
-    middle_flow: any;
-    exit_flow: {
-        reduction_block: ReductionBlockParams;
-        separable_conv: SeparableConvParams;
-    };
-};
diff --git a/build/src/xception/types.js b/build/src/xception/types.js
deleted file mode 100644
index 718fd38..0000000
--- a/build/src/xception/types.js
+++ /dev/null
@@ -1,2 +0,0 @@
-export {};
-//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/build/src/xception/types.js.map b/build/src/xception/types.js.map
deleted file mode 100644
index e49bdd1..0000000
--- a/build/src/xception/types.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"types.js","sourceRoot":"","sources":["../../../src/xception/types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/dist/face-api.esm-nobundle.js b/dist/face-api.esm-nobundle.js
new file mode 100644
index 0000000..2ab39e5
--- /dev/null
+++ b/dist/face-api.esm-nobundle.js
@@ -0,0 +1,9 @@
+
+  /*
+  Face-API
+  homepage: <https://github.com/vladmandic/face-api>
+  author: <https://github.com/vladmandic>'
+  */
+
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);import*as dist_star from"@tensorflow/tfjs/dist/index.js";import*as tfjs_backend_wasm_star from"@tensorflow/tfjs-backend-wasm";var require_tfjs_esm=__commonJS(exports=>{__exportStar(exports,dist_star);__exportStar(exports,tfjs_backend_wasm_star)}),require_isNodejs=__commonJS((exports,module)=>{__export(exports,{isNodejs:()=>isNodejs3});function isNodejs3(){return typeof global=="object"&&!0&&typeof module!="undefined"&&typeof process!="undefined"&&!!process.version}}),tf42=__toModule(require_tfjs_esm()),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});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var 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});var tf=__toModule(require_tfjs_esm()),Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven(num){return num%2===0}function round(num,prec=2){let f=Math.pow(10,prec);return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&0<=num&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(Math.pow(this.x,2)+Math.pow(this.y,2))}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${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`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),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)}`);let[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}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(){let[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(){let[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,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,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){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{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()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{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})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmount2=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmount2,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],paddingTensorPrepend=isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,tensorsToStack=[paddingTensorPrepend,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{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){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},options);return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),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){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class 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)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{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){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!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]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){let extension={detection};return Object.assign({},sourceObj,extension)}function createBrowserEnv(){let fetch=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},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}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}let 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}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=function(){if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=function(){if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},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}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var isNodejs=__toModule(require_isNodejs()),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(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{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}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}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={}));var DrawTextFieldOptions=class{constructor(options={}){let{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}},DrawTextField=class{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){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions(Object.assign({},defaultDrawLabelOptions,drawLabelOptions))}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){let detectionsArray=Array.isArray(detections)?detections:[detections];detectionsArray.forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}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)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{if(!(buf instanceof Blob))return reject("bufferToImage - expected buf to be of type: Blob");let reader=new FileReader;reader.onload=()=>{if(typeof reader.result!="string")return reject("bufferToImage - expected reader.result to be a string, in onload");let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}import{Tensor as Tensor2,browser as browser2,cast as cast2,image,stack,tidy as tidy4}from"@tensorflow/tfjs-core";function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;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)){let 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}let 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");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tidy4(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof Tensor2){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=image.resizeBilinear(imgTensor,[inputSize,inputSize])),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}`)}),batchTensor=stack(inputTensors.map(t=>cast2(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3);return batchTensor})}};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");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height));return boxes.map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)),faceTensors=boxes.map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]));return faceTensors})}async function fetchOrThrow(url,init){let fetch=env.getEnv().fetch,res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),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: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm()),tf8=__toModule(require_tfjs_esm()),NeuralNetwork=class{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){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{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})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){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`);let 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`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(filePath2=>readFile(filePath2).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{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");let 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}),{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}}},tf10=__toModule(require_tfjs_esm()),tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let 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])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,filterSize,mappedPrefix){let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm()),SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return function(prefix){let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return function(originalPath,paramRank,mappedPath){let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return function(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),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)}},tf17=__toModule(require_tfjs_esm()),tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams3(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),fc=extractFCParams(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap3(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams3(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap3(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{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)}},FaceExpressionNet=class 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){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){let extension={expressions};return Object.assign({},sourceObj,extension)}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){let faceExpressionsArray=Array.isArray(faceExpressions)?faceExpressions:[faceExpressions];faceExpressionsArray.forEach(e=>{let 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");let sorted=expr.asSortedArray(),resultsToDisplay=sorted.filter(expr2=>expr2.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0),drawTextField=new DrawTextField(resultsToDisplay.map(expr2=>`${expr2.expression} (${round(expr2.probability)})`),anchor);drawTextField.draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims),extension={landmarks,unshiftedLandmarks,alignedRect};return Object.assign({},sourceObj,extension)}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,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)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{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(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let 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){let faceLandmarksArray=Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks];faceLandmarksArray.forEach(f=>{let 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)})}var tf20=__toModule(require_tfjs_esm()),tf19=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams5(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory3(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),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: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory3(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap5(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory3(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),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)}};function extractParams7(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap7(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams7(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap7(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm()),FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w),landmarkTensors=output.mul(tf21.fill([batchSize,136],inputSize,"float32")).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(()=>{let 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){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),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)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),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)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm()),tf24=__toModule(require_tfjs_esm()),tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),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: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams9(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory5(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return{params,paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap9(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory6(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),fc=weightMap.fc;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let 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 disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3],isAdjustShape=pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2];if(isAdjustShape){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf27.scalar(256)),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);let globalAvg=out.mean([1,2]),fullyConnected=tf27.matMul(globalAvg,params.fc);return fullyConnected})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap9(weightMap)}extractParams(weights){return extractParams9(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){let extension={descriptor};return Object.assign({},sourceObj,extension)}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){let extension={age};return Object.assign({},sourceObj,extension)}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){let extension={gender,genderProbability};return Object.assign({},sourceObj,extension)}var tf34=__toModule(require_tfjs_esm()),tf28=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return 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`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),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(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),box_predictor_0={box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1={box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2={box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3={box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4={box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},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){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory7(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),extra_dim=tf28.tensor3d(extractWeights(5118*4),[1,5118,4]),output_layer={extra_dim};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${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){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory8(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm()),tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),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,out=pointwiseConvLayer(x,params.conv_0,[2,2]),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];if(convPairParams.forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou3=IOU(boxes,c.boxIndex,selected[j]);if(iou3===0)continue;if(c.score*=suppressFunc(iou3),c.score<=scoreThreshold)break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],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){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),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(()=>{let batchSize=boxPredictions.shape[0],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]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm()),tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||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}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{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={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),iouThreshold=.5,indices=nonMaxSuppression2(boxes,scoresData,maxResults,iouThreshold,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[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)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap11(weightMap)}extractParams(weights){return extractParams11(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.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)],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)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",tf39=__toModule(require_tfjs_esm()),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: ${JSON.stringify(config2.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm()),tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv3(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory9(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams13(weights,config2,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory9(extractWeights,paramMappings),params;if(config2.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config2.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),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: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory10(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap13(weightMap,config2){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory10(weightMap,paramMappings),params;if(config2.withSeparableConvs){let 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")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}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={}));var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.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}},TinyYolov2Base2=class 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);return 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),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv3(x,params.conv0);return 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,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),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={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]),indices=nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0),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){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,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){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),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]}),results=[],scoresData=await scoresTensor.array(),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++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,width2=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,height2=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-width2/2,y=ctY-height2/2,pos={row,col,anchor},{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})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,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=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config2=Object.assign({},{withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"]},withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0});super(config2)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){let 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)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf41=__toModule(require_tfjs_esm()),tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,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)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config2={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config2)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){let 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)}},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},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov23=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,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)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,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)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{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)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask,descriptors=await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}));return descriptors.map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class 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}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),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)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,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=>{let detections=await this.run();return res(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async res=>{let detection=await this.run();return res(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+Math.pow(diff,2),0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,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){let 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){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{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)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var version="0.8.9",node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",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.esm-nobundle.js.map
diff --git a/dist/face-api.esm-nobundle.js.map b/dist/face-api.esm-nobundle.js.map
new file mode 100644
index 0000000..1d8334c
--- /dev/null
+++ b/dist/face-api.esm-nobundle.js.map
@@ -0,0 +1,7 @@
+{
+  "version": 3,
+  "sources": ["../src/tfjs/tf-browser.js", "../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/utils/index.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
+  "sourcesContent": ["// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\nexport * from '@tensorflow/tfjs/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n/*\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n  tfjs,\n  'tfjs-core': versionCore,\n  'tfjs-data': versionData,\n  'tfjs-layers': versionLayers,\n  'tfjs-converter': versionConverter,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-backend-wasm': version_wasm,\n};\n*/\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../dist/tfjs.esm.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '../dist/tfjs.esm.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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+  "names": []
+}
diff --git a/dist/face-api.esm-nobundle.json b/dist/face-api.esm-nobundle.json
new file mode 100644
index 0000000..14b49e4
--- /dev/null
+++ b/dist/face-api.esm-nobundle.json
@@ -0,0 +1,2736 @@
+{
+  "inputs": {
+    "dist/tfjs.esm.js": {
+      "bytes": 244,
+      "imports": []
+    },
+    "src/draw/drawContour.ts": {
+      "bytes": 520,
+      "imports": []
+    },
+    "src/classes/Dimensions.ts": {
+      "bytes": 731,
+      "imports": [
+        {
+          "path": "src/utils/index.ts"
+        }
+      ]
+    },
+    "src/utils/index.ts": {
+      "bytes": 1770,
+      "imports": [
+        {
+          "path": "dist/tfjs.esm.js"
+        },
+        {
+          "path": "src/classes/index.ts"
+        },
+        {
+          "path": "src/classes/Dimensions.ts"
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diff --git a/dist/face-api.esm.js b/dist/face-api.esm.js
index c08252e..9e4eb8e 100644
--- a/dist/face-api.esm.js
+++ b/dist/face-api.esm.js
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Buffer shape=${this.shape}`;throw new Error(s)}e++}let r=t[t.length-1];for(let o=0;o<t.length-1;++o)r+=this.strides[o]*t[o];return this.values[r]}locToIndex(t){if(this.rank===0)return 0;if(this.rank===1)return t[0];let e=t[t.length-1];for(let r=0;r<t.length-1;++r)e+=this.strides[r]*t[r];return e}indexToLoc(t){if(this.rank===0)return[];if(this.rank===1)return[t];let e=new Array(this.shape.length);for(let r=0;r<e.length-1;++r)e[r]=Math.floor(t/this.strides[r]),t-=e[r]*this.strides[r];return e[e.length-1]=t,e}get rank(){return this.shape.length}toTensor(){return Oo().makeTensor(this.values,this.shape,this.dtype)}}let Oo=null,yc=null,EN=null;function wP(n){Oo=n}function vP(n){yc=n}function TP(n){EN=n}class ot{constructor(t,e,r,o){this.kept=!1,this.isDisposedInternal=!1,this.shape=t.slice(),this.dtype=e||"float32",this.size=G(t),this.strides=Yt(t),this.dataId=r,this.id=o,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let t=await this.data();return yc.buffer(this.shape,this.dtype,t)}bufferSync(){return yc.buffer(this.shape,this.dtype,this.dataSync())}async array(){let t=await this.data();return Er(this.shape,t)}arraySync(){return Er(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let t=Oo().read(this.dataId);if(this.dtype==="string"){let e=await t;try{return e.map(r=>Uu(r))}catch(r){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return t}dataSync(){this.throwIfDisposed();let t=Oo().readSync(this.dataId);if(this.dtype==="string")try{return t.map(e=>Uu(e))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return t}async bytes(){this.throwIfDisposed();let t=await Oo().read(this.dataId);return this.dtype==="string"?t:new Uint8Array(t.buffer)}dispose(){if(this.isDisposed)return;Oo().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(t=!1){return yc.print(this,t)}clone(){return this.throwIfDisposed(),yc.clone(this)}toString(t=!1){let e=this.dataSync();return bP(e,this.shape,this.dtype,t)}cast(t){return this.throwIfDisposed(),yc.cast(this,t)}variable(t=!0,e,r){return this.throwIfDisposed(),Oo().makeVariable(this,t,e,r)}}Object.defineProperty(ot,Symbol.hasInstance,{value:n=>!!n&&n.data!=null&&n.dataSync!=null&&n.throwIfDisposed!=null});class Ku extends ot{constructor(t,e,r,o){super(t.shape,t.dtype,t.dataId,o);this.trainable=e,this.name=r}assign(t){if(t.dtype!==this.dtype)throw new Error(`dtype of the new value (${t.dtype}) and previous value (${this.dtype}) must match`);if(!lt(t.shape,this.shape))throw new Error(`shape of the new value (${t.shape}) and previous value (${this.shape}) must match`);Oo().disposeTensor(this),this.dataId=t.dataId,Oo().incRef(this,null)}dispose(){Oo().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Ku,Symbol.hasInstance,{value:n=>n instanceof ot&&n.assign!=null&&n.assign instanceof Function});(function(n){n.R0="R0",n.R1="R1",n.R2="R2",n.R3="R3",n.R4="R4",n.R5="R5",n.R6="R6"})(i.Rank||(i.Rank={}));var Ex;(function(n){n.float32="float32",n.int32="int32",n.bool="int32",n.complex64="complex64"})(Ex||(Ex={}));var Dx;(function(n){n.float32="float32",n.int32="int32",n.bool="bool",n.complex64="complex64"})(Dx||(Dx={}));var Ax;(function(n){n.float32="float32",n.int32="float32",n.bool="float32",n.complex64="complex64"})(Ax||(Ax={}));var Fx;(function(n){n.float32="complex64",n.int32="complex64",n.bool="complex64",n.complex64="complex64"})(Fx||(Fx={}));let kP={float32:Ax,int32:Ex,bool:Dx,complex64:Fx};function jn(n,t){if(n==="string"||t==="string"){if(n==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${n} with ${t}`)}return kP[n][t]}function Vf(n){return jn(n,"int32")}function Je(n,t){if(n.dtype===t.dtype)return[n,t];let e=jn(n.dtype,t.dtype);return[n.cast(e),t.cast(e)]}function DN(n,t){_(n.dtype===t.dtype,()=>`The dtypes of the first(${n.dtype}) and second(${t.dtype}) input must match`)}function Gf(n,t){return t.some(e=>e.id===n.id)}function cs(n){let t=[],e=new Set;return AN(n,t,e),t}function AN(n,t,e){if(n==null)return;if(n instanceof ot){t.push(n);return}if(!NP(n))return;let r=n;for(let o in r){let s=r[o];e.has(s)||(e.add(s),AN(s,t,e))}}function NP(n){return Array.isArray(n)||typeof n=="object"}var _P=Object.freeze({__proto__:null,makeTypesMatch:Je,assertTypesMatch:DN,isTensorInList:Gf,getTensorsInContainer:cs});class FN{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}}class bc{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new FN}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let r=t[e],o=await this.initializeBackend(r).success;if(o){await this.setBackend(r);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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){let{name:t,asyncInit:e}=this.initializeBackendsAndReturnBest();if(e)throw new Error(`The highest priority backend '${t}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(t)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(t){if(!(t in this.registry))if(t in this.registryFactory){let{asyncInit:e}=this.initializeBackend(t);if(e)return null}else return null;return this.registry[t]}findBackendFactory(t){return t in this.registryFactory?this.registryFactory[t].factory:null}registerBackend(t,e,r=1){return t in this.registryFactory?(console.warn(`${t} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[t]={factory:e,priority:r},!0)}async setBackend(t){if(this.registryFactory[t]==null)throw new Error(`Backend name '${t}' not found in registry`);if(this.backendName=t,this.registry[t]==null){this.backendInstance=null;let{success:e,asyncInit:r}=this.initializeBackend(t),o=r?await e:e;if(!o)return!1}return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new fP(this.backendInstance),!0}setupRegisteredKernels(){let t=Mf(this.backendName);t.forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){let e=Mf(t);e.forEach(r=>{r.disposeFunc!=null&&r.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let r=e.factory();if(r&&!(r instanceof d)&&typeof r.then=="function"){let o=++this.pendingBackendInitId,s=r.then(c=>o<this.pendingBackendInitId?!1:(this.registry[t]=c,this.pendingBackendInit=null,!0)).catch(c=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${t} failed`),console.warn(c.stack||c.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=r,{success:!0,asyncInit:!1}}catch(r){return console.warn(`Initialization of backend ${t} failed`),console.warn(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(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((t,e)=>this.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let r=t[e],{success:o,asyncInit:s}=this.initializeBackend(r);if(s||o)return{name:r,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(t,e){let r=this.state.tensorInfo.get(e),o=r.backend,s=this.readSync(e);o.disposeData(e),r.backend=t,t.move(e,s,r.shape,r.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(t,e){let r=null;if(e==null){if(typeof t!="function")throw new Error("Please provide a function to tidy()");e=t}else{if(typeof t!="string"&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof e!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=t}let o;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,r){t();try{let o=r();return e(),o}catch(o){throw e(),o}}nextTensorId(){return bc.nextTensorId++}nextVariableId(){return bc.nextVariableId++}clone(t){let e=this.makeTensorFromDataId(t.dataId,t.shape,t.dtype),r={x:t},o=c=>({x:()=>{let l="float32",p={x:c},f={dtype:l};return X.runKernelFunc(m=>m.cast(c,l),p,null,cc,f)}}),s=[];return this.addTapeNode(this.state.activeScope.name,r,[e],o,s,{}),e}runKernel(t,e,r,o,s){let c=null,l=null;return this.runKernelFunc(c,e,l,t,r,o,s)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,r){let o=this.backend.numDataIds(),s=0;r.forEach(p=>{s+=p.dtype==="complex64"?3:1});let c=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],l=o-e-s-c;if(l>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${l} data ids) after running '${t}'`)}runKernelFunc(t,e,r,o,s,c,l){let p,f=[],m=this.isTapeOn();o==null&&(o=this.state.activeScope!=null?this.state.activeScope.name:"");let y=this.state.numBytes,b=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let v,T=Cx(o,this.backendName),N;if(T!=null)v=()=>{let D=this.backend.numDataIds();N=T.kernelFunc({inputs:e,attrs:s,backend:this.backend});let I=Array.isArray(N)?N:[N];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,D,I);let P=I.map(({dataId:E,shape:L,dtype:B})=>this.makeTensorFromDataId(E,L,B));if(m){let E=this.getTensorsForGradient(o,e,P);if(E==null){l==null&&(l=[]);let L=P.filter((B,q)=>l[q]);E=(c||[]).slice().concat(L)}f=this.saveTensorsForBackwardMode(E)}return P};else{let D=I=>{if(!m)return;f=I.map(P=>this.keep(this.clone(P)))};v=()=>{let I=this.backend.numDataIds();N=this.tidy(()=>t(this.backend,D));let P=Array.isArray(N)?N:[N];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,I,P),P}}let S;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?p=v():(S=this.profiler.profileKernel(o,e,()=>v()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(S),p=S.outputs)}),m&&this.addTapeNode(o,e,p,r,f,s),this.state.profiling&&this.state.activeProfile.kernels.push({name:o,bytesAdded:this.state.numBytes-y,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-b,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(e).map(D=>e[D]!=null?e[D].shape:null),outputShapes:p.map(D=>D.shape),kernelTimeMs:S.timeMs,extraInfo:S.extraInfo}),Array.isArray(N)?p:p[0]}saveTensorsForBackwardMode(t){let e=t.map(r=>this.keep(this.clone(r)));return e}getTensorsForGradient(t,e,r){let o=Sx(t);if(o!=null){let s=o.inputsToSave||[],c=o.outputsToSave||[],l;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),l=Object.keys(e).map(f=>e[f])):l=s.map(f=>e[f]);let p=r.filter((f,m)=>c[m]);return l.concat(p)}return null}makeTensor(t,e,r,o){if(t==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",o=o||this.backend;let s=t;r==="string"&&as(t[0])&&(s=t.map(p=>zf(p)));let c=o.write(s,e,r),l=new ot(e,r,c,this.nextTensorId());if(this.incRef(l,o),r==="string"){let p=this.state.tensorInfo.get(c),f=jk(s);this.state.numBytes+=f-p.bytes,p.bytes=f}return l}makeTensorFromDataId(t,e,r,o){r=r||"float32";let s=new ot(e,r,t,this.nextTensorId());return this.incRef(s,o),s}makeVariable(t,e=!0,r,o){r=r||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new Ku(t,e,r,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}incRef(t,e){let r=this.state.tensorInfo.has(t.dataId)?this.state.tensorInfo.get(t.dataId).refCount:0;if(this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++,r===0){this.state.numDataBuffers++;let o=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(o=t.size*Pb(t.dtype)),this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:o,refCount:0}),this.state.numBytes+=o}this.state.tensorInfo.get(t.dataId).refCount++,t instanceof Ku||this.track(t)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;this.state.numTensors--,t.dtype==="string"&&this.state.numStringTensors--;let e=this.state.tensorInfo.get(t.dataId),r=e.refCount;r<=1?(t.dtype!=="complex64"&&(this.state.numBytes-=e.bytes),this.state.numDataBuffers--,e.backend.disposeData(t.dataId),this.state.tensorInfo.delete(t.dataId)):this.state.tensorInfo.get(t.dataId).refCount--}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,r,o,s,c){let l={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:r,saved:s},p=Sx(t);p!=null&&(o=p.gradFunc),o!=null&&(l.gradient=f=>(f=f.map((m,y)=>{if(m==null){let b=r[y],v=ac(b.size,b.dtype);return this.makeTensor(v,b.shape,b.dtype)}return m}),o(f.length>1?f:f[0],s,c))),this.state.activeTape.push(l)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=cs(t),r=new Set(e.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let c=this.state.activeScope.track[s];!c.kept&&!r.has(c.id)&&c.dispose()}let o=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],e.forEach(s=>{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,r,o=!1){if(_(e.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));_(s instanceof ot,()=>"The result y returned by f() must be a tensor.");let c=gP(this.state.activeTape,e,s);if(!o&&c.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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B=(J,it)=>{let[pt,ht,dt,ft]=it,ut=Dd(Q(J,dt.shape),dt,s),bt,yt;if(!e&&!r?(bt=ge(ut,ht,!1,!0),yt=ge(pt,ut,!0,!1)):!e&&r?(bt=ge(ut,ht,!1,!1),yt=ge(ut,pt,!0,!1)):e&&!r?(bt=ge(ht,ut,!1,!0),yt=ge(pt,ut,!1,!1)):(bt=ge(ht,ut,!0,!0),yt=ge(ut,pt,!0,!0)),o!=null){let xt=Ad(ft,ut);return[bt,yt,xt]}else return[bt,yt]},q=J=>{let it=J.fusedBatchMatMul({a:I,b:P,transposeA:e,transposeB:r,bias:E,activation:s,preluActivationWeights:L});return it},H={a:I,b:P,bias:E,preluActivationWeights:L},Z={transposeA:e,transposeB:r,activation:s};if(o==null){let J=zo((it,pt,ht)=>{let dt=X.runKernelFunc(q,H,null,Pf,Z);return ht([it,pt,dt]),{value:Q(dt,D),gradFunc:B}});return J(I,P)}else{let J=zo((it,pt,ht,dt)=>{let ft=X.runKernelFunc(q,H,null,Pf,Z);return dt([it,pt,ft,ht]),{value:Q(ft,D),gradFunc:B}});return J(I,P,E)}}let Pd=j({fusedMatMul_:gz});var yz=Object.freeze({__proto__:null,conv2d:qw,depthwiseConv2d:cC,matMul:Pd});function bz(n){return Gw(n,.54,.46)}let xz=j({hammingWindow_:bz});function wz(n){return 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${r.rank}.`),_(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),_(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let o=r,s=!1;r.rank===3&&(s=!0,o=Q(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[c,l]=t,p={images:o},f={alignCorners:e,size:t},m=(b,v)=>(v([o]),b.resizeNearestNeighbor(o,c,l,e)),y=X.runKernelFunc(m,p,null,mx,f);return s?Q(y,[y.shape[1],y.shape[2],y.shape[3]]):y}let fC=j({resizeNearestNeighbor_:Xz});function Yz(n,t,e){_(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),_(e%1===0,()=>`bandPart(): numUpper must be an integer, got ${e}.`);let r=M(n,"a","bandPart");_(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let o=r.shape,[s,c]=r.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(e<=c))throw new Error(`bandPart(): numUpper (${e}) must not be greater than the number of columns (${c}).`);t<0&&(t=s),e<0&&(e=c);let l=Q(hp(0,s,1,"int32"),[-1,1]),p=hp(0,c,1,"int32"),f=Dt(l,p),m=Yr(ai(f,Et(+t,"int32")),ds(f,Et(-e,"int32"))),y=xe([s,c],r.dtype);return Q(ur(mo(Q(r,[-1,s,c])).map(b=>Xn(m,b,y))),o)}let Jz=j({bandPart_:Yz});function Zz(n){let t;if(Array.isArray(n)){t=!1,_(n!=null&&n.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let o=n[0].shape[0];for(let s=1;s<n.length;++s)_(n[s].shape[0]===o,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${n[s].shape[0]} vs. ${o})`)}else t=!0,n=Tr(n,n.shape[0],0).map(o=>li(o,[0]));_(n.length<=n[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${n.length}) exceeds number of dimensions (${n[0].shape[0]}).`);let e=[],r=n;for(let o=0;o<n.length;++o)e.push(X.tidy(()=>{let s=r[o];if(o>0)for(let c=0;c<o;++c){let l=nt(zt(nt(e[c],s)),e[c]);s=Dt(s,l)}return Bt(s,Ed(s,"euclidean"))}));return t?ur(e,0):e}let Qz=j({gramSchmidt_:Zz});function tW(n,t=!1){if(_(n.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${n.rank}`),n.rank===2)return dC(n,t);{let e=n.shape.slice(0,n.shape.length-2).reduce((p,f)=>p*f),r=mo(Q(n,[e,n.shape[n.shape.length-2],n.shape[n.shape.length-1]]),0),o=[],s=[];r.forEach(p=>{let[f,m]=dC(p,t);o.push(f),s.push(m)});let c=Q(ur(o,0),n.shape),l=Q(ur(s,0),n.shape);return[c,l]}}function dC(n,t=!1){return X.tidy(()=>{_(n.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${n.shape.length}D Tensor.`);let e=n.shape[0],r=n.shape[1],o=hd(e),s=ni(n),c=ui([[1]],[1,1]),l=ni(c),p=e>=r?r:e;for(let f=0;f<p;++f){let m=s,y=l,b=o;[l,s,o]=X.tidy(()=>{let v=ce(s,[f,f],[e-f,1]),T=Ed(v),N=ce(s,[f,f],[1,1]),S=Xn(Fr(N,0),ui([[-1]]),ui([[1]])),D=Dt(N,nt(S,T)),I=Bt(v,D);I.shape[0]===1?l=ni(c):l=Qe([c,ce(I,[1,0],[I.shape[0]-1,I.shape[1]])],0);let P=tn(Bt(ge(S,D),T)),E=ce(s,[f,0],[e-f,r]),L=nt(P,l),B=Kt(l);if(f===0)s=Dt(E,ge(L,ge(B,E)));else{let Z=Dt(E,ge(L,ge(B,E)));s=Qe([ce(s,[0,0],[f,r]),Z],0)}let q=Kt(L),H=ce(o,[0,f],[e,o.shape[1]-f]);if(f===0)o=Dt(H,ge(ge(H,l),q));else{let Z=Dt(H,ge(ge(H,l),q));o=Qe([ce(o,[0,0],[e,f]),Z],1)}return[l,s,o]}),Xt([m,y,b])}return!t&&e>r&&(o=ce(o,[0,0],[e,r]),s=ce(s,[0,0],[r,r])),[o,s]})}let eW=j({qr_:tW});(function(n){n[n.NONE=0]="NONE",n[n.MEAN=1]="MEAN",n[n.SUM=2]="SUM",n[n.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(i.Reduction||(i.Reduction={}));function nW(n,t,e=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let r=M(n,"losses","computeWeightedLoss"),o=null;t!=null&&(o=M(t,"weights","computeWeightedLoss"));let s=o==null?r:nt(r,o);if(e===i.Reduction.NONE)return s;if(e===i.Reduction.SUM)return zt(s);if(e===i.Reduction.MEAN){if(o==null)return en(s);{let c=r.size/o.size,l=Bt(zt(s),zt(o));return c>1?Bt(l,Et(c)):l}}if(e===i.Reduction.SUM_BY_NONZERO_WEIGHTS){if(o==null)return Bt(zt(s),Et(r.size));{let c=nt(o,ho(r.shape)),l=$t(zt(ci(c,Et(0))),"float32");return Bt(zt(s),l)}}throw Error(`Unknown reduction: ${e}`)}let ms=j({computeWeightedLoss_:nW});function rW(n,t,e,r=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=M(n,"labels","absoluteDifference"),s=M(t,"predictions","absoluteDifference"),c=null;e!=null&&(c=M(e,"weights","absoluteDifference")),W(o.shape,s.shape,"Error in absoluteDifference: ");let l=bn(Dt(o,s));return ms(l,c,r)}let oW=j({absoluteDifference_:rW});function sW(n,t,e,r,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"labels","cosineDistance"),c=M(t,"predictions","cosineDistance"),l=null;r!=null&&(l=M(r,"weights","cosineDistance")),W(s.shape,c.shape,"Error in cosineDistance: ");let p=Et(1),f=Dt(p,zt(nt(s,c),e,!0));return ms(f,l,o)}let iW=j({cosineDistance_:sW});function aW(n,t,e,r=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=M(n,"labels","hingeLoss"),s=M(t,"predictions","hingeLoss"),c=null;e!=null&&(c=M(e,"weights","hingeLoss")),W(o.shape,s.shape,"Error in hingeLoss: ");let l=Et(1);o=Dt(nt(Et(2),o),l);let p=Vo(Dt(l,nt(o,s)));return ms(p,c,r)}let cW=j({hingeLoss_:aW});function lW(n,t,e,r=1,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"labels","huberLoss"),c=M(t,"predictions","huberLoss"),l=null;e!=null&&(l=M(e,"weights","huberLoss")),W(s.shape,c.shape,"Error in huberLoss: ");let p=Et(r),f=bn(Dt(c,s)),m=oa(f,p),y=Dt(f,m),b=Tt(nt(Et(.5),De(m)),nt(p,y));return ms(b,l,o)}let uW=j({huberLoss_:lW});function pW(n,t,e,r=1e-7,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"labels","logLoss"),c=M(t,"predictions","logLoss"),l=null;e!=null&&(l=M(e,"weights","logLoss")),W(s.shape,c.shape,"Error in logLoss: ");let p=Et(1),f=Et(r),m=tn(nt(s,wr(Tt(c,f)))),y=nt(Dt(p,s),wr(Tt(Dt(p,c),f))),b=Dt(m,y);return ms(b,l,o)}let hW=j({logLoss_:pW});function fW(n,t,e,r=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let o=M(n,"labels","meanSquaredError"),s=M(t,"predictions","meanSquaredError"),c=null;e!=null&&(c=M(e,"weights","meanSquaredError")),W(o.shape,s.shape,"Error in meanSquaredError: ");let l=gp(o,s);return ms(l,c,r)}let dW=j({meanSquaredError_:fW});function mW(n,t){let e=M(n,"labels","sigmoidCrossEntropyWithLogits"),r=M(t,"logits","sigmoidCrossEntropyWithLogits");W(e.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Vo(r),s=nt(r,e),c=dd(Ar(tn(bn(r))));return Tt(Dt(o,s),c)}function gW(n,t,e,r=0,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"multiClassLabels","sigmoidCrossEntropy"),c=M(t,"logits","sigmoidCrossEntropy"),l=null;if(e!=null&&(l=M(e,"weights","sigmoidCrossEntropy")),W(s.shape,c.shape,"Error in sigmoidCrossEntropy: "),r>0){let f=Et(r),m=Et(1),y=Et(.5);s=Tt(nt(s,Dt(m,f)),nt(y,f))}let p=mW(s,c);return ms(p,l,o)}let yW=j({sigmoidCrossEntropy_:gW});function bW(n,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${e}`);let r=zo((o,s,c)=>{let l=!0,p=_w(s,[e],l),f=Dt($t(s,"float32"),p);c([o,f]);let m=tn(nt(f,o)),y=zt(m,[e]),b=(v,T)=>{let[N,S]=T,D=Rn(v.shape,[e]);return[nt(Q(v,D),Dt($t(N,"float32"),Ar(S))),nt(Q(v,D),Dt(Ar(S),$t(N,"float32")))]};return{value:y,gradFunc:b}});return r(n,t)}function xW(n,t,e,r=0,o=i.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=M(n,"onehotLabels","softmaxCrossEntropy"),c=M(t,"logits","softmaxCrossEntropy"),l=null;if(e!=null&&(l=M(e,"weights","softmaxCrossEntropy")),W(s.shape,c.shape,"Error in softmaxCrossEntropy: "),r>0){let f=Et(r),m=Et(1),y=Et(s.shape[1]);s=Tt(nt(s,Dt(m,f)),Bt(f,y))}let p=bW(s,c);return ms(p,l,o)}let wW=j({softmaxCrossEntropy_:xW});let vW={fft:dp,ifft:Rc,rfft:mp,irfft:Sd},TW={hammingWindow:xz,hannWindow:lC,frame:uC,stft:kz},pi={flipLeftRight:Sz,resizeNearestNeighbor:fC,resizeBilinear:hC,rotateWithOffset:Iz,cropAndResize:_z,nonMaxSuppression:Dz,nonMaxSuppressionAsync:Bz,nonMaxSuppressionWithScore:Wz,nonMaxSuppressionWithScoreAsync:Gz,nonMaxSuppressionPadded:qz,nonMaxSuppressionPaddedAsync:jz},mC={bandPart:Jz,gramSchmidt:Qz,qr:eW},kW={absoluteDifference:oW,computeWeightedLoss:ms,cosineDistance:iW,hingeLoss:cW,huberLoss:uW,logLoss:hW,meanSquaredError:dW,sigmoidCrossEntropy:yW,softmaxCrossEntropy:wW};class gs extends Qi{minimize(t,e=!1,r){let{value:o,grads:s}=this.computeGradients(t,r);if(r!=null){let c=r.map(l=>({name:l.name,tensor:s[l.name]}));this.applyGradients(c)}else this.applyGradients(s);return Xt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Nw(t,e)}dispose(){this.iterations_!=null&&Xt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Et(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}}Object.defineProperty(gs,Symbol.hasInstance,{value:n=>n.minimize!=null&&n.computeGradients!=null&&n.applyGradients!=null});class bp extends gs{constructor(t,e,r=null){super();this.learningRate=t,this.rho=e,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r],c=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${r}/accum_grad`,variable:rt(()=>re(s).variable(c))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${r}/accum_var`,variable:rt(()=>re(s).variable(c))});let l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;let p=this.accumulatedGrads[o].variable,f=this.accumulatedUpdates[o].variable;rt(()=>{let m=Tt(nt(p,this.rho),nt(De(l),1-this.rho)),y=nt(Bt(Pn(Tt(f,this.epsilon)),Pn(Tt(p,this.epsilon))),l),b=Tt(nt(f,this.rho),nt(De(y),1-this.rho));p.assign(m),f.assign(b);let v=Tt(nt(y,-this.learningRate),s);s.assign(v)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Xt(this.accumulatedGrads.map(t=>t.variable)),Xt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,r=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}}bp.className="Adadelta",vt(bp);class xp extends gs{constructor(t,e=.1){super();this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r];if(this.accumulatedGrads[o]==null){let p=!1;this.accumulatedGrads[o]={originalName:`${r}/accumulator`,variable:rt(()=>Cc(s.shape,this.initialAccumulatorValue).variable(p))}}let c=Array.isArray(t)?t[o].tensor:t[r];if(c==null)return;let l=this.accumulatedGrads[o].variable;rt(()=>{let p=Tt(l,De(c));l.assign(p);let f=Tt(nt(Bt(c,Pn(Tt(p,X.backend.epsilon()))),-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Xt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}}xp.className="Adagrad",vt(xp);class wp extends gs{constructor(t,e,r,o=null){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],rt(()=>{this.accBeta1=Et(e).variable(),this.accBeta2=Et(r).variable()}),o==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);rt(()=>{let r=Dt(1,this.accBeta1),o=Dt(1,this.accBeta2);e.forEach((s,c)=>{let l=X.registeredVariables[s],p=!1;this.accumulatedFirstMoment[c]==null&&(this.accumulatedFirstMoment[c]={originalName:`${s}/m`,variable:rt(()=>re(l).variable(p))}),this.accumulatedSecondMoment[c]==null&&(this.accumulatedSecondMoment[c]={originalName:`${s}/v`,variable:rt(()=>re(l).variable(p))});let f=Array.isArray(t)?t[c].tensor:t[s];if(f==null)return;let m=this.accumulatedFirstMoment[c].variable,y=this.accumulatedSecondMoment[c].variable,b=Tt(nt(m,this.beta1),nt(f,1-this.beta1)),v=Tt(nt(y,this.beta2),nt(De(f),1-this.beta2)),T=Bt(b,r),N=Bt(v,o);m.assign(b),y.assign(v);let S=Tt(nt(Bt(T,Tt(Pn(N),this.epsilon)),-this.learningRate),l);l.assign(S)}),this.accBeta1.assign(nt(this.accBeta1,this.beta1)),this.accBeta2.assign(nt(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Xt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Xt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),rt(()=>{this.accBeta1.assign(fo(this.beta1,this.iterations_+1)),this.accBeta2.assign(fo(this.beta2,this.iterations_+1))});let e=t.length/2,r=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}}wp.className="Adam",vt(wp);class vp extends gs{constructor(t,e,r,o=null,s=0){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],rt(()=>{this.iteration=Et(0).variable(),this.accBeta1=Et(e).variable()}),o==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);rt(()=>{let r=Dt(1,this.accBeta1),o=Bt(-this.learningRate,Tt(nt(this.iteration,this.decay),1));e.forEach((s,c)=>{let l=X.registeredVariables[s],p=!1;this.accumulatedFirstMoment[c]==null&&(this.accumulatedFirstMoment[c]={originalName:`${s}/m`,variable:re(l).variable(p)}),this.accumulatedWeightedInfNorm[c]==null&&(this.accumulatedWeightedInfNorm[c]={originalName:`${s}/v`,variable:re(l).variable(p)});let f=Array.isArray(t)?t[c].tensor:t[s];if(f==null)return;let m=this.accumulatedFirstMoment[c].variable,y=this.accumulatedWeightedInfNorm[c].variable,b=Tt(nt(m,this.beta1),nt(f,1-this.beta1)),v=nt(y,this.beta2),T=bn(f),N=Xr(v,T);m.assign(b),y.assign(N);let S=Tt(nt(Bt(o,r),Bt(b,Tt(N,this.epsilon))),l);l.assign(S)}),this.iteration.assign(Tt(this.iteration,1)),this.accBeta1.assign(nt(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Xt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Xt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){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(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}}vp.className="Adamax",vt(vp);class Mc extends gs{constructor(t){super();this.learningRate=t,this.setLearningRate(t)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=Array.isArray(t)?t[o].tensor:t[r];if(s==null)return;let c=X.registeredVariables[r];rt(()=>{let l=Tt(nt(this.c,s),c);c.assign(l)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Sn(Et(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}}Mc.className="SGD",vt(Mc);class Tp extends Mc{constructor(t,e,r=!1){super(t);this.learningRate=t,this.momentum=e,this.useNesterov=r,this.accumulations=[],this.m=Et(this.momentum)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r];if(this.accumulations[o]==null){let 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gs{constructor(t,e=.9,r=0,o=null,s=!1){super();if(this.learningRate=t,this.decay=e,this.momentum=r,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=X.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r],c=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${r}/rms`,variable:rt(()=>re(s).variable(c))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${r}/momentum`,variable:rt(()=>re(s).variable(c))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${r}/mg`,variable:rt(()=>re(s).variable(c))});let l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;let p=this.accumulatedMeanSquares[o].variable,f=this.accumulatedMoments[o].variable;rt(()=>{let m=Tt(nt(p,this.decay),nt(De(l),1-this.decay));if(this.centered){let y=this.accumulatedMeanGrads[o].variable,b=Tt(nt(y,this.decay),nt(l,1-this.decay)),v=Bt(nt(l,this.learningRate),Pn(Dt(m,Tt(De(b),this.epsilon)))),T=Tt(nt(f,this.momentum),v);p.assign(m),y.assign(b),f.assign(T);let N=Dt(s,T);s.assign(N)}else{let y=Tt(nt(p,this.decay),nt(De(l),1-this.decay)),b=Tt(nt(f,this.momentum),Bt(nt(l,this.learningRate),Pn(Tt(y,this.epsilon))));p.assign(y),f.assign(b);let v=Dt(s,b);s.assign(v)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Xt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Xt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Xt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return 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Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new Go(`Layer ${this.name} is not connected, no input to return.`);return pr(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Go(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Go(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. 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Input received: ${t}`);for(let r=0;r<t.length;r++){let o=t[r],s=e[r];if(s==null)continue;let c=o.rank;if(s.ndim!=null&&c!==s.ndim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${c}`);if(s.maxNDim!=null&&c>s.maxNDim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${c}`);if(s.minNDim!=null&&c<s.minNDim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${c}.`);if(s.dtype!=null&&o.dtype!==s.dtype)throw new Y(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${o.dtype}.`);if(s.axes){let l=o.shape;for(let p in s.axes){let f=Number(p),m=s.axes[p],y=f>=0?l[f]:l[l.length+f];if(m!=null&&[m,null].indexOf(y)===-1)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected axis ${f} of input shape to have value ${m} but got shape ${l}.`)}}if(s.shape!=null)for(let l=0;l<s.shape.length;++l){let p=s.shape[l],f=o.shape[l];if(p!=null&&f!=null&&p!==f)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${o.shape}.`)}}}call(t,e){return t}invokeCallHook(t,e){this._callHook!=null&&this._callHook(t,e)}setCallHook(t){this._callHook=t}clearCallHook(){this._callHook=null}apply(t,e){e=e||{},this.assertNotDisposed();let r=ze(t),o=!0;for(let c of r)if(!(c instanceof xo)){o=!1;break}let s=!0;for(let c of r)if(c instanceof xo){s=!1;break}if(o===s)throw new Y("Arguments to apply() must be all SymbolicTensors or all Tensors");return fa(this.name,()=>{if(!this.built){this.assertInputCompatibility(t);let c=[];for(let l of ze(t))c.push(l.shape);this.build(pr(c)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(t),s){let c=this.call(t,e),l=ze(c),p=[];for(let f of l)r.indexOf(f)!==-1&&(f=f.clone()),p.push(f);if(c=pr(p),this.activityRegularizer!=null)throw new Ut("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}else{let c=xG(t),l=this.computeOutputShape(c),p,f=wG(t);if(this.warnOnIncompatibleInputShape(Array.isArray(t)?c[0]:c),l!=null&&l.length>0&&Array.isArray(l[0])?p=l.map((m,y)=>new xo(f,m,this,ze(t),e,this.name,y)):p=new xo(f,l,this,ze(t),e,this.name),this.addInboundNode(t,p,null,null,c,l,e),this._refCount++,this.activityRegularizer!=null)throw new Ut("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return p}})}warnOnIncompatibleInputShape(t){if(this.batchInputShape==null)return;if(t.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(t)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let e=!1;this.batchInputShape.forEach((r,o)=>{r!=null&&t[o]!=null&&t[o]!==r&&(e=!0)}),e&&console.warn(`The shape of the input tensor (${JSON.stringify(t)}) 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 Go(`The layer ${this.name} has never been called and thus has no defined output shape.`);let t=[];for(let e of this.inboundNodes){let r=JSON.stringify(e.outputShapes);t.indexOf(r)===-1&&t.push(r)}if(t.length===1){let e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&e.length===1?e[0]:e}else throw new Go(`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 Jr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return rm(this.weights)}build(t){this.built=!0}getWeights(t=!1){return Iv(t?this.trainableWeights:this.weights)}setWeights(t){rt(()=>{let e=this.weights;if(e.length!==t.length)throw new Y(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${t.length}, but the layer was expecting ${e.length} weights. Provided weights: ${t}...`);if(e.length===0)return;let r=[],o=Iv(e);for(let s=0;s<o.length;++s){let c=o[s],l=e[s],p=t[s];if(!lt(c.shape,p.shape))throw new Y(`Layer weight shape ${c.shape} not compatible with provided weight shape ${p.shape}`);r.push([l,p])}Ev(r)})}addWeight(t,e,r,o,s,c,l){if(this._addedWeightNames.indexOf(t)!==-1)throw new Y(`Duplicate weight name ${t} for layer ${this.name}`);this._addedWeightNames.push(t),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(o=je("zeros"));let p=o.apply(e,r),f=new bo(p,r,t,c,l);return p.dispose(),s!=null&&this.addLoss(()=>s.apply(f.read())),c==null&&(c=!0),c?this._trainableWeights.push(f):this._nonTrainableWeights.push(f),f}setFastWeightInitDuringBuild(t){this.fastWeightInitDuringBuild=t}addLoss(t){if(t==null||Array.isArray(t)&&t.length===0)return;t=ze(t),this._losses!==void 0&&this._losses!==null&&this.losses.push(...t)}computeOutputShape(t){return t}computeMask(t,e){if(!this.supportsMasking){if(e!=null)if(Array.isArray(e))e.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return e}addInboundNode(t,e,r,o,s,c,l=null){let p=ze(t);e=ze(e),r=ze(r),o=ze(o),s=nm(s),c=nm(c);let f=[],m=[],y=[];for(let b of p)f.push(b.sourceLayer),m.push(b.nodeIndex),y.push(b.tensorIndex);new om({outboundLayer:this,inboundLayers:f,nodeIndices:m,tensorIndices:y,inputTensors:p,outputTensors:e,inputMasks:r,outputMasks:o,inputShapes:s,outputShapes:c},l);for(let b=0;b<e.length;b++)e[b].sourceLayer=this,e[b].nodeIndex=this.inboundNodes.length-1,e[b].tensorIndex=b}getConfig(){let t={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(t.batchInputShape=this.batchInputShape),this.dtype!=null&&(t.dtype=this.dtype),t}disposeWeights(){return this.weights.forEach(t=>t.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let t=0;return--this._refCount===0&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}}}function xG(n){n=ze(n);let t=[];for(let e of n)t.push(e.shape);return pr(t)}function wG(n){return"float32"}function XC(n,t,e){if((t==null||e!=null&&e>0)&&(t=n.sourceLayer,e=n.nodeIndex),t.inboundNodes.length===0)return[n];{let r=t.inboundNodes[e];if(r.inboundLayers.length===0)return r.inputTensors;{let o=[];for(let s=0;s<r.inboundLayers.length;s++){let c=r.inputTensors[s],l=r.inboundLayers[s],p=r.nodeIndices[s],f=XC(c,l,p);for(let m of f)o.indexOf(m)===-1&&o.push(m)}return o}}}class Gc extends ye{constructor(t){super({dtype:t.dtype,name:t.name!=null?t.name:em("input").toString()});if(t.batchSize==null&&(t.batchSize=null),t.sparse==null&&(t.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=t.sparse,t.inputShape!=null&&t.batchInputShape!=null)throw new Y("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let e=t.batchInputShape;if(e==null){if(t.inputShape==null)throw new Y("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");e=[t.batchSize].concat(t.inputShape)}else if(t.batchSize!=null)throw new Y("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=t.dtype||"float32";this.batchInputShape=e,this.dtype=r,this.inputSpec=[{shape:e}];let o=new xo(this.dtype,this.batchInputShape,this,[],{},this.name);o.nodeIndex=0,o.tensorIndex=0,new om({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[o],outputTensors:[o],inputMasks:[null],outputMasks:[null],inputShapes:[e],outputShapes:[e]})}apply(t,e){throw new Y(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}Gc.className="InputLayer",vt(Gc);function YC(n){if(n.batchShape==null&&n.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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Found: ${this.outputs.map(I=>I.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let I of this.outputs){let P=I.sourceLayer,E=I.nodeIndex,L=I.tensorIndex;this.outputLayers.push(P),this.outputLayersNodeIndices.push(E),this.outputLayersTensorIndices.push(L)}for(let I of this.inputs){let P=I.sourceLayer,E=I.nodeIndex,L=I.tensorIndex;Pr(E===0,"input layer has >1 nodes"),Pr(L===0,"input layer has >1 tensors"),this.inputLayers.push(P),this.inputLayersNodeIndices.push(E),this.inputLayersTensorIndices.push(L)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let I=0;I<this.inputLayers.length;I++){let P=this.inputLayers[I];if(!(P instanceof Gc))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${t.inputs}. 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let t=[];for(let e of this.layers)t=t.concat(e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.layers)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let r of this.layers)e.push(...r.trainableWeights);return e.concat(t)}return t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(t,e=!0){let r={},o=0;for(let c of this.layers)for(let l of c.weights){if(r[l.originalName]!=null)throw new Y(`Duplicate weight name: ${l.originalName}`);r[l.originalName]=l,o++}let s=[];for(let c in t){let l=c;if(r[c]==null){let p=c.split("/"),f=p.slice(0,-2).concat([p[p.length-1]]);l=f.join("/")}if(r[l]!=null)s.push([r[l],t[c]]);else if(e)throw new Y(`Provided weight data has no target variable: ${c}`);delete r[l]}if(e){let c=[];for(let l in r)c.push(l);if(c.length>0)throw new Y(`${c.length} of ${o} weights are not set: ${c}`)}Ev(s)}updatedConfig(){let t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion=`tfjs-layers ${fm}`,e.backend="TensorFlow.js",e}toJSON(t,e=!0){let r=Mv(this.updatedConfig());return e?JSON.stringify(r):r}call(t,e){return rt(()=>{t=ze(t);let r=new yi;for(let o=0;o<this.inputs.length;++o)r.add(this.inputs[o],t[o]);return Op(this.outputs,r,e)})}computeMask(t,e){return rt(()=>{t=ze(t);let r;return e==null?r=pa(null,t.length):r=ze(e),this.runInternalGraph(t,r)[1]})}computeOutputShape(t){let e=nm(t);if(e.length!==this.inputLayers.length)throw new Y(`Invalid inputShape argument ${t}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let l=0;l<e.length;l++){let p=this.inputLayers[l],f=e[l],m=p.name+"_0_0";r[m]=f}let o=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(Gd);if(o.length>1)for(let l of o){let p=this.nodesByDepth[l];for(let f of p){let m=f.outboundLayer;if(this.inputLayers.map(N=>N.id).indexOf(m.id)!==-1)continue;let y=[];for(let N=0;N<f.inboundLayers.length;N++){let S=f.inboundLayers[N],D=f.nodeIndices[N],I=f.tensorIndices[N],P=`${S.name}_${D}_${I}`,E=r[P];y.push(E)}let b=m.computeOutputShape(pr(y)),v=nm(b),T=m.inboundNodes.indexOf(f);for(let N=0;N<v.length;N++){let S=`${m.name}_${T}_${N}`;r[S]=v[N]}}}let s=[],c=[];for(let l=0;l<this.outputLayers.length;l++){let p=this.outputLayers[l],f=this.outputLayersNodeIndices[l],m=this.outputLayersTensorIndices[l],y=`${p.name}_${f}_${m}`;c.push(y)}for(let l=0;l<c.length;l++){let p=c[l];Pr(p in r),s.push(r[p])}return pr(s)}runInternalGraph(t,e){e==null&&(e=pa(null,t.length));let r={};for(let p=0;p<this.inputs.length;++p){let f=this.inputs[p],m=t[p],y=e[p];r[f.id]=[m,y]}let o=Object.keys(this.nodesByDepth).map(p=>parseInt(p,10)).sort(Gd);for(let p of o){let f=this.nodesByDepth[p];for(let m of f){let y=m.outboundLayer,b=m.inputTensors,v=m.outputTensors,T=new Array;for(let N of b)N.id in r&&T.push(r[N.id]);if(T.length===b.length){let N={},S,D,I,P;if(m.callArgs!=null&&(N=m.callArgs),T.length===1){let[E,L]=T[0];N.mask==null&&(N.mask=L),I=ze(y.call(E,N)),P=ze(y.computeMask(E,L)),S=[E],D=[L]}else S=T.map(E=>E[0]),D=T.map(E=>E[1]),N.mask==null&&(N.mask=D),I=ze(y.call(S,N)),P=ze(y.computeMask(S,D));if(y.activityRegularizer)throw new Ut("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let E=0;E<v.length;++E){let L=v[E],B=I[E],q=P[E];r[L.id]=[B,q]}}}}let s=[],c=[],l=[];for(let p of this.outputs){Pr(p.id in r,`Could not compute output ${p.name} : ${p.id}`);let[f,m]=r[p.id];l.push(f.shape),s.push(f),c.push(m)}return[s,c,l]}buildNodeConversionMap(t){let e={},r;for(let o of this.layers){r=o instanceof vo?1:0;for(let s=0;s<o.inboundNodes.length;s++){let c=vo.nodeKey(o,s);this.containerNodes.has(c)&&(e[c]=r,r+=1)}}return e}getLayer(t,e){if(e!=null){if(this.layers.length<=e)throw new Y(`Was asked to retrieve layer at index ${e}, but model only has ${this.layers.length} layer(s).`);return this.layers[e]}else if(t==null)throw new Y("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===t)return r;throw new Y(`No such layer: ${t}`)}calculateLosses(){return rt(()=>{let t=[];for(let e of this.layers)for(let r=0;r<e.inboundNodes.length;++r){let o=vo.nodeKey(e,r);this.containerNodes.has(o)&&t.push(...e.calculateLosses())}return t})}getConfig(){let t={name:this.name},e=this.buildNodeConversionMap(this.layers),r=[];for(let c of this.layers){let l=c.getClassName(),p=c.getConfig(),f=[];for(let y=0;y<c.inboundNodes.length;y++){let b=c.inboundNodes[y],v=vo.nodeKey(c,y),T={};if(this.containerNodes.has(v)){if(b.callArgs)try{JSON.stringify(b.callArgs),T=b.callArgs}catch(N){console.warn(`Layer ${c.name} was passed non-serializable keyword arguments: ${b.callArgs}. 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Provided ${e} not understood: ${JSON.stringify(n)}`)}function mS(n,t){return dS(n,t,"classWeight")}function lst(n,t){return dS(n,t,"sampleWeight")}async function gS(n,t,e,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(e!=null){let o=rt(()=>{if(n.shape.length===1)return n.clone();if(n.shape.length===2)if(n.shape[1]>1){let l=1;return n.argMax(l)}else{if(n.shape[1]===1)return n.reshape([n.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${n.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${n.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await o.data());Xt(o);let c=[];return s.forEach(l=>{if(e[l]==null)throw new Error(`classWeight must contain all classes in the training data. 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c=0;c<o.length;++c){if(r&&!o[c].trainable)continue;e.push({name:o[c].originalName,tensor:s[c]})}return e}set stopTraining(t){this.stopTraining_=t}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(t){this.optimizer_!==t&&(this.optimizer_=t,this.isOptimizerOwned=!1)}dispose(){let t=super.dispose();if(t.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let e=ed().numTensors;this.optimizer_.dispose(),t.numDisposedVariables+=e-ed().numTensors}return t}getLossIdentifiers(){let t;if(typeof this.loss=="string")t=bs(this.loss);else if(Array.isArray(this.loss)){for(let e of this.loss)if(typeof e!="string")throw new Error("Serialization of non-string loss is not supported.");t=this.loss.map(e=>bs(e))}else{let e=Object.keys(this.loss);t={};let r=this.loss;for(let o of e)if(typeof r[o]=="string")t[o]=bs(r[o]);else throw new Error("Serialization of non-string loss is not supported.")}return t}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[bs(pm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(t=>bs(pm(t)));{let t={};for(let e in this.metrics)t[e]=bs(pm(this.metrics[e]));return t}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(t){if(t.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(t.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(t.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let e=Pp(t.optimizer_config),r=wo(e),o;if(typeof t.loss=="string")o=ha(t.loss);else if(Array.isArray(t.loss))o=t.loss.map(c=>ha(c));else if(t.loss!=null){o={};for(let c in t.loss)o[c]=ha(t.loss[c])}let s;if(Array.isArray(t.metrics))s=t.metrics.map(c=>ha(c));else if(t.metrics!=null){s={};for(let c in t.metrics)s[c]=ha(t.metrics[c])}this.compile({loss:o,metrics:s,optimizer:r})}async save(t,e){if(typeof t=="string"){let f=Mx(t);if(f.length===0)throw new Y(`Cannot find any save handlers for URL '${t}'`);if(f.length>1)throw new Y(`Found more than one (${f.length}) save handlers for URL '${t}'`);t=f[0]}if(t.save==null)throw new Y("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Ox(this.getNamedWeights(e)),o=!1,s=null,c=this.toJSON(s,o),l={modelTopology:c,format:lU,generatedBy:`TensorFlow.js tfjs-layers v${fm}`,convertedBy:null},p=e==null?!1:e.includeOptimizer;if(p&&this.optimizer!=null){l.trainingConfig=this.getTrainingConfig();let f="optimizer",{data:m,specs:y}=await Ox(await this.optimizer.getWeights(),f);r.specs.push(...y),r.data=Hf([r.data,m])}if(this.userDefinedMetadata!=null){let f=!0;uS(this.userDefinedMetadata,this.name,f),l.userDefinedMetadata=this.userDefinedMetadata}return l.weightData=r.data,l.weightSpecs=r.specs,t.save(l)}setUserDefinedMetadata(t){uS(t,this.name),this.userDefinedMetadata=t}getUserDefinedMetadata(){return this.userDefinedMetadata}}ws.className="Model",vt(ws);class NS extends ws{}NS.className="Functional",vt(NS);async function uU(n,t){"modelTopology"in n||(n={modelTopology:n}),n=n;let e=n.modelTopology;e.model_config!=null&&(e=e.model_config);let r=Pp(e),o=wo(r,t);if(n.weightsManifest!=null){let s=await JN(n.weightsManifest,n.pathPrefix,o.weights.map(l=>l.originalName)),c={};for(let l of o.weights)c[l.originalName]=s[l.originalName];o.loadWeights(c),Xt(s)}return o}async function pU(n,t){if(t==null&&(t={}),typeof n=="string"){let e=Bx(n,t);if(e.length===0)e.push(Xf(n,t));else if(e.length>1)throw new Y(`Found more than one (${e.length}) load handlers for URL '${n}'`);n=e[0]}return hU(n,void 0,t)}async function hU(n,t,e){if(e==null&&(e={}),n.load==null)throw new Y("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await n.load(),o=r.modelTopology;o.model_config!=null&&(o=o.model_config);let s=e.strict==null?!0:e.strict,c=r.weightData!=null&&r.weightSpecs!=null&&s,l=wo(Pp(o),t,c),p=r.trainingConfig;if(p!=null&&l.loadTrainingConfig(p),r.userDefinedMetadata!=null&&l.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new Y("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:f,optimizerWeights:m}=fU(r.weightData,r.weightSpecs);l.loadWeights(f,s),l.optimizer!=null&&m.length>0&&await l.optimizer.setWeights(m),Xt(f),Xt(m.map(y=>y.tensor))}return l}function fU(n,t){let e=qf(n,t),r={},o=[];return t.forEach(s=>{s.group==="optimizer"?o.push({name:s.name,tensor:e[s.name]}):r[s.name]=e[s.name]}),{modelWeights:r,optimizerWeights:o}}class ga extends ws{constructor(t){super({inputs:[],outputs:[]});if(t=t||{},this.trainable=!0,this.built=!1,this.name=t.name!=null?t.name:em("sequential_"),t.layers!=null)for(let e of t.layers)this.add(e)}checkShape(t){let e=t.inboundNodes[0].outputTensors[0].shape;if(e.some(r=>r<0))throw new Y(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let e=t instanceof ga||t instanceof ws,r;if(e){if(r=t,r.outputs.length!==1)throw new Y("All layers in a Sequential model should have a single output tensor. 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compiled before being used.");return this.model.evaluate(t,e,r)}async evaluateDataset(t,e){if(!this.built)throw new Jr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),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(t){this.model.optimizer=t}async fit(t,e,r={}){if(!this.built)throw new Jr("The model needs to be compiled before being used.");return this.model.fit(t,e,r)}async fitDataset(t,e){if(!this.built)throw new Jr("The model needs to be compiled before being used.");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,r={},o=!1){let s,c={};if(e instanceof Array){if(!(e[0].className!=null)||e[0].className==="Merge")throw new Y("Legacy serialization format not supported yet.");s=e}else _(e.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=e.layers,delete e.layers,c=e;let l=new t(c);if(!(l instanceof ga))throw new Ut(`Sequential.fromConfig called on non-Sequential input: ${l}`);for(let p of s){let f=void 0,m=wo(p,f,o);o&&m.setFastWeightInitDuringBuild(!0),l.add(m)}return l}set stopTraining(t){if(this.model==null)throw new Y("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new Y("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let r={};r.className=e.getClassName(),r.config=e.getConfig(),t.push(r)}return{name:this.name,layers:t}}}ga.className="Sequential",vt(ga);function dU(n){return new ws(n)}function mU(n){return new ga(n)}function gU(n,t){return t==null&&(t={}),pU(n,t)}function _S(n){return YC(n)}function yU(n,t){Lr.registerCallbackConstructor(n,t)}class kr extends Qi{getConfig(){return{}}}class CS extends kr{apply(t,e=1){return qV(t,e)}}CS.className="elu",vt(CS);class SS extends kr{apply(t){return Td(t)}}SS.className="selu",vt(SS);class $S extends kr{apply(t){return Vo(t)}}$S.className="relu",vt($S);class IS extends kr{apply(t){return rt(()=>oa(6,Vo(t)))}}IS.className="relu6",vt(IS);class ES extends kr{apply(t){return t}}ES.className="linear",vt(ES);class DS extends kr{apply(t){return Bo(t)}}DS.className="sigmoid",vt(DS);class AS extends kr{apply(t){return jV(t)}}AS.className="hardSigmoid",vt(AS);class FS extends kr{apply(t){return 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MS{constructor(t){super();Hv(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return rt(()=>{let e=xe([1]);return this.hasL1&&(e=Tt(e,zt(nt(this.l1,bn(t))))),this.hasL2&&(e=Tt(e,zt(nt(this.l2,Ap(t))))),e.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}}Mp.className="L1L2",vt(Mp);function bU(n){return Hv(n),new Mp({l1:n!=null?n.l1:null,l2:0})}function xU(n){return Hv(n),new Mp({l2:n!=null?n.l2:null,l1:0})}let BS={l1l2:"L1L2"};function Pe(n){return lv(n)}function zS(n,t={}){return Sp(n,Dr.getMap().classNameMap,t,"regularizer")}function Ke(n){if(n==null)return null;if(typeof n=="string"){let t=n in BS?BS[n]:n,e={className:t,config:{}};return zS(e)}else return n instanceof MS?n:zS(n)}class jv extends ye{constructor(t){super(t==null?{}:t);this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=Qt(t);let r=Vo(t);return 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Received: ${n.length} elements.`);for(let r=0;r<t;++r){let o=n[r];if(!BV(o))throw new Y(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(n)} including a non-integer number ${o}`)}return n}function To(n,t,e,r,o=1){if(n==null)return n;let s=t+(t-1)*(o-1),c;return e==="same"?c=n:c=n-s+1,Math.floor((c+r-1)/r)}function dm(n,t,e,r){if(n==null)return null;if(r==="valid")n=n*t+di([e-t,0]);else if(r==="same")n=n*t;else throw new Y(`Unsupport padding mode: ${r}.`);return n}function Qv(n,t){return rt(()=>(nn(t),t==="channelsFirst"?Kt(n,[0,2,3,1]):n))}function WS(n,t){return rt(()=>(nn(t),t==="channelsFirst"?Kt(n,[0,2,3,4,1]):n))}function VS(n,t,e,r=1,o="valid",s,c=1){return rt(()=>{if(s==null&&(s=go()),nn(s),n.shape.length!==3)throw new Y(`The input of a conv1dWithBias operation should be 3, but is ${n.shape.length} instead.`);if(t.shape.length!==3)throw new Y(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new Y(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(n=Kt(n,[0,2,1])),o==="causal")throw new Ut("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let l=cd(n,t,r,o==="same"?"same":"valid","NWC",c);return e!=null&&(l=qo(l,e)),l})}function ust(n,t,e=1,r="valid",o,s=1){return rt(()=>(nn(o),VS(n,t,null,e,r,o,s)))}function pst(n,t,e=[1,1],r="valid",o,s){return rt(()=>(nn(o),t0(n,t,null,e,r,o,s)))}function t0(n,t,e,r=[1,1],o="valid",s,c,l=null){return rt(()=>{if(s==null&&(s=go()),nn(s),n.rank!==3&&n.rank!==4)throw new Y(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${n.rank}.`);if(t.rank!==3&&t.rank!==4)throw new Y(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${n.rank}.`);let p=Qv(n,s);if(o==="causal")throw new Ut("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return p=qw({x:p,filter:t,strides:r,pad:o==="same"?"same":"valid",dilations:c,dataFormat:"NHWC",bias:e,activation:l}),s==="channelsFirst"&&(p=Kt(p,[0,3,1,2])),p})}function hst(n,t,e=[1,1,1],r="valid",o,s){return rt(()=>(nn(o),GS(n,t,null,e,r,o,s)))}function GS(n,t,e,r=[1,1,1],o="valid",s,c){return rt(()=>{if(s==null&&(s=go()),nn(s),n.rank!==4&&n.rank!==5)throw new Y(`conv3dWithBias expects input to be of rank 4 or 5, but received ${n.rank}.`);if(t.rank!==4&&t.rank!==5)throw new Y(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${n.rank}.`);let l=WS(n,s);if(o==="causal")throw new Ut("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return l=gw(l,t,r,o==="same"?"same":"valid","NDHWC",c),e!=null&&(l=qo(l,e)),s==="channelsFirst"&&(l=Kt(l,[0,4,1,2,3])),l})}class mm extends ye{constructor(t,e){super(e);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",mm.verifyArgs(e),this.rank=t,$n(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ut(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Hc(e.kernelSize,t,"kernelSize"),this.strides=Hc(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,Or(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,nn(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Tn(e.biasConstraint),this.biasRegularizer=Ke(e.biasRegularizer),this.activityRegularizer=Ke(e.activityRegularizer),this.dilationRate=Hc(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new Y(`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 Y(`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 Y(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(Pr("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!pv(t.kernelSize,"number",1,3))throw new Y(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:bi(this.activation),useBias:this.useBias,biasInitializer:rn(this.biasInitializer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),biasConstraint:vn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}class jc extends mm{constructor(t,e){super(t,e);this.kernel=null,jc.verifyArgs(e),this.filters=e.filters,$n(this.filters,"filters"),this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Tn(e.kernelConstraint),this.kernelRegularizer=Ke(e.kernelRegularizer)}build(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Y(`The channel dimension of the input should be defined. Found ${t[e]}`);let r=t[e],o=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",o,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:{[e]:r}}],this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r,o=this.bias==null?null:this.bias.read(),s=FC(this.activation.getClassName());if(s!=null&&this.rank===2)r=t0(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)r=VS(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=t0(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=GS(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ut("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(t){t=Re(t);let e=[],r=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<r.length;++s){let c=To(r[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(c)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:rn(this.kernelInitializer),kernelRegularizer:Pe(this.kernelRegularizer),kernelConstraint:vn(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new Y(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}}class Kc extends jc{constructor(t){super(2,t);Kc.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!pv(t.kernelSize,"number",1,2))throw new Y(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}}Kc.className="Conv2D",vt(Kc);class Bp extends jc{constructor(t){super(3,t);Bp.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new Y(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}}Bp.className="Conv3D",vt(Bp);class e0 extends Kc{constructor(t){super(t);if(this.inputSpec=[new In({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new Y(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Re(t),t.length!==4)throw new Y("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Y("The channel dimension of the inputs should be defined. Found `None`.");let r=t[e],o=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new In({ndim:4,axes:{[e]:r}})],this.built=!0}call(t,e){return rt(()=>{let r=Qt(t);if(r.shape.length!==4)throw new Y(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let o=r.shape,s=o[0],c,l;this.dataFormat==="channelsFirst"?(c=2,l=3):(c=1,l=2);let p=o[c],f=o[l],m=this.kernelSize[0],y=this.kernelSize[1],b=this.strides[0],v=this.strides[1],T=dm(p,b,m,this.padding),N=dm(f,v,y,this.padding),S=[s,T,N,this.filters];this.dataFormat!=="channelsLast"&&(r=Kt(r,[0,2,3,1]));let D=ld(r,this.kernel.read(),S,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(D=Kt(D,[0,3,1,2])),this.bias!=null&&(D=qo(D,this.bias.read(),this.dataFormat)),this.activation!=null&&(D=this.activation.apply(D)),D})}computeOutputShape(t){t=Re(t);let e=t.slice(),r,o,s;this.dataFormat==="channelsFirst"?(r=1,o=2,s=3):(r=3,o=1,s=2);let c=this.kernelSize[0],l=this.kernelSize[1],p=this.strides[0],f=this.strides[1];return e[r]=this.filters,e[o]=dm(e[o],p,c,this.padding),e[s]=dm(e[s],f,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}}e0.className="Conv2DTranspose",vt(e0);class US extends jc{constructor(t,e){super(t,e);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new Y("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new Y("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new Y(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ke(e.depthwiseRegularizer),this.depthwiseConstraint=Tn(e.depthwiseConstraint),this.pointwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ke(e.pointwiseRegularizer),this.pointwiseConstraint=Tn(e.pointwiseConstraint)}build(t){if(t=Re(t),t.length<this.rank+2)throw new Y(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new Y(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let r=t[e],o=this.kernelSize.concat([r,this.depthMultiplier]),s=[];for(let l=0;l<this.rank;++l)s.push(1);s.push(r*this.depthMultiplier,this.filters);let c=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,c,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,c,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,c,this.biasConstraint):this.bias=null,this.inputSpec=[new In({ndim:this.rank+2,axes:{[e]:r}})],this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r;if(this.rank===1)throw new Ut("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Kt(t,[0,2,3,1])),r=Rw(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=qo(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=Kt(r,[0,3,1,2])),r})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=rn(this.depthwiseInitializer),t.pointwiseInitializer=rn(this.pointwiseInitializer),t.depthwiseRegularizer=Pe(this.depthwiseRegularizer),t.pointwiseRegularizer=Pe(this.pointwiseRegularizer),t.depthwiseConstraint=vn(this.depthwiseConstraint),t.pointwiseConstraint=vn(this.pointwiseConstraint),t}}US.className="SeparableConv";class n0 extends US{constructor(t){super(2,t)}}n0.className="SeparableConv2D",vt(n0);class zp extends jc{constructor(t){super(1,t);zp.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!pv(t.kernelSize,"number",1,1))throw new Y(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}}zp.className="Conv1D",vt(zp);class r0 extends ye{constructor(t){super(t);typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return rt(()=>{if(t=Qt(t),this.dataFormat==="channelsLast"){let r=qd(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return qd(r,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=qd(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return qd(r,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}r0.className="Cropping2D",vt(r0);class o0 extends ye{constructor(t){super(t);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],r=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,r]}else{let e=t[1]==null?null:this.size[0]*t[1],r=t[2]==null?null:this.size[1]*t[2];return[t[0],e,r,t[3]]}}call(t,e){return rt(()=>{let r=Qt(t),o=r.shape;if(this.dataFormat==="channelsFirst"){r=Kt(r,[0,2,3,1]);let s=this.size[0]*o[2],c=this.size[1]*o[3],l=r.resizeNearestNeighbor([s,c]);return Kt(l,[0,3,1,2])}else{let s=this.size[0]*o[1],c=this.size[1]*o[2];return r.resizeNearestNeighbor([s,c])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}o0.className="UpSampling2D",vt(o0);function wU(n,t,e=[1,1],r="valid",o,s){return rt(()=>{o==null&&(o=go()),nn(o);let c=Qv(n,o);if(n.rank!==4)throw new Y(`Input for depthwiseConv2d is required to be 4-D, but is instead ${n.rank}-D`);if(t.rank!==4)throw new Y(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return c=na(c,t,e,r==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(c=Kt(c,[0,3,1,2])),c})}class s0 extends mm{constructor(t){super(2,t);this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=je(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Tn(t.depthwiseConstraint),this.depthwiseRegularizer=Ke(t.depthwiseRegularizer)}build(t){if(t=Re(t),t.length<4)throw new Y(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new Y(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let r=t[e],o=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r=wU(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=qo(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=To(e,this.kernelSize[0],this.padding,this.strides[0]),c=To(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,c]:[t[0],s,c,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=rn(this.depthwiseInitializer),t.depthwiseRegularizer=Pe(this.depthwiseRegularizer),t.depthwiseConstraint=vn(this.depthwiseRegularizer),t}}s0.className="DepthwiseConv2D",vt(s0);function qS(n,t,e,r){if(Array.isArray(n)){if(t!=null||e!=null)throw new Y("When inputs is an array, neither initialState or constants should be provided");r!=null&&(e=n.slice(n.length-r,n.length),n=n.slice(0,n.length-r)),n.length>1&&(t=n.slice(1,n.length)),n=n[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:n,initialState:t,constants:e}}function HS(n,t,e,r=!1,o,s,c=!1,l=!1){return rt(()=>{let p=t.shape.length;if(p<3)throw new Y(`Input should be at least 3D, but is ${p}D.`);let f=[1,0].concat(yo(2,p));if(t=Kt(t,f),s!=null)throw new Ut("The rnn() functoin of the deeplearn.js backend does not support constants yet.");c&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===p-1&&(o=cr(o,-1)),o=Kt(o,f)),r&&(t=Rr(t,0),o!=null&&(o=Rr(o,0)));let m=[],y,b=e,v=t.shape[0],T=mo(t),N;o!=null&&(N=mo(o));for(let D=0;D<v;++D){let I=T[D],P=rt(()=>n(I,b));if(o==null)y=P[0],b=P[1];else{let E=rt(()=>{let L=N[D],B=qn(L).sub(L),q=P[0].mul(L).add(b[0].mul(B)),H=b.map((Z,J)=>P[1][J].mul(L).add(Z.mul(B)));return{output:q,newStates:H}});y=E.output,b=E.newStates}l&&m.push(y)}let S;if(l){let D=1;S=ur(m,D)}return[y,S,b]})}class ko extends ye{constructor(t){super(t);let e;if(t.cell==null)throw new Y("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new bm({cells:t.cell}):e=t.cell,e.stateSize==null)throw new Y("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new In({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return yo(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){$v(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let r=e[0],o;if(this.returnSequences?o=[t[0],t[1],r]:o=[t[0],r],this.returnState){let s=[];for(let c of e)s.push([t[0],c]);return[o].concat(s)}else return o}computeMask(t,e){return rt(()=>{Array.isArray(e)&&(e=e[0]);let r=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[r].concat(o)}else return r})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let r=0;r<t;++r)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){let e=null;if(this.numConstants!=null)throw new Ut("Constants support is not implemented in RNN yet.");$v(t)&&(t=t[0]),t=t;let r=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new In({shape:[r,null,...o]});let s=[t[0]].concat(t.slice(2));if(e!=null)throw new Ut("Constants support is not implemented in RNN yet.");this.cell.build(s);let c;if(Array.isArray(this.cell.stateSize)?c=this.cell.stateSize:c=[this.cell.stateSize],this.stateSpec!=null){if(!lt(this.stateSpec.map(l=>l.shape[l.shape.length-1]),c))throw new Y(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=c.map(l=>new In({shape:[null,l]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){rt(()=>{if(!this.stateful)throw new Go("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new Y("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(o=>xe([r,o])):this.states_=[xe([r,this.cell.stateSize])];else if(t==null)Xt(this.states_),this.keptStates!=null&&(Xt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>xe([r,o])):this.states_[0]=xe([r,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Y(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Xt(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],c=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,l=[r,c];if(!lt(s.shape,l))throw new Y(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Sn(o.clone()))})}apply(t,e){let r=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=qS(t,r,o,this.numConstants);t=s.inputs,r=s.initialState,o=s.constants;let c=[],l=[];if(r!=null){e.initialState=r,c=c.concat(r),this.stateSpec=[];for(let f of r)this.stateSpec.push(new In({shape:f.shape}));l=l.concat(this.stateSpec)}o!=null&&(e.constants=o,c=c.concat(o),this.numConstants=o.length);let p=c[0]instanceof xo;if(p){let f=[t].concat(c),m=this.inputSpec.concat(l),y=this.inputSpec;this.inputSpec=m;let b=super.apply(f,e);return this.inputSpec=y,b}else return super.apply(t,e)}call(t,e){return rt(()=>{let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Qt(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let c=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==c)throw new Y(`RNN Layer has ${c} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let l={training:o},p=(T,N)=>{let S=this.cell.call([T].concat(N),l);return[S[0],S.slice(1)]},f=HS(p,t,s,this.goBackwards,r,null,this.unroll,this.returnSequences),m=f[0],y=f[1],b=f[2];this.stateful&&this.resetStates(b,o);let v=this.returnSequences?y:m;return this.returnState?[v].concat(b):v})}getInitialState(t){return rt(()=>{let e=xe(t.shape);return e=zt(e,[1,2]),e=Dp(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?xv(e,[1,r]):e):this.cell.stateSize>1?[xv(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===ko.className&&(e.cell={className:this.cell.getClassName(),config:r}),Object.assign({},r,t,e)}static fromConfig(t,e,r={}){let o=e.cell,s=wo(o,r);return new t(Object.assign(e,{cell:s}))}}ko.className="RNN",vt(ko);class Xc extends ye{}class gm extends Xc{constructor(t){super(t);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,$n(this.units,"units"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Vc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Vc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Re(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return rt(()=>{if(t=t,t.length!==2)throw new Y(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let r=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wi({ones:()=>qn(t),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wi({ones:()=>qn(r),rate:this.recurrentDropout,training:o}));let s,c=this.dropoutMask,l=this.recurrentDropoutMask;c!=null?s=Uo(nt(t,c),this.kernel.read()):s=Uo(t,this.kernel.read()),this.bias!=null&&(s=qo(s,this.bias.read())),l!=null&&(r=nt(r,l));let p=Tt(s,Uo(r,this.recurrentKernel.read()));return this.activation!=null&&(p=this.activation.apply(p)),[p,p]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:bi(this.activation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},t,e)}}gm.className="SimpleRNNCell",vt(gm);class i0 extends ko{constructor(t){t.cell=new gm(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}}i0.className="SimpleRNN",vt(i0);class ym extends Xc{constructor(t){super(t);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",t.resetAfter)throw new Y("GRUCell does not support reset_after parameter set to true.");this.units=t.units,$n(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Vc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Vc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Re(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return rt(()=>{if(t=t,t.length!==2)throw new Y(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wi({ones:()=>qn(t),rate:this.dropout,training:r,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wi({ones:()=>qn(o),rate:this.recurrentDropout,training:r,count:3}));let s=this.dropoutMask,c=this.recurrentDropoutMask,l,p,f;0<this.dropout&&this.dropout<1&&(t=nt(t,s[0]));let m=Uo(t,this.kernel.read());this.useBias&&(m=qo(m,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=nt(o,c[0]));let y=this.recurrentKernel.read(),[b,v]=Tr(y,[2*this.units,this.units],y.rank-1),T=Uo(o,b),[N,S,D]=Tr(m,3,m.rank-1),[I,P]=Tr(T,2,T.rank-1);l=this.recurrentActivation.apply(Tt(N,I)),p=this.recurrentActivation.apply(Tt(S,P));let E=Uo(nt(p,o),v);f=this.activation.apply(Tt(D,E));let L=Tt(nt(l,o),nt(Tt(1,tn(l)),f));return[L,L]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:bi(this.activation),recurrentActivation:bi(this.recurrentActivation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},t,e)}}ym.className="GRUCell",vt(ym);class a0 extends ko{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new ym(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}a0.className="GRU",vt(a0);class Wp extends Xc{constructor(t){super(t);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=t.units,$n(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Vc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Vc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Re(t);let r=t[t.length-1];this.kernel=this.addWeight("kernel",[r,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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,c=this.units;o=new(e=class extends Qr{apply(p,f){let m=s.apply([c]),y=new jd().apply([c]),b=s.apply([c*2]);return VC(VC(m,y),b)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return rt(()=>{let r=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new Y(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wi({ones:()=>qn(t),rate:this.dropout,training:r,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wi({ones:()=>qn(o),rate:this.recurrentDropout,training:r,count:4}));let c=this.dropoutMask,l=this.recurrentDropoutMask,p,f,m,y;0<this.dropout&&this.dropout<1&&(t=nt(t,c[0]));let b=Uo(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=nt(o,l[0])),b=Tt(b,Uo(o,this.recurrentKernel.read())),this.useBias&&(b=qo(b,this.bias.read()));let[v,T,N,S]=Tr(b,4,b.rank-1);p=this.recurrentActivation.apply(v),f=this.recurrentActivation.apply(T),m=Tt(nt(f,s),nt(p,this.activation.apply(N))),y=this.recurrentActivation.apply(S);let D=nt(y,this.activation.apply(m));return[D,D,m]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:bi(this.activation),recurrentActivation:bi(this.recurrentActivation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},t,e)}}Wp.className="LSTMCell",vt(Wp);class c0 extends ko{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Wp(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}c0.className="LSTM",vt(c0);class bm extends Xc{constructor(t){super(t);this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return rt(()=>{t=t;let r=t.slice(1),o=[];for(let l of this.cells.slice().reverse())Array.isArray(l.stateSize)?o.push(r.splice(0,l.stateSize.length)):o.push(r.splice(0,1));o.reverse();let s=[],c;for(let l=0;l<this.cells.length;++l){let p=this.cells[l];r=o[l],l===0?c=[t[0]].concat(r):c=[c[0]].concat(r),c=p.call(c,e),s.push(c.slice(1))}r=[];for(let l of s.slice().reverse())r.push(...l);return[c[0]].concat(r)})}build(t){$v(t)&&(t=t[0]),t=t;let e;this.cells.forEach((r,o)=>{fa(`RNNCell_${o}`,()=>{r.build(t),Array.isArray(r.stateSize)?e=r.stateSize[0]:e=r.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),r=this.cells.map(e),o={cells:r};return Object.assign({},t,o)}static fromConfig(t,e,r={}){let o=[];for(let s of e.cells)o.push(wo(s,r));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let r of this.cells)e.push(...r.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Iv(t)}setWeights(t){let e=[];for(let r of this.cells){let o=r.weights.length,s=t.splice(o);for(let c=0;c<r.weights.length;++c)e.push([r.weights[c],s[c]])}Ev(e)}}bm.className="StackedRNNCells",vt(bm);function wi(n){let{ones:t,rate:e,training:r=!1,count:o=1}=n,s=()=>UC(t(),e),c=()=>Fp(s,t,r);if(!o||o<=1)return Sn(c().clone());let l=Array(o).fill(void 0).map(c);return l.map(p=>Sn(p.clone()))}var vU=function(n,t){var e={};for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&t.indexOf(r)<0&&(e[r]=n[r]);if(n!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,r=Object.getOwnPropertySymbols(n);o<r.length;o++)t.indexOf(r[o])<0&&Object.prototype.propertyIsEnumerable.call(n,r[o])&&(e[r[o]]=n[r[o]]);return e};class fst extends Xc{}class jS extends ko{constructor(t){if(t.unroll)throw new Ut("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new Ut("It is not possible at the moment to stack convolutional cells.");super(t);this.inputSpec=[new In({ndim:5})]}call(t,e){return rt(()=>{if(this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new Y("ConvRNN2D cell does not support constants");let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return rt(()=>{let{stateSize:e}=this.cell,r=t.shape,o=this.computeSingleOutputShape(r),s=[o[0],...o.slice(2)],c=xe(s);return Array.isArray(e)?Array(e.length).fill(c):[c]})}resetStates(t,e=!1){rt(()=>{if(!this.stateful)throw new Go("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,o=this.computeSingleOutputShape(r),s=[o[0],...o.slice(2)],c=r[0];if(c==null)throw new Y("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(()=>xe(s)):this.states_=[xe(s)];else if(t==null)Xt(this.states_),this.keptStates!=null&&(Xt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xe(s)):this.states_[0]=xe(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Y(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Xt(this.states_);for(let l=0;l<this.states_.length;++l){let p=t[l],f=s;if(!lt(p.shape,f))throw new Y(`State ${l} is incompatible with layer ${this.name}: expected shape=${f}, received shape=${p.shape}`);this.states_[l]=p}}this.states_=this.states_.map(l=>Sn(l.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:r,kernelSize:o,padding:s,strides:c,dilationRate:l}=this.cell,p=e==="channelsFirst",f=t[p?3:2],m=t[p?4:3],y=To(f,o[0],s,c[0],l[0]),b=To(m,o[1],s,c[1],l[1]),v=[...t.slice(0,2),...p?[r,y,b]:[y,b,r]];return v}}jS.className="ConvRNN2D";class xm extends Wp{constructor(t){let{filters:e,kernelSize:r,strides:o,padding:s,dataFormat:c,dilationRate:l}=t;super(Object.assign({},t,{units:e}));this.filters=e,$n(this.filters,"filters"),this.kernelSize=Hc(r,2,"kernelSize"),this.kernelSize.forEach(p=>$n(p,"kernelSize")),this.strides=Hc(o||1,2,"strides"),this.strides.forEach(p=>$n(p,"strides")),this.padding=s||"valid",Or(this.padding),this.dataFormat=c||"channelsLast",nn(this.dataFormat),this.dilationRate=Hc(l||1,2,"dilationRate"),this.dilationRate.forEach(p=>$n(p,"dilationRate"))}build(t){var e;t=Re(t);let r=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[r]==null)throw new Y(`The channel dimension of the input should be defined. Found ${t[r]}`);let o=t[r],s=4,c=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",c,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let l=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",l,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let p;if(this.unitForgetBias){let f=this.biasInitializer,m=this.filters;p=new(e=class extends Qr{apply(b,v){let T=f.apply([m]),N=ho([m]),S=f.apply([m*2]);return bv([T,N,S])}},e.className="CustomInit",e)}else p=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,p,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return rt(()=>{if(t.length!==3)throw new Y(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training||!1,o=t[0],s=t[1],c=t[2],l=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wi({ones:()=>qn(o),rate:this.dropout,training:r,count:l}));let p=this.dropoutMask,f=(kt,Nt,At)=>!Nt||!Nt[At]?kt:nt(Nt[At],kt),m=f(o,p,0),y=f(o,p,1),b=f(o,p,2),v=f(o,p,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wi({ones:()=>qn(s),rate:this.recurrentDropout,training:r,count:l}));let T=this.recurrentDropoutMask,N=f(s,T,0),S=f(s,T,1),D=f(s,T,2),I=f(s,T,3),P=3,[E,L,B,q]=Tr(this.kernel.read(),l,P),[H,Z,J,it]=this.useBias?Tr(this.bias.read(),l):[null,null,null,null];m=this.inputConv(m,E,H,this.padding),y=this.inputConv(y,L,Z,this.padding),b=this.inputConv(b,B,J,this.padding),v=this.inputConv(v,q,it,this.padding);let[pt,ht,dt,ft]=Tr(this.recurrentKernel.read(),l,P);N=this.recurrentConv(N,pt),S=this.recurrentConv(S,ht),D=this.recurrentConv(D,dt),I=this.recurrentConv(I,ft);let ut=this.recurrentActivation.apply(Tt(m,N)),bt=this.recurrentActivation.apply(Tt(y,S)),yt=Tt(nt(bt,c),nt(ut,this.activation.apply(Tt(b,D)))),xt=nt(this.recurrentActivation.apply(Tt(v,I)),this.activation.apply(yt));return[xt,xt,yt]})}getConfig(){let t=super.getConfig(),{units:e}=t,r=vU(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},r,o)}inputConv(t,e,r,o){let s=fs(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?qo(s,r,this.dataFormat):s}recurrentConv(t,e){let r=1;return fs(t,e,r,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}xm.className="ConvLSTM2DCell",vt(xm);class l0 extends jS{constructor(t){let e=new xm(t);super(Object.assign({},t,{cell:e}))}static fromConfig(t,e){return new t(e)}}l0.className="ConvLSTM2D",vt(l0);class wm extends ye{constructor(t){super(t);this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,r=[];for(let o=0;o<this.noiseShape.length;++o)r.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return r}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(r),c=Fp(()=>UC(r,this.rate,s,this.seed),()=>r,o);return c}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}}wm.className="Dropout",vt(wm);class u0 extends wm{constructor(t){super(t);this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}}u0.className="SpatialDropout1D",vt(u0);class p0 extends ye{constructor(t){super(t);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,$n(this.units,"units"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Tn(t.kernelConstraint),this.biasConstraint=Tn(t.biasConstraint),this.kernelRegularizer=Ke(t.kernelRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.activityRegularizer=Ke(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Re(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Re(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=FC(this.activation.getClassName()),s;return o!=null?s=Uo(r,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Uo(r,this.kernel.read()),this.bias!=null&&(s=qo(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:bi(this.activation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),biasConstraint:vn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}p0.className="Dense",vt(p0);class h0 extends ye{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Re(t);for(let e of t.slice(1))if(e==null)throw new Y(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],fi(t,1)]}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(this.dataFormat==="channelsFirst"&&r.rank>1){let o=[0];for(let s=2;s<r.rank;++s)o.push(s);o.push(1),r=r.transpose(o)}return UV(r)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}}h0.className="Flatten",vt(h0);class f0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.activation=xi(t.activation)}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);return this.activation.apply(r)})}getConfig(){let t={activation:bi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}}f0.className="Activation",vt(f0);class d0 extends ye{constructor(t){super(t);this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return rt(()=>(t=Qt(t),VV(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}}d0.className="RepeatVector",vt(d0);class m0 extends ye{constructor(t){super(t);this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let r="Total size of new array must be unchanged.",o=e.slice(),s=1,c=null;for(let p=0;p<o.length;++p){let f=o[p];if(this.isUnknown(f))if(c===null)c=p;else throw new Y("Can only specifiy one unknown dimension.");else s*=f}let l=fi(t);if(c!==null){if(s===0||l%s!==0)throw new Y(r);o[c]=l/s}else if(l!==s)throw new Y(r);return o}computeOutputShape(t){let e=!1;for(let r=0;r<t.length;++r)if(this.isUnknown(t[r])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=r.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return r.reshape(s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}}m0.className="Reshape",vt(m0);class g0 extends ye{constructor(t){super(t);if(t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=yo(1,t.dims.length+1);if(!lt(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new In({ndim:this.dims.length+1})]}computeOutputShape(t){t=Re(t);let e=t.slice();return this.dims.forEach((r,o)=>{e[o+1]=t[r]}),e}call(t,e){return Kt(Qt(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}}g0.className="Permute",vt(g0);class y0 extends ye{constructor(t){super(t==null?{}:t);this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let r=Qt(t),o=-1;return Zu(ci(r,this.maskValue),o)}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=-1,s=!0,c=Zu(ci(r,this.maskValue),o,s),l=r.mul(c.asType(r.dtype));return l})}}y0.className="Masking",vt(y0);class b0 extends ye{constructor(t){super(t);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ze(t.inputLength))}this.inputDim=t.inputDim,$n(this.inputDim,"inputDim"),this.outputDim=t.outputDim,$n(this.outputDim,"outputDim"),this.embeddingsInitializer=je(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ke(t.embeddingsRegularizer),this.activityRegularizer=Ke(t.activityRegularizer),this.embeddingsConstraint=Tn(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return rt(()=>this.maskZero?(t=Qt(t),ci(t,re(t))):null)}computeOutputShape(t){if(t=Re(t),this.inputLength==null)return[...t,this.outputDim];let e=ze(this.inputLength);if(e.length!==t.length-1)throw new Y(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let r=0;for(let o=0;o<e.length;++o){let s=e[o],c=t[o+1];if(s!=null&&c!=null&&s!==c)throw new Y(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);s==null&&(e[r]=c),r++}}return[t[0],...e,this.outputDim]}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);r.dtype!=="int32"&&(r=Ep(r,"int32"));let o=GC(this.embeddings.read(),r.as1D());return o.reshape(Re(this.computeOutputShape(r.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:rn(this.embeddingsInitializer),embeddingsRegularizer:Pe(this.embeddingsRegularizer),activityRegularizer:Pe(this.activityRegularizer),embeddingsConstraint:vn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}}b0.className="Embedding",vt(b0);class ya extends ye{constructor(t){super(t||{});this.supportsMasking=!0}mergeFunction(t){throw new Ut}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let r=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],c=e[o];if(s==null||c==null||s<0||c<0)r.push(null);else if(s===1)r.push(c);else if(c===1)r.push(s);else{if(s!==c)throw new Y("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));r.push(s)}}return r}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Re(t)]),t=t,t.length<2)throw new Y(`A merge layer should be called on an Array of at least 2 inputs. Got ${t.length} input(s).`);let e=[];for(let s of t)s!=null&&s[0]!==null&&e.push(s[0]);if(e=hi(e),e.length>1)throw new Y(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let r=t[0]==null?null:t[0].slice(1);for(let s=1;s<t.length;++s){let c=t[s]==null?null:t[s].slice(1);r=this.computeElementwiseOpOutputShape(r,c)}let o=t.map(s=>s.length);t.indexOf(null)===-1&&hi(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return rt(()=>{if(t=t,this.reshapeRequired){let r=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=di(o);for(let c of t){let l=c.rank;for(let p=0;p<s-l;++p)c=Dp(c,1);r.push(c)}return this.mergeFunction(r)}else{let s=!1;for(let p of t){let f=p.rank;if(f==null){let m=p.shape,y=m[0],b=m.slice(1).concat([y]),v=p.reshape([y].concat(fi(m.slice(1))));v=Kt(v,[1,0]),v=v.reshape(b),r.push(v),s=!0}else if(f>1){let m=yo(1,f).concat([0]);r.push(Kt(p,m)),s=!0}else r.push(p)}let c=this.mergeFunction(r),l=c.rank;if(s){if(l==null){let p=c.shape,f=p.length,m=p[f-1],y=[m].concat(p.slice(0,p.length-1));c=Kt(c.reshape([-1,m]),[1,0]).reshape(y)}else if(l>1){let p=[l-1].concat(yo(0,l-1));c=Kt(c,p)}}return c}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o<t.length;++o){let s=t[o]==null?null:t[o].slice(1);e=this.computeElementwiseOpOutputShape(e,s)}let r=[];for(let o of t)o!=null&&o[0]!==null&&r.push(o[0]);return r=hi(r),r.length===1?e=r.concat(e):e=[null].concat(e),e}computeMask(t,e){return rt(()=>{if(e==null)return null;if(!Array.isArray(e))throw new Y("`mask` should be an Array");if(!Array.isArray(t))throw new Y("`inputs` should be an Array");if(e.length!==t.length)throw new Y(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:cr(o,0));let r=e[0];for(let o=1;o<e.length-1;++o)r=Yr(r,e[o]);return r})}}class Vp extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Tt(e,t[r]);return e})}}Vp.className="Add",vt(Vp);function dst(n){if(Array.isArray(n)){let t=new Vp({});return t.apply(n)}else return new Vp(n)}class Gp extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=nt(e,t[r]);return e})}}Gp.className="Multiply",vt(Gp);function mst(n){if(Array.isArray(n)){let t=new Gp({});return t.apply(n)}else return new Gp(n)}class Up extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Tt(e,t[r]);return nt(1/t.length,e)})}}Up.className="Average",vt(Up);function gst(n){if(Array.isArray(n)){let t=new Up({});return t.apply(n)}else return new Up(n)}class qp extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=Xr(e,t[r]);return e})}}qp.className="Maximum",vt(qp);function yst(n){if(Array.isArray(n)){let t=new qp({});return t.apply(n)}else return new qp(n)}class Hp extends ya{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=oa(e,t[r]);return e})}}Hp.className="Minimum",vt(Hp);function bst(n){if(Array.isArray(n)){let t=new Hp({});return t.apply(n)}else return new Hp(n)}class jp extends ya{constructor(t){super(t);this.DEFAULT_AXIS=-1,t==null&&(t={}),this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){if(!(Array.isArray(t)&&Array.isArray(t[0]))||t.length===1)throw new Y("A `Concatenate` layer should be called on a list of at least 2 inputs");t=t;let e=!0;for(let o of t)if(o!=null){e=!1;break}if(e)return;let r=[];for(let o=0;o<t.length;++o){let s=t[o].slice();s.splice(this.axis,1);let c=!1;for(let l of r)if(lt(l,s)){c=!0;break}c||r.push(s)}if(r.length>1)throw new Y("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return rt(()=>bv(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new Y("A `Concatenate` layer should be called on a list of inputs.");let e=t,r=e[0].slice(),o=this.axis<0?r.length+this.axis:this.axis;for(let s of e.slice(1)){if(r[o]==null||s[o]==null){r[o]=null;break}r[o]+=s[o]}return r}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new Y("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new Y("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new Y(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return rt(()=>{let r=!0;if(e.forEach(c=>{if(c!=null){r=!1;return}}),r)return null;let o=[];for(let c=0;c<t.length;++c)e[c]==null?o.push(qn(t[c]).asType("bool")):e[c].rank<t[c].rank?o.push(cr(e[c],-1)):o.push(e[c]);let s=Qe(o,this.axis);return rd(s,-1,!1)})}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}}jp.className="Concatenate",vt(jp);function xst(n){if(Array.isArray(n)){let t=new jp({});return t.apply(n)}else return new jp(n)}function Kp(n,t){for(;n<0;)n+=t;return n}function TU(n,t,e){if(n.shape.length>3||t.shape.length>3)throw new Ut("batchDot is not implemented for tensors of 4D or higher rank yet");if(_(n.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${n.shape.length}`),_(n.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),n.dtype==="complex64"||t.dtype==="complex64")throw new Ut("batchDot is not implemented for complex64-type Tensors yet.");let r=n.shape.length,o=t.shape.length;e==null&&(e=[r-1,o-2]);let s=e;return rt(()=>{let c;if(r>o){c=r-o;let p=[];for(let f=0;f<c;++f)p.push(1);t=t.reshape(t.shape.concat(p))}else if(o>r){c=o-r;let p=[];for(let f=0;f<c;++f)p.push(1);n=n.reshape(n.shape.concat(p))}else c=0;let l;if(n.shape.length===2&&t.shape.length===2)s[0]===s[1]?l=n.mul(t).sum(s[0]):l=n.transpose([1,0]).mul(t).sum(s[1]);else{let p=s[0]!==n.shape.length-1,f=s[1]===t.shape.length-1;l=n.matMul(t,p,f)}if(c>0){let p;r>o?p=r+o-3:p=r-1;let f=[];for(let m=p;m<p+c;++m)f.push(m);l=l.squeeze(f)}return l.shape.length===1&&(l=l.expandDims(1)),l})}class x0 extends ya{constructor(t){super(t);this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){_(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],r=t[1];if(e.length>3||r.length>3)throw new Ut("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,r);if(e[o[0]]!==r[o[1]])throw new Y(`Dimension incompatibility: ${e[o[0]]} !== ${r[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new Y(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],r=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,c)=>Kp(s,t[c].shape.length)):o=[Kp(this.axes,e.shape.length),Kp(this.axes,r.shape.length)],this.normalize&&(e=sm(e,o[0]),r=sm(r,o[1])),TU(e,r,o)}interpretAxes(t,e){let r;return Array.isArray(this.axes)?r=this.axes:r=[Kp(this.axes,t.length),Kp(this.axes,e.length)],r}computeOutputShape(t){_(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),r=t[1].slice();if(e.length>3||r.length>3)throw new Ut("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,r);e.splice(o[0],1),r.splice(o[1],1),r.splice(0,1);let s=e.concat(r);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}}x0.className="Dot",vt(x0);class w0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=()=>Hd(r.shape,0,this.stddev).add(r),s=Fp(o,()=>r,e.training||!1);return s})}}w0.className="GaussianNoise",vt(w0);class v0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(this.rate>0&&this.rate<1){let o=()=>{let s=Math.sqrt(this.rate/(1-this.rate));return r.mul(Hd(r.shape,1,s))};return Fp(o,()=>r,e.training||!1)}return r})}}v0.className="GaussianDropout",vt(v0);class T0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||Qt(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return rt(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(t),o=()=>{let s=Qt(t),c=1.6732632423543772,l=1.0507009873554805,p=-c*l,f=ds(aa(r),this.rate);f=Ep(f,"float32");let m=((1-this.rate)*(1+this.rate*p**2))**-.5,y=-m*p*this.rate,b=s.mul(f).add(f.add(-1).mul(p));return b.mul(m).add(y)};return Fp(o,()=>Qt(t),e.training||!1)}return t})}}T0.className="AlphaDropout",vt(T0);function Xp(n,t,e,r,o,s=.001){let c;if(n.rank===2)c=b_(n,t,e,r,o,s);else if(n.rank===3)c=x_(n,t,e,r,o,s);else if(n.rank===4)c=w_(n,t,e,r,o,s);else throw new Ut(`batchNormalization is not implemented for array of rank ${n.rank} yet`);return c}function kU(n,t,e,r,o=.001){return rt(()=>{let s=xd(n,r),c=s.mean,l=s.variance,p=Xp(n,c,l,e,t,o);return[p,c,l]})}function NU(n,t,e,r,o=.001){return rt(()=>{let s=xd(n,r),c=s.mean,l=s.variance,p=[];for(let T of yo(0,n.rank))r.indexOf(T)!==-1?p.push(1):p.push(n.shape[T]);let f=c.reshape(p),m=l.reshape(p),y=t==null?null:t.reshape(p),b=e==null?null:e.reshape(p),v=Xp(n,f,m,b,y,o);return[v,c,l]})}function _U(n,t,e,r,o=.001){return lt(r.slice().sort(),yo(0,n.rank-1))?kU(n,t,e,r,o):NU(n,t,e,r,o)}class k0 extends ye{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=je(t.betaInitializer||"zeros"),this.gammaInitializer=je(t.gammaInitializer||"ones"),this.movingMeanInitializer=je(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=je(t.movingVarianceInitializer||"ones"),this.betaConstraint=Tn(t.betaConstraint),this.gammaConstraint=Tn(t.gammaConstraint),this.betaRegularizer=Ke(t.betaRegularizer),this.gammaRegularizer=Ke(t.gammaRegularizer)}build(t){t=Re(t);let e=this.axis>=0?this.axis:this.axis+t.length,r=t[e];if(r==null)throw new Y(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new In({ndim:t.length,axes:{[e]:r}})];let o=[r];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return rt(()=>{let r=e.training==null?!1:e.training,o=Qt(t),s=o.shape,c=s.length,l=yo(0,c),p=this.axis>=0?this.axis:this.axis+c;l.splice(p,1);let f=pa(1,c);f[p]=s[p];let m=l.slice();m.sort();let y=!lt(m,yo(0,c).slice(0,c-1)),b=()=>{if(y){let I=this.movingMean.read().reshape(f),P=this.movingVariance.read().reshape(f),E=this.center?this.beta.read().reshape(f):null,L=this.scale?this.gamma.read().reshape(f):null;return Xp(o,I,P,E,L,this.epsilon)}else return Xp(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return b();let[v,T,N]=_U(o,this.gamma.read(),this.beta.read(),l,this.epsilon),S=(I,P,E)=>{rt(()=>{let L=1-E,B=I.read(),q=B.sub(P).mul(L);I.write(B.sub(q))})},D=()=>{S(this.movingMean,T,this.momentum),S(this.movingVariance,N,this.momentum)};return D(),v})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:rn(this.betaInitializer),gammaInitializer:rn(this.gammaInitializer),movingMeanInitializer:rn(this.movingMeanInitializer),movingVarianceInitializer:rn(this.movingVarianceInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer),betaConstraint:vn(this.betaConstraint),gammaConstraint:vn(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}}k0.className="BatchNormalization",vt(k0);class N0 extends ye{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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(let e of this.axis)if(!Number.isInteger(e))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=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=je(t.betaInitializer||"zeros"),this.gammaInitializer=je(t.gammaInitializer||"ones"),this.betaRegularizer=Ke(t.betaRegularizer),this.gammaRegularizer=Ke(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Re(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=e);for(let s of this.axis)if(s<0||s>=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==hi(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let r=Qt(t),o=r.shape,s=o.length;return rt(()=>{let c=!0,{mean:l,variance:p}=xd(r,this.axis,c),f=pa(1,s);for(let N of this.axis)f[N]=o[N];let m=N=>N!=null&&N.shape.length!==s&&this.axis!==[s-1]?N.reshape(f):N,y=m(this.gamma.read()),b=m(this.beta.read()),v=[],T=[];for(let N=0;N<s;++N)this.axis.indexOf(N)!==-1?(v.push(o[N]),T.push(1)):(v.push(1),T.push(o[N]));return l=l.tile(v),p=p.tile(v),y=y.tile(T),b=b.tile(T),Xp(r,l,p,b,y,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:rn(this.betaInitializer),gammaInitializer:rn(this.gammaInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}}N0.className="LayerNormalization",vt(N0);function wst(n,t){return rt(()=>{if(n.rank!==3)throw new Y(`temporalPadding expects input tensor to be 3-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new Y(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);let e=[[0,0],t,[0,0]];return Wo(n,e)})}function CU(n,t,e){return rt(()=>{if(n.rank!==4)throw new Y(`temporalPadding expects input tensor to be 4-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new Y("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=go()),e!=="channelsLast"&&e!=="channelsFirst")throw new Y(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return e==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Wo(n,r)})}class _0 extends ye{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?go():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding=="number")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new Y(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,r;if(typeof t.padding[0]=="number")e=[t.padding[0],t.padding[0]],r=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new Y(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new Y(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);r=t.padding[1]}this.padding=[e,r]}this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){t=Re(t);let e,r;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?r=t[3]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],t[1],e,r]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?r=t[2]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],e,r,t[3]])}call(t,e){return rt(()=>CU(Qt(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}_0.className="ZeroPadding2D",vt(_0);function vm(n,t,e,r,o,s){return rt(()=>{nn(o),OC(s),Or(r),e==null&&(e=[1,1]),r==null&&(r="valid"),o==null&&(o=go()),s==null&&(s="max"),n=Qv(n,o);let c,l=r==="same"?"same":"valid";return s==="max"?c=lp(n,t,e,l):c=ep(n,t,e,l),o==="channelsFirst"&&(c=Kt(c,[0,3,1,2])),c})}function KS(n,t,e,r,o,s){return rt(()=>{nn(o),OC(s),Or(r),e==null&&(e=[1,1,1]),r==null&&(r="valid"),o==null&&(o=go()),s==null&&(s="max"),n=WS(n,o);let c,l=r==="same"?"same":"valid";return s==="max"?c=Cw(n,t,e,l):c=fw(n,t,e,l),o==="channelsFirst"&&(c=Kt(c,[0,4,1,2,3])),c})}class XS extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new Y(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if($n(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new Y(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,Or(this.padding),this.inputSpec=[new In({ndim:3})]}computeOutputShape(t){t=Re(t);let e=To(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return rt(()=>{this.invokeCallHook(t,e),t=Dp(Qt(t),2);let r=this.poolingFunction(Qt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return li(r,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}}class C0 extends XS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),vm(t,e,r,o,s,"max")}}C0.className="MaxPooling1D",vt(C0);class S0 extends XS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),vm(t,e,r,o,s,"avg")}}S0.className="AveragePooling1D",vt(S0);class YS extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new Y(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];$n(this.poolSize,"poolSize"),$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),Or(this.padding),this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=To(e,this.poolSize[0],this.padding,this.strides[0]),r=To(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r]:[t[0],e,r,t[3]]}call(t,e){return rt(()=>(this.invokeCallHook(t,e),this.poolingFunction(Qt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class $0 extends YS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),vm(t,e,r,o,s,"max")}}$0.className="MaxPooling2D",vt($0);class I0 extends YS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),vm(t,e,r,o,s,"avg")}}I0.className="AveragePooling2D",vt(I0);class JS extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new Y(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];$n(this.poolSize,"poolSize"),$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),Or(this.padding),this.inputSpec=[new In({ndim:5})]}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=To(e,this.poolSize[0],this.padding,this.strides[0]),r=To(r,this.poolSize[1],this.padding,this.strides[1]),o=To(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r,o]:[t[0],e,r,o,t[4]]}call(t,e){return rt(()=>(this.invokeCallHook(t,e),this.poolingFunction(Qt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class E0 extends JS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),KS(t,e,r,o,s,"max")}}E0.className="MaxPooling3D",vt(E0);class D0 extends JS{constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Or(o),KS(t,e,r,o,s,"avg")}}D0.className="AveragePooling3D",vt(D0);class ZS extends ye{constructor(t){super(t);this.inputSpec=[new In({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Ut}}class A0 extends ZS{constructor(t){super(t||{})}call(t,e){return rt(()=>{let r=Qt(t);return en(r,1)})}}A0.className="GlobalAveragePooling1D",vt(A0);class F0 extends ZS{constructor(t){super(t||{})}call(t,e){return rt(()=>{let r=Qt(t);return lr(r,1)})}}F0.className="GlobalMaxPooling1D",vt(F0);class QS extends ye{constructor(t){super(t);this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Ut}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class R0 extends QS{call(t,e){return rt(()=>{let r=Qt(t);return this.dataFormat==="channelsLast"?en(r,[1,2]):en(r,[2,3])})}}R0.className="GlobalAveragePooling2D",vt(R0);class P0 extends QS{call(t,e){return rt(()=>{let r=Qt(t);return this.dataFormat==="channelsLast"?lr(r,[1,2]):lr(r,[2,3])})}}P0.className="GlobalMaxPooling2D",vt(P0);class t$ extends ye{constructor(t){super(t);this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,r={}){let o=e.layer,s=wo(o,r);delete e.layer;let c={layer:s};return Object.assign(c,e),new t(c)}}class O0 extends t${constructor(t){super(t);this.supportsMasking=!0}build(t){if(t=Re(t),t.length<3)throw new Y(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Re(t);let e=[t[0]].concat(t.slice(2)),r=this.layer.computeOutputShape(e),o=t[1];return[r[0],o].concat(r.slice(1))}call(t,e){return rt(()=>{t=Qt(t);let r=(c,l)=>{let p=Qt(this.layer.call(c,e));return[p,[]]},o=HS(r,t,[],!1,null,null,!1,!0),s=o[1];return s})}}O0.className="TimeDistributed",vt(O0);function SU(n){zc(OV,"BidirectionalMergeMode",n)}let $U="concat";class L0 extends t${constructor(t){super(t);let e=t.layer.getConfig(),r={};r.className=t.layer.getClassName(),r.config=e,this.forwardLayer=wo(r),e.goBackwards=!(e.goBackwards===!0);let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=wo(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?$U:t.mergeMode,SU(this.mergeMode),t.weights)throw new Ut("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,r=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,r)),this.backwardLayer.setWeights(t.slice(r))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let r,o,s;return this.returnState&&(s=e.slice(1)),r=e[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,o=[r]):this.mergeMode==null?o=[r,r.slice()]:o=[r],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[r].concat(s).concat(s.slice()):pr(o)}apply(t,e){let r=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=qS(t,r,o,this.numConstants);if(t=s.inputs,r=s.initialState,o=s.constants,Array.isArray(t)&&(r=t.slice(1),t=t[0]),(r==null||r.length===0)&&o==null)return super.apply(t,e);let c=[],l=[];if(r!=null){let f=r.length;if(f%2>0)throw new Y("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=r,c.push(...r);let m=r.map(y=>new In({shape:y.shape}));this.forwardLayer.stateSpec=m.slice(0,f/2),this.backwardLayer.stateSpec=m.slice(f/2),l.push(...m)}if(o!=null)throw new Ut("Support for constants in Bidirectional layers is not implemented yet.");let p=c[0]instanceof xo;for(let f of c)if(f instanceof xo!==p)throw new Y("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(p){let f=[t].concat(c),m=this.inputSpec.concat(l),y=this.inputSpec;this.inputSpec=m;let b=super.apply(f,e);return this.inputSpec=y,b}else return super.apply(t,e)}call(t,e){return rt(()=>{let r=e.initialState,o,s;if(r==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let p=r.slice(0,r.length/2),f=r.slice(r.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:p})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:f}))}let c;this.returnState&&(Array.isArray(o)&&(c=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=Rr(s,1));let l;return this.mergeMode==="concat"?l=bv([o,s]):this.mergeMode==="sum"?l=Tt(o,s):this.mergeMode==="ave"?l=nt(.5,Tt(o,s)):this.mergeMode==="mul"?l=nt(o,s):this.mergeMode==null&&(l=[o,s]),this.returnState?this.mergeMode==null?l.concat(c):[l].concat(c):l})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){fa(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),fa(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[e,e]:r=e:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let o=this.forwardLayer.states,s=o.map(c=>null);return Array.isArray(r)?r.concat(s).concat(s):[r].concat(s).concat(s)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let r=wo(e.layer);if(delete e.layer,e.numConstants!=null)throw new Ut("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=r,new t(o)}}L0.className="Bidirectional",vt(L0);function IU(n){return new Gc(n)}function EU(n){return new Yv(n)}function DU(n){return new jv(n)}function AU(n){return new Kv(n)}function FU(n){return new Xv(n)}function RU(n){return new Zv(n)}function PU(n){return new Jv(n)}function OU(n){return new zp(n)}function LU(n){return new Kc(n)}function MU(n){return new e0(n)}function BU(n){return 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gH=Object.freeze({__proto__:null,json:mH});let yH=[{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:"F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bH=Object.freeze({__proto__:null,json:yH});let 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wH=Object.freeze({__proto__:null,json:xH});let vH=[{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}]}];var TH=Object.freeze({__proto__:null,json:vH});let kH=[{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"}]}];var NH=Object.freeze({__proto__:null,json:kH});let _H=[{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"}]}];var CH=Object.freeze({__proto__:null,json:_H});let SH=[{tfOpName:"HashTable",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"HashTableV2",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"LookupTableImport",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableImportV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFind",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFindV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]}];var $H=Object.freeze({__proto__:null,json:SH});let IH=[{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"}]}];var EH=Object.freeze({__proto__:null,json:IH});let DH=[{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}]}];var AH=Object.freeze({__proto__:null,json:DH});let FH=[{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}]}];var RH=Object.freeze({__proto__:null,json:FH});let PH=[{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}]}];var OH=Object.freeze({__proto__:null,json:PH});let LH=[{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"}]}];var MH=Object.freeze({__proto__:null,json:LH});let BH=[{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}]}];var zH=Object.freeze({__proto__:null,json:BH});let WH=[{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}]}];var VH=Object.freeze({__proto__:null,json:WH});let GH=[{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:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{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:[]}];var UH=Object.freeze({__proto__:null,json:GH});class u${static get Instance(){return this._instance||(this._instance=new this)}constructor(){let t=[hH,dH,gH,bH,wH,TH,NH,AH,EH,CH,RH,OH,MH,zH,VH,UH,$H],e=[].concat(...t.map(r=>r.json));this.opMappers=e.reduce((r,o)=>(r[o.tfOpName]=o,r),{})}transformGraph(t,e={}){let r=t.node,o=[],s=[],c=[],l=r.reduce((N,S)=>(N[S.name]=this.mapNode(S),S.op.startsWith("Placeholder")?o.push(N[S.name]):S.op==="Const"?s.push(N[S.name]):(S.input==null||S.input.length===0)&&c.push(N[S.name]),N),{}),p=[],f=[],m={},y={};e!=null&&(m=this.mapSignatureEntries(e.inputs),y=this.mapSignatureEntries(e.outputs));let b=Object.keys(l);b.forEach(N=>{let S=l[N];S.inputNames.forEach(D=>{let[I]=vs(D);S.inputs.push(l[I]),l[I].children.push(S)})}),Object.keys(y).length===0?b.forEach(N=>{let S=l[N];S.children.length===0&&f.push(S)}):Object.keys(y).forEach(N=>{let[S]=vs(N),D=l[S];D!=null&&(D.signatureKey=y[N],f.push(D))}),Object.keys(m).length>0?Object.keys(m).forEach(N=>{let[S]=vs(N),D=l[S];D&&(D.signatureKey=m[N],p.push(D))}):p=o;let v={};t.library!=null&&t.library.function!=null&&(v=t.library.function.reduce((N,S)=>(N[S.signature.name]=this.mapFunction(S),N),{}));let T={nodes:l,inputs:p,outputs:f,weights:s,placeholders:o,signature:e,functions:v};return c.length>0&&(T.initNodes=c),T}mapSignatureEntries(t){return Object.keys(t||{}).reduce((e,r)=>(e[t[r].name]=r,e),{})}mapNode(t){let e=l$(t.op)||this.opMappers[t.op]||{};t.attr==null&&(t.attr={});let r={name:t.name,op:t.op,category:e.category,inputNames:(t.input||[]).map(o=>o.startsWith("^")?o.substr(1):o),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:t.attr};return e.inputs!=null&&(r.inputParams=e.inputs.reduce((o,s)=>(o[s.name]={type:s.type,inputIndexStart:s.start,inputIndexEnd:s.end},o),{})),e.attrs!=null&&(r.attrParams=e.attrs.reduce((o,s)=>{let c=s.type,l;switch(s.type){case"string":l=V0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=V0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"string[]":l=Y0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=Y0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"number":l=U0(t.attr,s.tfName,s.defaultValue||0),l===void 0&&!!s.tfDeprecatedName&&(l=U0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"number[]":l=X0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=X0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool":l=G0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=G0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool[]":l=Z0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=Z0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape":l=K0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=K0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape[]":l=J0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=J0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype":l=H0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=H0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype[]":l=j0(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=j0(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"func":l=h$(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=h$(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${s.type} for op: ${t.op}`)}return o[s.name]={value:l,type:c},o},{})),r}mapFunction(t){let e=t.nodeDef,r=[],o=[],s={};e!=null&&(s=e.reduce((y,b)=>(y[b.name]=this.mapNode(b),b.op==="Const"&&o.push(y[b.name]),y),{}));let c=[],l=[];t.signature.inputArg.forEach(y=>{let[b]=vs(y.name),v={name:b,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:q0(y.type),type:"dtype"}},children:[]};v.signatureKey=y.name,c.push(v),s[b]=v});let p=Object.keys(s);p.forEach(y=>{let b=s[y];b.inputNames.forEach(v=>{let[T]=vs(v);b.inputs.push(s[T]),s[T].children.push(b)})});let f=t.ret;t.signature.outputArg.forEach(y=>{let[b,v]=vs(f[y.name]),T=s[b];T!=null&&(T.defaultOutput=v,l.push(T))});let m=this.mapArgsToSignature(t);return{nodes:s,inputs:c,outputs:l,weights:o,placeholders:r,signature:m}}mapArgsToSignature(t){return{methodName:t.signature.name,inputs:t.signature.inputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r),e),{}),outputs:t.signature.outputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r,t.ret),e),{})}}mapArgToTensorInfo(t,e){let r=t.name;return e!=null&&(r=e[r]),{name:r,dtype:t.type}}}function qH(n){let t=ct().global;if(typeof t.atob!="undefined")return t.atob(n);if(typeof Buffer!="undefined")return new Buffer(n,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function p$(n,t){let e=Array.isArray(n)?String.fromCharCode.apply(null,n):qH(n);return t?e:e.toLowerCase()}function V0(n,t,e,r=!1){let o=n[t];return o!=null?p$(o.s,r):e}function G0(n,t,e){let r=n[t];return r?r.b:e}function U0(n,t,e){let r=n[t]||{},o=r.i!=null?r.i:r.f!=null?r.f:e;return typeof o=="number"?o:parseInt(o,10)}function q0(n){typeof n=="string"&&(n=No[n]);switch(n){case No.DT_FLOAT:return"float32";case No.DT_INT32:case No.DT_INT64:case No.DT_INT8:case No.DT_UINT8:return"int32";case No.DT_BOOL:return"bool";case No.DT_DOUBLE:return"float32";case No.DT_STRING:return"string";default:return null}}function h$(n,t,e){let r=n[t];return r&&r.func?r.func.name:e}function H0(n,t,e){let r=n[t];return r&&r.type?q0(r.type):e}function j0(n,t,e){let r=n[t];return r&&r.list&&r.list.type?r.list.type.map(o=>q0(o)):e}function f$(n){return n.unknownRank?void 0:n.dim!=null?n.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function 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G0(this.node.rawAttrs,t,e);if(r.shape!=null)return K0(this.node.rawAttrs,t,e);if(r.type!=null)return H0(this.node.rawAttrs,t,e);if(r.list!=null){if(r.list.i!=null||r.list.f!=null)return X0(this.node.rawAttrs,t,e);if(r.list.s!=null)return Y0(this.node.rawAttrs,t,e);if(r.list.shape!=null)return J0(this.node.rawAttrs,t,e);if(r.list.b!=null)return Z0(this.node.rawAttrs,t,e);if(r.list.type!=null)return j0(this.node.rawAttrs,t,e)}return e}}let jH=(n,t,e)=>{switch(n.op){case"BiasAdd":case"AddV2":case"Add":return[Tt(A("a",n,t,e),A("b",n,t,e))];case"AddN":return[g_(A("tensors",n,t,e))];case"FloorMod":case"Mod":return[bd(A("a",n,t,e),A("b",n,t,e))];case"Mul":return[nt(A("a",n,t,e),A("b",n,t,e))];case"RealDiv":case"Div":return[Bt(A("a",n,t,e),A("b",n,t,e))];case"DivNoNan":return[xw(A("a",n,t,e),A("b",n,t,e))];case"FloorDiv":return[nd(A("a",n,t,e),A("b",n,t,e))];case"Sub":return[Dt(A("a",n,t,e),A("b",n,t,e))];case"Minimum":return[oa(A("a",n,t,e),A("b",n,t,e))];case"Maximum":return[Xr(A("a",n,t,e),A("b",n,t,e))];case"Pow":return[fo(A("a",n,t,e),A("b",n,t,e))];case"SquaredDifference":return[gp(A("a",n,t,e),A("b",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Tst="arithmetic";let 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TypeError(`Node type ${n.op} is not implemented`)}},kst="basic_math";function to(n,t,e=""){_(XH(n,t),()=>e+` Shapes ${n} and ${t} must match`)}function XH(n,t){if(n.length!==t.length)return!1;for(let e=0;e<n.length;e++)if(n[e]!==-1&&t[e]!==-1&&n[e]!==t[e])return!1;return!0}class YH{constructor(t,e,r,o,s,c,l){this.name=t,this.dtype=e,this.maxSize=r,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=c,this.clearAfterRead=l,this.tensors=[],this.closed_=!1,this.idTensor=Et(0),Sn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.tensor.id))&&e.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||t>=this.size())throw new Error(`Tried to read from index ${t}, but array size is: ${this.size()}`);let 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-          because the value dtype is ${e.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=e.shape),to(this.elementShape,e.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${t}.`),r.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been read.`);if(r.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been written.`);r.tensor=e,Sn(e),r.written=!0,this.tensors[t]=r}writeMany(t,e){if(t.length!==e.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${t.length} is not the same as tensors size: ${e.length}.`);t.forEach((r,o)=>this.write(r,e[o]))}gather(t,e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${e}`);if(t)t=t.slice(0,this.size());else{t=[];for(let o=0;o<this.size();o++)t.push(o)}if(t.length===0)return un([],[0].concat(this.elementShape));let r=this.readMany(t);return to(this.elementShape,r[0].shape,"TensorArray shape mismatch: "),ur(r,0)}concat(t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${t}`);if(this.size()===0)return un([],[0].concat(this.elementShape));let e=[];for(let o=0;o<this.size();o++)e.push(o);let r=this.readMany(e);return to(this.elementShape,r[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${r[0].shape})`),Qe(r,0)}scatter(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(!this.dynamicSize&&r>=this.maxSize)throw new Error(`Max index must be < array size (${r}  vs. ${this.maxSize})`);this.writeMany(t,mo(e,0))}split(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);let r=0,o=t.map(p=>(r+=p,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
+
+  /*
+  Face-API
+  homepage: <https://github.com/vladmandic/face-api>
+  author: <https://github.com/vladmandic>'
+  */
+
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all6)=>{__markAsModule(target);for(var name in all6)__defProp(target,name,{get:all6[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return 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copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports3,module)=>{(function(pool62,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten52(options.entropy?[seed,tostring(pool62)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool62),(options.pass||callback||function(prng2,seed2,is_math_call,state6){return state6&&(state6.S&&copy(state6,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten52(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten52(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser2=global2.navigator,plugins=browser2&&browser2.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool62)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool62),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS2((exports3,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_string_decoder=__commonJS2(()=>{}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports3,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer11=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert32(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert32(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir&&(scriptDirectory=_scriptDir),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert32(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:165,maximum:165+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert32(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert32(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack92=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack92===0&&(stack92=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack92!==0&&stackRestore(stack92),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer12){GROWABLE_HEAP_I8().set(array2,buffer12)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer11,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer11=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer11=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer11=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer11.byteLength,updateGlobalBufferAndViews(buffer11),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks3){for(;callbacks3.length>0;){var callback=callbacks3.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert32(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports5=instance.exports;if(Module.asm=exports5,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports4=Module.instantiateWasm(info,receiveInstance);return exports4}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=12976,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack92=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack92),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer11.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests())return;for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),context.initExtensionsDone)return;context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer12=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer12,0)),buffer12.length=0):buffer12.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert32(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer12){HEAP8.set(array2,buffer12)}var buffer11,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer11=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks3){for(;callbacks3.length>0;){var callback=callbacks3.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). 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fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports4=Module.instantiateWasm(info,receiveInstance);return exports4}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var 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Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolBackprop="AvgPoolBackprop",AvgPool3D="AvgPool3D",AvgPool3DBackprop="AvgPool3DBackprop",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",Div="Div",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNBackprop="LRNBackprop",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolBackprop="MaxPoolBackprop",MaxPool3D="MaxPool3D",MaxPool3DBackprop="MaxPool3DBackprop",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multiply="Multiply",Negate="Negate",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",SelectV2="SelectV2",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config2]=value,[backend3]=key.split("_");backend3===backendName&&result.push(config2)}return result}function registerKernel(config2){let{kernelName,backendName}=config2,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config2)}function registerGradient(config2){let{kernelName}=config2;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config2)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){let kernels=getKernelsForBackend(registeredBackendName);kernels.forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}let kernelProfile={kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")};return kernelProfile}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape2,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape2.length;i++){let node2=tape2[i],nodeInputs=node2.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node2.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node2.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape2.length-1;i>=0;i--){let node2=tape2[i],nodeInputs=node2.inputs;for(let j=0;j<node2.outputs.length;j++)if(tensorsLeadToY[node2.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node2.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape2.length;i++){let node2=tape2[i];if(nodesFromX[node2.id]&&nodesToY[node2.id]){let prunedInputs={};for(let inputName in node2.inputs){let nodeInput=node2.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node2);prunedNode.inputs=prunedInputs,prunedNode.outputs=node2.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy22,add33){for(let i=filteredTape.length-1;i>=0;i--){let node2=filteredTape[i],dys=[];if(node2.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node2.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);let inputGradients=node2.gradient(dys);for(let inputName in node2.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy22(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node2.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add33(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
+`)),lines.join(`
+`)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){let offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString(val,pad11,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`:isString(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),rightPad(valStr,pad11)}function boolNumToString(v){return v===0?"false":"true"}function subTensorToString(vals,shape,dtype,strides,padPerCol,isLast=!0){let storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){let complexTuple=createComplexTuples(vals);return[valToString(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}let sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
+`;for(let i=2;i<rank;i++)newLineSep+=`
+`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){if(this.isDisposed)return;trackerFn().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank22){Rank22.R0="R0",Rank22.R1="R1",Rank22.R2="R2",Rank22.R3="R3",Rank22.R4="R4",Rank22.R5="R5",Rank22.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap22){UpcastInt32AndMap22.float32="float32",UpcastInt32AndMap22.int32="int32",UpcastInt32AndMap22.bool="int32",UpcastInt32AndMap22.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap22){UpcastBoolAndMap22.float32="float32",UpcastBoolAndMap22.int32="int32",UpcastBoolAndMap22.bool="bool",UpcastBoolAndMap22.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap22){UpcastFloat32AndMap22.float32="float32",UpcastFloat32AndMap22.int32="float32",UpcastFloat32AndMap22.bool="float32",UpcastFloat32AndMap22.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap22){UpcastComplex64AndMap22.float32="complex64",UpcastComplex64AndMap22.int32="complex64",UpcastComplex64AndMap22.bool="complex64",UpcastComplex64AndMap22.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor168,tensorList){return tensorList.some(x=>x.id===tensor168.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container2,list,seen){if(container2==null)return;if(container2 instanceof Tensor){list.push(container2);return}if(!isIterable(container2))return;let iterable=container2;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV52){this.ENV=ENV52,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],success=await this.initializeBackend(backendName).success;if(success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName),result=asyncInit?await success:success;if(!result)return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){let kernels=getKernelsForBackend(this.backendName);kernels.forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){let kernels=getKernelsForBackend(backendName);kernels.forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend3=registryFactoryEntry.factory();if(backend3&&!(backend3 instanceof KernelBackend)&&typeof backend3.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend3.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend3,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend3,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend3,backend3.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend3=>backend3.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3,kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(({dataId,shape,dtype})=>this.makeTensorFromDataId(dataId,shape,dtype));if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{let saveFunc=tensors=>{if(!isTapeOn)return;saved=tensors.map(tensor168=>this.keep(this.clone(tensor168)))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){let saved=tensors.map(tensor168=>this.keep(this.clone(tensor168)));return saved}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend3){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend3=backend3||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend3.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend3),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend3){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend3),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend3){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend3||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId),refCount=info.refCount;refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor168=this.state.activeScope.track[i];!tensor168.kept&&!tensorsToTrackInParentSet.has(tensor168.id)&&tensor168.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor168=>{!tensor168.kept&&tensor168.scopeId===oldScope.id&&this.track(tensor168)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node2=>{for(let tensor168 of node2.saved)tensor168.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.readSync(dataId)}read(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment15=new Environment(ns);ns._tfengine=new Engine(environment15)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.add(a,b);return save([a,b]),res},inputs,null,Add)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype==null)return;if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let skipTypedArray=!0,values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],skipTypedArray);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);let tensors=arg;return tensors.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName),parseAsDtype)}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real82,imag82){let $real=convertToTensor(real82,"real","complex"),$imag=convertToTensor(imag82,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let forward=backend3=>backend3.complex($real,$imag),inputs={real:$real,imag:$imag};return ENGINE.runKernelFunc(forward,inputs,null,Complex)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor4(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor168=>tensor168.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer11,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer11.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer11.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer11.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer11.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real82=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real82.length;i++)real82[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor4(real82,shape,"float32"),imageTensor=tensor4(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor4(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer11){if(useNodeBuffer)return Buffer.from(buffer11).toString("base64");let buf=new Uint8Array(buffer11),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer11=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer11.set([s.charCodeAt(i)],i);return buffer11.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer11=>{totalByteLength+=buffer11.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer11=>{temp.set(new Uint8Array(buffer11),offset),offset+=buffer11.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)===0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer11=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer11);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer11)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[],routers=handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters;return routers.forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),modelStore=modelTx.objectStore(MODEL_STORE_NAME),getRequest=modelStore.get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME),putModelRequest=modelStore.put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),store=tx.objectStore(INFO_STORE_NAME),getAllInfoRequest=store.getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME),deleteModelRequest=modelStore.delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology21=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{return this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology21),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata})),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology21=JSON.parse(this.LS.getItem(this.keys.topology));if(topology21==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology21;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,out.userDefinedMetadata=metadata.userDefinedMetadata}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}},URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url),manager=ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);return manager.removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){let deleteSource=!1;return cloneModelInternal(sourceURL,destURL,deleteSource)}async function moveModel(sourceURL,destURL){let deleteSource=!0;return cloneModelInternal(sourceURL,destURL,deleteSource)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}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}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernelFunc(backend3=>backend3.cast($x,dtype),inputs,null,Cast,attrs)}var cast=op({cast_});function clone_(x){let $x=convertToTensor(x,"x","clone",null),forward=()=>ENGINE.makeTensorFromDataId($x.dataId,$x.shape,$x.dtype),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Identity)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest},modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1&&resolve({modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy,userDefinedMetadata:modelJSON.userDefinedMetadata})},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,responses=loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction),bufferPromises=responses.map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1,buffers=loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction);return buffers}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){let fetchWeights=fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}),loadWeights2=weightsLoaderFactory(fetchWeights);return loadWeights2(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
+Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer11=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer11,groupBufferOffset),groupBufferOffset+=buffer11.byteLength}let weightsEntries=groupWeightsToFetch[i];weightsEntries.forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata,weightsManifest};init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;if(weightsManifest!=null){let results=await this.loadWeights(weightsManifest);[weightSpecs,weightData]=results}let artifacts={modelTopology,weightSpecs,weightData,userDefinedMetadata,generatedBy,convertedBy,format},initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){if(arguments.length===1){let isModelArtifacts=modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null;return isModelArtifacts?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts}))}else return console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig})}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function reshape_(x,shape){let $x=convertToTensor(x,"x","reshape",null),inputs={x:$x},attrs={shape},forward=(backend3,save)=>(shape=inferFromImplicitShape(shape,$x.size),assert($x.size===sizeFromShape(shape),()=>"new shape and old shape must have the same number of elements."),save([$x]),backend3.reshape($x,shape));return ENGINE.runKernelFunc(forward,inputs,null,Reshape,attrs)}var reshape=op({reshape_});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{save([$a,$b]);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;assert($a.rank>=2&&$b.rank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`),assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShapeOuterDims=batchDimA>batchDimB?outerDimsA:outerDimsB,outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),res3d=backend3.batchMatMul(a3D,b3D,transposeA,transposeB);return reshape(res3d,outShape)},inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernelFunc(forward,inputs,null,BatchMatMul,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let $indices=convertToTensor(indices,"indices","oneHot","int32"),outShape=[...$indices.shape,depth],forward=(backend3,save)=>(save([$indices]),reshape(backend3.oneHot(reshape($indices,[$indices.size]),depth,onValue,offValue),outShape)),inputs={indices:$indices},attrs={depth,onValue,offValue};return ENGINE.runKernelFunc(forward,inputs,null,OneHot,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernelFunc(backend3=>backend3.transpose($x,perm),inputs,null,Transpose,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}let kernel=getKernel(FromPixels,ENGINE.backendName);if(kernel!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}let outShape=[height,width,numChannels];return tensor3d(values,outShape,"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor168,indices){if(tensor168.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor168.rank}.`);if(indices.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indices.rank-1]>tensor168.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank-1]} vs. ${tensor168.rank}`);if(tensor168.size===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor168.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor168.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensor168.rank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor168.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export2(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config2){return new cls(config2)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon32){return epsilon32==null&&(epsilon32=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon32))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`);for(let i=0;i<expectedFlat.length;++i){let a=actualFlat[i],e=expectedFlat[i];if(!predicate(a,e))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp132=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp132,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon32){if(epsilon32==null&&(epsilon32=testEpsilon()),!areClose(a,e,epsilon32))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon32){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon32)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}var version="2.7.0";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine15(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container2){let tensors=getTensorsInContainer(container2);tensors.forEach(tensor168=>tensor168.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend2(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.add($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Add)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.floorDiv($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,FloorDiv)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let forward=(backend3,save)=>{let res=backend3.realDivide($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE.runKernelFunc(forward,inputs,null,Div,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.multiply($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Multiply)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>(save([$x]),$x.dtype==="complex64"?backend3.complexAbs($x):backend3.abs($x)),inputs,null,Abs)}var abs=op({abs_});function acos_(x){let $x=convertToTensor(x,"x","acos"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.acos($x);return save([$x]),res},inputs,null,Acos)}var acos=op({acos_});function acosh_(x){let $x=convertToTensor(x,"x","acosh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.acosh($x);return save([$x]),res},inputs,null,Acosh)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let forward=(backend3,save)=>{let res=backend3.addN($tensors);return save($tensors),res},inputs=$tensors;return ENGINE.runKernelFunc(forward,inputs,null,AddN)}var addN=op({addN_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function all_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","all","bool"),forward=backend3=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank);permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let res=backend3.all($x,axes);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,origAxes);return reshape(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,All,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","any","bool"),forward=backend3=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank);permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let res=backend3.any($x,axes);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,origAxes);return reshape(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Any,attrs)}var any=op({any_});function argMax_(x,axis=0){let $x=convertToTensor(x,"x","argMax"),forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam(axis,$x.shape),permutedAxes=getAxesPermutation(axes,$x.rank);return permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank)),backend3.argMax($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,ArgMax,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let $x=convertToTensor(x,"x","argMin"),forward=(backend3,save)=>{save([$x]),axis==null&&(axis=0);let axes=parseAxisParam(axis,$x.shape),permutedAxes=getAxesPermutation(axes,$x.rank);return permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank)),backend3.argMin($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,ArgMin,attrs)}var argMin=op({argMin_});function asin_(x){let $x=convertToTensor(x,"x","asin"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.asin($x);return save([$x]),res},inputs,null,Asin)}var asin=op({asin_});function asinh_(x){let $x=convertToTensor(x,"x","asinh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.asinh($x);return save([$x]),res},inputs,null,Asinh)}var asinh=op({asinh_});function atan_(x){let $x=convertToTensor(x,"x","atan"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.atan($x);return save([$x]),res},inputs,null,Atan)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.atan2($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Atan2)}var atan2=op({atan2_});function atanh_(x){let $x=convertToTensor(x,"x","atanh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.atanh($x);return save([$x]),res},inputs,null,Atanh)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad11,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad11,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad11,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad11,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad11,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad11,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad11,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad11,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad11,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad11,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=conditionalRound((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert(isInt(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=conditionalRound((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputDepths),()=>`The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);let outputRows=conditionalRound((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert(isInt(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad11,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad11=="number"){let padType=pad11===0?"VALID":"NUMBER";padInfo={top:pad11,bottom:pad11,left:pad11,right:pad11,type:padType};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad11,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad11==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad11==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad11=="object"){let top=dataFormat==="channelsLast"?pad11[1][0]:pad11[2][0],bottom=dataFormat==="channelsLast"?pad11[1][1]:pad11[2][1],left=dataFormat==="channelsLast"?pad11[2][0]:pad11[3][0],right=dataFormat==="channelsLast"?pad11[2][1]:pad11[3][1],padType=top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT";padInfo={top,bottom,left,right,type:padType},outHeight=conditionalRound((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=conditionalRound((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad11}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad11,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad11=="number"){let padType=pad11===0?"VALID":"NUMBER";padInfo={top:pad11,bottom:pad11,left:pad11,right:pad11,front:pad11,back:pad11,type:padType};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad11,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad11==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad11==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad11}`);return{padInfo,outDepth,outHeight,outWidth}}function conditionalRound(value,roundingMode){if(!roundingMode)return value;switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function avgPool_(x,filterSize,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo(x4D.shape,filterSize,strides,1,pad11,dimRoundingMode);return save([x4D]),convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual(convInfo.inShape,convInfo.outShape)?x4D.clone():backend3.avgPool(x4D,convInfo)},inputs={x:x4D},attrs={filterSize,strides,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad11,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1,1]);let convInfo=computePool3DInfo(x5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode,dataFormat);return save([x5D]),backend3.avgPool3d(x5D,convInfo)},inputs={x:x5D},attrs={filterSize,strides,pad:pad11,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool3D,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat");$tensors[0].dtype==="complex64"&&$tensors.forEach(tensor168=>{if(tensor168.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor168.dtype}. `)});let forward=(backend3,save)=>{let $axis=parseAxisParam(axis,$tensors[0].shape)[0],outShape=computeOutShape2($tensors.map(t=>t.shape),$axis);if(sizeFromShape(outShape)===0)return tensor4([],outShape);if($tensors=$tensors.filter(t=>t.size>0),$tensors.length===1)return $tensors[0];let shapes=$tensors.map(t=>t.shape);assertParamsConsistent(shapes,$axis);let res=backend3.concat($tensors,$axis);return save($tensors),res},inputs=$tensors,attr={axis};return ENGINE.runKernelFunc(forward,inputs,null,Concat,attr)}var concat=op({concat_});function sigmoid_(x){let $x=convertToTensor(x,"x","sigmoid"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sigmoid($x);return save([res]),res},inputs,null,Sigmoid)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice");if($x.rank===0)throw new Error("Slicing scalar is not possible");let forward=(backend3,save)=>{let[begin_,size_]=parseSliceParams($x,begin,size);return assertParamsValid($x,begin_,size_),save([$x]),backend3.slice($x,begin_,size_)},inputs={x:$x},attrs={begin,size};return ENGINE.runKernelFunc(forward,inputs,null,Slice,attrs)}var slice=op({slice_});function tanh_(x){let $x=convertToTensor(x,"x","tanh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let y=backend3.tanh($x);return save([y]),y},inputs,null,Tanh)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod52=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod52===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod52}`);let forward=backend3=>backend3.batchToSpaceND($x,blockShape,crops),inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernelFunc(forward,inputs,null,BatchToSpaceND,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean7,variance,offset,scale22,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let x4D=xAs4D($x),forward=(backend3,save)=>(save([x4D,$mean,$variance,$scale]),backend3.batchNorm(x4D,as1DOr4D($mean),as1DOr4D($variance),as1DOr4D($offset),as1DOr4D($scale),varianceEpsilon)),inputs={x:x4D,scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernelFunc(forward,inputs,null,FusedBatchNorm,attrs);return reshape(res,$x.shape)}function as1DOr4D(x){return x==null?null:x.rank===0?reshape(x,[x.size]):x.rank===1?x:x.rank===2?reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean7,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean7,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean7,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);let axes=reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0);if(axes.length===0)return clone(input2);let forward=backend3=>backend3.tile(input2,reps),inputs={x:input2},attrs={shape,inputShape};return ENGINE.runKernelFunc(forward,inputs,null,BroadcastTo,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let $x=convertToTensor(x,"x","ceil"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.ceil($x),inputs,null,Ceil)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.clip($x,clipValueMin,clipValueMax);return save([$x]),res},inputs,null,ClipByValue,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let forward=(backend3,save)=>{let $dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2d(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,Conv2D,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad11,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),strides=[1,stride],dilations=[1,dilation],conv2dDataFormat="NHWC",res=conv2d(input4D,filter4D,strides,pad11,conv2dDataFormat,dilations,dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad11,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let dilations=1,$dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(xShape4D,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2dDerInput(dy4D,filter,convInfo);return save([dy4D,filter]),res2},inputs={dy:dy4D,filter},attrs={strides,pad:pad11,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernelFunc(forward,inputs,null,Conv2DBackpropInput,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad11,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad11,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let forward=(backend3,save)=>{let convInfo=computeConv3DInfo(x5D.shape,$filter.shape,strides,dilations,pad11),res2=backend3.conv3d(x5D,$filter,convInfo);return save([x5D,$filter]),res2},inputs={x:x5D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,Conv3D,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad11){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo(xShape5D,filter.shape,strides,dilations,pad11);return backend3.conv3dDerInput(dy5D,filter,convInfo)},inputs={dy:dy5D,filter},attrs={pad:pad11,strides,inputShape:xShape5D},res=ENGINE.runKernelFunc(forward,inputs,null,Conv3DBackpropInputV2,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad11){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad11)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let $x=convertToTensor(x,"x","cos"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.cos($x);return save([$x]),res},inputs,null,Cos)}var cos=op({cos_});function cosh_(x){let $x=convertToTensor(x,"x","cosh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.cosh($x);return save([$x]),res},inputs,null,Cosh)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse12=!1){let $x=convertToTensor(x,"x","cumsum"),forward=(backend3,save)=>{let permutation=getAxesPermutation([axis],$x.rank),permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation));let permutedAxis=getInnerMostAxes(1,$x.rank)[0],value=backend3.cumsum(permutedX,permutedAxis,exclusive,reverse12);if(save([$x]),permutation!=null){let reversePermutation=getUndoAxesPermutation(permutation);value=transpose(value,reversePermutation)}return value},inputs={x:$x},attrs={axis,exclusive,reverse:reverse12};return ENGINE.runKernelFunc(forward,inputs,null,Cumsum,attrs)}var cumsum=op({cumsum_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
+    ${$x.shape}`),assert(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputWidth} and ${blockSize} for depthToSpace with input shape
+        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let forward=backend3=>backend3.depthToSpace($x,blockSize,dataFormat),inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernelFunc(forward,inputs,null,DepthToSpace,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!0),res2=backend3.depthwiseConv2D(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNative,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let $x=convertToTensor(x,"x","diag"),forward=backend3=>{let flat=reshape($x,[$x.size]),result=backend3.diag(flat),outShape=[...x.shape,...x.shape];return reshape(result,outShape)},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Diag)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad11,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1,b=outShape[outShape.length-1-i]||1;b>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.equal($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Equal)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let forward=(backend3,save)=>{let res=backend3.select($condition,$broadcastedA,$broadcastedB);return save([$condition]),res},inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernelFunc(forward,inputs,null,SelectV2)}var where=op({where_});function zerosLike_(x){let $x=convertToTensor(x,"x","zerosLike"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.zerosLike($x),inputs,null,ZerosLike)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros10=zerosLike(divResult),bEqualsZero=equal($b,zeros10);return where(bEqualsZero,zeros10,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul($t1,t22D);return t1t2}}var dot=op({dot_});function elu_(x){let $x=convertToTensor(x,"x","elu"),forward=(backend3,save)=>{let y=backend3.elu($x);return save([y]),y},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Elu)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.erf($x);return save([$x]),res},inputs,null,Erf)}var erf=op({erf_});function exp_(x){let $x=convertToTensor(x,"x","exp"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.exp($x);return save([res]),res},inputs,null,Exp)}var exp=op({exp_});function expandDims_(x,axis=0){let parseAs=null,$x=convertToTensor(x,"x","expandDims",parseAs);assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let newShape=$x.shape.slice();return axis<0&&(assert(-($x.rank+1)<=axis,()=>`Axis must be in the interval [${-($x.rank+1)}, ${$x.rank}]`),axis=$x.rank+axis+1),newShape.splice(axis,0,1),reshape($x,newShape)}var expandDims=op({expandDims_});function expm1_(x){let $x=convertToTensor(x,"x","expm1"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.expm1($x);return save([$x]),res},inputs,null,Expm1)}var expm1=op({expm1_});function tile_(x,reps){let parseAs=null,$x=convertToTensor(x,"x","tile",parseAs);assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let forward=(backend3,save)=>{let res=backend3.tile($x,reps);return save([$x]),res},inputsToSave=[$x],inputs={x:$x},attrs={reps};return ENGINE.runKernelFunc(forward,inputs,null,Tile,attrs,inputsToSave)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernelFunc(backend3=>backend3.fill(shape,value,dtype),{},null,Fill,attrs)}function floor_(x){let $x=convertToTensor(x,"x","floor"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.floor($x),inputs,null,Floor)}var floor=op({floor_}),segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis){let dimSize=x.shape[axis],outputShape=[],batchSize=1,sliceSize=1;for(let i=0;i<axis;i++)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=0;i<indices.rank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<x.rank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,dimSize,outputShape}}function gather_(x,indices,axis=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis},forward=(backend3,save)=>{let parsedAxis=parseAxisParam(axis,$x.shape)[0],shapeInfo=collectGatherOpShapeInfo($x,$indices,parsedAxis),res=backend3.gather($x,reshape($indices,[$indices.size]),parsedAxis);return save([$x,$indices]),reshape(res,shapeInfo.outputShape)};return ENGINE.runKernelFunc(forward,inputs,null,GatherV2,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.greater($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Greater)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.greaterEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,GreaterEqual)}var greaterEqual=op({greaterEqual_});function imag_(input2){let $input=convertToTensor(input2,"input","imag"),forward=backend3=>backend3.imag($input),inputs={input:$input};return ENGINE.runKernelFunc(forward,inputs,null,Imag)}var imag=op({imag_});function isFinite_(x){let $x=convertToTensor(x,"x","isFinite"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isFinite($x),inputs,null,IsFinite)}var isFinite2=op({isFinite_});function isInf_(x){let $x=convertToTensor(x,"x","isInf"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isInf($x),inputs,null,IsInf)}var isInf=op({isInf_});function isNaN_(x){let $x=convertToTensor(x,"x","isNaN"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isNaN($x),inputs,null,IsNan)}var isNaN2=op({isNaN_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.maximum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Maximum)}var maximum=op({maximum_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");let shape=[],inferredShape=[];return makeTensor(value,shape,inferredShape,dtype)}function leakyRelu_(x,alpha=.2){let $x=convertToTensor(x,"x","leakyRelu");return maximum(mul(scalar(alpha),$x),$x)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.less($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Less)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.lessEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,LessEqual)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernelFunc(backend3=>backend3.linspace(start,stop,num),{},null,LinSpace,attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
+               rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let forward=(backend3,save)=>{let y=backend3.localResponseNormalization4D(x4D,depthRadius,bias,alpha,beta);return save([x4D,y]),y},inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernelFunc(forward,inputs,null,LRN,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let $x=convertToTensor(x,"x","log"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.log($x);return save([$x]),res},inputs,null,Log)}var log=op({log_});function log1p_(x){let $x=convertToTensor(x,"x","log1p"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.log1p($x);return save([$x]),res},inputs,null,Log1p)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad",null),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads",null),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable32=>!variable32.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable32=>variable32.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){let numNullGradients=grads2.filter(g=>g==null).length;if(numNullGradients>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
+    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let $x=convertToTensor(x,"x","neg"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.neg($x),inputs,null,Negate)}var neg=op({neg_});function softplus_(x){let $x=convertToTensor(x,"x","softplus"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.softplus($x);return save([$x]),res},inputs,null,Softplus)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid"),customOp=customGrad(x2=>{let value=neg(softplus(neg(x2))),gradFunc=dy=>{let derX=mul(dy,sigmoid(neg(x2)));return derX};return{value,gradFunc}});return customOp($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","max"),forward=(backend3,save)=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank),maxInput=$x;permutedAxes!=null&&(maxInput=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,maxInput.rank));let y=backend3.max(maxInput,axes);permutedAxes!=null&&maxInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim(res.shape,parseAxisParam(axis,$x.shape));res=reshape(res,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Max,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.subtract($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Sub)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam(axis,$x.shape),permutation=getAxesPermutation(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation),reductionAxes=getInnerMostAxes(reductionAxes.length,$x.rank));let value=backend3.sum(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim(value.shape,axes);value=reshape(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Sum,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);let forward=(backend3,save)=>{let keepDims=!0,xMax=max(logits,axis,!0),shifted=sub(logits,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),value},inputs={logits:$logits},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,LogSoftmax,attrs)}var logSoftmax=op({logSoftmax_});function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernelFunc(backend3=>backend3.logicalAnd($a,$b),inputs,null,LogicalAnd)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let $x=convertToTensor(x,"x","logicalNot","bool"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.logicalNot($x),inputs,null,LogicalNot)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernelFunc(backend3=>backend3.logicalOr($a,$b),inputs,null,LogicalOr)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo(x4D.shape,filterSize,strides,1,pad11,dimRoundingMode),y;return convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual(convInfo.inShape,convInfo.outShape)?y=x4D.clone():y=backend3.maxPool(x4D,convInfo),save([x4D,y]),y},inputs={x:x4D},attrs={filterSize,strides,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad11,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1,1]);let convInfo=computePool3DInfo(x5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode,dataFormat),y=backend3.maxPool3d(x5D,convInfo);return save([x5D,y]),y},inputs={x:x5D},attrs={filterSize,strides,pad:pad11,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool3D,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad11,includeBatchInIndex=!1){let $x=convertToTensor(x,"x","maxPoolWithArgmax"),inputs={x:$x},attrs={filterSize,strides,pad:pad11,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real82=zeros(shape,"float32"),imag82=zeros(shape,"float32");return complex(real82,imag82)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real82=ones2(shape,"float32"),imag82=zeros(shape,"float32");return complex(real82,imag82)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function mean_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","mean"),axes=parseAxisParam(axis,$x.shape),shapes=computeOutAndReduceShapes($x.shape,axes),reduceShape=shapes[1],reduceSize=sizeFromShape(reduceShape),inputs={x:$x},attrs={axis,keepDims},forward=()=>{let reduceSizeScalar=scalar(reduceSize),xReduce=reduceSizeScalar.dtype===$x.dtype?$x:cast($x,reduceSizeScalar.dtype),res=div(xReduce,reduceSizeScalar);return sum2(res,axis,keepDims)},customOp=customGrad(x2=>{let value=ENGINE.runKernelFunc(forward,inputs,null,Mean,attrs),gradFunc=dy=>{let expandedDyShape=x2.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=div(mul(expandedDy,ones2(x2.shape,"float32")),reduceSize);return derX};return{value,gradFunc}});return customOp($x)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","min"),forward=(backend3,save)=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank),minInput=$x;permutedAxes!=null&&(minInput=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let y=backend3.min(minInput,axes);permutedAxes!=null&&minInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim(res.shape,origAxes);res=reshape(y,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Min,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.minimum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Minimum)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.mod($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Mod)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={},inputsToSave=[$x],outputsToSave=[];return ENGINE.runKernelFunc((backend3,save)=>(save([$x]),backend3.square($x)),{x:$x},null,"Square",attrs,inputsToSave,outputsToSave)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let logits2D=origRank===1?reshape($logits,[1,-1]):$logits,res=ENGINE.runKernelFunc(backend3=>backend3.multinomial(logits2D,normalized,numSamples,seed),{logits2D});return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.notEqual($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,NotEqual)}var notEqual=op({notEqual_});function real_(input2){let $input=convertToTensor(input2,"input","real"),forward=backend3=>backend3.real($input),inputs={input:$input};return ENGINE.runKernelFunc(forward,inputs,null,Real)}var real=op({real_});function onesLike_(x){let $x=convertToTensor(x,"x","onesLike"),forward=(backend3,save)=>{if($x.dtype==="complex64"){let r=onesLike(real($x)),i=zerosLike(imag($x));return complex(r,i)}return backend3.onesLike($x)},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,OnesLike)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let forward=(backend3,save)=>(save([$x]),backend3.pad($x,paddings,constantValue)),attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,PadV2,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let forward=backend3=>backend3.spaceToBatchND($x,blockShape,paddings),inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernelFunc(forward,inputs,null,SpaceToBatchND,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad11,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad11===0&&(pad11="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad11),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad11==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad11:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),forwardOp=poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad),y=forwardOp(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let dilatedFilterShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)),padExtraShape=dilatedFilterShape.map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp132){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp132,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp},forward=(backend3,save)=>{let y=backend3.pow($base,$exp);return save([$base,$exp,y]),y};return ENGINE.runKernelFunc(forward,inputs,null,Pow)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),forward=(backend3,save)=>{let res=backend3.prelu($x,$alpha);return save([$x,$alpha]),res},inputs={x:$x,alpha:$alpha};return ENGINE.runKernelFunc(forward,inputs,null,Prelu)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let forward=backend3=>{let axes=parseAxisParam(axis,$x.shape),permutation=getAxesPermutation(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation),reductionAxes=getInnerMostAxes(reductionAxes.length,$x.rank));let value=backend3.prod(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim(value.shape,axes);value=reshape(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Prod,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_}),seedrandom=__toModule2(require_seedrandom2()),MPRandGauss=class{constructor(mean7,stdDeviation,dtype,truncated,seed){this.mean=mean7,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul64=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul64,resultY=this.mean+this.stdDev*v2*mul64,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min82=0,max102=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min82,this.range=max102-min82,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min82} - ${max102} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean7=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean7,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");let shape=null;return makeTensor(values,shape,inferredShape,dtype)}function range(start,stop,step9=1,dtype="float32"){if(step9===0)throw new Error("Cannot have a step of zero");let forward=()=>{let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step9<0,decreasingRangePositiveStep=stop<start&&step9>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return zeros([0],dtype);let numElements=Math.abs(Math.ceil((stop-start)/step9)),values=makeZerosTypedArray(numElements,dtype);stop<start&&step9===1&&(step9=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d(values,dtype)},attrs={start,stop,step:step9,dtype};return ENGINE.runKernelFunc(forward,{},null,Range,attrs)}function reciprocal_(x){let $x=convertToTensor(x,"x","reciprocal"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.reciprocal($x);return save([$x]),res},inputs,null,Reciprocal)}var reciprocal=op({reciprocal_});function relu_(x){let $x=convertToTensor(x,"x","relu"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast($x,"int32"):backend3.relu($x)),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Relu)}var relu=op({relu_});function relu6_(x){let $x=convertToTensor(x,"x","relu6"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast($x,"int32"):backend3.relu6($x)),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Relu6)}var relu6=op({relu6_});function reverse_(x,axis){let $x=convertToTensor(x,"x","reverse"),forward=backend3=>{let axes=parseAxisParam(axis,$x.shape);if($x.rank===0)return clone($x);let res=backend3.reverse($x,axes);return reshape(res,$x.shape)},inputs={x:$x},attrs={dims:axis};return ENGINE.runKernelFunc(forward,inputs,null,Reverse,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let $x=convertToTensor(x,"x","round"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.round($x),inputs,null,Round)}var round=op({round_});function rsqrt_(x){let $x=convertToTensor(x,"x","rsqrt"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.rsqrt($x);return save([$x]),res},inputs,null,Rsqrt)}var rsqrt=op({rsqrt_});function selu_(x){let $x=convertToTensor(x,"x","selu"),forward=(backend3,save)=>{let res=backend3.selu($x);return save([$x]),res},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Selu)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad11,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad11,dataFormat,dilation),pointwiseStride=1,res=conv2d(depthwise,$pointwiseFilter,pointwiseStride,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer11=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer11.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer11.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let $x=convertToTensor(x,"x","sign"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.sign($x),inputs,null,Sign)}var sign=op({sign_});function sin_(x){let $x=convertToTensor(x,"x","sin"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sin($x);return save([$x]),res},inputs,null,Sin)}var sin=op({sin_});function sinh_(x){let $x=convertToTensor(x,"x","sinh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sinh($x);return save([$x]),res},inputs,null,Sinh)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernelFunc((backend3,save)=>{let y=backend3.softmax($logits,dim);return save([y]),y},inputs,null,Softmax,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=input2.as2D(batch,innerDimensionSize),result=backend3.fft(input2D);return result.reshape(input2.shape)},inputs,null,FFT)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=reshape(input2,[batch,innerDimensionSize]),result=backend3.ifft(input2D);return reshape(result,input2.shape)},inputs,null,IFFT)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}function split_(x,numOrSizeSplits,axis=0){let $x=convertToTensor(x,"x","split"),forward=(backend3,_)=>{let $axis=parseAxisParam(axis,$x.shape)[0],splitSizes=prepareSplitSize($x,numOrSizeSplits,$axis);return backend3.split($x,splitSizes,$axis)},inputs={x:$x},attr={numOrSizeSplits,axis};return ENGINE.runKernelFunc(forward,inputs,null,SplitV,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let $x=convertToTensor(x,"x","sqrt"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sqrt($x);return save([$x]),res},inputs,null,Sqrt)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.squaredDifference($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE.runKernelFunc(forward,inputs,null,SquaredDifference,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack");if(assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length===1)return expandDims($tensors[0],axis);let rank=$tensors[0].rank,shape=$tensors[0].shape,dtype=$tensors[0].dtype;assert(axis<=rank,()=>"Axis must be <= rank of the tensor"),$tensors.forEach(t=>{assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=$tensors.map(t=>expandDims(t,axis));return concat(expandedTensors,axis)}var stack=op({stack_});function step_(x,alpha=0){let $x=convertToTensor(x,"x","step"),inputs={x:$x},attrs={alpha};return ENGINE.runKernelFunc(backend3=>backend3.step($x,alpha),inputs,null,Step,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let $x=convertToTensor(x,"x","stridedSlice"),forward=backend3=>{strides==null&&(strides=new Array(begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=$x.rank-begin.length,expandAxes=maskToAxes(newAxisMask),newShape=$x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)}),$x=reshape($x,newShape);let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes($x.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided)return reshape(slice($x,begin,size),outShape);let res=backend3.stridedSlice($x,begin,end,strides);return reshape(res,outShape)},inputs={x:$x},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernelFunc(forward,inputs,null,StridedSlice,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let $x=convertToTensor(x,"x","tan"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.tan($x);return save([$x]),res},inputs,null,Tan)}var tan=op({tan_});function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernelFunc(b=>b.topk($x,k,sorted),inputs,null,TopK,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean7=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean7,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique",null);assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments},forward=(backend3,save)=>{let res=backend3.unsortedSegmentSum($x,$segmentIds,numSegments);return save([$segmentIds]),res};return ENGINE.runKernelFunc(forward,inputs,null,UnsortedSegmentSum,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`),axis<0&&(axis+=$x.shape.length);let inputs={value:$x},attrs={axis},forward=backend3=>backend3.unstack($x,axis);return ENGINE.runKernelFunc(forward,inputs,null,Unpack,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor168,mask,axis){let $tensor=convertToTensor(tensor168,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor168!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp132){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp132.shape,"Error in powStrict: "),pow(base2,exp132)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm52=normImpl(x,ord,axis),keepDimsShape=norm52.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm52.shape,axes)}return reshape(norm52,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step9,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step9!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step9,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let forward=backend3=>backend3.scatterND($indices,$updates,shape),inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernelFunc(forward,inputs,null,ScatterNd,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernelFunc(backend3=>backend3.sparseToDense($sparseIndices,$sparseValues,outputShape,$defaultValue),inputs,null,SparseToDense,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),$x=convertToTensor(x,"x","gatherND"),forward=backend3=>backend3.gatherND($x,$indices),inputs={params:$x,indices:$indices};return ENGINE.runKernelFunc(forward,inputs,null,GatherNd)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor4(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_,fused_ops_exports={};__export2(fused_ops_exports,{conv2d:()=>conv2d5,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad11,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let dilations=1,$dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(x4D.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat);return backend3.conv2dDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad11,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,Conv2DBackpropFilter,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>{let gradientMode=gradientDepth>0;return!gradientMode||activation2==="linear"};function fusedConv2d_({x,filter,strides,pad:pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad11,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad11),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad11),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},forward=backend3=>{let res=backend3.fusedConv2d({input:x4D,filter:$filter,convInfo,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return res},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2};if(bias==null){let customOp=customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedConv2D,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOp(x4D,$filter)}else{let customOpWithBias=customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedConv2D,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOpWithBias(x4D,$filter,$bias)}}var conv2d5=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad11,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo(x.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!0);return backend3.depthwiseConv2DDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad11,dimRoundingMode,dilations,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropFilter,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad11,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo(xShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0);return backend3.depthwiseConv2DDerInput(dy4D,filter,convInfo)},inputs={dy:dy4D,filter},attrs={strides,pad:pad11,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropInput,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad11,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad11,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad11,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},forward=backend3=>{let res=backend3.fusedDepthwiseConv2D({input:x4D,filter:$filter,convInfo,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return res},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2};if(bias==null){let customOp=customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedDepthwiseConv2D,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOp(x4D,$filter)}else{let customOpWithBias=customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedDepthwiseConv2D,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOpWithBias(x4D,$filter,$bias)}}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},forward=backend3=>{let y=backend3.fusedBatchMatMul({a:a3D,b:b3D,transposeA,transposeB,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return y},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2};if(bias==null){let customOp=customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,_FusedMatMul,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}});return customOp(a3D,b3D)}else{let customOpWithBias=customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,_FusedMatMul,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}});return customOpWithBias(a3D,b3D,$bias)}}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal22,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal22.size;)output.push(slice(signal22,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal22.size;){let padLen=start+frameLength-signal22.size,pad11=concat([slice(signal22,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad11),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal22,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal22,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method,extrapolationValue){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32");method=method||"bilinear",extrapolationValue=extrapolationValue||0;let numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let forward=backend3=>backend3.cropAndResize($image,$boxes,$boxInd,cropSize,method,extrapolationValue),inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize},res=ENGINE.runKernelFunc(forward,inputs,null,CropAndResize,attrs);return res}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},res=ENGINE.runKernel(FlipLeftRight,inputs,{});return res}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center},res=ENGINE.runKernel(RotateWithOffset,inputs,attrs);return res}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernelFunc(b=>b.nonMaxSuppression($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold),{boxes:$boxes,scores:$scores},null,NonMaxSuppressionV3,attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0).selectedIndices}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale22=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou3=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou3>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale22,iou3),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices:tensor1d(selectedIndices,"int32")};return returnScoresTensor&&(result.selectedScores=tensor1d(selectedScores,"float32")),returnValidOutputs&&(result.validOutputs=scalar(validOutputs,"int32")),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale22,iou3){let weight=Math.exp(scale22*iou3*iou3);return iou3<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),res=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,forward=(backend3,save)=>(save([batchImages]),backend3.resizeBilinear(batchImages,newHeight,newWidth,alignCorners)),inputs={images:batchImages},attrs={alignCorners,size},res=ENGINE.runKernelFunc(forward,inputs,null,ResizeBilinear,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,inputs={images:batchImages},attrs={alignCorners,size},forward=(backend3,save)=>(save([batchImages]),backend3.resizeNearestNeighbor(batchImages,newHeight,newWidth,alignCorners)),res=ENGINE.runKernelFunc(forward,inputs,null,ResizeNearestNeighbor,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction22){Reduction22[Reduction22.NONE=0]="NONE",Reduction22[Reduction22.MEAN=1]="MEAN",Reduction22[Reduction22.SUM=2]="SUM",Reduction22[Reduction22.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses8,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses8,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses8=abs(sub($labels,$predictions));return computeWeightedLoss(losses8,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses8=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses8,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses8=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses8,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses8=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses8,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon32=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon32),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses8=sub(l13,l23);return computeWeightedLoss(losses8,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses8=squaredDifference($labels,$predictions);return computeWeightedLoss(losses8,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses8=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses8,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);let customOp=customGrad((labels2,logits2,save)=>{let keepDims=!0,lse=logSumExp(logits2,[dim],keepDims),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2)),value=sum2(costVector,[dim]),gradFunc=(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]};return{value,gradFunc}});return customOp(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses8=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses8,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon32=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon32,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables5=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.rho,config2.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables5=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon,config2.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);varNames.forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config2){return new cls(config2.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.momentum,config2.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon32,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables5=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables5.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.decay,config2.momentum,config2.epsilon,config2.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon32,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon32=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon32)}static adadelta(learningRate=.001,rho=.95,epsilon32=null){return new AdadeltaOptimizer(learningRate,rho,epsilon32)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon32=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon32,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,linspaceImpl:()=>linspaceImpl,log:()=>log6,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod52,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod52),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2===1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod52,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod52):reshapedPermuted.push(inputShape[0]*prod52);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log6(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real82,imag82){if(real82.length!==imag82.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real82.length}, imag: ${imag82.length}.`);let result=new Float32Array(real82.length*2);for(let i=0;i<result.length;i+=2)result[i]=real82[i/2],result[i+1]=imag82[i/2];return result}function splitRealAndImagArrays(complex11){let real82=new Float32Array(complex11.length/2),imag82=new Float32Array(complex11.length/2);for(let i=0;i<complex11.length;i+=2)real82[i/2]=complex11[i],imag82[i/2]=complex11[i+1];return{real:real82,imag:imag82}}function complexWithEvenIndex(complex11){let len=Math.ceil(complex11.length/4),real82=new Float32Array(len),imag82=new Float32Array(len);for(let i=0;i<complex11.length;i+=4)real82[Math.floor(i/4)]=complex11[i],imag82[Math.floor(i/4)]=complex11[i+1];return{real:real82,imag:imag82}}function complexWithOddIndex(complex11){let len=Math.floor(complex11.length/4),real82=new Float32Array(len),imag82=new Float32Array(len);for(let i=2;i<complex11.length;i+=4)real82[Math.floor(i/4)]=complex11[i],imag82[Math.floor(i/4)]=complex11[i+1];return{real:real82,imag:imag82}}function getComplexWithIndex(complex11,index){let real82=complex11[index*2],imag82=complex11[index*2+1];return{real:real82,imag:imag82}}function assignToTypedArray(data,real82,imag82,index){data[index*2]=real82,data[index*2+1]=imag82}function exponents(n,inverse){let real82=new Float32Array(n/2),imag82=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real82[i]=Math.cos(x),imag82[i]=Math.sin(x)}return{real:real82,imag:imag82}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real82=Math.cos(x),imag82=Math.sin(x);return{real:real82,imag:imag82}}function castTensor(x,dtype,backend3){if(dtype==="complex64"){if(x.dtype==="complex64")return x.clone();let zerosTensor=zeros(x.shape),floatX=cast(x,"float32"),result=backend3.complex(floatX,zerosTensor);return zerosTensor.dispose(),floatX.dispose(),result}if(!hasEncodingLoss(x.dtype,dtype))return ENGINE.makeTensorFromDataId(x.dataId,x.shape,dtype);if(x.dtype==="complex64"){let real82=backend3.real(x),result=cast(real82,dtype);return real82.dispose(),result}if(dtype==="int32")return backend3.int(x);if(dtype==="bool"){let zero=scalar(0,x.dtype),result=backend3.notEqual(x,zero);return zero.dispose(),result}else throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}function reshapeTensor(x,shape){return ENGINE.makeTensorFromDataId(x.dataId,shape,x.dtype)}function linspaceImpl(start,stop,num){let step9=(stop-start)/(num-1),values=makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d(values,"float32")}var kernel_impls_exports={};__export2(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,split:()=>split5,tile:()=>tile4,topkImpl:()=>topkImpl,whereImpl:()=>whereImpl});function split5(x,sizeSplits,axis){let begin=new Array(x.rank).fill(0),size=x.shape.slice();return sizeSplits.map(s=>{let sliceSize=[...size];sliceSize[axis]=s;let sliceT=slice(x,begin,sliceSize);return begin[axis]+=s,sliceT})}function tile4(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result.toTensor()}function topkImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[tensor4(allTopKVals,outputShape,xDtype),tensor4(allTopKIndices,outputShape,"int32")]}var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}},acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}},acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}},addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)};return{a:derA,b:derB}}},addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}},argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}},asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}},atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)};return{a:derA,b:derB}}},atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}},atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dBackprop_(dy,input2,filterSize,strides,dilations=[1,1,1],pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dBackprop"),$input=convertToTensor(input2,"input","avgPool3dBackprop"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool3DInfo(input5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode);return backend3.avgPool3dBackprop(dy5D,input5D,convInfo)},inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool3DBackprop,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dBackprop=op({avgPool3dBackprop_}),avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dBackprop(dy,x,filterSize,strides,$dilations,pad11,dimRoundingMode)}}};function avgPoolBackprop_(dy,input2,filterSize,strides,pad11){let $dy=convertToTensor(dy,"dy","avgPoolBackprop"),$input=convertToTensor(input2,"input","avgPoolBackprop");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);let forward=backend3=>{let convInfo=computePool2DInfo(input4D.shape,filterSize,strides,1,pad11);return backend3.avgPoolBackprop(dy4D,input4D,convInfo)},inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad11},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPoolBackprop,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolBackprop=op({avgPoolBackprop_}),avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad11}=attrs;return{x:()=>avgPoolBackprop(dy,x,filterSize,strides,pad11)}}},batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}},batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}},broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}},castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})},ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})},clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}},concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]),derTensors=split(dy,sizeSplits,$axis);return derTensors.map(t=>()=>t)}},conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad11,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad11,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad11,dataFormat)}}},conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad11,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad11,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad11,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad11){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo(x5D.shape,filterShape,strides,dilations,pad11);return backend3.conv3dDerFilter(x5D,dy5D,convInfo)},inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad11,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,Conv3DBackpropFilterV2,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_}),conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad11}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad11),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad11)}}},cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}},coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}},cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse12}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse12);return permutation!=null&&(out=transpose(out,permutation)),out}}}},depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad11,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad11,dilations,dimRoundingMode)}}},dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}},divGradConfig={kernelName:Div,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))};return{a:derA,b:derB}}},eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,backPropKernelFunc=backend3=>backend3.eluDer(dy,y),inputs={dy,y};return{x:()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,EluGrad)}}},erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}},expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}},expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}},floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})},floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))};return{a:derA,b:derB}}},fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean7,variance,scale22]=saved,scaleValue=scale22==null?scalar(1):scale22,reductionAxes=getReductionAxes(mean7.shape,x.shape),tileShape=[];if(mean7.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}let xMinusMean=sub(x,mean7),dyTimesScaleValue=mul(dy,scaleValue),oneOverSqrtVariance=rsqrt(add2(variance,scalar(varianceEpsilon))),minusHalfRCube=mul(mul(mul(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar(-.5)),derX=()=>mean7.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean7.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),derMean=()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean7.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean7.shape)},derVariance=()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean7.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean7.shape)},derScale=()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean7.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean7.shape)},derOffset=()=>{let offsetDer=dy;return mean7.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean7.shape)};return{x:derX,mean:derMean,variance:derVariance,scale:derScale,offset:derOffset}}},gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0],derX=()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad};return{x:derX,indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat(arrays){let result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}var greaterEqualGradConfig={kernelName:GreaterEqual,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}},identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})},isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})},isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})},isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})},log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}},logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}},logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax62=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax62))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let forward=backend3=>backend3.LRNGrad(dy,x,y,depthRadius,bias,alpha,beta),inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernelFunc(forward,inputs,null,LRNBackprop,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_}),lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape(y,expandShapeToKeepDim(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape(dy,expandShapeToKeepDim(dy.shape,origAxes))),{x:()=>{let dx=mul(dy,cast(equal(xOrig,y),dy.dtype));return dx}}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}},maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul(dy,cast(greaterEqual(a,b),"float32")),derB=()=>mul(dy,cast(less(a,b),"float32"));return{a:derA,b:derB}}};function maxPool3dBackprop_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dBackprop"),$input=convertToTensor(input2,"input","maxPool3dBackprop"),$output=convertToTensor(output,"output","maxPool3dBackprop"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool3DInfo(input5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode);return backend3.maxPool3dBackprop(dy5D,input5D,output5D,convInfo)},inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool3DBackprop,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dBackprop=op({maxPool3dBackprop_}),maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dBackprop(dy,x,y,filterSize,strides,$dilations,pad11,dimRoundingMode)}}};function maxPoolBackprop_(dy,input2,output,filterSize,strides,pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolBackprop"),$input=convertToTensor(input2,"input","maxPoolBackprop"),$output=convertToTensor(output,"output","maxPoolBackprop");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool2DInfo($input.shape,filterSize,strides,1,pad11,dimRoundingMode);return backend3.maxPoolBackprop($dy,$input,$output,convInfo)},inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad11,dimRoundingMode};return ENGINE.runKernelFunc(forward,inputs,null,MaxPoolBackprop,attrs)}var maxPoolBackprop=op({maxPoolBackprop_}),maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad11}=attrs;return{x:()=>maxPoolBackprop(dy,x,y,filterSize,strides,pad11)}}},minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}},minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul(dy,cast(lessEqual(a,b),"float32")),derB=()=>mul(dy,cast(greater(a,b),"float32"));return{a:derA,b:derB}}},mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},derB=()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}},multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}},negateGradConfig={kernelName:Negate,gradFunc:dy=>({x:()=>neg(dy)})},oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}},onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},padV2GradConfig={kernelName:PadV2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},powGradConfig={kernelName:Pow,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp132=b,outShape=assertAndGetBroadcastShape(base2.shape,exp132.shape),derBase=()=>{let expFloat=cast(exp132,"float32"),res=mul(dy,mul(expFloat,pow(base2,sub(expFloat,scalar(1))))),reduceAxes=getReductionAxes(base2.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,base2.shape)},derExp=()=>{let condition=greater(base2,0),logBase=where(condition,log(base2),zerosLike(base2)),res=mul(dy,mul(y,logBase)),reduceAxes=getReductionAxes(exp132.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,exp132.shape)};return{a:derBase,b:derExp}}},preluGradConfig={kernelName:Prelu,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha)),alpha:()=>{let res=where(mask,zerosLike(dy),mul(dy,x)),reduceAxes=getReductionAxes(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,alpha.shape)}}}},reciprocalGradConfig={kernelName:Reciprocal,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,neg(square(x)))}}},relu6GradConfig={kernelName:Relu6,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul(lessEqual(x,6),step(x));return{x:()=>mul(dy,cast(mask,"float32"))}}},reluGradConfig={kernelName:Relu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,cast(step(x),"float32"))}}},reshapeGradConfig={kernelName:Reshape,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape(dy,x.shape)}}},resizeBilinearGradConfig={kernelName:ResizeBilinear,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeBilinearBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,ResizeBilinearGrad,attrs);return{images:imagesDer}}},resizeNearestNeighborGradConfig={kernelName:ResizeNearestNeighbor,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeNearestNeighborBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,ResizeNearestNeighborGrad,attrs);return{images:imagesDer}}},reverseGradConfig={kernelName:Reverse,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam(dims,dy.shape);return{x:()=>reverse(dy,axes)}}},roundGradConfig={kernelName:Round,gradFunc:dy=>({x:()=>zerosLike(dy)})},rsqrtGradConfig={kernelName:Rsqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg(div(dy,mul(pow(x,1.5),2)))}}},selectV2PoolGradConfig={kernelName:SelectV2,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast(zerosLike(condition),"float32"),t:()=>mul(dy,cast(condition,dy.dtype)),e:()=>mul(dy,cast(logicalNot(condition),dy.dtype))}}},seluGradConfig={kernelName:Selu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale22=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale22),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}},sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}},signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})},sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}},sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}},sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let i=0;i<dy.rank;i++)paddings.push([begin_[i],inputShape[i]-begin_[i]-size_[i]]);return{x:()=>pad(dy,paddings)}}},softmaxGradConfig={kernelName:Softmax,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul(dy,y);return{logits:()=>sub(dyTimesY,mul(sum2(dyTimesY,[dim],keepDims),y))}}},softplusGradConfig={kernelName:Softplus,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,sigmoid(x))}}},spaceToBatchNDGradConfig={kernelName:SpaceToBatchND,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND(dy,blockShape,paddings)}}},splitVGradConfig={kernelName:SplitV,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat(dy,axis)}}},sqrtGradConfig={kernelName:Sqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,mul(sqrt(cast(x,"float32")),2))}}},squareGradConfig={kernelName:Square,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,mul(cast(x,"float32"),2))}}},squaredDifferenceGradConfig={kernelName:SquaredDifference,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar(2),derA=()=>mul(dy,mul(two,sub(a,b))),derB=()=>mul(dy,mul(two,sub(b,a)));return{a:derA,b:derB}}},stepGradConfig={kernelName:Step,gradFunc:dy=>({x:()=>zerosLike(dy)})},subGradConfig={kernelName:Sub,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)};return{a:derA,b:derB}}},sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs,axes=parseAxisParam(axis,x.shape);axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}},tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}},tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}},tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs,derX=()=>{let xGrad=zerosLike(x);if(x.rank===1)for(let i=0;i<reps[0];++i)xGrad=add2(xGrad,slice(dy,[i*x.shape[0]],[x.shape[0]]));else if(x.rank===2)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1]],[x.shape[0],x.shape[1]]));else if(x.rank===3)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2]],[x.shape[0],x.shape[1],x.shape[2]]));else if(x.rank===4)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)for(let l=0;l<reps[3];++l)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2],l*x.shape[3]],[x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);return xGrad};return{x:derX}}},transposeGradConfig={kernelName:Transpose,gradFunc:(dy,saved,attrs)=>{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}},unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}},unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved,derX=()=>gatherDropNegatives(dy,segmentIds);return{x:derX}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims(isPositive,i+1);isPositive=logicalAnd(isPositive,ones2(gathered.shape,"bool"));let zeroSlice=zerosLike(gathered);return where(isPositive,gathered,zeroSlice)}var zerosLikeGradConfig={kernelName:ZerosLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negateGradConfig,oneHotGradConfig,onesLikeGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectV2PoolGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad11,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean7,variance,offset,scale22,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean7,variance,offset,scale22,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min82,max102){return this.throwIfDisposed(),clipByValue(this,min82,max102)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad11,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad11,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad11,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse12){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse12)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad11,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad11,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad11,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding2,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding2,dilationRate,strides)};Tensor.prototype.powStrict=function(exp132){return this.throwIfDisposed(),powStrict(this,exp132)};Tensor.prototype.pow=function(exp132){return this.throwIfDisposed(),pow(this,exp132)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad11,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad11,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon(){return _epsilon==null&&(_epsilon=backend2().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;i<numValues;i++)newArray=newArray.concat(value);return newArray}else{let newArray=new Array(numValues);return newArray.fill(value),newArray}}function assert2(val,message){if(!val)throw new AssertionError(message)}function count(array2,refernce){let counter=0;for(let item of array2)item===refernce&&counter++;return counter}function singletonOrArray(xs){return xs.length===1?xs[0]:xs}function toList(x){return Array.isArray(x)?x:[x]}function toSnakeCase(name){let intermediate=name.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2"),insecure=intermediate.replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return insecure[0]!=="_"?insecure:"private"+insecure}function toCamelCase(identifier){return identifier.length<=1||identifier.indexOf("_")===-1?identifier:identifier.replace(/[_]+(\w|$)/g,(m,p1)=>p1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config2){if(config2==null||typeof config2!="object")return;if(Array.isArray(config2))config2.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config2);for(let field of fields){let value=config2[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config2[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config2=identifier;if(config2.className==null||config2.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config2)}.
+'className' and 'config' must set.`);let className=config2.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config2.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config2.config);let returnObj=fromConfig(cls,config2.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config2.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique5(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value==null)return;if(values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult,f2=(...args)=>{let now22=util_exports.now();return now22-lastTime<waitMs||(lastTime=now22,lastResult=f(...args)),lastResult};return f2}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let className=identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config2={className,config:{}};return deserializeConstraint(config2)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config2){return new MinMaxNorm(config2)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones8,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros9});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod52=1;for(let i=begin;i<end;++i)prod52*=array2[i];return prod52}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min6(array2){return min(toArray1D(array2)).dataSync()[0]}function max8(array2){return max(toArray1D(array2)).dataSync()[0]}function range4(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast48(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile8(y,[1,n,1])})}function flatten3(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile8(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean7=0,stddev=1,dtype,seed){return randomNormal(shape,mean7,stddev,dtype,seed)}function dot5(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather7(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square24(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu6(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training5=!1){return training5?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale22=this.scale;if(this.mode==="fanIn"?scale22/=Math.max(1,fanIn):this.mode==="fanOut"?scale22/=Math.max(1,fanOut):scale22/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale22);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale22);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={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 deserializeInitializer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config2={};return config2.className=className,config2.config={},deserializeInitializer(config2)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros9(){return new Zeros}function ones8(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config2){return new VarianceScaling(config2)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add31,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d5,conv2d:()=>conv2d10,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d3,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot6,dropout:()=>dropout3,elu:()=>elu7,embedding:()=>embedding,flatten:()=>flatten4,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum9,minimum:()=>minimum7,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu6,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape87,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax4,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),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(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{let variable32=variableAndValue[0];variable32.write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,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 name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`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 AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}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(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec==null)continue;let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape==null)return;if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) 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 AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node2 of this.inboundNodes){let shapeString=JSON.stringify(node2.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`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 RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses8){if(losses8==null||Array.isArray(losses8)&&losses8.length===0)return;losses8=toList(losses8),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses8)}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config2={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config2.batchInputShape=this.batchInputShape),this.dtype!=null&&(config2.dtype=this.dtype),config2}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount===0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor168,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor168.sourceLayer,nodeIndex=tensor168.nodeIndex),layer.inboundNodes.length===0)return[tensor168];{let node2=layer.inboundNodes[nodeIndex];if(node2.inboundLayers.length===0)return node2.inputTensors;{let sourceTensors=[];for(let i=0;i<node2.inboundLayers.length;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config2){if(config2.batchShape==null&&config2.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config2.batchShape!=null&&config2.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config2.batchShape;config2.shape!=null&&batchShape==null&&(batchShape=[null].concat(config2.shape));let dtype=config2.dtype;dtype==null&&(dtype="float32");let inputLayer2=new InputLayer({batchInputShape:batchShape,name:config2.name,dtype,sparse:config2.sparse}),outputs=inputLayer2.inboundNodes[0].outputTensors;return outputs[0]}async function resolveScalarsInLogs(logs5){if(logs5==null)return;let promises=[],keys=[],scalarsToDispose=[];for(let key in logs5){let value=logs5[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs5[keys[i]]=values[i][0];dispose(scalarsToDispose)}}function disposeTensorsInLogs(logs5){if(logs5==null)return;for(let key in logs5){let value=logs5[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs5){}async onEpochEnd(epoch,logs5){}async onBatchBegin(batch,logs5){}async onBatchEnd(batch,logs5){}async onTrainBegin(logs5){}async onTrainEnd(logs5){}setModel(model2){}},CallbackList=class{constructor(callbacks3,queueLength=10){callbacks3==null&&(callbacks3=[]),this.callbacks=callbacks3,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs5)}async onEpochEnd(epoch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs5)}async onBatchBegin(batch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs5)}async onBatchEnd(batch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs5)}async onTrainBegin(logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onTrainBegin(logs5)}async onTrainEnd(logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onTrainEnd(logs5)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs5){logs5==null&&(logs5={});let batchSize=logs5.size==null?0:logs5.size;this.seen+=batchSize;for(let key in logs5){let value=logs5[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs5){if(logs5!=null)for(let key of this.params.metrics){if(this.totals[key]==null)continue;typeof this.totals[key]=="number"?logs5[key]=this.totals[key]/this.seen:tidy(()=>{let log102=mul(div(1,this.seen),this.totals[key]);logs5[key]=log102,this.totals[key].dispose(),keep(logs5[key])})}}},History=class extends BaseCallback{async onTrainBegin(logs5){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs5){logs5==null&&(logs5={}),this.epoch.push(epoch);for(let key in logs5)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs5[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n){let tensorToDispose=this.history[keys[n]][indices[n]];tensorToDispose.dispose(),this.history[keys[n]][indices[n]]=values[n][0]}}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs5){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.yield(epoch,batch,logs5))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs5){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs5),await this.epochBegin(epoch,logs5))}async onEpochEnd(epoch,logs5){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.epochEnd(epoch,logs5))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs5){this.batchBegin!=null&&(await resolveScalarsInLogs(logs5),await this.batchBegin(batch,logs5))}async onBatchEnd(batch,logs5){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.batchEnd(batch,logs5))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs5)),await Promise.all(ps)}async onTrainBegin(logs5){this.trainBegin!=null&&(await resolveScalarsInLogs(logs5),await this.trainBegin(logs5))}async onTrainEnd(logs5){this.trainEnd!=null&&(await resolveScalarsInLogs(logs5),await this.trainEnd(logs5))}};function standardizeCallbacks(callbacks3,yieldEvery){if(callbacks3==null&&(callbacks3={}),callbacks3 instanceof BaseCallback)return[callbacks3];if(Array.isArray(callbacks3)&&callbacks3[0]instanceof BaseCallback)return callbacks3;let callbackConfigs=toList(callbacks3);return callbackConfigs.map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors){let constructors=CallbackConstructorRegistry.constructors[+levelName];constructors.forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks3,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks3!=null&&actualCallbacks.push(...callbacks3),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config2,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square24(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm52=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm52)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square24(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square24(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square24(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg202=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg202,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten3(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast48(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast48(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys){if(typeof key!="string")return!1;if(!plainObjectCheck(x[key]))return!1}return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node2 of layer.inboundNodes)if(nodes.indexOf(node2)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node2 of layer.inboundNodes){if(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node2)===-1)continue;for(let i=0;i<node2.inboundLayers.length;++i){let inboundLayer=node2.inboundLayers[i].name,inboundLayerIndex=node2.nodeIndices[i],inboundTensorIndex=node2.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.7.0";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training5=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training5||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(srcLayer instanceof InputLayer)continue;let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training5||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training5||dispose(tensorsToDispose)}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack92=[],marks=[];for(stack92.push(fetch3);stack92.length>0;){let top=stack92[stack92.length-1];if(visited.has(top.name)){stack92.pop();continue}let topIsMarked=marks[marks.length-1]===stack92.length-1;if(top.inputs.length===0||topIsMarked)stack92.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack92.length-1);for(let input2 of top.inputs){if(recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),visited.has(input2.name))continue;stack92.push(input2)}}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique5(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique5(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor168,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor168.sourceLayer,nodeIndex=tensor168.nodeIndex,tensorIndex=tensor168.tensorIndex);let node2=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node2)!==-1)throw new RuntimeError(`The tensor ${tensor168.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node2)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node2)===-1&&nodesInProgress2.push(node2);let numInboundLayers=node2.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],tensorIndex2=node2.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node2);nodesInProgress2.indexOf(node2)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node2),1);nodesInDecreasingDepth.push(node2)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node2 of reversedNodesInDecreasingDepth){nodeIDToNode[node2.id]=node2,node2.id in nodesDepths||(nodesDepths[node2.id]=0);let depth=nodesDepths[node2.id],previousDepth=layersDepths[node2.outboundLayer.id]==null?0:layersDepths[node2.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node2.outboundLayer.id]=depth,layerIDToLayer[node2.outboundLayer.id]=node2.outboundLayer,nodesDepths[node2.id]=depth;for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node2 of nodesByDepth[depth]){let layer=node2.outboundLayer;if(layer!=null){for(let x of node2.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node2.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container2 of this.internalContainerRefs)result.numDisposedVariables+=container2.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("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 weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/"),shortenNameArray=tokens.slice(0,-2).concat([tokens[tokens.length-1]]);validatedName=shortenNameArray.join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;j<node2.inboundLayers.length;j++){let inboundLayer=node2.inboundLayers[j],nodeIndex2=node2.nodeIndices[j],tensorIndex=node2.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node2);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer,referenceInputTensors=node2.inputTensors,referenceOutputTensors=node2.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node2.callArgs!=null&&(kwargs=node2.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor168,mask]=tensorMap[x.id];outputShapes.push(tensor168.shape),outputTensors.push(tensor168),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses8=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses8.push(...layer.calculateLosses())}return losses8})}getConfig(){let config2={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node2=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node2.callArgs)try{JSON.stringify(node2.callArgs),kwargs=node2.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node2.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node2.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],tensorIndex=node2.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config2.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}config2.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}return config2.outputLayers=modelOutputs,config2}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config2.customObjects!=null?config2.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer;let inboundNodesData=layerData.inboundNodes;inboundNodesData.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config2.name,layersFromConfig=config2.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config2.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layer=createdLayers[layerName],layerOutputTensors=layer.inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config2.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layer=createdLayers[layerName],layerOutputTensors=layer.inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("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(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses8,sampleWeights){return mul(losses8,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=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 ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset5,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks3=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks3,verbose,args.epochs,null,null,getStepsPerEpoch(dataset5,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset5.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset5.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset5,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset5.size)&&(stepsPerEpoch=dataset5.size),stepsPerEpoch}function isDatasetObject(dataset5){return typeof dataset5.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset5,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset5)?dataset5:await dataset5.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather7(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks3,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range4(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks3,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks3=standardizeCallbacks(args.callbacks,args.yieldEvery),out=await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks3,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null);return out}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor168=tensors[i];if(tensor168.rank===1)outs.push(expandDims2(tensor168,1));else{if(tensor168.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor168)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors==null)return;let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor168=tensors[name];oldTensorIds.indexOf(tensor168.id)===-1&&tensorsToDispose.push(tensor168)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique5(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique5(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss==null)continue;if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);let theLosses=args.loss;lossFunctions=theLosses.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let outputMetrics=nestedMetrics[i],handleMetrics=metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else{let metricFn=get2(metric);weightedMetricFn=metricFn,metricName=metricNamePrefix+getLossOrMetricName(metric)}let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}};handleMetrics(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){if(this.collectedTrainableWeights==null)return;this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset5,args){return this.makeTestFunction(),evaluateDataset(this,dataset5,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor168,i)=>{tensor168==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchOuts=tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)});batchOuts.forEach((batchOut,i)=>outsBatches[i].push(batchOut))}return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i],lossFn=this.feedLossFns[i];lossFn===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!==0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range4(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;if(count(outLabels,label)>1){let dupIndex=count(outLabels.slice(0,i),label);newLabel+=`_${dupIndex}`}dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=lossFunction(targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables5=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0,totalLossValue=this.optimizer_.minimize(totalLossFunction,returnCost,variables5);return[totalLossValue].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset5,args){return fitDataset(this,dataset5,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],trainFunction=this.makeTrainFunction(),losses8=trainFunction(inputs.concat(targets)),lossValues=[];for(let loss of losses8){let v=await loss.data();lossValues.push(v[0])}return dispose(losses8),singletonOrArray(lossValues)}getNamedWeights(config2){let namedWeights=[],trainableOnly=config2!=null&&config2.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i){if(trainableOnly&&!weights[i].trainable)continue;namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]})}return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer7){this.optimizer_!==optimizer7&&(this.optimizer_=optimizer7,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses8=this.loss;for(let outputName of outputNames)if(typeof losses8[outputName]=="string")lossNames[outputName]=toSnakeCase(losses8[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer7=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer:optimizer7})}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config2)),returnString=!1,unusedArg=null,modelConfig=this.toJSON(unusedArg,returnString),modelArtifacts={modelTopology:modelConfig,format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null},includeOptimizer=config2==null?!1:config2.includeOptimizer;if(includeOptimizer&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer11,specs){let name2Tensor=io_exports.decodeWeights(buffer11,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){let shape=layer.inboundNodes[0].outputTensors[0].shape;if(shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("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(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))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(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),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{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),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 LayersModel({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(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset5,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset5,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),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(optimizer7){this.model.optimizer=optimizer7}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset5,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset5,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config2 instanceof Array){if(!(config2[0].className!=null)||config2[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config2}else util_exports.assert(config2.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config2.layers,delete config2.layers,extraModelConfig=config2;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let customObjects2,layer=deserialize(conf,customObjects2,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config2){return new Sequential(config2)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config2){return Input(config2)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu6(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config2={};return config2.className="linear",config2.config={},deserializeActivation(config2)}if(typeof identifier=="string"){let config2={};return config2.className=identifier,config2.config={},deserializeActivation(config2)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square24(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config2){return new cls({l1:config2.l1,l2:config2.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let className=identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config2={className,config:{}};return deserializeRegularizer(config2)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config2={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast48(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding2,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding2==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding2){if(dimSize==null)return null;if(padding2==="valid")dimSize=dimSize*strideSize+max8([kernelSize-strideSize,0]);else if(padding2==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding2}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding2="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding2==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding2="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding2==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding2="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding2==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`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 ValueError(`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 ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config2={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,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:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config2={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config2=super.getConfig();return delete config2.dilationRate,config2}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config2){super(rank,config2);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config2.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config2.kernelInitializer!=null||config2.kernelRegularizer!=null||config2.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config2.padding!=null&&config2.padding!=="same"&&config2.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config2.padding)}`);this.depthMultiplier=config2.depthMultiplier==null?1:config2.depthMultiplier,this.depthwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config2.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config2.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config2.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config2.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.kernelInitializer,delete config2.kernelRegularizer,delete config2.kernelConstraint,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config2.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config2}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.dataFormat,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config2={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=input2.resizeNearestNeighbor([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return input2.resizeNearestNeighbor([height,width])}})}getConfig(){let config2={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding2="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding2==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config2=super.getConfig();return config2.depthMultiplier=this.depthMultiplier,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config2}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range4(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state6,i)=>stepOutputs[1][i].mul(stepMask).add(state6.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;if(needPerStepOutputs){let axis=1;outputs=stack(perStepOutputs,axis)}return[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range4(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`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=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training5=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("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(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training5===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state6=>keep(state6.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state6 of initialState)this.stateSpec.push(new InputSpec({shape:state6.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length);let isTensor2=additionalInputs[0]instanceof SymbolicTensor;if(isTensor2){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training:training5},step9=(inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},rnnOutputs=rnn(step9,inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training5);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile8(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile8(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config2={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config2.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config2.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cellConfig=config2.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config2,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.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(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training5=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training:training5}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot5(mul(inputs,dpMask),this.kernel.read()):h=dot5(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot5(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config2)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return new cls(config2)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);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",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,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(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training5=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot5(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot5(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot5(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config2)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);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=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,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 biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training5=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot5(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot5(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config2)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),cellConfigs=this.cells.map(getCellConfig),config2={cells:cellConfigs};return Object.assign({},baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cells=[];for(let cellConfig of config2.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones92,rate,training:training5=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones92(),rate),createMask=()=>inTrainPhase(droppedInputs,ones92,training5);if(!count2||count2<=1)return keep(createMask().clone());let masks=Array(count2).fill(void 0).map(createMask);return masks.map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t},ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training5=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],batchSize=inputShape[0];if(batchSize==null)throw new ValueError("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(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training5?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state6=>keep(state6.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding:padding2,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding2,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding2,strides[1],dilationRate[1]),outShape=[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]];return outShape}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding:padding2,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding2||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training5=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training:training5,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config2={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config2)}inputConv(x,w,b,padding2){let out=conv2d(x,w,this.strides,padding2||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){let strides=1;return conv2d(x,w,strides,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config2){return new cls(config2)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training5=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2),output=inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training5);return output}return inputs})}getConfig(){let config2={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,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]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot5(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot5(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config2={};this.dataFormat!=null&&(config2.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config2={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config2={n:this.n},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!==0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config2={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range4(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config2={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={maskValue:this.maskValue};return Object.assign(config2,baseConfig),config2}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims),output=input2.mul(booleanMask.asType(input2.dtype));return output})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);input2.dtype!=="int32"&&(input2=cast48(input2,"int32"));let output=gather7(this.embeddings.read(),input2.as1D());return output.reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config2={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique5(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique5(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max8(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range4(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range4(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique5(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(allNoneShape)return;let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config2={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config2={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={stddev:this.stddev};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),noised=()=>randomNormal2(input2.shape,0,this.stddev).add(input2),output=inTrainPhase(noised,()=>input2,kwargs.training||!1);return output})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.rate>0&&this.rate<1){let noised=()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))};return inTrainPhase(noised,()=>input2,kwargs.training||!1)}return input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs),droppedInputs=()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale22=1.0507009873554805,alphaP=-alpha*scale22,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast48(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate,x=input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));return x.mul(a).add(b)};return inTrainPhase(droppedInputs,()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean7,variance,beta,gamma,epsilon32=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean7,variance,beta,gamma,epsilon32);else if(x.rank===3)out=batchNorm3d(x,mean7,variance,beta,gamma,epsilon32);else if(x.rank===4)out=batchNorm4d(x,mean7,variance,beta,gamma,epsilon32);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean7=meanAndVariance.mean,variance=meanAndVariance.variance,normed=batchNormalization(x,mean7,variance,beta,gamma,epsilon32);return[normed,mean7,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean7=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range4(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean7.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape),normed=batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon32);return[normed,mean7,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range4(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training5=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range4(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range4(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training5)return normalizeInference();let[normedTraining,mean7,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable32,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable32.read(),updateDelta=origValue.sub(value).mul(decay);variable32.write(origValue.sub(updateDelta))})},updateMovingMeanAndVariance=()=>{doMovingAverage(this.movingMean,mean7,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)};return updateMovingMeanAndVariance(),normedTraining})}getConfig(){let config2={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.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(let axis of this.axis)if(!Number.isInteger(axis))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=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.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]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique5(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean7,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale22=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean7=mean7.tile(momentsTiling),variance=variance.tile(momentsTiling),scale22=scale22.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean7,variance,offset,scale22,this.epsilon)})}getConfig(){let config2={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding2,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding2==null&&(padding2=[[1,1],[1,1]]),padding2.length!==2||padding2[0].length!==2||padding2[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding2[0],padding2[1]]:pattern=[[0,0],padding2[0],padding2[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config2={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding2,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding2),strides==null&&(strides=[1,1]),padding2==null&&(padding2="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding2==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding2,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding2),strides==null&&(strides=[1,1,1]),padding2==null&&(padding2="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding2==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool3d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool3d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config2={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}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(weights){this.layer.setWeights(weights)}getConfig(){let config2={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config2,customObjects={}){let layerConfig=config2.layer,layer=deserialize(layerConfig,customObjects);delete config2.layer;let newConfig={layer};return Object.assign(newConfig,config2),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let step9=(inputs2,states)=>{let output=getExactlyOneTensor(this.layer.call(inputs2,kwargs));return[output,[]]},rnnOutputs=rnn(step9,inputs,[],!1,null,null,!1,!0),y=rnnOutputs[1];return y})}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=!(layerConfig.goBackwards===!0);let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state6=>new InputSpec({shape:state6.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor168 of additionalInputs)if(tensor168 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let states=this.forwardLayer.states,stateMask=states.map(state6=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config2={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static fromConfig(cls,config2){let rnnLayer=deserialize(config2.layer);if(delete config2.layer,config2.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config2;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu7(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu6(args){return new PReLU(args)}function softmax4(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d5(args){return new Conv1D(args)}function conv2d10(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d3(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten4(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape87(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add31(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum9(args){return new Maximum2(args)}function minimum7(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot6(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config2){return new L1L2(config2)}function l12(config2){return l1(config2)}function l22(config2){return l2(config2)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less7(currVal,prevVal){return currVal<prevVal}function greater11(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less7:this.mode==="max"?this.monitorFunc=greater11:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater11:this.monitorFunc=less7,this.monitorFunc===less7&&(this.minDelta*=-1)}async onTrainBegin(logs5){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less7?Infinity:-Infinity}async onEpochEnd(epoch,logs5){await resolveScalarsInLogs(logs5);let current=this.getMonitorValue(logs5);if(current==null)return;this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0))}async onTrainEnd(logs5){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs5){logs5==null&&(logs5={});let monitorValue=logs5[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs5)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node2,tensorMap,context,resourceManager){let inputParam=node2.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node2.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors"){let inputs=node2.inputNames.slice(start,end);return inputs.map(name=>getTensor(name,tensorMap,context,resourceManager))}let tensor168=getTensor(node2.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor168.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor168.shape,data)}let attrParam=node2.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor168=resourceManager.getHashTableHandleByName(nodeName);if(tensor168!=null)return tensor168}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");if(parts.length===1)return[name,0];let nodeName=parts[0];return[nodeName,Number(parts[parts.length-1])]}function getPadding(node2,tensorMap,context){let pad11=getParamValue("pad",node2,tensorMap,context);if(pad11==="explicit"){pad11=getParamValue("explicitPaddings",node2,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad11[i*2],explicitPadding[i][1]=pad11[i*2+1];return explicitPadding}return pad11}function cloneTensor(tensor168){return tensor168.kept?tensor168:clone(tensor168)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var 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json17=[{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:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{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:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops692=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops692.map(op22=>op22.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op.startsWith("Placeholder")?placeholders.push(map[node2.name]):node2.op==="Const"?weights.push(map[node2.name]):(node2.input==null||node2.input.length===0)&&initNodes.push(map[node2.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node2=nodes[key];node2.children.length===0&&outputs.push(node2)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2!=null&&(node2.signatureKey=outputNodeNameToKey[name],outputs.push(node2))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2&&(node2.signatureKey=inputNodeNameToKey[name],inputs.push(node2))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node2){let mapper=getRegisteredOp(node2.op)||this.opMappers[node2.op]||{};node2.attr==null&&(node2.attr={});let newNode={name:node2.name,op:node2.op,category:mapper.category,inputNames:(node2.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node2.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node2.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node2.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op==="Const"&&weights.push(map[node2.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node2={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node2.signatureKey=arg.name,inputs.push(node2),nodes[nodeName]=node2});let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node2=nodes[nodeName];node2!=null&&(node2.defaultOutput=index,outputs.push(node2))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){typeof value=="string"&&(value=DataType[value]);switch(value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){return shape.unknownRank?void 0:shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node2,tensorMap,context){this.node=node2,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node2.inputNames.map(name=>this.getInput(name)),node2.rawAttrs!=null&&(this.attrs=Object.keys(node2.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node2,tensorMap,context)=>{switch(node2.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node2,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp2=(node2,tensorMap,context)=>{switch(node2.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node2,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node2,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node2,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node2,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node2,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node2,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node2,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node2,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node2,tensorMap,context),getParamValue("imag",node2,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node2,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node2,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node2,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node2,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node2,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node2,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node2,tensorMap,context))];case"Log":return[log(getParamValue("x",node2,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node2,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node2,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node2,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node2,tensorMap,context))];case"Real":return[real(getParamValue("x",node2,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node2,tensorMap,context))];case"Round":return[round(getParamValue("x",node2,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node2,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node2,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node2,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node2,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node2,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node2,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node2,tensorMap,context))];case"Square":return[square(getParamValue("x",node2,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node2,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node2,tensorMap,context))];case"Relu6":case"ClipByValue":return[clipByValue(getParamValue("x",node2,tensorMap,context),getParamValue("clipValueMin",node2,tensorMap,context),getParamValue("clipValueMax",node2,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node2.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node2,tensorMap,context),getParamValue("axes",node2,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor168=>{(keepIds==null||!keepIds.has(tensor168.tensor.id))&&tensor168.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor168){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
+          because the value dtype is ${tensor168.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor168.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor168.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor168,keep(tensor168),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i<this.size();i++)indices.push(i)}if(indices.length===0)return tensor4([],[0].concat(this.elementShape));let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,"TensorArray shape mismatch: "),stack(tensors,0)}concat(dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);if(this.size()===0)return tensor4([],[0].concat(this.elementShape));let indices=[];for(let i=0;i<this.size();i++)indices.push(i);let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`),concat(tensors,0)}scatter(indices,tensor168){if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor168.dtype}`);if(indices.length!==tensor168.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor168.shape[0]}`);let maxIndex=Math.max(...indices);if(!this.dynamicSize&&maxIndex>=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor168,0))}split(length,tensor168){if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor168.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor168.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${r}, and tensor's shape is: ${e.shape}`);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${t.length}), and the TensorArray is not marked as dynamically resizeable`);let s=r===0?0:e.size/r,c=[];rt(()=>{e=Q(e,[1,r,s]);for(let p=0;p<t.length;++p){let f=p===0?0:o[p-1],m=[0,f,0],y=[1,t[p],s];c[p]=Q(ce(e,m,y),this.elementShape)}return c});let l=[];for(let p=0;p<t.length;p++)l[p]=p;this.writeMany(l,c)}}class Yc{constructor(t,e,r,o=-1){this.tensors=t,this.elementShape=e,this.elementDtype=r,t!=null&&t.forEach(s=>{if(r!==s.dtype)throw new Error(`Invalid data types; op elements ${r}, but list elements ${s.dtype}`);to(e,s.shape,"TensorList shape mismatch: "),Sn(s)}),this.idTensor=Et(0),this.maxNumElements=o,Sn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Yc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.id))&&e.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(t,e,r=-1){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(r!==-1&&this.tensors.length!==r)throw new Error(`Operation expected a list with ${r} elements but got a list with ${this.tensors.length} elements.`);return to(t,this.elementShape,"TensorList shape mismatch: "),rt(()=>{let o=this.tensors.map(s=>Q(s,t));return ur(o,0)})}popBack(t,e){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let r=this.tensors.pop();return to(r.shape,t,"TensorList shape mismatch: "),Q(r,t)}pushBack(t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(to(t.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Sn(t),this.tensors.push(t)}resize(t){if(t<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${t}`);if(this.maxNumElements!==-1&&t>this.maxNumElements)throw new Error(`TensorListResize input size ${t} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=t}getItem(t,e,r){if(r!==this.elementDtype)throw new Error(`Invalid data types; op elements ${r}, but list elements ${this.elementDtype}`);if(t<0||t>this.tensors.length)throw new Error(`Trying to access element ${t} in a list with ${this.tensors.length} elements.`);if(this.tensors[t]==null)throw new Error(`element at index ${t} is null.`);return to(this.tensors[t].shape,e,"TensorList shape mismatch: "),this.tensors[t]}setItem(t,e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(t<0||this.maxNumElements!==-1&&t>=this.maxNumElements)throw new Error(`Trying to set element ${t} in a list with max ${this.maxNumElements} elements.`);to(this.elementShape,e.shape,"TensorList shape mismatch: "),Sn(e),this.tensors[t]=e}gather(t,e,r){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);return to(this.elementShape,r,"TensorList shape mismatch: "),t=t.slice(0,this.size()),t.length===0?un([],[0].concat(this.elementShape)):rt(()=>{let o=t.map(s=>Q(this.tensors[s],r));return ur(o,0)})}concat(t,e){if(!!t&&t!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${t}`);return to(this.elementShape,e,"TensorList shape mismatch: "),this.size()===0?un([],[0].concat(this.elementShape)):rt(()=>{let r=this.tensors.map(o=>Q(o,e));return Qe(r,0)})}}function JH(n,t,e){let r=n.dtype;if(n.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${n.shape}`);if(n.dtype!==e)throw new Error(`Invalid data types; op elements ${n.dtype}, but list elements ${e}`);let o=n.shape.slice(1);to(o,t,"TensorList shape mismatch: ");let s=mo(n);return new Yc(s,t,r)}function ZH(n,t,e){return new Yc([],n,t,e)}function QH(n,t,e,r){if(t.length!==n.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${n.shape[0]}`);let o=Math.max(...t);if(r!=null&&r!==-1&&o>=r)throw new Error(`Max index must be < array size (${o}  vs. ${r})`);let s=new Yc([],e,n.dtype,r),c=mo(n,0);return t.forEach((l,p)=>{s.setItem(l,c[p])}),s}function tj(n,t,e){let r=0,o=t.map(p=>(r+=p,r));if(r!==n.shape[0])throw new Error(`Expected sum of lengths to be equal to
+        ${totalLength}, and tensor's shape is: ${tensor168.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor168.size/totalLength,tensors=[];tidy(()=>{tensor168=reshape(tensor168,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices2=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors[i]=reshape(slice(tensor168,indices2,sizes),this.elementShape)}return tensors});let indices=[];for(let i=0;i<length.length;i++)indices[i]=i;this.writeMany(indices,tensors)}},TensorList=class{constructor(tensors,elementShape,elementDtype,maxNumElements=-1){this.tensors=tensors,this.elementShape=elementShape,this.elementDtype=elementDtype,tensors!=null&&tensors.forEach(tensor168=>{if(elementDtype!==tensor168.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor168.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor168.shape,"TensorList shape mismatch: "),keep(tensor168)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor168=>{(keepIds==null||!keepIds.has(tensor168.id))&&tensor168.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor168=>reshape(tensor168,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor168=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor168.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor168,elementShape)}pushBack(tensor168){if(tensor168.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor168.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor168),this.tensors.push(tensor168)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor168){if(tensor168.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor168.shape,"TensorList shape mismatch: "),keep(tensor168),this.tensors[elementIndex]=tensor168}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor4([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor4([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor168,elementShape,elementDtype){let dtype=tensor168.dtype;if(tensor168.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor168.shape}`);if(tensor168.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${elementDtype}`);let outputShape=tensor168.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor168);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor168,indices,elementShape,numElements){if(indices.length!==tensor168.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor168.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);let list=new TensorList([],elementShape,tensor168.dtype,numElements),tensors=unstack(tensor168,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split9(tensor168,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor168.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${r}, and tensor's shape is: ${n.shape}`);let s=r===0?0:n.size/r,c=rt(()=>{let p=[];n=Q(n,[1,r,s]);for(let f=0;f<t.length;++f){let m=f===0?0:o[f-1],y=[0,m,0],b=[1,t[f],s];p[f]=Q(ce(n,y,b),e)}return n.dispose(),p}),l=new Yc([],e,n.dtype,t.length);for(let p=0;p<c.length;p++)l.setItem(p,c[p]);return l}let ej=async(n,t,e)=>{switch(n.op){case"If":case"StatelessIf":{let r=A("thenBranch",n,t,e),o=A("elseBranch",n,t,e),s=A("cond",n,t,e),c=A("args",n,t,e),l=await s.data();return l[0]?e.functionMap[r].executeFunctionAsync(c,e.tensorArrayMap,e.tensorListMap):e.functionMap[o].executeFunctionAsync(c,e.tensorArrayMap,e.tensorListMap)}case"While":case"StatelessWhile":{let r=A("body",n,t,e),o=A("cond",n,t,e),s=A("args",n,t,e),c=await e.functionMap[o].executeFunctionAsync(s,e.tensorArrayMap,e.tensorListMap),l=s.map(m=>m.id),p=await c[0].data();c.forEach(m=>{!m.kept&&l.indexOf(m.id)===-1&&m.dispose()});let f=s;for(;p[0];){let m=f;f=await e.functionMap[r].executeFunctionAsync(f,e.tensorArrayMap,e.tensorListMap);let y=f.map(v=>v.id);m.forEach(v=>{!v.kept&&l.indexOf(v.id)===-1&&y.indexOf(v.id)===-1&&v.dispose()});let b=await e.functionMap[o].executeFunctionAsync(f,e.tensorArrayMap,e.tensorListMap);p=await b[0].data(),b.forEach(v=>{!v.kept&&l.indexOf(v.id)===-1&&y.indexOf(v.id)===-1&&v.dispose()})}return f}case"LoopCond":{let r=A("pred",n,t,e);return[Ts(r)]}case"Switch":{let r=A("pred",n,t,e),o=A("data",n,t,e);return o.kept||(o=Ts(o)),(await r.data())[0]?[void 0,o]:[o,void 0]}case"Merge":{let r=n.inputNames.find(o=>fr(o,t,e)!==void 0);if(r){let o=fr(r,t,e);return[Ts(o)]}return}case"Enter":{let r=A("frameName",n,t,e),o=A("tensor",n,t,e);return e.enterFrame(r),[Ts(o)]}case"Exit":{let r=A("tensor",n,t,e);return e.exitFrame(),[Ts(r)]}case"NextIteration":{let r=A("tensor",n,t,e);return e.nextIteration(),[Ts(r)]}case"TensorArrayV3":{let r=A("size",n,t,e),o=A("dtype",n,t,e),s=A("elementShape",n,t,e),c=A("dynamicSize",n,t,e),l=A("clearAfterRead",n,t,e),p=A("identicalElementShapes",n,t,e),f=A("name",n,t,e),m=new YH(f,o,r,s,p,c,l);return e.addTensorArray(m),[m.idTensor,Et(1)]}case"TensorArrayWriteV3":{let r=A("tensorArrayId",n,t,e),o=A("index",n,t,e),s=A("tensor",n,t,e),c=e.getTensorArray(r.id);return c.write(o,s),[c.idTensor]}case"TensorArrayReadV3":{let r=A("tensorArrayId",n,t,e),o=A("index",n,t,e),s=e.getTensorArray(r.id);return[s.read(o)]}case"TensorArrayGatherV3":{let r=A("tensorArrayId",n,t,e),o=A("indices",n,t,e),s=A("dtype",n,t,e),c=e.getTensorArray(r.id);return[c.gather(o,s)]}case"TensorArrayScatterV3":{let r=A("tensorArrayId",n,t,e),o=A("indices",n,t,e),s=A("tensor",n,t,e),c=e.getTensorArray(r.id);return c.scatter(o,s),[c.idTensor]}case"TensorArrayConcatV3":{let r=A("tensorArrayId",n,t,e),o=e.getTensorArray(r.id),s=A("dtype",n,t,e);return[o.concat(s)]}case"TensorArraySplitV3":{let r=A("tensorArrayId",n,t,e),o=A("tensor",n,t,e),s=A("lengths",n,t,e),c=e.getTensorArray(r.id);return c.split(s,o),[c.idTensor]}case"TensorArraySizeV3":{let r=A("tensorArrayId",n,t,e),o=e.getTensorArray(r.id);return[Et(o.size(),"int32")]}case"TensorArrayCloseV3":{let r=A("tensorArrayId",n,t,e),o=e.getTensorArray(r.id);return o.clearAndClose(),[o.idTensor]}case"TensorListSetItem":{let r=A("tensorListId",n,t,e),o=A("index",n,t,e),s=A("tensor",n,t,e),c=e.getTensorList(r.id);return c.setItem(o,s),[c.idTensor]}case"TensorListGetItem":{let r=A("tensorListId",n,t,e),o=A("index",n,t,e),s=A("elementShape",n,t,e),c=A("elementDType",n,t,e),l=e.getTensorList(r.id);return[l.getItem(o,s,c)]}case"TensorListScatterV2":case"TensorListScatter":{let r=A("indices",n,t,e),o=A("tensor",n,t,e),s=A("elementShape",n,t,e),c=A("numElements",n,t,e),l=QH(o,r,s,c);return e.addTensorList(l),[l.idTensor]}case"TensorListReserve":{let r=A("elementShape",n,t,e),o=A("elementDType",n,t,e),s=A("numElements",n,t,e),c=ZH(r,o,s);return e.addTensorList(c),[c.idTensor]}case"TensorListGather":{let r=A("tensorListId",n,t,e),o=A("indices",n,t,e),s=A("elementShape",n,t,e),c=A("elementDType",n,t,e),l=e.getTensorList(r.id);return[l.gather(o,c,s)]}case"TensorListStack":{let r=A("tensorListId",n,t,e),o=A("elementShape",n,t,e),s=A("elementDType",n,t,e),c=A("numElements",n,t,e),l=e.getTensorList(r.id);return[l.stack(o,s,c)]}case"TensorListFromTensor":{let r=A("tensor",n,t,e),o=A("elementShape",n,t,e),s=A("elementDType",n,t,e),c=JH(r,o,s);return e.addTensorList(c),[c.idTensor]}case"TensorListConcat":{let r=A("tensorListId",n,t,e),o=e.getTensorList(r.id),s=A("dtype",n,t,e),c=A("elementShape",n,t,e);return[o.concat(s,c)]}case"TensorListPushBack":{let r=A("tensorListId",n,t,e),o=A("tensor",n,t,e),s=e.getTensorList(r.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let 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f=A("strides",n,t,e),m=Nm(n,t,e),y=A("dataFormat",n,t,e).toUpperCase(),b=A("dilations",n,t,e),[v,T]=A("args",n,t,e);return{stride:f,pad:m,dataFormat:y,dilations:b,biasArg:v,preluArg:T,activationFunc:o}}let nj=(n,t,e)=>{switch(n.op){case"Conv1D":{let r=A("stride",n,t,e),o=A("pad",n,t,e),s=A("dataFormat",n,t,e).toUpperCase(),c=A("dilation",n,t,e);return[cd(A("x",n,t,e),A("filter",n,t,e),r,o,s,c)]}case"Conv2D":{let 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s=A("x",n,t,e),c=A("data",n,t,e),l=A("message",n,t,e),p=A("summarize",n,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(l);for(let f=0;f<c.length;f++)console.log(Array.prototype.slice.call(c[f].dataSync()).slice(0,p));return[s];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ist="graph";class aj{constructor(t,e){this.keyDType=t,this.valueDType=e,this.handle=Et(0),this.tensorMap=new Map,Sn(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(t=>t.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),rt(()=>{let o=mo(e),s=r.length,c=o.length;_(s===c,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${c} elements.`);for(let l=0;l<s;l++){let p=r[l],f=o[l];Sn(f),this.tensorMap.set(p,f)}return this.handle})}async find(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return rt(()=>{let o=[];for(let s=0;s<r.length;s++){let c=r[s],l=this.findWithDefault(c,e);o.push(l)}return ur(o)})}findWithDefault(t,e){let r=this.tensorMap.get(t);return r!=null?r:e}checkKeyAndValueTensor(t,e){if(t.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${t.dtype}`);if(e.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`)}}let cj=async(n,t,e,r)=>{switch(n.op){case"HashTable":case"HashTableV2":{let o=A("keyDType",n,t,e),s=A("valueDType",n,t,e),c=new aj(o,s);return r.addHashTable(n.name,c),[c.handle]}case"LookupTableImport":case"LookupTableImportV2":{let o=A("tableHandle",n,t,e,r),s=A("keys",n,t,e),c=A("values",n,t,e),l=r.getHashTableById(o.id);return[await l.import(s,c)]}case"LookupTableFind":case"LookupTableFindV2":{let 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uj=(n,t,e)=>{switch(n.op){case"Equal":return[po(A("a",n,t,e),A("b",n,t,e))];case"NotEqual":return[ci(A("a",n,t,e),A("b",n,t,e))];case"Greater":return[Fr(A("a",n,t,e),A("b",n,t,e))];case"GreaterEqual":return[ds(A("a",n,t,e),A("b",n,t,e))];case"Less":return[ap(A("a",n,t,e),A("b",n,t,e))];case"LessEqual":return[ai(A("a",n,t,e),A("b",n,t,e))];case"LogicalAnd":return[Yr(A("a",n,t,e),A("b",n,t,e))];case"LogicalNot":return[cp(A("a",n,t,e))];case"LogicalOr":return[yd(A("a",n,t,e),A("b",n,t,e))];case"Select":case"SelectV2":return[Xn(A("condition",n,t,e),A("a",n,t,e),A("b",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ast="logical";let pj=(n,t,e)=>{switch(n.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[ge(A("a",n,t,e),A("b",n,t,e),A("transposeA",n,t,e),A("transposeB",n,t,e))];case"Transpose":return[Kt(A("x",n,t,e),A("perm",n,t,e))];case"_FusedMatMul":let[r,o]=A("fusedOps",n,t,e),s=r==="biasadd",c=o==="prelu",l=A("numArgs",n,t,e);if(s){if(c&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!c&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,f]=A("args",n,t,e);return[Pd({a:A("a",n,t,e),b:A("b",n,t,e),transposeA:A("transposeA",n,t,e),transposeB:A("transposeB",n,t,e),bias:p,activation:o,preluActivationWeights:f})];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Fst="matrices";let hj=(n,t,e)=>{switch(n.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[ea(A("x",n,t,e),A("mean",n,t,e),A("variance",n,t,e),A("offset",n,t,e),A("scale",n,t,e),A("epsilon",n,t,e))];case"FusedBatchNormV3":return[ea(A("x",n,t,e),A("mean",n,t,e),A("variance",n,t,e),A("offset",n,t,e),A("scale",n,t,e),A("epsilon",n,t,e))];case"LRN":return[kw(A("x",n,t,e),A("radius",n,t,e),A("bias",n,t,e),A("alpha",n,t,e),A("beta",n,t,e))];case"Softmax":return[ca(A("x",n,t,e))];case"LogSoftmax":return[gd(A("x",n,t,e))];case"SparseToDense":return[Vw(A("sparseIndices",n,t,e),A("outputShape",n,t,e),A("sparseValues",n,t,e),A("defaultValue",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Rst="normalization";let fj=(n,t,e)=>{switch(n.op){case"Max":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[lr(A("x",n,t,e),r,o)]}case"Mean":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[en(A("x",n,t,e),r,o)]}case"Min":{let 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r=A("axis",n,t,e);return[li(A("x",n,t,e),r)]}case"Reshape":return[Q(A("x",n,t,e),A("shape",n,t,e))];case"MirrorPad":return[Sw(A("x",n,t,e),A("padding",n,t,e),A("mode",n,t,e))];case"PadV2":case"Pad":return[Wo(A("x",n,t,e),A("padding",n,t,e),A("constantValue",n,t,e))];case"SpaceToBatchND":{let r=A("blockShape",n,t,e),o=A("paddings",n,t,e);return[up(A("x",n,t,e),r,o)]}case"BatchToSpaceND":{let r=A("blockShape",n,t,e),o=A("crops",n,t,e);return[np(A("x",n,t,e),r,o)]}case"DepthToSpace":{let r=A("blockSize",n,t,e),o=A("dataFormat",n,t,e).toUpperCase();return[yw(A("x",n,t,e),r,o)]}case"BroadcastTo":return[rp(A("x",n,t,e),A("shape",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Mst="transformation";function m$(n,t,e,r){let o=((s,c,l)=>{switch(s.category){case"arithmetic":return rt(()=>jH(s,c,l));case"basic_math":return rt(()=>KH(s,c,l));case"control":return ej(s,c,l);case"convolution":return rt(()=>nj(s,c,l));case"creation":return rt(()=>rj(s,c,l));case"dynamic":return oj(s,c,l);case"evaluation":return rt(()=>sj(s,c,l));case"image":return rt(()=>lj(s,c,l));case"graph":return rt(()=>ij(s,c,l));case"logical":return rt(()=>uj(s,c,l));case"matrices":return rt(()=>pj(s,c,l));case"normalization":return rt(()=>hj(s,c,l));case"reduction":return rt(()=>fj(s,c,l));case"slice_join":return rt(()=>dj(s,c,l));case"spectral":return rt(()=>mj(s,c,l));case"transformation":return rt(()=>gj(s,c,l));case"hash_table":return cj(s,c,l,r);case"custom":let p=l$(s.op);if(p&&p.customExecutor)return p.customExecutor(new HH(s,c,l));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(n,t,e);return qi(o)?o.then(s=>[].concat(s)):[].concat(o)}class g${constructor(t={},e={},r={},o={}){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=r,this.functionMap=o,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let r=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(r))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}}function y$(n,t,e,r){let o=new Set,s=[],c=null,l=null,p=new Set,f=Object.keys(n).map(b=>Nr(b)[0]),m=[];r!=null&&(m=r.map(b=>Nr(b.name)[0]));let y=[...t];for(;y.length>0;){let b=y.pop();if((b$(b)||vj(b)||Tj(b))&&(c==null&&(c=b,l=c.children.map(v=>v.name).filter(v=>o.has(v)))),o.add(b.name),e[b.name]!=null)continue;if(f.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){s.push(b.name);continue}b.inputs.forEach(v=>{if(p.has(v.name))return;p.add(v.name),y.push(v)})}return{inputs:n,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:c,syncInputs:l}}function yj(n,t,e){let{usedNodes:r,inputs:o}=e,s=[],c=Object.keys(o).map(m=>Nr(m)[0]).map(m=>n.nodes[m]),l=n.initNodes;c.forEach(m=>{r.has(m.name)&&s.push(m)}),n.weights.forEach(m=>{r.has(m.name)&&s.push(m)}),l!=null&&l.forEach(m=>{r.has(m.name)&&s.push(m)});let p=new Set,f=[];for(;s.length>0;){let m=s.pop();p.add(m.name),t[m.name]||f.push(m),m.children.forEach(y=>{!p.has(y.name)&&r.has(y.name)&&y.inputs.every(b=>p.has(b.name))&&s.push(y)})}return f}let bj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],xj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],wj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function b$(n){return bj.indexOf(n.op)>=0}function vj(n){return xj.indexOf(n.op)>=0}function Tj(n){return wj.indexOf(n.op)>=0}class _m{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(r=>{this._functionExecutorMap[r]=new _m(t.functions[r],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(t){let e=Object.keys(t).map(r=>t[r].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let r=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return r.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let r=y$(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:c}=r;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. 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m}processChildNodes(t,e,r,o,s,c){t.children.forEach(l=>{let[p]=vs(l.name,r);if(s[p]||!c.has(l.name))return;l.op==="Merge"?l.inputNames.some(f=>!!fr(f,o,r))&&(s[p]=!0,e.push({contexts:r.currentContext,node:l})):l.inputNames.every(f=>!!fr(f,o,r))&&(s[p]=!0,e.push({contexts:r.currentContext,node:l}))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let r=t[e],[o]=Nr(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let c=s.attrParams.shape.value,l=c.length===r.shape.length&&r.shape.every((p,f)=>c[f]===-1||c[f]===p);_(l,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${c}], but was [${r.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&_(r.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(t){let e={};for(let r in t)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let o=this._signature.inputs[r];e[o.name]=t[r]}else e[r]=t[r];return e}checkInputs(t){let e=Object.keys(t).filter(r=>{let[o]=Nr(r);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[e]!=null){let r=this._signature.outputs[e];return r.name}return e},{})}checkOutputs(t){t.forEach(e=>{let[r]=Nr(e);if(!this.graph.nodes[r])throw new Error(`The output '${e}' is not found in the graph`)})}}class kj{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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save(t,e){if(typeof t=="string"){let r=Mx(t);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${t}'`);t=r[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}predict(t,e){return this.execute(t,this.outputNodes)}normalizeInputs(t){if(!(t instanceof ot)&&!Array.isArray(t))return t;if(t=Array.isArray(t)?t:[t],t.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${t.length} input tensors.`);return this.inputNodes.reduce((e,r,o)=>(e[r]=t[o],e),{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}execute(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=this.executor.execute(t,e);return r.length>1?r:r[0]}async executeAsync(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=await this.executor.executeAsync(t,e);return r.length>1?r:r[0]}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,r)=>(e[r]=[t[r]],e),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function Cj(n,t={}){if(n==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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N${constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),r=this.get(e);return this.set(e,this.pop()),r}}class Sm extends N${constructor(){super(Sm.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),r=this.length();for(let o=0;o<r;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}}Sm.INITIAL_CAPACITY=32;function _$(n){return new Rj(n)}function Bst(n){let t=n;return Yp(()=>({value:t++,done:!1}))}function Yp(n){return new Pj(n)}function C$(n,t){return new $$(n,t)}function zst(n,t,e){return C$(Yp(n).take(t),e)}function Fj(n,t=vi.FAIL){return new Uj(n,t)}class En{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],r=await t.next();for(;!r.done;)e.push(r.value),r=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),r=t(e.value);for(;!e.done&&r;)e=await this.next(),r=t(e.value)}handleErrors(t){return new Vj(this,t)}filter(t){return new zj(this,t)}map(t){return new Wj(this,t)}mapAsync(t){return new S$(this,t)}serialMapAsync(t){return new S$(this,t).serial()}flatmap(t){return new Gj(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new Bj(this,t,e)}columnMajorBatch(t,e=!0,r=T$){let o=this.rowMajorBatch(t,e);return o.map(s=>$j(s,r))}concatenate(t,e){return new $$(_$([this,t]),e)}take(t){return t<0||t==null?this:new Mj(this,t)}skip(t){return t<0||t==null?this:new Lj(this,t)}prefetch(t){return new I$(this,t)}shuffle(t,e){return new qj(this,t,e)}serial(){return new Oj(this)}}class Rj extends En{constructor(t){super();this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:Dj(t),done:!1}}}class Pj extends En{constructor(t){super();this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}}class Oj extends En{constructor(t){super();this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class Lj extends En{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;Xt(t.value)}return this.upstream.next()}}class Mj extends En{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class Bj extends En{constructor(t,e,r=!0){super();this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}}class zj extends En{constructor(t,e){super();this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Xt(t.value)}}}class Wj extends En{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=cs(t.value),r=this.transform(t.value),o=cs(r);for(let s of e)Gf(s,o)||s.dispose();return{value:r,done:!1}}}class Vj extends En{constructor(t,e){super();this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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(t){if(!this.handler(t))return{value:null,done:!0}}}}class S$ extends En{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=cs(t.value),r=await this.transform(t.value),o=cs(r);for(let s of e)Gf(s,o)||s.dispose();return{value:r,done:!1}}}class t1 extends En{constructor(){super();this.outputQueue=new Sm,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class Gj extends t1{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=cs(t.value),r=this.transform(t.value),o=cs(r);this.outputQueue.pushAll(r);for(let s of e)Gf(s,o)||s.dispose();return!0}}class $$ extends En{constructor(t,e){super();this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){let t="TODO: fill in upstream of chained summaries";return`${t} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}}var vi;(function(n){n[n.FAIL=0]="FAIL",n[n.SHORTEST=1]="SHORTEST",n[n.LONGEST=2]="LONGEST"})(vi||(vi={}));class Uj extends En{constructor(t,e=vi.FAIL){super();this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){let t="TODO: fill in upstream of zip summaries";return`{${t}} -> Zip`}async nextState(t){await t;let e=0,r=0;function o(c){if(c instanceof En){let l=c.next();return{value:l.then(p=>(e++,p.done&&r++,p.value)),recurse:!1}}else return{value:null,recurse:!0}}let s=await k$(this.iterators,o);if(e===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case vi.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case vi.SHORTEST:return{value:null,done:!0};case vi.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class I$ extends En{constructor(t,e){super();this.upstream=t,this.bufferSize=e,this.buffer=new N$(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}}class qj extends I${constructor(t,e,r){super(t,e);this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=Fc(r||or().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}}class Zc{constructor(){this.size=null}batch(t,e=!0){let r=this;_(t>0,()=>`batchSize needs to be positive, but it is
-      ${t}`);let o;return this.size===Infinity||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),_r(async()=>(await r.iterator()).columnMajorBatch(t,e,Kj),o)}concatenate(t){let e=this,r;return this.size===Infinity||t.size===Infinity?r=Infinity:this.size!=null&&t.size!=null?r=this.size+t.size:r=null,_r(async()=>(await e.iterator()).concatenate(await t.iterator()),r)}filter(t){let e=this,r;return this.size===Infinity?r=Infinity:r=null,_r(async()=>(await e.iterator()).filter(o=>rt(()=>t(o))),r)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return _r(async()=>(await e.iterator()).map(r=>rt(()=>t(r))),this.size)}mapAsync(t){let e=this;return _r(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return _r(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,r;return this.size!=null&&t>0?r=this.size*t:t===0?r=0:this.size!=null&&(t===void 0||t<0)?r=Infinity:r=null,_r(async()=>{let o=Yp(async()=>({value:await e.iterator(),done:!1}));return C$(o.take(t))},r)}skip(t){let e=this,r;return this.size!=null&&t>=0&&this.size>=t?r=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?r=0:r=null,_r(async()=>(await e.iterator()).skip(t),r)}shuffle(t,e,r=!0){if(t==null||t<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 (${this.size} elements)`);let o=this,s=Fc(e||or().toString());return _r(async()=>{let c=s.int32();return r&&(c+=s.int32()),(await o.iterator()).shuffle(t,c.toString())},this.size)}take(t){let e=this,r;return this.size!=null&&this.size>t?r=t:this.size!=null&&this.size<=t?r=this.size:r=null,_r(async()=>(await e.iterator()).take(t),r)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}Zc.MAX_BUFFER_SIZE=1e4;function _r(n,t=null){return new class extends Zc{constructor(){super(...arguments);this.size=t}async iterator(){return n()}}}function Hj(n){return _r(async()=>_$(n),n.length)}function jj(n){if(!Jc(n))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(n))for(let e=0;e<n.length;e++)t=t==null?n[e].size:Math.min(t,n[e].size);else if(n instanceof Object)for(let e in n)t=t==null?n[e].size:Math.min(t,n[e].size);return _r(async()=>{let e=await k$(n,r=>{if(r instanceof Zc)return{value:r.iterator(),recurse:!1};if(Jc(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Fj(e,vi.SHORTEST)},t)}function Kj(n){if(n===null)return null;let t=n[0];if(Ij(t)){let e=Xj(n);return{value:e,recurse:!1}}return{value:null,recurse:!0}}function Xj(n){if(n.length===0)throw new Error("Can't make a batch of zero elements.");return n[0]instanceof ot?ur(n):un(n)}class E$ extends Zc{constructor(t){super();this.input=t}async iterator(){let t=await this.input.iterator(),e=t.decodeUTF8(),r=e.split(`
-`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o));return r}}let $m='"',Jp=Symbol("out"),D$=Symbol("field"),Im=Symbol("quote"),e1=Symbol("quoteafterquote"),A$=Symbol("quoteinquote");class F$ extends Zc{constructor(t,e){super();this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new E$(t),e||(e={}),this.hasHeader=!(e.hasHeader===!1),this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(_(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&_(t.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 ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),r=Object.keys(e).filter(o=>e[o]>1);if(_(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs)for(let o of Object.keys(this.columnConfigs)){let s=this.fullColumnNames.indexOf(o);if(s===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await this.base.iterator(),e=await t.next();if(e.done)throw new Error("No data was found for CSV parsing.");let r=e.value,o=this.parseRow(r,!1);return o}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),r={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let c=this.fullColumnNames[s],l=this.columnConfigs?this.columnConfigs[c]:null;if(this.configuredColumnsOnly&&!l)continue;{let p=e[s],f=null;if(p==="")if(l&&l.default!==void 0)f=l.default;else{if(l&&(l.required||l.isLabel))throw new Error(`Required column ${c} is empty in this line: ${t}`);f=void 0}else{let m=Number(p);if(isNaN(m))l&&l.dtype==="bool"?f=this.getBoolean(p):f=p;else if(!l||!l.dtype)f=m;else switch(l.dtype){case"float32":f=m;break;case"int32":f=Math.floor(m);break;case"bool":f=this.getBoolean(p);break;default:f=m}}l&&l.isLabel?o[c]=f:r[c]=f}}return Object.keys(o).length===0?r:{xs:r,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let r=[],o=0,s=t.length,c=Jp;for(let l=0;l<s;l++)switch(c){case Jp:switch(t.charAt(l)){case $m:o=l+1,c=Im;break;case this.delimiter:if(o=l+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),c=Jp;break;default:c=D$,o=l;break}break;case D$:switch(t.charAt(l)){case this.delimiter:r.push(t.substring(o,l)),c=Jp,o=l+1;break;default:}break;case Im:switch(t.charAt(l)){case $m:c=e1;break;default:}break;case e1:switch(t.charAt(l)){case this.delimiter:r.push(t.substring(o,l-1)),c=Jp,o=l+1;break;case $m:c=Im;break;default:c=A$;break}break;case A$:switch(t.charAt(l)){case $m:c=Im;break;default:}break;default:}if(c===e1?r.push(t.substring(o,s-1)):r.push(t.substring(o)),e&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}}class n1 extends En{constructor(t){super();this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=!(t.includeSpectrogram===!1),this.includeWaveform=t.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(t={}){if(ct().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let e=new n1(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,r=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(r.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(r.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],r=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,r=new Float32Array(t.length*e);return t.forEach((o,s)=>r.set(o,s*e)),r}getTensorFromAudioDataArray(t,e){let r=new Float32Array(G(e));return r.set(t,r.length-t.length),un(r,e)}}class r1 extends En{constructor(t,e){super();if(this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=vr([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-r)/2,c=(1-o)/2,l=s+r,p=o+c;this.cropBox=ui([c,s,p,l],[1,4])}else this.cropBox=ui([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(ct().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let r=new r1(t,e);return await r.start(),r}async start(){this.webcamConfig.facingMode&&_(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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=e_(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return rt(()=>{let e=t.toFloat().expandDims(0),r;r=pi.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=r.shape;return r.reshape(o.slice(1))})}async capture(){return(await this.next()).value}stop(){let t=this.stream.getTracks();t.forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class R${}class P$ extends En{split(t){return new Yj(this,t)}}class Yj extends P${constructor(t,e){super();this.upstream=t,this.impl=new Jj(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Jj extends t1{constructor(t,e){super();this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let r of e.slice(0,-1))this.outputQueue.push(r);return this.carryover=e[e.length-1],!0}}class Zj extends En{decodeUTF8(){return new Qj(this)}}class Qj extends P${constructor(t){super();this.upstream=t,this.impl=new t6(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class t6 extends t1{constructor(t){super();if(this.upstream=t,ct().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=require("string_decoder");this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let r;return ct().get("IS_BROWSER")?r=this.decoder.decode(e,{stream:!0}):r=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(r),!0}}class O$ extends Zj{constructor(t,e={}){super();this.file=t,this.options=e,_(t instanceof Uint8Array||(ct().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};let t=new Promise((e,r)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=l=>{let p=s.result;if(p instanceof ArrayBuffer&&(p=new Uint8Array(p)),!(p instanceof Uint8Array))return r(new TypeError("FileReader returned unknown type."));e(p)},s.onabort=l=>r(new Error("Aborted")),s.onerror=l=>r(new Error(l.type));let c=this.file.slice(this.offset,o);s.readAsArrayBuffer(c)}this.offset=o});return{value:await t,done:!1}}}async function e6(n,t={}){let e,r;typeof n=="string"?e=n:(e=n.url,r=n6(n));let o=await SN(e,r);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new O$(s,t)}else throw new Error(o.statusText)}let n6=n=>{let t={method:n.method,headers:n.headers,body:n.body,mode:n.mode,credentials:n.credentials,cache:n.cache,redirect:n.redirect,referrer:n.referrer,integrity:n.integrity};return t};function L$(n){return typeof n=="string"&&n.substr(0,7)==="file://"}class M$ extends R${constructor(t,e={}){super();this.input=t,this.options=e}async iterator(){if(L$(this.input)&&ct().get("IS_NODE")){let t=require("fs");this.input=t.readFileSync(this.input.substr(7))}return new O$(this.input,this.options)}}class B$ extends R${constructor(t,e={}){super();this.url=t,this.fileOptions=e}async iterator(){return L$(this.url)?new M$(this.url,this.fileOptions).iterator():e6(this.url,this.fileOptions)}}function r6(n,t={}){return new F$(new B$(n),t)}function o6(n){let t=Yp(n);return _r(async()=>t)}function s6(n){return _r(async()=>{let t=await n();return Yp(()=>t.next())})}async function i6(n,t){return r1.create(n,t)}async function a6(n){return n1.create(n)}let z$="2.7.0";var c6=Object.freeze({__proto__:null,array:Hj,Dataset:Zc,zip:jj,CSVDataset:F$,TextLineDataset:E$,csv:r6,func:o6,generator:s6,microphone:a6,webcam:i6,FileDataSource:M$,URLDataSource:B$,version_data:z$});function Ct(n,t){Array.isArray(n)||(n=[n]),n.forEach(e=>{e!=null&&_(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}let l6=Od,u6=sv,p6=iv,h6=av,f6=Id;class d6 extends d{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new h(this,ps())}write(t,e,r){this.firstUse&&(this.firstUse=!1,ct().get("IS_NODE")&&Bc(`
+        ${totalLength}, and tensor's shape is: ${tensor168.shape}`);let elementPerRow=totalLength===0?0:tensor168.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor168=reshape(tensor168,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor168,indices,sizes),elementShape)}return tensor168.dispose(),tensors2}),list=new TensorList([],elementShape,tensor168.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node2,tensorMap,context)=>{switch(node2.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node2,tensorMap,context),elseFunc=getParamValue("elseBranch",node2,tensorMap,context),cond=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context),condValue=await cond.data();return condValue[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node2,tensorMap,context),condFunc=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor168=>tensor168.id),condValue=await condResult[0].data();condResult.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&tensor168.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor168=>tensor168.id);origResult.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&resultIds.indexOf(tensor168.id)===-1&&tensor168.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&resultIds.indexOf(tensor168.id)===-1&&tensor168.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node2,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node2.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node2,tensorMap,context),data=getParamValue("tensor",node2,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node2,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node2,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node2,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node2,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node2,tensorMap,context),name=getParamValue("name",node2,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context),readTensorArray=context.getTensorArray(readId.id);return[readTensorArray.read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),gatherDtype=getParamValue("dtype",node2,tensorMap,context),gatherTensorArray=context.getTensorArray(gatherId.id);return[gatherTensorArray.gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node2,tensorMap,context),scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node2,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node2,tensorMap,context),splitTensor=getParamValue("tensor",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node2,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node2,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":{let elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),tensorList=context.getTensorList(gatherId.id);return[tensorList.gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor168=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),tensorList=fromTensor(tensor168,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node2,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),tensorList=split9(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node2,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node2,tensorMap,context),pad11=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context);return{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}}var executeOp4=(node2,tensorMap,context)=>{switch(node2.op){case"Conv1D":{let stride=getParamValue("stride",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node2,tensorMap,context);return[conv1d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),stride,pad11,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad11,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad11,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad11,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node2,tensorMap,context),stride=getParamValue("strides",node2,tensorMap,context),pad11=getPadding(node2,tensorMap,context);return[conv2dTranspose(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),shape,[stride[1],stride[2]],pad11)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getPadding(node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad11,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv3d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2],stride[3]],pad11,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11)]}case"MaxPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node2,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad11)]}case"MaxPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad11)]}case"Dilation2D":{let strides=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[strideHeight,strideWidth],pad11,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp5=(node2,tensorMap,context)=>{switch(node2.op){case"Fill":{let shape=getParamValue("shape",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),value=getParamValue("value",node2,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),num=getParamValue("num",node2,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node2,tensorMap,context),numSamples=getParamValue("numSamples",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node2,tensorMap,context),depth=getParamValue("depth",node2,tensorMap,context),onValue=getParamValue("onValue",node2,tensorMap,context),offValue=getParamValue("offValue",node2,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node2,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node2,tensorMap,context),getParamValue("minval",node2,tensorMap,context),getParamValue("maxval",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"Range":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),step9=getParamValue("step",node2,tensorMap,context);return[range(start,stop,step9,getParamValue("dtype",node2,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node2,tensorMap,context),mean7=getParamValue("mean",node2,tensorMap,context),stdDev=getParamValue("stdDev",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[truncatedNormal(shape,mean7,stdDev,getParamValue("dtype",node2,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function nmsParams(node2,tensorMap,context){let boxes=getParamValue("boxes",node2,tensorMap,context),scores=getParamValue("scores",node2,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node2,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node2,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node2,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node2,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node2,tensorMap,context)=>{switch(node2.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node2,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node2,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node2,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context));default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp7=(node2,tensorMap,context)=>{switch(node2.op){case"TopKV2":{let x=getParamValue("x",node2,tensorMap,context),k=getParamValue("k",node2,tensorMap,context),sorted=getParamValue("sorted",node2,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node2,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp8=(node2,tensorMap,context)=>{switch(node2.op){case"Const":return tensorMap[node2.name];case"PlaceholderWithDefault":let def=getParamValue("default",node2,tensorMap,context);return[getTensor(node2.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node2.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node2,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node2,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node2,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node2,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node2,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node2,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node2,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context),message=getParamValue("message",node2,tensorMap,context),summarize=getParamValue("summarize",node2,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node2,tensorMap,context,resourceManager)=>{switch(node2.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node2,tensorMap,context),valueDType=getParamValue("valueDType",node2,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node2.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context),hashTable2=resourceManager.getHashTableById(handle.id);return[await hashTable2.import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context),hashTable2=resourceManager.getHashTableById(handle.id);return[await hashTable2.find(keys,defaultValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp10=(node2,tensorMap,context)=>{switch(node2.op){case"ResizeBilinear":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners)]}case"CropAndResize":{let image3=getParamValue("image",node2,tensorMap,context),boxes=getParamValue("boxes",node2,tensorMap,context),boxInd=getParamValue("boxInd",node2,tensorMap,context),cropSize=getParamValue("cropSize",node2,tensorMap,context),method=getParamValue("method",node2,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node2,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp11=(node2,tensorMap,context)=>{switch(node2.op){case"Equal":return[equal(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Less":return[less(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node2,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node2,tensorMap,context),getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp12=(node2,tensorMap,context)=>{switch(node2.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context),getParamValue("transposeA",node2,tensorMap,context),getParamValue("transposeB",node2,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node2,tensorMap,context),getParamValue("perm",node2,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node2,tensorMap,context),b:getParamValue("b",node2,tensorMap,context),transposeA:getParamValue("transposeA",node2,tensorMap,context),transposeB:getParamValue("transposeB",node2,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp13=(node2,tensorMap,context)=>{switch(node2.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node2,tensorMap,context),getParamValue("radius",node2,tensorMap,context),getParamValue("bias",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context),getParamValue("beta",node2,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node2,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node2,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node2,tensorMap,context),getParamValue("outputShape",node2,tensorMap,context),getParamValue("sparseValues",node2,tensorMap,context),getParamValue("defaultValue",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp14=(node2,tensorMap,context)=>{switch(node2.op){case"Max":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[max(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[mean(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[min(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[sum2(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[all(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[any(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMax(getParamValue("x",node2,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMin(getParamValue("x",node2,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[prod(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node2,tensorMap,context),exclusive=getParamValue("exclusive",node2,tensorMap,context),reverse12=getParamValue("reverse",node2,tensorMap,context);return[cumsum(getParamValue("x",node2,tensorMap,context),axis,exclusive,reverse12)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp15=(node2,tensorMap,context)=>{switch(node2.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),inputs=getParamValue("tensors",node2,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"GatherV2":case"Gather":{let axis=getParamValue("axis",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis)]}case"ReverseV2":case"Reverse":{let axis=getParamValue("axis",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[slice(getParamValue("x",node2,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node2,tensorMap,context),end=getParamValue("end",node2,tensorMap,context),strides=getParamValue("strides",node2,tensorMap,context),beginMask=getParamValue("beginMask",node2,tensorMap,context),endMask=getParamValue("endMask",node2,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node2,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node2,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node2,tensorMap,context),tensor168=getParamValue("x",node2,tensorMap,context);return[stridedSlice(tensor168,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node2,tensorMap,context),tensors=getParamValue("tensors",node2,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor168=>{let sameShape=util_exports.arraysEqual(tensor168.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor168).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor168:reshape(tensor168,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node2,tensorMap,context),tensor168=getParamValue("tensor",node2,tensorMap,context);return unstack(tensor168,axis)}case"Tile":{let reps=getParamValue("reps",node2,tensorMap,context);return[tile(getParamValue("x",node2,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node2,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node2,tensorMap,context),tensor168=getParamValue("x",node2,tensorMap,context);return split(tensor168,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context),shape=getParamValue("shape",node2,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node2,tensorMap,context),shape=getParamValue("outputShape",node2,tensorMap,context),sparseValues=getParamValue("sparseValues",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp16=(node2,tensorMap,context)=>{switch(node2.op){case"FFT":return[fft(getParamValue("x",node2,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node2,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node2,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp17=(node2,tensorMap,context)=>{switch(node2.op){case"Cast":return[cast(getParamValue("x",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node2,tensorMap,context);return[expandDims(getParamValue("x",node2,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node2,tensorMap,context);return[squeeze(getParamValue("x",node2,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("mode",node2,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("constantValue",node2,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),paddings=getParamValue("paddings",node2,tensorMap,context);return[spaceToBatchND(getParamValue("x",node2,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),crops=getParamValue("crops",node2,tensorMap,context);return[batchToSpaceND(getParamValue("x",node2,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node2,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function executeOp18(node2,tensorMap,context,resourceManager){let value=((node22,tensorMap2,context2)=>{switch(node22.category){case"arithmetic":return tidy(()=>executeOp(node22,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node22,tensorMap2,context2));case"control":return executeOp3(node22,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node22,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node22,tensorMap2,context2));case"dynamic":return executeOp6(node22,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node22,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node22,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node22,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node22,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node22,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node22,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node22,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node22,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node22,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node22,tensorMap2,context2));case"hash_table":return executeOp9(node22,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node22.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node22,tensorMap2,context2));throw TypeError(`Custom op ${node22.op} is not registered.`);default:throw TypeError(`Unknown op '${node22.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node2,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),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++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node2=>parseNodeName(node2.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node2=frontier.pop();if((isControlFlow(node2)||isDynamicShape(node2)||isHashTable(node2))&&dynamicNode==null&&(dynamicNode=node2,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node2.name),weightMap[node2.name]!=null)continue;if(inputNodeNames.indexOf(node2.name)!==-1)continue;if(initNodeNames.indexOf(node2.name)!==-1)continue;if(node2.inputs.length===0){missingInputs.push(node2.name);continue}node2.inputs.forEach(input2=>{if(seen.has(input2.name))return;seen.add(input2.name),frontier.push(input2)})}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node2=>{usedNodes.has(node2.name)&&frontier.push(node2)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node2=frontier.pop();seen.add(node2.name),weightMap[node2.name]||orderedNodes.push(node2),node2.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node2){return CONTROL_FLOW_OPS.indexOf(node2.op)>=0}function isDynamicShape(node2){return DYNAMIC_SHAPE_OPS.indexOf(node2.op)>=0}function isHashTable(node2){return HASH_TABLE_OPS.indexOf(node2.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],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(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor168=>tensor168.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node2=>node2.signatureKey||node2.name)}get outputNodes(){return this._outputs.map(node2=>{let name=node2.signatureKey||node2.name;return node2.defaultOutput?`${name}:${node2.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node2=>node2.name).sort(),sortedOutputs=outputs.map(node2=>node2.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node2=orderedNodes[i];if(!tensorsMap[node2.name]){let tensors=executeOp18(node2,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node2.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node2.name]=tensors,this.checkTensorForDisposal(node2.name,node2,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor168=>tensor168.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node2,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){if(node2.category==="control"||outputNames.indexOf(nodeName)!==-1)return;tensorMap[nodeName].forEach(tensor168=>{tensor168!=null&&(intermediateTensorConsumerCount[tensor168.id]=(intermediateTensorConsumerCount[tensor168.id]||0)+node2.children.length)}),node2.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor168=>{if(tensor168&&!tensorsToKeep.has(tensor168.id)){let count2=intermediateTensorConsumerCount[tensor168.id];count2===1?(tensor168.dispose(),delete intermediateTensorConsumerCount[tensor168.id]):count2!=null&&intermediateTensorConsumerCount[tensor168.id]--}})}})}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{let tensorArray=tensorMap[key];tensorArray.forEach(tensor168=>{tensor168&&!tensor168.isDisposed&&!keepIds.has(tensor168.id)&&tensor168.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor168,index)=>(map[this.inputs[index].name]=tensor168,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack92=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node2=>({node:node2,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack92.length>0;){let promises=this.processStack(inputNodes,stack92,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node2=>!isControlFlow(node2)&&!getTensor(node2.name,tensorsMap,context)).map(node2=>node2.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack92,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack92.length>0;){let item=stack92.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack92,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack92,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack92,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node2,stack92,context,tensorMap,added,usedNodes){node2.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);if(added[nodeName]||!usedNodes.has(childNode.name))return;childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack92.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack92.push({contexts:context.currentContext,node:childNode}))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor168=>tensor168.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node2=this.graph.nodes[nodeName];if(node2.attrParams.shape&&node2.attrParams.shape.value){let shape=node2.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node2.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node2.attrParams.dtype&&node2.attrParams.dtype.value&&util_exports.assert(input2.dtype===node2.attrParams.dtype.value,()=>`The dtype of dict['${node2.name}'] provided in model.execute(dict) must be ${node2.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor168=this._signature.inputs[inputName];result[tensor168.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null){let tensor168=this._signature.outputs[name];return tensor168.name}return name},{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature={};this.artifacts.userDefinedMetadata!=null&&(signature=this.artifacts.userDefinedMetadata.signature),this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config2){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version6="2.7.0",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version8,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule2(require_seedrandom2()),seedrandom2=__toModule2(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}let result=deepMapInternal(input2,mapFn,seen);return result}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container2){return deepMap(container2,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){let rowBatches=this.rowMajorBatch(batchSize,smallLastBatch);return rowBatches.map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){let upstreamSummaries="TODO: fill in upstream of chained summaries";return`${upstreamSummaries} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){let upstreamSummaries="TODO: fill in upstream of zip summaries";return`{${upstreamSummaries}} -> Zip`}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container2){if(container2 instanceof LazyIterator){let result=container2.next();return{value:result.then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}}else return{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max102){return Math.floor(this.random()*max102)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
+      ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset5){let base2=this,size;return this.size===Infinity||dataset5.size===Infinity?size=Infinity:this.size!=null&&dataset5.size!=null?size=this.size+dataset5.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset5.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size<count2||count2===void 0||count2<0)?size=0:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<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)`);let base2=this,random=seedrandom3.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}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()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i<datasets.length;i++)size=size==null?datasets[i].size:Math.min(size,datasets[i].size);else if(datasets instanceof Object)for(let ds in datasets)size=size==null?datasets[ds].size:Math.min(size,datasets[ds].size);return datasetFromIteratorFn(async()=>{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];if(canTensorify(exampleRow)){let value=batchConcat(rows);return{value,recurse:!1}}return{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor4(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){let inputIterator=await this.input.iterator(),utf8Iterator=inputIterator.decodeUTF8(),lineIterator=utf8Iterator.split(`
+`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line));return lineIterator}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=!(csvConfig.hasHeader===!1),this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.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 ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs)for(let key of Object.keys(this.columnConfigs)){let index=this.fullColumnNames.indexOf(key);if(index===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let iter=await this.base.iterator(),firstElement=await iter.next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value,headers=this.parseRow(firstLine,!1);return headers}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config2=this.columnConfigs?this.columnConfigs[key]:null;if(this.configuredColumnsOnly&&!config2)continue;{let value=values[i],parsedValue=null;if(value==="")if(config2&&config2.default!==void 0)parsedValue=config2.default;else{if(config2&&(config2.required||config2.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config2&&config2.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config2||!config2.dtype)parsedValue=valueAsNum;else switch(config2.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config2&&config2.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}},MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))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=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=!(microphoneConfig.includeSpectrogram===!1),this.includeWaveform=microphoneConfig.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(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!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}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.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 spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},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(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor4(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.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(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){let tracks=this.stream.getTracks();tracks.forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.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};let chunk=new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice21=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice21)}this.offset=end});return{value:await chunk,done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>{let init2={method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity};return init2};function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version8="2.7.0",seedrandom4=__toModule2(require_seedrandom2());function assertNotComplex(tensor168,opName){Array.isArray(tensor168)||(tensor168=[tensor168]),tensor168.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl,split10=kernel_impls_exports.split,tile9=kernel_impls_exports.tile,topkImpl2=kernel_impls_exports.topkImpl,whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine15())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
 ============================
 Hi there \u{1F44B}. 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.
-============================`));let o={};return this.data.set(o,{values:t,dtype:r,refCount:1}),o}makeTensorInfo(t,e,r){let o;if(e==="string"&&r!=null&&r.length>0&&as(r[0])){let s=r.map(c=>zf(c));o=this.write(s,t,e)}else o=this.write(r,t,e);return{dataId:o,shape:t,dtype:e}}incRef(t){let e=this.data.get(t);e.refCount++}decRef(t){if(this.data.has(t)){let e=this.data.get(t);e.refCount--}}move(t,e,r,o){this.data.set(t,{values:e,dtype:o,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(t){return this.readSync(t)}readSync(t){let{dtype:e,complexTensorInfos:r}=this.data.get(t);if(e==="complex64"){let o=this.readSync(r.real.dataId),s=this.readSync(r.imag.dataId);return ys(o,s)}return this.data.get(t).values}bufferSync(t){let e=this.readSync(t.dataId),r=e;if(t.dtype==="string")try{r=e.map(o=>Uu(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Se(t.shape,t.dtype,r)}makeOutput(t,e,r){let o=this.write(t,e,r);return ps().makeTensorFromDataId(o,e,r,this)}disposeData(t){if(this.data.has(t)){let{complexTensorInfos:e}=this.data.get(t);e!=null&&(this.disposeData(e.real.dataId),this.disposeData(e.imag.dataId)),this.data.delete(t)}}disposeIntermediateTensorInfo(t){let e=t.dataId;if(this.data.has(e)){let r=this.data.get(e);r.refCount--,r.refCount<1&&this.disposeData(e)}}async time(t){let e=or();t();let r=or()-e;return{kernelMs:r}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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yt=f[ht+bt],xt=f[dt+bt],kt=f[ft+bt],Nt=f[ut+bt],At=yt+(kt-yt)*it,It=xt+(Nt-xt)*it,St=At+(It-At)*E;m[v++]=St}}}return un(m,[s,e,r,p])}resizeBilinearBackprop(t,e,r){Ct([t,e],"resizeBilinearBackprop");let[o,s,c,l]=e.shape,[,p,f]=t.shape,m=new Float32Array(o*s*c*l),y=[r&&p>1?s-1:s,r&&f>1?c-1:c],b=[r&&p>1?p-1:p,r&&f>1?f-1:f],v=y[0]/b[0],T=y[1]/b[1],N=this.readSync(t.dataId),S=0;for(let D=0;D<o;D++){let I=D*e.strides[0];for(let P=0;P<p;P++){let E=P*v,L=Math.floor(E),B=Math.min(Math.ceil(E),s-1),q=I+L*e.strides[1],H=I+B*e.strides[1],Z=E-L,J=1-Z;for(let it=0;it<f;it++){let pt=it*T,ht=Math.floor(pt),dt=Math.min(Math.ceil(pt),c-1),ft=pt-ht,ut=1-ft,bt=q+ht*e.strides[2],yt=q+dt*e.strides[2],xt=H+ht*e.strides[2],kt=H+dt*e.strides[2],Nt=J*ut,At=J*ft,It=Z*ut,St=Z*ft;for(let Lt=0;Lt<l;Lt++){let Ht=N[S++];m[bt+Lt]+=Ht*Nt,m[yt+Lt]+=Ht*At,m[xt+Lt]+=Ht*It,m[kt+Lt]+=Ht*St}}}}return Oc(m,[o,c,s,l],e.dtype)}resizeNearestNeighbor(t,e,r,o){Ct(t,"resizeNearestNeighbor");let[s,c,l,p]=t.shape,f=this.readSync(t.dataId),m=new Float32Array(s*e*r*p),y=[o&&e>1?c-1:c,o&&r>1?l-1:l],b=[o&&e>1?e-1:e,o&&r>1?r-1:r],v=y[0]/b[0],T=y[1]/b[1],N=0;for(let S=0;S<s;S++){let D=S*t.strides[0];for(let I=0;I<e;I++){let P=v*I,E=Math.min(c-1,o?Math.round(P):Math.floor(P)),L=D+E*t.strides[1];for(let B=0;B<r;B++){let q=T*B,H=Math.min(l-1,o?Math.round(q):Math.floor(q)),Z=L+H*t.strides[2];for(let J=0;J<p;J++){let it=f[Z+J];m[N++]=it}}}}return un(m,[s,e,r,p],t.dtype)}resizeNearestNeighborBackprop(t,e,r){Ct([t,e],"resizeNearestNeighborBackprop");let[o,s,c,l]=e.shape,[,p,f]=t.shape,m=new Float32Array(o*s*c*l),y=this.readSync(t.dataId),b=[r&&p>1?s-1:s,r&&f>1?c-1:c],v=[r&&p>1?p-1:p,r&&f>1?f-1:f],T=b[0]/v[0],N=b[1]/v[1],S=1/T,D=1/N,I=Math.ceil(S)*2+2,P=Math.ceil(D)*2+2;for(let E=0;E<o;E++){let L=E*e.strides[0];for(let B=0;B<s;B++){let q=L+B*e.strides[1],H=Math.floor(B*S),Z=Math.floor(H-I/2);for(let J=0;J<c;J++){let it=q+J*e.strides[2],pt=Math.floor(J*D),ht=Math.floor(pt-P/2);for(let dt=0;dt<l;dt++){let ft=0;for(let ut=0;ut<I;ut++){let bt=ut+Z;if(bt<0||bt>=p)continue;let yt=L+bt*t.strides[1],xt=bt*T,kt=Math.min(s-1,r?Math.round(xt):Math.floor(xt));if(B!==kt)continue;for(let Nt=0;Nt<P;Nt++){let At=Nt+ht;if(At<0||At>=f)continue;let It=yt+At*t.strides[2],St=At*N,Lt=Math.min(c-1,r?Math.round(St):Math.floor(St));J===Lt&&(ft+=y[It+dt])}}m[it+dt]=ft}}}}return Oc(m,e.shape,e.dtype)}localResponseNormalization4D(t,e,r,o,s){Ct(t,"localResponseNormalization4D");let c=t.shape[3],l=c-1,p=this.readSync(t.dataId),f=t.size,m=new Float32Array(f);function y(b){let v=b%c,T=b-v+Math.max(0,v-e),N=b-v+Math.min(v+e,l),S=0;for(;T<=N;T++){let D=p[T];S+=D*D}return S}for(let b=0;b<f;b++){let v=y(b),T=p[b]*Math.pow(r+o*v,-s);m[b]=T}return Oc(m,t.shape)}LRNGrad(t,e,r,o,s,c,l){Ct(t,"LRNGrad");let p=t.shape[3],f=this.readSync(t.dataId),m=this.readSync(e.dataId),y=this.readSync(r.dataId),b=new Float32Array(t.size),v=t.size;for(let T=0;T<v;T++){let N=T%p,S=T-N+Math.max(0,N-o),D=T-N+Math.min(p,N+o+1),I=0;for(let P=S;P<D;P++)I+=Math.pow(m[P],2);I=c*I+s;for(let P=S;P<D;P++){let E=-2*c*l*m[P]*y[T]/I;T===P&&(E+=Math.pow(I,-l)),E*=f[T],b[P]+=E}}return Oc(b,t.shape)}multinomial(t,e,r,o){Ct(t,"multinomial");let s=e?t:ca(t),c=s.shape[0],l=s.shape[1],p=xe([c,r],"int32"),f=this.readSync(p.dataId),m=this.readSync(s.dataId);for(let y=0;y<c;++y){let b=y*l,v=new Float32Array(l-1);v[0]=m[b];for(let S=1;S<v.length;++S)v[S]=v[S-1]+m[b+S];let T=Fc(o.toString()),N=y*r;for(let S=0;S<r;++S){let D=T();f[N+S]=v.length;for(let I=0;I<v.length;I++)if(D<v[I]){f[N+S]=I;break}}}return p}oneHot(t,e,r,o){Ct(t,"oneHot");let s=new Float32Array(t.size*e);s.fill(o);let c=this.readSync(t.dataId);for(let l=0;l<t.size;++l)c[l]>=0&&c[l]<e&&(s[l*e+c[l]]=r);return ui(s,[t.size,e],"int32")}nonMaxSuppression(t,e,r,o,s){Ct(t,"nonMaxSuppression");let c=this.readSync(t.dataId),l=this.readSync(e.dataId);return l6(c,l,r,o,s)}depthToSpace(t,e,r){_(r==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${r}`),_(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`);let o=t.shape[0],s=t.shape[1],c=t.shape[2],l=t.shape[3],p=s*e,f=c*e,m=l/(e*e),y=this.readSync(t.dataId),b=new Float32Array(o*p*f*m),v=0;for(let T=0;T<o;++T)for(let N=0;N<p;++N){let S=Math.floor(N/e),D=N%e;for(let I=0;I<f;++I){let P=Math.floor(I/e),E=I%e,L=(D*e+E)*m;for(let B=0;B<m;++B){let q=B+L,H=q+l*(P+c*(S+s*T));b[v++]=y[H]}}}return Oc(b,[o,p,f,m])}broadcastedBinaryOp(t,e,r,o){let s=le(t.shape,e.shape),c=Se(s,r),l=this.readSync(t.dataId),p=this.readSync(e.dataId),f=ra(t.shape,s),m=ra(e.shape,s),y=c.values;if(f.length+m.length===0)for(let b=0;b<y.length;++b)y[b]=o(l[b%l.length],p[b%p.length]);else{let b=this.bufferSync(t),v=this.bufferSync(e);for(let T=0;T<y.length;++T){let N=c.indexToLoc(T),S=N.slice(-t.rank);f.forEach(E=>S[E]=0);let D=b.locToIndex(S),I=N.slice(-e.rank);m.forEach(E=>I[E]=0);let P=v.locToIndex(I);y[T]=o(l[D],p[P])}}return c.toTensor()}split(t,e,r){return 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Ht=D[St];St=It+kt*I[2]+ut*I[1]+J*I[0];let oe=D[St];St=It+Nt*I[2]+ut*I[1]+J*I[0];let de=D[St],ie=Lt+(Ht-Lt)*At,we=oe+(de-oe)*At;St=It+yt*P[2]+ht*P[1]+E*P[0],T.values[St]=ie+(we-ie)*bt}}}else for(let ft=0;ft<v;++ft){let ut=v>1?q*(f-1)+ft*pt:.5*(q+Z)*(f-1);if(ut<0||ut>f-1){for(let xt=0;xt<m;xt++){let kt=xt+ft*P[2]+ht*P[1]+E*P[0];T.values[kt]=c}continue}let bt=Math.round(ut),yt=Math.round(dt);for(let xt=0;xt<m;xt++){let kt=xt+bt*I[2]+yt*I[1]+J*I[0],Nt=xt+ft*P[2]+ht*P[1]+E*P[0];T.values[Nt]=D[kt]}}}}return T.toTensor()}sparseToDense(t,e,r,o){let{sliceRank:s,numUpdates:c,sliceSize:l,strides:p,outputSize:f}=Tc(e,t,r),m=!1;return this.scatter(t,e,r,f,l,c,s,p,o,m)}gatherND(t,e){let r=e.shape,o=r[r.length-1],[s,c,l,p]=Yf(t,e);if(c===0)return un([],s,t.dtype);let f=new hn([c,l],t.dtype),m=this.readSync(e.dataId),y=this.readSync(t.dataId);for(let b=0;b<c;b++){let v=[],T=0;for(let N=0;N<o;N++){let S=m[b*o+N];T+=S*p[N],v.push(S)}if(T<0||T>=t.size/l)throw new Error(`Invalid indices: ${v} does not 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ie=At+de*ht,we=de*b.strideWidth-D;for(let pe=0;pe<T;++pe){let Xe=we+pe*S;if(Xe<0||Xe>=b.inWidth)continue;let on=Ht+pe*B[1],sn=oe+Xe*Z,Ae=on;for(let Ye=0;Ye<b.inChannels;++Ye){let Le=ft[sn+Ye*J];for(let Ln=0;Ln<b.outChannels;++Ln)bt[ie+Ln*dt]+=Le*ut[Ae+Ln];Ae+=b.outChannels}}}}}}return e.makeTensorInfo(E.shape,E.dtype,bt)}let kK={kernelName:uf,backendName:"cpu",kernelFunc:uI};function NK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,pad:l,dataFormat:p,dimRoundingMode:f,filterShape:m}=r;Ct([o,s],"conv2dBackpropFilter");let y=si(p),b=Un(o.shape,m,c,1,l,f,!1,y),{strideHeight:v,strideWidth:T,filterHeight:N,filterWidth:S}=b,D=b.dataFormat==="channelsLast",I=new hn(b.filterShape,"float32"),P=b.padInfo.left,E=b.padInfo.top,L=e.data.get(o.dataId).values,B=e.data.get(s.dataId).values,q=new hn(o.shape,o.dtype,L),H=new hn(s.shape,s.dtype,B);for(let Z=0;Z<N;++Z){let J=Math.max(0,Math.ceil((E-Z)/v)),it=Math.min(b.outHeight,(b.inHeight+E-Z)/v);for(let pt=0;pt<S;++pt){let ht=Math.max(0,Math.ceil((P-pt)/T)),dt=Math.min(b.outWidth,(b.inWidth+P-pt)/T);for(let ft=0;ft<b.inChannels;++ft)for(let ut=0;ut<b.outChannels;++ut){let bt=0;for(let yt=0;yt<b.batchSize;++yt)for(let xt=J;xt<it;++xt){let kt=Z+xt*v-E;for(let Nt=ht;Nt<dt;++Nt){let At=pt+Nt*T-P;D?bt+=q.get(yt,kt,At,ft)*H.get(yt,xt,Nt,ut):bt+=q.get(yt,ft,kt,At)*H.get(yt,ut,xt,Nt)}}I.set(bt,Z,pt,ft,ut)}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let _K={kernelName:Hb,backendName:"cpu",kernelFunc:NK};function CK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{inputShape:c,strides:l,pad:p,dataFormat:f,dimRoundingMode:m}=r;Ct([o,s],"conv2dBackpropInput");let y=Yt(s.shape),b=Yt(o.shape),v=si(f),T=Un(c,s.shape,l,1,p,m,!1,v),N=new hn(T.inShape,"float32"),S=N.values,D=e.data.get(o.dataId).values,I=e.data.get(s.dataId).values,[P,E,L]=y,{batchSize:B,filterHeight:q,filterWidth:H,inChannels:Z,inHeight:J,inWidth:it,outChannels:pt,outHeight:ht,outWidth:dt,strideHeight:ft,strideWidth:ut}=T;v=T.dataFormat;let bt=q-1-T.padInfo.top,yt=H-1-T.padInfo.left,xt=v==="channelsLast",kt=N.strides[0],Nt=xt?N.strides[1]:N.strides[2],At=xt?N.strides[2]:1,It=xt?1:N.strides[1],St=b[0],Lt=xt?b[1]:b[2],Ht=xt?b[2]:1,oe=xt?1:b[1];for(let de=0;de<B;++de)for(let ie=0;ie<Z;++ie)for(let we=0;we<J;++we){let pe=we-bt,Xe=Math.max(0,Math.ceil(pe/ft)),on=Math.min(ht,(q+pe)/ft);for(let sn=0;sn<it;++sn){let Ae=sn-yt,Ye=Math.max(0,Math.ceil(Ae/ut)),Le=Math.min(dt,(H+Ae)/ut),Ln=0;for(let Mn=Xe;Mn<on;++Mn){let Co=Mn*ft-pe;for(let Br=Ye;Br<Le;++Br){let va=Br*ut-Ae,So=St*de+Lt*Mn+Ht*Br,Ko=P*(q-1-Co)+E*(H-1-va)+L*ie;for(let Ni=0;Ni<pt;++Ni){let _i=D[So+oe*Ni],Ci=I[Ko+Ni];Ln+=_i*Ci}}}let $s=kt*de+Nt*we+At*sn+It*ie;S[$s]=Ln}}return e.makeTensorInfo(N.shape,N.dtype,N.values)}let SK={kernelName:pf,backendName:"cpu",kernelFunc:CK};function $K(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dilations:p}=r;Ct([o,s],"conv3d");let f=ri(o.shape,s.shape,c,p,l),{filterDepth:m,filterHeight:y,filterWidth:b,dilationDepth:v,dilationHeight:T,dilationWidth:N,padInfo:S}=f,D=S.front,I=S.left,P=S.top,E=new hn(f.outShape,o.dtype),L=e.data.get(o.dataId).values,B=e.data.get(s.dataId).values,q=E.values,H=Yt(o.shape),Z=Yt(s.shape);for(let J=0;J<f.batchSize;++J){let it=J*H[0],pt=J*E.strides[0];for(let ht=0;ht<f.outDepth;++ht){let dt=pt+ht*E.strides[1],ft=ht*f.strideDepth-D;for(let ut=0;ut<m;++ut){let bt=ft+ut*v;if(bt<0||bt>=f.inDepth)continue;let yt=ut*Z[0],xt=it+bt*H[1];for(let kt=0;kt<f.outHeight;++kt){let Nt=dt+kt*E.strides[2],At=kt*f.strideHeight-P;for(let It=0;It<y;++It){let St=At+It*T;if(St<0||St>=f.inHeight)continue;let Lt=yt+It*Z[1],Ht=xt+St*H[2];for(let oe=0;oe<f.outWidth;++oe){let de=Nt+oe*f.outChannels,ie=oe*f.strideWidth-I;for(let we=0;we<b;++we){let pe=ie+we*N;if(pe<0||pe>=f.inWidth)continue;let Xe=Lt+we*Z[2],on=Ht+pe*f.inChannels,sn=Xe;for(let Ae=0;Ae<f.inChannels;++Ae){let Ye=L[on+Ae];for(let Le=0;Le<f.outChannels;++Le)q[de+Le]+=Ye*B[sn+Le];sn+=f.outChannels}}}}}}}}return e.makeTensorInfo(E.shape,E.dtype,E.values)}let IK={kernelName:hf,backendName:"cpu",kernelFunc:$K};function EK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,pad:l,filterShape:p}=r;Ct([o,s],"conv3dBackpropFilterV2");let f=Yt(o.shape),m=Yt(s.shape),y=ri(o.shape,p,c,1,l),b=y.strideDepth,v=y.strideHeight,T=y.strideWidth,N=y.filterDepth,S=y.filterHeight,D=y.filterWidth,I=new hn(y.filterShape,"float32"),P=I.values,[E,L,B,q]=I.strides,H=e.data.get(s.dataId).values,[Z,J,it,pt]=m,ht=e.data.get(o.dataId).values,[dt,ft,ut,bt]=f,yt=y.padInfo.front,xt=y.padInfo.left,kt=y.padInfo.top;for(let Nt=0;Nt<N;++Nt){let At=Math.max(0,Math.ceil((yt-Nt)/b)),It=Math.min(y.outDepth,(y.inDepth+yt-Nt)/b),St=Nt*E;for(let Lt=0;Lt<S;++Lt){let Ht=Math.max(0,Math.ceil((kt-Lt)/v)),oe=Math.min(y.outHeight,(y.inHeight+kt-Lt)/v),de=Lt*L+St;for(let ie=0;ie<D;++ie){let we=Math.max(0,Math.ceil((xt-ie)/T)),pe=Math.min(y.outWidth,(y.inWidth+xt-ie)/T),Xe=ie*B+de;for(let on=0;on<y.inChannels;++on){let sn=on*q+Xe;for(let Ae=0;Ae<y.outChannels;++Ae){let Ye=0;for(let Le=0;Le<y.batchSize;++Le){let Ln=Le*dt,$s=Le*Z;for(let Mn=At;Mn<It;++Mn){let Co=Nt+Mn*b-yt,Br=Co*ft+Ln,va=Mn*J+$s;for(let So=Ht;So<oe;++So){let Ko=Lt+So*v-kt,Ni=Ko*ut+Br,_i=So*it+va;for(let Ci=we;Ci<pe;++Ci){let fl=ie+Ci*T-xt,_1=fl*bt+Ni,C1=Ci*pt+_i;Ye+=ht[_1+on]*H[C1+Ae]}}}}P[sn+Ae]=Ye}}}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let DK={kernelName:jb,backendName:"cpu",kernelFunc:EK};function AK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{pad:c,strides:l,inputShape:p}=r;Ct([o],"conv3dBackpropInputV2");let f=Yt(o.shape),m=Yt(s.shape),y=ri(p,s.shape,l,1,c),b=new hn(y.inShape,"float32"),v=b.values,[T,N,S,D]=b.strides,I=e.data.get(o.dataId).values,[P,E,L,B]=f,q=e.data.get(s.dataId).values,[H,Z,J,it]=m,{batchSize:pt,filterDepth:ht,filterHeight:dt,filterWidth:ft,inChannels:ut,inDepth:bt,inHeight:yt,inWidth:xt,outChannels:kt,outDepth:Nt,outHeight:At,outWidth:It,strideDepth:St,strideHeight:Lt,strideWidth:Ht}=y,oe=ht-1-y.padInfo.front,de=dt-1-y.padInfo.top,ie=ft-1-y.padInfo.left;for(let we=0;we<pt;++we)for(let pe=0;pe<ut;++pe)for(let Xe=0;Xe<bt;++Xe){let on=Xe-oe,sn=Math.max(0,Math.ceil(on/St)),Ae=Math.min(Nt,(ht+on)/St);for(let Ye=0;Ye<yt;++Ye){let Le=Ye-de,Ln=Math.max(0,Math.ceil(Le/Lt)),$s=Math.min(At,(dt+Le)/Lt);for(let Mn=0;Mn<xt;++Mn){let Co=Mn-ie,Br=Math.max(0,Math.ceil(Co/Ht)),va=Math.min(It,(ft+Co)/Ht),So=0;for(let Ko=sn;Ko<Ae;++Ko){let Ni=Ko*St-on;for(let _i=Ln;_i<$s;++_i){let Ci=_i*Lt-Le;for(let fl=Br;fl<va;++fl){let _1=fl*Ht-Co,C1=P*we+E*Ko+L*_i+B*fl,E9=H*(ht-1-Ni)+Z*(dt-1-Ci)+J*(ft-1-_1)+it*pe;for(let Um=0;Um<kt;++Um){let D9=I[C1+Um],A9=q[E9+Um];So+=D9*A9}}}}v[T*we+N*Xe+S*Ye+D*Mn+pe]=So}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}let FK={kernelName:Kb,backendName:"cpu",kernelFunc:AK};let RK=Ee(lc,n=>Math.cos(n)),PK={kernelName:lc,backendName:"cpu",kernelFunc:RK};let OK=Ee(pu,n=>Math.cosh(n)),LK={kernelName:pu,backendName:"cpu",kernelFunc:OK};function pI(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dilations:p,dimRoundingMode:f}=r;Ct([o,s],"depthwiseConv2DNative");let m=Yt(o.shape),y=Yt(s.shape),b=p;b==null&&(b=[1,1]),_(fn(c,b),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${c} and dilations '${b}'`);let v=Un(o.shape,s.shape,c,b,l,f,!0),{filterHeight:T,filterWidth:N,dilationHeight:S,dilationWidth:D,padInfo:I}=v,P=I.left,E=I.top,L=v.outChannels/v.inChannels,B=new hn(v.outShape,o.dtype),q=e.data.get(o.dataId).values,H=e.data.get(s.dataId).values,Z=B.values;for(let J=0;J<v.batchSize;++J){let it=J*m[0],pt=J*B.strides[0];for(let ht=0;ht<v.outHeight;++ht){let dt=pt+ht*B.strides[1],ft=ht*v.strideHeight-P;for(let ut=0;ut<T;++ut){let bt=ft+ut*S;if(bt<0||bt>=v.inHeight)continue;let yt=ut*y[0],xt=it+bt*m[1];for(let kt=0;kt<v.outWidth;++kt){let Nt=dt+kt*B.strides[2],At=kt*v.strideWidth-E;for(let It=0;It<N;++It){let St=At+It*D;if(St<0||St>=v.inWidth)continue;let Lt=yt+It*y[1],Ht=xt+St*v.inChannels,oe=Nt,de=Lt;for(let ie=0;ie<v.inChannels;++ie){let we=q[Ht+ie];for(let pe=0;pe<L;++pe)Z[oe+pe]+=we*H[de+pe];oe+=L,de+=L}}}}}}return e.makeTensorInfo(B.shape,B.dtype,B.values)}let MK={kernelName:ff,backendName:"cpu",kernelFunc:pI};function BK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,dilations:l,pad:p,dimRoundingMode:f,filterShape:m}=r;Ct([o,s],"depthwiseConv2dNativeBackpropFilter");let y=Un(o.shape,m,c,l,p,f,!0),{strideHeight:b,strideWidth:v,filterHeight:T,filterWidth:N}=y,S=new hn(y.filterShape,"float32"),D=y.padInfo.left,I=y.padInfo.top,P=y.outChannels/y.inChannels,E=e.data.get(o.dataId).values,L=new hn(o.shape,o.dtype,E),B=e.data.get(s.dataId).values,q=new hn(s.shape,s.dtype,B);for(let H=0;H<T;++H){let Z=Math.max(0,Math.ceil((I-H)/b)),J=Math.min(y.outHeight,(y.inHeight+I-H)/b);for(let it=0;it<N;++it){let pt=Math.max(0,Math.ceil((D-it)/v)),ht=Math.min(y.outWidth,(y.inWidth+D-it)/v);for(let dt=0;dt<y.outChannels;++dt){let ft=Math.trunc(dt/P),ut=dt%P,bt=0;for(let yt=0;yt<y.batchSize;++yt)for(let xt=Z;xt<J;++xt){let kt=H+xt*b-I;for(let Nt=pt;Nt<ht;++Nt){let At=it+Nt*v-D;bt+=L.get(yt,kt,At,ft)*q.get(yt,xt,Nt,dt)}}S.set(bt,H,it,ft,ut)}}}return e.makeTensorInfo(S.shape,S.dtype,S.values)}let zK={kernelName:Yb,backendName:"cpu",kernelFunc:BK};function WK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{strides:c,dilations:l,pad:p,dimRoundingMode:f,inputShape:m}=r;Ct([o,s],"depthwiseConv2DNativeBackpropInput");let y=Yt(o.shape),b=Yt(s.shape),v=Un(m,s.shape,c,l,p,f,!0),T=new hn(v.inShape,"float32"),N=T.values,[S,D,I]=T.strides,P=e.data.get(o.dataId).values,[E,L,B]=y,q=e.data.get(s.dataId).values,[H,Z,J]=b,{batchSize:it,filterHeight:pt,filterWidth:ht,inChannels:dt,inHeight:ft,inWidth:ut,outChannels:bt,outHeight:yt,outWidth:xt,strideHeight:kt,strideWidth:Nt}=v,At=pt-1-v.padInfo.top,It=ht-1-v.padInfo.left,St=bt/dt;for(let Lt=0;Lt<it;++Lt)for(let Ht=0;Ht<dt;++Ht)for(let oe=0;oe<ft;++oe){let de=oe-At,ie=Math.max(0,Math.ceil(de/kt)),we=Math.min(yt,(pt+de)/kt);for(let pe=0;pe<ut;++pe){let Xe=pe-It,on=Math.max(0,Math.ceil(Xe/Nt)),sn=Math.min(xt,(ht+Xe)/Nt),Ae=0;for(let Ye=ie;Ye<we;++Ye){let Le=Ye*kt-de;for(let Ln=on;Ln<sn;++Ln){let $s=Ln*Nt-Xe,Mn=E*Lt+L*Ye+B*Ln,Co=H*(pt-1-Le)+Z*(ht-1-$s)+J*Ht;for(let Br=0;Br<St;++Br){let va=Ht*St+Br,So=P[Mn+va],Ko=q[Co+Br];Ae+=So*Ko}}}N[S*Lt+D*oe+I*pe+Ht]=Ae}}return e.makeTensorInfo(T.shape,T.dtype,T.values)}let VK={kernelName:Jb,backendName:"cpu",kernelFunc:WK};let GK={kernelName:df,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o}=n,{strides:s,pad:c,dilations:l}=e,p=t,f=p.data.get(r.dataId).values,m=r.shape.length,y=p.data.get(o.dataId).values,b=o.shape.length,{batchSize:v,inHeight:T,inWidth:N,inChannels:S,outHeight:D,outWidth:I,padInfo:P,strideHeight:E,strideWidth:L,filterHeight:B,filterWidth:q,dilationHeight:H,dilationWidth:Z,outShape:J}=od(r.shape,o.shape,s,c,"NHWC",l),it=G(J),pt=J.length,ht=rr(r.dtype,it);for(let ft=0;ft<v;++ft)for(let ut=0;ut<D;++ut){let bt=ut*E-P.top;for(let yt=0;yt<I;++yt){let xt=yt*L-P.left;for(let kt=0;kt<S;++kt){let Nt=Number.MIN_SAFE_INTEGER;for(let It=0;It<B;++It){let St=bt+It*H;if(St>=0&&St<T)for(let Lt=0;Lt<q;++Lt){let Ht=xt+Lt*Z;if(Ht>=0&&Ht<N){let oe=Kr([ft,St,Ht,kt],m,Yt(r.shape)),de=Kr([It,Lt,kt],b,Yt(o.shape)),ie=f[oe]+y[de];ie>Nt&&(Nt=ie)}}}let At=Kr([ft,ut,yt,kt],pt,Yt(J));ht[At]=Nt}}}let dt=p.write(Qs(ht,r.dtype),J,r.dtype);return{dataId:dt,shape:J,dtype:r.dtype}}};let UK={kernelName:gf,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o,dy:s}=n,{strides:c,pad:l,dilations:p}=e,f=t,m=Er(r.shape,f.data.get(r.dataId).values),y=Er(o.shape,f.data.get(o.dataId).values),{batchSize:b,inHeight:v,inWidth:T,inChannels:N,outHeight:S,outWidth:D,padInfo:I,strideHeight:P,strideWidth:E,filterHeight:L,filterWidth:B,dilationHeight:q,dilationWidth:H,outShape:Z}=od(r.shape,o.shape,c,l,"NHWC",p);_(s.rank===Z.length,()=>`Error in ${gf}, dy must have the same rank as output ${Z.length}, but got ${s.rank}`);let J=Er(Z,f.data.get(s.dataId).values),it=Lb(o.shape,o.dtype);for(let ht=0;ht<b;++ht)for(let dt=0;dt<S;++dt){let ft=dt*P-I.top;for(let ut=0;ut<D;++ut){let bt=ut*E-I.left;for(let yt=0;yt<N;++yt){let xt=Number.MIN_SAFE_INTEGER,kt=0,Nt=0;for(let At=0;At<L;++At){let It=ft+At*q;if(It>=0&&It<v)for(let St=0;St<B;++St){let Lt=bt+St*H;if(Lt>=0&&Lt<T){let Ht=m[ht][It][Lt][yt]+y[At][St][yt];Ht>xt&&(xt=Ht,kt=At,Nt=St)}}}it[kt][Nt][yt]+=J[ht][dt][ut][yt]}}}let pt=f.write(Qs(it,r.dtype),o.shape,o.dtype);return{dataId:pt,shape:o.shape,dtype:o.dtype}}};let qK={kernelName:mf,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o,dy:s}=n,{strides:c,pad:l,dilations:p}=e,f=t,m=Er(r.shape,f.data.get(r.dataId).values),y=Er(o.shape,f.data.get(o.dataId).values),{batchSize:b,inHeight:v,inWidth:T,inChannels:N,outHeight:S,outWidth:D,padInfo:I,strideHeight:P,strideWidth:E,filterHeight:L,filterWidth:B,dilationHeight:q,dilationWidth:H,outShape:Z}=od(r.shape,o.shape,c,l,"NHWC",p);_(s.rank===Z.length,()=>`Error in ${mf}, dy must have the same rank as output ${Z.length}, but got ${s.rank}`);let J=Er(Z,f.data.get(s.dataId).values),it=Lb(r.shape,r.dtype);for(let ht=0;ht<b;++ht)for(let dt=0;dt<S;++dt){let ft=dt*P-I.top;for(let ut=0;ut<D;++ut){let bt=ut*E-I.left;for(let yt=0;yt<N;++yt){let xt=Number.MIN_SAFE_INTEGER,kt=ft<0?0:ft,Nt=bt<0?0:bt;for(let At=0;At<L;++At){let It=ft+At*q;if(It>=0&&It<v)for(let St=0;St<B;++St){let Lt=bt+St*H;if(Lt>=0&&Lt<T){let Ht=m[ht][It][Lt][yt]+y[At][St][yt];Ht>xt&&(xt=Ht,kt=It,Nt=Lt)}}}it[ht][kt][Nt][yt]+=J[ht][dt][ut][yt]}}}let pt=f.write(Qs(it,r.dtype),r.shape,r.dtype);return{dataId:pt,shape:r.shape,dtype:r.dtype}}};let HK=Ti((n,t)=>n/t),jK=Qc(uc,HK),l1={kernelName:uc,backendName:"cpu",kernelFunc:jK};let KK=Yw,XK=Jw,YK=Zw,JK=Qw,ZK=tv,QK=ev,t5=Ee(fu,n=>{let t=Math.sign(n),e=Math.abs(n),r=1/(1+KK*e);return t*(1-((((QK*r+ZK)*r+JK)*r+YK)*r+XK)*r*Math.exp(-e*e))}),e5={kernelName:fu,backendName:"cpu",kernelFunc:t5};function hI(n,t,e){let r=n.shape,o=r[0],s=r[1],c=e.data.get(n.dataId),l=c.complexTensorInfos.real,p=c.complexTensorInfos.imag,f=[o,s],m=G(f),y=Ce("float32",m),b=Ce("float32",m);for(let S=0;S<o;S++){let D=s1({inputs:{x:l},backend:e,attrs:{begin:[S,0],size:[1,s]}}),I=s1({inputs:{x:p},backend:e,attrs:{begin:[S,0],size:[1,s]}}),P=_o({inputs:{real:D,imag:I},backend:e}),{real:E,imag:L}=n5(P,t,e),B=ys(E,L);for(let q=0;q<s;q++){let H=nv(B,q);y[S*s+q]=H.real,b[S*s+q]=H.imag}e.disposeIntermediateTensorInfo(D),e.disposeIntermediateTensorInfo(I),e.disposeIntermediateTensorInfo(P)}let v=e.makeTensorInfo(f,"float32",y),T=e.makeTensorInfo(f,"float32",b),N=_o({inputs:{real:v,imag:T},backend:e});return e.disposeIntermediateTensorInfo(v),e.disposeIntermediateTensorInfo(T),N}function n5(n,t,e){let r=G(n.shape),o=e.data.get(n.dataId),s=e.data.get(o.complexTensorInfos.real.dataId).values,c=e.data.get(o.complexTensorInfos.imag.dataId).values;if(r5(r)){let l=u1(s,c,r,t,e),p=[n.shape[0],n.shape[1]];if(t){let f=e.makeTensorInfo(p,"float32",l.real),m=e.makeTensorInfo(p,"float32",l.imag),y=e.makeTensorInfo([],"float32",CN(r,"float32")),b=ba({inputs:{x:y},backend:e}),v=l1.kernelFunc({inputs:{a:f,b:y},backend:e}),T=l1.kernelFunc({inputs:{a:m,b},backend:e}),N=e.data.get(v.dataId).values,S=e.data.get(T.dataId).values;return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v),e.disposeIntermediateTensorInfo(T),{real:N,imag:S}}return l}else{let l=ys(s,c),p=o5(l,r,t);return gC(p)}}function r5(n){return(n&n-1)===0}function u1(n,t,e,r,o){if(e===1)return{real:n,imag:t};let 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${n}.`)});let{simpleAbsImpl:nX,addImpl:rX,ceilImpl:oX,expImpl:sX,expm1Impl:iX,floorImpl:aX,logImpl:cX,maxImpl:lX,multiplyImpl:uX,rsqrtImpl:pX,sliceImpl:hX,subImpl:fX,transposeImpl:y1,uniqueImpl:dX}=U6;class mX{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,c)=>`T${c}`);let r=[];this.variableNames.forEach(s=>{r.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
+============================`));let dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){let tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){let dataId=this.write(values,shape,dtype);return engine15().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){let{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){let start=util_exports.now();f();let kernelMs=util_exports.now()-start;return{kernelMs}}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(x,begin,end,strides){assertNotComplex(x,"stridedSlice");let outShape=slice_util_exports.computeOutShape(begin,end,strides);if(outShape.some(axis=>axis===0))return tensor4([],outShape);let buffer11=buffer(outShape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<buffer11.size;i++){let loc=buffer11.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];buffer11.set(xBuf.get(...newLoc),...loc)}return buffer11.toTensor()}diag(x){let xVals=this.readSync(x.dataId),buffer11=buffer([x.size,x.size],x.dtype),vals=buffer11.values;for(let i=0;i<xVals.length;i++)vals[i*x.size+i]=xVals[i];return buffer11.toTensor()}unstack(x,axis){let num=x.shape[axis],outShape=new Array(x.rank-1),outIndex=0;for(let i=0;i<x.rank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let begin=new Array(x.rank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++)begin[axis]=i,res[i]=slice(x,begin,size).reshape(outShape);return res}reverse(x,axis){assertNotComplex(x,"reverse");let buffer11=buffer(x.shape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<buffer11.size;i++){let outLoc=buffer11.indexToLoc(i),inLoc=outLoc.slice();axis.forEach(ax=>inLoc[ax]=x.shape[ax]-1-inLoc[ax]),buffer11.set(xBuf.get(...inLoc),...outLoc)}return buffer11.toTensor()}neg(x){return assertNotComplex(x,"neg"),mul(scalar(-1),x)}addN(tensors){assertNotComplex(tensors,"addN");let vals=tensors.map(t=>this.readSync(t.dataId)),result=buffer(tensors[0].shape,tensors[0].dtype),resultVals=result.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<resultVals.length;j++)resultVals[j]+=currVals[j]}return result.toTensor()}softmax(logits,dim){let axes=util_exports.parseAxisParam([dim],logits.shape),maxLogit=max(logits,axes),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),a=sub(logits,maxLogit.reshape(expandedShape)),b=exp(a),sumExp=this.sum(b,axes).reshape(expandedShape);return div(b,sumExp)}pow(a,b){return assertNotComplex([a,b],"pow"),this.broadcastedBinaryOp(a,b,a.dtype,(aValue,bValue)=>Math.pow(aValue,bValue))}floorDiv(a,b){assertNotComplex([a,b],"floorDiv");let op22=(a6,b2)=>Math.floor(a6/b2),outputDtype="int32";return this.broadcastedBinaryOp(a,b,outputDtype,op22)}sum(x,axes){assertNotComplex(x,"sum"),backend_util_exports.assertAxesAreInnerMostDims("sum",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),resultDtype=upcastType(x.dtype,"int32"),result=zeros(outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum29=0;for(let j=0;j<reduceSize;++j)sum29+=aVals[offset+j];vals[i]=sum29}return result}prod(x,axes){assertNotComplex(x,"sum");let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),resultDtype=upcastType(x.dtype,"int32"),result=zeros(outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,prod52=1;for(let j=0;j<reduceSize;++j)prod52*=aVals[offset+j];vals[i]=prod52}return result}unsortedSegmentSum(x,segmentIds,numSegments){assertNotComplex(x,"unsortedSegmentSum");let res=[],numIters=x.rank-segmentIds.rank;for(let i=0;i<numIters;++i)segmentIds=segmentIds.expandDims(i+1);for(let i=0;i<numSegments;++i){let segmentId=scalar(i,"int32"),mask=equal(segmentId,segmentIds).asType("float32"),sum29=mask.mul(x).sum(0);res.push(sum29)}return stack(res)}argMin(x,axis){assertNotComplex(x,"argMin");let axes=[axis];backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min82=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min82&&(min82=value,minIndex=j)}vals[i]=minIndex}return result}argMax(x,axis){assertNotComplex(x,"argMax");let axes=[axis];backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max102=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max102&&(max102=value,maxIndex=j)}vals[i]=maxIndex}return result}cumsum(x,axis,exclusive,reverse12){if(assertNotComplex(x,"cumsum"),axis!==x.rank-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank-1} but got axis=${axis}`);let resultDtype=upcastType(x.dtype,"int32"),result=zeros(x.shape,resultDtype),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId),finalDim=x.shape[x.rank-1],indexAdjuster=reverse12?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}return result}equal(a,b){return assertNotComplex([a,b],"equal"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal===bVal?1:0)}notEqual(a,b){return assertNotComplex([a,b],"notEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal!==bVal?1:0)}less(a,b){return assertNotComplex([a,b],"less"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal<bVal?1:0)}lessEqual(a,b){return assertNotComplex([a,b],"lessEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal<=bVal?1:0)}greater(a,b){return assertNotComplex([a,b],"greater"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal>bVal?1:0)}greaterEqual(a,b){return assertNotComplex([a,b],"greaterEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal>=bVal?1:0)}logicalAnd(a,b){return assertNotComplex([a,b],"logicalAnd"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal&&bVal)}logicalOr(a,b){return assertNotComplex([a,b],"logicalOr"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal||bVal)}select(condition,a,b){assertNotComplex([condition,a,b],"select");let values=this.readSync(condition.dataId),aValues=this.readSync(a.dataId),bValues=this.readSync(b.dataId),result=zeros(a.shape,upcastType(a.dtype,b.dtype)),newValues=this.readSync(result.dataId),index=0,offset=condition.rank===0||condition.rank>1||a.rank===1?1:util_exports.sizeFromShape(a.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=aValues[i]:newValues[index++]=bValues[i];return result}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}topk(x,k,sorted){assertNotComplex(x,"topk");let xVals=this.readSync(x.dataId);return topkImpl2(xVals,x.shape,x.dtype,k,sorted)}min(x,axes){assertNotComplex(x,"min"),backend_util_exports.assertAxesAreInnerMostDims("min",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min82=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min82&&(min82=value)}vals[i]=min82}return result}minimum(a,b){return assertNotComplex([a,b],"minimum"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>Math.min(aVal,bVal))}mod(a,b){return assertNotComplex([a,b],"mod"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>{let rem=aVal%bVal;return aVal<0&&bVal<0||aVal>=0&&bVal>=0?rem:(rem+bVal)%bVal})}maximum(a,b){return assertNotComplex([a,b],"maximum"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>Math.max(aVal,bVal))}all(x,axes){assertNotComplex(x,"all"),backend_util_exports.assertAxesAreInnerMostDims("all",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all52=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all52=all52&&value}vals[i]=all52}return result}any(x,axes){assertNotComplex(x,"any"),backend_util_exports.assertAxesAreInnerMostDims("any",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}return result}squaredDifference(a,b){return assertNotComplex([a,b],"squaredDifference"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>{let diff=aVal-bVal;return diff*diff})}eluDer(dy,y){assertNotComplex([dy,y],"eluDer");let resultValues=new Float32Array(y.size),values=this.readSync(y.dataId),dyValues=this.readSync(dy.dataId);for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return this.makeOutput(resultValues,y.shape,"float32")}atan2(a,b){return assertNotComplex([a,b],"atan2"),this.broadcastedBinaryOp(a,b,a.dtype,(aValue,bValue)=>Math.atan2(aValue,bValue))}tile(x,reps){return assertNotComplex(x,"tile"),tile9(this.bufferSync(x),reps)}gather(x,indices,axis){assertNotComplex([x,indices],"gather");let newShape=x.shape.slice(),indicesValues=this.readSync(indices.dataId);newShape[axis]=indicesValues.length;let result=buffer(newShape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<result.size;++i){let newLoc=result.indexToLoc(i),originalLoc=newLoc.slice();originalLoc[axis]=indicesValues[newLoc[axis]];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result.toTensor()}batchToSpaceND(x,blockShape,crops){assertNotComplex([x],"batchToSpaceND");let prod52=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod52),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod52),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length);return transpose(x.reshape(reshaped),permuted).reshape(reshapedPermuted).slice(sliceBeginCoords,sliceSize)}pool3d(x,convInfo,poolType){assertNotComplex(x,"pool3d");let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,xValues=this.readSync(x.dataId),output=buffer(convInfo.outShape,x.dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*x.strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*x.strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*x.strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*x.strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output.toTensor()}avgPool3d(x,convInfo){return assertNotComplex(x,"avgPool3d"),this.pool3d(x,convInfo,"avg").toFloat()}avgPool3dBackprop(dy,x,convInfo){assertNotComplex([dy,x],"avgPool3dBackprop");let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=this.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth)continue;for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow)continue;for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let pixel=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel);dotProd+=pixel}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return dx.toTensor()}maxPool3d(x,convInfo){return assertNotComplex(x,"maxPool3d"),this.pool3d(x,convInfo,"max").toFloat()}maxPool3dPositions(x,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=this.bufferSync(x);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions.toTensor()}maxPool3dBackprop(dy,x,y,convInfo){assertNotComplex([x,y],"maxPool3dBackprop");let maxPositions=this.maxPool3dPositions(x,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),maxPosBuf=this.bufferSync(maxPositions),dyBuf=this.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth)continue;for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow)continue;for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;if(mask===0)continue;let pixel=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel);dotProd+=pixel*mask}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return dx.toTensor()}resizeBilinear(x,newHeight,newWidth,alignCorners){assertNotComplex(x,"resizeBilinear");let[batch,oldHeight,oldWidth,numChannels]=x.shape,xValues=this.readSync(x.dataId),result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow=effectiveRowSizeRatio*r,sourceRowFloor=Math.floor(sourceFracRow),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*x.strides[0]+sourceRowFloor*x.strides[1],botRowOffset=b*x.strides[0]+sourceRowCeil*x.strides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=effectiveColSizeRatio*c,sourceColFloor=Math.floor(sourceFracCol),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*x.strides[2],botLeftOffset=botRowOffset+sourceColFloor*x.strides[2],topRightOffset=topRowOffset+sourceColCeil*x.strides[2],botRightOffest=botRowOffset+sourceColCeil*x.strides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return tensor4(result,[batch,newHeight,newWidth,numChannels])}resizeBilinearBackprop(dy,x,alignCorners){assertNotComplex([dy,x],"resizeBilinearBackprop");let[batch,xHeight,xWidth,depth]=x.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=this.readSync(dy.dataId),offset=0;for(let b=0;b<batch;b++){let bOffset=b*x.strides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*x.strides[1],bottomDxROffset=bOffset+bottomDxRIndex*x.strides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*x.strides[2],topRightRCOffset=topDxROffset+rightDxCIndex*x.strides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*x.strides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*x.strides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return tensor4d(output,[batch,xWidth,xHeight,depth],x.dtype)}resizeNearestNeighbor(x,newHeight,newWidth,alignCorners){assertNotComplex(x,"resizeNearestNeighbor");let[batch,oldHeight,oldWidth,numChannels]=x.shape,xValues=this.readSync(x.dataId),output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*x.strides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow)),rowOffset=batchOffset+sourceNearestRow*x.strides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol)),colOffset=rowOffset+sourceNearestCol*x.strides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return tensor4(output,[batch,newHeight,newWidth,numChannels],x.dtype)}resizeNearestNeighborBackprop(dy,x,alignCorners){assertNotComplex([dy,x],"resizeNearestNeighborBackprop");let[batch,xHeight,xWidth,depth]=x.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=this.readSync(dy.dataId),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*x.strides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*x.strides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*x.strides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(dyR<0||dyR>=yHeight)continue;let dyROffset=batchOffset+dyR*dy.strides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r!==sourceNearestRow)continue;for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(dyC<0||dyC>=yWidth)continue;let dyCOffset=dyROffset+dyC*dy.strides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}output[colOffset+d]=accum}}}}return tensor4d(output,x.shape,x.dtype)}localResponseNormalization4D(x,depthRadius,bias,alpha,beta){assertNotComplex(x,"localResponseNormalization4D");let channels=x.shape[3],maxD=channels-1,xValues=this.readSync(x.dataId),size=x.size,result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum29=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum29+=z*z}return sum29}for(let offset=0;offset<size;offset++){let sum29=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum29,-beta);result[offset]=val}return tensor4d(result,x.shape)}LRNGrad(dy,inputImage,outputImage,depthRadius,bias,alpha,beta){assertNotComplex(dy,"LRNGrad");let channels=dy.shape[3],dyValues=this.readSync(dy.dataId),inputImageValues=this.readSync(inputImage.dataId),outputImageValues=this.readSync(outputImage.dataId),result=new Float32Array(dy.size),size=dy.size;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm52=0;for(let k=depthBegin;k<depthEnd;k++)norm52+=Math.pow(inputImageValues[k],2);norm52=alpha*norm52+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*inputImageValues[k]*outputImageValues[offset]/norm52;offset===k&&(dyi+=Math.pow(norm52,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return tensor4d(result,dy.shape)}multinomial(logits,normalized,numSamples,seed){assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax(logits),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],res=zeros([batchSize,numSamples],"int32"),resVals=this.readSync(res.dataId),probVals=this.readSync(probabilities.dataId);for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return res}oneHot(indices,depth,onValue,offValue){assertNotComplex(indices,"oneHot");let res=new Float32Array(indices.size*depth);res.fill(offValue);let indicesVal=this.readSync(indices.dataId);for(let event=0;event<indices.size;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return tensor2d(res,[indices.size,depth],"int32")}nonMaxSuppression(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){assertNotComplex(boxes,"nonMaxSuppression");let boxesVals=this.readSync(boxes.dataId),scoresVals=this.readSync(scores.dataId);return nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold)}depthToSpace(x,blockSize,dataFormat){util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=this.readSync(x.dataId),result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inD=d+offsetD,inputIdx=inD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return tensor4d(result,[batchSize,outputHeight,outputWidth,outputDepth])}broadcastedBinaryOp(a,b,dtype,op22){let newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),result=buffer(newShape,dtype),aVals=this.readSync(a.dataId),bVals=this.readSync(b.dataId),aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),resVals=result.values;if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resVals.length;++i)resVals[i]=op22(aVals[i%aVals.length],bVals[i%bVals.length]);else{let aBuf=this.bufferSync(a),bBuf=this.bufferSync(b);for(let i=0;i<resVals.length;++i){let loc=result.indexToLoc(i),aLoc=loc.slice(-a.rank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=aBuf.locToIndex(aLoc),bLoc=loc.slice(-b.rank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=bBuf.locToIndex(bLoc);resVals[i]=op22(aVals[aIndex],bVals[bIndex])}}return result.toTensor()}split(x,sizeSplits,axis){return split10(x,sizeSplits,axis)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(images,boxes,boxIndex,cropSize,method,extrapolationValue){let[batch,imageHeight,imageWidth,numChannels]=images.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=this.readSync(boxes.dataId),boxIndVals=this.readSync(boxIndex.dataId),imageVals=this.readSync(images.dataId),inStride=images.strides,outStride=output.strides;for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(bInd>=batch)continue;let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}return output.toTensor()}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1;return this.scatter(sparseIndices,sparseValues,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices)}gatherND(x,indices){let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(x,indices);if(numSlices===0)return tensor4([],resultShape,x.dtype);let buffer11=new TensorBuffer([numSlices,sliceSize],x.dtype),indicesData=this.readSync(indices.dataId),xData=this.readSync(x.dataId);for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=x.size/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);for(let k=0;k<sliceSize;k++)buffer11.values[i*sliceSize+k]=xData[flattenIndex*sliceSize+k]}return buffer11.toTensor().reshape(resultShape)}scatterND(indices,updates,shape){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),defaultValue=scalar(0),sumDupeIndices=!0;return this.scatter(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices)}onesLike(x){if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");return fill(x.shape,1,x.dtype)}zerosLike(x){let values=util_exports.getArrayFromDType(x.dtype,util_exports.sizeFromShape(x.shape));return this.makeOutput(values,x.shape,x.dtype)}linspace(start,stop,num){return backend_util_exports.linspaceImpl(start,stop,num)}scatter(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=this.readSync(indices.dataId),updatesData=this.readSync(updates.dataId);if(outputSize===0)return tensor4([],shape,updates.dtype);let buffer11=new TensorBuffer(flattenShape,updates.dtype);buffer11.values.fill(this.readSync(defaultValue.dataId)[0]);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?buffer11.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:buffer11.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return buffer11.toTensor().reshape(shape)}},shared_exports={};__export2(shared_exports,{addImpl:()=>addImpl,ceilImpl:()=>ceilImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,multiplyImpl:()=>multiplyImpl,notEqualImpl:()=>notEqualImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,subImpl:()=>subImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs9=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape));if(x.dtype!=="complex64"){let values=cpuBackend.data.get(x.dataId).values;resultValues=simpleAbsImpl(values)}else{let complexVals=cpuBackend.data.get(x.dataId),real82=complexVals.complexTensorInfos.real,imag82=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real82.dataId).values,imagVals=cpuBackend.data.get(imag82.dataId).values;for(let i=0;i<realVals.length;i++){let real9=realVals[i],imag9=imagVals[i];resultValues[i]=Math.hypot(real9,imag9)}}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs9};function createSimpleBinaryKernelImpl(op22){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op22(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op22(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex9(args){let{inputs,backend:backend3}=args,{real:real82,imag:imag82}=inputs,realVals=backend3.data.get(real82.dataId).values,imagVals=backend3.data.get(imag82.dataId).values,complexInfo=backend3.makeTensorInfo(real82.shape,"complex64"),complex11=backend3.data.get(complexInfo.dataId);return complex11.complexTensorInfos={real:backend3.makeTensorInfo(real82.shape,"float32",realVals),imag:backend3.makeTensorInfo(imag82.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex9};function identity2(args){let{inputs,backend:backend3}=args,{x}=inputs;return backend3.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,real82=backend3.data.get(input2.dataId).complexTensorInfos.real,realVal=backend3.data.get(real82.dataId).values;return backend3.makeTensorInfo(real82.shape,real82.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real6};function cast49(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend3});let zerosTensor=zeros(x.shape),floatX=cast49({inputs:{x},backend:backend3,attrs:{dtype:"float32"}}),result=complex9({inputs:{real:floatX,imag:zerosTensor},backend:backend3});return zerosTensor.dispose(),backend3.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real6({inputs:{input:x},backend:backend3}),result=cast49({inputs:{x:realPart},backend:backend3,attrs:{dtype}});return backend3.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend3});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend3.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend3.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend3.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend3.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast49};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend3})=>{let{a,b}=inputs,cpuBackend=backend3;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend3})=>{let{a,b}=inputs,cpuBackend=backend3;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast49({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast49({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex9({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op22){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op22(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op22(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add32=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add32};function createSimpleUnaryImpl(op22){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op22(values[i],attrs);return newValues}}function unaryKernelFunc(name,op22,dtype){return({inputs,attrs,backend:backend3})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend3,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op22(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend3})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend3,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil4=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil4},expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp12=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp12},expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm14=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm14},floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor6=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor6},logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log9=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log9};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max102=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max102&&(max102=value)}vals[i]=max102}return vals}var multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2},notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2},rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt5=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt5};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return vals.subarray(flatOffset,flatOffset+length)}let outVals=util_exports.getTypedArrayFromDType(dtype,length);for(let i=0;i<length;++i){let rank=size.length,strides=util_exports.computeStrides(size),loc=util_exports.indexToLoc(i,rank,strides),xLoc=loc.map((idx,j)=>idx+begin[j]),xIndex=util_exports.locToIndex(xLoc,shape.length,xStrides);outVals[i]=vals[xIndex]}return outVals}function slice19(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend3.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend3.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice19},squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2},subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub34=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub34};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version10="2.7.0";registerBackend("cpu",()=>new MathBackendCPU,1);var elu8=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu8},preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu7(args){let{inputs,backend:backend3}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend3.data.get(x.dataId).values,bVals=backend3.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend3.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu7},relu9=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu9},relu66=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu66};function applyActivation2(backend3,x,activation2,preluActivationWeights){if(activation2==="linear")return identity2({inputs:{x},backend:backend3});if(activation2==="relu")return relu9({inputs:{x},backend:backend3});if(activation2==="elu")return elu8({inputs:{x},backend:backend3});if(activation2==="relu6")return relu66({inputs:{x},backend:backend3});if(activation2==="prelu")return prelu7({inputs:{x,alpha:preluActivationWeights},backend:backend3});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape88(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend3.incRef(x.dataId);let xData=backend3.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real82=xData.complexTensorInfos.real,imag82=xData.complexTensorInfos.imag;real82.shape=$shape,imag82.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape88};function batchMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShapeOuterDims=batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2),outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape88({inputs:{x:a},backend:backend3,attrs:{shape:a3dShape}}),b3d=reshape88({inputs:{x:b},backend:backend3,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend3.data.get(a3d.dataId).values,b3dValues=backend3.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend3.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum29=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum29+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum29}}return backend3.disposeIntermediateTensorInfo(a3d),backend3.disposeIntermediateTensorInfo(b3d),backend3.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2}=attrs,current,addRes,activationRes,intermediates=[],matMulRes=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend3});current=matMulRes,bias&&(addRes=add32({inputs:{a:current,b:bias},backend:backend3}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend3,current,activation2,preluActivationWeights),intermediates.push(current),current=activationRes);for(let i of intermediates)backend3.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul},acos4=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos4},acosh4=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh4},asin4=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin4},asinh4=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh4},atan5=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan5},atanh4=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh4};function pool5(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function avgPool2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend3});else{let xValues=backend3.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer11=pool5(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend3.makeTensorInfo(convInfo.outShape,x.dtype,buffer11.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPoolBackprop2(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolBackprop");let{filterSize,strides,pad:pad11}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend3.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR)continue;for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let pixel=dyBuf.get(b,dyR,dyC,d);dotProd+=pixel}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolBackpropConfig={kernelName:AvgPoolBackprop,backendName:"cpu",kernelFunc:avgPoolBackprop2};function batchNorm2(args){let{inputs,backend:backend3,attrs}=args,{x,scale:scale22,offset,mean:mean7,variance}=inputs;util_exports.assert(mean7.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean7.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale22==null||mean7.shape.length===scale22.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean7,variance,scale22,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend3.data.get(x.dataId).values,mVals=backend3.data.get(mean7.dataId).values,varVals=backend3.data.get(variance.dataId).values,sVals=scale22?backend3.data.get(scale22.dataId).values:new Float32Array([1]),offVals=offset?backend3.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend3.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2},clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip};function imag6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,imag82=backend3.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend3.data.get(imag82.dataId).values;return backend3.makeTensorInfo(imag82.shape,imag82.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag6};function concat17(args){let{inputs,backend:backend3,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend3.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real6({inputs:{input:t},backend:backend3})),imags=$inputs.map(t=>imag6({inputs:{input:t},backend:backend3})),realConcated=concat17({inputs:reals,backend:backend3,attrs:{axis:$axis}}),imagConcated=concat17({inputs:imags,backend:backend3,attrs:{axis:$axis}}),result=complex9({inputs:{real:realConcated,imag:imagConcated},backend:backend3});return reals.forEach(r=>backend3.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend3.disposeIntermediateTensorInfo(i)),backend3.disposeIntermediateTensorInfo(realConcated),backend3.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis)),shape=[-1,innerSize];return reshape88({inputs:{x:t},backend:backend3,attrs:{shape}})});outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let outVals=util_exports.getTypedArrayFromDType($inputs[0].dtype,util_exports.sizeFromShape(outShape));if(inputs2D[0].shape[0]===1){let offset=0;inputs2D.forEach(t=>{let val=backend3.data.get(t.dataId).values,size=util_exports.sizeFromShape(t.shape);outVals.set(val,offset),offset+=size})}else{let colOffset=0;inputs2D.forEach(t=>{let tVals=backend3.data.get(t.dataId).values,tIdx=0;for(let row=0;row<t.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<t.shape[1];++col)outVals[resIdx+col]=tVals[tIdx++]}colOffset+=t.shape[1]})}let finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend3.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend3.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat17};function conv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,pad:pad11,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad11,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend3.data.get(x.dataId).values,dyVals=backend3.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend3.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad11,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad11,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend3.data.get(dy.dataId).values,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad11),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(xF<0||xF>=convInfo.inDepth)continue;let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,pad:pad11,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad11),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend3.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xF=wF+yF*strideDepth-frontPad,xOffset2=xF*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad,xOffset3=xR*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad,xOffset4=xC*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend3.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{pad:pad11,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad11),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2},cos6=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos6},cosh5=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh5};function depthwiseConv2dNative(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad11,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad11,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend3.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend3.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend3.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad11,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2},dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}let dataId=cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype);return{dataId,shape:outShape,dtype:x.dtype}}},dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients8=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients8[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}let dataId=cpuBackend.write(util_exports.toTypedArray(gradients8,x.dtype),filter.shape,filter.dtype);return{dataId,shape:filter.shape,dtype:filter.dtype}}},dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients8=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients8[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}let dataId=cpuBackend.write(util_exports.toTypedArray(gradients8,x.dtype),x.shape,x.dtype);return{dataId,shape:x.shape,dtype:x.dtype}}},divImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div35=binaryKernelFunc(Div,divImpl),divConfig={kernelName:Div,backendName:"cpu",kernelFunc:div35},p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf4=unaryKernelFunc(Erf,xi=>{let sign52=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign52*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf4};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice19({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice19({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex9({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real82,imag:imag82}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real82,imag82);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex9({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=divConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=divConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)===0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex9({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex9({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex9({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex9({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex9({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add32({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub34({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real6({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real6({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag6({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag6({inputs:{input:subPart},backend:cpuBackend}),$real=concat17({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat17({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real82=0,imag82=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real82+=term.real*e.real-term.imag*e.imag,imag82+=term.real*e.imag+term.imag*e.real}inverse&&(real82/=size,imag82/=size),backend_util_exports.assignToTypedArray(ret,real82,imag82,r)}return ret}function fft6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape88({inputs:{x:input2},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend3),resultReshaped=reshape88({inputs:{x:result},backend:backend3,attrs:{shape:input2.shape}});return backend3.disposeIntermediateTensorInfo(input2D),backend3.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft6};function fill5(args){let{backend:backend3,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend3.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill5};function fillValues(values,value,dtype){dtype==="string",values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,cpuBackend=backend3,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}let dataId=cpuBackend.write(output,image3.shape,image3.dtype);return{dataId,shape:image3.shape,dtype:image3.dtype}}};function fusedConv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2}=attrs,result=conv2D({inputs:{x,filter},backend:backend3,attrs:{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add32({inputs:{a:result,b:bias},backend:backend3}),backend3.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend3,result,activation2,preluActivationWeights),backend3.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend3,attrs:{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add32({inputs:{a:result,b:bias},backend:backend3}),backend3.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend3,result,activation2,preluActivationWeights),backend3.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function ifft6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape88({inputs:{x:input2},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend3),resultReshaped=reshape88({inputs:{x:result},backend:backend3,attrs:{shape:input2.shape}});return backend3.disposeIntermediateTensorInfo(input2D),backend3.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft6},isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3},isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2},isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3},log1p5=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p5},logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2},maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend3,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes);outShape=newShape}return{dataId,shape:outShape,dtype:x.dtype}}};function maxPool2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend3});else{let xValues=backend3.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer11=pool5(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend3.makeTensorInfo(convInfo.outShape,x.dtype,buffer11.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPoolBackprop2(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolBackprop");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),xValues=backend3.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend3.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR)continue;for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;if(mask===0)continue;let pixel=dyBuf.get(b,dyR,dyC,d);dotProd+=pixel*mask}}dx.set(dotProd,b,dxR,dxC,d)}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolBackpropConfig={kernelName:MaxPoolBackprop,backendName:"cpu",kernelFunc:maxPoolBackprop2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool5(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{filterSize,strides,pad:pad11,includeBatchInIndex}=attrs,cpuBackend=backend3;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad11),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function mirrorPad2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend3.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}let outId=backend3.write(resVals,outShape,x.dtype);return{dataId:outId,shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2},nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl,nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs,cpuBackend=backend3;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=cpuBackend.data.get(boxes.dataId).values,scoresVals=cpuBackend.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[selectedIndices,validOutputs]}},nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl,nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs,cpuBackend=backend3;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=cpuBackend.data.get(boxes.dataId).values,scoresVals=cpuBackend.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[selectedIndices,selectedScores]}};function padV2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend3.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let coords2=util_exports.indexToLoc(i,xRank,xStrides),outCoords=coords2.map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}let outId=backend3.write(resVals,outShape,x.dtype);return{dataId:outId,shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2},reciprocal4=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal4},rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend3,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}let dataId=cpuBackend.write(output,image3.shape,image3.dtype);return{dataId,shape:image3.shape,dtype:image3.dtype}}},round4=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2===0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round4},scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu5=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu5},sigmoid7=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid7},sign4=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign4},sin5=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin5},sinh5=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh5},epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus5=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus5};function transpose18(args){let{inputs,attrs,backend:backend3}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend3.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape),dataId=backend3.write(result,newShape,x.dtype);return{dataId,shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose18};function spaceToBatchND2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod52=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend3,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod52,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod52,!1),reshapeInputs={x:paddedX},reshapeAttrs={shape:reshapedPaddedShape},paddedXReshaped=reshape88({inputs:reshapeInputs,backend:backend3,attrs:reshapeAttrs}),transposeInputs={x:paddedXReshaped},transposeAttrs={perm:permutedReshapedPaddedPermutation},paddedXT=transpose18({inputs:transposeInputs,backend:backend3,attrs:transposeAttrs}),resultReshapeInputs={x:paddedXT},resultReshapeAttrs={shape:flattenShape},result=reshape88({inputs:resultReshapeInputs,backend:backend3,attrs:resultReshapeAttrs});return backend3.disposeIntermediateTensorInfo(paddedX),backend3.disposeIntermediateTensorInfo(paddedXReshaped),backend3.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2},sqrt13=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt13},squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend3})=>{let{x}=inputs,cpuBackend=backend3;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}let dataId=cpuBackend.write(newValues,x.shape,x.dtype);return{dataId,shape:x.shape,dtype:x.dtype}}},step8=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step8},tan4=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan4},tanh6=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh6};function unique6(args){let{inputs,attrs,backend:backend3}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend3.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend3.makeTensorInfo(outputShape,x.dtype,outputValues),backend3.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique6},kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,asinConfig,asinhConfig,atanConfig,atanhConfig,avgPoolConfig,avgPoolBackpropConfig,batchMatMulConfig,batchNormConfig,castConfig,ceilConfig,clipConfig,complexConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,divConfig,eluConfig,erfConfig,expConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,logConfig,log1pConfig,logicalNotConfig,maxPoolConfig,maxPoolBackpropConfig,maxPoolWithArgmaxConfig,maxConfig,mirrorPadConfig,multiplyConfig,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,padV2Config,preluConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softplusConfig,spaceToBatchNDConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,subConfig,tanConfig,tanhConfig,transposeConfig,uniqueConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
+`),pad11=shaderLines.length.toString().length+2,linesWithLineNumbers=shaderLines.map((line,lineNumber2)=>util_exports.rightPad((lineNumber2+1).toString(),pad11)+line),maxLineLength=0;for(let i=0;i<linesWithLineNumbers.length;i++)maxLineLength=Math.max(linesWithLineNumbers[i].length,maxLineLength);let beforeErrorLines=linesWithLineNumbers.slice(0,lineNumber-1),errorLine=linesWithLineNumbers.slice(lineNumber-1,lineNumber),afterErrorLines=linesWithLineNumbers.slice(lineNumber);console.log(beforeErrorLines.join(`
+`)),console.log(shaderInfoLog.split(`
+`)[0]),console.log(`%c ${util_exports.rightPad(errorLine[0],maxLineLength)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(afterErrorLines.join(`
+`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer11=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer11}function createStaticIndexBuffer(gl,data){let buffer11=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer11}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max102=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max102+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer11,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1],isScalar=shape.length===0||shape.length===1&&shape[0]===1;return isScalar||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2){let squeezeResult=util_exports.squeezeShape(logShape);logShape=squeezeResult.newShape}let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2===0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2))return!0;if(!shape1.length||!shape2.length)return!0;if(shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols)return!0;if(isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){let ext=gl.getExtension(extensionName);return ext!=null}function isWebGLVersionEnabled(webGLVersion){try{let gl=getWebGLContext(webGLVersion);if(gl!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;let isFrameBufferComplete=createFloatTextureAndBindToFramebuffer(gl);return isFrameBufferComplete}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1;if(!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}let isFrameBufferComplete=createFloatTextureAndBindToFramebuffer(gl);return isFrameBufferComplete}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){if(webGLVersion!==2)return!1;let gl=getWebGLContext(webGLVersion),isEnabled=gl.fenceSync!=null;return isEnabled}function assertNotComplex2(tensor168,opName){Array.isArray(tensor168)||(tensor168=[tensor168]),tensor168.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{let useUniforms=ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return useUniforms?4:0});ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});var{simpleAbsImpl:simpleAbsImplCPU,addImpl:addImplCPU,ceilImpl:ceilImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,multiplyImpl:multiplyImplCPU,rsqrtImpl:rsqrtImplCPU,sliceImpl:sliceImplCPU,subImpl:subImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports,AddNProgram=class{constructor(outputShape,shapes){this.outputShape=[],this.outputShape=outputShape,this.variableNames=shapes.map((_,i)=>`T${i}`);let snippets=[];this.variableNames.forEach(variable32=>{snippets.push(`float v${variable32} = get${variable32}AtOutCoords();`)});let operation211=this.variableNames.map(variable32=>`v${variable32}`).join(" + ");this.userCode=`
       void main() {
-        ${r.join(`
+        ${snippets.join(`
         `)}
 
-        float result = ${o};
+        float result = ${operation211};
         setOutput(result);
       }
-    `}}class gX{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,c)=>`T${c}`);let r=[];this.variableNames.forEach(s=>{r.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
+    `}},AddNPackedProgram=class{constructor(outputShape,shapes){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape,this.variableNames=shapes.map((_,i)=>`T${i}`);let snippets=[];this.variableNames.forEach(variable32=>{snippets.push(`vec4 v${variable32} = get${variable32}AtOutCoords();`)});let operation211=this.variableNames.map(variable32=>`v${variable32}`).join(" + ");this.userCode=`
       void main() {
-        ${r.join(`
+        ${snippets.join(`
         `)}
 
-        vec4 result = ${o};
+        vec4 result = ${operation211};
         setOutput(result);
       }
-    `}}class yX{constructor(t,e,r){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:c}=t;r||this.variableNames.push("bestIndicesA"),this.outputShape=[s,c];let l=e==="max"?">":"<",p=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}},ArgMinMaxProgram=class{constructor(reduceInfo,op22,firstPass){this.variableNames=["A"];let{windowSize,batchSize,outSize}=reduceInfo;firstPass||this.variableNames.push("bestIndicesA"),this.outputShape=[batchSize,outSize];let compOp=op22==="max"?">":"<",indexSnippet=firstPass?"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 * ${o};
+        int inOffset = outIdx * ${windowSize};
 
         int bestIndex = inOffset;
         float bestValue = getA(batch, bestIndex);
 
-        for (int i = 0; i < ${o}; i++) {
-          int inIdx = ${p};
+        for (int i = 0; i < ${windowSize}; i++) {
+          int inIdx = ${indexSnippet};
           float candidate = getA(batch, inIdx);
-          if (candidate ${l} bestValue) {
+          if (candidate ${compOp} bestValue) {
             bestValue = candidate;
             bestIndex = inIdx;
           }
         }
         setOutput(float(bestIndex));
       }
-    `}}function wI(n,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${n}.${e}`)}function Yn(n,t){return t===1?[n]:wI(n,t)}function bX(n,t){if(n===1)return"rc";let e="";for(let r=0;r<n;r++)e+=t[r],r<n-1&&(e+=",");return e}function Jn(){let n,t,e,r,o,s,c,l,p,f;return ct().getNumber("WEBGL_VERSION")===2?(n="#version 300 es",t="in",e="out",r="in",o="texture",s="outputColor",c="out vec4 outputColor;",l=`
+    `}};function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}function getGlslDifferences(){let version19,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version19="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -80,7 +87,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       }
 
       #define isnan(value) isnan_custom(value)
-    `,p="",f=`
+    `,defineSpecialInf="",defineRound=`
       #define round(value) newRound(value)
       int newRound(float value) {
         return int(floor(value + 0.5));
@@ -89,7 +96,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 newRound(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `):(n="",t="attribute",e="varying",r="varying",o="texture2D",s="gl_FragColor",c="",l=`
+    `):(version19="",attribute="attribute",varyingVs="varying",varyingFs="varying",texture2D="texture2D",output="gl_FragColor",defineOutput="",defineSpecialNaN=`
       #define isnan(value) isnan_custom(value)
       bool isnan_custom(float val) {
         return (val > 0. || val < 1. || val == 0.) ? false : true;
@@ -97,7 +104,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       bvec4 isnan_custom(vec4 val) {
         return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
       }
-    `,p=`
+    `,defineSpecialInf=`
       uniform float INFINITY;
 
       bool isinf(float val) {
@@ -106,7 +113,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       bvec4 isinf(vec4 val) {
         return equal(abs(val), vec4(INFINITY));
       }
-    `,f=`
+    `,defineRound=`
       int round(float value) {
         return int(floor(value + 0.5));
       }
@@ -114,11 +121,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:n,attribute:t,varyingVs:e,varyingFs:r,texture2D:o,output:s,defineOutput:c,defineSpecialNaN:l,defineSpecialInf:p,defineRound:f}}function xa(n,t,e="index"){let r=Yt(t);return r.map((o,s)=>{let c=`int ${n[s]} = ${e} / ${o}`,l=s===r.length-1?`int ${n[s+1]} = ${e} - ${n[s]} * ${o}`:`index -= ${n[s]} * ${o}`;return`${c}; ${l};`}).join("")}function Lm(n){return n.length===1?`${n[0]}`:`vec${n.length}(${n.join(",")})`}function Yst(n,t){if(n.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${n.length} and ${t.length}`);let e=[],r=Math.floor(n.length/4),o=n.length%4;for(let s=0;s<r;s++){let c=n.slice(s*4,s*4+4),l=t.slice(s*4,s*4+4);e.push(`${Lm(c)}, ${Lm(l)}`)}if(o!==0){let s=n.slice(r*4),c=t.slice(r*4);s.length===1&&(s=s.map(l=>`float(${l})`),c=c.map(l=>`float(${l})`)),e.push(`${Lm(s)}, ${Lm(c)}`)}return e.map((s,c)=>`dot(${s})`).join("+")}function b1(n){let t=Yt(n).map(e=>e.toString());return`
+    `),{version:version19,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound}}function getLogicalCoordinatesFromFlatIndex(coords2,shape,index="index"){let strides=util_exports.computeStrides(shape);return strides.map((stride,i)=>{let line1=`int ${coords2[i]} = ${index} / ${stride}`,line2=i===strides.length-1?`int ${coords2[i+1]} = ${index} - ${coords2[i]} * ${stride}`:`index -= ${coords2[i]} * ${stride}`;return`${line1}; ${line2};`}).join("")}function getFlatIndexFrom3D(shape){let strides=util_exports.computeStrides(shape).map(d=>d.toString());return`
   int getFlatIndex(ivec3 coords) {
-    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
+    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
   }
-`}let vI=`
+`}var ENCODE_FLOAT_SNIPPET=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -157,27 +164,27 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`;let{getBroadcastDims:TI}=ov;function xX(n,t,e,r){let o=[];n.forEach(T=>{let N=G(T.shapeInfo.logicalShape);T.shapeInfo.isUniform?o.push(`uniform float ${T.name}${N>1?`[${N}]`:""};`):(o.push(`uniform sampler2D ${T.name};`),o.push(`uniform int offset${T.name};`))});let s=o.join(`
-`),c=n.map(T=>wX(T,t,r)).join(`
-`),l=t.texShape,p=Jn(),f=kX(p),m,y,b=CX(p);t.isPacked?(m=vX(t.logicalShape,l),y=_X(p)):(m=TX(t.logicalShape,l),y=NX(p)),r&&(b+=EX);let v=[b,f,y,s,m,c,e].join(`
-`);return v}function sl(n){let t=n.shapeInfo.logicalShape;switch(t.length){case 0:return VX(n);case 1:return UX(n);case 2:return HX(n);case 3:return KX(n);case 4:return YX(n);case 5:return JX(n);case 6:return ZX(n);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function kI(n){let t=n.shapeInfo.logicalShape;switch(t.length){case 0:return WX(n);case 1:return GX(n);case 2:return qX(n);case 3:return jX(n);default:return XX(n)}}function wX(n,t,e=!1){let r="";e?r+=kI(n):r+=sl(n);let o=n.shapeInfo.logicalShape,s=t.logicalShape;return o.length<=s.length&&(e?r+=QX(n,t):r+=tY(n,t)),r}function vX(n,t){switch(n.length){case 0:return NI();case 1:return DX(n,t);case 2:return BX(n,t);case 3:return FX(n,t);default:return PX(n,t)}}function TX(n,t){switch(n.length){case 0:return NI();case 1:return AX(n,t);case 2:return zX(n,t);case 3:return RX(n,t);case 4:return OX(n,t);case 5:return LX(n,t);case 6:return MX(n,t);default:throw new Error(`${n.length}-D output sampling is not yet supported`)}}function kX(n){return`
+`,{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
+`),inputSamplingSnippet=inputsInfo.map(x=>getInputSamplingSnippet(x,outputShape,usesPackedTextures)).join(`
+`),outTexShape=outputShape.texShape,glsl=getGlslDifferences(),floatTextureSampleSnippet=getFloatTextureSampleSnippet(glsl),outputSamplingSnippet,floatTextureSetOutputSnippet,shaderPrefix=getShaderPrefix(glsl);outputShape.isPacked?(outputSamplingSnippet=getPackedOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRGBASnippet(glsl)):(outputSamplingSnippet=getOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRSnippet(glsl)),usesPackedTextures&&(shaderPrefix+=SHADER_PACKED_PREFIX);let source=[shaderPrefix,floatTextureSampleSnippet,floatTextureSetOutputSnippet,inputPrefixSnippet,outputSamplingSnippet,inputSamplingSnippet,userCode].join(`
+`);return source}function getSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getSamplerScalar(inInfo);case 1:return getSampler1D(inInfo);case 2:return getSampler2D(inInfo);case 3:return getSampler3D(inInfo);case 4:return getSampler4D(inInfo);case 5:return getSampler5D(inInfo);case 6:return getSampler6D(inInfo);default:throw new Error(`${shape.length}-D input sampling is not yet supported`)}}function getPackedSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getPackedSamplerScalar(inInfo);case 1:return getPackedSampler1D(inInfo);case 2:return getPackedSampler2D(inInfo);case 3:return getPackedSampler3D(inInfo);default:return getPackedSamplerND(inInfo)}}function getInputSamplingSnippet(inInfo,outShapeInfo,usesPackedTextures=!1){let res="";usesPackedTextures?res+=getPackedSamplerFromInInfo(inInfo):res+=getSamplerFromInInfo(inInfo);let inShape=inInfo.shapeInfo.logicalShape,outShape=outShapeInfo.logicalShape;return inShape.length<=outShape.length&&(usesPackedTextures?res+=getPackedSamplerAtOutputCoords(inInfo,outShapeInfo):res+=getSamplerAtOutputCoords(inInfo,outShapeInfo)),res}function getPackedOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(outShape,outTexShape);case 2:return getOutputPacked2DCoords(outShape,outTexShape);case 3:return getOutputPacked3DCoords(outShape,outTexShape);default:return getOutputPackedNDCoords(outShape,outTexShape)}}function getOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(outShape,outTexShape);case 2:return getOutput2DCoords(outShape,outTexShape);case 3:return getOutput3DCoords(outShape,outTexShape);case 4:return getOutput4DCoords(outShape,outTexShape);case 5:return getOutput5DCoords(outShape,outTexShape);case 6:return getOutput6DCoords(outShape,outTexShape);default:throw new Error(`${outShape.length}-D output sampling is not yet supported`)}}function getFloatTextureSampleSnippet(glsl){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
-      return ${n.texture2D}(textureSampler, uv).r;
+      return ${glsl.texture2D}(textureSampler, uv).r;
     }
-  `}function NX(n){return`
+  `}function getFloatTextureSetRSnippet(glsl){return`
     void setOutput(float val) {
-      ${n.output} = vec4(val, 0, 0, 0);
+      ${glsl.output} = vec4(val, 0, 0, 0);
     }
-  `}function _X(n){return`
+  `}function getFloatTextureSetRGBASnippet(glsl){return`
     void setOutput(vec4 val) {
-      ${n.output} = val;
+      ${glsl.output} = val;
     }
-  `}function CX(n){let t=`${n.version}
+  `}function getShaderPrefix(glsl){let SHADER_PREFIX=`${glsl.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
-    ${n.varyingFs} vec2 resultUV;
-    ${n.defineOutput}
+    ${glsl.varyingFs} vec2 resultUV;
+    ${glsl.defineOutput}
     const vec2 halfCR = vec2(0.5, 0.5);
 
     struct ivec5
@@ -200,9 +207,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     };
 
     uniform float NAN;
-    ${n.defineSpecialNaN}
-    ${n.defineSpecialInf}
-    ${n.defineRound}
+    ${glsl.defineSpecialNaN}
+    ${glsl.defineSpecialInf}
+    ${glsl.defineRound}
 
     int imod(int x, int y) {
       return x - y * (x / y);
@@ -227,10 +234,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${SX}
-    ${$X}
-    ${IX}
-  `;return t}let SX=`
+    ${SAMPLE_1D_SNIPPET}
+    ${SAMPLE_2D_SNIPPET}
+    ${SAMPLE_3D_SNIPPET}
+  `;return SHADER_PREFIX}var SAMPLE_1D_SNIPPET=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -242,7 +249,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,$X=`
+`,SAMPLE_2D_SNIPPET=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -250,7 +257,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,IX=`
+`,SAMPLE_3D_SNIPPET=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -259,7 +266,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,EX=`
+`,SHADER_PACKED_PREFIX=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -270,503 +277,503 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function NI(){return`
+`;function getOutputScalarCoords(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function DX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return e[0]===1?`
+  `}function getOutputPacked1DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];return packedTexShape[0]===1?`
       int getOutputCoords() {
-        return 2 * int(resultUV.x * ${e[1]}.0);
+        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
       }
-    `:e[1]===1?`
+    `:packedTexShape[1]===1?`
       int getOutputCoords() {
-        return 2 * int(resultUV.y * ${e[0]}.0);
+        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
       }
     `:`
     int getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
-      return 2 * (resTexRC.x * ${e[1]} + resTexRC.y);
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
     }
-  `}function AX(n,t){return t[0]===1?`
+  `}function getOutput1DCoords(shape,texShape){return texShape[0]===1?`
       int getOutputCoords() {
-        return int(resultUV.x * ${t[1]}.0);
+        return int(resultUV.x * ${texShape[1]}.0);
       }
-    `:t[1]===1?`
+    `:texShape[1]===1?`
       int getOutputCoords() {
-        return int(resultUV.y * ${t[0]}.0);
+        return int(resultUV.y * ${texShape[0]}.0);
       }
     `:`
     int getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${t[0]}, ${t[1]}));
-      return resTexRC.x * ${t[1]} + resTexRC.y;
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      return resTexRC.x * ${texShape[1]} + resTexRC.y;
     }
-  `}function FX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[2]/2),o=r*Math.ceil(n[1]/2);return`
+  `}function getOutputPacked3DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texelsInLogicalRow=Math.ceil(shape[2]/2),texelsInBatch=texelsInLogicalRow*Math.ceil(shape[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
 
-      int b = index / ${o};
-      index -= b * ${o};
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
 
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
 
       return ivec3(b, r, c);
     }
-  `}function RX(n,t){let e=xa(["r","c","d"],n);return`
+  `}function getOutput3DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      ${e}
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
       return ivec3(r, c, d);
     }
-  `}function PX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[n.length-1]/2),o=r*Math.ceil(n[n.length-2]/2),s=o,c="",l="b, r, c";for(let p=2;p<n.length-1;p++)s*=n[n.length-p-1],c=`
-      int b${p} = index / ${s};
-      index -= b${p} * ${s};
-    `+c,l=`b${p}, `+l;return`
-    ivec${n.length} getOutputCoords() {
+  `}function getOutputPackedNDCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texelsInLogicalRow=Math.ceil(shape[shape.length-1]/2),texelsInBatch=texelsInLogicalRow*Math.ceil(shape[shape.length-2]/2),texelsInBatchN=texelsInBatch,batches="",coords2="b, r, c";for(let b=2;b<shape.length-1;b++)texelsInBatchN*=shape[shape.length-b-1],batches=`
+      int b${b} = index / ${texelsInBatchN};
+      index -= b${b} * ${texelsInBatchN};
+    `+batches,coords2=`b${b}, `+coords2;return`
+    ivec${shape.length} getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
 
-      ${c}
+      ${batches}
 
-      int b = index / ${o};
-      index -= b * ${o};
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
 
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
 
-      return ivec${n.length}(${l});
+      return ivec${shape.length}(${coords2});
     }
-  `}function OX(n,t){let e=xa(["r","c","d","d2"],n);return`
+  `}function getOutput4DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2"],shape);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      ${e}
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
       return ivec4(r, c, d, d2);
     }
-  `}function LX(n,t){let e=xa(["r","c","d","d2","d3"],n);return`
+  `}function getOutput5DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],shape);return`
     ivec5 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
-                             ${t[1]}));
+      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
+                             ${texShape[1]}));
 
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
 
-      ${e}
+      ${coordsFromIndexSnippet}
 
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function MX(n,t){let e=xa(["r","c","d","d2","d3","d4"],n);return`
+  `}function getOutput6DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],shape);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
 
-      ${e}
+      ${coordsFromIndexSnippet}
 
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function BX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(lt(n,t))return`
+  `}function getOutputPacked2DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];if(util_exports.arraysEqual(shape,texShape))return`
       ivec2 getOutputCoords() {
-        return 2 * ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
+        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
       }
-    `;let r=Math.ceil(n[1]/2);return`
+    `;let texelsInLogicalRow=Math.ceil(shape[1]/2);return`
     ivec2 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
 
-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
 
       return ivec2(r, c);
     }
-  `}function zX(n,t){return lt(n,t)?`
+  `}function getOutput2DCoords(shape,texShape){return util_exports.arraysEqual(shape,texShape)?`
       ivec2 getOutputCoords() {
-        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
+        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
       }
-    `:n[1]===1?`
+    `:shape[1]===1?`
       ivec2 getOutputCoords() {
         ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(${t[0]}, ${t[1]}));
-        int index = resTexRC.x * ${t[1]} + resTexRC.y;
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
         return ivec2(index, 0);
       }
-    `:n[0]===1?`
+    `:shape[0]===1?`
       ivec2 getOutputCoords() {
         ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(${t[0]}, ${t[1]}));
-        int index = resTexRC.x * ${t[1]} + resTexRC.y;
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
         return ivec2(0, index);
       }
     `:`
     ivec2 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      int r = index / ${n[1]};
-      int c = index - r * ${n[1]};
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      int r = index / ${shape[1]};
+      int c = index - r * ${shape[1]};
       return ivec2(r, c);
     }
-  `}function wa(n){return`offset${n}`}function WX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=Jn();return`
-    vec4 ${e}() {
-      return ${r.texture2D}(${t}, halfCR);
+  `}function getFlatOffsetUniformName(texName){return`offset${texName}`}function getPackedSamplerScalar(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),glsl=getGlslDifferences();return`
+    vec4 ${funcName}() {
+      return ${glsl.texture2D}(${texName}, halfCR);
     }
-  `}function VX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`float ${e}() {return ${t};}`;let[r,o]=n.shapeInfo.texShape;if(r===1&&o===1)return`
-      float ${e}() {
-        return sampleTexture(${t}, halfCR);
+  `}function getSamplerScalar(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1);if(inputInfo.shapeInfo.isUniform)return`float ${funcName}() {return ${texName};}`;let[texNumR,texNumC]=inputInfo.shapeInfo.texShape;if(texNumR===1&&texNumC===1)return`
+      float ${funcName}() {
+        return sampleTexture(${texName}, halfCR);
       }
-    `;let[s,c]=n.shapeInfo.texShape,l=wa(t);return`
-    float ${e}() {
-      vec2 uv = uvFromFlat(${s}, ${c}, ${l});
-      return sampleTexture(${t}, uv);
+    `;let[tNumR,tNumC]=inputInfo.shapeInfo.texShape,offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}() {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
+      return sampleTexture(${texName}, uv);
     }
-  `}function GX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=n.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=Jn();return`
-    vec4 ${e}(int index) {
+  `}function getPackedSampler1D(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],glsl=getGlslDifferences();return`
+    vec4 ${funcName}(int index) {
       vec2 uv = packedUVfrom1D(
-        ${o[0]}, ${o[1]}, index);
-      return ${s.texture2D}(${t}, uv);
+        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
+      return ${glsl.texture2D}(${texName}, uv);
     }
-  `}function UX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`
-      float ${e}(int index) {
-        ${il(n)}
+  `}function getSampler1D(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1);if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int index) {
+        ${getUniformSampler(inputInfo)}
       }
-    `;let r=n.shapeInfo.texShape,o=r[0],s=r[1];if(s===1&&o===1)return`
-      float ${e}(int index) {
-        return sampleTexture(${t}, halfCR);
+    `;let texShape=inputInfo.shapeInfo.texShape,tNumR=texShape[0],tNumC=texShape[1];if(tNumC===1&&tNumR===1)return`
+      float ${funcName}(int index) {
+        return sampleTexture(${texName}, halfCR);
       }
-    `;let c=wa(t);return s===1?`
-      float ${e}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${c}) + 0.5) / ${o}.0);
-        return sampleTexture(${t}, uv);
+    `;let offset=getFlatOffsetUniformName(texName);return tNumC===1?`
+      float ${funcName}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `:o===1?`
-      float ${e}(int index) {
-        vec2 uv = vec2((float(index + ${c}) + 0.5) / ${s}.0, 0.5);
-        return sampleTexture(${t}, uv);
+    `:tNumR===1?`
+      float ${funcName}(int index) {
+        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
+        return sampleTexture(${texName}, uv);
       }
     `:`
-    float ${e}(int index) {
-      vec2 uv = uvFromFlat(${o}, ${s}, index + ${c});
-      return sampleTexture(${t}, uv);
+    float ${funcName}(int index) {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
     }
-  `}function qX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape,s=o[0],c=o[1],l=Jn();if(o!=null&&lt(t,o))return`
-      vec4 ${r}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${s}.0);
+  `}function getPackedSampler2D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1],glsl=getGlslDifferences();if(texShape!=null&&util_exports.arraysEqual(shape,texShape))return`
+      vec4 ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
 
-        return ${l.texture2D}(${e}, uv);
+        return ${glsl.texture2D}(${texName}, uv);
       }
-    `;let p=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],f=Math.ceil(t[1]/2);return`
-    vec4 ${r}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${f}, ${p[0]}, ${p[1]}, row, col);
-      return ${l.texture2D}(${e}, uv);
+    `;let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],valuesPerRow=Math.ceil(shape[1]/2);return`
+    vec4 ${funcName}(int row, int col) {
+      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
     }
-  `}function HX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape;if(o!=null&&lt(t,o)){let y=o[0],b=o[1];return`
-    float ${r}(int row, int col) {
-      vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
-      return sampleTexture(${e}, uv);
+  `}function getSampler2D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape;if(texShape!=null&&util_exports.arraysEqual(shape,texShape)){let texNumR2=texShape[0],texNumC2=texShape[1];return`
+    float ${funcName}(int row, int col) {
+      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);
+      return sampleTexture(${texName}, uv);
     }
-  `}let{newShape:s,keptDims:c}=ln(t),l=s;if(l.length<t.length){let y=al(n,l),b=["row","col"];return`
-      ${sl(y)}
-      float ${r}(int row, int col) {
-        return ${r}(${cl(b,c)});
+  `}let{newShape,keptDims}=util_exports.squeezeShape(shape),squeezedShape=newShape;if(squeezedShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["row","col"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
       }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col) {
-        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
-        ${il(n)}
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col) {
+        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
+        ${getUniformSampler(inputInfo)}
       }
-    `;let p=o[0],f=o[1],m=wa(e);return f===1?`
-    float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2(0.5, (index + 0.5) / ${p}.0);
-      return sampleTexture(${e}, uv);
+    `;let texNumR=texShape[0],texNumC=texShape[1],offset=getFlatOffsetUniformName(texName);return texNumC===1?`
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
     }
-  `:p===1?`
-    float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2((index + 0.5) / ${f}.0, 0.5);
-      return sampleTexture(${e}, uv);
+  `:texNumR===1?`
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
+      return sampleTexture(${texName}, uv);
     }
   `:`
-  float ${r}(int row, int col) {
+  float ${funcName}(int row, int col) {
     // Explicitly use integer operations as dot() only works on floats.
-    int index = row * ${t[1]} + col + ${m};
-    vec2 uv = uvFromFlat(${p}, ${f}, index);
-    return sampleTexture(${e}, uv);
+    int index = row * ${shape[1]} + col + ${offset};
+    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+    return sampleTexture(${texName}, uv);
   }
-`}function jX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape,s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(t[0]===1){let y=t.slice(1),b=[1,2],v=al(n,y),T=["b","row","col"];return`
-        ${kI(v)}
-        vec4 ${r}(int b, int row, int col) {
-          return ${r}(${cl(T,b)});
+`}function getPackedSampler3D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];if(shape[0]===1){let squeezedShape=shape.slice(1),keptDims=[1,2],newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["b","row","col"];return`
+        ${getPackedSamplerFromInInfo(newInputInfo)}
+        vec4 ${funcName}(int b, int row, int col) {
+          return ${funcName}(${getSqueezedParams(params,keptDims)});
         }
-      `}let c=s[0],l=s[1],p=Math.ceil(t[2]/2),f=p*Math.ceil(t[1]/2),m=Jn();return`
-    vec4 ${r}(int b, int row, int col) {
+      `}let texNumR=packedTexShape[0],texNumC=packedTexShape[1],valuesPerRow=Math.ceil(shape[2]/2),texelsInBatch=valuesPerRow*Math.ceil(shape[1]/2),glsl=getGlslDifferences();return`
+    vec4 ${funcName}(int b, int row, int col) {
       vec2 uv = packedUVfrom3D(
-        ${c}, ${l}, ${f}, ${p}, b, row, col);
-      return ${m.texture2D}(${e}, uv);
+        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
     }
-  `}function KX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[1]*t[2],s=t[2],{newShape:c,keptDims:l}=ln(t),p=c;if(p.length<t.length){let T=al(n,p),N=["row","col","depth"];return`
-        ${sl(T)}
-        float ${r}(int row, int col, int depth) {
-          return ${r}(${cl(N,l)});
+  `}function getSampler3D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride0=shape[1]*shape[2],stride1=shape[2],{newShape,keptDims}=util_exports.squeezeShape(shape),squeezedShape=newShape;if(squeezedShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["row","col","depth"];return`
+        ${getSamplerFromInInfo(newInputInfo)}
+        float ${funcName}(int row, int col, int depth) {
+          return ${funcName}(${getSqueezedParams(params,keptDims)});
         }
-      `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth) {
+      `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
-                          vec3(${o}, ${s}, 1)));
-        ${il(n)}
+                          vec3(${stride0}, ${stride1}, 1)));
+        ${getUniformSampler(inputInfo)}
       }
-    `;let f=n.shapeInfo.texShape,m=f[0],y=f[1],b=n.shapeInfo.flatOffset;if(y===o&&b==null)return`
-        float ${r}(int row, int col, int depth) {
+    `;let texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1],flatOffset=inputInfo.shapeInfo.flatOffset;if(texNumC===stride0&&flatOffset==null)return`
+        float ${funcName}(int row, int col, int depth) {
           float texR = float(row);
-          float texC = dot(vec2(col, depth), vec2(${s}, 1));
+          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
           vec2 uv = (vec2(texC, texR) + halfCR) /
-                     vec2(${y}.0, ${m}.0);
-          return sampleTexture(${e}, uv);
+                     vec2(${texNumC}.0, ${texNumR}.0);
+          return sampleTexture(${texName}, uv);
         }
-      `;if(y===s&&b==null)return`
-    float ${r}(int row, int col, int depth) {
-      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
+      `;if(texNumC===stride1&&flatOffset==null)return`
+    float ${funcName}(int row, int col, int depth) {
+      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
       float texC = float(depth);
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}.0, ${m}.0);
-      return sampleTexture(${e}, uv);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
     }
-  `;let v=wa(e);return`
-      float ${r}(int row, int col, int depth) {
+  `;let offset=getFlatOffsetUniformName(texName);return`
+      float ${funcName}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
-        int index = row * ${o} + col * ${s} + depth + ${v};
-        vec2 uv = uvFromFlat(${m}, ${y}, index);
-        return sampleTexture(${e}, uv);
+        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
+        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+        return sampleTexture(${texName}, uv);
       }
-  `}function XX(n){let t=n.shapeInfo.logicalShape,e=t.length,r=n.name,o="get"+r.charAt(0).toUpperCase()+r.slice(1),s=n.shapeInfo.texShape,c=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],l=c[0],p=c[1],f=Math.ceil(t[e-1]/2),m=f*Math.ceil(t[e-2]/2),y="int b, int row, int col",b=`b * ${m} + (row / 2) * ${f} + (col / 2)`;for(let T=2;T<e-1;T++)y=`int b${T}, `+y,m*=t[e-T-1],b=`b${T} * ${m} + `+b;let v=Jn();return`
-    vec4 ${o}(${y}) {
-      int index = ${b};
-      int texR = index / ${p};
-      int texC = index - texR * ${p};
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${l});
-      return ${v.texture2D}(${r}, uv);
+  `}function getPackedSamplerND(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,rank=shape.length,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texNumR=packedTexShape[0],texNumC=packedTexShape[1],valuesPerRow=Math.ceil(shape[rank-1]/2),texelsInBatch=valuesPerRow*Math.ceil(shape[rank-2]/2),params="int b, int row, int col",index=`b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;for(let b=2;b<rank-1;b++)params=`int b${b}, `+params,texelsInBatch*=shape[rank-b-1],index=`b${b} * ${texelsInBatch} + `+index;let glsl=getGlslDifferences();return`
+    vec4 ${funcName}(${params}) {
+      int index = ${index};
+      int texR = index / ${texNumC};
+      int texC = index - texR * ${texNumC};
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
+      return ${glsl.texture2D}(${texName}, uv);
     }
-  `}function YX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[3],s=t[2]*o,c=t[1]*s,{newShape:l,keptDims:p}=ln(t);if(l.length<t.length){let T=al(n,l),N=["row","col","depth","depth2"];return`
-      ${sl(T)}
-      float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${cl(N,p)});
+  `}function getSampler4D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride2=shape[3],stride1=shape[2]*stride2,stride0=shape[1]*stride1,{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
       }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth, int depth2) {
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
-                          vec4(${c}, ${s}, ${o}, 1)));
-        ${il(n)}
+                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
+        ${getUniformSampler(inputInfo)}
       }
-    `;let f=n.shapeInfo.flatOffset,m=n.shapeInfo.texShape,y=m[0],b=m[1];if(b===c&&f==null)return`
-      float ${r}(int row, int col, int depth, int depth2) {
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
         float texR = float(row);
         float texC =
             dot(vec3(col, depth, depth2),
-                vec3(${s}, ${o}, 1));
+                vec3(${stride1}, ${stride2}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${b}.0, ${y}.0);
-        return sampleTexture(${e}, uv);
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;if(b===o&&f==null)return`
-      float ${r}(int row, int col, int depth, int depth2) {
+    `;if(texNumC===stride2&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
         float texR = dot(vec3(row, col, depth),
-                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
+                         vec3(${shape[1]*shape[2]}, ${shape[2]}, 1));
         float texC = float(depth2);
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${b}.0, ${y}.0);
-        return sampleTexture(${e}, uv);
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;let v=wa(e);return`
-    float ${r}(int row, int col, int depth, int depth2) {
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${c} + col * ${s} +
-          depth * ${o} + depth2;
-      vec2 uv = uvFromFlat(${y}, ${b}, index + ${v});
-      return sampleTexture(${e}, uv);
+      int index = row * ${stride0} + col * ${stride1} +
+          depth * ${stride2} + depth2;
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
     }
-  `}function JX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,c=t[2]*s,l=t[1]*c,{newShape:p,keptDims:f}=ln(t);if(p.length<t.length){let N=al(n,p),S=["row","col","depth","depth2","depth3"];return`
-      ${sl(N)}
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        return ${r}(${cl(S,f)});
+  `}function getSampler5D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride3=shape[4],stride2=shape[3]*stride3,stride1=shape[2]*stride2,stride0=shape[1]*stride1,{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2","depth3"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
       }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
         float index = dot(
           vec4(row, col, depth, depth2),
-          vec4(${l}, ${c}, ${s}, ${o})) +
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
           depth3;
-        ${il(n)}
+        ${getUniformSampler(inputInfo)}
       }
-    `;let m=n.shapeInfo.flatOffset,y=n.shapeInfo.texShape,b=y[0],v=y[1];if(v===l&&m==null)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-                         vec4(${c}, ${s}, ${o}, 1));
+                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${v}.0, ${b}.0);
-        return sampleTexture(${e}, uv);
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;if(v===o&&m==null)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
+    `;if(texNumC===stride3&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
         float texR = dot(
           vec4(row, col, depth, depth2),
-          vec4(${t[1]*t[2]*t[3]},
-               ${t[2]*t[3]}, ${t[3]}, 1));
+          vec4(${shape[1]*shape[2]*shape[3]},
+               ${shape[2]*shape[3]}, ${shape[3]}, 1));
         int texC = depth3;
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${v}.0, ${b}.0);
-        return sampleTexture(${e}, uv);
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;let T=wa(e);return`
-    float ${r}(int row, int col, int depth, int depth2, int depth3) {
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${l} + col * ${c} + depth * ${s} +
-          depth2 * ${o} + depth3 + ${T};
-      vec2 uv = uvFromFlat(${b}, ${v}, index);
-      return sampleTexture(${e}, uv);
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
     }
-  `}function ZX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=ln(t);if(o.length<t.length){let S=al(n,o),D=["row","col","depth","depth2","depth3","depth4"];return`
-      ${sl(S)}
-      float ${r}(int row, int col, int depth,
+  `}function getSampler6D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2","depth3","depth4"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${r}(${cl(D,s)});
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
       }
-    `}let c=t[5],l=t[4]*c,p=t[3]*l,f=t[2]*p,m=t[1]*f;if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth,
+    `}let stride4=shape[5],stride3=shape[4]*stride4,stride2=shape[3]*stride3,stride1=shape[2]*stride2,stride0=shape[1]*stride1;if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
         int index = round(dot(
           vec4(row, col, depth, depth2),
-          vec4(${m}, ${f}, ${p}, ${l})) +
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
           dot(
             vec2(depth3, depth4),
-            vec2(${c}, 1)));
-        ${il(n)}
+            vec2(${stride4}, 1)));
+        ${getUniformSampler(inputInfo)}
       }
-    `;let y=n.shapeInfo.flatOffset,b=n.shapeInfo.texShape,v=b[0],T=b[1];if(T===m&&y==null)return`
-      float ${r}(int row, int col, int depth,
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${f}, ${p}, ${l}, ${c})) +
+          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
                float(depth4);
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${T}.0, ${v}.0);
-        return sampleTexture(${e}, uv);
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;if(T===c&&y==null)return`
-      float ${r}(int row, int col, int depth,
+    `;if(texNumC===stride4&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         float texR = dot(vec4(row, col, depth, depth2),
-          vec4(${t[1]*t[2]*t[3]*t[4]},
-               ${t[2]*t[3]*t[4]},
-               ${t[3]*t[4]},
-               ${t[4]})) + float(depth3);
+          vec4(${shape[1]*shape[2]*shape[3]*shape[4]},
+               ${shape[2]*shape[3]*shape[4]},
+               ${shape[3]*shape[4]},
+               ${shape[4]})) + float(depth3);
         int texC = depth4;
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${T}.0, ${v}.0);
-        return sampleTexture(${e}, uv);
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;let N=wa(e);return`
-    float ${r}(int row, int col, int depth,
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(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 * ${m} + col * ${f} + depth * ${p} +
-          depth2 * ${l} + depth3 * ${c} + depth4 + ${N};
-      vec2 uv = uvFromFlat(${v}, ${T}, index);
-      return sampleTexture(${e}, uv);
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
     }
-  `}function il(n){let t=n.name,e=G(n.shapeInfo.logicalShape);return e<2?`return ${t};`:`
-    for (int i = 0; i < ${e}; i++) {
+  `}function getUniformSampler(inputInfo){let texName=inputInfo.name,inSize=util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);return inSize<2?`return ${texName};`:`
+    for (int i = 0; i < ${inSize}; i++) {
       if (i == index) {
-        return ${t}[i];
+        return ${texName}[i];
       }
     }
-  `}function QX(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),o="get"+r+"AtOutCoords",s=n.shapeInfo.logicalShape.length,c=t.logicalShape.length,l=TI(n.shapeInfo.logicalShape,t.logicalShape),p=Oe(c),f=c-s,m,y=["x","y","z","w","u","v"];s===0?m="":c<2&&l.length>=1?m="coords = 0;":m=l.map(I=>`coords.${y[I+f]} = 0;`).join(`
-`);let b="";c<2&&s>0?b="coords":b=n.shapeInfo.logicalShape.map((I,P)=>`coords.${y[P+f]}`).join(", ");let v="return outputValue;",T=G(n.shapeInfo.logicalShape),N=T===1,S=G(t.logicalShape),D=S===1;if(s===1&&!N&&!D)v=`
+  `}function getPackedSamplerAtOutputCoords(inputInfo,outShapeInfo){let texName=inputInfo.name,texFuncSnippet=texName.charAt(0).toUpperCase()+texName.slice(1),funcName="get"+texFuncSnippet+"AtOutCoords",inRank=inputInfo.shapeInfo.logicalShape.length,outRank=outShapeInfo.logicalShape.length,broadcastDims=getBroadcastDims2(inputInfo.shapeInfo.logicalShape,outShapeInfo.logicalShape),type=getCoordsDataType(outRank),rankDiff=outRank-inRank,coordsSnippet,fields=["x","y","z","w","u","v"];inRank===0?coordsSnippet="":outRank<2&&broadcastDims.length>=1?coordsSnippet="coords = 0;":coordsSnippet=broadcastDims.map(d=>`coords.${fields[d+rankDiff]} = 0;`).join(`
+`);let unpackedCoordsSnippet="";outRank<2&&inRank>0?unpackedCoordsSnippet="coords":unpackedCoordsSnippet=inputInfo.shapeInfo.logicalShape.map((s,i)=>`coords.${fields[i+rankDiff]}`).join(", ");let output="return outputValue;",inSize=util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape),isInputScalar=inSize===1,outSize=util_exports.sizeFromShape(outShapeInfo.logicalShape),isOutputScalar=outSize===1;if(inRank===1&&!isInputScalar&&!isOutputScalar)output=`
       return vec4(outputValue.xy, outputValue.xy);
-    `;else if(N&&!D)c===1?v=`
+    `;else if(isInputScalar&&!isOutputScalar)outRank===1?output=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
-      `:v=`
+      `:output=`
         return vec4(outputValue.x);
-      `;else if(l.length){let I=s-2,P=s-1;l.indexOf(I)>-1&&l.indexOf(P)>-1?v="return vec4(outputValue.x);":l.indexOf(I)>-1?v="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":l.indexOf(P)>-1&&(v="return vec4(outputValue.xx, outputValue.zz);")}return`
-    vec4 ${o}() {
-      ${p} coords = getOutputCoords();
-      ${m}
-      vec4 outputValue = get${r}(${b});
-      ${v}
+      `;else if(broadcastDims.length){let rows=inRank-2,cols=inRank-1;broadcastDims.indexOf(rows)>-1&&broadcastDims.indexOf(cols)>-1?output="return vec4(outputValue.x);":broadcastDims.indexOf(rows)>-1?output="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":broadcastDims.indexOf(cols)>-1&&(output="return vec4(outputValue.xx, outputValue.zz);")}return`
+    vec4 ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
+      ${output}
     }
-  `}function tY(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),o="get"+r+"AtOutCoords",s=t.texShape,c=n.shapeInfo.texShape,l=n.shapeInfo.logicalShape.length,p=t.logicalShape.length;if(!n.shapeInfo.isUniform&&l===p&&n.shapeInfo.flatOffset==null&&lt(c,s))return`
-      float ${o}() {
-        return sampleTexture(${e}, resultUV);
+  `}function getSamplerAtOutputCoords(inputInfo,outShapeInfo){let texName=inputInfo.name,texFuncSnippet=texName.charAt(0).toUpperCase()+texName.slice(1),funcName="get"+texFuncSnippet+"AtOutCoords",outTexShape=outShapeInfo.texShape,inTexShape=inputInfo.shapeInfo.texShape,inRank=inputInfo.shapeInfo.logicalShape.length,outRank=outShapeInfo.logicalShape.length;if(!inputInfo.shapeInfo.isUniform&&inRank===outRank&&inputInfo.shapeInfo.flatOffset==null&&util_exports.arraysEqual(inTexShape,outTexShape))return`
+      float ${funcName}() {
+        return sampleTexture(${texName}, resultUV);
       }
-    `;let f=Oe(p),m=TI(n.shapeInfo.logicalShape,t.logicalShape),y=p-l,b,v=["x","y","z","w","u","v"];l===0?b="":p<2&&m.length>=1?b="coords = 0;":b=m.map(N=>`coords.${v[N+y]} = 0;`).join(`
-`);let T="";return p<2&&l>0?T="coords":T=n.shapeInfo.logicalShape.map((N,S)=>`coords.${v[S+y]}`).join(", "),`
-    float ${o}() {
-      ${f} coords = getOutputCoords();
-      ${b}
-      return get${r}(${T});
+    `;let type=getCoordsDataType(outRank),broadcastDims=getBroadcastDims2(inputInfo.shapeInfo.logicalShape,outShapeInfo.logicalShape),rankDiff=outRank-inRank,coordsSnippet,fields=["x","y","z","w","u","v"];inRank===0?coordsSnippet="":outRank<2&&broadcastDims.length>=1?coordsSnippet="coords = 0;":coordsSnippet=broadcastDims.map(d=>`coords.${fields[d+rankDiff]} = 0;`).join(`
+`);let unpackedCoordsSnippet="";return outRank<2&&inRank>0?unpackedCoordsSnippet="coords":unpackedCoordsSnippet=inputInfo.shapeInfo.logicalShape.map((s,i)=>`coords.${fields[i+rankDiff]}`).join(", "),`
+    float ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      return get${texFuncSnippet}(${unpackedCoordsSnippet});
     }
-  `}function Oe(n){if(n<=1)return"int";if(n===2)return"ivec2";if(n===3)return"ivec3";if(n===4)return"ivec4";if(n===5)return"ivec5";if(n===6)return"ivec6";throw Error(`GPU for rank ${n} is not yet supported`)}function al(n,t){let e=JSON.parse(JSON.stringify(n));return e.shapeInfo.logicalShape=t,e}function cl(n,t){return t.map(e=>n[e]).join(", ")}class eY{constructor(t,e,r,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,_(t.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],c=Math.ceil(s/e);this.outputShape=t.slice(0,-1),c>1&&this.outputShape.push(c),o||this.variableNames.push("bestIndicesA");let l=this.outputShape,p=l.length,f=Oe(p),m=Yn("coords",p),y,b;if(c===1){b=p+1;let H=Oe(b);y=`
-        ${H} sourceLocR = ${H}(${m.join()}, 0);
-        ++${m[p-1]};
-        ${H} sourceLocG = ${H}(${m.join()}, 0);
-        ++${m[p-2]};
-        ${H} sourceLocA = ${H}(${m.join()}, 0);
-        --${m[p-1]};
-        ${H} sourceLocB = ${H}(${m.join()}, 0);
-        --${m[p-2]};`}else b=p,y=`
-        ${f} sourceLocR = coords;
-        ++${m[p-1]};
-        ${f} sourceLocG = coords;
-        ++${m[p-2]};
-        ${f} sourceLocA = coords;
-        --${m[p-1]};
-        ${f} sourceLocB = coords;
-        --${m[p-2]};`;let v=["x","y","z","w","u","v"].slice(0,b),T="."+v[b-1],N=v.map(H=>"int "+H),S=Yn("sourceLocR",b-1).concat("inIdx.r"),D=Yn("sourceLocG",b-1).concat("inIdx.g"),I=Yn("sourceLocB",b-1).concat("inIdx.b"),P=Yn("sourceLocA",b-1).concat("inIdx.a"),E=r==="max"?"greaterThan":"lessThan",L=o?"":`
-          inIdx = round(vec4(getBestIndicesAChannel(${S.join()}),
-                             getBestIndicesAChannel(${D.join()}),
-                             getBestIndicesAChannel(${I.join()}),
-                             getBestIndicesAChannel(${P.join()})));`,B=`vec4(
-            getAChannel(${S.join()}),
-            hasNextCol ? getAChannel(${D.join()}) : 0.,
-            hasNextRow ? getAChannel(${I.join()}) : 0.,
-            hasNextRow && hasNextCol ? getAChannel(${P.join()}) : 0.)`,q=o?"":`
-      float getBestIndicesAChannel(${N.join()}) {
-        return getChannel(getBestIndicesA(${v.join()}),
-                                          vec2(${v.slice(-2).join()}));
+  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}var ArgMinMaxPackedProgram=class{constructor(shape,windowSize,op22,firstPass){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,util_exports.assert(shape.length>2,()=>`Packed arg${op22.charAt(0).toUpperCase()+op22.slice(1)} supports only inputs with rank above 2.`);let inSize=shape[shape.length-1],outSize=Math.ceil(inSize/windowSize);this.outputShape=shape.slice(0,-1),outSize>1&&this.outputShape.push(outSize),firstPass||this.variableNames.push("bestIndicesA");let outShape=this.outputShape,rank=outShape.length,dtype=getCoordsDataType(rank),coords2=getChannels("coords",rank),sourceLocSetup,sourceRank;if(outSize===1){sourceRank=rank+1;let sourceLocDType=getCoordsDataType(sourceRank);sourceLocSetup=`
+        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0);
+        ++${coords2[rank-1]};
+        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0);
+        ++${coords2[rank-2]};
+        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0);
+        --${coords2[rank-1]};
+        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0);
+        --${coords2[rank-2]};`}else sourceRank=rank,sourceLocSetup=`
+        ${dtype} sourceLocR = coords;
+        ++${coords2[rank-1]};
+        ${dtype} sourceLocG = coords;
+        ++${coords2[rank-2]};
+        ${dtype} sourceLocA = coords;
+        --${coords2[rank-1]};
+        ${dtype} sourceLocB = coords;
+        --${coords2[rank-2]};`;let channels=["x","y","z","w","u","v"].slice(0,sourceRank),inChannel="."+channels[sourceRank-1],intChannels=channels.map(x=>"int "+x),srcRCoords=getChannels("sourceLocR",sourceRank-1).concat("inIdx.r"),srcGCoords=getChannels("sourceLocG",sourceRank-1).concat("inIdx.g"),srcBCoords=getChannels("sourceLocB",sourceRank-1).concat("inIdx.b"),srcACoords=getChannels("sourceLocA",sourceRank-1).concat("inIdx.a"),compOp=op22==="max"?"greaterThan":"lessThan",fetchCandidateIdx=firstPass?"":`
+          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
+                             getBestIndicesAChannel(${srcGCoords.join()}),
+                             getBestIndicesAChannel(${srcBCoords.join()}),
+                             getBestIndicesAChannel(${srcACoords.join()})));`,fetchValue=`vec4(
+            getAChannel(${srcRCoords.join()}),
+            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
+            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`,getBestIndicesAChannelSnippet=firstPass?"":`
+      float getBestIndicesAChannel(${intChannels.join()}) {
+        return getChannel(getBestIndicesA(${channels.join()}),
+                                          vec2(${channels.slice(-2).join()}));
       }`;this.userCode=`
-      float getAChannel(${N.join()}) {
-        return getChannel(getA(${v.join()}),
-                               vec2(${v.slice(-2).join()}));
+      float getAChannel(${intChannels.join()}) {
+        return getChannel(getA(${channels.join()}),
+                               vec2(${channels.slice(-2).join()}));
       }
-      ${q}
+      ${getBestIndicesAChannelSnippet}
       void main() {
-        ${f} coords = getOutputCoords();
-        bool hasNextCol = ${m[p-1]} < ${l[p-1]-1};
-        bool hasNextRow = ${m[p-2]} < ${l[p-2]-1};
-        ${y}
-        ivec4 srcIdx = ivec4(sourceLocR${T}, sourceLocG${T},
-          sourceLocB${T}, sourceLocA${T}) * ${e};
+        ${dtype} coords = getOutputCoords();
+        bool hasNextCol = ${coords2[rank-1]} < ${outShape[rank-1]-1};
+        bool hasNextRow = ${coords2[rank-2]} < ${outShape[rank-2]-1};
+        ${sourceLocSetup}
+        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
+          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
         ivec4 inIdx = srcIdx;
         vec4 bestIndex = vec4(inIdx);
-        vec4 bestValue = ${B};
+        vec4 bestValue = ${fetchValue};
 
-        for (int i = 0; i < ${e}; i++) {
+        for (int i = 0; i < ${windowSize}; i++) {
           inIdx = srcIdx;
-          ${L}
-          vec4 candidate = ${B};
+          ${fetchCandidateIdx}
+          vec4 candidate = ${fetchValue};
           bvec4 nan = isnan(candidate);
           bvec4 replace = bvec4(
-            vec4(${E}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
+            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
 
           bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                            replace.y  ? candidate.y : bestValue.y,
@@ -777,9 +784,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(bestIndex);
       }
-    `}}class nY{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=t.dilationHeight,l=t.dilationWidth,p=t.effectiveFilterHeight,f=t.effectiveFilterWidth,m=p-1-t.padInfo.top,y=f-1-t.padInfo.left,b=1/(e*r);this.userCode=`
-      const ivec2 pads = ivec2(${m}, ${y});
-      const float avgMultiplier = float(${b});
+    `}},AvgPool2DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,avgMultiplier=1/(filterHeight*filterWidth);this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -793,20 +800,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         // 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 < ${p};
-            wR += ${c}) {
-          float dyR = float(dyRCorner + wR) / ${o}.0;
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
           }
           int idyR = int(dyR);
 
-          for (int wC = 0; wC < ${f};
-            wC+= ${l}) {
-            float dyC = float(dyCCorner + wC) / ${s}.0;
+          for (int wC = 0; wC < ${effectiveFilterWidth};
+            wC+= ${dilationWidth}) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                 fract(dyC) > 0.0) {
               continue;
             }
@@ -819,9 +826,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(dotProd);
       }
-    `}}class rY{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,o=t.filterWidth,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=t.dilationDepth,f=t.dilationHeight,m=t.dilationWidth,y=t.effectiveFilterDepth,b=t.effectiveFilterHeight,v=t.effectiveFilterWidth,T=y-1-t.padInfo.front,N=b-1-t.padInfo.top,S=v-1-t.padInfo.left,D=1/(e*r*o);this.userCode=`
-      const ivec3 pads = ivec3(${T}, ${N}, ${S});
-      const float avgMultiplier = float(${D});
+    `}},AvgPool3DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy"],this.outputShape=convInfo.inShape;let filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,avgMultiplier=1/(filterDepth*filterHeight*filterWidth);this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -838,30 +845,30 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${y};
-            wD += ${p}) {
-          float dyD = float(dyDCorner + wD) / ${s}.0;
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
 
-          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
             continue;
           }
           int idyD = int(dyD);
 
-          for (int wR = 0; wR < ${b};
-              wR += ${f}) {
-            float dyR = float(dyRCorner + wR) / ${c}.0;
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                 fract(dyR) > 0.0) {
               continue;
             }
             int idyR = int(dyR);
 
-            for (int wC = 0; wC < ${v};
-                wC += ${m}) {
-              float dyC = float(dyCCorner + wC) / ${l}.0;
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                   fract(dyC) > 0.0) {
                 continue;
               }
@@ -875,10 +882,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(dotProd);
       }
-    `}}let _I=`
+    `}},CHECK_NAN_SNIPPET=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,oY=`
+`,INT_DIV=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -888,7 +895,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   } else {
     return NAN;
   }
-`,sY=`
+`,POW=`
 if(a < 0.0 && floor(b) < b){
   return NAN;
 }
@@ -897,14 +904,14 @@ if (b == 0.0) {
 }
 return (round(mod(b, 2.0)) != 1) ?
     pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,Jst="return (a - b) * (a - b);",iY="return float(a == b);",aY="return float(a < b);",cY="return float(a <= b);",lY="return float(a > b);",uY="return float(a >= b);",pY="return float(a >= 1.0 && b >= 1.0);",hY="return float(a >= 1.0 || b >= 1.0);",fY=_I+`
+`,EQUAL="return float(a == b);",LESS="return float(a < b);",LESS_EQUAL="return float(a <= b);",GREATER="return float(a > b);",GREATER_EQUAL="return float(a >= b);",LOGICAL_AND="return float(a >= 1.0 && b >= 1.0);",LOGICAL_OR="return float(a >= 1.0 || b >= 1.0);",MAX=CHECK_NAN_SNIPPET+`
   return max(a, b);
-`,dY=_I+`
+`,MIN=CHECK_NAN_SNIPPET+`
   return min(a, b);
-`,mY=`if (b == 0.0) return NAN;
-  return mod(a, b);`,gY="return (b >= 1.0) ? a : a * (b + 1.0);",CI="return (a < 0.) ? b * a : a;";class Hn{constructor(t,e,r){this.variableNames=["A","B"],this.outputShape=le(e,r),this.userCode=`
+`,MOD=`if (b == 0.0) return NAN;
+  return mod(a, b);`,ELU_DER="return (b >= 1.0) ? a : a * (b + 1.0);",PRELU="return (a < 0.) ? b * a : a;",BinaryOpProgram=class{constructor(op22,aShape,bShape){this.variableNames=["A","B"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
       float binaryOperation(float a, float b) {
-        ${t}
+        ${op22}
       }
 
       void main() {
@@ -912,12 +919,12 @@ return (round(mod(b, 2.0)) != 1) ?
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}}let Mm=`
+    `}},CHECK_NAN_SNIPPET2=`
   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;
-`,yY=`
+`,INT_DIV2=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -938,7 +945,7 @@ return (round(mod(b, 2.0)) != 1) ?
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,bY=`
+`,POW2=`
   // 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);
@@ -952,71 +959,69 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+Mm+`
+  `+CHECK_NAN_SNIPPET2+`
   return result;
-`,SI=`
+`,PRELU2=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`,xY=`
+`,ELU_DER2=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,wY=`
+`,EQUAL2=`
   return vec4(equal(a, b));
-`,Zst=`
-  return vec4(notEqual(a, b));
-`,vY=`
+`,LESS2=`
   return vec4(lessThan(a, b));
-`,TY=`
+`,LESS_EQUAL2=`
   return vec4(lessThanEqual(a, b));
-`,kY=`
+`,GREATER2=`
   return vec4(greaterThan(a, b));
-`,NY=`
+`,GREATER_EQUAL2=`
   return vec4(greaterThanEqual(a, b));
-`,_Y=`
+`,LOGICAL_AND2=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,CY=`
+`,LOGICAL_OR2=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,SY=`
+`,MAX2=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+Mm+`
+  `+CHECK_NAN_SNIPPET2+`
   return result;
-`,$Y=`
+`,MIN2=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+Mm+`
+  `+CHECK_NAN_SNIPPET2+`
   return result;
-`,IY=`
+`,MOD2=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+Mm+`
+  `+CHECK_NAN_SNIPPET2+`
   return result;
-`;class Ns{constructor(t,e,r,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=le(e,r);let s=this.outputShape.length,c="";if(o)if(s===0||G(this.outputShape)===1)c=`
+`,BinaryOpPackedProgram=class{constructor(op22,aShape,bShape,checkOutOfBounds=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape);let rank=this.outputShape.length,checkOutOfBoundsString="";if(checkOutOfBounds)if(rank===0||util_exports.sizeFromShape(this.outputShape)===1)checkOutOfBoundsString=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
-        `;else{let l=Oe(s);if(c=`
-          ${l} coords = getOutputCoords();
-        `,s===1)c+=`
+        `;else{let dtype=getCoordsDataType(rank);if(checkOutOfBoundsString=`
+          ${dtype} coords = getOutputCoords();
+        `,rank===1)checkOutOfBoundsString+=`
             result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
             result.z = 0.;
             result.w = 0.;
-          `;else{let p=Yn("coords",s);c+=`
+          `;else{let channels=getChannels("coords",rank);checkOutOfBoundsString+=`
             bool nextRowOutOfBounds =
-              (${p[s-2]} + 1) >= ${this.outputShape[s-2]};
+              (${channels[rank-2]} + 1) >= ${this.outputShape[rank-2]};
             bool nextColOutOfBounds =
-              (${p[s-1]} + 1) >= ${this.outputShape[s-1]};
+              (${channels[rank-1]} + 1) >= ${this.outputShape[rank-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) {
-        ${t}
+        ${op22}
       }
 
       void main() {
@@ -1024,11 +1029,11 @@ return (round(mod(b, 2.0)) != 1) ?
         vec4 b = getBAtOutCoords();
 
         vec4 result = binaryOperation(a, b);
-        ${c}
+        ${checkOutOfBoundsString}
 
         setOutput(result);
       }
-    `}}class EY{constructor(t){this.variableNames=["A"],this.outputShape=t,this.userCode=`
+    `}},ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1041,7 +1046,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(t,e){return(r,o)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class DY{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=`
+    `}getCustomSetupFunc(min82,max102){return(gpgpu,webGLProgram)=>{this.minLoc==null&&(this.minLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"minVal"),this.maxLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"maxVal")),gpgpu.gl.uniform1f(this.minLoc,min82),gpgpu.gl.uniform1f(this.maxLoc,max102)}}},ClipPackedProgram=class{constructor(aShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=aShape,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1055,7 +1060,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(t,e){return(r,o)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class AY{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=`
+    `}getCustomSetupFunc(min82,max102){return(gpgpu,webGLProgram)=>{this.minLoc==null&&(this.minLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"minVal"),this.maxLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"maxVal")),gpgpu.gl.uniform1f(this.minLoc,min82),gpgpu.gl.uniform1f(this.maxLoc,max102)}}},ComplexAbsProgram=class{constructor(shape){this.variableNames=["real","imag"],this.outputShape=shape,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1068,7 +1073,7 @@ return (round(mod(b, 2.0)) != 1) ?
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}}class FY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,c=t.dataFormat==="channelsLast";this.userCode=`
+    `}},Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -1080,22 +1085,22 @@ return (round(mod(b, 2.0)) != 1) ?
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int b = 0; b < ${t.batchSize}; b++) {
-          for (int yR = 0; yR < ${t.outHeight}; yR++) {
-            int xR = wR + yR * ${e} - ${o};
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int yC = 0; yC < ${t.outWidth}; yC++) {
-              int xC = wC + yC * ${r} - ${s};
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
 
-              if (xC < 0 || xC >= ${t.inWidth}) {
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                 continue;
               }
 
-              if (${c}) {
+              if (${isChannelsLast}) {
                 float dyValue = getDy(b, yR, yC, d2);
                 float xValue = getX(b, xR, xC, d1);
                 dotProd += (xValue * dyValue);
@@ -1110,45 +1115,45 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class RY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=t.dataFormat==="channelsLast",l=e-1-t.padInfo.top,p=r-1-t.padInfo.left,f=c?1:2,m=c?2:3,y=c?3:1;this.userCode=`
-      const ivec2 pads = ivec2(${l}, ${p});
+    `}},Conv2DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,isChannelsLast=convInfo.dataFormat==="channelsLast",padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left,rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords[0];
-        int d1 = coords[${y}];
+        int d1 = coords[${channelDim}];
 
-        ivec2 dyCorner = ivec2(coords[${f}], coords[${m}]) - pads;
+        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - 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 < ${e}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${o}.0;
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
           }
           int idyR = int(dyR);
 
-          int wRPerm = ${e} - 1 - wR;
+          int wRPerm = ${filterHeight} - 1 - wR;
 
-          for (int wC = 0; wC < ${r}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${s}.0;
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                 fract(dyC) > 0.0) {
               continue;
             }
             int idyC = int(dyC);
 
-            int wCPerm = ${r} - 1 - wC;
+            int wCPerm = ${filterWidth} - 1 - wC;
 
-            for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
+            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
 
-              if (${c}) {
+              if (${isChannelsLast}) {
                 float xValue = getDy(batch, idyR, idyC, d2);
                 float wValue = getW(wRPerm, wCPerm, d1, d2);
                 dotProd += xValue * wValue;
@@ -1163,7 +1168,7 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class PY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,r=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,c=t.padInfo.top,l=t.padInfo.left;this.userCode=`
+    `}},Conv3DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -1174,25 +1179,25 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float dotProd = 0.0;
 
-        for (int b = 0; b < ${t.batchSize}; b++) {
-          for (int yF = 0; yF < ${t.outDepth}; yF++) {
-            int xF = wF + yF * ${e} - ${s};
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
+            int xF = wF + yF * ${strideDepth} - ${padFront};
 
-            if (xF < 0 || xF >= ${t.inDepth}) {
+            if (xF < 0 || xF >= ${convInfo.inDepth}) {
               continue;
             }
 
-            for (int yR = 0; yR < ${t.outHeight}; yR++) {
-              int xR = wR + yR * ${r} - ${c};
+            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+              int xR = wR + yR * ${strideHeight} - ${padTop};
 
-              if (xR < 0 || xR >= ${t.inHeight}) {
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                 continue;
               }
 
-              for (int yC = 0; yC < ${t.outWidth}; yC++) {
-                int xC = wC + yC * ${o} - ${l};
+              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+                int xC = wC + yC * ${strideWidth} - ${padLeft};
 
-                if (xC < 0 || xC >= ${t.inWidth}) {
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                   continue;
                 }
 
@@ -1205,8 +1210,8 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class OY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,o=t.filterWidth,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=e-1-t.padInfo.front,f=r-1-t.padInfo.top,m=o-1-t.padInfo.left;this.userCode=`
-      const ivec3 pads = ivec3(${p}, ${f}, ${m});
+    `}},Conv3DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padFront=filterDepth-1-convInfo.padInfo.front,padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -1220,39 +1225,39 @@ return (round(mod(b, 2.0)) != 1) ?
         int dyCCorner = dyCorner.z;
 
         float dotProd = 0.0;
-        for (int wF = 0; wF < ${e}; wF++) {
-          float dyF = float(dyFCorner + wF) / ${s}.0;
+        for (int wF = 0; wF < ${filterDepth}; wF++) {
+          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;
 
-          if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
+          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
             continue;
           }
           int idyF = int(dyF);
 
-          int wFPerm = ${e} - 1 - wF;
+          int wFPerm = ${filterDepth} - 1 - wF;
 
-          for (int wR = 0; wR < ${r}; wR++) {
-            float dyR = float(dyRCorner + wR) / ${c}.0;
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
               fract(dyR) > 0.0) {
               continue;
             }
             int idyR = int(dyR);
 
-            int wRPerm = ${r} - 1 - wR;
+            int wRPerm = ${filterHeight} - 1 - wR;
 
-            for (int wC = 0; wC < ${o}; wC++) {
-              float dyC = float(dyCCorner + wC) / ${l}.0;
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                   fract(dyC) > 0.0) {
                 continue;
               }
               int idyC = int(dyC);
 
-              int wCPerm = ${o} - 1 - wC;
+              int wCPerm = ${filterWidth} - 1 - wC;
 
-              for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
+              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
                 float xValue = getDy(batch, idyF, idyR, idyC, d2);
                 float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                 dotProd += xValue * wValue;
@@ -1262,30 +1267,30 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class LY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,c=t.outChannels/t.inChannels;this.userCode=`
+    `}},DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.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 * ${c} + dm;
+        int d2 = d1 * ${channelMul} + dm;
 
         float dotProd = 0.0;
 
         // TO DO: Vec4 over the batch size
-        for (int b = 0; b < ${t.batchSize}; b++) {
-          for (int yR = 0; yR < ${t.outHeight}; yR++) {
-            int xR = wR + yR * ${e} - ${o};
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int yC = 0; yC < ${t.outWidth}; yC++) {
-              int xC = wC + yC * ${r} - ${s};
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
 
-              if (xC < 0 || xC >= ${t.inWidth}) {
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                 continue;
               }
 
@@ -1297,8 +1302,8 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class MY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=e-1-t.padInfo.top,l=r-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=`
-      const ivec2 pads = ivec2(${c}, ${l});
+    `}},DepthwiseConv2DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -1310,30 +1315,30 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float dotProd = 0.0;
 
-        for (int wR = 0; wR < ${e}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${o}.0;
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
           }
           int idyR = int(dyR);
 
-          int wRPerm = ${e} - 1 - wR;
+          int wRPerm = ${filterHeight} - 1 - wR;
 
-          for (int wC = 0; wC < ${r}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${s}.0;
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                 fract(dyC) > 0.0) {
               continue;
             }
             int idyC = int(dyC);
 
-            int wCPerm = ${r} - 1 - wC;
+            int wCPerm = ${filterWidth} - 1 - wC;
 
             // TO DO: Vec4 over the channelMul
-            for (int dm = 0; dm < ${p}; dm++) {
-              int d2 = d1 * ${p} + dm;
+            for (int dm = 0; dm < ${channelMul}; dm++) {
+              int d2 = d1 * ${channelMul} + dm;
               float xValue = getDy(batch, idyR, idyC, d2);
               float wValue = getW(wRPerm, wCPerm, d1, dm);
               dotProd += xValue * wValue;
@@ -1342,47 +1347,47 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class $I{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let s=t.padInfo.top,c=t.padInfo.left,l=t.strideHeight,p=t.strideWidth,f=t.dilationHeight,m=t.dilationWidth,y=t.filterHeight,b=t.filterWidth,v=Math.floor(t.inChannels/4)*4,T=t.inChannels%4,N=t.dataFormat==="channelsLast",S=N?1:2,D=N?2:3,I=N?3:1,P="",E="";r&&(o?P=`float activation(float a) {
+    `}},Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:P=`
+          ${activation2}
+        }`:activationSnippet=`
           float activation(float x) {
-            ${r}
+            ${activation2}
           }
-        `,E="result = activation(result);");let L=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${P}
+        `,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
 
-      const ivec2 strides = ivec2(${l}, ${p});
-      const ivec2 pads = ivec2(${s}, ${c});
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords[0];
-        int d2 = coords[${I}];
+        int d2 = coords[${channelDim}];
 
         ivec2 xRCCorner =
-            ivec2(coords[${S}], coords[${D}]) * strides - pads;
+            ivec2(coords[${rowDim}], coords[${colDim}]) * 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 < ${y}; wR++) {
-          int xR = xRCorner + wR * ${f};
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * ${dilationHeight};
 
-          if (xR < 0 || xR >= ${t.inHeight}) {
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
             continue;
           }
 
-          for (int wC = 0; wC < ${b}; wC++) {
-            int xC = xCCorner + wC * ${m};
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * ${dilationWidth};
 
-            if (xC < 0 || xC >= ${t.inWidth}) {
+            if (xC < 0 || xC >= ${convInfo.inWidth}) {
               continue;
             }
 
-            for (int d1 = 0; d1 < ${v}; d1 += 4) {
+            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
               vec4 wValues = vec4(
                 getW(wR, wC, d1, d2),
                 getW(wR, wC, d1 + 1, d2),
@@ -1390,7 +1395,7 @@ return (round(mod(b, 2.0)) != 1) ?
                 getW(wR, wC, d1 + 3, d2)
               );
 
-              if (${N}) {
+              if (${isChannelsLast}) {
                 vec4 xValues = vec4(
                   getX(batch, xR, xC, d1),
                   getX(batch, xR, xC, d1 + 1),
@@ -1409,57 +1414,57 @@ return (round(mod(b, 2.0)) != 1) ?
               }
             }
 
-            if (${T===1}) {
+            if (${inputDepthVec4Remainder===1}) {
 
-              if (${N}) {
+              if (${isChannelsLast}) {
                 dotProd +=
-                    getX(batch, xR, xC, ${v}) *
-                    getW(wR, wC, ${v}, d2);
+                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
               } else {
                 dotProd +=
-                    getX(batch, ${v}, xR, xC) *
-                    getW(wR, wC, ${v}, d2);
+                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
               }
 
-            } else if (${T===2}) {
+            } else if (${inputDepthVec4Remainder===2}) {
               vec2 wValues = vec2(
-                getW(wR, wC, ${v}, d2),
-                getW(wR, wC, ${v} + 1, d2)
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
               );
 
-              if (${N}) {
+              if (${isChannelsLast}) {
                 vec2 xValues = vec2(
-                  getX(batch, xR, xC, ${v}),
-                  getX(batch, xR, xC, ${v} + 1)
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
                 );
                 dotProd += dot(xValues, wValues);
               } else {
                 vec2 xValues = vec2(
-                  getX(batch, ${v}, xR, xC),
-                  getX(batch, ${v} + 1, xR, xC)
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
                 );
                 dotProd += dot(xValues, wValues);
               }
 
-            } else if (${T===3}) {
+            } else if (${inputDepthVec4Remainder===3}) {
               vec3 wValues = vec3(
-                getW(wR, wC, ${v}, d2),
-                getW(wR, wC, ${v} + 1, d2),
-                getW(wR, wC, ${v} + 2, d2)
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
               );
 
-              if (${N}) {
+              if (${isChannelsLast}) {
                 vec3 xValues = vec3(
-                  getX(batch, xR, xC, ${v}),
-                  getX(batch, xR, xC, ${v} + 1),
-                  getX(batch, xR, xC, ${v} + 2)
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
                 );
                 dotProd += dot(xValues, wValues);
               } else {
                 vec3 xValues = vec3(
-                  getX(batch, ${v}, xR, xC),
-                  getX(batch, ${v} + 1, xR, xC),
-                  getX(batch, ${v} + 2, xR, xC)
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
                 );
                 dotProd += dot(xValues, wValues);
               }
@@ -1469,13 +1474,13 @@ return (round(mod(b, 2.0)) != 1) ?
         }
 
         float result = dotProd;
-        ${L}
-        ${E}
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
         setOutput(result);
       }
-    `}}class BY{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,r=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=t.dilationDepth,f=t.dilationHeight,m=t.dilationWidth,y=t.filterDepth,b=t.filterHeight,v=t.filterWidth,T=Math.floor(t.inChannels/4)*4,N=t.inChannels%4;this.userCode=`
-      const ivec3 strides = ivec3(${s}, ${c}, ${l});
-      const ivec3 pads = ivec3(${e}, ${r}, ${o});
+    `}},Conv3DProgram=class{constructor(convInfo){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4;this.userCode=`
+      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -1491,28 +1496,28 @@ return (round(mod(b, 2.0)) != 1) ?
         // y(yF, yR, yC, d2). ? = to be determined. : = across all
         // values in that axis.
         float dotProd = 0.0;
-        for (int wF = 0; wF < ${y}; wF++) {
-          int xF = xFCorner + wF * ${p};
+        for (int wF = 0; wF < ${filterDepth}; wF++) {
+          int xF = xFCorner + wF * ${dilationDepth};
 
-          if (xF < 0 || xF >= ${t.inDepth}) {
+          if (xF < 0 || xF >= ${convInfo.inDepth}) {
             continue;
           }
 
-          for (int wR = 0; wR < ${b}; wR++) {
-            int xR = xRCorner + wR * ${f};
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            int xR = xRCorner + wR * ${dilationHeight};
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int wC = 0; wC < ${v}; wC++) {
-              int xC = xCCorner + wC * ${m};
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              int xC = xCCorner + wC * ${dilationWidth};
 
-              if (xC < 0 || xC >= ${t.inWidth}) {
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                 continue;
               }
 
-              for (int d1 = 0; d1 < ${T}; d1 += 4) {
+              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
                 vec4 xValues = vec4(
                   getX(batch, xF, xR, xC, d1),
                   getX(batch, xF, xR, xC, d1 + 1),
@@ -1529,30 +1534,30 @@ return (round(mod(b, 2.0)) != 1) ?
                 dotProd += dot(xValues, wValues);
               }
 
-              if (${N===1}) {
+              if (${inputDepthVec4Remainder===1}) {
                 dotProd +=
-                  getX(batch, xF, xR, xC, ${T}) *
-                  getW(wF, wR, wC, ${T}, d2);
-              } else if (${N===2}) {
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
+              } else if (${inputDepthVec4Remainder===2}) {
                 vec2 xValues = vec2(
-                  getX(batch, xF, xR, xC, ${T}),
-                  getX(batch, xF, xR, xC, ${T} + 1)
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
                 );
                 vec2 wValues = vec2(
-                  getW(wF, wR, wC, ${T}, d2),
-                  getW(wF, wR, wC, ${T} + 1, d2)
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
                 );
                 dotProd += dot(xValues, wValues);
-              } else if (${N===3}) {
+              } else if (${inputDepthVec4Remainder===3}) {
                 vec3 xValues = vec3(
-                  getX(batch, xF, xR, xC, ${T}),
-                  getX(batch, xF, xR, xC, ${T} + 1),
-                  getX(batch, xF, xR, xC, ${T} + 2)
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
                 );
                 vec3 wValues = vec3(
-                  getW(wF, wR, wC, ${T}, d2),
-                  getW(wF, wR, wC, ${T} + 1, d2),
-                  getW(wF, wR, wC, ${T} + 2, d2)
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
                 );
                 dotProd += dot(xValues, wValues);
               }
@@ -1561,26 +1566,26 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class II{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let s=t.inHeight,c=t.inWidth,l=t.padInfo.top,p=t.padInfo.left,f=t.strideHeight,m=t.strideWidth,y=t.dilationHeight,b=t.dilationWidth,v=t.filterHeight,T=t.filterWidth,N=t.outChannels/t.inChannels,S="",D="";r&&(o?S=`float activation(float a) {
+    `}},DepthwiseConv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let xNumRows=convInfo.inHeight,xNumCols=convInfo.inWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,channelMul=convInfo.outChannels/convInfo.inChannels,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:S=`
+          ${activation2}
+        }`:activationSnippet=`
           float activation(float x) {
-            ${r}
+            ${activation2}
           }
-        `,D="result = activation(result);");let I=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${S}
+        `,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
 
-      const ivec2 strides = ivec2(${f}, ${m});
-      const ivec2 pads = ivec2(${l}, ${p});
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
-        int d1 = d2 / ${N};
-        int q = d2 - d1 * ${N};
+        int d1 = d2 / ${channelMul};
+        int q = d2 - d1 * ${channelMul};
 
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
@@ -1589,17 +1594,17 @@ return (round(mod(b, 2.0)) != 1) ?
         // ? = 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 < ${v}; wR++) {
-          int xR = xRCorner + wR * ${y};
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * ${dilationHeight};
 
-          if (xR < 0 || xR >= ${s}) {
+          if (xR < 0 || xR >= ${xNumRows}) {
             continue;
           }
 
-          for (int wC = 0; wC < ${T}; wC++) {
-            int xC = xCCorner + wC * ${b};
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * ${dilationWidth};
 
-            if (xC < 0 || xC >= ${c}) {
+            if (xC < 0 || xC >= ${xNumCols}) {
               continue;
             }
 
@@ -1610,139 +1615,139 @@ return (round(mod(b, 2.0)) != 1) ?
         }
 
         float result = dotProd;
-        ${I}
-        ${D}
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
         setOutput(result);
       }
-    `}}class EI{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;let s=t.inHeight,c=t.inWidth,l=t.padInfo.top,p=t.padInfo.left,f=t.strideHeight,m=t.strideWidth,y=t.dilationHeight,b=t.dilationWidth,v=t.filterHeight,T=t.filterWidth,N=T,S="int xR; int xC; int xCOffset;";for(let E=0;E<v;E++)for(let L=0;L<T;L++)S+=`
-          vec4 xTexelR${E}C${L*2} = vec4(0.);
-          vec4 wR${E}C${L} = vec4(0.);
-          vec4 xR${E}C${L} = vec4(0.);`;for(let E=0;E<v;E++)for(let L=0;L<N;L++){let B=L*2;if(S+=`
-          xR = xRCorner + ${E*y};
-          xC = xCCorner + ${B*b};
-        `,m===1){if(B<T&&(p%2===1?S+=`
+    `}},DepthwiseConvPacked2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=convInfo.outShape;let xNumRows=convInfo.inHeight,xNumCols=convInfo.inWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,texelsAcross=filterWidth,mainLoop="int xR; int xC; int xCOffset;";for(let r=0;r<filterHeight;r++)for(let c=0;c<filterWidth;c++)mainLoop+=`
+          vec4 xTexelR${r}C${c*2} = vec4(0.);
+          vec4 wR${r}C${c} = vec4(0.);
+          vec4 xR${r}C${c} = vec4(0.);`;for(let r=0;r<filterHeight;r++)for(let texelC=0;texelC<texelsAcross;texelC++){let c=texelC*2;if(mainLoop+=`
+          xR = xRCorner + ${r*dilationHeight};
+          xC = xCCorner + ${c*dilationWidth};
+        `,strideWidth===1){if(c<filterWidth&&(padLeft%2===1?mainLoop+=`
                 xCOffset = xC + 1;
-                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${c}) {
-                  xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
+                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
 
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${c}) {
-                    xTexelR${E}C${B}.zw = vec2(0.);
+                  if(xCOffset + 1 >= ${xNumCols}) {
+                    xTexelR${r}C${c}.zw = vec2(0.);
                   }
                 } else {
-                  xTexelR${E}C${B} = vec4(0.);
+                  xTexelR${r}C${c} = vec4(0.);
                 }
 
                 xCOffset = xC + 1 - 2;
-                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${c}) {
+                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                   vec4 previous = getX(batch, xR, xCOffset, d1);
 
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${c}) {
+                  if(xCOffset + 1 >= ${xNumCols}) {
                     previous.zw = vec2(0.);
                   }
 
-                  xR${E}C${B} = vec4(previous.zw, xTexelR${E}C${B}.xy);
+                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);
                 } else {
-                  xR${E}C${B} = vec4(0, 0, xTexelR${E}C${B}.xy);
+                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);
                 }
-              `:S+=`
-                if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${c}) {
-                  xTexelR${E}C${B} = getX(batch, xR, xC, d1);
+              `:mainLoop+=`
+                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
                 } else {
-                  xTexelR${E}C${B} = vec4(0.);
+                  xTexelR${r}C${c} = vec4(0.);
                 }
 
-                xR${E}C${B} = xTexelR${E}C${B};
-              `,B+1<T)){let q=p%2===0?k(b):b;b%2===0&&p%2===1||b%2!==0&&p%2!==1?(S+=`
-                  xCOffset = xC + ${p%2} + ${q};
+                xR${r}C${c} = xTexelR${r}C${c};
+              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2===0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2===0&&padLeft%2===1||dilationWidth%2!==0&&padLeft%2!==1?(mainLoop+=`
+                  xCOffset = xC + ${padLeft%2} + ${nextTexelOffset};
 
-                  if(xR >= 0 && xR < ${s} &&
-                    xCOffset >= 0 && xCOffset < ${c}) {
-                    xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
+                  if(xR >= 0 && xR < ${xNumRows} &&
+                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                    xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                   }
-                `,b>1&&(S+=`
+                `,dilationWidth>1&&(mainLoop+=`
                     xCOffset -= 2;
-                    if(xR >= 0 && xR < ${s} &&
-                      xCOffset >= 0 && xCOffset < ${c}) {
-                      xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
+                    if(xR >= 0 && xR < ${xNumRows} &&
+                      xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
                     } else {
-                      xTexelR${E}C${B} = vec4(0.);
+                      xTexelR${r}C${c} = vec4(0.);
                     }
-                  `),S+=`
-                  xR${E}C${B+1} = vec4(
-                    xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.xy);
-                `):S+=`
-                  xCOffset = xC + ${q};
+                  `),mainLoop+=`
+                  xR${r}C${c+1} = vec4(
+                    xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.xy);
+                `):mainLoop+=`
+                  xCOffset = xC + ${nextTexelOffset};
 
-                  if(xR >= 0 && xR < ${s} &&
-                    xCOffset >= 0 && xCOffset < ${c}) {
-                    xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
+                  if(xR >= 0 && xR < ${xNumRows} &&
+                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                    xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                   }
 
-                  xR${E}C${B+1} = xTexelR${E}C${B+2};
-                `}}else B<T&&(S+=`
-              if(xR >= 0 && xR < ${s}) {
-            `,p%2===1?(S+=`
-                xCOffset = xC + 1 - ${m};
-                if(xCOffset >= 0 && xCOffset < ${c}) {
-                  xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
+                  xR${r}C${c+1} = xTexelR${r}C${c+2};
+                `}}else c<filterWidth&&(mainLoop+=`
+              if(xR >= 0 && xR < ${xNumRows}) {
+            `,padLeft%2===1?(mainLoop+=`
+                xCOffset = xC + 1 - ${strideWidth};
+                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
                 } else {
-                  xTexelR${E}C${B} = vec4(0.);
+                  xTexelR${r}C${c} = vec4(0.);
                 }
 
-                if(xC + 1 >= 0 && xC + 1 < ${c}) {
-                  xTexelR${E}C${B+2} = getX(batch, xR, xC + 1, d1);
+                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {
+                  xTexelR${r}C${c+2} = getX(batch, xR, xC + 1, d1);
                 } else {
-                  xTexelR${E}C${B+2} = vec4(0.);
+                  xTexelR${r}C${c+2} = vec4(0.);
                 }
 
-                xR${E}C${B} = vec4(
-                  xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.zw);
-              `,B+1<T&&(S+=`
+                xR${r}C${c} = vec4(
+                  xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.zw);
+              `,c+1<filterWidth&&(mainLoop+=`
                   vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${m};
-                  if(xCOffset >= 0 && xCOffset < ${c}) {
+                  xCOffset = xC + 1 + ${strideWidth};
+                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
-                  xR${E}C${B+1} = vec4(xTexelR${E}C${B+2}.xy, final.xy);
-                `)):(S+=`
-                if(xC >= 0 && xC < ${c}) {
-                  xTexelR${E}C${B} = getX(batch, xR, xC, d1);
+                  xR${r}C${c+1} = vec4(xTexelR${r}C${c+2}.xy, final.xy);
+                `)):(mainLoop+=`
+                if(xC >= 0 && xC < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
                 } else {
-                  xTexelR${E}C${B} = vec4(0.);
+                  xTexelR${r}C${c} = vec4(0.);
                 }
 
-                xCOffset = xC + ${m};
-                if(xCOffset >= 0 && xCOffset < ${c}) {
-                  xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
+                xCOffset = xC + ${strideWidth};
+                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                 } else {
-                  xTexelR${E}C${B+2} = vec4(0.);
+                  xTexelR${r}C${c+2} = vec4(0.);
                 }
 
-                xR${E}C${B} = vec4(
-                  xTexelR${E}C${B}.xy, xTexelR${E}C${B+2}.xy);
-              `,B+1<T&&(S+=`
-                  xR${E}C${B+1} = vec4(
-                    xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.zw);
-                `)),S+="}");B<T&&(S+=`
-            vec4 wTexelR${E}C${B} = getW(${E}, ${B}, d1, q);
-            wR${E}C${B} = vec4(wTexelR${E}C${B}.xz, wTexelR${E}C${B}.xz);
-          `,B+1<T&&(S+=`
-              vec4 wTexelR${E}C${B+1} = getW(${E}, ${B+1}, d1, q);
-              wR${E}C${B+1} =
-                vec4(wTexelR${E}C${B+1}.xz, wTexelR${E}C${B+1}.xz);`))}for(let E=0;E<v;E++)for(let L=0;L<T;L++)S+=`dotProd += xR${E}C${L} * wR${E}C${L};`;let D="",I="";r&&(o?D=`vec4 activation(vec4 a) {
+                xR${r}C${c} = vec4(
+                  xTexelR${r}C${c}.xy, xTexelR${r}C${c+2}.xy);
+              `,c+1<filterWidth&&(mainLoop+=`
+                  xR${r}C${c+1} = vec4(
+                    xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.zw);
+                `)),mainLoop+="}");c<filterWidth&&(mainLoop+=`
+            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);
+            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);
+          `,c+1<filterWidth&&(mainLoop+=`
+              vec4 wTexelR${r}C${c+1} = getW(${r}, ${c+1}, d1, q);
+              wR${r}C${c+1} =
+                vec4(wTexelR${r}C${c+1}.xz, wTexelR${r}C${c+1}.xz);`))}for(let r=0;r<filterHeight;r++)for(let c=0;c<filterWidth;c++)mainLoop+=`dotProd += xR${r}C${c} * wR${r}C${c};`;let activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:D=`vec4 activation(vec4 x) {
-          ${r}
-        }`,I="result = activation(result);");let P=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${D}
+          ${activation2}
+        }`:activationSnippet=`vec4 activation(vec4 x) {
+          ${activation2}
+        }`,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
 
-      const ivec2 strides = ivec2(${f}, ${m});
-      const ivec2 pads = ivec2(${l}, ${p});
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
 
@@ -1757,16 +1762,16 @@ return (round(mod(b, 2.0)) != 1) ?
 
         vec4 dotProd = vec4(0.);
 
-        ${S}
+        ${mainLoop}
 
         vec4 result = dotProd;
-        ${P}
-        ${I}
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
         setOutput(result);
       }
-    `}}class zY{constructor(t,e,r,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[c,l,p,f]=t,[m]=e,[y,b]=r;this.outputShape=[m,y,b,f];let v=o==="bilinear"?1:0,[T,N]=[`${l-1}.0`,`${p-1}.0`],[S,D,I]=y>1?[`${(l-1)/(y-1)}`,"(y2-y1) * height_ratio",`y1*${T} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${T}`],[P,E,L]=b>1?[`${(p-1)/(b-1)}`,"(x2-x1) * width_ratio",`x1*${N} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${N}`];this.userCode=`
-      const float height_ratio = float(${S});
-      const float width_ratio = float(${P});
+    `}},CropAndResizeProgram=class{constructor(imageShape,boxShape,cropSize,method,extrapolationValue){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[batch,imageHeight,imageWidth,depth]=imageShape,[numBoxes]=boxShape,[cropHeight,cropWidth]=cropSize;this.outputShape=[numBoxes,cropHeight,cropWidth,depth];let methodId=method==="bilinear"?1:0,[inputHeightFloat,inputWidthFloat]=[`${imageHeight-1}.0`,`${imageWidth-1}.0`],[heightRatio,heightScale,inY]=cropHeight>1?[`${(imageHeight-1)/(cropHeight-1)}`,"(y2-y1) * height_ratio",`y1*${inputHeightFloat} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${inputHeightFloat}`],[widthRatio,widthScale,inX]=cropWidth>1?[`${(imageWidth-1)/(cropWidth-1)}`,"(x2-x1) * width_ratio",`x1*${inputWidthFloat} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${inputWidthFloat}`];this.userCode=`
+      const float height_ratio = float(${heightRatio});
+      const float width_ratio = float(${widthRatio});
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -1782,26 +1787,26 @@ return (round(mod(b, 2.0)) != 1) ?
 
         // get image in batch index
         int bInd = round(getBoxInd(b));
-        if(bInd < 0 || bInd >= ${c}) {
+        if(bInd < 0 || bInd >= ${batch}) {
           return;
         }
 
-        float height_scale = ${D};
-        float width_scale = ${E};
+        float height_scale = ${heightScale};
+        float width_scale = ${widthScale};
 
-        float in_y = ${I};
-        if( in_y < 0.0 || in_y > ${T} ) {
-          setOutput(float(${s}));
+        float in_y = ${inY};
+        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
+          setOutput(float(${extrapolationValue}));
           return;
         }
-        float in_x = ${L};
-        if( in_x < 0.0 || in_x > ${N} ) {
-          setOutput(float(${s}));
+        float in_x = ${inX};
+        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
+          setOutput(float(${extrapolationValue}));
           return;
         }
 
         vec2 sourceFracIndexCR = vec2(in_x,in_y);
-        if(${v} == 1) {
+        if(${methodId} == 1) {
           // Compute the four integer indices.
           ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
           ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
@@ -1825,30 +1830,30 @@ return (round(mod(b, 2.0)) != 1) ?
           setOutput(newValue);
         }
       }
-    `}}class DI{constructor(t,e,r){this.variableNames=["x"],this.outputShape=t;let o=t.length,s=e?"0.0":`getX(${AI(o,"coords")})`,c=t[t.length-1],l="",p="";e?(l=r?`end != ${c-1}`:"end != 0",p=r?"end + 1":"end - 1"):(l=r?`end + pow2 < ${c}`:"end >= pow2",p=r?"end + pow2":"end - pow2"),this.userCode=`
+    `}},CumSumProgram=class{constructor(shape,exclusive,reverse12){this.variableNames=["x"],this.outputShape=shape;let rank=shape.length,val=exclusive?"0.0":`getX(${getCoords(rank,"coords")})`,length=shape[shape.length-1],condition="",idxString="";exclusive?(condition=reverse12?`end != ${length-1}`:"end != 0",idxString=reverse12?"end + 1":"end - 1"):(condition=reverse12?`end + pow2 < ${length}`:"end >= pow2",idxString=reverse12?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
-        ${Oe(o)} coords = getOutputCoords();
-        int end = ${FI(o,"coords")};
-        float val = ${s};
+        ${getCoordsDataType(rank)} coords = getOutputCoords();
+        int end = ${getFinalCoord(rank,"coords")};
+        float val = ${val};
         int pow2 = int(pow(2.0, index));
-        if (${l}) {
-          int idx = ${p};
-          ${FI(o,"coords")} = idx;
-          val += getX(${AI(o,"coords")});
+        if (${condition}) {
+          int idx = ${idxString};
+          ${getFinalCoord(rank,"coords")} = idx;
+          val += getX(${getCoords(rank,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(t){return(e,r)=>{this.index==null&&(this.index=e.getUniformLocation(r,"index")),e.gl.uniform1f(this.index,t)}}}function AI(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.x, ${t}.y`;if(n===3)return`${t}.x, ${t}.y, ${t}.z`;if(n===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}function FI(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.y`;if(n===3)return`${t}.z`;if(n===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}class WY{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=nh.DENSE;let e=oh(t),r=Jn();this.outputShape=t,this.userCode=`
+    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}var DecodeMatrixProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=PackingScheme.DENSE;let texShape=getDenseTexShape(outputShape),glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${xa(["r","c","d"],t)}
+        ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],outputShape)}
         return ivec3(r, c, d);
       }
 
       void main() {
         ivec2 resTexRC = ivec2(resultUV.yx *
-          vec2(${e[0]}, ${e[1]}));
-        int index = 4 * (resTexRC.x * ${e[1]} + resTexRC.y);
+          vec2(${texShape[0]}, ${texShape[1]}));
+        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);
 
         vec4 result = vec4(0.);
 
@@ -1858,18 +1863,18 @@ return (round(mod(b, 2.0)) != 1) ?
           result[i] = getA(rc.x, rc.y, rc.z);
         }
 
-        ${r.output} = result;
+        ${glsl.output} = result;
       }
-    `}}class VY{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=nh.DENSE;let e=oh(t),r=Jn();this.outputShape=t,this.userCode=`
+    `}},DecodeMatrixPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=PackingScheme.DENSE;let texShape=getDenseTexShape(outputShape),glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${xa(["r","c","d"],t)}
+        ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],outputShape)}
         return ivec3(r, c, d);
       }
 
       void main() {
         ivec2 resTexRC = ivec2(resultUV.yx *
-          vec2(${e[0]}, ${e[1]}));
-        int index = 4 * (resTexRC.x * ${e[1]} + resTexRC.y);
+          vec2(${texShape[0]}, ${texShape[1]}));
+        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);
 
         vec4 result = vec4(0.);
 
@@ -1879,9 +1884,9 @@ return (round(mod(b, 2.0)) != 1) ?
           result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
         }
 
-        ${r.output} = result;
+        ${glsl.output} = result;
       }
-    `}}class GY{constructor(t,e,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=r,this.userCode=`
+    `}},DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -1889,40 +1894,40 @@ return (round(mod(b, 2.0)) != 1) ?
       int w = ${this.getWidthCoordString()};
       int d = ${this.getDepthCoordString()};
 
-      int in_h = h / ${e};
-      int offset_h = imod(h, ${e});
-      int in_w = w / ${e};
-      int offset_w = imod(w, ${e});
-      int offset_d = (offset_h * ${e} + offset_w) *
+      int in_h = h / ${blockSize};
+      int offset_h = imod(h, ${blockSize});
+      int in_w = w / ${blockSize};
+      int offset_w = imod(w, ${blockSize});
+      int offset_d = (offset_h * ${blockSize} + 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 UY{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
+  `}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)"}},DiagProgram=class{constructor(size){this.variableNames=["X"],this.outputShape=[size,size],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}}class qY{constructor(t){this.variableNames=["A"],this.outTexUsage=Mr.DOWNLOAD;let e=Jn();this.outputShape=t,this.userCode=`
-      ${vI}
+    `}},EncodeFloatProgram=class{constructor(outputShape){this.variableNames=["A"],this.outTexUsage=TextureUsage.DOWNLOAD;let glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
+      ${ENCODE_FLOAT_SNIPPET}
 
       void main() {
         float x = getAAtOutCoords();
-        ${e.output} = encode_float(x);
+        ${glsl.output} = encode_float(x);
       }
-    `}}class HY{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Mr.DOWNLOAD;let e=Jn();this.outputShape=t,this.userCode=`
-      ${vI}
+    `}},EncodeFloatPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=TextureUsage.DOWNLOAD;let glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
+      ${ENCODE_FLOAT_SNIPPET}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
-        ${e.output} = encode_float(x);
+        ${glsl.output} = encode_float(x);
       }
-    `}}class jY{constructor(t,e,r=!1){this.variableNames=["A"];let o=Jn(),[s,c]=e;this.outputShape=t;let l="result";r&&(l="floor(result * 255. + 0.5)"),this.userCode=`
-      ${b1(t)}
+    `}},EncodeMatrixProgram=class{constructor(outputShape,texShape,inputIsUnsignedByte=!1){this.variableNames=["A"];let glsl=getGlslDifferences(),[height,width]=texShape;this.outputShape=outputShape;let output="result";inputIsUnsignedByte&&(output="floor(result * 255. + 0.5)"),this.userCode=`
+      ${getFlatIndexFrom3D(outputShape)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1932,10 +1937,10 @@ return (round(mod(b, 2.0)) != 1) ?
 
         flatIndex = idiv(flatIndex, 4, 1.);
 
-        int r = flatIndex / ${c};
-        int c = imod(flatIndex, ${c});
-        vec2 uv = (vec2(c, r) + halfCR) / vec2(${c}.0, ${s}.0);
-        vec4 values = ${o.texture2D}(A, uv);
+        int r = flatIndex / ${width};
+        int c = imod(flatIndex, ${width});
+        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
+        vec4 values = ${glsl.texture2D}(A, uv);
 
         float result;
 
@@ -1949,38 +1954,38 @@ return (round(mod(b, 2.0)) != 1) ?
           result = values[3];
         }
 
-        ${o.output} = vec4(${l}, 0., 0., 0.);
+        ${glsl.output} = vec4(${output}, 0., 0., 0.);
       }
-    `}}class KY{constructor(t,e,r=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let o=Jn(),[s,c]=e;this.outputShape=t;let l="",p="result";r&&(p="floor(result * 255. + 0.5)");for(let f=0;f<=1;f++)for(let m=0;m<=1;m++){let y=f*2+m;l+=`
+    `}},EncodeMatrixPackedProgram=class{constructor(outputShape,texShape,inputIsUnsignedByte=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let glsl=getGlslDifferences(),[height,width]=texShape;this.outputShape=outputShape;let mainLoop="",output="result";inputIsUnsignedByte&&(output="floor(result * 255. + 0.5)");for(let row=0;row<=1;row++)for(let col=0;col<=1;col++){let channel=row*2+col;mainLoop+=`
           localCoords = coords;
-          if(localCoords[2] + ${m} < ${t[2]}) {
-            localCoords[2] += ${m};
-            if(localCoords[1] + ${f} < ${t[1]}) {
-              localCoords[1] += ${f};
+          if(localCoords[2] + ${col} < ${outputShape[2]}) {
+            localCoords[2] += ${col};
+            if(localCoords[1] + ${row} < ${outputShape[1]}) {
+              localCoords[1] += ${row};
 
               flatIndex = getFlatIndex(localCoords);
               offset = imod(flatIndex, 4);
 
               flatIndex = idiv(flatIndex, 4, 1.);
 
-              r = flatIndex / ${c};
-              c = imod(flatIndex, ${c});
-              uv = (vec2(c, r) + halfCR) / vec2(${c}.0, ${s}.0);
-              values = ${o.texture2D}(A, uv);
+              r = flatIndex / ${width};
+              c = imod(flatIndex, ${width});
+              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
+              values = ${glsl.texture2D}(A, uv);
 
               if(offset == 0) {
-                result[${y}] = values[0];
+                result[${channel}] = values[0];
               } else if(offset == 1) {
-                result[${y}] = values[1];
+                result[${channel}] = values[1];
               } else if(offset == 2) {
-                result[${y}] = values[2];
+                result[${channel}] = values[2];
               } else {
-                result[${y}] = values[3];
+                result[${channel}] = values[3];
               }
             }
           }
         `}this.userCode=`
-      ${b1(t)}
+      ${getFlatIndexFrom3D(outputShape)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1991,66 +1996,66 @@ return (round(mod(b, 2.0)) != 1) ?
         vec2 uv;
         vec4 values;
 
-        ${l}
+        ${mainLoop}
 
-        ${o.output} = ${p};
+        ${glsl.output} = ${output};
       }
-    `}}class XY{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.outputShape=t,this.userCode=`
+    `}},FillProgram=class{constructor(shape,value){this.outputShape=[],this.variableNames=["x"],this.outputShape=shape,this.userCode=`
       uniform float value;
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}getCustomSetupFunc(t){return(e,r)=>{this.valueLoc==null&&(this.valueLoc=e.getUniformLocationNoThrow(r,"value")),e.gl.uniform1f(this.valueLoc,t)}}}class YY{constructor(t,e,r){this.variableNames=["A","indices"];let o=t.slice();o[r]=e,this.outputShape=o,this.rank=o.length;let s=Oe(this.rank),c=JY(t,r);this.userCode=`
+    `}getCustomSetupFunc(value){return(gpgpu,webGLProgram)=>{this.valueLoc==null&&(this.valueLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"value")),gpgpu.gl.uniform1f(this.valueLoc,value)}}},GatherProgram=class{constructor(aShape,indicesLength,axis){this.variableNames=["A","indices"];let outputShape=aShape.slice();outputShape[axis]=indicesLength,this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),sourceCoords=getSourceCoords2(aShape,axis);this.userCode=`
       void main() {
-        ${s} resRC = getOutputCoords();
-        setOutput(getA(${c}));
+        ${dtype} resRC = getOutputCoords();
+        setOutput(getA(${sourceCoords}));
       }
-    `}}function JY(n,t){let e=n.length;if(e>4)throw Error(`Gather for rank ${e} is not yet supported`);if(e===1)return"int(getIndices(resRC))";let r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let s=0;s<n.length;s++)s===t?o.push(`int(getIndices(${r[s]}))`):o.push(`${r[s]}`);return o.join()}class ZY{constructor(t,e,r){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=r;let o=Oe(e.length),s=Oe(r.length),c=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
-        ${o} strides = ${o}(${this.strides});
+    `}};function getSourceCoords2(aShape,axis){let rank=aShape.length;if(rank>4)throw Error(`Gather for rank ${rank} is not yet supported`);if(rank===1)return"int(getIndices(resRC))";let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w"],sourceCoords=[];for(let i=0;i<aShape.length;i++)i===axis?sourceCoords.push(`int(getIndices(${currentCoords[i]}))`):sourceCoords.push(`${currentCoords[i]}`);return sourceCoords.join()}var GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${stridesType} strides = ${stridesType}(${this.strides});
          void main() {
-          ${s} coords = getOutputCoords();
+          ${dtype} coords = getOutputCoords();
           int flattenIndex = 0;
           for (int j = 0; j < ${this.sliceDim}; j++) {
             int index = round(getIndices(coords[0], j));
-            flattenIndex += index * ${c};
+            flattenIndex += index * ${strideString};
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}}function QY(n){let t=Jn(),e=`${t.version}
+      `}};function createVertexShader2(gl){let glsl=getGlslDifferences(),vertexShaderSource=`${glsl.version}
     precision highp float;
-    ${t.attribute} vec3 clipSpacePos;
-    ${t.attribute} vec2 uv;
-    ${t.varyingVs} vec2 resultUV;
+    ${glsl.attribute} vec3 clipSpacePos;
+    ${glsl.attribute} vec2 uv;
+    ${glsl.varyingVs} vec2 resultUV;
 
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return A8(n,e)}function t7(n){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return M8(n,t)}function e7(n){let t=new Uint16Array([0,1,2,2,1,3]);return B8(n,t)}function ih(n,t,e,r,o,s){W8(t,e);let c=z8(n),l=n.TEXTURE_2D;return Rt(n,()=>n.bindTexture(l,c)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_MIN_FILTER,n.NEAREST)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_MAG_FILTER,n.NEAREST)),Rt(n,()=>n.texImage2D(l,0,r,t,e,0,o,s,null)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null)),c}function RI(n){return n.internalFormatFloat}function n7(n,t,e,r){let[o,s]=rh(t,e);return ih(n,o,s,RI(r),r.textureFormatFloat,n.FLOAT)}function PI(n){return n.internalFormatHalfFloat}function r7(n,t,e,r){let[o,s]=rh(t,e);return ih(n,o,s,PI(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function OI(n){return n.downloadTextureFormat}function o7(n,t,e,r){let[o,s]=rh(t,e);return ih(n,o,s,OI(r),n.RGBA,n.UNSIGNED_BYTE)}function LI(n){return n.internalFormatPackedFloat}function s7(n,t,e,r){let[o,s]=nl(t,e);return ih(n,o,s,LI(r),n.RGBA,n.FLOAT)}function MI(n){return n.internalFormatPackedHalfFloat}function i7(n,t,e,r){let[o,s]=nl(t,e);return ih(n,o,s,MI(r),n.RGBA,r.textureTypeHalfFloat)}function a7(n,t,e){let r=0,o=3*4,s=3*4+2*4;Rt(n,()=>n.bindBuffer(n.ARRAY_BUFFER,e));let c=gI(n,t,"clipSpacePos",e,3,s,r);return c&&gI(n,t,"uv",e,2,s,o)}function c7(n,t,e,r,o,s){Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t));let c,l,p;o instanceof Uint8Array?(c=new Uint8Array(e*r*4),l=n.UNSIGNED_BYTE,p=n.RGBA):(c=new Float32Array(e*r*4),l=n.FLOAT,p=s.internalFormatPackedFloat),c.set(o),Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,p,e,r,0,n.RGBA,l,c)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function l7(n,t,e){Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t)),e.data instanceof Uint8Array?Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,e.width,e.height,0,n.RGBA,n.UNSIGNED_BYTE,e.data)):Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,n.RGBA,n.UNSIGNED_BYTE,e)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function u7(n,t,e,r){let o=n.createBuffer();Rt(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,o));let s=4,c=4,l=s*c*t*e;return Rt(n,()=>n.bufferData(n.PIXEL_PACK_BUFFER,l,n.STREAM_READ)),Rt(n,()=>n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,0)),Rt(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,null)),o}function p7(n,t,e){let r=n,o=new Float32Array(e);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,o),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),o}function h7(n,t,e,r){let[o,s]=rh(t,e),c=4,l=new Uint8Array(_8(t*e,c));return Rt(n,()=>n.readPixels(0,0,o,s,r.downloadTextureFormat,n.UNSIGNED_BYTE,l)),new Float32Array(l.buffer)}function f7(n,t,e,r,o,s,c,l){let p=n,f=new Float32Array(C8(s,c));return p.bindBuffer(p.PIXEL_PACK_BUFFER,t),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,f),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),f}function d7(n,t,e){let r=new Float32Array(t*e*4);return Rt(n,()=>n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,r)),r}class m7{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let e=ct().getNumber("WEBGL_VERSION");t!=null?(this.gl=t,T8(e,t)):this.gl=jo(e);let r="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(ct().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",c="OES_texture_half_float";if(this.textureFloatExtension=Dm(this.gl,s),eo(this.gl,c))this.textureHalfFloatExtension=Dm(this.gl,c);else if(ct().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(r),eo(this.gl,o))this.colorBufferHalfFloatExtension=Dm(this.gl,o);else if(ct().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(r="EXT_color_buffer_float",eo(this.gl,r))this.colorBufferFloatExtension=this.gl.getExtension(r);else if(eo(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=t7(this.gl),this.indexBuffer=e7(this.gl),this.framebuffer=V8(this.gl),this.textureConfig=h1(this.gl,this.textureHalfFloatExtension)}get debug(){return ct().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.");let t=this.gl;Rt(t,()=>t.finish()),Rt(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),Rt(t,()=>t.deleteFramebuffer(this.framebuffer)),Rt(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),Rt(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),Rt(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),n7(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),r7(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),o7(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),l7(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,r,o){this.throwIfDisposed(),c7(this.gl,t,e,r,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),i7(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),s7(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(yI(this.gl,this.framebuffer),this.outputTexture=null),Rt(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,r){return this.downloadMatrixDriver(t,()=>h7(this.gl,e,r,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,r,o,s,c){return f7(this.gl,t,e,r,o,s,c,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return p7(this.gl,t,e)}createBufferFromTexture(t,e,r){this.bindTextureToFrameBuffer(t);let o=u7(this.gl,e,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,r;if(ct().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),r=()=>{let c=o.clientWaitSync(s,0,0);return c===o.ALREADY_SIGNALED||c===o.CONDITION_SATISFIED},e=s}else ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),r=()=>this.isQueryAvailable(e,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):r=()=>!0;return{query:e,isFencePassed:r}}downloadMatrixFromPackedTexture(t,e,r){return this.downloadMatrixDriver(t,()=>d7(this.gl,e,r))}createProgram(t){this.throwIfDisposed();let e=this.gl,r=F8(e,t),o=QY(e),s=O8(e);return Rt(e,()=>e.attachShader(s,o)),Rt(e,()=>e.attachShader(s,r)),L8(e,s),this.debug&&f1(e,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=a7(e,this.program,this.vertexBuffer)),s}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&Rt(this.gl,()=>this.gl.deleteProgram(t))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&f1(this.gl,this.program),Rt(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,r=!0){return this.throwIfDisposed(),r?U8(this.gl,t,e):q8(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),Rt(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,r){this.throwIfDisposed(),this.throwIfNoProgram(),H8(this.gl,t,e,r)}setOutputMatrixTexture(t,e,r){this.setOutputMatrixTextureDriver(t,r,e)}setOutputPackedMatrixTexture(t,e,r){this.throwIfDisposed();let[o,s]=nl(e,r);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,r,o){this.setOutputMatrixWriteRegionDriver(r,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,r,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&f1(this.gl,this.program),Am(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;this.debug&&this.debugValidate(),Rt(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Rt(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Dm(this.gl,ct().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(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=r.createQuery();return r.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,r=this.getQueryTimerExtensionWebGL2();e.endQuery(r.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await _e(()=>this.disposed||this.isQueryAvailable(t,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let r=this.gl,o=r.getQueryParameter(t,r.QUERY_RESULT);return o/1e6}else{let r=this.getQueryTimerExtensionWebGL1(),o=r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT);return o/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let r=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=r.getQueryParameter(t,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),o=r.getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=g7(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:r}=this.itemsToPoll[e];r()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;_e(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(t){this.throwIfDisposed(),d1(this.gl,t,this.framebuffer),this.debug&&Am(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(d1(this.gl,this.outputTexture,this.framebuffer),this.debug&&Am(this.gl)):yI(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let r=e();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(t,e,r){this.throwIfDisposed();let o=this.gl;d1(o,t,this.framebuffer),this.debug&&Am(o),this.outputTexture=t,Rt(o,()=>o.viewport(0,0,e,r)),Rt(o,()=>o.scissor(0,0,e,r))}setOutputMatrixWriteRegionDriver(t,e,r,o){this.throwIfDisposed(),Rt(this.gl,()=>this.gl.scissor(t,e,r,o))}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 g7(n){let t=0;for(;t<n.length;++t){let e=n[t]();if(!e)break}return t-1}function y7(n,t,e,r){let o=t.userCode,s=e.map((v,T)=>{let N={logicalShape:v.shape,texShape:v.isUniform?null:v.texData.texShape,isUniform:v.isUniform,isPacked:v.isUniform?!1:v.texData.isPacked,flatOffset:null};return v.texData!=null&&v.texData.slice!=null&&v.texData.slice.flatOffset>0&&(N.flatOffset=v.texData.slice.flatOffset),{name:t.variableNames[T],shapeInfo:N}}),c=s.map(v=>v.shapeInfo),l={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},p=xX(s,l,o,t.packedInputs),f=n.createProgram(p),m=null,y=n.getUniformLocation(f,"NAN",!1);ct().getNumber("WEBGL_VERSION")===1&&(m=n.getUniformLocation(f,"INFINITY",!1));let b={};for(let v=0;v<t.variableNames.length;v++){let T=t.variableNames[v],N=!1;b[T]=n.getUniformLocation(f,T,N),b[`offset${T}`]=n.getUniformLocation(f,`offset${T}`,N)}return{program:t,source:p,webGLProgram:f,uniformLocations:b,inShapeInfos:c,outShapeInfo:l,infLoc:m,nanLoc:y}}function BI(n,t){if(n.length!==t.length)throw Error(`Binary was compiled with ${n.length} inputs, but was executed with ${t.length} inputs`);n.forEach((e,r)=>{let o=e.logicalShape,s=t[r],c=s.shape;if(!lt(o,c))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${c} must match`);if(e.isUniform&&s.isUniform)return;let l=e.texShape,p=s.isUniform?null:s.texData.texShape;if(!lt(l,p))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${l} and ${p} must match`)})}function b7(n,t,e,r,o){BI(t.inShapeInfos,e),BI([t.outShapeInfo],[r]);let s=r.texData.texture,c=r.texData.texShape;r.texData.isPacked?n.setOutputPackedMatrixTexture(s,c[0],c[1]):n.setOutputMatrixTexture(s,c[0],c[1]),n.setProgram(t.webGLProgram),ct().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&n.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&n.gl.uniform1f(t.nanLoc,NaN),e.forEach((l,p)=>{let f=t.program.variableNames[p],m=t.uniformLocations[f],y=t.uniformLocations[`offset${f}`];if(m==null)return;if(l.isUniform){if(G(l.shape)<2)n.gl.uniform1f(m,l.uniformValues[0]);else{let b=l.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),n.gl.uniform1fv(m,b)}return}l.texData.slice!=null&&y!=null&&n.gl.uniform1i(y,l.texData.slice.flatOffset),n.setInputMatrixTexture(l.texData.texture,m,p)}),o!=null&&o(n,t.webGLProgram),n.executeProgram()}function x7(n,t,e){let r="";t.concat(e).forEach(c=>{let l=c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0,p=c.isUniform?"uniform":c.texData.texShape;r+=`${c.shape}_${p}_${l}`});let o=n.userCode,s=n.constructor.name;return s+="_"+r+"_"+o,s}class w7{constructor(t,e,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let{filterWidth:o,inChannels:s,strideWidth:c,strideHeight:l,padInfo:p,outWidth:f,dilationWidth:m,dilationHeight:y,dataFormat:b}=r,{left:v,top:T}=p,N=s*o,S=Jn(),D=b==="channelsLast",I=D?0:1,P=D?1:2,E="";for(let L=0;L<=1;L++)for(let B=0;B<=1;B++)E+=`
-          blockIndex = rc.y + ${B};
-          pos = rc.x + ${L};
+    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer));let success=bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset);return success&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer11=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11));let bytesPerFloat=4,valuesPerTexel=4,bufferSizeBytes=bytesPerFloat*valuesPerTexel*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer11}function downloadFloat32MatrixFromBuffer(gl,buffer11,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer11,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().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(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().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(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().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.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer11,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer11,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer11,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer11,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().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(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl,timeElapsedNanos=gl2.getQueryParameter(query,gl2.QUERY_RESULT);return timeElapsedNanos/1e6}else{let ext=this.getQueryTimerExtensionWebGL1(),timeElapsedNanos=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT);return timeElapsedNanos/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){if(this.itemsToPoll.push({isDoneFn,resolveFn}),this.itemsToPoll.length>1)return;util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}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 linearSearchLastTrue(arr){let i=0;for(;i<arr.length;++i){let isDone=arr[i]();if(!isDone)break}return i-1}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(s.isUniform&&input2.isUniform)return;let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc==null)return;if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var Im2ColPackedProgram=class{constructor(outputShape,inputShape,convInfo){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let{filterWidth,inChannels,strideWidth,strideHeight,padInfo,outWidth,dilationWidth,dilationHeight,dataFormat}=convInfo,{left,top}=padInfo,itemsPerBlockRow=inChannels*filterWidth,glsl=getGlslDifferences(),isChannelsLast=dataFormat==="channelsLast",rowDim=isChannelsLast?0:1,colDim=isChannelsLast?1:2,unrolled="";for(let row=0;row<=1;row++)for(let col=0;col<=1;col++)unrolled+=`
+          blockIndex = rc.y + ${col};
+          pos = rc.x + ${row};
 
-          if(blockIndex < ${t[1]} && pos < ${t[0]}) {
-            offsetY = int(blockIndex / (${f})) * ${l} - ${T};
-            d0 = offsetY + ${y} * (pos / ${N});
+          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {
+            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};
+            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});
 
-            if(d0 < ${e[I]} && d0 >= 0) {
+            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {
 
-              offsetX = int(mod(float(blockIndex), ${f}.) * ${c}. - ${v}.);
-              d1 = offsetX + ${m} * (int(mod(float(pos), ${N}.) / ${s}.));
+              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);
+              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));
 
-              if(d1 < ${e[P]} && d1 >= 0) {
+              if(d1 < ${inputShape[colDim]} && d1 >= 0) {
 
-                ch = int(mod(float(pos), ${s}.));
+                ch = int(mod(float(pos), ${inChannels}.));
 
-                if (${D}) {
+                if (${isChannelsLast}) {
                   innerDims = vec2(d1, ch);
-                  result[${L*2+B}] = getChannel(
+                  result[${row*2+col}] = getChannel(
                     getA(d0, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 } else {
                   innerDims = vec2(d0, d1);
-                  result[${L*2+B}] = getChannel(
+                  result[${row*2+col}] = getChannel(
                     getA(ch, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 }
@@ -2066,11 +2071,11 @@ return (round(mod(b, 2.0)) != 1) ?
         int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
         vec2 innerDims;
 
-        ${E}
+        ${unrolled}
 
-        ${S.output} = result;
+        ${glsl.output} = result;
       }
-    `}}class v7{constructor(t,e,r,o,s){this.variableNames=["x"],this.outputShape=[];let c=e,l=t[3]-1;this.outputShape=t;let p,f=`float(${r}) + float(${o}) * sum`;s===.5?p=`inversesqrt(${f})`:s===1?p=`1.0/(${f})`:p=`exp(log(${f}) * float(-${s}));`,this.userCode=`
+    `}},LRNProgram=class{constructor(xShape,radius,bias,alpha,beta){this.variableNames=["x"],this.outputShape=[];let rad=radius,maxD=xShape[3]-1;this.outputShape=xShape;let powOperator,basis=`float(${bias}) + float(${alpha}) * sum`;beta===.5?powOperator=`inversesqrt(${basis})`:beta===1?powOperator=`1.0/(${basis})`:powOperator=`exp(log(${basis}) * float(-${beta}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -2079,17 +2084,17 @@ return (round(mod(b, 2.0)) != 1) ?
         int d = coords[3];
         float x = getX(b, r, c, d);
         float sum = 0.0;
-        for (int j = -${c}; j <= ${c}; j++) {
+        for (int j = -${rad}; j <= ${rad}; j++) {
           int idx = d + j;
-          if (idx >= 0 && idx <=  ${l}) {
+          if (idx >= 0 && idx <=  ${maxD}) {
             float z = getX(b, r, c, idx);
             sum += z * z;
           }
         }
-        float val = x * ${p};
+        float val = x * ${powOperator};
         setOutput(val);
       }
-    `}}class T7{constructor(t,e,r,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=r,this.alpha=o,this.beta=s,this.userCode=`
+    `}},LRNGradProgram=class{constructor(inputShape,depthRadius,bias,alpha,beta){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=inputShape,this.depth=inputShape[3],this.depthRadius=depthRadius,this.bias=bias,this.alpha=alpha,this.beta=beta,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -2098,9 +2103,9 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float result = 0.0;
         for (int d = 0; d < ${this.depth}; ++d) {
-          int depthBegin = int(max(0.0, float(d - ${e})));
+          int depthBegin = int(max(0.0, float(d - ${depthRadius})));
           int depthEnd = int(min(float(${this.depth}),
-              float(d + ${e} + 1)));
+              float(d + ${depthRadius} + 1)));
 
           const int MIN_DEPTH_BEGIN = 0;
           const int MAX_DEPTH_END = ${this.depth};
@@ -2118,19 +2123,19 @@ return (round(mod(b, 2.0)) != 1) ?
             }
           }
 
-          norm = float(${o}) * norm + float(${r});
+          norm = float(${alpha}) * norm + float(${bias});
 
           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(${o})
-                * float(${s})
+              float dyi = -2.0 * float(${alpha})
+                * float(${beta})
                 * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                 / norm;
               if (k == d) {
-                dyi += pow(norm, -1.0 * ${s});
+                dyi += pow(norm, -1.0 * ${beta});
               }
               if (k == coords[3]) {
                 dyi *= getDy(b, r, c, d);
@@ -2144,7 +2149,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
       setOutput(result);
       }
-    `}}class k7{constructor(t,e,r,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let c=e,l=t[3]-1;this.outputShape=t;let p,f=`float(${r}) + float(${o}) * sum`;s===.5?p=`inversesqrt(${f})`:s===1?p=`1.0/(${f})`:p=`exp(log(${f}) * float(-${s}));`,this.userCode=`
+    `}},LRNPackedProgram=class{constructor(xShape,radius,bias,alpha,beta){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let rad=radius,maxD=xShape[3]-1;this.outputShape=xShape;let powOperator,basis=`float(${bias}) + float(${alpha}) * sum`;beta===.5?powOperator=`inversesqrt(${basis})`:beta===1?powOperator=`1.0/(${basis})`:powOperator=`exp(log(${basis}) * float(-${beta}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -2168,7 +2173,7 @@ return (round(mod(b, 2.0)) != 1) ?
             getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
         );
 
-        int firstChannel = d - ${c};
+        int firstChannel = d - ${rad};
         vec2 cache = vec2(0.);
         if(firstChannel >= 0){
           vec4 firstChannelFrag = getX(b, r, c, firstChannel);
@@ -2179,10 +2184,10 @@ return (round(mod(b, 2.0)) != 1) ?
         }
 
         ivec2 depth = ivec2(d, d + 1);
-        for (int j = - ${c}; j <= ${c}; j++) {
+        for (int j = - ${rad}; j <= ${rad}; j++) {
           ivec2 idx = depth + j;
           bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
-          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${l}));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));
 
           bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
           bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
@@ -2203,11 +2208,11 @@ return (round(mod(b, 2.0)) != 1) ?
             sum += z * z;
           }
         }
-        vec4 result = xAtOutputCoords * ${p};
+        vec4 result = xAtOutputCoords * ${powOperator};
         setOutput(result);
       }
-    `}}class N7{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,r=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=s-1-t.padInfo.top,p=c-1-t.padInfo.left,f=s*c-1;this.userCode=`
-      const ivec2 pads = ivec2(${l}, ${p});
+    `}},MaxPool2DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy","maxPos"],this.outputShape=convInfo.inShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,lastIndex=effectiveFilterHeight*effectiveFilterWidth-1;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -2221,30 +2226,30 @@ return (round(mod(b, 2.0)) != 1) ?
         // 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 < ${s};
-          wR += ${o}) {
-          float dyR = float(dyRCorner + wR) / ${e}.0;
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+          wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
           }
           int idyR = int(dyR);
 
-          for (int wC = 0; wC < ${c}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${r}.0;
+          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                 fract(dyC) > 0.0) {
               continue;
             }
             int idyC = int(dyC);
 
             float dyValue = getDy(b, idyR, idyC, d);
-            int maxPosValue = ${f} - int(getMaxPos(b, idyR, idyC, d));
+            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));
 
             // Get the current value, check it against the value from the
             // position matrix.
-            int curPosValue = wR * ${c} + wC;
+            int curPosValue = wR * ${effectiveFilterWidth} + wC;
             float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
 
             dotProd += dyValue * mask;
@@ -2252,8 +2257,8 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class _7{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,r=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,c=t.dilationHeight,l=t.dilationWidth,p=t.effectiveFilterDepth,f=t.effectiveFilterHeight,m=t.effectiveFilterWidth,y=p-1-t.padInfo.front,b=f-1-t.padInfo.top,v=m-1-t.padInfo.left,T=p*f*m-1;this.userCode=`
-      const ivec3 pads = ivec3(${y}, ${b}, ${v});
+    `}},MaxPool3DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy","maxPos"],this.outputShape=convInfo.inShape;let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,lastIndex=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1;this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -2270,44 +2275,44 @@ return (round(mod(b, 2.0)) != 1) ?
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${p};
-           wD += ${s}) {
-          float dyD = float(dyDCorner + wD) / ${e}.0;
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+           wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
 
-          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
             continue;
           }
           int idyD = int(dyD);
 
-          for (int wR = 0; wR < ${f};
-              wR += ${c}) {
-            float dyR = float(dyRCorner + wR) / ${r}.0;
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                 fract(dyR) > 0.0) {
               continue;
             }
             int idyR = int(dyR);
 
-            for (int wC = 0; wC < ${m};
-                wC += ${l}) {
-              float dyC = float(dyCCorner + wC) / ${o}.0;
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                   fract(dyC) > 0.0) {
                 continue;
               }
               int idyC = int(dyC);
 
               float dyValue = getDy(batch, idyD, idyR, idyC, ch);
-              int maxPosValue = ${T} -
+              int maxPosValue = ${lastIndex} -
                   int(getMaxPos(batch, idyD, idyR, idyC, ch));
 
               // Get the current value, check it against the value from the
               // position matrix.
               int curPosValue =
-                  wD * ${f} * ${m} +
-                  wR * ${m} + wC;
+                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                  wR * ${effectiveFilterWidth} + wC;
               float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
 
               dotProd += dyValue * mask;
@@ -2316,28 +2321,28 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class x1{constructor(t,e,r,o=!1,s=!1,c=!1,l=null,p=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r;let f=o?t[1]:t[2],m=Math.ceil(f/2),y=o?"i * 2, rc.y":"rc.y, i * 2",b=s?"rc.z, i * 2":"i * 2, rc.z",v=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],T=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],N="",S="";l&&(p?N=`vec4 activation(vec4 a) {
+    `}},MatMulPackedProgram=class{constructor(aShape,bShape,outputShape,transposeA=!1,transposeB=!1,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let sharedDim=transposeA?aShape[1]:aShape[2],sharedDimensionPacked=Math.ceil(sharedDim/2),aSample=transposeA?"i * 2, rc.y":"rc.y, i * 2",bSample=transposeB?"rc.z, i * 2":"i * 2, rc.z",aSwizzle=transposeA?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],bSwizzle=transposeB?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${l}
-        }`:N=`vec4 activation(vec4 x) {
-          ${l}
-        }`,S="result = activation(result);");let D=c?"result += getBiasAtOutCoords();":"";c&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights");let I="rc.x",P="rc.x";t[0]<e[0]?I=`int(min(float(rc.x), ${t[0]-1}.))`:e[0]<t[0]&&(P=`int(min(float(rc.x), ${e[0]-1}.))`),this.userCode=`
-      ${N}
+          ${activation2}
+        }`:activationSnippet=`vec4 activation(vec4 x) {
+          ${activation2}
+        }`,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights");let batchASnippet="rc.x",batchBSnippet="rc.x";aShape[0]<bShape[0]?batchASnippet=`int(min(float(rc.x), ${aShape[0]-1}.))`:bShape[0]<aShape[0]&&(batchBSnippet=`int(min(float(rc.x), ${bShape[0]-1}.))`),this.userCode=`
+      ${activationSnippet}
 
-      const float sharedDimension = ${m}.0;
+      const float sharedDimension = ${sharedDimensionPacked}.0;
 
       vec4 dot2x2ARowBCol(ivec3 rc) {
         vec4 result = vec4(0);
-        for (int i = 0; i < ${m}; i++) {
-          int batchA = ${I};
-          int batchB = ${P};
-          vec4 a = getMatrixA(batchA, ${y});
-          vec4 b = getMatrixB(batchB, ${b});
+        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
+          int batchA = ${batchASnippet};
+          int batchB = ${batchBSnippet};
+          vec4 a = getMatrixA(batchA, ${aSample});
+          vec4 b = getMatrixB(batchB, ${bSample});
 
           // These swizzled products need to be separately added.
           // See: https://github.com/tensorflow/tfjs/issues/1735
-          result += (${v[0]} * ${T[0]});
-          result += (${v[1]} * ${T[1]});
+          result += (${aSwizzle[0]} * ${bSwizzle[0]});
+          result += (${aSwizzle[1]} * ${bSwizzle[1]});
         }
         return result;
       }
@@ -2346,13 +2351,13 @@ return (round(mod(b, 2.0)) != 1) ?
         ivec3 rc = getOutputCoords();
         vec4 result = dot2x2ARowBCol(rc);
 
-        ${D}
+        ${addBiasSnippet}
 
-        ${S}
+        ${applyActivationSnippet}
 
         setOutput(result);
       }
-    `}}class C7{constructor(t,e,r){this.variableNames=["probs"],this.outputShape=[t,r],this.userCode=`
+    `}},MultinomialProgram=class{constructor(batchSize,numOutcomes,numSamples){this.variableNames=["probs"],this.outputShape=[batchSize,numSamples],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -2362,7 +2367,7 @@ return (round(mod(b, 2.0)) != 1) ?
         float r = random(seed);
         float cdf = 0.0;
 
-        for (int i = 0; i < ${e-1}; i++) {
+        for (int i = 0; i < ${numOutcomes-1}; i++) {
           cdf += getProbs(batch, i);
 
           if (r < cdf) {
@@ -2372,96 +2377,96 @@ return (round(mod(b, 2.0)) != 1) ?
         }
 
         // If no other event happened, last event happened.
-        setOutput(float(${e-1}));
+        setOutput(float(${numOutcomes-1}));
       }
-    `}getCustomSetupFunc(t){return(e,r)=>{this.seedLoc==null&&(this.seedLoc=e.getUniformLocation(r,"seed")),e.gl.uniform1f(this.seedLoc,t)}}}class S7{constructor(t,e,r,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
+    `}getCustomSetupFunc(seed){return(gpgpu,webGLProgram)=>{this.seedLoc==null&&(this.seedLoc=gpgpu.getUniformLocation(webGLProgram,"seed")),gpgpu.gl.uniform1f(this.seedLoc,seed)}}},OneHotProgram=class{constructor(numIndices,depth,onValue,offValue){this.variableNames=["indices"],this.outputShape=[numIndices,depth],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
-        setOutput(mix(float(${o}), float(${r}),
+        setOutput(mix(float(${offValue}), float(${onValue}),
                       float(index == coords.y)));
       }
-    `}}class $7{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;let e=t.length;if(e===0)this.userCode=`
+    `}},PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let r=Yn("rc",e),o=Oe(e),s=E7(e,t,r),c=D7(e,t[t.length-1],t[t.length-2],r),l=A7(t,r);this.userCode=`
+      `;else{let channels=getChannels("rc",rank),dtype=getCoordsDataType(rank),outOfBoundsCondition=getOutOfBoundsCondition(rank,outputShape,channels),setup38=getSetup(rank,outputShape[outputShape.length-1],outputShape[outputShape.length-2],channels),output=getOutput(outputShape,channels);this.userCode=`
         void main() {
-          ${o} rc = getOutputCoords();
+          ${dtype} rc = getOutputCoords();
 
-          if(${s}) {
+          if(${outOfBoundsCondition}) {
             setOutput(vec4(0));
           } else {
-            ${c}
+            ${setup38}
 
-            setOutput(vec4(${l}));
+            setOutput(vec4(${output}));
           }
         }
-      `}}}function I7(n,t){let e=[];for(let r=0;r<=1;r++)for(let o=0;o<=1;o++){let s=`${r===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let c=2;c<n;c++)s=`${t[t.length-1-c]},`+s;e.push(s)}return e}function E7(n,t,e){if(n===1)return`rc > ${t[0]}`;let r="";for(let o=n-2;o<n;o++)r+=`${e[o]} >= ${t[o]}`,o<n-1&&(r+="||");return r}function D7(n,t,e,r){if(n===1)return"";let o=r.slice(-2);return`
-    int r = ${o[0]};
-    int c = ${o[1]};
+      `}}};function getSourceCoordsArr(rank,dims){let coords2=[];for(let row=0;row<=1;row++)for(let col=0;col<=1;col++){let coord=`${row===0?"r":"rp1"}, ${col===0?"c":"cp1"}`;for(let d=2;d<rank;d++)coord=`${dims[dims.length-1-d]},`+coord;coords2.push(coord)}return coords2}function getOutOfBoundsCondition(rank,shape,dims){if(rank===1)return`rc > ${shape[0]}`;let cond="";for(let i=rank-2;i<rank;i++)cond+=`${dims[i]} >= ${shape[i]}`,i<rank-1&&(cond+="||");return cond}function getSetup(rank,cols,rows,dims){if(rank===1)return"";let innerDims=dims.slice(-2);return`
+    int r = ${innerDims[0]};
+    int c = ${innerDims[1]};
     int rp1 = r + 1;
     int cp1 = c + 1;
 
-    bool cEdge = cp1 >= ${t};
-    bool rEdge = rp1 >= ${e};
-  `}function A7(n,t){let e=n.length,r=I7(e,t);return e===1?`getA(rc),
-            rc + 1 >= ${n[0]} ? 0. : getA(rc + 1),
-            0, 0`:`getA(${r[0]}),
-          cEdge ? 0. : getA(${r[1]}),
-          rEdge ? 0. : getA(${r[2]}),
-          rEdge || cEdge ? 0. : getA(${r[3]})`}class F7{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((f,m)=>f[0]+t[m]+f[1]);let o=t.length,s=Oe(o),c=e.map(f=>f[0]).join(","),l=e.map((f,m)=>f[0]+t[m]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
-        int start = ${c};
-        int end = ${l};
+    bool cEdge = cp1 >= ${cols};
+    bool rEdge = rp1 >= ${rows};
+  `}function getOutput(shape,dims){let rank=shape.length,sourceCoords=getSourceCoordsArr(rank,dims);return rank===1?`getA(rc),
+            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),
+            0, 0`:`getA(${sourceCoords[0]}),
+          cEdge ? 0. : getA(${sourceCoords[1]}),
+          rEdge ? 0. : getA(${sourceCoords[2]}),
+          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var PadProgram=class{constructor(xShape,paddings,constantValue){this.variableNames=["x"],this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,type=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),unpackedCoords=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,rank);if(rank===1){this.userCode=`
+        int start = ${start};
+        int end = ${end};
 
         void main() {
           int outC = getOutputCoords();
           if (outC < start || outC >= end) {
-            setOutput(float(${r}));
+            setOutput(float(${constantValue}));
           } else {
             setOutput(getX(outC - start));
           }
         }
       `;return}this.userCode=`
-      ${s} start = ${s}(${c});
-      ${s} end = ${s}(${l});
+      ${type} start = ${type}(${start});
+      ${type} end = ${type}(${end});
 
       void main() {
-        ${s} outC = getOutputCoords();
+        ${type} outC = getOutputCoords();
         if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
-          setOutput(float(${r}));
+          setOutput(float(${constantValue}));
         } else {
-          ${s} coords = outC - start;
-          setOutput(getX(${p}));
+          ${type} coords = outC - start;
+          setOutput(getX(${unpackedCoords}));
         }
       }
-    `}}class R7{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((N,S)=>N[0]+t[S]+N[1]);let o=t.length,s=Oe(o),c=e.map(N=>N[0]).join(","),l=e.map((N,S)=>N[0]+t[S]).join(","),p=Yn("rc",o),f=Yn("source",o),m=`${p[o-1]} < ${this.outputShape[o-1]}`,y=o===1?"source":`vec2(${f.slice(-2).join()})`,b=[`${s} rc = outputLoc;`,`${p[o-1]} += 1;
-       if(${m}) {
-      `,o===1?"":`}
+    `}},PadPackedProgram=class{constructor(xShape,paddings,constantValue){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),coords2=getChannels("rc",rank),source=getChannels("source",rank),cLimit=`${coords2[rank-1]} < ${this.outputShape[rank-1]}`,innerDims=rank===1?"source":`vec2(${source.slice(-2).join()})`,componentSetup=[`${dtype} rc = outputLoc;`,`${coords2[rank-1]} += 1;
+       if(${cLimit}) {
+      `,rank===1?"":`}
        rc = outputLoc;
-       ${p[o-2]} += 1;
-       if(${p[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":`  ${p[o-1]} += 1;
-         if(${m}) {`],v=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",T="";for(let N=0,S=o===1?2:4;N<S;N++)T+=`
-        ${b[N]}
-        if (${v}) {
-          result[${N}] = float(${r});
+       ${coords2[rank-2]} += 1;
+       if(${coords2[rank-2]} < ${this.outputShape[rank-2]}) {`,rank===1?"":`  ${coords2[rank-1]} += 1;
+         if(${cLimit}) {`],paddingArea=rank===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",mainLoop="";for(let i=0,j=rank===1?2:4;i<j;i++)mainLoop+=`
+        ${componentSetup[i]}
+        if (${paddingArea}) {
+          result[${i}] = float(${constantValue});
         } else {
-          ${s} source = rc - start;
-          result[${N}] = getChannel(getX(${f.join()}), ${y});
+          ${dtype} source = rc - start;
+          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
         }
-      `;T+=o===1?"} ":"}}",this.userCode=`
-      const ${s} start = ${s}(${c});
-      const ${s} end = ${s}(${l});
+      `;mainLoop+=rank===1?"} ":"}}",this.userCode=`
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
 
       void main() {
-        ${s} outputLoc = getOutputCoords();
+        ${dtype} outputLoc = getOutputCoords();
         vec4 result = vec4(0.);
-        ${T}
+        ${mainLoop}
         setOutput(result);
       }
-    `}}class ah{constructor(t,e,r,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let c=t.filterWidth,l=t.strideHeight,p=t.strideWidth,f=t.dilationHeight,m=t.dilationWidth,y=t.effectiveFilterHeight,b=t.effectiveFilterWidth,v=t.padInfo.top,T=t.padInfo.left;this.outputShape=t.outShape;let N=e==="avg",S=`((batch  * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,D=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,I="0.0";if(N||(I="-1.0 / 1e-20"),r){let H=">=";this.userCode=`
-        const ivec2 strides = ivec2(${l}, ${p});
-        const ivec2 pads = ivec2(${v}, ${T});
+    `}},Pool2DProgram=class{constructor(convInfo,poolType,computePositions,flattenPositions=!1,includeBatchInIndex=!1){if(this.variableNames=["x"],poolType==="avg"&&computePositions)throw new Error("Cannot compute positions for average pool.");let filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.outputShape=convInfo.outShape;let isAvgPool=poolType==="avg",batchFlattenPositionStr=`((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`,flattenPositionStr=`(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`,initializationValue="0.0";if(isAvgPool||(initializationValue="-1.0 / 1e-20"),computePositions){let compareOp2=">=";this.userCode=`
+        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+        const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
         void main() {
           ivec4 coords = getOutputCoords();
@@ -2479,19 +2484,19 @@ return (round(mod(b, 2.0)) != 1) ?
           int minMaxPosition = 0;
           float avgValue = 0.0;
 
-          for (int wR = 0; wR < ${y};
-              wR += ${f}) {
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
             int xR = xRCorner + wR;
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int wC = 0; wC < ${b};
-                wC += ${m}) {
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
               int xC = xCCorner + wC;
 
-              if (xC < 0 || xC >= ${t.inWidth}) {
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                 continue;
               }
 
@@ -2501,31 +2506,31 @@ return (round(mod(b, 2.0)) != 1) ?
               // use the current value.
               float currMinMaxValue = mix(
                   value, minMaxValue, minMaxValueFound);
-              if (value ${H} currMinMaxValue) {
+              if (value ${compareOp2} currMinMaxValue) {
                 minMaxValue = value;
                 minMaxValueFound = 1.0;
-                minMaxPosition = ${o?s?S:D:`wR * ${b} + wC`};
+                minMaxPosition = ${flattenPositions?includeBatchInIndex?batchFlattenPositionStr:flattenPositionStr:`wR * ${effectiveFilterWidth} + wC`};
               }
             }
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let P="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / count");let L=Math.floor(c/4)*4,B=c%4,q=`
-      if (${N}) {
+      `;return}let compareOp="max",returnValue=`${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;poolType==="avg"&&(returnValue="avgValue / count");let filterWidthNearestVec4=Math.floor(filterWidth/4)*4,filterWidthVec4Remainder=filterWidth%4,updateSnippet=`
+      if (${isAvgPool}) {
         avgValue += dot(values, ones);
       } else {
-        minMaxValue = ${P}(values, minMaxValue);
+        minMaxValue = ${compareOp}(values, minMaxValue);
       }
     `;this.userCode=`
-      const ivec2 strides = ivec2(${l}, ${p});
-      const ivec2 pads = ivec2(${v}, ${T});
-      const float initializationValue = ${I};
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
       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 >= ${t.inWidth}) {
+        if (xC < 0 || xC >= ${convInfo.inWidth}) {
           return initializationValue;
         }
         count += 1.0;
@@ -2543,33 +2548,33 @@ return (round(mod(b, 2.0)) != 1) ?
 
         // max/min x(?, ?, d) to get y(yR, yC, d).
         // ? = to be determined
-        vec4 minMaxValue = vec4(${I});
+        vec4 minMaxValue = vec4(${initializationValue});
         float avgValue = 0.0;
         count = 0.0;
 
-        for (int wR = 0; wR < ${y};
-            wR += ${f}) {
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
           int xR = xRCorner + wR;
 
-          if (xR < 0 || xR >= ${t.inHeight}) {
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
             continue;
           }
 
-          for (int wC = 0; wC < ${L}; wC += 4) {
-            int xC = xCCorner + wC * ${m};
+          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+            int xC = xCCorner + wC * ${dilationWidth};
 
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${m}, d),
-              getValue(batch, xR, xC + 2 * ${m}, d),
-              getValue(batch, xR, xC + 3 * ${m}, d)
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
+              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
             );
 
-            ${q}
+            ${updateSnippet}
           }
 
-          int xC = xCCorner + ${L};
-          if (${B===1}) {
+          int xC = xCCorner + ${filterWidthNearestVec4};
+          if (${filterWidthVec4Remainder===1}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               initializationValue,
@@ -2577,33 +2582,33 @@ return (round(mod(b, 2.0)) != 1) ?
               initializationValue
             );
 
-            ${q}
-          } else if (${B===2}) {
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder===2}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${m}, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
               initializationValue,
               initializationValue
             );
 
-            ${q}
-          } else if (${B===3}) {
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder===3}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${m}, d),
-              getValue(batch, xR, xC + 2 * ${m}, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
               initializationValue
             );
 
-            ${q}
+            ${updateSnippet}
           }
         }
-        setOutput(${E});
+        setOutput(${returnValue});
       }
-    `}}class w1{constructor(t,e,r,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let c=t.filterWidth,l=t.strideDepth,p=t.strideHeight,f=t.strideWidth,m=t.dilationDepth,y=t.dilationHeight,b=t.dilationWidth,v=t.effectiveFilterDepth,T=t.effectiveFilterHeight,N=t.effectiveFilterWidth,S=t.padInfo.front,D=t.padInfo.top,I=t.padInfo.left;this.outputShape=t.outShape;let P=e==="avg",E="0.0";if(P||(E="-1.0 / 1e-20"),r){let J=">=";this.userCode=`
+    `}},Pool3DProgram=class{constructor(convInfo,poolType,computePositions,flattenPositions=!1,includeBatchInIndex=!1){if(this.variableNames=["x"],poolType==="avg"&&computePositions)throw new Error("Cannot compute positions for average pool.");let filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.outputShape=convInfo.outShape;let isAvgPool=poolType==="avg",initializationValue="0.0";if(isAvgPool||(initializationValue="-1.0 / 1e-20"),computePositions){let compareOp2=">=";this.userCode=`
         const ivec3 strides =
-            ivec3(${l}, ${p}, ${f});
-        const ivec3 pads = ivec3(${S}, ${D}, ${I});
+            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
 
         void main() {
           ivec5 coords = getOutputCoords();
@@ -2621,27 +2626,27 @@ return (round(mod(b, 2.0)) != 1) ?
           float minMaxValueFound = 0.0;
           int minMaxPosition = 0;
 
-          for (int wD = 0; wD < ${v};
-              wD += ${m}) {
+          for (int wD = 0; wD < ${effectiveFilterDepth};
+              wD += ${dilationDepth}) {
             int xD = xDCorner + wD;
 
-            if (xD < 0 || xD >= ${t.inDepth}) {
+            if (xD < 0 || xD >= ${convInfo.inDepth}) {
               continue;
             }
 
-            for (int wR = 0; wR < ${T};
-                wR += ${y}) {
+            for (int wR = 0; wR < ${effectiveFilterHeight};
+                wR += ${dilationHeight}) {
               int xR = xRCorner + wR;
 
-              if (xR < 0 || xR >= ${t.inHeight}) {
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                 continue;
               }
 
-              for (int wC = 0; wC < ${N};
-                  wC += ${b}) {
+              for (int wC = 0; wC < ${effectiveFilterWidth};
+                  wC += ${dilationWidth}) {
                 int xC = xCCorner + wC;
 
-                if (xC < 0 || xC >= ${t.inWidth}) {
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                   continue;
                 }
 
@@ -2651,34 +2656,34 @@ return (round(mod(b, 2.0)) != 1) ?
                 // use the current value.
                 float currMinMaxValue = mix(
                     value, minMaxValue, minMaxValueFound);
-                if (value ${J} currMinMaxValue) {
+                if (value ${compareOp2} currMinMaxValue) {
                   minMaxValue = value;
                   minMaxValueFound = 1.0;
-                  minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${T} * ${N} +
-                      wR * ${N} + wC`};
+                  minMaxPosition = ${flattenPositions?includeBatchInIndex?`(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`:`((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`:`wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                      wR * ${effectiveFilterWidth} + wC`};
                 }
               }
             }
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let L="max",B=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(B="avgValue / count");let q=Math.floor(c/4)*4,H=c%4,Z=`
-      if (${P}) {
+      `;return}let compareOp="max",returnValue=`${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;poolType==="avg"&&(returnValue="avgValue / count");let filterWidthNearestVec4=Math.floor(filterWidth/4)*4,filterWidthVec4Remainder=filterWidth%4,updateSnippet=`
+      if (${isAvgPool}) {
         avgValue += dot(values, ones);
       } else {
-        minMaxValue = ${L}(values, minMaxValue);
+        minMaxValue = ${compareOp}(values, minMaxValue);
       }
     `;this.userCode=`
       const ivec3 strides =
-        ivec3(${l}, ${p}, ${f});
-      const ivec3 pads = ivec3(${S}, ${D}, ${I});
-      const float initializationValue = ${E};
+        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
       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 >= ${t.inWidth}) {
+        if (xC < 0 || xC >= ${convInfo.inWidth}) {
           return initializationValue;
         }
         count += 1.0;
@@ -2697,41 +2702,41 @@ return (round(mod(b, 2.0)) != 1) ?
 
         // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
         // ? = to be determined
-        vec4 minMaxValue = vec4(${E});
+        vec4 minMaxValue = vec4(${initializationValue});
         float avgValue = 0.0;
         count = 0.0;
 
-        for (int wD = 0; wD < ${v};
-            wD += ${m}) {
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
           int xD = xDCorner + wD;
 
-          if (xD < 0 || xD >= ${t.inDepth}) {
+          if (xD < 0 || xD >= ${convInfo.inDepth}) {
             continue;
           }
 
-          for (int wR = 0; wR < ${T};
-            wR += ${y}) {
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
             int xR = xRCorner + wR;
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int wC = 0; wC < ${q}; wC += 4) {
-              int xC = xCCorner + wC * ${b};
+            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+              int xC = xCCorner + wC * ${dilationWidth};
 
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${b}, ch),
-                getValue(batch, xD, xR, xC + 2 * ${b}, ch),
-                getValue(batch, xD, xR, xC + 3 * ${b}, ch)
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
               );
 
-              ${Z}
+              ${updateSnippet}
             }
 
-            int xC = xCCorner + ${q};
-            if (${H===1}) {
+            int xC = xCCorner + ${filterWidthNearestVec4};
+            if (${filterWidthVec4Remainder===1}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 initializationValue,
@@ -2739,57 +2744,57 @@ return (round(mod(b, 2.0)) != 1) ?
                 initializationValue
               );
 
-              ${Z}
-            } else if (${H===2}) {
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder===2}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${b}, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                 initializationValue,
                 initializationValue
               );
 
-              ${Z}
-            } else if (${H===3}) {
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder===3}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${b}, ch),
-                getValue(batch, xD, xR, xC + 2 * ${b}, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
                 initializationValue
               );
 
-              ${Z}
+              ${updateSnippet}
             }
           }
-          setOutput(${B});
+          setOutput(${returnValue});
         }
       }
-    `}}class zI{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:o,inSize:s,outSize:c}=t;this.outputShape=[o,c];let l="0.0",p="";e==="prod"?l="1.0":e==="min"?(l="1.0 / 1e-20",p="min"):e==="max"&&(l="-1.0 / 1e-20",p="max");let f=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?f="sumValue":e==="prod"?f="prodValue":e==="all"?f="allValue":e==="any"&&(f="anyValue");let m=Math.floor(r/4)*4,y=r%4,b=`
-      if (${e==="sum"}) {
+    `}},ReduceProgram=class{constructor(reduceInfo,reduceType){this.variableNames=["x"];let{windowSize,batchSize,inSize,outSize}=reduceInfo;this.outputShape=[batchSize,outSize];let initializationValue="0.0",compareOp="";reduceType==="prod"?initializationValue="1.0":reduceType==="min"?(initializationValue="1.0 / 1e-20",compareOp="min"):reduceType==="max"&&(initializationValue="-1.0 / 1e-20",compareOp="max");let returnValue=`${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;reduceType==="sum"?returnValue="sumValue":reduceType==="prod"?returnValue="prodValue":reduceType==="all"?returnValue="allValue":reduceType==="any"&&(returnValue="anyValue");let windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
+      if (${reduceType==="sum"}) {
         sumValue += dot(values, ones);
-      } else if (${e==="prod"}) {
+      } else if (${reduceType==="prod"}) {
         vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
         prodValue *= tmp[0] * tmp[1];
       } else {
-        minMaxValue = ${p}(values, minMaxValue);
+        minMaxValue = ${compareOp}(values, minMaxValue);
       }
-    `,v="vec4";e==="all"?(l="1.0",b=`
+    `,vecType="vec4";reduceType==="all"?(initializationValue="1.0",updateSnippet=`
         bool reducedAllValue = all(values);
         float floatedReducedAllValue = float(reducedAllValue);
         allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
-      `,v="bvec4"):e==="any"&&(l="0.0",b=`
+      `,vecType="bvec4"):reduceType==="any"&&(initializationValue="0.0",updateSnippet=`
         bool reducedAnyValue = any(values);
         float floatedReducedAnyValue = float(reducedAnyValue);
         anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
-      `,v="bvec4");let T="";s%r>0&&(T=`
-        if (inIdx < 0 || inIdx >= ${s}) {
+      `,vecType="bvec4");let checkOutOfBounds="";inSize%windowSize>0&&(checkOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
           return initializationValue;
         }
       `),this.userCode=`
-      const float initializationValue = ${l};
+      const float initializationValue = ${initializationValue};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
-        ${T}
+        ${checkOutOfBounds}
         return getX(batch, inIdx);
       }
 
@@ -2797,71 +2802,71 @@ return (round(mod(b, 2.0)) != 1) ?
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int outIdx = coords[1];
-        int inOffset = outIdx * ${r};
+        int inOffset = outIdx * ${windowSize};
 
-        vec4 minMaxValue = vec4(${l});
+        vec4 minMaxValue = vec4(${initializationValue});
         float prodValue = 1.0;
         float sumValue = 0.0;
         float allValue = 1.0;
         float anyValue = 0.0;
 
-        for (int i = 0; i < ${m}; i += 4) {
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
           int inIdx = inOffset + i;
-          ${v} values = ${v}(
+          ${vecType} values = ${vecType}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
             getValue(batch, inIdx + 2),
             getValue(batch, inIdx + 3)
           );
 
-          ${b}
+          ${updateSnippet}
         }
 
-        int inIdx = inOffset + ${m};
-        if (${y===1}) {
-          ${v} values = ${v}(
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===1}) {
+          ${vecType} values = ${vecType}(
             getValue(batch, inIdx),
             initializationValue,
             initializationValue,
             initializationValue
           );
 
-          ${b}
-        } else if (${y===2}) {
-          ${v} values = ${v}(
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===2}) {
+          ${vecType} values = ${vecType}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
             initializationValue,
             initializationValue
           );
 
-          ${b}
-        } else if (${y===3}) {
-          ${v} values = ${v}(
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===3}) {
+          ${vecType} values = ${vecType}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
             getValue(batch, inIdx + 2),
             initializationValue
           );
 
-          ${b}
+          ${updateSnippet}
         }
-        setOutput(${f});
+        setOutput(${returnValue});
       }
-    `}}class WI{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let r="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),r+=`
-        ${s}
-        ${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
+    `}},ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2===1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
+        ${thisRC}
+        ${i>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[${o}] =
+          result[${i}] =
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
-        ${o>0?"}":""}
+        ${i>0?"}":""}
       `}this.userCode=`
-      ${P7(e)}
-      ${b1(t)}
+      ${getReshapedInputCoords(inputShape)}
+      ${getFlatIndexFrom3D(outputShape)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -2869,19 +2874,19 @@ return (round(mod(b, 2.0)) != 1) ?
         vec4 result = vec4(0.);
 
         ivec3 thisRC;
-        int rows = ${t[1]};
-        int cols = ${t[2]};
+        int rows = ${outputShape[1]};
+        int cols = ${outputShape[2]};
 
-        ${r}
+        ${mainLoop}
 
         setOutput(result);
       }
-    `}}function P7(n){let t=xa(["r","c","d"],n);return`
+    `}};function getReshapedInputCoords(shape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape);return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t}
+      ${coordsFromIndexSnippet}
       return ivec3(r, c, d);
     }
-  `}class O7{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,o,s]=e.shape,[,c,l]=t.shape,p=[r&&c>1?o-1:o,r&&l>1?s-1:s],f=[r&&c>1?c-1:c,r&&l>1?l-1:l],m=p[0]/f[0],y=p[1]/f[1],b=1/m,v=1/y,T=Math.ceil(b)*2+2,N=Math.ceil(v)*2+2;this.userCode=`
+  `}var ResizeBilinearBackpropProgram=class{constructor(dy,x,alignCorners){this.variableNames=["dy"],this.outputShape=[],this.outputShape=x.shape;let[,xHeight,xWidth]=x.shape,[,yHeight,yWidth]=dy.shape,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -2891,14 +2896,14 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${m});
-        const float widthScale = float(${y});
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
 
-        const float invHeightScale = float(${b});
-        const float invWidthScale = float(${v});
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
 
-        const int winHeight = int(${T});
-        const int winWidth = int(${N});
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
 
         // Compute bounds for where in dy we will look
         float startRLerp = floor(float(r) * invHeightScale);
@@ -2912,7 +2917,7 @@ return (round(mod(b, 2.0)) != 1) ?
           int dyR = dyROffset + startDyR;
 
           // Guard against the window exceeding the bounds of dy
-          if (dyR < 0 || dyR >= ${c}) {
+          if (dyR < 0 || dyR >= ${yHeight}) {
             continue;
           }
 
@@ -2920,19 +2925,19 @@ return (round(mod(b, 2.0)) != 1) ?
             int dyC = dyCOffset + startDyC;
 
             // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${l}) {
+            if (dyC < 0 || dyC >= ${yWidth}) {
               continue;
             }
 
             float dxR = float(dyR) * heightScale;
             int topDxRIndex = int(floor(dxR));
-            int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));
+            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight-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), ${s-1}.0));
+            int rightDxCIndex = int(min(ceil(dxC), ${xWidth-1}.0));
             float dxCLerp = dxC - float(leftDxCIndex);
             float inverseDxCLerp = 1.0 - dxCLerp;
 
@@ -2962,11 +2967,11 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(accumulator);
       }
-    `}}class L7{constructor(t,e,r,o){this.variableNames=["A"],this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r];this.userCode=`
+    `}},ResizeBilinearProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${f[0]/m[0]},
-          ${f[1]/m[1]});
-      const vec2 inputShapeRC = vec2(${c}.0, ${l}.0);
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -2995,13 +3000,13 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(newValue);
       }
-    `}}class M7{constructor(t,e,r,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r];this.userCode=`
+    `}},ResizeBilinearPackedProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
-          ${f[0]/m[0]},
-          ${f[1]/m[1]},
-          ${f[1]/m[1]});
-      const vec3 inputShapeRC = vec3(${c}.0, ${l}.0,
-                                     ${l}.0);
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
+                                     ${oldWidth}.0);
 
       float getAValue(int b, int r, int c, int d) {
         return getChannel(getA(b, r, c, d), vec2(c, d));
@@ -3023,8 +3028,8 @@ return (round(mod(b, 2.0)) != 1) ?
           min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
 
         // Should we calculate next column and row elements in 2x2 packed cell.
-        bool hasNextCol = d < ${p-1};
-        bool hasNextRow = coords.z < ${r-1};
+        bool hasNextCol = d < ${depth-1};
+        bool hasNextRow = coords.z < ${newWidth-1};
 
         // In parallel, construct four corners for all four components in
         // packed 2x2 cell.
@@ -3072,7 +3077,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(newValue);
       }
-    `}}class B7{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,o,s]=e.shape,[,c,l]=t.shape,p=[r&&c>1?o-1:o,r&&l>1?s-1:s],f=[r&&c>1?c-1:c,r&&l>1?l-1:l],m=p[0]/f[0],y=p[1]/f[1],b=1/m,v=1/y,T=Math.ceil(b)*2+2,N=Math.ceil(v)*2+2;this.userCode=`
+    `}},ResizeNearestNeigborBackpropProgram=class{constructor(dy,x,alignCorners){this.variableNames=["dy"],this.outputShape=[],this.outputShape=x.shape;let[,xHeight,xWidth]=x.shape,[,yHeight,yWidth]=dy.shape,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3082,14 +3087,14 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${m});
-        const float widthScale = float(${y});
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
 
-        const float invHeightScale = float(${b});
-        const float invWidthScale = float(${v});
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
 
-        const int winHeight = int(${T});
-        const int winWidth = int(${N});
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
 
         // Compute bounds for where in dy we will look
         float startRLerp = floor(float(r) * invHeightScale);
@@ -3103,7 +3108,7 @@ return (round(mod(b, 2.0)) != 1) ?
           int dyR = dyROffset + startDyR;
 
           // Guard against the window exceeding the bounds of dy
-          if (dyR < 0 || dyR >= ${c}) {
+          if (dyR < 0 || dyR >= ${yHeight}) {
             continue;
           }
 
@@ -3111,26 +3116,26 @@ return (round(mod(b, 2.0)) != 1) ?
             int dyC = dyCOffset + startDyC;
 
             // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${l}) {
+            if (dyC < 0 || dyC >= ${yWidth}) {
               continue;
             }
 
             float sourceFracRow =
-              float(${p[0]}) *
-                (float(dyR) / float(${f[0]}));
+              float(${effectiveXSize[0]}) *
+                (float(dyR) / float(${effectiveYSize[0]}));
 
             float sourceFracCol =
-                float(${p[1]}) *
-                  (float(dyC) / float(${f[1]}));
+                float(${effectiveXSize[1]}) *
+                  (float(dyC) / float(${effectiveYSize[1]}));
 
             int sourceNearestRow = int(min(
-                float(int(${o}) - 1),
-                ${r} ? float(round(sourceFracRow)) :
+                float(int(${xHeight}) - 1),
+                ${alignCorners} ? float(round(sourceFracRow)) :
                                   float(floor(sourceFracRow))));
 
             int sourceNearestCol = int(min(
-                float(int(${s}) - 1),
-                ${r} ? float(round(sourceFracCol)) :
+                float(int(${xWidth}) - 1),
+                ${alignCorners} ? float(round(sourceFracCol)) :
                                   float(floor(sourceFracCol))));
 
             if (r == sourceNearestRow && c == sourceNearestCol) {
@@ -3142,11 +3147,11 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(accumulator);
       }
-    `}}class z7{constructor(t,e,r,o){this.variableNames=["A"],this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r],y=o?"0.5":"0.0";this.userCode=`
+    `}},ResizeNearestNeighborProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],roundBase=alignCorners?"0.5":"0.0";this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${f[0]/m[0]},
-          ${f[1]/m[1]});
-      const vec2 inputShapeRC = vec2(${c}.0, ${l}.0);
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -3159,90 +3164,90 @@ return (round(mod(b, 2.0)) != 1) ?
 
         // Compute the coordinators of nearest neighbor point.
         ivec2 sourceNearestRC = ivec2(
-          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${y})));
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
 
         float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
 
         setOutput(newValue);
       }
-    `}}class W7{constructor(t,e){this.variableNames=["x"];let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=t,r===1){this.userCode=`
+    `}},ReverseProgram=class{constructor(xShape,axis){this.variableNames=["x"];let rank=xShape.length;if(rank>4)throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);if(this.outputShape=xShape,rank===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
-          setOutput(getX(${t[0]} - coord - 1));
+          setOutput(getX(${xShape[0]} - coord - 1));
         }
-      `;return}let o=l=>e.indexOf(l)!==-1&&t[l]!==1?`${t[l]} - coords[${l}] - 1`:`coords[${l}]`,s=t.map((l,p)=>o(p)).join(","),c=Oe(r);this.userCode=`
+      `;return}let getInCoord=i=>axis.indexOf(i)!==-1&&xShape[i]!==1?`${xShape[i]} - coords[${i}] - 1`:`coords[${i}]`,inCoords=xShape.map((_,i)=>getInCoord(i)).join(","),type=getCoordsDataType(rank);this.userCode=`
       void main() {
-        ${c} coords = getOutputCoords();
-        setOutput(getX(${s}));
+        ${type} coords = getOutputCoords();
+        setOutput(getX(${inCoords}));
       }
-    `}}class V7{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=t;let o=Yn("rc",r),s=`${o[r-1]} + 1 < ${this.outputShape[r-1]}`,c=`${o[r-2]} + 1 < ${this.outputShape[r-2]}`,l=Oe(r);r===1?this.userCode=`
+    `}},ReversePackedProgram=class{constructor(xShape,axis){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let rank=xShape.length;if(rank>4)throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);this.outputShape=xShape;let channels=getChannels("rc",rank),nextColumn=`${channels[rank-1]} + 1 < ${this.outputShape[rank-1]}`,nextRow=`${channels[rank-2]} + 1 < ${this.outputShape[rank-2]}`,type=getCoordsDataType(rank);rank===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
-          result.r = getChannel(getX(${t[0]} - rc - 1),
-            ${t[0]} - rc - 1);
-          if(${s}){
-              result.g = getChannel(getX(${t[0]} - (rc  + 1) - 1),
-                ${t[0]} - (rc  + 1) - 1);
+          result.r = getChannel(getX(${xShape[0]} - rc - 1),
+            ${xShape[0]} - rc - 1);
+          if(${nextColumn}){
+              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
+                ${xShape[0]} - (rc  + 1) - 1);
           }
           setOutput(result);
         }
       `:this.userCode=`
         void main() {
-          ${l} rc = getOutputCoords();
+          ${type} rc = getOutputCoords();
           vec4 result = vec4(0.);
-          result.r = ${p(o.slice())};
-          if(${s}){
-            result.g = ${f(o.slice())};
+          result.r = ${getR(channels.slice())};
+          if(${nextColumn}){
+            result.g = ${getG(channels.slice())};
           }
-          if(${c}) {
-            result.b = ${m(o.slice())};
-            if(${s}) {
-              result.a = ${y(o.slice())};
+          if(${nextRow}) {
+            result.b = ${getB(channels.slice())};
+            if(${nextColumn}) {
+              result.a = ${getA(channels.slice())};
             }
           }
           setOutput(result);
         }
-    `;function p(T){return b(T)}function f(T){return T[r-1]="("+T[r-1]+" + 1)",b(T)}function m(T){return T[r-2]="("+T[r-2]+" + 1)",b(T)}function y(T){return T[r-1]="("+T[r-1]+" + 1)",T[r-2]="("+T[r-2]+" + 1)",b(T)}function b(T){let N=t.map((I,P)=>v(P,T)),S=N.join(","),D=N.slice(-2).join(",");return`getChannel(getX(${S}), vec2(${D}))`}function v(T,N){return e.indexOf(T)!==-1&&t[T]!==1?`${t[T]} - ${N[T]} - 1`:`${N[T]}`}}}class VI{constructor(t,e,r,o,s,c,l=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=c;let p=Oe(s.length),f=Oe(c.length),m="";r===1?m="i":r===2&&(m="i, j");let y=`getIndices(${m})`,b="";o===1?b="i":o===2&&(b="i, coords[1]");let v=`getUpdates(${b})`,T=e>1?"strides[j]":"strides";this.userCode=`
-        ${p} strides = ${p}(${s});
+    `;function getR(channels2){return getChannel(channels2)}function getG(channels2){return channels2[rank-1]="("+channels2[rank-1]+" + 1)",getChannel(channels2)}function getB(channels2){return channels2[rank-2]="("+channels2[rank-2]+" + 1)",getChannel(channels2)}function getA(channels2){return channels2[rank-1]="("+channels2[rank-1]+" + 1)",channels2[rank-2]="("+channels2[rank-2]+" + 1)",getChannel(channels2)}function getChannel(channels2){let inCoordsArray=xShape.map((_,i)=>getInCoord(i,channels2)),inCoords=inCoordsArray.join(","),innerDims=inCoordsArray.slice(-2).join(",");return`getChannel(getX(${inCoords}), vec2(${innerDims}))`}function getInCoord(i,channels1){return axis.indexOf(i)!==-1&&xShape[i]!==1?`${xShape[i]} - ${channels1[i]} - 1`:`${channels1[i]}`}}},ScatterProgram=class{constructor(updateSize,sliceDim,indicesRank,updatesRank,strides,shape,summingDupeIndex=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),indicesString="";indicesRank===1?indicesString="i":indicesRank===2&&(indicesString="i, j");let indicesSnippet=`getIndices(${indicesString})`,updatesString="";updatesRank===1?updatesString="i":updatesRank===2&&(updatesString="i, coords[1]");let updatesSnippet=`getUpdates(${updatesString})`,strideString=sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${stridesType} strides = ${stridesType}(${strides});
 
         void main() {
-          ${f} coords = getOutputCoords();
+          ${dtype} coords = getOutputCoords();
           float sum = 0.0;
           bool found = false;
-          for (int i = 0; i < ${t}; i++) {
+          for (int i = 0; i < ${updateSize}; i++) {
             int flattenedIndex = 0;
-            for (int j = 0; j < ${e}; j++) {
-              int index = round(${y});
-              flattenedIndex += index * ${T};
+            for (int j = 0; j < ${sliceDim}; j++) {
+              int index = round(${indicesSnippet});
+              flattenedIndex += index * ${strideString};
             }
             if (flattenedIndex == coords[0]) {
-              sum += ${v};
+              sum += ${updatesSnippet};
               found = true;
             }
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}}class G7{constructor(t,e){this.variableNames=["x","segmentIds"];let r=t.windowSize,o=t.batchSize,s=t.inSize,c=t.numSegments,l=c*Math.ceil(s/r);this.outputShape=[o,l];let p="0.0",f="sumValue",m=Math.floor(r/4)*4,y=r%4,b=`
+      `}},SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
         sumValue += dot(values, segFilter);
-    `,v="";s%r>0&&(v=`
-        if (inIdx < 0 || inIdx >= ${s}) {
+    `,checkValueOutOfBounds="";inSize%windowSize>0&&(checkValueOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
           return initializationValue;
         }
-      `);let T="";s%r>0&&(T=`
-        if (inIdx < 0 || inIdx >= ${s}) {
+      `);let checkSegmentIdOutOfBounds="";inSize%windowSize>0&&(checkSegmentIdOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
           return -1.0;
         }
       `),this.userCode=`
-      const float initializationValue = ${p};
+      const float initializationValue = ${initializationValue};
 
       float getValue(int batch, int inIdx) {
-        ${v}
+        ${checkValueOutOfBounds}
         return getX(batch, inIdx);
       }
 
       float getSegmentIdAtIndex(int inIdx) {
-        ${T}
+        ${checkSegmentIdOutOfBounds}
         return getSegmentIds(inIdx);
       }
 
@@ -3251,12 +3256,12 @@ return (round(mod(b, 2.0)) != 1) ?
         int batch = coords[0];
         int outIdx = coords[1];
         int inOffset = int(floor(float(outIdx) / float(
-          ${c})) * float(${r}));
-        int currentSeg = int(mod(float(outIdx), float(${c})));
+          ${numSegments})) * float(${windowSize}));
+        int currentSeg = int(mod(float(outIdx), float(${numSegments})));
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${m}; i += 4) {
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -3272,11 +3277,11 @@ return (round(mod(b, 2.0)) != 1) ?
             int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
           );
 
-          ${b}
+          ${updateSnippet}
         }
 
-        int inIdx = inOffset + ${m};
-        if (${y===1}) {
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===1}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             initializationValue,
@@ -3293,8 +3298,8 @@ return (round(mod(b, 2.0)) != 1) ?
             0
           );
 
-          ${b}
-        } else if (${y===2}) {
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -3309,8 +3314,8 @@ return (round(mod(b, 2.0)) != 1) ?
               0
           );
 
-          ${b}
-        } else if (${y===3}) {
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -3325,77 +3330,77 @@ return (round(mod(b, 2.0)) != 1) ?
             0
           );
 
-          ${b}
+          ${updateSnippet}
         }
-        setOutput(${f});
+        setOutput(${returnValue});
       }
-    `}}class U7{constructor(t,e,r){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)s="resRC",o="resRC";else{let l=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],f=[];for(let m=0;m<e.length;m++)f.push(`${l[m]}`),m<t&&p.push(`${l[m]}`);o=p.join(),s=f.join()}let c=Oe(r);this.userCode=`
+    `}},SelectProgram=class{constructor(cRank,shape,rank){this.variableNames=["c","a","b"],this.outputShape=shape;let cCoords,abCoords;if(rank>4)throw Error(`Where for rank ${rank} is not yet supported`);if(rank===1)abCoords="resRC",cCoords="resRC";else{let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w"],cCoordVars=[],abCoordVars=[];for(let i=0;i<shape.length;i++)abCoordVars.push(`${currentCoords[i]}`),i<cRank&&cCoordVars.push(`${currentCoords[i]}`);cCoords=cCoordVars.join(),abCoords=abCoordVars.join()}let dtype=getCoordsDataType(rank);this.userCode=`
       void main() {
-        ${c} resRC = getOutputCoords();
-        float cVal = getC(${o});
+        ${dtype} resRC = getOutputCoords();
+        float cVal = getC(${cCoords});
         if (cVal >= 1.0) {
-          setOutput(getA(${s}));
+          setOutput(getA(${abCoords}));
         } else {
-          setOutput(getB(${s}));
+          setOutput(getB(${abCoords}));
         }
       }
-    `}}class q7{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Oe(this.rank),r=`uniform int start[${this.rank}];`,o=H7(this.rank),s,c=t.map((l,p)=>`sourceLoc.${v1[p]} = start[${p}] + coords.${v1[p]};`);s=`
-        ${e} sourceLoc;
-        ${e} coords = getOutputCoords();
-        ${c.join(`
+    `}},SliceProgram=class{constructor(destSize){this.variableNames=["source"],this.outputShape=destSize,this.rank=destSize.length;let dtype=getCoordsDataType(this.rank),uniformPart=`uniform int start[${this.rank}];`,sourceCoords=getCoords2(this.rank),body,coordSum=destSize.map((_,i)=>`sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`);body=`
+        ${dtype} sourceLoc;
+        ${dtype} coords = getOutputCoords();
+        ${coordSum.join(`
 `)}
       `,this.userCode=`
-      ${r}
+      ${uniformPart}
       void main() {
-        ${s}
-        setOutput(getSource(${o}));
+        ${body}
+        setOutput(getSource(${sourceCoords}));
       }
-    `}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}let v1=["x","y","z","w","u","v"];function H7(n){if(n===1)return"sourceLoc";if(n<=6)return v1.slice(0,n).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${n} is not yet supported`)}class j7{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;let e=Oe(this.rank),r=Yn("coords",this.rank),o=Yn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,c=`getChannel(getSource(${o.join()}), ${s})`,l=`
-      result.x = ${c};
-      if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
-        ++${o[this.rank-1]};
-        result.y = ${c};
-        --${o[this.rank-1]};
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{if(this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null))return;gpgpu.gl.uniform1iv(this.startLoc,start)}}},coords=["x","y","z","w","u","v"];function getCoords2(rank){if(rank===1)return"sourceLoc";if(rank<=6)return coords.slice(0,rank).map(x=>"sourceLoc."+x).join(",");throw Error(`Slicing for rank ${rank} is not yet supported`)}var SlicePackedProgram=class{constructor(destSize){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=destSize,this.rank=destSize.length;let dtype=getCoordsDataType(this.rank),coords2=getChannels("coords",this.rank),sourceLoc=getChannels("sourceLoc",this.rank),innerDims=this.rank===1?"sourceLoc":`vec2(${sourceLoc.slice(-2).join()})`,getChannel=`getChannel(getSource(${sourceLoc.join()}), ${innerDims})`,upperRow=`
+      result.x = ${getChannel};
+      if (++${coords2[this.rank-1]} < ${destSize[this.rank-1]}) {
+        ++${sourceLoc[this.rank-1]};
+        result.y = ${getChannel};
+        --${sourceLoc[this.rank-1]};
       }
-    `,p=this.rank===1?"":`
-      --${r[this.rank-1]};
-      if (++${r[this.rank-2]} < ${t[this.rank-2]}) {
-        ++${o[this.rank-2]};
-        result.z = ${c};
-        if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
-          ++${o[this.rank-1]};
-          result.w = ${c};
+    `,lowerRow=this.rank===1?"":`
+      --${coords2[this.rank-1]};
+      if (++${coords2[this.rank-2]} < ${destSize[this.rank-2]}) {
+        ++${sourceLoc[this.rank-2]};
+        result.z = ${getChannel};
+        if (++${coords2[this.rank-1]} < ${destSize[this.rank-1]}) {
+          ++${sourceLoc[this.rank-1]};
+          result.w = ${getChannel};
         }
       }
-    `,f=this.rank<=4?`sourceLoc = coords +
-            ${e}(${t.map((m,y)=>`start[${y}]`).join()});`:t.map((m,y)=>`${o[y]} = ${r[y]} + start[${y}];`).join(`
+    `,sourceLocSetup=this.rank<=4?`sourceLoc = coords +
+            ${dtype}(${destSize.map((_,i)=>`start[${i}]`).join()});`:destSize.map((_,i)=>`${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join(`
 `);this.userCode=`
       uniform int start[${this.rank}];
       void main() {
-        ${e} coords = getOutputCoords();
-        ${e} sourceLoc;
-        ${f}
+        ${dtype} coords = getOutputCoords();
+        ${dtype} sourceLoc;
+        ${sourceLocSetup}
         vec4 result = vec4(0.);
-        ${l}
-        ${p}
+        ${upperRow}
+        ${lowerRow}
         setOutput(result);
       }
-    `}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}class K7{constructor(t,e,r){this.variableNames=["x"],this.outputShape=r;let o=r.length,s=Oe(r.length),c=Oe(r.length),l="";if(o===1)l="coords * strides + begin";else{let p=0;l=r.map((f,m)=>(p++,r.length===1?`coords * strides[${m}] + begin[${m}]`:`coords[${p-1}] * strides[${m}] + begin[${m}]`)).join(",")}this.userCode=`
-      ${s} begin = ${s}(${t});
-      ${s} strides = ${s}(${e});
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{if(this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null))return;gpgpu.gl.uniform1iv(this.startLoc,start)}}},StridedSliceProgram=class{constructor(begin,strides,size){this.variableNames=["x"],this.outputShape=size;let rank=size.length,inputDtype=getCoordsDataType(size.length),dtype=getCoordsDataType(size.length),newCoords="";if(rank===1)newCoords="coords * strides + begin";else{let outputAxis=0;newCoords=size.map((_,i)=>(outputAxis++,size.length===1?`coords * strides[${i}] + begin[${i}]`:`coords[${outputAxis-1}] * strides[${i}] + begin[${i}]`)).join(",")}this.userCode=`
+      ${inputDtype} begin = ${inputDtype}(${begin});
+      ${inputDtype} strides = ${inputDtype}(${strides});
 
       void main() {
-        ${c} coords = getOutputCoords();
-        setOutput(getX(${l}));
+        ${dtype} coords = getOutputCoords();
+        setOutput(getX(${newCoords}));
       }
-    `}}class X7{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,r){let o=UI(e,r),s=qI(t,o,r);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let c=GI(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=c,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let l;return o===On.PACKED_2X2_FLOAT32?l=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===On.PACKED_2X2_FLOAT16?l=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===On.UNPACKED_FLOAT32?l=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===On.UNPACKED_FLOAT16?l=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===On.PACKED_4X1_UNSIGNED_BYTE&&(l=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(l),this.numUsedTextures++,this._numBytesAllocated+=c,this.log(),l}releaseTexture(t,e,r,o){if(this.freeTextures==null)return;let s=UI(r,o),c=qI(e,s,o);c in this.freeTextures||(this.freeTextures[c]=[]);let l=GI(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),p=ct().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t),this._numBytesAllocated-=l):(this.freeTextures[c].push(t),this.numFreeTextures++,this._numBytesFree+=l),this.numUsedTextures--;let f=this.usedTextures[c],m=f.indexOf(t);if(m<0)throw new Error("Cannot release a texture that was never provided by this texture manager");f.splice(m,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function Y7(n,t){let e=n;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===n.RGBA)return 16;if(t===e.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function GI(n,t,e,r,o){let s=J7(t,r),c;if(o){let[p,f]=nl(n[0],n[1]);c=p*f}else{let[p,f]=rh(n[0],n[1]);c=p*f}let l=Y7(e,s);return c*l}function J7(n,t){switch(n){case On.PACKED_2X2_FLOAT32:return LI(t);case On.PACKED_2X2_FLOAT16:return MI(t);case On.UNPACKED_FLOAT32:return RI(t);case On.UNPACKED_FLOAT16:return PI(t);case On.PACKED_4X1_UNSIGNED_BYTE:return OI(t);default:throw new Error(`Unknown physical texture type ${n}`)}}function Z7(n){return ct().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?n?On.PACKED_2X2_FLOAT32:On.UNPACKED_FLOAT32:n?On.PACKED_2X2_FLOAT16:On.UNPACKED_FLOAT16}function UI(n,t){if(n===Mr.UPLOAD)return On.PACKED_2X2_FLOAT32;if(n===Mr.RENDER||n==null)return Z7(t);if(n===Mr.DOWNLOAD||n===Mr.PIXELS)return On.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${n}`)}function qI(n,t,e){return`${n[0]}_${n[1]}_${t}_${e}`}class Q7{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let c=0;c<r.length;c++)r[c]=t[c]*e[c];this.outputShape=r,this.rank=r.length;let o=Oe(this.rank),s=tJ(t);this.userCode=`
+    `}},TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures==null)return;let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}log(){if(!this.logEnabled)return;let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}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(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F)return 16;if(internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement22=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement22}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var TileProgram=class{constructor(aShape,reps){this.variableNames=["A"];let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[i]*reps[i];this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),sourceCoords=getSourceCoords3(aShape);this.userCode=`
       void main() {
-        ${o} resRC = getOutputCoords();
-        setOutput(getA(${s}));
+        ${dtype} resRC = getOutputCoords();
+        setOutput(getA(${sourceCoords}));
       }
-    `}}function tJ(n){let t=n.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${n[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let o=0;o<n.length;o++)r.push(`imod(${e[o]}, ${n[o]})`);return r.join()}class ue{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.userCode=`
+    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       float unaryOperation(float x) {
-        ${e}
+        ${opSnippet}
       }
 
       void main() {
@@ -3404,22 +3409,22 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(y);
       }
-    `}}let _s="if (isnan(x)) return x;",eJ="return x;",HI="return abs(x);",jI=_s+`
+    `}},CHECK_NAN_SNIPPET3="if (isnan(x)) return x;",LINEAR="return x;",ABS="return abs(x);",RELU=CHECK_NAN_SNIPPET3+`
   return (x < 0.0) ? 0.0 : x;
-`,KI=_s+`
+`,RELU6=CHECK_NAN_SNIPPET3+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,XI="return (x >= 0.0) ? x : (exp(x) - 1.0);",nJ=`
+`,ELU2="return (x >= 0.0) ? x : (exp(x) - 1.0);",SELU=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
-  float scaleAlpha = ${zd};
-  float scale = ${Wd};
+  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};
+  float scale = ${backend_util_exports.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`;function rJ(n=0){return _s+`
-    return x > 0.0 ? 1.0 : float(${n});
-  `}let YI="return -x;",JI="return ceil(x);",ZI="return floor(x);",oJ=`
+`;function STEP(alpha=0){return CHECK_NAN_SNIPPET3+`
+    return x > 0.0 ? 1.0 : float(${alpha});
+  `}var NEG="return -x;",CEIL="return ceil(x);",FLOOR="return floor(x);",SIGN=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,sJ="return float(isnan(x));",iJ="return float(isinf(x));",aJ="return float(!isnan(x) && !isinf(x));",cJ=`
+`,IS_NAN="return float(isnan(x));",IS_INF="return float(isinf(x));",IS_FINITE="return float(!isnan(x) && !isinf(x));",ROUND=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3434,8 +3439,8 @@ return (round(mod(b, 2.0)) != 1) ?
       return base + 1.0;
     }
   }
-`,QI="return exp(x);",tE="return exp(x) - 1.0;",lJ=`if (x < 0.0) return NAN;
-  return log(x);`,uJ="return log(1.0 + x);",pJ="return sqrt(x);",hJ="return inversesqrt(x);",fJ="return 1.0 / (1.0 + exp(-1.0 * x));",dJ=`
+`,EXP="return exp(x);",EXPM1="return exp(x) - 1.0;",LOG=`if (x < 0.0) return NAN;
+  return log(x);`,LOG1P="return log(1.0 + x);",SQRT="return sqrt(x);",RSQRT="return inversesqrt(x);",SIGMOID="return 1.0 / (1.0 + exp(-1.0 * x));",SOFTPLUS=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3455,47 +3460,47 @@ return (round(mod(b, 2.0)) != 1) ?
     result = log(exp_x + 1.0);
   }
   return result;
-`,mJ=_s+`
+`,ASIN=CHECK_NAN_SNIPPET3+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,gJ=_s+`
+`,ACOS=CHECK_NAN_SNIPPET3+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,yJ=_s+`
+`,ATAN=CHECK_NAN_SNIPPET3+`
   return atan(x);
-`,bJ=`
+`,SINH=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,xJ=`
+`,COSH=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,wJ=`
+`,TANH=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,vJ=_s+"return log(x + sqrt(x * x + 1.0));",TJ=_s+`
+`,ASINH=CHECK_NAN_SNIPPET3+"return log(x + sqrt(x * x + 1.0));",ACOSH=CHECK_NAN_SNIPPET3+`
   if (x < 1.0) return NAN;
-  return log(x + sqrt(x * x - 1.0));`,kJ=_s+`
+  return log(x + sqrt(x * x - 1.0));`,ATANH=CHECK_NAN_SNIPPET3+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,NJ=`
+  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ERF=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
-  float p = ${Yw};
-  float a1 = ${Jw};
-  float a2 = ${Zw};
-  float a3 = ${Qw};
-  float a4 = ${tv};
-  float a5 = ${ev};
+  float p = ${backend_util_exports.ERF_P};
+  float a1 = ${backend_util_exports.ERF_A1};
+  float a2 = ${backend_util_exports.ERF_A2};
+  float a3 = ${backend_util_exports.ERF_A3};
+  float a4 = ${backend_util_exports.ERF_A4};
+  float a5 = ${backend_util_exports.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));
-`,_J="return 1.0 / x;",CJ="return float(!(x >= 1.0));",Bm="return x;";let SJ="return x;",$J=`
+`,RECIPROCAL="return 1.0 / x;",LOGICAL_NOT="return float(!(x >= 1.0));",CLONE="return x;",LINEAR2="return x;",LOG2=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -3504,7 +3509,7 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,eE=`
+`,RELU2=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3514,7 +3519,7 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,nE=`
+`,RELU62=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3524,7 +3529,7 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,rE=`
+`,ELU3=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3533,9 +3538,9 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`;class ch{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=`
+`,UnaryOpPackedProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=aShape,this.userCode=`
       vec4 unaryOperation(vec4 x) {
-        ${e}
+        ${opSnippet}
       }
 
       void main() {
@@ -3544,104 +3549,104 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(y);
       }
-    `}}class IJ{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;let e=t.length,r=Yn("rc",e),o=Oe(e),s=bX(e,r),c=r.slice(-2),l=e<=1?"rc":`vec2(${c.join(",")})`;this.userCode=`
+    `}},UnpackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=outputShape;let rank=outputShape.length,channels=getChannels("rc",rank),dtype=getCoordsDataType(rank),sourceCoords=getSourceCoords(rank,channels),innerDims=channels.slice(-2),coords2=rank<=1?"rc":`vec2(${innerDims.join(",")})`;this.userCode=`
       void main() {
-        ${o} rc = getOutputCoords();
-        vec4 packedInput = getA(${s});
+        ${dtype} rc = getOutputCoords();
+        vec4 packedInput = getA(${sourceCoords});
 
-        setOutput(getChannel(packedInput, ${l}));
+        setOutput(getChannel(packedInput, ${coords2}));
       }
-    `}}let{segment_util:oE}=ov,EJ=sv,DJ=iv,AJ=av,FJ=Id,RJ=1e-7,PJ=1e-4,zm={};function OJ(n){return n in zm||(zm[n]={}),zm[n]}function Wm(n,t=!1){if(n==="linear")return t?SJ:eJ;if(n==="relu")return t?eE:jI;if(n==="elu")return t?rE:XI;if(n==="relu6")return t?nE:KI;if(n==="prelu")return t?SI:CI;throw new Error(`Activation ${n} has not been implemented for the WebGL backend.`)}let LJ=128,MJ=600;function BJ(){return ct().global.screen==null?1024:ct().global.screen.height*ct().global.screen.width*window.devicePixelRatio*MJ/1024/1024}let sE=1e3;class zJ extends d{constructor(t){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,!ct().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(t==null){let e=jo(ct().getNumber("WEBGL_VERSION"));this.binaryCache=OJ(ct().getNumber("WEBGL_VERSION")),this.gpgpu=new m7(e),this.canvas=e.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=t,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=t.gl.canvas;this.textureManager=new X7(this.gpgpu),this.numMBBeforeWarning=BJ(),this.texData=new h(this,ps())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(t,e,r){if((ct().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ct().getBool("DEBUG"))&&this.checkNumericalProblems(t),r==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={};return this.texData.set(o,{shape:e,dtype:r,values:t,usage:Mr.UPLOAD,refCount:1,complexParentRefCount:0}),o}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,r,o){if(ct().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:r,dtype:o,values:e,usage:Mr.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(t){let e=t.dataId;if(this.texData.has(e)){let r=this.texData.get(e);r.refCount--,r.refCount<1&&this.disposeData(e)}}readSync(t){let e=this.texData.get(t),{values:r,dtype:o,complexTensorInfos:s,slice:c,shape:l,isPacked:p}=e;if(c!=null){let b;p?b=new ch(l,Bm):b=new ue(l,Bm);let v=this.runWebGLProgram(b,[{dataId:t,shape:l,dtype:o}],o),T=this.readSync(v.dataId);return this.disposeIntermediateTensorInfo(v),T}if(r!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return r;let f=this.activeTimers!=null,m;f&&(m=or());let y;if(o==="complex64"){let b=this.readSync(s.real.dataId),v=this.readSync(s.imag.dataId);y=ys(b,v)}else y=this.getValuesFromTexture(t);return f&&(this.downloadWaitMs+=or()-m),this.convertAndCacheOnCPU(t,y)}async read(t){if(this.pendingRead.has(t)){let T=this.pendingRead.get(t);return new Promise(N=>T.push(N))}let e=this.texData.get(t),{values:r,shape:o,slice:s,dtype:c,complexTensorInfos:l,isPacked:p}=e;if(s!=null){let T;p?T=new ch(o,Bm):T=new ue(o,Bm);let N=this.runWebGLProgram(T,[{dataId:t,shape:o,dtype:c}],c),S=this.read(N.dataId);return this.disposeIntermediateTensorInfo(N),S}if(r!=null)return this.convertAndCacheOnCPU(t);if(!ct().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ct().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let f=null,m;if(c!=="complex64"&&ct().get("WEBGL_BUFFER_SUPPORTED")){m=this.decode(t);let T=this.texData.get(m.dataId);f=this.gpgpu.createBufferFromTexture(T.texture,...oh(o))}this.pendingRead.set(t,[]),c!=="complex64"&&await this.gpgpu.createAndWaitForFence();let y;if(c==="complex64"){let T=await Promise.all([this.read(l.real.dataId),this.read(l.imag.dataId)]),N=T[0],S=T[1];y=ys(N,S)}else if(f==null)y=this.getValuesFromTexture(t);else{let T=G(o);y=this.gpgpu.downloadFloat32MatrixFromBuffer(f,T)}m!=null&&this.disposeIntermediateTensorInfo(m);let b=this.convertAndCacheOnCPU(t,y),v=this.pendingRead.get(t);return this.pendingRead.delete(t),v.forEach(T=>T(b)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t),this.pendingDeletes--),b}checkNumericalProblems(t){if(t==null)return;for(let e=0;e<t.length;e++){let r=t[e];if(!E8(r))throw ct().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:r,isPacked:o}=this.texData.get(t),s=G(e);if(ct().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let b=this.decode(t),v=this.texData.get(b.dataId),T=this.gpgpu.downloadMatrixFromPackedTexture(v.texture,...oh(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(b),T}let c=ct().getBool("WEBGL_PACK")&&o===!0,l=c?m1(e):e,p=c?new HY(l):new qY(l),f=this.runWebGLProgram(p,[{shape:l,dtype:r,dataId:t}],"float32"),m=this.texData.get(f.dataId),y=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(m.texture,m.texShape[0],m.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(f),y}async time(t){let e=this.activeTimers,r=[],o=!1;this.programTimersStack==null?(this.programTimersStack=r,o=!0):this.activeTimers.push(r),this.activeTimers=r,t();let s=tt(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),c=tt(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let l={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);l.kernelMs=C(p),l.getExtraProfileInfo=()=>p.map((f,m)=>({name:c[m],ms:f})).map(f=>`${f.name}: ${f.ms}`).join(", ")}else l.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,l}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:or(),endMs:null}}endTimer(t){return ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=or(),t)}async getQueryTime(t){if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t){if(this.pendingDisposal.has(t))return;if(this.pendingRead.has(t)){this.pendingDisposal.add(t),this.pendingDeletes++;return}if(!this.texData.has(t))return;if(this.texData.get(t).complexParentRefCount>0){this.texData.get(t).refCount--;return}this.releaseGPUData(t);let{complexTensorInfos:e}=this.texData.get(t);e!=null&&(this.texData.get(e.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(e.real),this.texData.get(e.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(e.imag)),this.texData.delete(t)}releaseGPUData(t){let{texture:e,dtype:r,texShape:o,usage:s,isPacked:c,slice:l}=this.texData.get(t),p=l&&l.origDataId||t,f=this.dataRefCount.get(p);f>1?this.dataRefCount.set(p,f-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,r),this.textureManager.releaseTexture(e,o,s,c)));let m=this.texData.get(t);m.texture=null,m.texShape=null,m.isPacked=!1,m.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture}getDataInfo(t){return this.texData.get(t)}getCPUBackend(){return ct().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=ps().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(t,e=LJ){let r=this.getCPUBackend();return!this.warnedAboutCPUBackend&&r==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),r!=null&&t.every(o=>this.texData.get(o.dataId).texture==null&&G(o.shape)<e)}getGPGPUContext(){return this.gpgpu}slice(t,e,r){if(this.shouldExecuteOnCPU([t])){let c=hX(this.texData.get(t.dataId).values,e,r,t.shape,t.dtype);return this.makeOutput(r,t.dtype,c)}if(G(r)===0)return un([],r,t.dtype);let{isPacked:o}=this.texData.get(t.dataId),s=Yx(t.shape,e,r);if(o||!s){let c=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new j7(r):new q7(r),l=c.getCustomSetupFunc(e);return this.compileAndRun(c,[t],null,l)}return this.uploadToGPU(t.dataId),this.shallowSlice(t,e,r)}shallowSlice(t,e,r){let o=this.texData.get(t.dataId),s=this.makeOutput(r,t.dtype),c=this.texData.get(s.dataId);Object.assign(c,o),c.shape=r,c.dtype=t.dtype;let l=Jx(e,t.strides);o.slice&&(l+=o.slice.flatOffset),c.slice={flatOffset:l,origDataId:o.slice&&o.slice.origDataId||t.dataId};let p=this.dataRefCount.get(c.slice.origDataId)||1;return this.dataRefCount.set(c.slice.origDataId,p+1),s}stridedSlice(t,e,r,o){let s=this.tryRunOnCpuOrThrow([t],()=>this.cpuBackend.stridedSlice(t,e,r,o));if(s)return s;let c=Zf(e,r,o);if(c.some(p=>p===0))return un([],c);let l=new K7(e,o,c);return this.compileAndRun(l,[t])}reverse(t,e){let r=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new V7(t.shape,e):new W7(t.shape,e);return this.compileAndRun(r,[t])}neg(t){let e=this.tryRunOnCpuOrThrow([t],()=>this.cpuBackend.neg(t));if(e)return e;if(ct().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,YI,t.dtype);let r=new ue(t.shape,YI);return this.compileAndRun(r,[t])}batchMatMul(t,e,r,o){let s=r?t.shape[2]:t.shape[1],c=o?e.shape[1]:e.shape[2],l=r?t.shape[1]:t.shape[2],p=Math.max(t.shape[0],e.shape[0]);if((s===1||c===1)&&l>sE){r&&(t=Kt(t,[0,2,1])),o&&(e=Kt(e,[0,2,1]));let y=c===1?t:t.as3D(p,l,1),b=c===1?2:1,v=c===1?e.as3D(p,1,l):e,T=nt(y,v);return T.sum(b,!0)}let f=jn(t.dtype,e.dtype),m=new x1(t.shape,e.shape,[p,s,c],r,o);return this.compileAndRun(m,[t,e],f)}fusedBatchMatMul({a:t,b:e,transposeA:r,transposeB:o,bias:s,activation:c,preluActivationWeights:l}){let p=r?t.shape[2]:t.shape[1],f=o?e.shape[1]:e.shape[2],m=Math.max(t.shape[0],e.shape[0]),y=jn(t.dtype,e.dtype),b=s!=null,v=l!=null,T=c?Wm(c,!0):null,N=new x1(t.shape,e.shape,[m,p,f],r,o,b,T,v),S=[t,e];return s&&S.push(s),l&&S.push(l),this.compileAndRun(N,S,y)}localResponseNormalization4D(t,e,r,o,s){let c=ct().getBool("WEBGL_PACK_NORMALIZATION")?new k7(t.shape,e,r,o,s):new v7(t.shape,e,r,o,s);return this.compileAndRun(c,[t])}LRNGrad(t,e,r,o,s,c,l){let p=new T7(e.shape,o,s,c,l);return this.compileAndRun(p,[e,r,t])}tile(t,e){if(t.dtype==="string"){let o=this.readSync(t.dataId),s=o.map(l=>Uu(l)),c=Se(t.shape,t.dtype,s);return DJ(c,e)}let r=new Q7(t.shape,e);return this.compileAndRun(r,[t])}pad(t,e,r){let o=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new R7(t.shape,e,r):new F7(t.shape,e,r);return 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m.shape[1]===1?m:this.reduce(m,e,r)}argReduce(t,e,r=null){let o=t.shape[0],s=t.shape[1];r!=null&&(o=r.shape[0],s=r.shape[1]);let c=sp(s),l={windowSize:c,inSize:s,batchSize:o,outSize:Math.ceil(s/c)},p=new yX(l,e,r==null),f=[t];r!=null&&f.push(r);let m=this.compileAndRun(p,f,"int32");return m.shape[1]===1?m:this.argReduce(t,e,m)}argReducePacked(t,e,r=null){let o=r!=null?r.shape:t.shape,s=o[o.length-1],c=sp(s),l=new eY(o,c,e,r==null),p=r==null?[t]:[t,r],f=this.compileAndRun(l,p,"int32");return f.rank===t.rank?this.argReducePacked(t,e,f):f}sum(t,e){sr("sum",e,t.rank);let[r,o]=Fn(t.shape,e),s=G(o),c=t.as2D(-1,s),l=Vf(t.dtype);return this.reduce(c,"sum",l).reshape(r)}prod(t,e){let r=this.tryRunOnCpuOrThrow([t],()=>this.cpuBackend.prod(t,e));if(r)return r;let[o,s]=Fn(t.shape,e),c=G(s),l=t.as2D(-1,c),p=Vf(t.dtype);return this.reduce(l,"prod",p).reshape(o)}unsortedSegmentSum(t,e,r){let o=0,s=ir([o],t.rank),c=t;s!=null&&(c=Kt(t,s),o=xr(1,t.rank)[0]);let l=oE.computeOutShape(c.shape,o,r),p=G([c.shape[o]]),f=c.as2D(-1,p),m=Vf(t.dtype),y=this.segOpCompute(f,"unsortedSegmentSum",e,m,r).reshape(l);return s!=null&&(y=Kt(y,Ju(s))),y}segOpCompute(t,e,r,o,s){let c=t.shape[0],l=t.shape[1],p=oE.segOpComputeOptimalWindowSize(l,s),f={windowSize:p,inSize:l,batchSize:c,numSegments:s},m=new G7(f,e),y=this.compileAndRun(m,[t,r],o);return y.shape[1]===s?y:(r=hp(0,s).tile([l/p]),this.segOpCompute(y,e,r,o,s))}argMinMaxReduce(t,e,r){let o=[e];if(sr("arg"+r.charAt(0).toUpperCase()+r.slice(1),o,t.rank),!ct().getBool("WEBGL_PACK_REDUCE")||t.rank<=2){let[s,c]=Fn(t.shape,o),l=G(c),p=t.as2D(-1,l);return this.argReduce(p,r).reshape(s)}return this.argReducePacked(t,r)}argMin(t,e){return this.argMinMaxReduce(t,e,"min")}argMax(t,e){return this.argMinMaxReduce(t,e,"max")}cumsum(t,e,r,o){if(e!==t.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${t.rank-1} but got axis=${e}`);let s=t.shape[e],c=t;for(let l=0;l<=Math.ceil(Math.log2(s))-1;l++){let 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y.reshape(r)}gatherND(t,e){let r=e.shape,o=r[r.length-1],[s,c,l,p]=Yf(t,e),f=e.reshape([c,o]),m=t.reshape([t.size/l,l]),y=new ZY(o,p,[c,l]),b=this.compileAndRun(y,[m,f]);return b.reshape(s)}fill(t,e,r){if(r=r||ic(e),r==="string"){let o=rr(r,G(t));return o.fill(e),ps().makeTensor(o,t,r,this)}else{let o=new XY(t,e),s=o.getCustomSetupFunc(e);return this.compileAndRun(o,[],r,s)}}onesLike(t){if(t.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(t.shape,1,t.dtype)}zerosLike(t){return this.fill(t.shape,t.dtype==="string"?"":0,t.dtype)}linspace(t,e,r){return rv(t,e,r)}makeTensorInfo(t,e,r){let o=this.write(r,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,r){let{dataId:o}=this.makeTensorInfo(t,e,r);return ps().makeTensorFromDataId(o,t,e,this)}unpackTensor(t){let e=new IJ(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new $7(t.shape),r=!0;return this.runWebGLProgram(e,[t],t.dtype,null,r)}packedReshape(t,e){let r=[rl(t.shape),...ol(t.shape)],o={dtype:t.dtype,shape:r,dataId:t.dataId},s=[rl(e),...ol(e)],c=new WI(s,r),l=!0,p=this.runWebGLProgram(c,[o],t.dtype,null,l);return{dataId:p.dataId,shape:e,dtype:p.dtype}}decode(t){let e=this.texData.get(t),{isPacked:r,shape:o,dtype:s}=e,c=m1(o),l;r?l=new VY(c):l=new WY(c);let p=!0,f=this.runWebGLProgram(l,[{shape:c,dtype:s,dataId:t}],s,null,p);return{dtype:s,shape:o,dataId:f.dataId}}runWebGLProgram(t,e,r,o,s=!1){let c=this.makeTensorInfo(t.outputShape,r),l=this.texData.get(c.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===nh.DENSE){let N=oh(t.outputShape);l.texShape=N.map(S=>S*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),G(c.shape)===0)return l.values=Ce(c.dtype,0),c;let p=[],f=e.map(N=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let S=this.texData.get(N.dataId);if(S.texture==null){if(!t.packedInputs&&G(N.shape)<=ct().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:N.shape,texData:null,isUniform:!0,uniformValues:S.values};t.packedInputs&&(S.isPacked=!0,S.shape=N.shape)}else if(!!S.isPacked!==!!t.packedInputs)N=S.isPacked?this.unpackTensor(N):this.packTensor(N),p.push(N),S=this.texData.get(N.dataId);else if(S.isPacked&&!Rm(S.shape,N.shape)){let D=N,I=N.shape;N.shape=S.shape,N=this.packedReshape(N,I),p.push(N),S=this.texData.get(N.dataId),D.shape=I}return this.uploadToGPU(N.dataId),{shape:N.shape,texData:S,isUniform:!1}});this.uploadToGPU(c.dataId);let m={shape:c.shape,texData:l,isUniform:!1},y=x7(t,f,m),b=this.getAndSaveBinary(y,()=>y7(this.gpgpu,t,f,m)),v=this.activeTimers!=null,T;if(v&&(T=this.startTimer()),b7(this.gpgpu,b,f,m,o),p.forEach(N=>this.disposeIntermediateTensorInfo(N)),v&&(T=this.endTimer(T),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(T)})),!ct().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let N=this.unpackTensor(c);return this.disposeIntermediateTensorInfo(c),N}return c}compileAndRun(t,e,r,o,s=!1){r=r||e[0].dtype;let c=this.runWebGLProgram(t,e,r,o,s);return ps().makeTensorFromDataId(c.dataId,c.shape,c.dtype)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!ct().getBool("IS_TEST")){let t=Object.keys(this.binaryCache);t.forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})}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=rt(()=>{if(!ct().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=ct().getBool("DEBUG");ct().set("DEBUG",!1);let e=this.abs(Et(1e-8)).dataSync()[0];if(ct().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?RJ:PJ}uploadToGPU(t){let e=this.texData.get(t),{shape:r,dtype:o,values:s,texture:c,usage:l,isPacked:p}=e;if(c!=null)return;let f=this.activeTimers!=null,m;f&&(m=or());let y=e.texShape;if(y==null&&(y=K8(r,p),e.texShape=y),s!=null){let b=m1(r),v,T=y[1],N=y[0],S=s instanceof Uint8Array;p?([T,N]=nl(y[0],y[1]),v=new KY(b,[N,T],S)):v=new jY(b,[N,T],S);let D=this.makeTensorInfo([N,T],o);S?this.texData.get(D.dataId).usage=Mr.PIXELS:this.texData.get(D.dataId).usage=Mr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(D.dataId),T,N,s);let I=!0,P=this.runWebGLProgram(v,[D],o,null,I),E=this.texData.get(P.dataId);e.texture=E.texture,e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,this.disposeIntermediateTensorInfo(D),this.texData.delete(P.dataId),e.values=null,f&&(this.uploadWaitMs+=or()-m)}else{let b=this.acquireTexture(y,l,o,p);e.texture=b}}convertAndCacheOnCPU(t,e){let r=this.texData.get(t),{dtype:o}=r;return this.releaseGPUData(t),e!=null&&(r.values=WJ(e,o)),r.values}acquireTexture(t,e,r,o){if(this.numBytesInGPU+=this.computeBytes(t,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*Pb(e)}tryRunOnCpuOrThrow(t,e){if(this.shouldExecuteOnCPU(t))try{return e()}catch(r){if(ct().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function WJ(n,t){if(t==="float32"||t==="complex64")return n;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(n.length):new Uint8Array(n.length);for(let r=0;r<e.length;++r)e[r]=Math.round(n[r]);return e}else throw new Error(`Unknown dtype ${t}`)}let VJ="2.7.0";function GJ(){ct().set("WEBGL_FORCE_F16_TEXTURES",!0)}Rx()&&ew("webgl",()=>new zJ,2);let Qst={forceHalfFloat:GJ};function Cs(n){let{inputs:t,backend:e}=n,{x:r}=t;return e.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}let UJ={kernelName:bu,backendName:"webgl",kernelFunc:Cs};function ll(n){let{inputs:t,backend:e}=n,{real:r,imag:o}=t,s=e.makeTensorInfo(r.shape,"complex64"),c=e.texData.get(s.dataId),l=Cs({inputs:{x:r},backend:e}),p=e.texData.get(l.dataId);p.complexParentRefCount++;let f=Cs({inputs:{x:o},backend:e}),m=e.texData.get(f.dataId);return m.complexParentRefCount++,c.complexTensorInfos={real:l,imag:f},s}let qJ={kernelName:lf,backendName:"webgl",kernelFunc:ll};let iE="if (isnan(x)) return x;",HJ=`
+    `}},{segment_util:segment_util2}=backend_util_exports,split11=kernel_impls_exports.split,tile10=kernel_impls_exports.tile,topkImpl3=kernel_impls_exports.topkImpl,whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}function mapActivationToShaderProgram(activation2,packed=!1){if(activation2==="linear")return packed?LINEAR2:LINEAR;if(activation2==="relu")return packed?RELU2:RELU;if(activation2==="elu")return packed?ELU3:ELU2;if(activation2==="relu6")return packed?RELU62:RELU6;if(activation2==="prelu")return packed?PRELU2:PRELU;throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`)}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){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,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine15())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice21,shape,isPacked}=texData;if(slice21!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice21,dtype,complexTensorInfos,isPacked}=texData;if(slice21!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer11=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer11=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer11==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer11,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}checkNumericalProblems(values){if(values==null)return;for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(this.pendingDisposal.has(dataId))return;if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(!this.texData.has(dataId))return;if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice21}=this.texData.get(dataId),key=slice21&&slice21.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine15().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!this.warnedAboutCPUBackend&&cpuBackend==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),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}slice(x,begin,size){if(this.shouldExecuteOnCPU([x])){let outValues=sliceImplCPU(this.texData.get(x.dataId).values,begin,size,x.shape,x.dtype);return this.makeOutput(size,x.dtype,outValues)}if(util_exports.sizeFromShape(size)===0)return tensor4([],size,x.dtype);let{isPacked}=this.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,begin,size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram(size):new SliceProgram(size),customSetup=program.getCustomSetupFunc(begin);return this.compileAndRun(program,[x],null,customSetup)}return this.uploadToGPU(x.dataId),this.shallowSlice(x,begin,size)}shallowSlice(x,begin,size){let xTexData=this.texData.get(x.dataId),t=this.makeOutput(size,x.dtype),newTexData=this.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,x.strides);xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=this.dataRefCount.get(newTexData.slice.origDataId)||1;return this.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}stridedSlice(x,begin,end,strides){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.stridedSlice(x,begin,end,strides));if(cpuRes)return cpuRes;let outShape=slice_util_exports.computeOutShape(begin,end,strides);if(outShape.some(axis=>axis===0))return tensor4([],outShape);let program=new StridedSliceProgram(begin,strides,outShape);return this.compileAndRun(program,[x])}reverse(x,axis){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ReversePackedProgram(x.shape,axis):new ReverseProgram(x.shape,axis);return this.compileAndRun(program,[x])}neg(x){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.neg(x));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,NEG,x.dtype);let program=new UnaryOpProgram(x.shape,NEG);return this.compileAndRun(program,[x])}batchMatMul(a,b,transposeA,transposeB){let outerShapeA=transposeA?a.shape[2]:a.shape[1],outerShapeB=transposeB?b.shape[1]:b.shape[2],sharedDim=transposeA?a.shape[1]:a.shape[2],batch=Math.max(a.shape[0],b.shape[0]);if((outerShapeA===1||outerShapeB===1)&&sharedDim>MATMUL_SHARED_DIM_THRESHOLD){transposeA&&(a=transpose(a,[0,2,1])),transposeB&&(b=transpose(b,[0,2,1]));let a3D=outerShapeB===1?a:a.as3D(batch,sharedDim,1),axis=outerShapeB===1?2:1,b3D=outerShapeB===1?b.as3D(batch,1,sharedDim):b,product=mul(a3D,b3D);return product.sum(axis,!0)}let dtype=upcastType(a.dtype,b.dtype),program=new MatMulPackedProgram(a.shape,b.shape,[batch,outerShapeA,outerShapeB],transposeA,transposeB);return this.compileAndRun(program,[a,b],dtype)}fusedBatchMatMul({a,b,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}){let outerShapeA=transposeA?a.shape[2]:a.shape[1],outerShapeB=transposeB?b.shape[1]:b.shape[2],batch=Math.max(a.shape[0],b.shape[0]),dtype=upcastType(a.dtype,b.dtype),hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,program=new MatMulPackedProgram(a.shape,b.shape,[batch,outerShapeA,outerShapeB],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[a,b];return bias&&inputs.push(bias),preluActivationWeights&&inputs.push(preluActivationWeights),this.compileAndRun(program,inputs,dtype)}localResponseNormalization4D(x,radius,bias,alpha,beta){let program=env().getBool("WEBGL_PACK_NORMALIZATION")?new LRNPackedProgram(x.shape,radius,bias,alpha,beta):new LRNProgram(x.shape,radius,bias,alpha,beta);return this.compileAndRun(program,[x])}LRNGrad(dy,inputImage,outputImage,depthRadius,bias,alpha,beta){let program=new LRNGradProgram(inputImage.shape,depthRadius,bias,alpha,beta);return this.compileAndRun(program,[inputImage,outputImage,dy])}tile(x,reps){if(x.dtype==="string"){let data=this.readSync(x.dataId),decodedData=data.map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData);return tile10(buf,reps)}let program=new TileProgram(x.shape,reps);return this.compileAndRun(program,[x])}pad(x,paddings,constantValue){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PadPackedProgram(x.shape,paddings,constantValue):new PadProgram(x.shape,paddings,constantValue);return this.compileAndRun(program,[x])}gather(x,indices,axis){let cpuRes=this.tryRunOnCpuOrThrow([x,indices],()=>this.cpuBackend.gather(x,indices,axis));if(cpuRes)return cpuRes;let program=new GatherProgram(x.shape,indices.size,axis);return this.compileAndRun(program,[x,indices])}batchToSpaceND(x,blockShape,crops){util_exports.assert(x.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod52=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod52),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod52),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length);return transpose(x.reshape(reshaped),permuted).reshape(reshapedPermuted).slice(sliceBeginCoords,sliceSize)}spaceToBatchND(x,blockShape,paddings){util_exports.assert(x.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod52=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=x.pad(completePaddings),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod52,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod52,!1),paddedXT=transpose(paddedX.reshape(reshapedPaddedShape),permutedReshapedPaddedPermutation);return reshape(paddedXT,flattenShape)}reduce(x,reduceType,dtype){let batchSize=x.shape[0],inSize=x.shape[1],windowSize=backend_util_exports.computeOptimalWindowSize(inSize),outSize=Math.ceil(inSize/windowSize),reduceInfo={windowSize,inSize,batchSize,outSize},program=new ReduceProgram(reduceInfo,reduceType),output=this.compileAndRun(program,[x],dtype);return output.shape[1]===1?output:this.reduce(output,reduceType,dtype)}argReduce(x,reduceType,bestIndicesA=null){let batchSize=x.shape[0],inSize=x.shape[1];bestIndicesA!=null&&(batchSize=bestIndicesA.shape[0],inSize=bestIndicesA.shape[1]);let windowSize=backend_util_exports.computeOptimalWindowSize(inSize),reduceInfo={windowSize,inSize,batchSize,outSize:Math.ceil(inSize/windowSize)},program=new ArgMinMaxProgram(reduceInfo,reduceType,bestIndicesA==null),inputs=[x];bestIndicesA!=null&&inputs.push(bestIndicesA);let output=this.compileAndRun(program,inputs,"int32");return output.shape[1]===1?output:this.argReduce(x,reduceType,output)}argReducePacked(x,reduceType,bestIndicesA=null){let inShape=bestIndicesA!=null?bestIndicesA.shape:x.shape,inSize=inShape[inShape.length-1],windowSize=backend_util_exports.computeOptimalWindowSize(inSize),program=new ArgMinMaxPackedProgram(inShape,windowSize,reduceType,bestIndicesA==null),inputs=bestIndicesA==null?[x]:[x,bestIndicesA],output=this.compileAndRun(program,inputs,"int32");return output.rank===x.rank?this.argReducePacked(x,reduceType,output):output}sum(x,axes){backend_util_exports.assertAxesAreInnerMostDims("sum",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize),outputDType=sumOutType(x.dtype);return this.reduce(a2D,"sum",outputDType).reshape(outShape)}prod(x,axes){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.prod(x,axes));if(cpuRes)return cpuRes;let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize),outputDType=sumOutType(x.dtype);return this.reduce(a2D,"prod",outputDType).reshape(outShape)}unsortedSegmentSum(x,segmentIds,numSegments){let axis=0,permutation=backend_util_exports.getAxesPermutation([axis],x.rank),permutedX=x;permutation!=null&&(permutedX=transpose(x,permutation),axis=backend_util_exports.getInnerMostAxes(1,x.rank)[0]);let outShape=segment_util2.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=permutedX.as2D(-1,inSize),outputDType=sumOutType(x.dtype),result=this.segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments).reshape(outShape);return permutation!=null&&(result=transpose(result,backend_util_exports.getUndoAxesPermutation(permutation))),result}segOpCompute(x,segOpType,segmentIds,dtype,numSegments){let batchSize=x.shape[0],inSize=x.shape[1],windowSize=segment_util2.segOpComputeOptimalWindowSize(inSize,numSegments),segOpInfo={windowSize,inSize,batchSize,numSegments},program=new SegmentOpProgram(segOpInfo,segOpType),output=this.compileAndRun(program,[x,segmentIds],dtype);return output.shape[1]===numSegments?output:(segmentIds=range(0,numSegments).tile([inSize/windowSize]),this.segOpCompute(output,segOpType,segmentIds,dtype,numSegments))}argMinMaxReduce(x,axis,reduceType){let axes=[axis];if(backend_util_exports.assertAxesAreInnerMostDims("arg"+reduceType.charAt(0).toUpperCase()+reduceType.slice(1),axes,x.rank),!env().getBool("WEBGL_PACK_REDUCE")||x.rank<=2){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.argReduce(a2D,reduceType).reshape(outShape)}return this.argReducePacked(x,reduceType)}argMin(x,axis){return this.argMinMaxReduce(x,axis,"min")}argMax(x,axis){return this.argMinMaxReduce(x,axis,"max")}cumsum(x,axis,exclusive,reverse12){if(axis!==x.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank-1} but got axis=${axis}`);let size=x.shape[axis],result=x;for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(x.shape,!1,reverse12),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=this.compileAndRun(program,[result],result.dtype,customSetup),prevResult.dispose()}if(exclusive){let program=new CumSumProgram(x.shape,exclusive,reverse12),prevResult=result;result=this.compileAndRun(program,[result]),prevResult.dispose()}return result}equal(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,EQUAL2,"bool");let program=new BinaryOpProgram(EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}less(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.less(a,b));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LESS2,"bool");let program=new BinaryOpProgram(LESS,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}lessEqual(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LESS_EQUAL2,"bool");let program=new BinaryOpProgram(LESS_EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}greater(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.greater(a,b));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,GREATER2,"bool");let program=new BinaryOpProgram(GREATER,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}greaterEqual(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,GREATER_EQUAL2,"bool");let program=new BinaryOpProgram(GREATER_EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}logicalNot(x){let program=new UnaryOpProgram(x.shape,LOGICAL_NOT);return this.compileAndRun(program,[x])}logicalAnd(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LOGICAL_AND2,"bool");let program=new BinaryOpProgram(LOGICAL_AND,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}logicalOr(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LOGICAL_OR2,"bool");let program=new BinaryOpProgram(LOGICAL_OR,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}select(condition,a,b){let program=new SelectProgram(condition.rank,a.shape,a.rank);return this.compileAndRun(program,[condition,a,b],upcastType(a.dtype,b.dtype))}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}topk(x,k,sorted){let xVals=x.dataSync();return topkImpl3(xVals,x.shape,x.dtype,k,sorted)}min(x,axes){backend_util_exports.assertAxesAreInnerMostDims("min",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"min",a2D.dtype).reshape(outShape)}minimum(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.minimum(a,b));if(cpuRes)return cpuRes;let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MIN2,a.shape,b.shape):new BinaryOpProgram(MIN,a.shape,b.shape);return this.compileAndRun(program,[a,b])}mod(a,b){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MOD2,a.shape,b.shape):new BinaryOpProgram(MOD,a.shape,b.shape);return this.compileAndRun(program,[a,b])}maximum(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.maximum(a,b));if(cpuRes)return cpuRes;let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MAX2,a.shape,b.shape):new BinaryOpProgram(MAX,a.shape,b.shape);return this.compileAndRun(program,[a,b])}all(x,axes){backend_util_exports.assertAxesAreInnerMostDims("all",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"all",a2D.dtype).reshape(outShape)}any(x,axes){backend_util_exports.assertAxesAreInnerMostDims("any",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"any",a2D.dtype).reshape(outShape)}floorDiv(a,b){let op22=INT_DIV,outputDtype="int32";if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,INT_DIV2,outputDtype);let program=new BinaryOpProgram(op22,a.shape,b.shape);return this.compileAndRun(program,[a,b],outputDtype)}packedUnaryOp(x,op22,dtype){let program=new UnaryOpPackedProgram(x.shape,op22);return this.compileAndRun(program,[x],dtype)}packedBinaryOp(a,b,op22,dtype,checkOutOfBounds=!1){let program=new BinaryOpPackedProgram(op22,a.shape,b.shape,checkOutOfBounds);return this.compileAndRun(program,[a,b],dtype)}makeComplexComponentTensorInfo(complexTensor,complexPart){return{dataId:complexPart.dataId,dtype:complexPart.dtype,shape:complexTensor.shape}}addN(tensors){if(tensors.length===1)return tensors[0];if(tensors.length>env().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(tensors.length/2),leftSide=this.addN(tensors.slice(0,midIndex)),rightSide=this.addN(tensors.slice(midIndex));return this.addN([leftSide,rightSide])}let dtype=tensors.map(t=>t.dtype).reduce((d1,d2)=>upcastType(d1,d2)),shapes=tensors.map(t=>t.shape),usePackedOp=env().getBool("WEBGL_PACK"),program=usePackedOp?new AddNPackedProgram(tensors[0].shape,shapes):new AddNProgram(tensors[0].shape,shapes);return this.compileAndRun(program,tensors,dtype)}pow(a,b){let usePackedOp=env().getBool("WEBGL_PACK_BINARY_OPERATIONS"),program=usePackedOp?new BinaryOpPackedProgram(POW2,a.shape,b.shape):new BinaryOpProgram(POW,a.shape,b.shape),dtype=upcastType(a.dtype,b.dtype);return this.compileAndRun(program,[a,b],dtype)}ceil(x){if(this.shouldExecuteOnCPU([x])){let outValues=ceilImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,CEIL,x.dtype);let program=new UnaryOpProgram(x.shape,CEIL);return this.compileAndRun(program,[x])}floor(x){if(this.shouldExecuteOnCPU([x])){let outValues=floorImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,FLOOR,x.dtype);let program=new UnaryOpProgram(x.shape,FLOOR);return this.compileAndRun(program,[x])}sign(x){let program=new UnaryOpProgram(x.shape,SIGN);return this.compileAndRun(program,[x])}isNaN(x){let program=new UnaryOpProgram(x.shape,IS_NAN);return this.compileAndRun(program,[x],"bool")}isInf(x){let program=new UnaryOpProgram(x.shape,IS_INF);return this.compileAndRun(program,[x],"bool")}isFinite(x){let program=new UnaryOpProgram(x.shape,IS_FINITE);return this.compileAndRun(program,[x],"bool")}round(x){let program=new UnaryOpProgram(x.shape,ROUND);return this.compileAndRun(program,[x])}exp(x){if(this.shouldExecuteOnCPU([x])){let outValues=expImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,EXP,x.dtype);let program=new UnaryOpProgram(x.shape,EXP);return this.compileAndRun(program,[x])}expm1(x){if(this.shouldExecuteOnCPU([x])){let outValues=expm1ImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,EXPM1,x.dtype);let program=new UnaryOpProgram(x.shape,EXPM1);return this.compileAndRun(program,[x])}softmax(logits,dim){let axes=util_exports.parseAxisParam([dim],logits.shape),maxLogit=max(logits,axes),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),a=sub(logits,maxLogit.reshape(expandedShape)),b=this.exp(a),sumExp=this.sum(b,axes).reshape(expandedShape);return div(b,sumExp)}log(x){if(this.shouldExecuteOnCPU([x])){let outValues=logImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,LOG2,x.dtype);let program=new UnaryOpProgram(x.shape,LOG);return this.compileAndRun(program,[x])}log1p(x){let program=new UnaryOpProgram(x.shape,LOG1P);return this.compileAndRun(program,[x])}sqrt(x){let program=new UnaryOpProgram(x.shape,SQRT);return this.compileAndRun(program,[x])}rsqrt(x){if(this.shouldExecuteOnCPU([x])){let outValues=rsqrtImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}let program=new UnaryOpProgram(x.shape,RSQRT);return this.compileAndRun(program,[x])}reciprocal(x){let program=new UnaryOpProgram(x.shape,RECIPROCAL);return this.compileAndRun(program,[x])}relu(x){let program;return env().getBool("WEBGL_PACK")?program=new UnaryOpPackedProgram(x.shape,RELU2):program=new UnaryOpProgram(x.shape,RELU),this.compileAndRun(program,[x])}relu6(x){let program;return env().getBool("WEBGL_PACK")?program=new UnaryOpPackedProgram(x.shape,RELU62):program=new UnaryOpProgram(x.shape,RELU6),this.compileAndRun(program,[x])}prelu(x,alpha){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(PRELU2,x.shape,alpha.shape):new BinaryOpProgram(PRELU,x.shape,alpha.shape);return this.compileAndRun(program,[x,alpha])}elu(x){if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ELU3,x.dtype);let program=new UnaryOpProgram(x.shape,ELU2);return this.compileAndRun(program,[x])}eluDer(dy,y){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(ELU_DER2,dy.shape,y.shape):new BinaryOpProgram(ELU_DER,dy.shape,y.shape);return this.compileAndRun(program,[dy,y])}selu(x){let program=new UnaryOpProgram(x.shape,SELU);return this.compileAndRun(program,[x])}clip(x,min82,max102){let program;env().getBool("WEBGL_PACK_CLIP")?program=new ClipPackedProgram(x.shape):program=new ClipProgram(x.shape);let customSetup=program.getCustomSetupFunc(min82,max102);return this.compileAndRun(program,[x],null,customSetup)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}complexAbs(x){let xData=this.texData.get(x.dataId),program=new ComplexAbsProgram(x.shape),inputs=[this.makeComplexComponentTensorInfo(x,xData.complexTensorInfos.real),this.makeComplexComponentTensorInfo(x,xData.complexTensorInfos.imag)];return this.compileAndRun(program,inputs)}sigmoid(x){let program=new UnaryOpProgram(x.shape,SIGMOID);return this.compileAndRun(program,[x])}softplus(x){let program=new UnaryOpProgram(x.shape,SOFTPLUS);return this.compileAndRun(program,[x])}asin(x){let program=new UnaryOpProgram(x.shape,ASIN);return this.compileAndRun(program,[x])}acos(x){let program=new UnaryOpProgram(x.shape,ACOS);return this.compileAndRun(program,[x])}atan(x){let program=new UnaryOpProgram(x.shape,ATAN);return this.compileAndRun(program,[x])}sinh(x){let program=new UnaryOpProgram(x.shape,SINH);return this.compileAndRun(program,[x])}cosh(x){let program=new UnaryOpProgram(x.shape,COSH);return this.compileAndRun(program,[x])}tanh(x){let program=new UnaryOpProgram(x.shape,TANH);return this.compileAndRun(program,[x])}asinh(x){let program=new UnaryOpProgram(x.shape,ASINH);return this.compileAndRun(program,[x])}acosh(x){let program=new UnaryOpProgram(x.shape,ACOSH);return this.compileAndRun(program,[x])}atanh(x){let program=new UnaryOpProgram(x.shape,ATANH);return this.compileAndRun(program,[x])}erf(x){let program=new UnaryOpProgram(x.shape,ERF);return this.compileAndRun(program,[x])}step(x,alpha){let program=new UnaryOpProgram(x.shape,STEP(alpha));return this.compileAndRun(program,[x])}conv2dByMatMul(x,filter,convInfo,bias,activation2,preluActivationWeights){let xShape=x.shape,xTexData=this.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!==0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape2=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped2=reshape(x,[1,targetShape2,convInfo.inChannels]),filterReshaped2=reshape(filter,[1,convInfo.inChannels,convInfo.outChannels]),result=this.fusedBatchMatMul({a:xReshaped2,b:filterReshaped2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights});return reshape(result,convInfo.outShape)}let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape(filter,[1,convInfo.inChannels,convInfo.outChannels]),pointwiseConv=this.fusedBatchMatMul({a:xReshaped,b:filterReshaped,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}),pointwiseConvTexData=this.texData.get(pointwiseConv.dataId);return util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,engine15().makeTensorFromDataId(pointwiseConv.dataId,convInfo.outShape,pointwiseConv.dtype)}conv2dWithIm2Row(x,filter,convInfo,bias,activation2,preluActivationWeights){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,xSqueezed=x.squeeze([0]),w2Row=filter.reshape([1,sharedDim,-1]),im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=this.compileAndRun(im2ColProgram,[xSqueezed]).reshape([1,x2ColShape[0],x2ColShape[1]]),hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2Col.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[im2Col,w2Row];bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights);let product=this.compileAndRun(matmulProgram,inputs);return isChannelsLast?product.reshape([1,outHeight,outWidth,convInfo.outChannels]):product.reshape([1,convInfo.outChannels,outHeight,outWidth])}fusedConv2d({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))return this.conv2dByMatMul(input2,filter,convInfo,bias,activation2,preluActivationWeights);if(env().getBool("WEBGL_CONV_IM2COL")&&input2.shape[0]===1)return this.conv2dWithIm2Row(input2,filter,convInfo,bias,activation2,preluActivationWeights);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[input2,filter];return bias&&inputs.push(bias),preluActivationWeights&&inputs.push(preluActivationWeights),this.compileAndRun(program,inputs)}conv2d(x,filter,convInfo){if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))return this.conv2dByMatMul(x,filter,convInfo);if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)return this.conv2dWithIm2Row(x,filter,convInfo);let program=new Conv2DProgram(convInfo);return this.compileAndRun(program,[x,filter])}conv2dDerInput(dy,filter,convInfo){let program=new Conv2DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}conv2dDerFilter(x,dy,convInfo){let program=new Conv2DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}fusedDepthwiseConv2D({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){let shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,inputs=[input2,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null;hasBias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights);let program;return shouldPackDepthwiseConv?(program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),this.compileAndRun(program,inputs)):(program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),this.compileAndRun(program,inputs))}depthwiseConv2D(x,filter,convInfo){let program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1?(program=new DepthwiseConvPacked2DProgram(convInfo),this.compileAndRun(program,[x,filter])):(program=new DepthwiseConv2DProgram(convInfo),this.compileAndRun(program,[x,filter]))}depthwiseConv2DDerInput(dy,filter,convInfo){let program=new DepthwiseConv2DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}depthwiseConv2DDerFilter(x,dy,convInfo){let program=new DepthwiseConv2DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}conv3d(x,filter,convInfo){let program=new Conv3DProgram(convInfo);return this.compileAndRun(program,[x,filter])}conv3dDerInput(dy,filter,convInfo){let program=new Conv3DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}conv3dDerFilter(x,dy,convInfo){let program=new Conv3DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}unstack(x,axis){let num=x.shape[axis],outShape=new Array(x.rank-1),outIndex=0;for(let i=0;i<x.rank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let begin=new Array(x.rank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++)begin[axis]=i,res[i]=this.slice(x,begin,size).reshape(outShape);return res}avgPool3d(x,convInfo){let program=new Pool3DProgram(convInfo,"avg",!1);return this.compileAndRun(program,[x],"float32")}avgPool3dBackprop(dy,x,convInfo){let avgPool3dBackpropProgram=new AvgPool3DBackpropProgram(convInfo);return this.compileAndRun(avgPool3dBackpropProgram,[dy],x.dtype)}maxPool3d(x,convInfo){let program=new Pool3DProgram(convInfo,"max",!1);return this.compileAndRun(program,[x],"float32")}maxPool3dBackprop(dy,x,y,convInfo){let getPositions=!0,maxPool3dPositionsProgram=new Pool3DProgram(convInfo,"max",getPositions),maxPool3dPositions=this.compileAndRun(maxPool3dPositionsProgram,[x]),maxPool3dBackPropProgram=new MaxPool3DBackpropProgram(convInfo),result=this.compileAndRun(maxPool3dBackPropProgram,[dy,maxPool3dPositions],x.dtype);return maxPool3dPositions.dispose(),result}resizeBilinear(x,newHeight,newWidth,alignCorners){let program=env().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ResizeBilinearPackedProgram(x.shape,newHeight,newWidth,alignCorners):new ResizeBilinearProgram(x.shape,newHeight,newWidth,alignCorners);return this.compileAndRun(program,[x],"float32")}resizeBilinearBackprop(dy,x,alignCorners){let program=new ResizeBilinearBackpropProgram(dy,x,alignCorners);return this.compileAndRun(program,[dy])}resizeNearestNeighbor(x,newHeight,newWidth,alignCorners){let program=new ResizeNearestNeighborProgram(x.shape,newHeight,newWidth,alignCorners);return this.compileAndRun(program,[x])}resizeNearestNeighborBackprop(dy,x,alignCorners){let program=new ResizeNearestNeigborBackpropProgram(dy,x,alignCorners);return this.compileAndRun(program,[dy])}multinomial(logits,normalized,numSamples,seed){let probs=normalized?logits:softmax(logits),batchSize=probs.shape[0],numOutcomes=probs.shape[1],program=new MultinomialProgram(batchSize,numOutcomes,numSamples),customSetup=program.getCustomSetupFunc(seed);return this.compileAndRun(program,[probs],"int32",customSetup)}oneHot(indices,depth,onValue,offValue){let program=new OneHotProgram(indices.size,depth,onValue,offValue);return this.compileAndRun(program,[indices])}diag(x){let program=new DiagProgram(x.size);return this.compileAndRun(program,[x])}cropAndResize(image3,boxes,boxIndex,cropSize,method,extrapolationValue){let program=new CropAndResizeProgram(image3.shape,boxes.shape,cropSize,method,extrapolationValue);return this.compileAndRun(program,[image3,boxes,boxIndex],"float32")}depthToSpace(x,blockSize,dataFormat){util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],program=new DepthToSpaceProgram(outputShape,blockSize,dataFormat);return this.compileAndRun(program,[x])}split(x,sizeSplits,axis){return split11(x,sizeSplits,axis)}scatterND(indices,updates,shape){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),flattenShape=[outputSize/sliceSize,sliceSize],flattenIndices=indices.reshape([numUpdates,sliceRank]),flattenX=updates.reshape([numUpdates,sliceSize]);if(outputSize===0)return backend_util_exports.reshapeTensor(tensor4([]),shape);let defaultValue=scalar(0),program=new ScatterProgram(numUpdates,sliceRank,flattenIndices.rank,flattenX.rank,strides,flattenShape),res=this.compileAndRun(program,[flattenX,flattenIndices,defaultValue]);return res.reshape(shape)}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){let{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.rank,sparseValues.rank,strides,[outputSize,1],sumDupeIndices),res=this.compileAndRun(program,[sparseValues,sparseIndices,defaultValue]);return res.reshape(outputShape)}gatherND(x,indices){let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(x,indices),flattenIndices=indices.reshape([numSlices,sliceRank]),flattenX=x.reshape([x.size/sliceSize,sliceSize]),program=new GatherNDProgram(sliceRank,strides,[numSlices,sliceSize]),res=this.compileAndRun(program,[flattenX,flattenIndices]);return res.reshape(resultShape)}fill(shape,value,dtype){if(dtype=dtype||util_exports.inferDtype(value),dtype==="string"){let values=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(shape));return values.fill(value),engine15().makeTensor(values,shape,dtype,this)}else{let program=new FillProgram(shape,value),customSetup=program.getCustomSetupFunc(value);return this.compileAndRun(program,[],dtype,customSetup)}}onesLike(x){if(x.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(x.shape,1,x.dtype)}zerosLike(x){return this.fill(x.shape,x.dtype==="string"?"":0,x.dtype)}linspace(start,stop,num){return backend_util_exports.linspaceImpl(start,stop,num)}makeTensorInfo(shape,dtype,values){let dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine15().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!==!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine15().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!env().getBool("IS_TEST")){let allKeys=Object.keys(this.binaryCache);allKeys.forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]})}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=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture!=null)return;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}tryRunOnCpuOrThrow(inputs,fn){if(this.shouldExecuteOnCPU(inputs))try{return fn()}catch(e){if(env().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version12="2.7.0";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);function identity3(args){let{inputs,backend:backend3}=args,{x}=inputs;return backend3.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig2={kernelName:Identity,backendName:"webgl",kernelFunc:identity3};function complex10(args){let{inputs,backend:backend3}=args,{real:real82,imag:imag82}=inputs,complexInfo=backend3.makeTensorInfo(real82.shape,"complex64"),complex11=backend3.texData.get(complexInfo.dataId),realTensorInfo=identity3({inputs:{x:real82},backend:backend3}),realData=backend3.texData.get(realTensorInfo.dataId);realData.complexParentRefCount++;let imagTensorInfo=identity3({inputs:{x:imag82},backend:backend3}),imagData=backend3.texData.get(imagTensorInfo.dataId);return imagData.complexParentRefCount++,complex11.complexTensorInfos={real:realTensorInfo,imag:imagTensorInfo},complexInfo}var complexConfig2={kernelName:Complex,backendName:"webgl",kernelFunc:complex10},CHECK_NAN_SNIPPET_UNARY="if (isnan(x)) return x;",CHECK_NAN_SNIPPET_BINARY=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,jJ=`
+`,CHECK_NAN_SNIPPET_BINARY_PACKED=`
   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 Vm(n){return({inputs:t,backend:e})=>{let{x:r}=t,o=e,s=new ue(r.shape,n);return o.runWebGLProgram(s,[r],r.dtype)}}function ul({opSnippet:n,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:r=!1,cpuKernelImpl:o,dtype:s}){return({inputs:c,backend:l})=>{let{a:p,b:f}=c,m=l;if(r&&p.dtype==="complex64"){let T=m.texData.get(p.dataId),N=m.texData.get(f.dataId),[S,D]=[[T.complexTensorInfos.real,N.complexTensorInfos.real],[T.complexTensorInfos.imag,N.complexTensorInfos.imag]].map(P=>{let[E,L]=P,B={dataId:E.dataId,dtype:E.dtype,shape:p.shape},q={dataId:L.dataId,dtype:L.dtype,shape:f.shape},H=new Hn(n,p.shape,f.shape);return m.runWebGLProgram(H,[B,q],jn(E.dtype,L.dtype))}),I=ll({inputs:{real:S,imag:D},backend:m});return m.disposeIntermediateTensorInfo(S),m.disposeIntermediateTensorInfo(D),I}let y=s||jn(p.dtype,f.dtype);if(m.shouldExecuteOnCPU([p,f])&&o!=null){let T=m.texData.get(p.dataId),N=m.texData.get(f.dataId),[S,D]=o(p.shape,f.shape,T.values,N.values,y),I=m.makeTensorInfo(D,y),P=m.texData.get(I.dataId);return P.values=S,I}let b=ct().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,v;return b?v=new Ns(t,p.shape,f.shape,e):v=new Hn(n,p.shape,f.shape),m.runWebGLProgram(v,[p,f],y)}}let aE="return a + b;",KJ=ul({opSnippet:aE,packedOpSnippet:aE,supportsComplex:!0,cpuKernelImpl:rX}),XJ={kernelName:Hi,backendName:"webgl",kernelFunc:KJ};let YJ=HJ+`
+`;function unaryKernelFunc2(opSnippet){return({inputs,backend:backend3})=>{let{x}=inputs,webglBackend=backend3,program=new UnaryOpProgram(x.shape,opSnippet);return webglBackend.runWebGLProgram(program,[x],x.dtype)}}function binaryKernelFunc2({opSnippet,packedOpSnippet,checkOutOfBounds=!1,supportsComplex=!1,cpuKernelImpl,dtype}){return({inputs,backend:backend3})=>{let{a,b}=inputs,webglBackend=backend3;if(supportsComplex&&a.dtype==="complex64"){let aData=webglBackend.texData.get(a.dataId),bData=webglBackend.texData.get(b.dataId),[real82,imag82]=[[aData.complexTensorInfos.real,bData.complexTensorInfos.real],[aData.complexTensorInfos.imag,bData.complexTensorInfos.imag]].map(complexParts=>{let[aPart,bPart]=complexParts,aHandle={dataId:aPart.dataId,dtype:aPart.dtype,shape:a.shape},bHandle={dataId:bPart.dataId,dtype:bPart.dtype,shape:b.shape},program2=new BinaryOpProgram(opSnippet,a.shape,b.shape);return webglBackend.runWebGLProgram(program2,[aHandle,bHandle],upcastType(aPart.dtype,bPart.dtype))}),complexOutput=complex10({inputs:{real:real82,imag:imag82},backend:webglBackend});return webglBackend.disposeIntermediateTensorInfo(real82),webglBackend.disposeIntermediateTensorInfo(imag82),complexOutput}let $dtype=dtype||upcastType(a.dtype,b.dtype);if(webglBackend.shouldExecuteOnCPU([a,b])&&cpuKernelImpl!=null){let aData=webglBackend.texData.get(a.dataId),bData=webglBackend.texData.get(b.dataId),[outValues,outShape]=cpuKernelImpl(a.shape,b.shape,aData.values,bData.values,$dtype),out=webglBackend.makeTensorInfo(outShape,$dtype),outData=webglBackend.texData.get(out.dataId);return outData.values=outValues,out}let shouldUsePackedProgram=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&packedOpSnippet!=null,program;return shouldUsePackedProgram?program=new BinaryOpPackedProgram(packedOpSnippet,a.shape,b.shape,checkOutOfBounds):program=new BinaryOpProgram(opSnippet,a.shape,b.shape),webglBackend.runWebGLProgram(program,[a,b],$dtype)}}var ADD="return a + b;",addKernelFunc=binaryKernelFunc2({opSnippet:ADD,packedOpSnippet:ADD,supportsComplex:!0,cpuKernelImpl:addImplCPU}),addConfig2={kernelName:Add,backendName:"webgl",kernelFunc:addKernelFunc},ATAN2=CHECK_NAN_SNIPPET_BINARY+`
   return atan(a, b);
-`,JJ=`
+`,ATAN2_PACKED=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+jJ+`
+  `+CHECK_NAN_SNIPPET_BINARY_PACKED+`
   return result;
-`,ZJ=ul({opSnippet:YJ,packedOpSnippet:JJ}),QJ={kernelName:sf,backendName:"webgl",kernelFunc:ZJ};function tZ(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;sh(o,"avgPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Kn(o.shape,s,c,f,l,p);if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))return Cs({inputs:{x:o},backend:e});let y=new ah(m,"avg",!1);return e.runWebGLProgram(y,[o],"float32")}let eZ={kernelName:au,backendName:"webgl",kernelFunc:tZ};function nZ(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s}=t,c=s;sh([o,s],"avgPoolBackprop");let{filterSize:l,strides:p,pad:f}=r,m=Kn(c.shape,l,p,1,f),y=new nY(m);return e.runWebGLProgram(y,[o],c.dtype)}let rZ={kernelName:af,backendName:"webgl",kernelFunc:nZ};class oZ{constructor(t,e,r,o,s,c){this.outputShape=[],this.variableNames=["x","mean","variance"],le(t,e),le(t,r);let l="0.0";o!=null&&(le(t,o),this.variableNames.push("offset"),l="getOffsetAtOutCoords()");let p="1.0";s!=null&&(le(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
+`,atan25=binaryKernelFunc2({opSnippet:ATAN2,packedOpSnippet:ATAN2_PACKED}),atan2Config={kernelName:Atan2,backendName:"webgl",kernelFunc:atan25};function avgPool3(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex2(x,"avgPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))return identity3({inputs:{x},backend:backend3});let avgPoolProgram=new Pool2DProgram(convInfo,"avg",!1);return backend3.runWebGLProgram(avgPoolProgram,[x],"float32")}var avgPoolConfig2={kernelName:AvgPool,backendName:"webgl",kernelFunc:avgPool3};function avgPoolBackprop3(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex2([dy,input2],"avgPoolBackprop");let{filterSize,strides,pad:pad11}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11),avgPoolBackpropProgram=new AvgPool2DBackpropProgram(convInfo);return backend3.runWebGLProgram(avgPoolBackpropProgram,[dy],x.dtype)}var avgPoolBackpropConfig2={kernelName:AvgPoolBackprop,backendName:"webgl",kernelFunc:avgPoolBackprop3},BatchNormProgram=class{constructor(xShape,meanShape,varianceShape,offsetShape,scaleShape,varianceEpsilon){this.outputShape=[],this.variableNames=["x","mean","variance"],backend_util_exports.assertAndGetBroadcastShape(xShape,meanShape),backend_util_exports.assertAndGetBroadcastShape(xShape,varianceShape);let offsetSnippet="0.0";offsetShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,offsetShape),this.variableNames.push("offset"),offsetSnippet="getOffsetAtOutCoords()");let scaleSnippet="1.0";scaleShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,scaleShape),this.variableNames.push("scale"),scaleSnippet="getScaleAtOutCoords()"),this.outputShape=xShape,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
         float variance = getVarianceAtOutCoords();
-        float offset = ${l};
-        float scale = ${p};
-        float inv = scale * inversesqrt(variance + float(${c}));
+        float offset = ${offsetSnippet};
+        float scale = ${scaleSnippet};
+        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}}class sZ{constructor(t,e,r,o,s,c){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],le(t,e),le(t,r);let l="vec4(0.0)";o!=null&&(le(t,o),this.variableNames.push("offset"),l="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(le(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
+    `}},BatchNormPackedProgram=class{constructor(xShape,meanShape,varianceShape,offsetShape,scaleShape,varianceEpsilon){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],backend_util_exports.assertAndGetBroadcastShape(xShape,meanShape),backend_util_exports.assertAndGetBroadcastShape(xShape,varianceShape);let offsetSnippet="vec4(0.0)";offsetShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,offsetShape),this.variableNames.push("offset"),offsetSnippet="getOffsetAtOutCoords()");let scaleSnippet="vec4(1.0)";scaleShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,scaleShape),this.variableNames.push("scale"),scaleSnippet="getScaleAtOutCoords()"),this.outputShape=xShape,this.userCode=`
       void main() {
-        vec4 offset = ${l};
-        vec4 scale = ${p};
+        vec4 offset = ${offsetSnippet};
+        vec4 scale = ${scaleSnippet};
 
         vec4 x = getXAtOutCoords();
         vec4 mean = getMeanAtOutCoords();
         vec4 variance = getVarianceAtOutCoords();
 
-        vec4 inv = scale * inversesqrt(variance + vec4(${c}));
+        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));
 
         setOutput((x - mean) * inv + offset);
       }
-    `}}let iZ=({inputs:n,backend:t,attrs:e})=>{let{x:r,mean:o,variance:s,offset:c,scale:l}=n;_(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||o.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=e;p==null&&(p=.001);let f=[r,o,s],m=null;c!=null&&(m=c.shape,f.push(c));let y=null;l!=null&&(y=l.shape,f.push(l));let b=ct().getBool("WEBGL_PACK_NORMALIZATION")?new sZ(r.shape,o.shape,s.shape,m,y,p):new oZ(r.shape,o.shape,s.shape,m,y,p),v=t.runWebGLProgram(b,f,f[0].dtype);return v},aZ={kernelName:yu,backendName:"webgl",kernelFunc:iZ};let cZ="return float(a != b);",cE=ul({opSnippet:cZ,dtype:"bool"}),lZ={kernelName:Su,backendName:"webgl",kernelFunc:cE};function T1(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.texData.get(r.dataId);return Cs({inputs:{x:o.complexTensorInfos.real},backend:e})}let uZ={kernelName:$f,backendName:"webgl",kernelFunc:T1};let pZ="return float(int(x));";function hZ(n,t){let e=new ue(n.shape,pZ),r=t.runWebGLProgram(e,[n],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function k1(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{dtype:s}=r;if(s==="complex64"){if(o.dtype==="complex64")return Cs({inputs:{x:o},backend:e});let c=xe(o.shape),l=k1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),p=ll({inputs:{real:l,imag:c},backend:e});return c.dispose(),e.disposeIntermediateTensorInfo(l),p}if(o.dtype==="complex64"){let c=T1({inputs:{input:o},backend:e}),l=k1({inputs:{x:c},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(c),l}if(!sc(o.dtype,s)){let c=Cs({inputs:{x:o},backend:e});return{dataId:c.dataId,shape:c.shape,dtype:s}}if(s==="int32")return hZ(o,e);if(s==="bool"){let c=e.makeTensorInfo([],"bool",Ce("bool",1)),l={a:o,b:c},p=cE({inputs:l,backend:e});return e.disposeIntermediateTensorInfo(c),p}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}let fZ={kernelName:cc,backendName:"webgl",kernelFunc:k1};class dZ{constructor(t){this.outputShape=[],this.outputShape=hs(t,1),this.variableNames=t.map((c,l)=>`T${l}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let c=1;c<e.length;c++)e[c]=e[c-1]+t[c][1];let r=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let c=1;c<e.length;c++){let l=e[c-1];r.push(`else if (yC < ${e[c]}) setOutput(getT${c}(yR, yC-${l}));`)}let o=e.length,s=e[e.length-1];r.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
+    `}},batchNorm3=({inputs,backend:backend3,attrs})=>{let{x,mean:mean7,variance,offset,scale:scale22}=inputs;util_exports.assert(mean7.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean7.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale22==null||mean7.shape.length===scale22.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let finalInputs=[x,mean7,variance],offsetShape=null;offset!=null&&(offsetShape=offset.shape,finalInputs.push(offset));let scaleShape=null;scale22!=null&&(scaleShape=scale22.shape,finalInputs.push(scale22));let program=env().getBool("WEBGL_PACK_NORMALIZATION")?new BatchNormPackedProgram(x.shape,mean7.shape,variance.shape,offsetShape,scaleShape,varianceEpsilon):new BatchNormProgram(x.shape,mean7.shape,variance.shape,offsetShape,scaleShape,varianceEpsilon),output=backend3.runWebGLProgram(program,finalInputs,finalInputs[0].dtype);return output},batchNormConfig2={kernelName:FusedBatchNorm,backendName:"webgl",kernelFunc:batchNorm3},NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputData=backend3.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend3})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real7},TO_INT="return float(int(x));";function int(input2,backend3){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend3.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast50(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend3});let zerosTensor=zeros(x.shape),floatX=cast50({inputs:{x},backend:backend3,attrs:{dtype:"float32"}}),result=complex10({inputs:{real:floatX,imag:zerosTensor},backend:backend3});return zerosTensor.dispose(),backend3.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real7({inputs:{input:x},backend:backend3}),result=cast50({inputs:{x:realPart},backend:backend3,attrs:{dtype}});return backend3.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend3});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend3);if(dtype==="bool"){let zerosTensorInfo=backend3.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),binaryInputs={a:x,b:zerosTensorInfo},result=notEqual3({inputs:binaryInputs,backend:backend3});return backend3.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast50},ConcatProgram=class{constructor(shapes){this.outputShape=[],this.outputShape=backend_util_exports.computeOutShape(shapes,1),this.variableNames=shapes.map((_,i)=>`T${i}`);let offsets=new Array(shapes.length-1);offsets[0]=shapes[0][1];for(let i=1;i<offsets.length;i++)offsets[i]=offsets[i-1]+shapes[i][1];let snippets=[`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<offsets.length;i++){let shift=offsets[i-1];snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`)}let lastIndex=offsets.length,lastShift=offsets[offsets.length-1];snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
         int yC = coords.y;
 
-        ${r.join(`
+        ${snippets.join(`
         `)}
       }
-    `}}class mZ{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=hs(t,e);let r=this.outputShape,o=r.length,s=Oe(o),c=Yn("coords",o),l=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((N,S)=>`T${S}`);let p=new Array(t.length-1);p[0]=t[0][e];for(let N=1;N<p.length;N++)p[N]=p[N-1]+t[N][e];let f=l[e],m=l.slice(-2),y=l.join(),b=`if (${f} < ${p[0]}) {
+    `}},ConcatPackedProgram=class{constructor(shapes,axis){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=backend_util_exports.computeOutShape(shapes,axis);let shape=this.outputShape,rank=shape.length,dtype=getCoordsDataType(rank),coords2=getChannels("coords",rank),channels=["x","y","z","w","u","v"].slice(0,rank);this.variableNames=shapes.map((_,i)=>`T${i}`);let offsets=new Array(shapes.length-1);offsets[0]=shapes[0][axis];for(let i=1;i<offsets.length;i++)offsets[i]=offsets[i-1]+shapes[i][axis];let channel=channels[axis],lastChannels=channels.slice(-2),allChannels=channels.join(),getValueSnippet=`if (${channel} < ${offsets[0]}) {
         return getChannel(
-            getT0(${y}), vec2(${m.join()}));
-        }`;for(let N=1;N<p.length;N++){let S=p[N-1];b+=`
-        if (${f} < ${p[N]}  && ${f} >= ${p[N-1]}) {
+            getT0(${allChannels}), vec2(${lastChannels.join()}));
+        }`;for(let i=1;i<offsets.length;i++){let shift2=offsets[i-1];getValueSnippet+=`
+        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i-1]}) {
           return getChannel(
-            getT${N}(${Gm(l,f,S)}),
-            vec2(${Gm(m,f,S)}));
-        }`}let v=p.length,T=p[p.length-1];b+=`
+            getT${i}(${shiftedChannels(channels,channel,shift2)}),
+            vec2(${shiftedChannels(lastChannels,channel,shift2)}));
+        }`}let lastIndex=offsets.length,shift=offsets[offsets.length-1];getValueSnippet+=`
         return getChannel(
-          getT${v}(${Gm(l,f,T)}),
-          vec2(${Gm(m,f,T)}));`,this.userCode=`
-      float getValue(${l.map(N=>"int "+N)}) {
-        ${b}
+          getT${lastIndex}(${shiftedChannels(channels,channel,shift)}),
+          vec2(${shiftedChannels(lastChannels,channel,shift)}));`,this.userCode=`
+      float getValue(${channels.map(x=>"int "+x)}) {
+        ${getValueSnippet}
       }
 
       void main() {
-        ${s} coords = getOutputCoords();
-        vec4 result = vec4(getValue(${c}), 0., 0., 0.);
+        ${dtype} coords = getOutputCoords();
+        vec4 result = vec4(getValue(${coords2}), 0., 0., 0.);
 
-        ${c[o-1]} = ${c[o-1]} + 1;
-        if (${c[o-1]} < ${r[o-1]}) {
-          result.g = getValue(${c});
+        ${coords2[rank-1]} = ${coords2[rank-1]} + 1;
+        if (${coords2[rank-1]} < ${shape[rank-1]}) {
+          result.g = getValue(${coords2});
         }
 
-        ${c[o-2]} = ${c[o-2]} + 1;
-        if (${c[o-2]} < ${r[o-2]}) {
-          result.a = getValue(${c});
+        ${coords2[rank-2]} = ${coords2[rank-2]} + 1;
+        if (${coords2[rank-2]} < ${shape[rank-2]}) {
+          result.a = getValue(${coords2});
         }
 
-        ${c[o-1]} = ${c[o-1]} - 1;
-        if (${c[o-2]} < ${r[o-2]} &&
-            ${c[o-1]} < ${r[o-1]}) {
-          result.b = getValue(${c});
+        ${coords2[rank-1]} = ${coords2[rank-1]} - 1;
+        if (${coords2[rank-2]} < ${shape[rank-2]} &&
+            ${coords2[rank-1]} < ${shape[rank-1]}) {
+          result.b = getValue(${coords2});
         }
         setOutput(result);
       }
-    `}}function Gm(n,t,e){let r=n.indexOf(t),o=n.map((s,c)=>c===r?`${s} - ${e}`:s);return o.join()}function lE(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.texData.get(r.dataId);return Cs({inputs:{x:o.complexTensorInfos.imag},backend:e})}let gZ={kernelName:wf,backendName:"webgl",kernelFunc:lE};function yZ(n,t,e){let r=[rl(n.shape),...ol(n.shape)],o={dtype:n.dtype,shape:r,dataId:n.dataId},s=[rl(t),...ol(t)],c=new WI(s,r),l=!0,p=e.runWebGLProgram(c,[o],n.dtype,null,l);return{dataId:p.dataId,shape:t,dtype:p.dtype}}function Ss(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{shape:s}=r,c=e,l=G(o.shape),p=Ge(s,l),f=G(p);_(l===f,()=>`The new shape (${p}) has ${f} elements and the old shape (${o.shape}) has ${l} elements. The new shape and old shape must have the same number of elements.`);let m=c.texData.get(o.dataId);return m.isPacked&&!Rm(o.shape,p)&&!(m.texture!==null&&Rm(m.shape,p))?yZ(o,p,c):(c.incRef(o.dataId),{dataId:o.dataId,shape:p,dtype:o.dtype})}let bZ={kernelName:Eu,backendName:"webgl",kernelFunc:Ss};function pl(n,t,e){let r=n[0].dtype;if(r==="complex64"){let f=n.map(T=>T1({inputs:{input:T},backend:e})),m=n.map(T=>lE({inputs:{input:T},backend:e})),y=pl(f,t,e),b=pl(m,t,e),v=ll({inputs:{real:y,imag:b},backend:e});return f.forEach(T=>e.disposeIntermediateTensorInfo(T)),m.forEach(T=>e.disposeIntermediateTensorInfo(T)),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),v}if(n.length>ct().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let f=Math.floor(n.length/2),m=pl(n.slice(0,f),t,e),y=pl(n.slice(f),t,e),b=pl([m,y],t,e);return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(y),b}if(ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&n[0].shape.length>1){let f=new mZ(n.map(m=>m.shape),t);return e.runWebGLProgram(f,n,r)}let o=hs(n.map(f=>f.shape),t),s=n.map(f=>Ss({inputs:{x:f},attrs:{shape:[-1,G(f.shape.slice(t))]},backend:e})),c=new dZ(s.map(f=>f.shape)),l=e.runWebGLProgram(c,s,r);s.forEach(f=>e.disposeIntermediateTensorInfo(f));let p=Ss({inputs:{x:l},attrs:{shape:o},backend:e});return e.disposeIntermediateTensorInfo(l),p}function xZ(n){let{inputs:t,backend:e,attrs:r}=n,{axis:o}=r,s=Vt(o,t[0].shape)[0],c=hs(t.map(f=>f.shape),s);if(G(c)===0)return e.makeTensorInfo(c,t[0].dtype,[]);let l=t.filter(f=>G(f.shape)>0);if(l.length===1)return l[0];let p=l.map(f=>f.shape);return id(p,s),pl(l,s,e)}let wZ={kernelName:uu,backendName:"webgl",kernelFunc:xZ};let vZ=iE+`
+    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel),res=channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c);return res.join()}function imag7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputData=backend3.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend3})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag7};function packedReshape(input2,afterShape,backend3){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=backend3.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}function reshape90(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{shape}=attrs,webglBackend=backend3,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);let xTexData=webglBackend.texData.get(x.dataId);return xTexData.isPacked&&!isReshapeFree(x.shape,$shape)&&!(xTexData.texture!==null&&isReshapeFree(xTexData.shape,$shape))?packedReshape(x,$shape,webglBackend):(webglBackend.incRef(x.dataId),{dataId:x.dataId,shape:$shape,dtype:x.dtype})}var reshapeConfig2={kernelName:Reshape,backendName:"webgl",kernelFunc:reshape90};function concatImpl(inputs,axis,backend3){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real7({inputs:{input:t},backend:backend3})),imags=inputs.map(t=>imag7({inputs:{input:t},backend:backend3})),realConcated=concatImpl(reals,axis,backend3),imagConcated=concatImpl(imags,axis,backend3),result2=complex10({inputs:{real:realConcated,imag:imagConcated},backend:backend3});return reals.forEach(r=>backend3.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend3.disposeIntermediateTensorInfo(i)),backend3.disposeIntermediateTensorInfo(realConcated),backend3.disposeIntermediateTensorInfo(imagConcated),result2}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl(inputs.slice(0,midIndex),axis,backend3),rightSide=concatImpl(inputs.slice(midIndex),axis,backend3),result2=concatImpl([leftSide,rightSide],axis,backend3);return backend3.disposeIntermediateTensorInfo(leftSide),backend3.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend3.runWebGLProgram(program2,inputs,dtype)}let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),tensors2D=inputs.map(x=>reshape90({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend3})),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend3.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend3.disposeIntermediateTensorInfo(r));let reshapedResult=reshape90({inputs:{x:result},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(result),reshapedResult}function concat18(args){let{inputs,backend:backend3,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend3.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl($inputs,$axis,backend3)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat18},COS=CHECK_NAN_SNIPPET_UNARY+`
   return cos(x);
-`,TZ=Vm(vZ),kZ={kernelName:lc,backendName:"webgl",kernelFunc:TZ};let NZ=`
+`,cos7=unaryKernelFunc2(COS),cosConfig2={kernelName:Cos,backendName:"webgl",kernelFunc:cos7},DIV=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,_Z=`
+return a / b;`,DIV_PACKED=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3659,21 +3664,21 @@ return a / b;`,_Z=`
   }
 
   return result;
-`,CZ=ul({opSnippet:NZ,packedOpSnippet:_Z,checkOutOfBounds:!0}),SZ={kernelName:uc,backendName:"webgl",kernelFunc:CZ};class uE{constructor(t,e,r){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,c=r?`${o}.0`:"1.0",l;if(t==="real")l="return real * expR - imag * expI;";else if(t==="imag")l="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
-      const float exponentMultiplier = ${s};
+`,div36=binaryKernelFunc2({opSnippet:DIV,packedOpSnippet:DIV_PACKED,checkOutOfBounds:!0}),divConfig2={kernelName:Div,backendName:"webgl",kernelFunc:div36},FFTProgram=class{constructor(component,inputShape,inverse){this.variableNames=["real","imag"];let innerDim=inputShape[1];this.outputShape=inputShape;let exponentMultiplierSnippet=inverse?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,resultDenominator=inverse?`${innerDim}.0`:"1.0",opString;if(component==="real")opString="return real * expR - imag * expI;";else if(component==="imag")opString="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);this.userCode=`
+      const float exponentMultiplier = ${exponentMultiplierSnippet};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
-        ${l}
+        ${opString}
       }
 
       float mulMatDFT(int batch, int index) {
-        float indexRatio = float(index) / float(${o});
+        float indexRatio = float(index) / float(${innerDim});
         float exponentMultiplierTimesIndexRatio =
             exponentMultiplier * indexRatio;
 
         float result = 0.0;
 
-        for (int i = 0; i < ${o}; i++) {
+        for (int i = 0; i < ${innerDim}; i++) {
           // x = (-2|2 * PI / N) * index * i;
           float x = exponentMultiplierTimesIndexRatio * float(i);
           float expR = cos(x);
@@ -3682,7 +3687,7 @@ return a / b;`,_Z=`
           float imag = getImag(batch, i);
 
           result +=
-              unaryOpComplex(real, expR, imag, expI) / ${c};
+              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
         }
 
         return result;
@@ -3692,29 +3697,29 @@ return a / b;`,_Z=`
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}}function pE(n,t,e){let r=e.texData.get(n.dataId),o=G(n.shape),s=n.shape[n.shape.length-1],c=o/s,l=Ss({inputs:{x:n},backend:e,attrs:{shape:[c,s]}}),p=l.shape,f=new uE("real",p,t),m=new uE("imag",p,t),y=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:p},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:p}],b=e.runWebGLProgram(f,y,"float32"),v=e.runWebGLProgram(m,y,"float32"),T=ll({inputs:{real:b,imag:v},backend:e});e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v);let N=Ss({inputs:{x:T},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(N),N}function $Z(n){let{inputs:t,backend:e}=n,{input:r}=t;return pE(r,!1,e)}let IZ={kernelName:yf,backendName:"webgl",kernelFunc:$Z};class EZ{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
+    `}};function fftImpl2(x,inverse,backend3){let xData=backend3.texData.get(x.dataId),inputSize=util_exports.sizeFromShape(x.shape),innerDimensionSize=x.shape[x.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape90({inputs:{x},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),xShape=input2D.shape,realProgram=new FFTProgram("real",xShape,inverse),imagProgram=new FFTProgram("imag",xShape,inverse),inputs=[{dataId:xData.complexTensorInfos.real.dataId,dtype:xData.complexTensorInfos.real.dtype,shape:xShape},{dataId:xData.complexTensorInfos.imag.dataId,dtype:xData.complexTensorInfos.imag.dtype,shape:xShape}],realPart=backend3.runWebGLProgram(realProgram,inputs,"float32"),imagPart=backend3.runWebGLProgram(imagProgram,inputs,"float32"),complexOutput=complex10({inputs:{real:realPart,imag:imagPart},backend:backend3});backend3.disposeIntermediateTensorInfo(realPart),backend3.disposeIntermediateTensorInfo(imagPart);let complexOutputReshaped=reshape90({inputs:{x:complexOutput},backend:backend3,attrs:{shape:x.shape}});return backend3.disposeIntermediateTensorInfo(complexOutputReshaped),complexOutputReshaped}function fft7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs;return fftImpl2(input2,!1,backend3)}var fftConfig2={kernelName:FFT,backendName:"webgl",kernelFunc:fft7},FlipLeftRightProgram=class{constructor(imageShape){this.variableNames=["Image"],this.outputShape=[];let imageWidth=imageShape[2];this.outputShape=imageShape,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
 
-          int coordX = ${e} - x;
+          int coordX = ${imageWidth} - x;
           float outputValue;
-          if(coordX >= 0 && coordX < ${e}) {
+          if(coordX >= 0 && coordX < ${imageWidth}) {
             outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
           } else {
             outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
           }
           setOutput(outputValue);
         }
-    `}}let DZ={kernelName:bf,backendName:"webgl",kernelFunc:({inputs:n,backend:t})=>{let{image:e}=n,r=t,o=new EZ(e.shape),s=r.runWebGLProgram(o,[e],e.dtype);return s}};class AZ{constructor(t){this.variableNames=["A"];let e=Jn(),[r,o]=t;this.outputShape=t,this.userCode=`
+    `}},flipLeftRightConfig2={kernelName:FlipLeftRight,backendName:"webgl",kernelFunc:({inputs,backend:backend3})=>{let{image:image3}=inputs,webglBackend=backend3,program=new FlipLeftRightProgram(image3.shape),output=webglBackend.runWebGLProgram(program,[image3],image3.dtype);return output}},FromPixelsProgram=class{constructor(outputShape){this.variableNames=["A"];let glsl=getGlslDifferences(),[height,width]=outputShape;this.outputShape=outputShape,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
         int texC = coords[1];
         int depth = coords[2];
-        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${r}.0);
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);
 
-        vec4 values = ${e.texture2D}(A, uv);
+        vec4 values = ${glsl.texture2D}(A, uv);
         float value;
         if (depth == 0) {
           value = values.r;
@@ -3728,7 +3733,7 @@ return a / b;`,_Z=`
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}}class FZ{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Jn(),[r,o]=t;this.outputShape=t,this.userCode=`
+    `}},FromPixelsPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let glsl=getGlslDifferences(),[height,width]=outputShape;this.outputShape=outputShape,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3743,8 +3748,8 @@ return a / b;`,_Z=`
             depth = coords[2] + col;
 
             vec2 uv = (vec2(texC, texR) + halfCR) /
-                       vec2(${o}.0, ${r}.0);
-            vec4 values = ${e.texture2D}(A, uv);
+                       vec2(${width}.0, ${height}.0);
+            vec4 values = ${glsl.texture2D}(A, uv);
             float value;
             if (depth == 0) {
               value = values.r;
@@ -3760,17 +3765,17 @@ return a / b;`,_Z=`
           }
         }
 
-        ${e.output} = result;
+        ${glsl.output} = result;
       }
-    `}}let RZ={kernelName:Ff,backendName:"webgl",kernelFunc:PZ},hl;function PZ(n){let{inputs:t,backend:e,attrs:r}=n,{pixels:o}=t,{numChannels:s}=r,c=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,l=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[p,f]=c?[o.videoWidth,o.videoHeight]:[o.width,o.height],m=[f,p],y=[f,p,s];(l||c)&&(hl==null&&(hl=document.createElement("canvas").getContext("2d")),hl.canvas.width=p,hl.canvas.height=f,hl.drawImage(o,0,0,p,f),o=hl.canvas);let b=e.makeTensorInfo(m,"int32");e.texData.get(b.dataId).usage=Mr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(b.dataId),o);let v=ct().getBool("WEBGL_PACK")?new FZ(y):new AZ(y),T=e.runWebGLProgram(v,[b],"int32");return e.disposeData(b.dataId),T}function OZ(n){let{inputs:t,backend:e}=n,{input:r}=t;return pE(r,!0,e)}let LZ={kernelName:xf,backendName:"webgl",kernelFunc:OZ};class hE{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:o,inSize:s,outSize:c}=t;this.outputShape=[o,c];let l=Math.floor(r/4)*4,p=r%4,f="sumValue += dot(values, ones);";if(e!=null){let y=1/e;f=`sumValue += dot(values * ${gt(y)?y.toPrecision(2):y}, ones);`}let m="";s%r>0&&(m=`
-        if (inIdx < 0 || inIdx >= ${s}) {
+    `}},fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend3,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend3.makeTensorInfo(texShape,"int32");backend3.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend3.gpgpu.uploadPixelDataToTexture(backend3.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend3.runWebGLProgram(program,[tempPixelHandle],"int32");return backend3.disposeData(tempPixelHandle.dataId),res}function ifft7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs;return fftImpl2(input2,!0,backend3)}var ifftConfig2={kernelName:IFFT,backendName:"webgl",kernelFunc:ifft7},MeanProgram=class{constructor(reduceInfo,divisor){this.variableNames=["x"];let{windowSize,batchSize,inSize,outSize}=reduceInfo;this.outputShape=[batchSize,outSize];let windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet="sumValue += dot(values, ones);";if(divisor!=null){let denominator=1/divisor;updateSnippet=`sumValue += dot(values * ${util_exports.isInt(denominator)?denominator.toPrecision(2):denominator}, ones);`}let checkOutOfBounds="";inSize%windowSize>0&&(checkOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
           return 0.0;
         }
       `),this.userCode=`
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
-        ${m}
+        ${checkOutOfBounds}
         return getX(batch, inIdx);
       }
 
@@ -3778,11 +3783,11 @@ return a / b;`,_Z=`
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int outIdx = coords[1];
-        int inOffset = outIdx * ${r};
+        int inOffset = outIdx * ${windowSize};
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${l}; i += 4) {
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -3791,141 +3796,141 @@ return a / b;`,_Z=`
             getValue(batch, inIdx + 3)
           );
 
-          ${f}
+          ${updateSnippet}
         }
 
-        int inIdx = inOffset + ${l};
-        if (${p===1}) {
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===1}) {
           vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
 
-          ${f}
-        } else if (${p===2}) {
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1), 0.0, 0.0);
 
-          ${f}
-        } else if (${p===3}) {
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
             getValue(batch, inIdx + 2), 0.0);
 
-          ${f}
+          ${updateSnippet}
         }
         setOutput(sumValue);
       }
-    `}}function MZ(n){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:n[1],r=sp(e);t.push({inSize:e,windowSize:r,outSize:Math.ceil(e/r)})}return t}function fE(n,t,e,r){let o=MZ(n.shape),s=n;for(let c=0;c<o.length;c++){let{inSize:l,windowSize:p,outSize:f}=o[c],m,y;e==="mean"?m=c===0?new hE({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f},l):new hE({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f}):m=new zI({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f},e),y=s,s=r.runWebGLProgram(m,[s],t),y.dataId!==n.dataId&&r.disposeIntermediateTensorInfo(y)}return s}function BZ(n,t,e,r){let o=G(t),s=G(n.shape),c=s/o,l=Ss({inputs:{x:n},attrs:{shape:[c,o]},backend:r}),p=fE(l,n.dtype,"max",r),f=Ss({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),f}class zZ{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let c=0;c<r.length;c++)r[c]=t[e[c]];this.outputShape=r,this.rank=r.length;let o=Oe(this.rank),s=WZ(e);this.userCode=`
+    `}};function getReductionStages(inShape){let stages=[];for(;stages.length===0||stages[stages.length-1].outSize!==1;){let outSize=stages.length?stages[stages.length-1].outSize:inShape[1],windowSize=backend_util_exports.computeOptimalWindowSize(outSize);stages.push({inSize:outSize,windowSize,outSize:Math.ceil(outSize/windowSize)})}return stages}function reduce(x,dtype,reductionType,backend3){let reductionStages=getReductionStages(x.shape),result=x;for(let i=0;i<reductionStages.length;i++){let{inSize,windowSize,outSize}=reductionStages[i],program,previousResult;reductionType==="mean"?program=i===0?new MeanProgram({windowSize,inSize,batchSize:x.shape[0],outSize},inSize):new MeanProgram({windowSize,inSize,batchSize:x.shape[0],outSize}):program=new ReduceProgram({windowSize,inSize,batchSize:x.shape[0],outSize},reductionType),previousResult=result,result=backend3.runWebGLProgram(program,[result],dtype),previousResult.dataId!==x.dataId&&backend3.disposeIntermediateTensorInfo(previousResult)}return result}function maxImpl2(x,reduceShape,outShape,backend3){let inSize=util_exports.sizeFromShape(reduceShape),xSize=util_exports.sizeFromShape(x.shape),batchSize=xSize/inSize,reshapedInput=reshape90({inputs:{x},attrs:{shape:[batchSize,inSize]},backend:backend3}),reduced=reduce(reshapedInput,x.dtype,"max",backend3),reshapedOutput=reshape90({inputs:{x:reduced},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(reshapedInput),backend3.disposeIntermediateTensorInfo(reduced),reshapedOutput}var TransposeProgram=class{constructor(aShape,newDim){this.variableNames=["A"];let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[newDim[i]];this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),switched=getSwitchedCoords(newDim);this.userCode=`
     void main() {
-      ${o} resRC = getOutputCoords();
-      setOutput(getA(${s}));
+      ${dtype} resRC = getOutputCoords();
+      setOutput(getA(${switched}));
     }
-    `}}function WZ(n){let t=n.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let o=0;o<n.length;o++)r[n[o]]=e[o];return r.join()}class VZ{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(t.length);for(let m=0;m<r.length;m++)r[m]=t[e[m]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Oe(this.rank),s=wI("rc",this.rank),c=new Array(this.rank);for(let m=0;m<e.length;m++)c[e[m]]=s[m];let l=`vec2(${c.slice(-2).join()})`,p=`++${s[this.rank-1]} < ${r[this.rank-1]}`,f=`getChannel(getA(${c.join()}), ${l})`;this.userCode=`
+    `}};function getSwitchedCoords(newDim){let rank=newDim.length;if(rank>6)throw Error(`Transpose for rank ${rank} is not yet supported`);let originalOrder=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],switchedCoords=new Array(rank);for(let i=0;i<newDim.length;i++)switchedCoords[newDim[i]]=originalOrder[i];return switchedCoords.join()}var TransposePackedProgram=class{constructor(aShape,newDim){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[newDim[i]];if(this.outputShape=outputShape,this.rank=outputShape.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let dtype=getCoordsDataType(this.rank),outputOrder=getVecChannels("rc",this.rank),switchedOrder=new Array(this.rank);for(let i=0;i<newDim.length;i++)switchedOrder[newDim[i]]=outputOrder[i];let innerDims=`vec2(${switchedOrder.slice(-2).join()})`,nextColumn=`++${outputOrder[this.rank-1]} < ${outputShape[this.rank-1]}`,getc=`getChannel(getA(${switchedOrder.join()}), ${innerDims})`;this.userCode=`
     void main() {
-      ${o} rc = getOutputCoords();
+      ${dtype} rc = getOutputCoords();
       vec4 result = vec4(0.);
-      result[0] = ${f};
-      if(${p}) {
-        result[1] = ${f};
+      result[0] = ${getc};
+      if(${nextColumn}) {
+        result[1] = ${getc};
       }
-      --${s[this.rank-1]};
-      if(++${s[this.rank-2]} < ${r[this.rank-2]}) {
-        result[2] = ${f};
-        if(${p}) {
-          result[3] = ${f};
+      --${outputOrder[this.rank-1]};
+      if(++${outputOrder[this.rank-2]} < ${outputShape[this.rank-2]}) {
+        result[2] = ${getc};
+        if(${nextColumn}) {
+          result[3] = ${getc};
         }
       }
       setOutput(result);
     }
-    `}}function N1(n,t,e){let r=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VZ(n.shape,t):new zZ(n.shape,t);return e.runWebGLProgram(r,[n],n.dtype)}let GZ={kernelName:Nu,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{reductionIndices:o,keepDims:s}=t,c=e,l=r.shape.length,p=Vt(o,r.shape),f=p,m=ir(f,l),y=m!=null,b=c.shouldExecuteOnCPU([r]),v=r;if(y){if(b){let I=c.texData.get(v.dataId),P=I.values,E=new Array(l);for(let q=0;q<E.length;q++)E[q]=r.shape[m[q]];let L=y1(P,r.shape,r.dtype,m,E);v=c.makeTensorInfo(E,r.dtype);let B=c.texData.get(v.dataId);B.values=L}else v=N1(r,m,c);f=xr(f.length,l)}sr("max",f,l);let[T,N]=Fn(v.shape,f),S=T;s&&(S=Rn(T,p));let D;if(b){let I=c.texData.get(v.dataId),P=I.values,E=lX(P,G(N),S,r.dtype);D=c.makeTensorInfo(S,r.dtype);let L=c.texData.get(D.dataId);L.values=E}else D=BZ(v,N,S,c);return y&&c.disposeIntermediateTensorInfo(v),D}};function UZ(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;sh(o,"maxPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Kn(o.shape,s,c,f,l,p);if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))return Cs({inputs:{x:o},backend:e});let y=new ah(m,"max",!1);return e.runWebGLProgram(y,[o],o.dtype)}let qZ={kernelName:_u,backendName:"webgl",kernelFunc:UZ};function HZ(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s,output:c}=t,l=s;sh([s,c],"maxPoolBackprop");let{filterSize:p,strides:f,pad:m,dimRoundingMode:y}=r,b=Kn(l.shape,p,f,1,m,y),v=!0,T=new ah(b,"max",v),N=e.runWebGLProgram(T,[l],l.dtype),S=new N7(b),D=e.runWebGLProgram(S,[o,N],l.dtype);return e.disposeIntermediateTensorInfo(N),D}let jZ={kernelName:Tf,backendName:"webgl",kernelFunc:HZ};function KZ(n,t,e,r){let o=new ah(e,"max",!1),s=r.runWebGLProgram(o,[n],"float32");o=new ah(e,"max",!0,!0,t);let c=r.runWebGLProgram(o,[n],"float32");return[s,c]}let XZ={kernelName:kf,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{filterSize:o,strides:s,pad:c,includeBatchInIndex:l}=t,p=e;_(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let f=[1,1];_(fn(s,f),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${f}'`);let m=Kn(r.shape,o,s,f,c),[y,b]=KZ(r,l,m,p);return[y,b]}};function YZ(n,t,e,r){let o=G(t),s=G(n.shape),c=s/o,l=Ss({inputs:{x:n},attrs:{shape:[c,o]},backend:r}),p=fE(l,"float32","mean",r),f=Ss({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),f}let JZ={kernelName:ix,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{keepDims:o,axis:s}=t,c=e,l=r.shape.length,p=Vt(s,r.shape),f=p,m=ir(f,l),y=m!=null,b=c.shouldExecuteOnCPU([r]),v=[],T=r;if(y){if(b){let P=c.texData.get(T.dataId),E=P.values,L=new Array(l);for(let H=0;H<L.length;H++)L[H]=r.shape[m[H]];let B=y1(E,r.shape,r.dtype,m,L);T=c.makeTensorInfo(L,r.dtype);let q=c.texData.get(T.dataId);q.values=B}else T=N1(r,m,c);v.push(T),f=xr(f.length,l)}sr("sum",f,l);let[N,S]=Fn(T.shape,f),D=N;o&&(D=Rn(N,p));let I=YZ(T,S,D,c);for(let P of v)c.disposeIntermediateTensorInfo(P);return I}};class ZZ{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((m,y)=>m[0]+t[y]+m[1]);let o=t.length,s=Oe(o),c=e.map(m=>m[0]).join(","),l=e.map((m,y)=>m[0]+t[y]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),f=r==="reflect"?0:1;if(o===1){this.userCode=`
-        int start = ${c};
-        int end = ${l};
+    `}};function transposeImpl2(x,perm,backend3){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new TransposePackedProgram(x.shape,perm):new TransposeProgram(x.shape,perm);return backend3.runWebGLProgram(program,[x],x.dtype)}var maxConfig2={kernelName:Max,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{reductionIndices,keepDims}=attrs,webglBackend=backend3,xRank=x.shape.length,origAxes=util_exports.parseAxisParam(reductionIndices,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),maxInputIsTransposed=permutedAxes!=null,shouldExecuteOnCPU=webglBackend.shouldExecuteOnCPU([x]),maxInput=x;if(maxInputIsTransposed){if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(maxInput.dataId),values=xTexData.values,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[permutedAxes[i]];let maxInputValues=transposeImplCPU(values,x.shape,x.dtype,permutedAxes,newShape);maxInput=webglBackend.makeTensorInfo(newShape,x.dtype);let maxInputData=webglBackend.texData.get(maxInput.dataId);maxInputData.values=maxInputValues}else maxInput=transposeImpl2(x,permutedAxes,webglBackend);axes=backend_util_exports.getInnerMostAxes(axes.length,xRank)}backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(maxInput.shape,axes),outShape=maxOutShape;keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes));let out;if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(maxInput.dataId),values=xTexData.values,outValues=maxImplCPU(values,util_exports.sizeFromShape(reduceShape),outShape,x.dtype);out=webglBackend.makeTensorInfo(outShape,x.dtype);let outData=webglBackend.texData.get(out.dataId);outData.values=outValues}else out=maxImpl2(maxInput,reduceShape,outShape,webglBackend);return maxInputIsTransposed&&webglBackend.disposeIntermediateTensorInfo(maxInput),out}};function maxPool3(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex2(x,"maxPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))return identity3({inputs:{x},backend:backend3});let maxPoolProgram=new Pool2DProgram(convInfo,"max",!1);return backend3.runWebGLProgram(maxPoolProgram,[x],x.dtype)}var maxPoolConfig2={kernelName:MaxPool,backendName:"webgl",kernelFunc:maxPool3};function maxPoolBackprop3(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex2([input2,output],"maxPoolBackprop");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),getPositions=!0,maxPoolPositionsProgram=new Pool2DProgram(convInfo,"max",getPositions),maxPoolPositions2=backend3.runWebGLProgram(maxPoolPositionsProgram,[x],x.dtype),maxPoolBackPropProgram=new MaxPool2DBackpropProgram(convInfo),result=backend3.runWebGLProgram(maxPoolBackPropProgram,[dy,maxPoolPositions2],x.dtype);return backend3.disposeIntermediateTensorInfo(maxPoolPositions2),result}var maxPoolBackpropConfig2={kernelName:MaxPoolBackprop,backendName:"webgl",kernelFunc:maxPoolBackprop3};function maxPoolWithArgmaxImpl2(x,includeBatchInIndex,convInfo,backend3){let program=new Pool2DProgram(convInfo,"max",!1),poolOutput=backend3.runWebGLProgram(program,[x],"float32");program=new Pool2DProgram(convInfo,"max",!0,!0,includeBatchInIndex);let indexOutput=backend3.runWebGLProgram(program,[x],"float32");return[poolOutput,indexOutput]}var maxPoolWithArgmaxConfig2={kernelName:MaxPoolWithArgmax,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{filterSize,strides,pad:pad11,includeBatchInIndex}=attrs,webglBackend=backend3;util_exports.assert(x.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);let dilations=[1,1];util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11),[result,indexes]=maxPoolWithArgmaxImpl2(x,includeBatchInIndex,convInfo,webglBackend);return[result,indexes]}};function meanImpl(x,reduceShape,outShape,backend3){let inSize=util_exports.sizeFromShape(reduceShape),xSize=util_exports.sizeFromShape(x.shape),batchSize=xSize/inSize,reshapedInput=reshape90({inputs:{x},attrs:{shape:[batchSize,inSize]},backend:backend3}),reduced=reduce(reshapedInput,"float32","mean",backend3),reshapedOutput=reshape90({inputs:{x:reduced},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(reshapedInput),backend3.disposeIntermediateTensorInfo(reduced),reshapedOutput}var meanConfig={kernelName:Mean,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{keepDims,axis}=attrs,webglBackend=backend3,xRank=x.shape.length,origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),meanInputIsTransposed=permutedAxes!=null,shouldExecuteOnCPU=webglBackend.shouldExecuteOnCPU([x]),intermediates=[],meanInput=x;if(meanInputIsTransposed){if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(meanInput.dataId),values=xTexData.values,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[permutedAxes[i]];let meanInputValues=transposeImplCPU(values,x.shape,x.dtype,permutedAxes,newShape);meanInput=webglBackend.makeTensorInfo(newShape,x.dtype);let meanInputData=webglBackend.texData.get(meanInput.dataId);meanInputData.values=meanInputValues}else meanInput=transposeImpl2(x,permutedAxes,webglBackend);intermediates.push(meanInput),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank)}backend_util_exports.assertAxesAreInnerMostDims("sum",axes,xRank);let[meanOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(meanInput.shape,axes),outShape=meanOutShape;keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(meanOutShape,origAxes));let out=meanImpl(meanInput,reduceShape,outShape,webglBackend);for(let i of intermediates)webglBackend.disposeIntermediateTensorInfo(i);return out}},MirrorPadProgram=class{constructor(xShape,paddings,mode){this.variableNames=["x"],this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),unpackedCoords=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,rank),offset=mode==="reflect"?0:1;if(rank===1){this.userCode=`
+        int start = ${start};
+        int end = ${end};
 
         void main() {
           int outC = getOutputCoords();
           if (outC < start) {
-            outC = start * 2 - outC - ${f};
+            outC = start * 2 - outC - ${offset};
           } else if(outC >= end) {
-            outC = (end - 1) * 2 - outC + ${f};
+            outC = (end - 1) * 2 - outC + ${offset};
           }
           setOutput(getX(outC - start));
         }
       `;return}this.userCode=`
-      ${s} start = ${s}(${c});
-      ${s} end = ${s}(${l});
+      ${dtype} start = ${dtype}(${start});
+      ${dtype} end = ${dtype}(${end});
 
       void main() {
-        ${s} outC = getOutputCoords();
-        for (int i = 0; i < ${o}; i++) {
+        ${dtype} outC = getOutputCoords();
+        for (int i = 0; i < ${rank}; i++) {
           if (outC[i] < start[i]) {
-            outC[i] = start[i] * 2 - outC[i] - ${f};
+            outC[i] = start[i] * 2 - outC[i] - ${offset};
           } else if(outC[i] >= end[i]) {
-            outC[i] = (end[i] - 1) * 2 - outC[i] + ${f};
+            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
           }
         }
-        ${s} coords = outC - start;
-        setOutput(getX(${p}));
+        ${dtype} coords = outC - start;
+        setOutput(getX(${unpackedCoords}));
       }
-    `}}class QZ{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((T,N)=>T[0]+t[N]+T[1]);let o=t.length,s=Oe(o),c=e.map(T=>T[0]).join(","),l=e.map((T,N)=>T[0]+t[N]).join(","),p=Yn("rc",o),f=Yn("source",o),m=`${p[o-1]} < ${this.outputShape[o-1]}`,y=o===1?"source":`vec2(${f.slice(-2).join()})`,b=r==="reflect"?0:1,v="";if(o===1){let T=`
-        ${s} source = rc;
+    `}},MirrorPadPackedProgram=class{constructor(xShape,paddings,mode){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),coords2=getChannels("rc",rank),source=getChannels("source",rank),cLimit=`${coords2[rank-1]} < ${this.outputShape[rank-1]}`,innerDims=rank===1?"source":`vec2(${source.slice(-2).join()})`,offset=mode==="reflect"?0:1,mainLoop="";if(rank===1){let padSetup=`
+        ${dtype} source = rc;
         if (source < start) {
-          source = start * 2 - source - ${b};
+          source = start * 2 - source - ${offset};
         } else if (source >= end) {
-          source = (end - 1) * 2 - source + ${b};
+          source = (end - 1) * 2 - source + ${offset};
         }
         source -= start;
-      `;v=`
-        ${s} rc = outputLoc;
-        ${T}
-        result[0] = getChannel(getX(${f.join()}), ${y});
-        ${p[o-1]} += 1;
-        if(${m}) {
-          ${T}
-          result[1] = getChannel(getX(${f.join()}), ${y});
+      `;mainLoop=`
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords2[rank-1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
         }
-      `}else{let T=`
-        ${s} source = rc;
-        ${s} lt = ${s}(lessThan(source, start));
-        ${s} gte = ${s}(greaterThanEqual(source, end));
-        ${s} orig = 1 - (lt + gte);
+      `}else{let padSetup=`
+        ${dtype} source = rc;
+        ${dtype} lt = ${dtype}(lessThan(source, start));
+        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
+        ${dtype} orig = 1 - (lt + gte);
         source = orig * source +
-                lt * (start * 2 - source - ${b}) +
-                gte * ((end - 1) * 2 - source + ${b});
+                lt * (start * 2 - source - ${offset}) +
+                gte * ((end - 1) * 2 - source + ${offset});
         source -= start;
-      `;v=`
-        ${s} rc = outputLoc;
-        ${T}
-        result[0] = getChannel(getX(${f.join()}), ${y});
-        ${p[o-1]} += 1;
-        if(${m}) {
-          ${T}
-          result[1] = getChannel(getX(${f.join()}), ${y});
+      `;mainLoop=`
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords2[rank-1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
         }
         rc = outputLoc;
-        ${p[o-2]} += 1;
-        if(${p[o-2]} < ${this.outputShape[o-2]}) {
-          ${T}
-          result[2] = getChannel(getX(${f.join()}), ${y});
-          ${p[o-1]} += 1;
-          if(${m}) {
-            ${T}
-            result[3] = getChannel(getX(${f.join()}), ${y});
+        ${coords2[rank-2]} += 1;
+        if(${coords2[rank-2]} < ${this.outputShape[rank-2]}) {
+          ${padSetup}
+          result[2] = getChannel(getX(${source.join()}), ${innerDims});
+          ${coords2[rank-1]} += 1;
+          if(${cLimit}) {
+            ${padSetup}
+            result[3] = getChannel(getX(${source.join()}), ${innerDims});
           }
         }
       `}this.userCode=`
-      const ${s} start = ${s}(${c});
-      const ${s} end = ${s}(${l});
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
 
       void main() {
-        ${s} outputLoc = getOutputCoords();
+        ${dtype} outputLoc = getOutputCoords();
         vec4 result = vec4(0.);
-        ${v}
+        ${mainLoop}
         setOutput(result);
       }
-    `}}let t9=({inputs:n,backend:t,attrs:e})=>{let{x:r}=n,{paddings:o,mode:s}=e,c=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new QZ(r.shape,o,s):new ZZ(r.shape,o,s),l=t.runWebGLProgram(c,[r],r.dtype);return l},e9={kernelName:Cu,backendName:"webgl",kernelFunc:t9};let dE={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class mE{constructor(t,e,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=le(e,r),this.userCode=`
+    `}},mirrorPadKernelFunc=({inputs,backend:backend3,attrs})=>{let{x}=inputs,{paddings,mode}=attrs,program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MirrorPadPackedProgram(x.shape,paddings,mode):new MirrorPadProgram(x.shape,paddings,mode),output=backend3.runWebGLProgram(program,[x],x.dtype);return output},mirrorPadConfig2={kernelName:MirrorPad,backendName:"webgl",kernelFunc:mirrorPadKernelFunc},COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},BinaryOpComplexProgram=class{constructor(op22,aShape,bShape){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
-        ${t}
+        ${op22}
       }
 
       void main() {
@@ -3935,36 +3940,36 @@ return a / b;`,_Z=`
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}}let gE="return a * b;";function n9(n){let{inputs:t,backend:e}=n,{a:r,b:o}=t,s=jn(r.dtype,o.dtype);if(r.dtype==="complex64"){let l=e.texData.get(r.dataId),p=e.texData.get(o.dataId),f=new mE(dE.REAL,r.shape,o.shape),m=new mE(dE.IMAG,r.shape,o.shape),y=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}],b=e.runWebGLProgram(f,y,"float32"),v=e.runWebGLProgram(m,y,"float32"),T=ll({inputs:{real:b,imag:v},backend:e});return e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v),T}if(e.shouldExecuteOnCPU([r,o])){let l=e.texData.get(r.dataId),p=e.texData.get(o.dataId),[f,m]=uX(r.shape,o.shape,l.values,p.values,s),y=e.makeTensorInfo(m,s),b=e.texData.get(y.dataId);return b.values=f,y}let c;return ct().getBool("WEBGL_PACK_BINARY_OPERATIONS")?c=new Ns(gE,r.shape,o.shape):c=new Hn(gE,r.shape,o.shape),e.runWebGLProgram(c,[r,o],s)}let r9={kernelName:pc,backendName:"webgl",kernelFunc:n9};let o9={kernelName:px,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Bc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l}=e,p=t,f=p.readSync(r.dataId),m=p.readSync(o.dataId),y=s,b=c,v=l;return Od(f,m,y,b,v)}};let s9=Ld,i9={kernelName:Nf,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Bc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:p}=e,f=t,m=f.readSync(r.dataId),y=f.readSync(o.dataId),{selectedIndices:b,validOutputs:v}=s9(m,y,s,c,l,p);return[b,v]}};let a9=Md,c9={kernelName:_f,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Bc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l,softNmsSigma:p}=e,f=t,m=f.readSync(r.dataId),y=f.readSync(o.dataId),b=s,v=c,T=l,N=p,{selectedIndices:S,selectedScores:D}=a9(m,y,b,v,T,N);return[S,D]}};class l9{constructor(t,e,r,o){this.variableNames=["Image"],this.outputShape=[];let s=t[1],c=t[2],l=Math.sin(e).toFixed(3),p=Math.cos(e).toFixed(3);this.outputShape=t;let[f,m]=jw(o,s,c),y=f.toFixed(3),b=m.toFixed(3),v="";typeof r=="number"?v=`float outputValue = ${r.toFixed(2)};`:v=`
-        vec3 fill = vec3(${r.join(",")});
+    `}},MUL="return a * b;";function multiply3(args){let{inputs,backend:backend3}=args,{a,b}=inputs,dtype=backend_util_exports.upcastType(a.dtype,b.dtype);if(a.dtype==="complex64"){let aData=backend3.texData.get(a.dataId),bData=backend3.texData.get(b.dataId),realProgram=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,a.shape,b.shape),imagProgram=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,a.shape,b.shape),inputs2=[{dataId:aData.complexTensorInfos.real.dataId,dtype:aData.complexTensorInfos.real.dtype,shape:a.shape},{dataId:aData.complexTensorInfos.imag.dataId,dtype:aData.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:bData.complexTensorInfos.real.dataId,dtype:bData.complexTensorInfos.real.dtype,shape:b.shape},{dataId:bData.complexTensorInfos.imag.dataId,dtype:bData.complexTensorInfos.imag.dtype,shape:b.shape}],realPart=backend3.runWebGLProgram(realProgram,inputs2,"float32"),imagPart=backend3.runWebGLProgram(imagProgram,inputs2,"float32"),complexOutput=complex10({inputs:{real:realPart,imag:imagPart},backend:backend3});return backend3.disposeIntermediateTensorInfo(realPart),backend3.disposeIntermediateTensorInfo(imagPart),complexOutput}if(backend3.shouldExecuteOnCPU([a,b])){let aData=backend3.texData.get(a.dataId),bData=backend3.texData.get(b.dataId),[outValues,outShape]=multiplyImplCPU(a.shape,b.shape,aData.values,bData.values,dtype),out=backend3.makeTensorInfo(outShape,dtype),outData=backend3.texData.get(out.dataId);return outData.values=outValues,out}let program;return env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?program=new BinaryOpPackedProgram(MUL,a.shape,b.shape):program=new BinaryOpProgram(MUL,a.shape,b.shape),backend3.runWebGLProgram(program,[a,b],dtype)}var multiplyConfig2={kernelName:Multiply,backendName:"webgl",kernelFunc:multiply3},nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold;return kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal)}},nonMaxSuppressionV4Impl3=kernel_impls_exports.nonMaxSuppressionV4Impl,nonMaxSuppressionV4Config2={kernelName:NonMaxSuppressionV4,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl3(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[selectedIndices,validOutputs]}},nonMaxSuppressionV5Impl3=kernel_impls_exports.nonMaxSuppressionV5Impl,nonMaxSuppressionV5Config2={kernelName:NonMaxSuppressionV5,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl3(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[selectedIndices,selectedScores]}},RotateProgram=class{constructor(imageShape,radians,fillValue,center){this.variableNames=["Image"],this.outputShape=[];let imageHeight=imageShape[1],imageWidth=imageShape[2],sinFactor=Math.sin(radians).toFixed(3),cosFactor=Math.cos(radians).toFixed(3);this.outputShape=imageShape;let[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),centerXString=centerX.toFixed(3),centerYString=centerY.toFixed(3),fillSnippet="";typeof fillValue=="number"?fillSnippet=`float outputValue = ${fillValue.toFixed(2)};`:fillSnippet=`
+        vec3 fill = vec3(${fillValue.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) - ${y}) * ${p} - (float(y) - ${b}) * ${l};
-          float coordYFloat = (float(x) - ${y}) * ${l} + (float(y) - ${b}) * ${p};
-          int coordX = int(round(coordXFloat + ${y}));
-          int coordY = int(round(coordYFloat + ${b}));
-          ${v}
-          if(coordX >= 0 && coordX < ${c} && coordY >= 0 && coordY < ${s}) {
+          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};
+          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};
+          int coordX = int(round(coordXFloat + ${centerXString}));
+          int coordY = int(round(coordYFloat + ${centerYString}));
+          ${fillSnippet}
+          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
             outputValue = getImage(coords[0], coordY, coordX, coords[3]);
           }
           setOutput(outputValue);
         }
-    `}}let u9={kernelName:Rf,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{image:r}=n,{radians:o,fillValue:s,center:c}=t,l=e,p=new l9(r.shape,o,s,c),f=l.runWebGLProgram(p,[r],r.dtype);return f}};let p9=iE+`
+    `}},rotateWithOffsetConfig2={kernelName:RotateWithOffset,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,webglBackend=backend3,program=new RotateProgram(image3.shape,radians,fillValue,center),output=webglBackend.runWebGLProgram(program,[image3],image3.dtype);return output}},SIN=CHECK_NAN_SNIPPET_UNARY+`
   return sin(x);
-`,h9=Vm(p9),f9={kernelName:hc,backendName:"webgl",kernelFunc:h9};let d9="return x * x;",m9=Vm(d9),g9={kernelName:Df,backendName:"webgl",kernelFunc:m9};let yE="return (a - b) * (a - b);",y9=ul({opSnippet:yE,packedOpSnippet:yE}),b9={kernelName:fc,backendName:"webgl",kernelFunc:y9};let bE="return a - b;",x9=ul({opSnippet:bE,packedOpSnippet:bE,supportsComplex:!0,cpuKernelImpl:fX}),w9={kernelName:dc,backendName:"webgl",kernelFunc:x9};let v9="return tan(x);",T9=Vm(v9),k9={kernelName:mc,backendName:"webgl",kernelFunc:T9};let N9={kernelName:Wu,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{perm:o}=t,s=e,c=r.shape.length,l=new Array(c);for(let f=0;f<l.length;f++)l[f]=r.shape[o[f]];let p;if(s.shouldExecuteOnCPU([r])){let f=s.texData.get(r.dataId),m=f.values,y=y1(m,r.shape,r.dtype,o,l);p=s.makeTensorInfo(l,r.dtype);let b=s.texData.get(p.dataId);b.values=y}else p=N1(r,o,s);return p}};function 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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:a,asyncInit:u}=this.initializeBackendsAndReturnBest();if(u)throw new Error(`The highest priority backend '${a}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(a)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(a){if(!(a in this.registry))if(a in this.registryFactory){let{asyncInit:u}=this.initializeBackend(a);if(u)return null}else return null;return this.registry[a]}findBackendFactory(a){return a in this.registryFactory?this.registryFactory[a].factory:null}registerBackend(a,u,h=1){return a in this.registryFactory?(console.warn(`${a} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[a]={factory:u,priority:h},!0)}async setBackend(a){if(this.registryFactory[a]==null)throw new Error(`Backend name '${a}' not found in registry`);if(this.backendName=a,this.registry[a]==null){this.backendInstance=null;let{success:u,asyncInit:h}=this.initializeBackend(a),d=h?await u:u;if(!d)return!1}return this.backendInstance=this.registry[a],this.setupRegisteredKernels(),this.profiler=new oT(this.backendInstance),!0}setupRegisteredKernels(){let a=eT(this.backendName);a.forEach(u=>{u.setupFunc!=null&&u.setupFunc(this.backendInstance)})}disposeRegisteredKernels(a){let u=eT(a);u.forEach(h=>{h.disposeFunc!=null&&h.disposeFunc(this.registry[a])})}initializeBackend(a){let u=this.registryFactory[a];if(u==null)throw new Error(`Cannot initialize backend ${a}, no registration found.`);try{let h=u.factory();if(h&&!(h instanceof q1)&&typeof h.then=="function"){let d=++this.pendingBackendInitId,g=h.then(x=>d<this.pendingBackendInitId?!1:(this.registry[a]=x,this.pendingBackendInit=null,!0)).catch(x=>(d<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${a} failed`),console.warn(x.stack||x.message)),!1));return this.pendingBackendInit=g,{success:g,asyncInit:!0}}else return this.registry[a]=h,{success:!0,asyncInit:!1}}catch(h){return console.warn(`Initialization of backend ${a} failed`),console.warn(h.stack||h.message),{success:!1,asyncInit:!1}}}removeBackend(a){if(!(a in this.registryFactory))throw new Error(`${a} backend not found in registry`);this.backendName===a&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,a in this.registry&&(this.disposeRegisteredKernels(a),this.registry[a].dispose(),delete this.registry[a]),delete this.registryFactory[a],this.backendName===a&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,u)=>this.registryFactory[u].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let a=this.getSortedBackends();for(let u=0;u<a.length;u++){let h=a[u],{success:d,asyncInit:g}=this.initializeBackend(h);if(g||d)return{name:h,asyncInit:g}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(a,u){let h=this.state.tensorInfo.get(u),d=h.backend,g=this.readSync(u);d.disposeData(u),h.backend=a,a.move(u,g,h.shape,h.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(a,u){let h=null;if(u==null){if(typeof a!="function")throw new Error("Please provide a function to tidy()");u=a}else{if(typeof a!="string"&&!(a instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof u!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");h=a}let d;return this.scopedRun(()=>this.startScope(h),()=>this.endScope(d),()=>(d=u(),d instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),d))}scopedRun(a,u,h){a();try{let d=h();return u(),d}catch(d){throw u(),d}}nextTensorId(){return Ea.nextTensorId++}nextVariableId(){return Ea.nextVariableId++}clone(a){let u=this.makeTensorFromDataId(a.dataId,a.shape,a.dtype),h={x:a},d=x=>({x:()=>{let w="float32",k={x},C={dtype:w};return V.runKernelFunc($=>$.cast(x,w),k,null,kl,C)}}),g=[];return this.addTapeNode(this.state.activeScope.name,h,[u],d,g,{}),u}runKernel(a,u,h,d,g){let x=null,w=null;return this.runKernelFunc(x,u,w,a,h,d,g)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(a,u,h){let d=this.backend.numDataIds(),g=0;h.forEach(k=>{g+=k.dtype==="complex64"?3:1});let x=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],w=d-u-g-x;if(w>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${w} data ids) after running '${a}'`)}runKernelFunc(a,u,h,d,g,x,w){let k,C=[],$=this.isTapeOn();d==null&&(d=this.state.activeScope!=null?this.state.activeScope.name:"");let F=this.state.numBytes,_=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let W,et=My(d,this.backendName),tt;if(et!=null)W=()=>{let mt=this.backend.numDataIds();tt=et.kernelFunc({inputs:u,attrs:g,backend:this.backend});let lt=Array.isArray(tt)?tt:[tt];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,mt,lt);let gt=lt.map(({dataId:_t,shape:Gt,dtype:se})=>this.makeTensorFromDataId(_t,Gt,se));if($){let _t=this.getTensorsForGradient(d,u,gt);if(_t==null){w==null&&(w=[]);let Gt=gt.filter((se,fe)=>w[fe]);_t=(x||[]).slice().concat(Gt)}C=this.saveTensorsForBackwardMode(_t)}return gt};else{let mt=lt=>{if(!$)return;C=lt.map(gt=>this.keep(this.clone(gt)))};W=()=>{let lt=this.backend.numDataIds();tt=this.tidy(()=>a(this.backend,mt));let gt=Array.isArray(tt)?tt:[tt];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,lt,gt),gt}}let G;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?k=W():(G=this.profiler.profileKernel(d,u,()=>W()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(G),k=G.outputs)}),$&&this.addTapeNode(d,u,k,h,C,g),this.state.profiling&&this.state.activeProfile.kernels.push({name:d,bytesAdded:this.state.numBytes-F,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-_,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(mt=>u[mt]!=null?u[mt].shape:null),outputShapes:k.map(mt=>mt.shape),kernelTimeMs:G.timeMs,extraInfo:G.extraInfo}),Array.isArray(tt)?k:k[0]}saveTensorsForBackwardMode(a){let u=a.map(h=>this.keep(this.clone(h)));return u}getTensorsForGradient(a,u,h){let d=tT(a);if(d!=null){let g=d.inputsToSave||[],x=d.outputsToSave||[],w;d.saveAllInputs?(U(Array.isArray(u),()=>"saveAllInputs is true, expected inputs to be an array."),w=Object.keys(u).map(C=>u[C])):w=g.map(C=>u[C]);let k=h.filter((C,$)=>x[$]);return w.concat(k)}return null}makeTensor(a,u,h,d){if(a==null)throw new Error("Values passed to engine.makeTensor() are null");h=h||"float32",d=d||this.backend;let g=a;h==="string"&&gh(a[0])&&(g=a.map(k=>O2(k)));let x=d.write(g,u,h),w=new z(u,h,x,this.nextTensorId());if(this.incRef(w,d),h==="string"){let k=this.state.tensorInfo.get(x),C=RE(g);this.state.numBytes+=C-k.bytes,k.bytes=C}return w}makeTensorFromDataId(a,u,h,d){h=h||"float32";let g=new z(u,h,a,this.nextTensorId());return this.incRef(g,d),g}makeVariable(a,u=!0,h,d){h=h||this.nextVariableId().toString(),d!=null&&d!==a.dtype&&(a=a.cast(d));let g=new Th(a,u,h,this.nextTensorId());if(this.state.registeredVariables[g.name]!=null)throw new Error(`Variable with name ${g.name} was already registered`);return this.state.registeredVariables[g.name]=g,this.incRef(g,this.backend),g}incRef(a,u){let h=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,h===0){this.state.numDataBuffers++;let d=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(d=a.size*FE(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:u||this.backend,dtype:a.dtype,shape:a.shape,bytes:d,refCount:0}),this.state.numBytes+=d}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Th||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let u=this.state.tensorInfo.get(a.dataId),h=u.refCount;h<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=u.bytes),this.state.numDataBuffers--,u.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let a in this.state.registeredVariables){let u=this.state.registeredVariables[a];this.disposeVariable(u)}}disposeVariable(a){this.disposeTensor(a),this.state.registeredVariables[a.name]!=null&&delete this.state.registeredVariables[a.name]}memory(){let a=this.backend.memory();return a.numTensors=this.state.numTensors,a.numDataBuffers=this.state.numDataBuffers,a.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(a.unreliable=!0,a.reasons==null&&(a.reasons=[]),a.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),a}async profile(a){this.state.profiling=!0;let u=this.state.numBytes,h=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await a(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-u,this.state.activeProfile.newTensors=this.state.numTensors-h;for(let d of this.state.activeProfile.kernels)d.kernelTimeMs=await d.kernelTimeMs,d.extraInfo=await d.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(a,u,h,d,g,x){let w={id:this.state.nextTapeNodeId++,kernelName:a,inputs:u,outputs:h,saved:g},k=tT(a);k!=null&&(d=k.gradFunc),d!=null&&(w.gradient=C=>(C=C.map(($,F)=>{if($==null){let _=h[F],W=Ia(_.size,_.dtype);return this.makeTensor(W,_.shape,_.dtype)}return $}),d(C.length>1?C:C[0],g,x))),this.state.activeTape.push(w)}keep(a){return a.kept=!0,a}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(a){let u={track:[],name:"unnamed scope",id:this.state.nextScopeId++};a&&(u.name=a),this.state.scopeStack.push(u),this.state.activeScope=u}endScope(a){let u=Wy(a),h=new Set(u.map(g=>g.id));for(let g=0;g<this.state.activeScope.track.length;g++){let x=this.state.activeScope.track[g];!x.kept&&!h.has(x.id)&&x.dispose()}let d=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],u.forEach(g=>{!g.kept&&g.scopeId===d.id&&this.track(g)})}gradients(a,u,h,d=!1){if(U(u.length>0,()=>"gradients() received an empty list of xs."),h!=null&&h.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${h.dtype}'`);let g=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",a));U(g instanceof z,()=>"The result y returned by f() must be a tensor.");let x=M2(this.state.activeTape,u,g);if(!d&&x.length===0&&u.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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et=Gr(i,u.shape,h,g,d,x,!0);return W.depthwiseConv2DDerInput(w,u,et)},$={dy:w,filter:u},F={strides:h,pad:d,dimRoundingMode:x,dilations:g,inputShape:i},_=V.runKernelFunc(C,$,null,QE,F);return k?K(_,[_.shape[1],_.shape[2],_.shape[3]]):_}var UD=O({depthwiseConv2dNativeBackpropInput_:Snt});function $nt(i){return Zy(i,.54,.46)}var qD=O({hammingWindow_:$nt});function Int(i){return Zy(i,.5,.5)}var tb=O({hannWindow_:Int});function Ent(i,a,u,h=!1,d=0){let g=0,x=[];for(;g+a<=i.size;)x.push(ve(i,g,a)),g+=u;if(h)for(;g<i.size;){let w=g+a-i.size,k=mn([ve(i,g,a-w),JT([w],d)]);x.push(k),g+=u}return x.length===0?ja([],[0,a]):K(mn(x),[x.length,a])}var eb=O({frame_:Ent});function Dnt(i,a,u,h,d=tb){h==null&&(h=VD(a));let g=eb(i,a,u),x=at(g,d(a)),w=[];for(let k=0;k<g.shape[0];k++)w.push(qa(ve(x,[k,0],[1,a]),h));return mn(w)}var HD=O({stft_:Dnt});function Ant(i,a,u,h,d,g){let x=R(i,"image","cropAndResize"),w=R(a,"boxes","cropAndResize","float32"),k=R(u,"boxInd","cropAndResize","int32");d=d||"bilinear",g=g||0;let C=w.shape[0];U(x.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${x.rank}.`),U(w.rank===2&&w.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${C},4] but had shape ${w.shape}.`),U(k.rank===1&&k.shape[0]===C,()=>`Error in cropAndResize: boxInd must be have size [${C}] but had shape ${w.shape}.`),U(h.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${h.length}.`),U(h[0]>=1&&h[1]>=1,()=>`cropSize must be atleast [1,1], but was ${h}`),U(d==="bilinear"||d==="nearest",()=>`method must be bilinear or nearest, but was ${d}`);let $=et=>et.cropAndResize(x,w,k,h,d,g),F={image:x,boxes:w,boxInd:k},_={method:d,extrapolationValue:g,cropSize:h},W=V.runKernelFunc($,F,null,YE,_);return W}var jD=O({cropAndResize_:Ant});function Fnt(i){let 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u=this.getClassifierChannelsIn(),h=this.getClassifierChannelsOut(),d=h*u+h,g=a.slice(0,a.length-d),x=a.slice(a.length-d);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(x)}},Ik=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Mi=class{constructor(a){if(a.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${a.length}`);Ik.forEach((u,h)=>{this[u]=a[h]})}asSortedArray(){return Ik.map(a=>({expression:a,probability:this[a]})).sort((a,u)=>u.probability-a.probability)}},gb=class extends Xh{constructor(a=new jh){super("FaceExpressionNet",a)}forwardInput(a){return Wl.tidy(()=>Wl.softmax(this.runNet(a)))}async forward(a){return this.forwardInput(await Ve(a))}async predictExpressions(a){let u=await Ve(a),h=await this.forwardInput(u),d=await Promise.all(Wl.unstack(h).map(async x=>{let w=await x.data();return x.dispose(),w}));h.dispose();let g=d.map(x=>new Mi(x));return u.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Ek(i){return i.expressions instanceof Mi}function yb(i,a){let u={expressions:a};return Object.assign({},i,u)}function xrt(i,a,u=.1,h){let d=Array.isArray(a)?a:[a];d.forEach(g=>{let x=g instanceof Mi?g:Ek(g)?g.expressions:void 0;if(!x)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let w=x.asSortedArray(),k=w.filter(F=>F.probability>u),C=Jo(g)?g.detection.box.bottomLeft:h||new Jt(0,0),$=new Ii(k.map(F=>`${F.expression} (${ka(F.probability)})`),C);$.draw(i)})}function Ja(i){return Jo(i)&&i.landmarks instanceof Wr&&i.unshiftedLandmarks instanceof Wr&&i.alignedRect instanceof Ue}function Vl(i,a){let{box:u}=i.detection,h=a.shiftBy(u.x,u.y),d=h.align(),{imageDims:g}=i.detection,x=new 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k=d(`${w}/separable_conv0`),C=d(`${w}/separable_conv1`),$=d(`${w}/separable_conv2`);return{separable_conv0:k,separable_conv1:C,separable_conv2:$}}return{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:g,extractMainBlockParams:x}}function wR(i,a){let u=[],{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:g,extractMainBlockParams:x}=Trt(i,u),w=h("entry_flow/conv_in"),k=g("entry_flow/reduction_block_0"),C=g("entry_flow/reduction_block_1"),$={conv_in:w,reduction_block_0:k,reduction_block_1:C},F={};Xo(a,0,1).forEach(tt=>{F[`main_block_${tt}`]=x(`middle_flow/main_block_${tt}`)});let _=g("exit_flow/reduction_block"),W=d("exit_flow/separable_conv"),et={reduction_block:_,separable_conv:W};return tr(i,u),{params:{entry_flow:$,middle_flow:F,exit_flow:et},paramMappings:u}}function vR(i,a,u){return pn.add(pn.conv2d(i,a.filters,u,"same"),a.bias)}function Rk(i,a,u=!0){let h=u?pn.relu(i):i;return h=yr(h,a.separable_conv0,[1,1]),h=yr(pn.relu(h),a.separable_conv1,[1,1]),h=pn.maxPool(h,[3,3],[2,2],"same"),h=pn.add(h,vR(i,a.expansion_conv,[2,2])),h}function krt(i,a){let u=yr(pn.relu(i),a.separable_conv0,[1,1]);return u=yr(pn.relu(u),a.separable_conv1,[1,1]),u=yr(pn.relu(u),a.separable_conv2,[1,1]),u=pn.add(u,i),u}var Pk=class extends Gn{constructor(a){super("TinyXception");this._numMainBlocks=a}forwardInput(a){let{params:u}=this;if(!u)throw new Error("TinyXception - load model before inference");return pn.tidy(()=>{let h=pn.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=Io(h,d).div(pn.scalar(256)),x=pn.relu(vR(g,u.entry_flow.conv_in,[2,2]));return x=Rk(x,u.entry_flow.reduction_block_0,!1),x=Rk(x,u.entry_flow.reduction_block_1),Xo(this._numMainBlocks,0,1).forEach(w=>{x=krt(x,u.middle_flow[`main_block_${w}`])}),x=Rk(x,u.exit_flow.reduction_block),x=pn.relu(yr(x,u.exit_flow.separable_conv,[1,1])),x})}async forward(a){return this.forwardInput(await Ve(a))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(a){return wR(a,this._numMainBlocks)}extractParams(a){return xR(a,this._numMainBlocks)}};function TR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=er(i),d=ub(u,a),g=d(512,1,"fc/age"),x=d(512,2,"fc/gender");if(h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{paramMappings:a,params:{fc:{age:g,gender:x}}}}function kR(i){let a=[],u=Sr(i,a);function h(g){let x=u(`${g}/weights`,2),w=u(`${g}/bias`,1);return{weights:x,bias:w}}let d={fc:{age:h("fc/age"),gender:h("fc/gender")}};return tr(i,a),{params:d,paramMappings:a}}var Hs;(function(i){i.FEMALE="female",i.MALE="male"})(Hs||(Hs={}));var bb=class extends Gn{constructor(a=new Pk(2)){super("AgeGenderNet");this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(a){let{params:u}=this;if(!u)throw new Error(`${this._name} - load model before inference`);return ss.tidy(()=>{let h=a instanceof Us?this.faceFeatureExtractor.forwardInput(a):a,d=ss.avgPool(h,[7,7],[2,2],"valid").as2D(h.shape[0],-1),g=Kh(d,u.fc.age).as1D(),x=Kh(d,u.fc.gender);return{age:g,gender:x}})}forwardInput(a){return ss.tidy(()=>{let{age:u,gender:h}=this.runNet(a);return{age:u,gender:ss.softmax(h)}})}async forward(a){return this.forwardInput(await Ve(a))}async predictAgeAndGender(a){let u=await Ve(a),h=await this.forwardInput(u),d=ss.unstack(h.age),g=ss.unstack(h.gender),x=d.map((k,C)=>({ageTensor:k,genderTensor:g[C]})),w=await Promise.all(x.map(async({ageTensor:k,genderTensor:C})=>{let $=(await k.data())[0],F=(await C.data())[0],_=F>.5,W=_?Hs.MALE:Hs.FEMALE,et=_?F:1-F;return k.dispose(),C.dispose(),{age:$,gender:W,genderProbability:et}}));return h.age.dispose(),h.gender.dispose(),u.isBatchInput?w:w[0]}getDefaultModelName(){return"age_gender_model"}dispose(a=!0){this.faceFeatureExtractor.dispose(a),super.dispose(a)}loadClassifierParams(a){let{params:u,paramMappings:h}=this.extractClassifierParams(a);this._params=u,this._paramMappings=h}extractClassifierParams(a){return TR(a)}extractParamsFromWeigthMap(a){let{featureExtractorMap:u,classifierMap:h}=mb(a);return this.faceFeatureExtractor.loadFromWeightMap(u),kR(h)}extractParams(a){let u=512*1+1+(512*2+2),h=a.slice(0,a.length-u),d=a.slice(a.length-u);return this.faceFeatureExtractor.extractWeights(h),this.extractClassifierParams(d)}};var $r=te(ee()),Yh=class extends Xh{postProcess(a,u,h){let d=h.map(({width:x,height:w})=>{let k=u/Math.max(w,x);return{width:x*k,height:w*k}}),g=d.length;return $r.tidy(()=>{let x=(F,_)=>$r.stack([$r.fill([68],F,"float32"),$r.fill([68],_,"float32")],1).as2D(1,136).as1D(),w=(F,_)=>{let{width:W,height:et}=d[F];return _(W,et)?Math.abs(W-et)/2:0},k=F=>w(F,(_,W)=>_<W),C=F=>w(F,(_,W)=>W<_),$=a.mul($r.fill([g,136],u,"float32")).sub($r.stack(Array.from(Array(g),(F,_)=>x(k(_),C(_))))).div($r.stack(Array.from(Array(g),(F,_)=>x(d[_].width,d[_].height))));return $})}forwardInput(a){return $r.tidy(()=>{let u=this.runNet(a);return this.postProcess(u,a.inputSize,a.inputDimensions.map(([h,d])=>({height:h,width:d})))})}async forward(a){return this.forwardInput(await Ve(a))}async detectLandmarks(a){let u=await Ve(a),h=$r.tidy(()=>$r.unstack(this.forwardInput(u))),d=await Promise.all(h.map(async(g,x)=>{let w=Array.from(await g.data()),k=w.filter(($,F)=>Xm(F)),C=w.filter(($,F)=>!Xm(F));return new bl(Array(68).fill(0).map(($,F)=>new Jt(k[F],C[F])),{height:u.getInputHeight(x),width:u.getInputWidth(x)})}));return h.forEach(g=>g.dispose()),u.isBatchInput?d:d[0]}getClassifierChannelsOut(){return 136}},Gl=class extends Yh{constructor(a=new jh){super("FaceLandmark68Net",a)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Bi=te(ee());function NR(i){let a=[],{extractDenseBlock3Params:u}=db(i,a),h={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return tr(i,a),{params:h,paramMappings:a}}function _R(i){let a=[],{extractWeights:u,getRemainingWeights:h}=er(i),{extractDenseBlock3Params:d}=hb(u,a),g=d(3,32,"dense0",!0),x=d(32,64,"dense1"),w=d(64,128,"dense2");if(h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{paramMappings:a,params:{dense0:g,dense1:x,dense2:w}}}var Ok=class extends Gn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(a){let{params:u}=this;if(!u)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Bi.tidy(()=>{let h=Bi.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=Io(h,d).div(Bi.scalar(255)),x=ab(g,u.dense0,!0);return x=ab(x,u.dense1),x=ab(x,u.dense2),x=Bi.avgPool(x,[14,14],[2,2],"valid"),x})}async forward(a){return this.forwardInput(await Ve(a))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(a){return NR(a)}extractParams(a){return _R(a)}},xb=class extends Yh{constructor(a=new Ok){super("FaceLandmark68TinyNet",a)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},CR=class extends Gl{};var jr=te(ee()),Ul=te(ee()),wb=te(ee());function SR(i,a){return wb.add(wb.mul(i,a.weights),a.biases)}function Lk(i,a,u,h,d="same"){let{filters:g,bias:x}=a.conv,w=Ul.conv2d(i,g,u,d);return w=Ul.add(w,x),w=SR(w,a.scale),h?Ul.relu(w):w}function $R(i,a){return Lk(i,a,[1,1],!0)}function Mk(i,a){return Lk(i,a,[1,1],!1)}function vb(i,a){return Lk(i,a,[2,2],!0,"valid")}var Ir=te(ee());function Nrt(i,a){function u(w,k,C){let $=i(w),F=$.length/(k*C*C);if(I1(F))throw new Error(`depth has to be an integer: ${F}, weights.length: ${$.length}, 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IR(i){let{extractWeights:a,getRemainingWeights:u}=er(i),h=[],{extractConvLayerParams:d,extractResidualLayerParams:g}=Nrt(a,h),x=d(4704,32,7,"conv32_down"),w=g(9216,32,3,"conv32_1"),k=g(9216,32,3,"conv32_2"),C=g(9216,32,3,"conv32_3"),$=g(36864,64,3,"conv64_down",!0),F=g(36864,64,3,"conv64_1"),_=g(36864,64,3,"conv64_2"),W=g(36864,64,3,"conv64_3"),et=g(147456,128,3,"conv128_down",!0),tt=g(147456,128,3,"conv128_1"),G=g(147456,128,3,"conv128_2"),mt=g(589824,256,3,"conv256_down",!0),lt=g(589824,256,3,"conv256_1"),gt=g(589824,256,3,"conv256_2"),_t=g(589824,256,3,"conv256_down_out"),Gt=Ir.tidy(()=>Ir.transpose(Ir.tensor2d(a(256*128),[128,256]),[1,0]));if(h.push({paramPath:"fc"}),u().length!==0)throw new Error(`weights remaing after extract: ${u().length}`);let se={conv32_down:x,conv32_1:w,conv32_2:k,conv32_3:C,conv64_down:$,conv64_1:F,conv64_2:_,conv64_3:W,conv128_down:et,conv128_1:tt,conv128_2:G,conv256_down:mt,conv256_1:lt,conv256_2:gt,conv256_down_out:_t,fc:Gt};return{params:se,paramMappings:h}}function _rt(i,a){let u=Sr(i,a);function h(x){let w=u(`${x}/scale/weights`,1),k=u(`${x}/scale/biases`,1);return{weights:w,biases:k}}function d(x){let w=u(`${x}/conv/filters`,4),k=u(`${x}/conv/bias`,1),C=h(x);return{conv:{filters:w,bias:k},scale:C}}function g(x){return{conv1:d(`${x}/conv1`),conv2:d(`${x}/conv2`)}}return{extractConvLayerParams:d,extractResidualLayerParams:g}}function ER(i){let 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w=x.mean([1,2]),k=jr.matMul(w,u.fc);return k})}async forward(a){return this.forwardInput(await Ve(a))}async computeFaceDescriptor(a){let u=await Ve(a),h=jr.tidy(()=>jr.unstack(this.forwardInput(u))),d=await Promise.all(h.map(g=>g.data()));return h.forEach(g=>g.dispose()),u.isBatchInput?d:d[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(a){return ER(a)}extractParams(a){return IR(a)}};function Crt(i){let a=new ql;return a.extractWeights(i),a}function Tb(i,a){let u={descriptor:a};return Object.assign({},i,u)}function Srt(i){return typeof i.age=="number"}function kb(i,a){let u={age:a};return Object.assign({},i,u)}function $rt(i){return(i.gender===Hs.MALE||i.gender===Hs.FEMALE)&&ml(i.genderProbability)}function Nb(i,a,u){let h={gender:a,genderProbability:u};return Object.assign({},i,h)}var Po=te(ee()),Ro=te(ee());function Irt(i,a){function u(k,C){let 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k=d(3,32,3,"mobilenetv1/conv_0"),C=g(32,64,"mobilenetv1/conv_1"),$=g(64,128,"mobilenetv1/conv_2"),F=g(128,128,"mobilenetv1/conv_3"),_=g(128,256,"mobilenetv1/conv_4"),W=g(256,256,"mobilenetv1/conv_5"),et=g(256,512,"mobilenetv1/conv_6"),tt=g(512,512,"mobilenetv1/conv_7"),G=g(512,512,"mobilenetv1/conv_8"),mt=g(512,512,"mobilenetv1/conv_9"),lt=g(512,512,"mobilenetv1/conv_10"),gt=g(512,512,"mobilenetv1/conv_11"),_t=g(512,1024,"mobilenetv1/conv_12"),Gt=g(1024,1024,"mobilenetv1/conv_13");return{conv_0:k,conv_1:C,conv_2:$,conv_3:F,conv_4:_,conv_5:W,conv_6:et,conv_7:tt,conv_8:G,conv_9:mt,conv_10:lt,conv_11:gt,conv_12:_t,conv_13:Gt}}function w(){let 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DR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=er(i),{extractMobilenetV1Params:d,extractPredictionLayerParams:g}=Irt(u,a),x=d(),w=g(),k=Ro.tensor3d(u(5118*4),[1,5118,4]),C={extra_dim:k};if(a.push({paramPath:"output_layer/extra_dim"}),h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{params:{mobilenetv1:x,prediction_layer:w,output_layer:C},paramMappings:a}}function Ert(i,a){let u=Sr(i,a);function h(C,$,F){let _=u(`${C}/Conv2d_${$}_pointwise/weights`,4,`${F}/filters`),W=u(`${C}/Conv2d_${$}_pointwise/convolution_bn_offset`,1,`${F}/batch_norm_offset`);return{filters:_,batch_norm_offset:W}}function d(C){let $=`mobilenetv1/conv_${C}`,F=`MobilenetV1/Conv2d_${C}_depthwise`,_=`${$}/depthwise_conv`,W=`${$}/pointwise_conv`,et=u(`${F}/depthwise_weights`,4,`${_}/filters`),tt=u(`${F}/BatchNorm/gamma`,1,`${_}/batch_norm_scale`),G=u(`${F}/BatchNorm/beta`,1,`${_}/batch_norm_offset`),mt=u(`${F}/BatchNorm/moving_mean`,1,`${_}/batch_norm_mean`),lt=u(`${F}/BatchNorm/moving_variance`,1,`${_}/batch_norm_variance`);return{depthwise_conv:{filters:et,batch_norm_scale:tt,batch_norm_offset:G,batch_norm_mean:mt,batch_norm_variance:lt},pointwise_conv:h("MobilenetV1",C,W)}}function g(){return{conv_0:h("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:d(1),conv_2:d(2),conv_3:d(3),conv_4:d(4),conv_5:d(5),conv_6:d(6),conv_7:d(7),conv_8:d(8),conv_9:d(9),conv_10:d(10),conv_11:d(11),conv_12:d(12),conv_13:d(13)}}function x(C,$){let F=u(`${C}/weights`,4,`${$}/filters`),_=u(`${C}/biases`,1,`${$}/bias`);return{filters:F,bias:_}}function w(C){let $=x(`Prediction/BoxPredictor_${C}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${C}/box_encoding_predictor`),F=x(`Prediction/BoxPredictor_${C}/ClassPredictor`,`prediction_layer/box_predictor_${C}/class_predictor`);return{box_encoding_predictor:$,class_predictor:F}}function k(){return{conv_0:h("Prediction",0,"prediction_layer/conv_0"),conv_1:h("Prediction",1,"prediction_layer/conv_1"),conv_2:h("Prediction",2,"prediction_layer/conv_2"),conv_3:h("Prediction",3,"prediction_layer/conv_3"),conv_4:h("Prediction",4,"prediction_layer/conv_4"),conv_5:h("Prediction",5,"prediction_layer/conv_5"),conv_6:h("Prediction",6,"prediction_layer/conv_6"),conv_7:h("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:w(0),box_predictor_1:w(1),box_predictor_2:w(2),box_predictor_3:w(3),box_predictor_4:w(4),box_predictor_5:w(5)}}return{extractMobilenetV1Params:g,extractPredictionLayerParams:k}}function AR(i){let a=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:h}=Ert(i,a),d=i["Output/extra_dim"];if(a.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Ds(d))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${d}`);let g={mobilenetv1:u(),prediction_layer:h(),output_layer:{extra_dim:d}};return tr(i,a),{params:g,paramMappings:a}}var js=te(ee()),zi=te(ee());function io(i,a,u){return zi.tidy(()=>{let h=zi.conv2d(i,a.filters,u,"same");return h=zi.add(h,a.batch_norm_offset),zi.clipByValue(h,0,6)})}var Drt=.0010000000474974513;function Art(i,a,u){return js.tidy(()=>{let h=js.depthwiseConv2d(i,a.filters,u,"same");return h=js.batchNorm(h,a.batch_norm_mean,a.batch_norm_variance,a.batch_norm_offset,a.batch_norm_scale,Drt),js.clipByValue(h,0,6)})}function Frt(i){return[2,4,6,12].some(a=>a===i)?[2,2]:[1,1]}function FR(i,a){return js.tidy(()=>{let u,h=io(i,a.conv_0,[2,2]),d=[a.conv_1,a.conv_2,a.conv_3,a.conv_4,a.conv_5,a.conv_6,a.conv_7,a.conv_8,a.conv_9,a.conv_10,a.conv_11,a.conv_12,a.conv_13];if(d.forEach((g,x)=>{let w=x+1,k=Frt(w);h=Art(h,g.depthwise_conv,k),h=io(h,g.pointwise_conv,[1,1]),w===11&&(u=h)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:h,conv11:u}})}function RR(i,a,u,h,d){let g=i.shape[0],x=Math.min(u,g),w=a.map(($,F)=>({score:$,boxIndex:F})).filter($=>$.score>d).sort(($,F)=>F.score-$.score),k=$=>$<=h?1:0,C=[];return w.forEach($=>{if(C.length>=x)return;let F=$.score;for(let _=C.length-1;_>=0;--_){let W=Rrt(i,$.boxIndex,C[_]);if(W===0)continue;if($.score*=k(W),$.score<=d)break}F===$.score&&C.push($.boxIndex)}),C}function Rrt(i,a,u){let h=i.arraySync(),d=Math.min(h[a][0],h[a][2]),g=Math.min(h[a][1],h[a][3]),x=Math.max(h[a][0],h[a][2]),w=Math.max(h[a][1],h[a][3]),k=Math.min(h[u][0],h[u][2]),C=Math.min(h[u][1],h[u][3]),$=Math.max(h[u][0],h[u][2]),F=Math.max(h[u][1],h[u][3]),_=(x-d)*(w-g),W=($-k)*(F-C);if(_<=0||W<=0)return 0;let et=Math.max(d,k),tt=Math.max(g,C),G=Math.min(x,$),mt=Math.min(w,F),lt=Math.max(G-et,0)*Math.max(mt-tt,0);return lt/(_+W-lt)}var Mt=te(ee());function Prt(i){let a=Mt.unstack(Mt.transpose(i,[1,0])),u=[Mt.sub(a[2],a[0]),Mt.sub(a[3],a[1])],h=[Mt.add(a[0],Mt.div(u[0],Mt.scalar(2))),Mt.add(a[1],Mt.div(u[1],Mt.scalar(2)))];return{sizes:u,centers:h}}function Ort(i,a){let{sizes:u,centers:h}=Prt(i),d=Mt.unstack(Mt.transpose(a,[1,0])),g=Mt.div(Mt.mul(Mt.exp(Mt.div(d[2],Mt.scalar(5))),u[0]),Mt.scalar(2)),x=Mt.add(Mt.mul(Mt.div(d[0],Mt.scalar(10)),u[0]),h[0]),w=Mt.div(Mt.mul(Mt.exp(Mt.div(d[3],Mt.scalar(5))),u[1]),Mt.scalar(2)),k=Mt.add(Mt.mul(Mt.div(d[1],Mt.scalar(10)),u[1]),h[1]);return Mt.transpose(Mt.stack([Mt.sub(x,g),Mt.sub(k,w),Mt.add(x,g),Mt.add(k,w)]),[1,0])}function PR(i,a,u){return Mt.tidy(()=>{let h=i.shape[0],d=Ort(Mt.reshape(Mt.tile(u.extra_dim,[h,1,1]),[-1,4]),Mt.reshape(i,[-1,4]));d=Mt.reshape(d,[h,d.shape[0]/h,4]);let g=Mt.sigmoid(Mt.slice(a,[0,0,1],[-1,-1,-1])),x=Mt.slice(g,[0,0,0],[-1,-1,1]);x=Mt.reshape(x,[h,x.shape[1]]);let w=Mt.unstack(d),k=Mt.unstack(x);return{boxes:w,scores:k}})}var Qh=te(ee()),Zh=te(ee());function Za(i,a){return Zh.tidy(()=>{let u=i.shape[0],h=Zh.reshape(Ya(i,a.box_encoding_predictor),[u,-1,1,4]),d=Zh.reshape(Ya(i,a.class_predictor),[u,-1,3]);return{boxPredictionEncoding:h,classPrediction:d}})}function OR(i,a,u){return Qh.tidy(()=>{let h=io(i,u.conv_0,[1,1]),d=io(h,u.conv_1,[2,2]),g=io(d,u.conv_2,[1,1]),x=io(g,u.conv_3,[2,2]),w=io(x,u.conv_4,[1,1]),k=io(w,u.conv_5,[2,2]),C=io(k,u.conv_6,[1,1]),$=io(C,u.conv_7,[2,2]),F=Za(a,u.box_predictor_0),_=Za(i,u.box_predictor_1),W=Za(d,u.box_predictor_2),et=Za(x,u.box_predictor_3),tt=Za(k,u.box_predictor_4),G=Za($,u.box_predictor_5),mt=Qh.concat([F.boxPredictionEncoding,_.boxPredictionEncoding,W.boxPredictionEncoding,et.boxPredictionEncoding,tt.boxPredictionEncoding,G.boxPredictionEncoding],1),lt=Qh.concat([F.classPrediction,_.classPrediction,W.classPrediction,et.classPrediction,tt.classPrediction,G.classPrediction],1);return{boxPredictions:mt,classPredictions:lt}})}var ao=class{constructor({minConfidence:a,maxResults:u}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=a||.5,this._maxResults=u||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${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}},Qa=class extends Gn{constructor(){super("SsdMobilenetv1")}forwardInput(a){let{params:u}=this;if(!u)throw new Error("SsdMobilenetv1 - load model before inference");return Po.tidy(()=>{let h=Po.cast(a.toBatchTensor(512,!1),"float32"),d=Po.sub(Po.mul(h,Po.scalar(.007843137718737125)),Po.scalar(1)),g=FR(d,u.mobilenetv1),{boxPredictions:x,classPredictions:w}=OR(g.out,g.conv11,u.prediction_layer);return PR(x,w,u.output_layer)})}async forward(a){return this.forwardInput(await Ve(a))}async locateFaces(a,u={}){let{maxResults:h,minConfidence:d}=new ao(u),g=await Ve(a),{boxes:x,scores:w}=this.forwardInput(g),k=x[0],C=w[0];for(let gt=1;gt<x.length;gt++)x[gt].dispose(),w[gt].dispose();let $=Array.from(await C.data()),F=.5,_=RR(k,$,h,F,d),W=g.getReshapedInputDimensions(0),et=g.inputSize,tt=et/W.width,G=et/W.height,mt=k.arraySync(),lt=_.map(gt=>{let[_t,Gt]=[Math.max(0,mt[gt][0]),Math.min(1,mt[gt][2])].map(_e=>_e*G),[se,fe]=[Math.max(0,mt[gt][1]),Math.min(1,mt[gt][3])].map(_e=>_e*tt);return new Ue($[gt],new yl(se,_t,fe-se,Gt-_t),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return k.dispose(),C.dispose(),lt}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(a){return AR(a)}extractParams(a){return DR(a)}};function LR(i){let a=new Qa;return a.extractWeights(i),a}function Lrt(i){return LR(i)}var MR=class extends Qa{},BR=.4,zR=[new Jt(.738768,.874946),new Jt(2.42204,2.65704),new Jt(4.30971,7.04493),new Jt(10.246,4.59428),new Jt(12.6868,11.8741)],WR=[new Jt(1.603231,2.094468),new Jt(6.041143,7.080126),new Jt(2.882459,3.518061),new Jt(4.266906,5.178857),new Jt(9.041765,10.66308)],VR=[117.001,114.697,97.404],GR="tiny_yolov2_model",UR="tiny_yolov2_separable_conv_model",He=te(ee()),_b=i=>typeof i=="number";function Bk(i){if(!i)throw new Error(`invalid config: ${i}`);if(typeof i.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${i.withSeparableConvs}`);if(!_b(i.iouThreshold)||i.iouThreshold<0||i.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${i.iouThreshold}`);if(!Array.isArray(i.classes)||!i.classes.length||!i.classes.every(a=>typeof a=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(i.classes)}`);if(!Array.isArray(i.anchors)||!i.anchors.length||!i.anchors.map(a=>a||{}).every(a=>_b(a.x)&&_b(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(i.anchors)}`);if(i.meanRgb&&(!Array.isArray(i.meanRgb)||i.meanRgb.length!==3||!i.meanRgb.every(_b)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(i.meanRgb)}`)}var lo=te(ee()),co=te(ee());function Hl(i){return co.tidy(()=>{let a=co.mul(i,co.scalar(.10000000149011612));return co.add(co.relu(co.sub(i,a)),a)})}function Ks(i,a){return lo.tidy(()=>{let u=lo.pad(i,[[0,0],[1,1],[1,1],[0,0]]);return u=lo.conv2d(u,a.conv.filters,[1,1],"valid"),u=lo.sub(u,a.bn.sub),u=lo.mul(u,a.bn.truediv),u=lo.add(u,a.conv.bias),Hl(u)})}var Wi=te(ee());function Xs(i,a){return Wi.tidy(()=>{let u=Wi.pad(i,[[0,0],[1,1],[1,1],[0,0]]);return u=Wi.separableConv2d(u,a.depthwise_filter,a.pointwise_filter,[1,1],"valid"),u=Wi.add(u,a.bias),Hl(u)})}var zk=te(ee());function Mrt(i,a){let u=Ll(i,a);function h(x,w){let k=zk.tensor1d(i(x)),C=zk.tensor1d(i(x));return a.push({paramPath:`${w}/sub`},{paramPath:`${w}/truediv`}),{sub:k,truediv:C}}function d(x,w,k){let C=u(x,w,3,`${k}/conv`),$=h(w,`${k}/bn`);return{conv:C,bn:$}}let g=Ml(i,a);return{extractConvParams:u,extractConvWithBatchNormParams:d,extractSeparableConvParams:g}}function qR(i,a,u,h){let{extractWeights:d,getRemainingWeights:g}=er(i),x=[],{extractConvParams:w,extractConvWithBatchNormParams:k,extractSeparableConvParams:C}=Mrt(d,x),$;if(a.withSeparableConvs){let[F,_,W,et,tt,G,mt,lt,gt]=h,_t=a.isFirstLayerConv2d?w(F,_,3,"conv0"):C(F,_,"conv0"),Gt=C(_,W,"conv1"),se=C(W,et,"conv2"),fe=C(et,tt,"conv3"),_e=C(tt,G,"conv4"),Ge=C(G,mt,"conv5"),Vt=lt?C(mt,lt,"conv6"):void 0,ln=gt?C(lt,gt,"conv7"):void 0,Ce=w(gt||lt||mt,5*u,1,"conv8");$={conv0:_t,conv1:Gt,conv2:se,conv3:fe,conv4:_e,conv5:Ge,conv6:Vt,conv7:ln,conv8:Ce}}else{let[F,_,W,et,tt,G,mt,lt,gt]=h,_t=k(F,_,"conv0"),Gt=k(_,W,"conv1"),se=k(W,et,"conv2"),fe=k(et,tt,"conv3"),_e=k(tt,G,"conv4"),Ge=k(G,mt,"conv5"),Vt=k(mt,lt,"conv6"),ln=k(lt,gt,"conv7"),Ce=w(gt,5*u,1,"conv8");$={conv0:_t,conv1:Gt,conv2:se,conv3:fe,conv4:_e,conv5:Ge,conv6:Vt,conv7:ln,conv8:Ce}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:$,paramMappings:x}}function Brt(i,a){let u=Sr(i,a);function h(w){let k=u(`${w}/sub`,1),C=u(`${w}/truediv`,1);return{sub:k,truediv:C}}function d(w){let k=u(`${w}/filters`,4),C=u(`${w}/bias`,1);return{filters:k,bias:C}}function g(w){let k=d(`${w}/conv`),C=h(`${w}/bn`);return{conv:k,bn:C}}let x=Bl(u);return{extractConvParams:d,extractConvWithBatchNormParams:g,extractSeparableConvParams:x}}function HR(i,a){let u=[],{extractConvParams:h,extractConvWithBatchNormParams:d,extractSeparableConvParams:g}=Brt(i,u),x;if(a.withSeparableConvs){let w=a.filterSizes&&a.filterSizes.length||9;x={conv0:a.isFirstLayerConv2d?h("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:w>7?g("conv6"):void 0,conv7:w>8?g("conv7"):void 0,conv8:h("conv8")}}else x={conv0:d("conv0"),conv1:d("conv1"),conv2:d("conv2"),conv3:d("conv3"),conv4:d("conv4"),conv5:d("conv5"),conv6:d("conv6"),conv7:d("conv7"),conv8:h("conv8")};return tr(i,u),{params:x,paramMappings:u}}var Wk;(function(i){i[i.XS=224]="XS",i[i.SM=320]="SM",i[i.MD=416]="MD",i[i.LG=608]="LG"})(Wk||(Wk={}));var is=class{constructor({inputSize:a,scoreThreshold:u}={}){this._name="TinyYolov2Options";if(this._inputSize=a||416,this._scoreThreshold=u||.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}},Vk=class extends Gn{constructor(a){super("TinyYolov2");Bk(a),this._config=a}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(a,u){let h=Ks(a,u.conv0);return h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv1),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv2),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv3),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv4),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ks(h,u.conv5),h=He.maxPool(h,[2,2],[1,1],"same"),h=Ks(h,u.conv6),h=Ks(h,u.conv7),Ya(h,u.conv8,"valid",!1)}runMobilenet(a,u){let h=this.config.isFirstLayerConv2d?Hl(Ya(a,u.conv0,"valid",!1)):Xs(a,u.conv0);return h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv1),h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv2),h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv3),h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv4),h=He.maxPool(h,[2,2],[2,2],"same"),h=Xs(h,u.conv5),h=He.maxPool(h,[2,2],[1,1],"same"),h=u.conv6?Xs(h,u.conv6):h,h=u.conv7?Xs(h,u.conv7):h,Ya(h,u.conv8,"valid",!1)}forwardInput(a,u){let{params:h}=this;if(!h)throw new Error("TinyYolov2 - load model before inference");return He.tidy(()=>{let d=He.cast(a.toBatchTensor(u,!1),"float32");return d=this.config.meanRgb?Io(d,this.config.meanRgb):d,d=d.div(He.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(d,h):this.runTinyYolov2(d,h)})}async forward(a,u){return await this.forwardInput(await Ve(a),u)}async detect(a,u={}){let{inputSize:h,scoreThreshold:d}=new is(u),g=await Ve(a),x=await this.forwardInput(g,h),w=He.tidy(()=>He.unstack(x)[0].expandDims()),k={width:g.getInputWidth(0),height:g.getInputHeight(0)},C=await this.extractBoxes(w,g.getReshapedInputDimensions(0),d);x.dispose(),w.dispose();let $=C.map(G=>G.box),F=C.map(G=>G.score),_=C.map(G=>G.classScore),W=C.map(G=>this.config.classes[G.label]),et=F1($.map(G=>G.rescale(h)),F,this.config.iouThreshold,!0),tt=et.map(G=>new Si(F[G],_[G],W[G],$[G],k));return tt}getDefaultModelName(){return""}extractParamsFromWeigthMap(a){return HR(a,this.config)}extractParams(a){let u=this.config.filterSizes||Vk.DEFAULT_FILTER_SIZES,h=u?u.length:void 0;if(h!==7&&h!==8&&h!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${h} filterSizes in config`);return qR(a,this.config,this.boxEncodingSize,u)}async extractBoxes(a,u,h){let{width:d,height:g}=u,x=Math.max(d,g),w=x/d,k=x/g,C=a.shape[1],$=this.config.anchors.length,[F,_,W]=He.tidy(()=>{let mt=a.reshape([C,C,$,this.boxEncodingSize]),lt=mt.slice([0,0,0,0],[C,C,$,4]),gt=mt.slice([0,0,0,4],[C,C,$,1]),_t=this.withClassScores?He.softmax(mt.slice([0,0,0,5],[C,C,$,this.config.classes.length]),3):He.scalar(0);return[lt,gt,_t]}),et=[],tt=await _.array(),G=await F.array();for(let mt=0;mt<C;mt++)for(let lt=0;lt<C;lt++)for(let gt=0;gt<$;gt++){let _t=lh(tt[mt][lt][gt][0]);if(!h||_t>h){let Gt=(lt+lh(G[mt][lt][gt][0]))/C*w,se=(mt+lh(G[mt][lt][gt][1]))/C*k,fe=Math.exp(G[mt][lt][gt][2])*this.config.anchors[gt].x/C*w,_e=Math.exp(G[mt][lt][gt][3])*this.config.anchors[gt].y/C*k,Ge=Gt-fe/2,Vt=se-_e/2,ln={row:mt,col:lt,anchor:gt},{classScore:Ce,label:rr}=this.withClassScores?await this.extractPredictedClass(W,ln):{classScore:1,label:0};et.push({box:new gl(Ge,Vt,Ge+fe,Vt+_e),score:_t,classScore:_t*Ce,label:rr,...ln})}}return F.dispose(),_.dispose(),W.dispose(),et}async extractPredictedClass(a,u){let{row:h,col:d,anchor:g}=u,x=await a.array();return Array(this.config.classes.length).fill(0).map((w,k)=>x[h][d][g][k]).map((w,k)=>({classScore:w,label:k})).reduce((w,k)=>w.classScore>k.classScore?w:k)}},jl=Vk;jl.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Kl=class extends jl{constructor(a=!0){let u=Object.assign({},{withSeparableConvs:a,iouThreshold:BR,classes:["face"]},a?{anchors:WR,meanRgb:VR}:{anchors:zR,withClassScores:!0});super(u)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(a,u){let h=await this.detect(a,u);return h.map(d=>new Ue(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?UR:GR}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}};function zrt(i,a=!0){let u=new Kl(a);return u.extractWeights(i),u}var Cb=class extends is{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},uo=class{async then(a){return a(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf=te(ee()),Gk=te(ee());async function tc(i,a,u,h,d=({alignedRect:g})=>g){let g=i.map(k=>Ja(k)?d(k):k.detection),x=h||(a instanceof Gk.Tensor?await Pl(a,g):await Rl(a,g)),w=await u(x);return x.forEach(k=>k instanceof Gk.Tensor&&k.dispose()),w}async function Xl(i,a,u,h,d){return tc([i],a,async g=>u(g[0]),h,d)}var jR=.4,KR=[new Jt(1.603231,2.094468),new Jt(6.041143,7.080126),new Jt(2.882459,3.518061),new Jt(4.266906,5.178857),new Jt(9.041765,10.66308)],XR=[117.001,114.697,97.404],Yl=class extends jl{constructor(){let a={withSeparableConvs:!0,iouThreshold:jR,classes:["face"],anchors:KR,meanRgb:XR,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(a)}get anchors(){return this.config.anchors}async locateFaces(a,u){let h=await this.detect(a,u);return h.map(d=>new Ue(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}},Ne={ssdMobilenetv1:new Qa,tinyFaceDetector:new Yl,tinyYolov2:new Kl,faceLandmark68Net:new Gl,faceLandmark68TinyNet:new xb,faceRecognitionNet:new ql,faceExpressionNet:new gb,ageGenderNet:new bb},YR=(i,a)=>Ne.ssdMobilenetv1.locateFaces(i,a),Wrt=(i,a)=>Ne.tinyFaceDetector.locateFaces(i,a),Vrt=(i,a)=>Ne.tinyYolov2.locateFaces(i,a),JR=i=>Ne.faceLandmark68Net.detectLandmarks(i),Grt=i=>Ne.faceLandmark68TinyNet.detectLandmarks(i),Urt=i=>Ne.faceRecognitionNet.computeFaceDescriptor(i),qrt=i=>Ne.faceExpressionNet.predictExpressions(i),Hrt=i=>Ne.ageGenderNet.predictAgeAndGender(i),ZR=i=>Ne.ssdMobilenetv1.load(i),jrt=i=>Ne.tinyFaceDetector.load(i),Krt=i=>Ne.tinyYolov2.load(i),Xrt=i=>Ne.faceLandmark68Net.load(i),Yrt=i=>Ne.faceLandmark68TinyNet.load(i),Jrt=i=>Ne.faceRecognitionNet.load(i),Zrt=i=>Ne.faceExpressionNet.load(i),Qrt=i=>Ne.ageGenderNet.load(i),tot=ZR,eot=YR,not=JR,Uk=class extends uo{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.extractedFaces=h}},Ql=class extends Uk{async run(){let a=await this.parentTask,u=await tc(a,this.input,async h=>await Promise.all(h.map(d=>Ne.faceExpressionNet.predictExpressions(d))),this.extractedFaces);return a.map((h,d)=>yb(h,u[d]))}withAgeAndGender(){return new Jl(this,this.input)}},tu=class extends Uk{async run(){let a=await this.parentTask;if(!a)return;let u=await Xl(a,this.input,h=>Ne.faceExpressionNet.predictExpressions(h),this.extractedFaces);return yb(a,u)}withAgeAndGender(){return new Zl(this,this.input)}},rc=class extends Ql{withAgeAndGender(){return new ec(this,this.input)}withFaceDescriptors(){return new Vi(this,this.input)}},oc=class extends tu{withAgeAndGender(){return new nc(this,this.input)}withFaceDescriptor(){return new Gi(this,this.input)}},qk=class extends uo{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.extractedFaces=h}},Jl=class extends qk{async run(){let a=await this.parentTask,u=await tc(a,this.input,async h=>await Promise.all(h.map(d=>Ne.ageGenderNet.predictAgeAndGender(d))),this.extractedFaces);return a.map((h,d)=>{let{age:g,gender:x,genderProbability:w}=u[d];return kb(Nb(h,x,w),g)})}withFaceExpressions(){return new Ql(this,this.input)}},Zl=class extends qk{async run(){let a=await this.parentTask;if(!a)return;let{age:u,gender:h,genderProbability:d}=await Xl(a,this.input,g=>Ne.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return kb(Nb(a,h,d),u)}withFaceExpressions(){return new tu(this,this.input)}},ec=class extends Jl{withFaceExpressions(){return new rc(this,this.input)}withFaceDescriptors(){return new Vi(this,this.input)}},nc=class extends Zl{withFaceExpressions(){return new oc(this,this.input)}withFaceDescriptor(){return new Gi(this,this.input)}},Sb=class extends uo{constructor(a,u){super();this.parentTask=a;this.input=u}},Vi=class extends Sb{async run(){let a=await this.parentTask,u=await tc(a,this.input,h=>Promise.all(h.map(d=>Ne.faceRecognitionNet.computeFaceDescriptor(d))),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return u.map((h,d)=>Tb(a[d],h))}withFaceExpressions(){return new rc(this,this.input)}withAgeAndGender(){return new ec(this,this.input)}},Gi=class extends Sb{async run(){let a=await this.parentTask;if(!a)return;let u=await Xl(a,this.input,h=>Ne.faceRecognitionNet.computeFaceDescriptor(h),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return Tb(a,u)}withFaceExpressions(){return new oc(this,this.input)}withAgeAndGender(){return new nc(this,this.input)}},$b=class extends uo{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.useTinyLandmarkNet=h}get landmarkNet(){return this.useTinyLandmarkNet?Ne.faceLandmark68TinyNet:Ne.faceLandmark68Net}},Ib=class extends $b{async run(){let a=await this.parentTask,u=a.map(g=>g.detection),h=this.input instanceof tf.Tensor?await Pl(this.input,u):await Rl(this.input,u),d=await Promise.all(h.map(g=>this.landmarkNet.detectLandmarks(g)));return h.forEach(g=>g instanceof tf.Tensor&&g.dispose()),a.map((g,x)=>Vl(g,d[x]))}withFaceExpressions(){return new rc(this,this.input)}withAgeAndGender(){return new ec(this,this.input)}withFaceDescriptors(){return new Vi(this,this.input)}},Eb=class extends $b{async run(){let a=await this.parentTask;if(!a)return;let{detection:u}=a,h=this.input instanceof tf.Tensor?await Pl(this.input,[u]):await Rl(this.input,[u]),d=await this.landmarkNet.detectLandmarks(h[0]);return h.forEach(g=>g instanceof tf.Tensor&&g.dispose()),Vl(a,d)}withFaceExpressions(){return new oc(this,this.input)}withAgeAndGender(){return new nc(this,this.input)}withFaceDescriptor(){return new Gi(this,this.input)}},Db=class extends uo{constructor(a,u=new ao){super();this.input=a;this.options=u}},ef=class extends Db{async run(){let{input:a,options:u}=this,h=u instanceof Cb?d=>Ne.tinyFaceDetector.locateFaces(d,u):u instanceof ao?d=>Ne.ssdMobilenetv1.locateFaces(d,u):u instanceof is?d=>Ne.tinyYolov2.locateFaces(d,u):null;if(!h)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return h(a)}runAndExtendWithFaceDetections(){return new Promise(async a=>{let u=await this.run();return a(u.map(h=>_a({},h)))})}withFaceLandmarks(a=!1){return new Ib(this.runAndExtendWithFaceDetections(),this.input,a)}withFaceExpressions(){return new Ql(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Jl(this.runAndExtendWithFaceDetections(),this.input)}},Ab=class extends Db{async run(){let a=await new ef(this.input,this.options),u=a[0];return a.forEach(h=>{h.score>u.score&&(u=h)}),u}runAndExtendWithFaceDetection(){return new Promise(async a=>{let u=await this.run();return a(u?_a({},u):void 0)})}withFaceLandmarks(a=!1){return new Eb(this.runAndExtendWithFaceDetection(),this.input,a)}withFaceExpressions(){return new tu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Zl(this.runAndExtendWithFaceDetection(),this.input)}};function rot(i,a=new ao){return new Ab(i,a)}function Fb(i,a=new ao){return new ef(i,a)}async function QR(i,a){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Fb(i,new ao(a?{minConfidence:a}:{})).withFaceLandmarks().withFaceDescriptors()}async function oot(i,a={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Fb(i,new is(a)).withFaceLandmarks().withFaceDescriptors()}var sot=QR;function Hk(i,a){if(i.length!==a.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let u=Array.from(i),h=Array.from(a);return Math.sqrt(u.map((d,g)=>d-h[g]).reduce((d,g)=>d+Math.pow(g,2),0))}var Rb=class{constructor(a,u=.6){this._distanceThreshold=u;let h=Array.isArray(a)?a:[a];if(!h.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let d=1,g=()=>`person ${d++}`;this._labeledDescriptors=h.map(x=>{if(x instanceof As)return x;if(x instanceof Float32Array)return new As(g(),[x]);if(x.descriptor&&x.descriptor instanceof Float32Array)return new As(g(),[x.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(a,u){return u.map(h=>Hk(h,a)).reduce((h,d)=>h+d,0)/(u.length||1)}matchDescriptor(a){return this.labeledDescriptors.map(({descriptors:u,label:h})=>new uh(h,this.computeMeanDistance(a,u))).reduce((u,h)=>u.distance<h.distance?u:h)}findBestMatch(a){let u=this.matchDescriptor(a);return u.distance<this.distanceThreshold?u:new uh("unknown",u.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(a=>a.toJSON())}}static fromJSON(a){let u=a.labeledDescriptors.map(h=>As.fromJSON(h));return new Rb(u,a.distanceThreshold)}};function iot(i){let a=new Yl;return a.extractWeights(i),a}function tP(i,a){let{width:u,height:h}=new Zn(a.width,a.height);if(u<=0||h<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:h})}`);if(Array.isArray(i))return i.map(d=>tP(d,{width:u,height:h}));if(Ja(i)){let d=i.detection.forSize(u,h),g=i.unshiftedLandmarks.forSize(d.box.width,d.box.height);return Vl(_a(i,d),g)}return Jo(i)?_a(i,i.detection.forSize(u,h)):i instanceof Wr||i instanceof Ue?i.forSize(u,h):i}var eP="0.8.8",cot=typeof process!="undefined",lot=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",uot={faceapi:eP,node:cot,browser:lot};export{bb as AgeGenderNet,gl as BoundingBox,Fe as Box,uo as ComposableTask,Vi as ComputeAllFaceDescriptorsTask,Sb as ComputeFaceDescriptorsTaskBase,Gi as ComputeSingleFaceDescriptorTask,Ib as DetectAllFaceLandmarksTask,ef as DetectAllFacesTask,$b as DetectFaceLandmarksTaskBase,Db as DetectFacesTaskBase,Eb as DetectSingleFaceLandmarksTask,Ab as DetectSingleFaceTask,Zn as Dimensions,Ik as FACE_EXPRESSION_LABELS,Ue as FaceDetection,MR as FaceDetectionNet,gb as FaceExpressionNet,Mi as FaceExpressions,Gl as FaceLandmark68Net,xb as FaceLandmark68TinyNet,CR as FaceLandmarkNet,Wr as FaceLandmarks,NE as FaceLandmarks5,bl as FaceLandmarks68,uh as FaceMatch,Rb as FaceMatcher,ql as FaceRecognitionNet,Hs as Gender,ph as LabeledBox,As as LabeledFaceDescriptors,Us as NetInput,Gn as NeuralNetwork,Si as ObjectDetection,Jt as Point,_E as PredictedBox,yl as Rect,Qa as SsdMobilenetv1,ao as SsdMobilenetv1Options,Yl as TinyFaceDetector,Cb as TinyFaceDetectorOptions,Kl as TinyYolov2,is as TinyYolov2Options,Wk as TinyYolov2SizeType,sot as allFaces,QR as allFacesSsdMobilenetv1,oot as allFacesTinyYolov2,V1 as awaitMediaLoaded,G1 as bufferToImage,Urt as computeFaceDescriptor,xl as createCanvas,dh as createCanvasFromMedia,Lrt as createFaceDetectionNet,Crt as createFaceRecognitionNet,LR as createSsdMobilenetv1,iot as createTinyFaceDetector,zrt as createTinyYolov2,Fb as detectAllFaces,JR as detectFaceLandmarks,Grt as detectFaceLandmarksTiny,not as detectLandmarks,rot as detectSingleFace,Fk as draw,$e as env,Hk as euclideanDistance,kb as extendWithAge,Tb as extendWithFaceDescriptor,_a as extendWithFaceDetection,yb as extendWithFaceExpressions,Vl as extendWithFaceLandmarks,Nb as extendWithGender,Pl as extractFaceTensors,Rl as extractFaces,grt as fetchImage,Sk as fetchJson,yrt as fetchNetWeights,Xa as fetchOrThrow,dr as getContext2dOrThrow,Sa as getMediaDimensions,U1 as imageTensorToCanvas,Ck as imageToSquare,V9 as inverseSigmoid,D1 as iou,tg as isMediaElement,fh as isMediaLoaded,Srt as isWithAge,Jo as isWithFaceDetection,Ek as isWithFaceExpressions,Ja as isWithFaceLandmarks,$rt as isWithGender,Qrt as loadAgeGenderModel,tot as loadFaceDetectionModel,Zrt as loadFaceExpressionModel,Xrt as loadFaceLandmarkModel,Yrt as loadFaceLandmarkTinyModel,Jrt as loadFaceRecognitionModel,ZR as loadSsdMobilenetv1Model,jrt as loadTinyFaceDetectorModel,Krt as loadTinyYolov2Model,$k as loadWeightMap,eot as locateFaces,brt as matchDimensions,A1 as minBbox,Ne as nets,F1 as nonMaxSuppression,Io as normalize,R1 as padToSquare,Hrt as predictAgeAndGender,qrt as recognizeFaceExpressions,tP as resizeResults,Ca as resolveInput,W9 as shuffleArray,lh as sigmoid,YR as ssdMobilenetv1,aot as tf,Wrt as tinyFaceDetector,Vrt as tinyYolov2,Ve as toNetInput,S1 as utils,Bk as validateConfig,uot as version};
+`,sin6=unaryKernelFunc2(SIN),sinConfig2={kernelName:Sin,backendName:"webgl",kernelFunc:sin6},SQUARE="return x * x;",square25=unaryKernelFunc2(SQUARE),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square25},SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3},SUB="return a - b;",subKernelFunc=binaryKernelFunc2({opSnippet:SUB,packedOpSnippet:SUB,supportsComplex:!0,cpuKernelImpl:subImplCPU}),subConfig2={kernelName:Sub,backendName:"webgl",kernelFunc:subKernelFunc},TAN="return tan(x);",tan5=unaryKernelFunc2(TAN),tanConfig2={kernelName:Tan,backendName:"webgl",kernelFunc:tan5},transposeConfig2={kernelName:Transpose,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{perm}=attrs,webglBackend=backend3,xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let out;if(webglBackend.shouldExecuteOnCPU([x])){let xTexData=webglBackend.texData.get(x.dataId),values=xTexData.values,outValues=transposeImplCPU(values,x.shape,x.dtype,perm,newShape);out=webglBackend.makeTensorInfo(newShape,x.dtype);let outData=webglBackend.texData.get(out.dataId);outData.values=outValues}else out=transposeImpl2(x,perm,webglBackend);return out}};function unique7(args){let{inputs,attrs,backend:backend3}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend3.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend3.makeTensorInfo(outputShape,x.dtype,outputValues),backend3.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique7},kernelConfigs2=[addConfig2,atan2Config,avgPoolConfig2,avgPoolBackpropConfig2,batchNormConfig2,castConfig2,complexConfig2,concatConfig2,cosConfig2,divConfig2,fftConfig2,flipLeftRightConfig2,fromPixelsConfig,identityConfig2,ifftConfig2,imagConfig2,maxConfig2,maxPoolConfig2,maxPoolBackpropConfig2,maxPoolWithArgmaxConfig2,meanConfig,mirrorPadConfig2,multiplyConfig2,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,realConfig2,reshapeConfig2,rotateWithOffsetConfig2,sinConfig2,squareConfig2,subConfig2,squaredDifferenceConfig2,tanConfig2,transposeConfig2,uniqueConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version14="2.7.0",version16={"tfjs-core":version,"tfjs-backend-cpu":version10,"tfjs-backend-webgl":version12,"tfjs-data":version8,"tfjs-layers":version2,"tfjs-converter":version6,tfjs:version14},CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend3){wasmFusedMatMul=backend3.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2}=attrs,aId=backend3.dataIdMap.get(a.dataId).id,bId=backend3.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend3.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend3.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc8;function setupFunc2(backend3){wasmFunc8=backend3.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend3,inputs:{x}}=args,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:kernelFunc3}}var absConfig2=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc8;function setupFunc2(backend3){wasmFunc8=backend3.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend3,inputs}=args,{a,b}=inputs,aId=backend3.dataIdMap.get(a.dataId).id,bId=backend3.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend3.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend3.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc8(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend3){wasmFunc=backend3.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend3}=args,out=backend3.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend3.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),inVals=backend3.typedArrayFromHeap(x),outVals=backend3.typedArrayFromHeap(out);return outVals.set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4},wasmTranspose;function setup2(backend3){wasmTranspose=backend3.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose19(args){let{inputs,backend:backend3,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend3});return cloned.shape=outShape,cloned}let out=backend3.makeOutput(outShape,x.dtype),xId=backend3.dataIdMap.get(x.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose19,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend3){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose19({inputs:{x},attrs:{perm:permutedAxes},backend:backend3});let xId=backend3.dataIdMap.get(x.dataId).id,transposedId=backend3.dataIdMap.get(xTransposed.dataId).id;transposedId!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend3){wasmFunc2=backend3.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend3,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend3.makeOutput(outShape,"int32"),outId=backend3.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend3.disposeData(transposed.dataId),out}var argMaxConfig={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3},wasmAvgPool;function setup4(backend3){wasmAvgPool=backend3.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend3}=args,x=inputs.x,xId=backend3.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape91(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape91},wasmBatchMatMul;function setup5(backend3){wasmBatchMatMul=backend3.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul2(args){let{inputs,backend:backend3,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShapeOuterDims=batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2),outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape91({inputs:{x:a},backend:backend3,attrs:{shape:a3dShape}}),b3d=reshape91({inputs:{x:b},backend:backend3,attrs:{shape:b3dShape}}),a3dId=backend3.dataIdMap.get(a3d.dataId).id,b3dId=backend3.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend3.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend3.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig2={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul2};function cast51(args){let{inputs:{x},attrs:{dtype},backend:backend3}=args,out=backend3.makeOutput(x.shape,dtype),inVals=backend3.typedArrayFromHeap(x),outVals=backend3.typedArrayFromHeap(out);return outVals.set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast51},wasmClip;function setup6(backend3){wasmClip=backend3.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat19(args){let{inputs,backend:backend3}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),out=backend3.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);backend_util_exports.assertParamsConsistent(shapes,axis);let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend3.typedArrayFromHeap(input2)),outVals=backend3.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat19},wasmConv2d;function setup7(backend3){wasmConv2d=backend3.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d11(args){let{inputs,attrs,backend:backend3}=args,{x,filter}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad11,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig2={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d11},wasmConv2DBackpropInput;function setup8(backend3){wasmConv2DBackpropInput=backend3.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput3(args){let{backend:backend3,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad11,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend3.makeOutput(convInfo.inShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,dyId=backend3.dataIdMap.get(dy.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig2={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput3},cosConfig3=createUnaryKernelConfig(Cos),InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend3){wasmCropAndResize=backend3.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize2(args){let{backend:backend3,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend3.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast51({backend:backend3,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend3.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend3.dataIdMap.get(boxes.dataId).id,boxIndId=backend3.dataIdMap.get(boxInd.dataId).id,out=backend3.makeOutput(outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend3.disposeData(castedData.dataId),out}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize2},wasmCumsum;function setup10(backend3){wasmCumsum=backend3.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum6(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse12}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose19({inputs:{x},attrs:{perm:permutation},backend:backend3}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend3.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend3.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend3.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse12?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose19({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend3}),backend3.disposeData(permutedX.dataId),backend3.disposeData(permutedOut.dataId)}return out}var cumsumConfig={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum6},wasmDepthToSpace;function setup11(backend3){wasmDepthToSpace=backend3.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace2(args){let{backend:backend3,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend3.makeOutput(outputShape,"float32"),xData=backend3.dataIdMap.get(x.dataId),xId=xData.id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend3.dataIdMap.get(out.dataId).id,channelsLast=dataFormat==="NHWC"?1:0;return wasmDepthToSpace(xId,blockSize,channelsLast,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace2},wasmDepthwiseConv2d;function setup12(backend3){wasmDepthwiseConv2d=backend3.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend3}=args,{x,filter}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad11,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5},supportsFullBroadcast2=!0,divConfig3=createBinaryKernelConfig(Div,supportsFullBroadcast2),supportsFullBroadcast3=!1,equalConfig=createBinaryKernelConfig(Equal,supportsFullBroadcast3,"bool"),expConfig2=createUnaryKernelConfig(Exp);function fill6(args){let{attrs:{shape,value,dtype},backend:backend3}=args,out=backend3.makeOutput(shape,dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(value),out}var fillConfig2={kernelName:Fill,backendName:"wasm",kernelFunc:fill6},wasmFlipLeftRight;function setup13(backend3){wasmFlipLeftRight=backend3.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend3}=args,{image:image3}=inputs,out=backend3.makeOutput(image3.shape,image3.dtype),imageId=backend3.dataIdMap.get(image3.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},supportsFullBroadcast4=!1,floorDivConfig=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast4),wasmBatchNorm;function setup14(backend3){wasmBatchNorm=backend3.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend3,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean7,variance,offset,scale:scale22}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,meanId=backend3.dataIdMap.get(mean7.dataId).id,varianceId=backend3.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend3.dataIdMap.get(offset.dataId).id:0,scaleId=scale22!=null?backend3.dataIdMap.get(scale22.dataId).id:0,out=backend3.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend3.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend3){wasmFusedConv2d=backend3.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d(args){let{inputs,attrs,backend:backend3}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dilations,dataFormat,dimRoundingMode,activation:activation2}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,outId),out}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d},wasmFusedDepthwiseConv2d;function setup16(backend3){wasmFusedDepthwiseConv2d=backend3.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend3}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dilations,dataFormat,dimRoundingMode,activation:activation2}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,outId),out}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend3){wasmGatherNd=backend3.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd(args){let{backend:backend3,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend3.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xData=backend3.dataIdMap.get(params.dataId),xId=xData.id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd},wasmGather;function setup18(backend3){wasmGather=backend3.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV2(args){let{backend:backend3,inputs,attrs}=args,{x,indices}=inputs,{axis}=attrs,newShape=x.shape.slice();newShape[axis]=util_exports.sizeFromShape(indices.shape);let stridesSize=x.shape.length-1,out=backend3.makeOutput(newShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let xData=backend3.dataIdMap.get(x.dataId),xId=xData.id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,outId=backend3.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(newShape)).buffer);wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,axis,outStridesBytes,outId);let parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis);return out.shape=shapeInfo.outputShape,out}var gatherV2Config={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV2},supportsFullBroadcast5=!1,greaterConfig=createBinaryKernelConfig(Greater,supportsFullBroadcast5,"bool"),supportsFullBroadcast6=!1,greaterEqualConfig=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessConfig=createBinaryKernelConfig(Less,supportsFullBroadcast7,"bool"),supportsFullBroadcast8=!1,lessEqualConfig=createBinaryKernelConfig(LessEqual,supportsFullBroadcast8,"bool"),logConfig2=createUnaryKernelConfig(Log),supportsFullBroadcast9=!1,logicalAndConfig=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast9,"bool"),wasmMax;function setup19(backend3){wasmMax=backend3.wasm.cwrap(Max,null,["number, number, number"])}function max9(args){let{backend:backend3,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max9},supportsFullBroadcast10=!1,maximumConfig=createBinaryKernelConfig(Maximum,supportsFullBroadcast10),wasmMaxPool;function setup20(backend3){wasmMaxPool=backend3.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend3}=args,x=inputs.x,xId=backend3.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMin;function setup21(backend3){wasmMin=backend3.wasm.cwrap(Min,null,["number, number, number"])}function min7(args){let{backend:backend3,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig={kernelName:Min,backendName:"wasm",setupFunc:setup21,kernelFunc:min7},supportsFullBroadcast11=!1,minimumConfig=createBinaryKernelConfig(Minimum,supportsFullBroadcast11),supportsFullBroadcast12=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast12),negateConfig=createUnaryKernelConfig(Negate);function parseResultStruct(backend3,resOffset){let result=new Int32Array(backend3.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend3.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc3;function setup22(backend3){wasmFunc3=backend3.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc3(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pSelectedScores),backend3.wasm._free(pValidOutputs);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices);return selectedIndicesTensor}var nonMaxSuppressionV3Config2={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup22,kernelFunc},wasmFunc4;function setup23(backend3){wasmFunc4=backend3.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV4(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pSelectedScores);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend3.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup23,kernelFunc:nonMaxSuppressionV4},wasmFunc5;function setup24(backend3){wasmFunc5=backend3.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pValidOutputs);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend3.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup24,kernelFunc:kernelFunc2},supportsFullBroadcast13=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast13,"bool"),wasmOneHot;function setup25(backend3){wasmOneHot=backend3.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot2(args){let{inputs,backend:backend3,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend3.makeOutput([...indices.shape,depth],"int32"),outId=backend3.dataIdMap.get(out.dataId).id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig={kernelName:OneHot,backendName:"wasm",setupFunc:setup25,kernelFunc:oneHot2};function onesLike2(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(1),out}var onesLikeConfig={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike2},wasmPadV2;function setup26(backend3){wasmPadV2=backend3.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad10(args){let{inputs:{x},backend:backend3,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(outShape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config2={kernelName:PadV2,backendName:"wasm",kernelFunc:pad10,setupFunc:setup26},supportsFullBroadcast14=!1,powConfig=createBinaryKernelConfig(Pow,supportsFullBroadcast14),wasmPrelu;function setup27(backend3){wasmPrelu=backend3.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu8(args){let{inputs,backend:backend3}=args,{x,alpha}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,weightsId=backend3.dataIdMap.get(alpha.dataId).id,out=backend3.makeOutput(x.shape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig2={kernelName:Prelu,backendName:"wasm",setupFunc:setup27,kernelFunc:prelu8},reluConfig2=createUnaryKernelConfig(Relu),relu6Config2=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup28(backend3){wasmResizeBilinear=backend3.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number"])}function resizeBilinear2(args){let{backend:backend3,inputs,attrs}=args,{images}=inputs,{alignCorners,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend3.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast51({backend:backend3,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend3.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend3.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend3.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,outId),castedData!=null&&backend3.disposeData(castedData.dataId),out}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup28,kernelFunc:resizeBilinear2},wasmReverse;function setup29(backend3){wasmReverse=backend3.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse11(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend3});let out=backend3.makeOutput(x.shape,x.dtype),xId=backend3.dataIdMap.get(x.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape91({inputs:{x:out},attrs:{shape:x.shape},backend:backend3})}var reverseConfig={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse11,setupFunc:setup29},wasmRotate;function setup30(backend3){wasmRotate=backend3.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend3,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend3.makeOutput(image3.shape,image3.dtype),imageId=backend3.dataIdMap.get(image3.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup30},rsqrtConfig2=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup31(backend3){wasmScatterNd=backend3.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd(args){let{backend:backend3,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend3.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,updatesData=backend3.dataIdMap.get(updates.dataId),updatesId=updatesData.id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup31,kernelFunc:scatterNd},wasmSelect;function setup32(backend3){wasmSelect=backend3.wasm.cwrap(SelectV2,null,["number","number","number","number","number"])}function select(args){let{inputs,backend:backend3}=args,{condition,t,e}=inputs,conditionId=backend3.dataIdMap.get(condition.dataId).id,tId=backend3.dataIdMap.get(t.dataId).id,eId=backend3.dataIdMap.get(e.dataId).id,out=backend3.makeOutput(t.shape,t.dtype),outId=backend3.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectV2Config={kernelName:SelectV2,backendName:"wasm",kernelFunc:select,setupFunc:setup32},wasmFunc6;function setup33(backend3){wasmFunc6=backend3.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid8(args){let{backend:backend3,inputs:{x}}=args,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc6(xId,outId),out}var sigmoidConfig2={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup33,kernelFunc:sigmoid8},sinConfig3=createUnaryKernelConfig(Sin);function slice20(args){let{inputs:{x},attrs:{begin,size},backend:backend3}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend3.typedArrayFromHeap(x),out=backend3.makeOutput(size_,x.dtype),outVals=backend3.typedArrayFromHeap(out),xStrides=util_exports.computeStrides(x.shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return outVals.set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}let rank=x.shape.length;return rank===2?slice2d3(xVals,xStrides[0],outVals,begin_,size_):rank===3?slice3d3(xVals,xStrides[0],xStrides[1],outVals,begin_,size_):rank===4?slice4d3(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_):genericSliceSlow(xVals,x,outVals,begin_,size_),out}function slice2d3(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d3(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d3(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}function genericSliceSlow(xVals,xInfo,outVals,begin,size){let outBuf=buffer(size,xInfo.dtype,outVals),xBuf=buffer(xInfo.shape,xInfo.dtype,xVals);for(let i=0;i<outBuf.size;++i){let loc=outBuf.indexToLoc(i),xLoc=loc.map((idx,j)=>idx+begin[j]);outVals[i]=xBuf.get(...xLoc)}}var sliceConfig2={kernelName:Slice,backendName:"wasm",kernelFunc:slice20},wasmFunc7;function setup34(backend3){wasmFunc7=backend3.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend3,inputs:{logits},attrs:{dim}}=args,xId=backend3.dataIdMap.get(logits.dataId).id,out=backend3.makeOutput(logits.shape,logits.dtype),outId=backend3.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId,channels,batch),out}var softmaxConfig={kernelName:Softmax,backendName:"wasm",setupFunc:setup34,kernelFunc:softmax5};function split12(args){let{inputs,attrs,backend:backend3}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice20({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend3});return begin[$axis]+=s,xSlice})}var splitVConfig={kernelName:SplitV,backendName:"wasm",kernelFunc:split12},sqrtConfig2=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup35(backend3){wasmStridedSlice=backend3.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice2(args){let{backend:backend3,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape91({inputs:{x},attrs:{shape:newShape},backend:backend3}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided){let xSliced=slice20({inputs:{x},attrs:{begin,size},backend:backend3});return reshape91({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend3})}let out=backend3.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend3.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend3.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape91({inputs:{x:out},attrs:{shape:outShape},backend:backend3})}var stridedSliceConfig={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup35,kernelFunc:stridedSlice2},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup36(backend3){wasmSum=backend3.wasm.cwrap(Sum,null,["number, number, number"])}function sum28(args){let{backend:backend3,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig={kernelName:Sum,backendName:"wasm",setupFunc:setup36,kernelFunc:sum28},tanhConfig2=createUnaryKernelConfig(Tanh),wasmTile;function setup37(backend3){wasmTile=backend3.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile11(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend3.makeOutput(newShape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig={kernelName:Tile,backendName:"wasm",setupFunc:setup37,kernelFunc:tile11};function unpack(args){let{inputs,backend:backend3,attrs}=args,{value}=inputs,{axis}=attrs,numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice20({inputs:{x:value},attrs:{begin,size},backend:backend3});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack};function zerosLike2(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(0),out}var zerosLikeConfig={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike2},kernelConfigs3=[absConfig2,addConfig3,addNConfig,argMaxConfig,avgPoolConfig3,batchMatMulConfig2,castConfig3,clipByValueConfig,concatConfig3,conv2DConfig2,conv2DBackpropInputConfig2,cosConfig3,cropAndResizeConfig,cumsumConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig2,divConfig3,equalConfig,expConfig2,fillConfig2,flipLeftRightConfig3,floorDivConfig,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig3,lessConfig,lessEqualConfig,logConfig2,logicalAndConfig,maxConfig3,maximumConfig,maxPoolConfig3,minConfig,minimumConfig,multiplyConfig3,negateConfig,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig,onesLikeConfig,padV2Config2,powConfig,preluConfig2,reluConfig2,relu6Config2,reshapeConfig3,resizeBilinearConfig,reverseConfig,rotateWithOffsetConfig3,rsqrtConfig2,scatterNdConfig,selectV2Config,sigmoidConfig2,sinConfig3,sliceConfig2,softmaxConfig,splitVConfig,sqrtConfig2,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig,subConfig3,sumConfig,tanhConfig2,tileConfig,transposeConfig3,unpackConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),WASM_PRIORITY=2,BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine15())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();f();let kernelMs=util_exports.now()-start;return{kernelMs}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer11=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer11,memoryOffset,size);case"int32":return new Int32Array(buffer11,memoryOffset,size);case"bool":return new Uint8Array(buffer11,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var _scriptDir = undefined; var WasmBackendModuleThreadedSimd = "+tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{if(initialized)return;if(initAborted)return;initAborted=!0;let rejectMsg="Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers";reject({message:rejectMsg})}})}function typedArrayFromBuffer(buffer11,dtype){switch(dtype){case"float32":return new Float32Array(buffer11);case"int32":return new Int32Array(buffer11);case"bool":return new Uint8Array(buffer11);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version17="2.7.0";var 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});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven2,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range2,round:()=>round2});var Dimensions=class{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)}};function isTensor(tensor163,dim){return tensor163 instanceof Tensor&&tensor163.shape.length===dim}function isTensor1D(tensor163){return isTensor(tensor163,1)}function isTensor2D(tensor163){return isTensor(tensor163,2)}function isTensor3D(tensor163){return isTensor(tensor163,3)}function isTensor4D(tensor163){return isTensor(tensor163,4)}function isFloat(num){return num%1!==0}function isEven2(num){return num%2===0}function round2(num,prec=2){let 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){let scale3=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale3),Math.round(height*scale3))}function getCenterPoint(pts){return pts.reduce((sum29,pt)=>sum29.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range2(num,start,step9){return Array(num).fill(0).map((_,i)=>start+i*step9)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&0<=num&&num<=1}var Point=class{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))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){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`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),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)}`);let[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}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(){let[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(){let[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,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,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){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{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()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{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})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min9,x)=>x<min9?x:min9,Infinity),minY=ys.reduce((min9,y)=>y<min9?y:min9,Infinity),maxX=xs.reduce((max11,x)=>max11<x?x:max11,0),maxY=ys.reduce((max11,y)=>max11<y?y:max11,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression2(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}function normalize(x,meanRgb){return tidy(()=>{let[r,g,b]=meanRgb,avg_r=fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=concat([avg_r,avg_g,avg_b],3);return sub(x,avg_rgb)})}function padToSquare(imgTensor,isCenterImage=!1){return tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmount2=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmount2,fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],paddingTensorPrepend=isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,tensorsToStack=[paddingTensorPrepend,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>cast(t,"float32"));return concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array2=inputArray.slice();for(let i=array2.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array2[i];array2[i]=array2[j],array2[j]=x}return array2}function sigmoid2(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{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){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},options);return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),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){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class 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)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round2(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{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(json18){let descriptors=json18.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json18.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!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]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){let extension={detection};return Object.assign({},sourceObj,extension)}function createBrowserEnv(){let fetch3=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},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:fetch3,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}let readFile=fs?function(filePath){return new Promise((res,rej)=>{fs.readFile(filePath,function(err,buffer6){return err?rej(err):res(buffer6)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)};return{readFile}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=function(){if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=function(){if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch3=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch:fetch3,...fileSystem}}function isBrowser2(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env18){environment=env18}function initialize(){if(isBrowser2())return setEnv(createBrowserEnv());if(isNodejs.isNodejs())return setEnv(createNodejsEnv())}function monkeyPatch(env18){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env18;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env18.createCanvasElement||(()=>new Canvas),environment.createImageElement=env18.createImageElement||(()=>new Image),environment.ImageData=env18.ImageData||environment.ImageData,environment.Video=env18.Video||environment.Video,environment.fetch=env18.fetch||environment.fetch,environment.readFile=env18.readFile||environment.readFile}var env2={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser:isBrowser2,isNodejs:isNodejs.isNodejs};initialize();function resolveInput(arg){return!env2.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env2.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}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={}));var DrawTextFieldOptions=class{constructor(options={}){let{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}},DrawTextField=class{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){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions(Object.assign({},defaultDrawLabelOptions,drawLabelOptions))}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){let detectionsArray=Array.isArray(detections)?detections:[detections];detectionsArray.forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round2(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}function isMediaLoaded(media){let{Image,Video}=env2.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env2.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)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{if(!(buf instanceof Blob))return reject("bufferToImage - expected buf to be of type: Blob");let reader=new FileReader;reader.onload=()=>{if(typeof reader.result!="string")return reject("bufferToImage - expected reader.result to be a string, in onload");let img=env2.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input2){let{Image,Video}=env2.getEnv();return input2 instanceof Image?new Dimensions(input2.naturalWidth,input2.naturalHeight):input2 instanceof Video?new Dimensions(input2.videoWidth,input2.videoHeight):new Dimensions(input2.width,input2.height)}function createCanvas2({width,height}){let{createCanvasElement}=env2.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env2.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas2({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env2.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await browser_exports.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input2){let{Image,Canvas,Video}=env2.getEnv();return input2 instanceof Image||input2 instanceof Canvas||input2 instanceof Video}var EPSILON_FLOAT323=1e-7,EPSILON_FLOAT163=1e-4,KernelBackend2=class{time(f){return notYetImplemented2("time")}read(dataId){return notYetImplemented2("read")}readSync(dataId){return notYetImplemented2("readSync")}numDataIds(){return notYetImplemented2("numDataIds")}disposeData(dataId){return notYetImplemented2("disposeData")}write(values,shape,dtype){return notYetImplemented2("write")}move(dataId,values,shape,dtype){return notYetImplemented2("move")}memory(){return notYetImplemented2("memory")}floatPrecision(){return 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notYetImplemented2("minimum")}mod(a,b){return notYetImplemented2("mod")}max(x,axes){return notYetImplemented2("max")}maximum(a,b){return notYetImplemented2("maximum")}all(x,axes){return notYetImplemented2("all")}any(x,axes){return notYetImplemented2("any")}squaredDifference(a,b){return notYetImplemented2("squaredDifference")}ceil(x){return notYetImplemented2("ceil")}floor(x){return notYetImplemented2("floor")}round(x){return notYetImplemented2("round")}sign(x){return notYetImplemented2("sign")}isNaN(x){return notYetImplemented2("isNaN")}isInf(x){return notYetImplemented2("isInf")}isFinite(x){return notYetImplemented2("isFinite")}pow(a,b){return notYetImplemented2("pow")}exp(x){return notYetImplemented2("exp")}expm1(x){return notYetImplemented2("expm1")}softmax(x,dim){return notYetImplemented2("softmax")}log(x){return notYetImplemented2("log")}log1p(x){return notYetImplemented2("log1p")}sqrt(x){return notYetImplemented2("sqrt")}rsqrt(x){return notYetImplemented2("rsqrt")}square(x){return notYetImplemented2("square")}reciprocal(x){return notYetImplemented2("reciprocal")}relu(x){return notYetImplemented2("relu")}relu6(x){return notYetImplemented2("relu6")}prelu(x,a){return notYetImplemented2("prelu")}elu(x){return notYetImplemented2("elu")}eluDer(dy,y){return notYetImplemented2("eluDer")}selu(x){return notYetImplemented2("selu")}int(x){return notYetImplemented2("int")}clip(x,min9,max11){return notYetImplemented2("clip")}abs(x){return notYetImplemented2("abs")}complexAbs(x){return notYetImplemented2("complexAbs")}sigmoid(x){return notYetImplemented2("sigmoid")}softplus(x){return notYetImplemented2("softplus")}sin(x){return notYetImplemented2("sin")}cos(x){return notYetImplemented2("cos")}tan(x){return notYetImplemented2("tan")}asin(x){return notYetImplemented2("asin")}acos(x){return notYetImplemented2("acos")}atan(x){return notYetImplemented2("atan")}atan2(a,b){return notYetImplemented2("atan2")}sinh(x){return notYetImplemented2("sinh")}cosh(x){return notYetImplemented2("cosh")}tanh(x){return notYetImplemented2("tanh")}asinh(x){return notYetImplemented2("asinh")}acosh(x){return notYetImplemented2("acosh")}atanh(x){return notYetImplemented2("atanh")}erf(x){return notYetImplemented2("erf")}step(x,alpha){return notYetImplemented2("step")}fusedConv2d({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){return notYetImplemented2("fusedConv2d")}conv2d(x,filter,convInfo){return notYetImplemented2("conv2d")}conv2dDerInput(dy,filter,convInfo){return notYetImplemented2("conv2dDerInput")}conv2dDerFilter(x,dY,convInfo){return notYetImplemented2("conv2dDerFilter")}fusedDepthwiseConv2D({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){return notYetImplemented2("fusedDepthwiseConv2D")}depthwiseConv2D(input2,filter,convInfo){return notYetImplemented2("depthwiseConv2D")}depthwiseConv2DDerInput(dy,filter,convInfo){return 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Abs2="Abs",Acos2="Acos",Acosh2="Acosh",Add3="Add",AddN2="AddN",All2="All",Any2="Any",ArgMax2="ArgMax",ArgMin2="ArgMin",Asin2="Asin",Asinh2="Asinh",Atan3="Atan",Atanh2="Atanh",Atan22="Atan2",AvgPool2="AvgPool",AvgPoolBackprop2="AvgPoolBackprop",AvgPool3D2="AvgPool3D",AvgPool3DBackprop2="AvgPool3DBackprop",BatchMatMul2="BatchMatMul",BatchToSpaceND2="BatchToSpaceND",BroadcastTo2="BroadcastTo",Cast2="Cast",Ceil2="Ceil",ClipByValue2="ClipByValue",Complex2="Complex",Concat2="Concat",Conv2D3="Conv2D",Conv2DBackpropFilter2="Conv2DBackpropFilter",Conv2DBackpropInput2="Conv2DBackpropInput",Conv3D3="Conv3D",Conv3DBackpropFilterV22="Conv3DBackpropFilterV2",Conv3DBackpropInputV22="Conv3DBackpropInputV2",Cos2="Cos",Cosh2="Cosh",Cumsum2="Cumsum",CropAndResize2="CropAndResize",DepthToSpace2="DepthToSpace",DepthwiseConv2dNative2="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter2="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput2="DepthwiseConv2dNativeBackpropInput",Dilation2D2="Dilation2D",Dilation2DBackpropInput2="Dilation2DBackpropInput",Dilation2DBackpropFilter2="Dilation2DBackpropFilter",Div2="Div",Elu3="Elu",EluGrad2="EluGrad",Erf2="Erf",Equal2="Equal",Exp2="Exp",Expm12="Expm1",FFT2="FFT",Fill2="Fill",FlipLeftRight2="FlipLeftRight",Floor2="Floor",FloorDiv2="FloorDiv",FusedBatchNorm2="FusedBatchNorm",GatherV22="GatherV2",Greater2="Greater",GreaterEqual2="GreaterEqual",Identity3="Identity",IFFT2="IFFT",Imag2="Imag",IsFinite2="IsFinite",IsInf2="IsInf",IsNan2="IsNan",Less2="Less",LessEqual2="LessEqual",Log2="Log",Log1p2="Log1p",LogicalAnd2="LogicalAnd",LogicalNot2="LogicalNot",LogicalOr2="LogicalOr",LogSoftmax3="LogSoftmax",LRN2="LRN",LRNBackprop2="LRNBackprop",Max2="Max",Maximum3="Maximum",MaxPool2="MaxPool",MaxPoolBackprop2="MaxPoolBackprop",MaxPool3D2="MaxPool3D",MaxPool3DBackprop2="MaxPool3DBackprop",Mean2="Mean",Min2="Min",Minimum3="Minimum",MirrorPad2="MirrorPad",Mod2="Mod",Multiply3="Multiply",Negate2="Negate",NotEqual2="NotEqual",NonMaxSuppressionV32="NonMaxSuppressionV3",NonMaxSuppressionV42="NonMaxSuppressionV4",NonMaxSuppressionV52="NonMaxSuppressionV5",OnesLike2="OnesLike",OneHot2="OneHot",PadV22="PadV2",Pow2="Pow",Prelu2="Prelu",Prod2="Prod",Range2="Range",Real2="Real",Reciprocal2="Reciprocal",Relu3="Relu",Reshape3="Reshape",ResizeNearestNeighbor2="ResizeNearestNeighbor",ResizeNearestNeighborGrad2="ResizeNearestNeighborGrad",ResizeBilinear2="ResizeBilinear",ResizeBilinearGrad2="ResizeBilinearGrad",Relu63="Relu6",Reverse2="Reverse",Round2="Round",Rsqrt2="Rsqrt",SelectV22="SelectV2",Selu3="Selu",Slice2="Slice",Sin2="Sin",Sinh2="Sinh",Sign2="Sign",Sigmoid3="Sigmoid",Softplus3="Softplus",Sqrt2="Sqrt",Sum2="Sum",SpaceToBatchND2="SpaceToBatchND",SplitV2="SplitV",Softmax4="Softmax",SquaredDifference2="SquaredDifference",Square2="Square",Sub2="Sub",StridedSlice2="StridedSlice",Tan2="Tan",Tanh3="Tanh",Tile2="Tile",TopK2="TopK",Transpose2="Transpose",Unique2="Unique",Unpack2="Unpack",UnsortedSegmentSum2="UnsortedSegmentSum",ZerosLike2="ZerosLike",Step2="Step",FromPixels2="FromPixels",RotateWithOffset2="RotateWithOffset";var kernelRegistry2=getGlobal2("kernelRegistry",()=>new Map),gradRegistry2=getGlobal2("gradRegistry",()=>new Map);function getKernel2(kernelName,backendName){let key=makeKey2(kernelName,backendName);return kernelRegistry2.get(key)}function getGradient2(kernelName){return gradRegistry2.get(kernelName)}function getKernelsForBackend2(backendName){let it=kernelRegistry2.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config2]=value,[backend3]=key.split("_");backend3===backendName&&result.push(config2)}return result}function registerGradient2(config2){let{kernelName}=config2;gradRegistry2.has(kernelName)&&(env12().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`)),gradRegistry2.set(kernelName,config2)}function makeKey2(kernelName,backendName){return`${backendName}_${kernelName}`}function noConversionNeeded2(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray2(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten2(a)),env12().getBool("DEBUG")&&checkConversionForErrors2(a,dtype),noConversionNeeded2(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now2(){return env12().platform.now()}function encodeString2(s,encoding="utf-8"){return encoding=encoding||"utf-8",env12().platform.encode(s,encoding)}function decodeString2(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env12().platform.decode(bytes,encoding)}var Profiler2=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger2)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors2(tensorVals,output.dtype,kernelName)})}let kernelProfile={kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")};return kernelProfile}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors2(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger2=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad2(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad2(name,25),rank=result.rank,size=result.size,shape=rightPad2(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY2(tape2,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape2.length;i++){let node2=tape2[i],nodeInputs=node2.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node2.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node2.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape2.length-1;i>=0;i--){let node2=tape2[i],nodeInputs=node2.inputs;for(let j=0;j<node2.outputs.length;j++)if(tensorsLeadToY[node2.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node2.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape2.length;i++){let node2=tape2[i];if(nodesFromX[node2.id]&&nodesToY[node2.id]){let prunedInputs={};for(let inputName in node2.inputs){let nodeInput=node2.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node2);prunedNode.inputs=prunedInputs,prunedNode.outputs=node2.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients2(tensorAccumulatedGradientMap,filteredTape,tidy3,add20){for(let i=filteredTape.length-1;i>=0;i--){let node2=filteredTape[i],dys=[];if(node2.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node2.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);let inputGradients=node2.gradient(dys);for(let inputName in node2.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy3(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node2.inputs[inputName];if(!arraysEqual2(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add20(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS2=20,FORMAT_NUM_FIRST_LAST_VALS2=3,FORMAT_NUM_SIG_DIGITS2=7;function tensorToString2(vals,shape,dtype,verbose){let strides=computeStrides2(shape),padPerCol=computeMaxSizePerColumn2(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString2(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
+`)),lines.join(`
+`)}function computeMaxSizePerColumn2(vals,shape,dtype,strides){let n=sizeFromShape2(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples2(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){let offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString2(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString2(val,pad7,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS2))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS2))}j`:isString2(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString2(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS2)).toString(),rightPad2(valStr,pad7)}function boolNumToString2(v){return v===0?"false":"true"}function subTensorToString2(vals,shape,dtype,strides,padPerCol,isLast=!0){let storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){let complexTuple=createComplexTuples2(vals);return[valToString2(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString2(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS2){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS2*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS2)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples2(firstVals),lastVals=createComplexTuples2(lastVals)),["["+firstVals.map((x,i)=>valToString2(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString2(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS2+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples2(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString2(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS2){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS2;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString2(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS2;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString2(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString2(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}let sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
+`;for(let i=2;i<rank;i++)newLineSep+=`
+`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples2(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer2=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape2(shape),values!=null){let n=values.length;assert3(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType2(dtype,this.size),this.strides=computeStrides2(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert3(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn2().makeTensor(this.values,this.shape,this.dtype)}},trackerFn2=null,opHandler3=null,deprecationWarningFn2=null;function setTensorTracker2(fn){trackerFn2=fn}function setOpHandler2(handler){opHandler3=handler}function setDeprecationWarningFn2(fn){deprecationWarningFn2=fn}var Tensor2=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape2(shape),this.strides=computeStrides2(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler3.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler3.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray2(this.shape,vals)}arraySync(){return toNestedArray2(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn2().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString2(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn2().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString2(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn2().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){if(this.isDisposed)return;trackerFn2().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler3.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler3.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString2(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler3.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn2().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor2,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable2=class extends Tensor2{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual2(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn2().disposeTensor(this),this.dataId=newValue.dataId,trackerFn2().incRef(this,null)}dispose(){trackerFn2().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable2,Symbol.hasInstance,{value:instance=>instance instanceof Tensor2&&instance.assign!=null&&instance.assign instanceof Function});var Rank2;(function(Rank3){Rank3.R0="R0",Rank3.R1="R1",Rank3.R2="R2",Rank3.R3="R3",Rank3.R4="R4",Rank3.R5="R5",Rank3.R6="R6"})(Rank2||(Rank2={}));var UpcastInt32AndMap2;(function(UpcastInt32AndMap3){UpcastInt32AndMap3.float32="float32",UpcastInt32AndMap3.int32="int32",UpcastInt32AndMap3.bool="int32",UpcastInt32AndMap3.complex64="complex64"})(UpcastInt32AndMap2||(UpcastInt32AndMap2={}));var UpcastBoolAndMap2;(function(UpcastBoolAndMap3){UpcastBoolAndMap3.float32="float32",UpcastBoolAndMap3.int32="int32",UpcastBoolAndMap3.bool="bool",UpcastBoolAndMap3.complex64="complex64"})(UpcastBoolAndMap2||(UpcastBoolAndMap2={}));var UpcastFloat32AndMap2;(function(UpcastFloat32AndMap3){UpcastFloat32AndMap3.float32="float32",UpcastFloat32AndMap3.int32="float32",UpcastFloat32AndMap3.bool="float32",UpcastFloat32AndMap3.complex64="complex64"})(UpcastFloat32AndMap2||(UpcastFloat32AndMap2={}));var UpcastComplex64AndMap2;(function(UpcastComplex64AndMap3){UpcastComplex64AndMap3.float32="complex64",UpcastComplex64AndMap3.int32="complex64",UpcastComplex64AndMap3.bool="complex64",UpcastComplex64AndMap3.complex64="complex64"})(UpcastComplex64AndMap2||(UpcastComplex64AndMap2={}));var upcastTypeMap2={float32:UpcastFloat32AndMap2,int32:UpcastInt32AndMap2,bool:UpcastBoolAndMap2,complex64:UpcastComplex64AndMap2};function upcastType2(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap2[typeA][typeB]}function makeTypesMatch2(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType2(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function getTensorsInContainer2(result){let list=[],seen=new Set;return walkTensorContainer2(result,list,seen),list}function walkTensorContainer2(container,list,seen){if(container==null)return;if(container instanceof Tensor2){list.push(container);return}if(!isIterable3(container))return;let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer2(val,list,seen))}}function isIterable3(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState2=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine2=class{constructor(ENV7){this.ENV=ENV7,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState2}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],success=await this.initializeBackend(backendName).success;if(success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName),result=asyncInit?await success:success;if(!result)return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler2(this.backendInstance),!0}setupRegisteredKernels(){let kernels=getKernelsForBackend2(this.backendName);kernels.forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){let kernels=getKernelsForBackend2(backendName);kernels.forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend3=registryFactoryEntry.factory();if(backend3&&!(backend3 instanceof KernelBackend2)&&typeof backend3.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend3.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend3,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend3,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend3,backend3.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine2.nextTensorId++}nextVariableId(){return Engine2.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE2.runKernelFunc(backend3=>backend3.cast(dy,dtype),gradInputs,null,Cast2,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3,kernel=getKernel2(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(({dataId,shape,dtype})=>this.makeTensorFromDataId(dataId,shape,dtype));if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{let saveFunc=tensors=>{if(!isTapeOn)return;saved=tensors.map(tensor163=>this.keep(this.clone(tensor163)))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){let saved=tensors.map(tensor163=>this.keep(this.clone(tensor163)));return saved}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient2(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert3(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend3){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend3=backend3||this.backend;let backendVals=values;dtype==="string"&&isString2(values[0])&&(backendVals=values.map(d=>encodeString2(d)));let dataId=backend3.write(backendVals,shape,dtype),t=new Tensor2(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend3),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray2(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend3){dtype=dtype||"float32";let t=new Tensor2(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend3),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable2(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend3){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement2(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend3||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable2||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId),refCount=info.refCount;refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient2(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray2(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer2(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor163=this.state.activeScope.track[i];!tensor163.kept&&!tensorsToTrackInParentSet.has(tensor163.id)&&tensor163.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor163=>{!tensor163.kept&&tensor163.scopeId===oldScope.id&&this.track(tensor163)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert3(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert3(y instanceof Tensor2,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY2(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones3(y.shape):dy,backpropagateGradients2(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add3);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node2=>{for(let tensor163 of node2.saved)tensor163.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert3(isFunction2(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert3(inputs.every(t=>t instanceof Tensor2),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert3(res.value instanceof Tensor2,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert3(isFunction2(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert3(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert3(grads2.every(t=>t instanceof Tensor2),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.readSync(dataId)}read(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.read(dataId)}async time(query){let start=now2(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now2()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState2;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine2.nextTensorId=0;Engine2.nextVariableId=0;function ones3(shape){let values=makeOnesTypedArray2(sizeFromShape2(shape),"float32");return ENGINE2.makeTensor(values,shape,"float32")}function getOrMakeEngine2(){let ns=getGlobalNamespace2();if(ns._tfengine==null){let environment12=new Environment2(ns);ns._tfengine=new Engine2(environment12)}return setEnvironmentGlobal2(ns._tfengine.ENV),setTensorTracker2(()=>ns._tfengine),ns._tfengine}var ENGINE2=getOrMakeEngine2();function add3(a,b){let inputs={a,b};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.add(a,b);return save([a,b]),res},inputs,null,Add3)}function isBrowser4(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var ENV6=env12();ENV6.registerFlag("DEBUG",()=>!1,debugValue=>{debugValue&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV6.registerFlag("IS_BROWSER",()=>isBrowser4());ENV6.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV6.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV6.registerFlag("PROD",()=>!1);ENV6.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV6.getBool("DEBUG"));ENV6.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV6.registerFlag("IS_TEST",()=>!1);function inferShape2(val,dtype){let firstElem=val;if(isTypedArray2(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray2(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env12().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency2(val,shape,[]),shape}function deepAssertShapeConsistency2(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray2(val)){assert3(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert3(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert3(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency2(val[i],subShape,indices.concat(i))}function assertDtype2(expectedDtype,actualDType,argName,functionName){if(expectedDtype==null)return;if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}function convertToTensor2(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor2)return assertDtype2(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype2(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype2(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray2(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape2(x,inferredDtype);!isTypedArray2(x)&&!Array.isArray(x)&&(x=[x]);let skipTypedArray=!0,values=inferredDtype!=="string"?toTypedArray2(x,inferredDtype):flatten2(x,[],skipTypedArray);return ENGINE2.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray2(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);let tensors=arg;return tensors.map((t,i)=>convertToTensor2(t,`${argName}[${i}]`,functionName),parseAsDtype)}var OP_SCOPE_SUFFIX2="__op";function op2(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX2;let f2=(...args)=>{ENGINE2.startScope(opName);try{let result=fn(...args);return isPromise2(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE2.endScope(result),result}catch(ex){throw ENGINE2.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_2(real9,imag9){let $real=convertToTensor2(real9,"real","complex"),$imag=convertToTensor2(imag9,"imag","complex");assertShapesMatch2($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let forward=backend3=>backend3.complex($real,$imag),inputs={real:$real,imag:$imag};return ENGINE2.runKernelFunc(forward,inputs,null,Complex2)}var complex2=op2({complex_:complex_2});function makeTensor2(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype2(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray2(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions2(shape);let providedSize=sizeFromShape2(shape),inferredSize=sizeFromShape2(inferredShape);assert3(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape2(shape.slice(i)):!0;assert3(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray2(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray2(values,dtype):flatten2(values,[],!0),ENGINE2.makeTensor(values,shape,dtype)}function tensor5(values,shape,dtype){let inferredShape=inferShape2(values,dtype);return makeTensor2(values,shape,inferredShape,dtype)}var useNodeBuffer2=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength2(str){return useNodeBuffer2?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String2(buffer6){if(useNodeBuffer2)return Buffer.from(buffer6).toString("base64");let buf=new Uint8Array(buffer6),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer2(str){if(useNodeBuffer2){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer6=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer6.set([s.charCodeAt(i)],i);return buffer6.buffer}function getModelArtifactsInfoForJSON2(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength2(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength2(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}var IORouterRegistry2=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry2.instance==null&&(IORouterRegistry2.instance=new IORouterRegistry2),IORouterRegistry2.instance}static registerSaveRouter(saveRouter){IORouterRegistry2.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry2.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry2.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry2.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[],routers=handlerType==="load"?IORouterRegistry2.getInstance().loadRouters:IORouterRegistry2.getInstance().saveRouters;return routers.forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}};var DATABASE_NAME2="tensorflowjs",DATABASE_VERSION2=1,MODEL_STORE_NAME2="models_store",INFO_STORE_NAME2="model_info_store";function getIndexedDBFactory2(){if(!env12().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase2(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME2,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME2,{keyPath:"modelPath"})}var BrowserIndexedDB2=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory2(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME2,DATABASE_VERSION2);openRequest.onupgradeneeded=()=>setUpDatabase2(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME2,"readonly"),modelStore=modelTx.objectStore(MODEL_STORE_NAME2),getRequest=modelStore.get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON2(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME2,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME2),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME2,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME2),putModelRequest=modelStore.put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME2);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB2.URL_SCHEME="indexeddb://";var indexedDBRouter2=url=>env12().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserIndexedDB2.URL_SCHEME))?browserIndexedDB2(url.slice(BrowserIndexedDB2.URL_SCHEME.length)):null;IORouterRegistry2.registerSaveRouter(indexedDBRouter2);IORouterRegistry2.registerLoadRouter(indexedDBRouter2);function browserIndexedDB2(modelPath){return new BrowserIndexedDB2(modelPath)}function maybeStripScheme3(key){return key.startsWith(BrowserIndexedDB2.URL_SCHEME)?key.slice(BrowserIndexedDB2.URL_SCHEME.length):key}var BrowserIndexedDBManager2=class{constructor(){this.indexedDB=getIndexedDBFactory2()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME2,DATABASE_VERSION2);openRequest.onupgradeneeded=()=>setUpDatabase2(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME2,"readonly"),store=tx.objectStore(INFO_STORE_NAME2),getAllInfoRequest=store.getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme3(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME2,DATABASE_VERSION2);openRequest.onupgradeneeded=()=>setUpDatabase2(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME2,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME2),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME2,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME2),deleteModelRequest=modelStore.delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}};var PATH_SEPARATOR2="/",PATH_PREFIX2="tensorflowjs_models",INFO_SUFFIX2="info",MODEL_TOPOLOGY_SUFFIX2="model_topology",WEIGHT_SPECS_SUFFIX2="weight_specs",WEIGHT_DATA_SUFFIX2="weight_data",MODEL_METADATA_SUFFIX2="model_metadata";function getModelKeys2(path){return{info:[PATH_PREFIX2,path,INFO_SUFFIX2].join(PATH_SEPARATOR2),topology:[PATH_PREFIX2,path,MODEL_TOPOLOGY_SUFFIX2].join(PATH_SEPARATOR2),weightSpecs:[PATH_PREFIX2,path,WEIGHT_SPECS_SUFFIX2].join(PATH_SEPARATOR2),weightData:[PATH_PREFIX2,path,WEIGHT_DATA_SUFFIX2].join(PATH_SEPARATOR2),modelMetadata:[PATH_PREFIX2,path,MODEL_METADATA_SUFFIX2].join(PATH_SEPARATOR2)}}function getModelPathFromKey2(key){let items=key.split(PATH_SEPARATOR2);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR2)}function maybeStripScheme4(key){return key.startsWith(BrowserLocalStorage2.URL_SCHEME)?key.slice(BrowserLocalStorage2.URL_SCHEME.length):key}var BrowserLocalStorage2=class{constructor(modelPath){if(!env12().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys2(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON2(modelArtifacts);try{return this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String2(modelArtifacts.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata})),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,out.userDefinedMetadata=metadata.userDefinedMetadata}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer2(weightDataBase64),out}};BrowserLocalStorage2.URL_SCHEME="localstorage://";var localStorageRouter2=url=>env12().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserLocalStorage2.URL_SCHEME))?browserLocalStorage2(url.slice(BrowserLocalStorage2.URL_SCHEME.length)):null;IORouterRegistry2.registerSaveRouter(localStorageRouter2);IORouterRegistry2.registerLoadRouter(localStorageRouter2);function browserLocalStorage2(modelPath){return new BrowserLocalStorage2(modelPath)}var BrowserLocalStorageManager2=class{constructor(){assert3(env12().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert3(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX2+PATH_SEPARATOR2,suffix=PATH_SEPARATOR2+INFO_SUFFIX2;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey2(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme4(path);let keys=getModelKeys2(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}};var URL_SCHEME_SUFFIX2="://",ModelStoreManagerRegistry2=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry2.instance==null&&(ModelStoreManagerRegistry2.instance=new ModelStoreManagerRegistry2),ModelStoreManagerRegistry2.instance}static registerManager(scheme,manager){assert3(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX2)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX2))),assert3(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry2.getInstance();assert3(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};var PlatformBrowser2=class{fetch(path,init2){return fetch(path,init2)}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}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env12().get("IS_BROWSER")){env12().setPlatform("browser",new PlatformBrowser2);try{ModelStoreManagerRegistry2.registerManager(BrowserLocalStorage2.URL_SCHEME,new BrowserLocalStorageManager2)}catch(err){}try{ModelStoreManagerRegistry2.registerManager(BrowserIndexedDB2.URL_SCHEME,new BrowserIndexedDBManager2)}catch(err){}}var getNodeFetch2={importFetch:()=>require_browser2()},systemFetch2,PlatformNode2=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env12().global.fetch!=null?env12().global.fetch(path,requestInits):(systemFetch2==null&&(systemFetch2=getNodeFetch2.importFetch()),systemFetch2(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env12().get("IS_NODE")&&env12().setPlatform("node",new PlatformNode2);function buffer2(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions2(shape),new TensorBuffer2(shape,dtype,values)}function cast_2(x,dtype){let $x=convertToTensor2(x,"x","cast");if(!isValidDtype2(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE2.runKernelFunc(backend3=>backend3.cast($x,dtype),inputs,null,Cast2,attrs)}var cast2=op2({cast_:cast_2});function clone_2(x){let $x=convertToTensor2(x,"x","clone",null),forward=()=>ENGINE2.makeTensorFromDataId($x.dataId,$x.shape,$x.dtype),inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Identity3)}var clone2=op2({clone_:clone_2});function print3(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine2();var opHandler4={buffer:buffer2,cast:cast2,clone:clone2,print:print3};setOpHandler2(opHandler4);function reshape_2(x,shape){let $x=convertToTensor2(x,"x","reshape",null),inputs={x:$x},attrs={shape},forward=(backend3,save)=>(shape=inferFromImplicitShape2(shape,$x.size),assert3($x.size===sizeFromShape2(shape),()=>"new shape and old shape must have the same number of elements."),save([$x]),backend3.reshape($x,shape));return ENGINE2.runKernelFunc(forward,inputs,null,Reshape3,attrs)}var reshape2=op2({reshape_:reshape_2});function matMul_2(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor2(a,"a","matMul"),$b=convertToTensor2(b,"b","matMul");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{save([$a,$b]);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape2(outerDimsA),batchDimB=sizeFromShape2(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;assert3($a.rank>=2&&$b.rank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`),assert3(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShapeOuterDims=batchDimA>batchDimB?outerDimsA:outerDimsB,outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape2($a,[batchDimA,innerShapeA,outerShapeA]):reshape2($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape2($b,[batchDimB,outerShapeB,innerShapeB]):reshape2($b,[batchDimB,innerShapeB,outerShapeB]),res3d=backend3.batchMatMul(a3D,b3D,transposeA,transposeB);return reshape2(res3d,outShape)},inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE2.runKernelFunc(forward,inputs,null,BatchMatMul2,attrs)}var matMul3=op2({matMul_:matMul_2});function oneHot_2(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let $indices=convertToTensor2(indices,"indices","oneHot","int32"),outShape=[...$indices.shape,depth],forward=(backend3,save)=>(save([$indices]),reshape2(backend3.oneHot(reshape2($indices,[$indices.size]),depth,onValue,offValue),outShape)),inputs={indices:$indices},attrs={depth,onValue,offValue};return ENGINE2.runKernelFunc(forward,inputs,null,OneHot2,attrs)}var oneHot3=op2({oneHot_:oneHot_2});function transpose_2(x,perm){let $x=convertToTensor2(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert3($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert3(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE2.runKernelFunc(backend3=>backend3.transpose($x,perm),inputs,null,Transpose2,attrs)}var transpose2=op2({transpose_:transpose_2}),browser_exports2={};__export(browser_exports2,{fromPixels:()=>fromPixels3,toPixels:()=>toPixels2});function tensor3d2(values,shape,dtype){if(assertNonNull2(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape2(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor2(values,shape,inferredShape,dtype)}var fromPixels2DContext3;function fromPixels_2(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}let kernel=getKernel2(FromPixels2,ENGINE2.backendName);if(kernel!=null){let inputs={pixels},attrs={numChannels};return ENGINE2.runKernel(FromPixels2,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext3==null&&(fromPixels2DContext3=document.createElement("canvas").getContext("2d")),fromPixels2DContext3.canvas.width=width,fromPixels2DContext3.canvas.height=height,fromPixels2DContext3.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext3.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}let outShape=[height,width,numChannels];return tensor3d2(values,outShape,"int32")}async function toPixels2(img,canvas){let $img=convertToTensor2(img,"img","toPixels");if(!(img instanceof Tensor2)){let originalImgTensor=$img;$img=cast2(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels3=op2({fromPixels_:fromPixels_2});function assertParamsValid2(input2,begin,size){let inputRank=input2.shape.length;assert3(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert3(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert3(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes2(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape5(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims2(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis2(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes2(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes2(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims2(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims2(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims2(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis2(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis2(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis2(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims2(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes2(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis2(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims2(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes2(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis2(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp2(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis2(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis2(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp2(0,start,axisSize-1),start}function stopForAxis2(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp2(0,stop,axisSize):stop=clamp2(-1,stop,axisSize-1),stop}function parseSliceParams2(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert3(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert3(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function deprecationWarn2(msg){env12().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn2(deprecationWarn2);function tidy2(nameOrFn,fn){return ENGINE2.tidy(nameOrFn,fn)}function dispose2(container){let tensors=getTensorsInContainer2(container);tensors.forEach(tensor163=>tensor163.dispose())}function add_2(a,b){let $a=convertToTensor2(a,"a","add"),$b=convertToTensor2(b,"b","add");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.add($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Add3)}var add4=op2({add_:add_2});function floorDiv_2(a,b){let $a=convertToTensor2(a,"a","floorDiv"),$b=convertToTensor2(b,"b","floorDiv");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.floorDiv($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,FloorDiv2)}var floorDiv2=op2({floorDiv_:floorDiv_2});function div_2(a,b){let $a=convertToTensor2(a,"a","div"),$b=convertToTensor2(b,"b","div");if([$a,$b]=makeTypesMatch2($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv2($a,$b);let forward=(backend3,save)=>{let res=backend3.realDivide($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE2.runKernelFunc(forward,inputs,null,Div2,attrs)}var div2=op2({div_:div_2});function mul_2(a,b){let $a=convertToTensor2(a,"a","mul"),$b=convertToTensor2(b,"b","mul");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.multiply($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Multiply3)}var mul2=op2({mul_:mul_2});function abs_2(x){let $x=convertToTensor2(x,"x","abs"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>(save([$x]),$x.dtype==="complex64"?backend3.complexAbs($x):backend3.abs($x)),inputs,null,Abs2)}var abs2=op2({abs_:abs_2});function acos_2(x){let $x=convertToTensor2(x,"x","acos"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.acos($x);return save([$x]),res},inputs,null,Acos2)}var acos2=op2({acos_:acos_2});function acosh_2(x){let $x=convertToTensor2(x,"x","acosh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.acosh($x);return save([$x]),res},inputs,null,Acosh2)}var acosh2=op2({acosh_:acosh_2});function axesAreInnerMostDims2(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations2(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes2(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim2(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations2(shape,reduceSubShape,axes)}function getAxesPermutation2(axes,rank){if(axesAreInnerMostDims2(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation2(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes2(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function all_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","all","bool"),forward=backend3=>{let origAxes=parseAxisParam2(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation2(axes,$x.rank);permutedAxes!=null&&($x=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank));let res=backend3.all($x,axes);if(keepDims){let newShape=expandShapeToKeepDim2(res.shape,origAxes);return reshape2(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,All2,attrs)}var all2=op2({all_:all_2});function any_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","any","bool"),forward=backend3=>{let origAxes=parseAxisParam2(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation2(axes,$x.rank);permutedAxes!=null&&($x=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank));let res=backend3.any($x,axes);if(keepDims){let newShape=expandShapeToKeepDim2(res.shape,origAxes);return reshape2(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Any2,attrs)}var any2=op2({any_:any_2});function argMax_2(x,axis=0){let $x=convertToTensor2(x,"x","argMax"),forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam2(axis,$x.shape),permutedAxes=getAxesPermutation2(axes,$x.rank);return permutedAxes!=null&&($x=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank)),backend3.argMax($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE2.runKernelFunc(forward,inputs,null,ArgMax2,attrs)}var argMax2=op2({argMax_:argMax_2});function argMin_2(x,axis=0){let $x=convertToTensor2(x,"x","argMin"),forward=(backend3,save)=>{save([$x]),axis==null&&(axis=0);let axes=parseAxisParam2(axis,$x.shape),permutedAxes=getAxesPermutation2(axes,$x.rank);return permutedAxes!=null&&($x=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank)),backend3.argMin($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE2.runKernelFunc(forward,inputs,null,ArgMin2,attrs)}var argMin2=op2({argMin_:argMin_2});function asin_2(x){let $x=convertToTensor2(x,"x","asin"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.asin($x);return save([$x]),res},inputs,null,Asin2)}var asin2=op2({asin_:asin_2});function asinh_2(x){let $x=convertToTensor2(x,"x","asinh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.asinh($x);return save([$x]),res},inputs,null,Asinh2)}var asinh2=op2({asinh_:asinh_2});function atan_2(x){let $x=convertToTensor2(x,"x","atan"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.atan($x);return save([$x]),res},inputs,null,Atan3)}var atan3=op2({atan_:atan_2});function atan2_2(a,b){let $a=convertToTensor2(a,"a","atan2"),$b=convertToTensor2(b,"b","atan2");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.atan2($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Atan22)}var atan22=op2({atan2_:atan2_2});function atanh_2(x){let $x=convertToTensor2(x,"x","atanh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.atanh($x);return save([$x]),res},inputs,null,Atanh2)}var atanh2=op2({atanh_:atanh_2});function computePool2DInfo2(inShape,filterSize,strides,dilations,pad7,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam2(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo2(inShape,filterShape,strides,dilations,pad7,roundingMode,!1,dataFormat)}function computePool3DInfo2(inShape,filterSize,strides,dilations,pad7,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam2(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo2(inShape,filterShape,strides,dilations,pad7,!1,$dataFormat,roundingMode)}function computeConv2DInfo2(inShape,filterShape,strides,dilations,pad7,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam2(strides),[dilationHeight,dilationWidth]=parseTupleParam2(dilations),effectiveFilterHeight=getEffectiveFilterSize2(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize2(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo2(pad7,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo2(inShape,filterShape,strides,dilations,pad7,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam2(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam2(dilations),effectiveFilterDepth=getEffectiveFilterSize2(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize2(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize2(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo2(pad7,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D2(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad2(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=conditionalRound2((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert3(isInt2(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound2((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert3(isInt2(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputRows,outputCols]}function computeOutputShape4D2(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad2(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=conditionalRound2((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode);assert3(isInt2(outputDepths),()=>`The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);let outputRows=conditionalRound2((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert3(isInt2(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound2((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert3(isInt2(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad2(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize2(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam2(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam2(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize2(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo2(pad7,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad7=="number"){let padType=pad7===0?"VALID":"NUMBER";padInfo={top:pad7,bottom:pad7,left:pad7,right:pad7,type:padType};let outShape=computeOutputShape2D2([inHeight,inWidth],filterHeight,strideHeight,pad7,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad7==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad7==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad7=="object"){let top=dataFormat==="channelsLast"?pad7[1][0]:pad7[2][0],bottom=dataFormat==="channelsLast"?pad7[1][1]:pad7[2][1],left=dataFormat==="channelsLast"?pad7[2][0]:pad7[3][0],right=dataFormat==="channelsLast"?pad7[2][1]:pad7[3][1],padType=top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT";padInfo={top,bottom,left,right,type:padType},outHeight=conditionalRound2((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=conditionalRound2((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad7}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo2(pad7,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad7=="number"){let padType=pad7===0?"VALID":"NUMBER";padInfo={top:pad7,bottom:pad7,left:pad7,right:pad7,front:pad7,back:pad7,type:padType};let outShape=computeOutputShape4D2([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad7,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad7==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad7==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad7}`);return{padInfo,outDepth,outHeight,outWidth}}function conditionalRound2(value,roundingMode){if(!roundingMode)return value;switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne2(param){let[dimA,dimB,dimC]=parseTupleParam2(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne2(strides,dilations){return tupleValuesAreOne2(strides)||tupleValuesAreOne2(dilations)}function convertConv2DDataFormat2(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function avgPool_2(x,filterSize,strides,pad7,dimRoundingMode){let $x=convertToTensor2(x,"x","avgPool","float32"),dilations=1;assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo2(x4D.shape,filterSize,strides,1,pad7,dimRoundingMode);return save([x4D]),convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual2(convInfo.inShape,convInfo.outShape)?x4D.clone():backend3.avgPool(x4D,convInfo)},inputs={x:x4D},attrs={filterSize,strides,pad:pad7,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,AvgPool2,attrs);return res=cast2(res,$x.dtype),reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool5=op2({avgPool_:avgPool_2});function assertParamsConsistent2(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert3(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert3(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert3(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape6(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}function concat_2(tensors,axis=0){assert3(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray2(tensors,"tensors","concat");$tensors[0].dtype==="complex64"&&$tensors.forEach(tensor163=>{if(tensor163.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor163.dtype}. `)});let forward=(backend3,save)=>{let $axis=parseAxisParam2(axis,$tensors[0].shape)[0],outShape=computeOutShape6($tensors.map(t=>t.shape),$axis);if(sizeFromShape2(outShape)===0)return tensor5([],outShape);if($tensors=$tensors.filter(t=>t.size>0),$tensors.length===1)return $tensors[0];let shapes=$tensors.map(t=>t.shape);assertParamsConsistent2(shapes,$axis);let res=backend3.concat($tensors,$axis);return save($tensors),res},inputs=$tensors,attr={axis};return ENGINE2.runKernelFunc(forward,inputs,null,Concat2,attr)}var concat2=op2({concat_:concat_2});function sigmoid_2(x){let $x=convertToTensor2(x,"x","sigmoid"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.sigmoid($x);return save([res]),res},inputs,null,Sigmoid3)}var sigmoid3=op2({sigmoid_:sigmoid_2});function slice_2(x,begin,size){let $x=convertToTensor2(x,"x","slice");if($x.rank===0)throw new Error("Slicing scalar is not possible");let forward=(backend3,save)=>{let[begin_,size_]=parseSliceParams2($x,begin,size);return assertParamsValid2($x,begin_,size_),save([$x]),backend3.slice($x,begin_,size_)},inputs={x:$x},attrs={begin,size};return ENGINE2.runKernelFunc(forward,inputs,null,Slice2,attrs)}var slice2=op2({slice_:slice_2});function tanh_2(x){let $x=convertToTensor2(x,"x","tanh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let y=backend3.tanh($x);return save([y]),y},inputs,null,Tanh3)}var tanh3=op2({tanh_:tanh_2});function batchToSpaceND_2(x,blockShape,crops){let $x=convertToTensor2(x,"x","batchToSpaceND"),prod6=blockShape.reduce((a,b)=>a*b);assert3($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert3(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert3($x.shape[0]%prod6===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod6}`);let forward=backend3=>backend3.batchToSpaceND($x,blockShape,crops),inputs={x:$x},attrs={blockShape,crops};return ENGINE2.runKernelFunc(forward,inputs,null,BatchToSpaceND2,attrs)}var batchToSpaceND2=op2({batchToSpaceND_:batchToSpaceND_2});function xAs4D2(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape2(x,[1,1,1,x.size]):x.rank===2?x4D=reshape2(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_2(x,mean7,variance,offset,scale3,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor2(x,"x","batchNorm"),$mean=convertToTensor2(mean7,"mean","batchNorm"),$variance=convertToTensor2(variance,"variance","batchNorm"),$scale;scale3!=null&&($scale=convertToTensor2(scale3,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor2(offset,"offset","batchNorm")),assert3($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert3($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert3($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let x4D=xAs4D2($x),forward=(backend3,save)=>(save([x4D,$mean,$variance,$scale]),backend3.batchNorm(x4D,as1DOr4D2($mean),as1DOr4D2($variance),as1DOr4D2($offset),as1DOr4D2($scale),varianceEpsilon)),inputs={x:x4D,scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE2.runKernelFunc(forward,inputs,null,FusedBatchNorm2,attrs);return reshape2(res,$x.shape)}function as1DOr4D2(x){return x==null?null:x.rank===0?reshape2(x,[x.size]):x.rank===1?x:x.rank===2?reshape2(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x}var batchNorm4=op2({batchNorm_:batchNorm_2});function broadcastTo_2(x,shape){let input2=convertToTensor2(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape2(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);let axes=reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0);if(axes.length===0)return clone2(input2);let forward=backend3=>backend3.tile(input2,reps),inputs={x:input2},attrs={shape,inputShape};return ENGINE2.runKernelFunc(forward,inputs,null,BroadcastTo2,attrs)}var broadcastTo2=op2({broadcastTo_:broadcastTo_2});function ceil_2(x){let $x=convertToTensor2(x,"x","ceil"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.ceil($x),inputs,null,Ceil2)}var ceil2=op2({ceil_:ceil_2});function clipByValue_2(x,clipValueMin,clipValueMax){let $x=convertToTensor2(x,"x","clipByValue");assert3(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.clip($x,clipValueMin,clipValueMax);return save([$x]),res},inputs,null,ClipByValue2,attrs)}var clipByValue2=op2({clipByValue_:clipByValue_2});function conv2d_2(x,filter,strides,pad7,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor2(x,"x","conv2d"),$filter=convertToTensor2(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert3($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert3(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let forward=(backend3,save)=>{let $dataFormat=convertConv2DDataFormat2(dataFormat),convInfo=computeConv2DInfo2(x4D.shape,$filter.shape,strides,dilations,pad7,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2d(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad7,dataFormat,dilations,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,Conv2D3,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d2=op2({conv2d_:conv2d_2});function conv1d_2(x,filter,stride,pad7,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor2(x,"x","conv1d"),$filter=convertToTensor2(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape2($x,[1,$x.shape[0],$x.shape[1]])),assert3(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert3($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`),assert3(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert3(eitherStridesOrDilationsAreOne2(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert3(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape2($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape2(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),strides=[1,stride],dilations=[1,dilation],conv2dDataFormat="NHWC",res=conv2d2(input4D,filter4D,strides,pad7,conv2dDataFormat,dilations,dimRoundingMode);return reshapedTo3D?reshape2(res,[res.shape[2],res.shape[3]]):reshape2(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d2=op2({conv1d_:conv1d_2});function conv2DBackpropInput_2(xShape,dy,filter,strides,pad7,dataFormat="NHWC",dimRoundingMode){assert3(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert3(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert3(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert3(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert3(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert3(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=(backend3,save)=>{let dilations=1,$dataFormat=convertConv2DDataFormat2(dataFormat),convInfo=computeConv2DInfo2(xShape4D,filter.shape,strides,dilations,pad7,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2dDerInput(dy4D,filter,convInfo);return save([dy4D,filter]),res2},inputs={dy:dy4D,filter},attrs={strides,pad:pad7,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE2.runKernelFunc(forward,inputs,null,Conv2DBackpropInput2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput4=op2({conv2DBackpropInput_:conv2DBackpropInput_2});function conv2dTranspose_2(x,filter,outputShape,strides,pad7,dimRoundingMode){let $x=convertToTensor2(x,"x","conv2dTranspose"),$filter=convertToTensor2(filter,"filter","conv2dTranspose");return conv2DBackpropInput4(outputShape,$x,$filter,strides,pad7,"NHWC",dimRoundingMode)}var conv2dTranspose3=op2({conv2dTranspose_:conv2dTranspose_2});function conv3DBackpropInput_2(xShape,dy,filter,strides,pad7){assert3(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert3(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert3(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert3(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert3(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert3(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo2(xShape5D,filter.shape,strides,dilations,pad7);return backend3.conv3dDerInput(dy5D,filter,convInfo)},inputs={dy:dy5D,filter},attrs={pad:pad7,strides,inputShape:xShape5D},res=ENGINE2.runKernelFunc(forward,inputs,null,Conv3DBackpropInputV22,attrs);return reshapedTo5D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput2=op2({conv3DBackpropInput_:conv3DBackpropInput_2});function cos_2(x){let $x=convertToTensor2(x,"x","cos"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.cos($x);return save([$x]),res},inputs,null,Cos2)}var cos2=op2({cos_:cos_2});function cosh_2(x){let $x=convertToTensor2(x,"x","cosh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.cosh($x);return save([$x]),res},inputs,null,Cosh2)}var cosh2=op2({cosh_:cosh_2});function cumsum_2(x,axis=0,exclusive=!1,reverse8=!1){let $x=convertToTensor2(x,"x","cumsum"),forward=(backend3,save)=>{let permutation=getAxesPermutation2([axis],$x.rank),permutedX=$x;permutation!=null&&(permutedX=transpose2($x,permutation));let permutedAxis=getInnerMostAxes2(1,$x.rank)[0],value=backend3.cumsum(permutedX,permutedAxis,exclusive,reverse8);if(save([$x]),permutation!=null){let reversePermutation=getUndoAxesPermutation2(permutation);value=transpose2(value,reversePermutation)}return value},inputs={x:$x},attrs={axis,exclusive,reverse:reverse8};return ENGINE2.runKernelFunc(forward,inputs,null,Cumsum2,attrs)}var cumsum2=op2({cumsum_:cumsum_2});function depthToSpace_2(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor2(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert3(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
+    ${$x.shape}`),assert3(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputWidth} and ${blockSize} for depthToSpace with input shape
+        ${$x.shape}`),assert3(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let forward=backend3=>backend3.depthToSpace($x,blockSize,dataFormat),inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE2.runKernelFunc(forward,inputs,null,DepthToSpace2,attrs)}var depthToSpace3=op2({depthToSpace_:depthToSpace_2});function depthwiseConv2d_2(x,filter,strides,pad7,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor2(x,"x","depthwiseConv2d"),$filter=convertToTensor2(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert3($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert3(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1]),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computeConv2DInfo2(x4D.shape,$filter.shape,strides,dilations,pad7,dimRoundingMode,!0),res2=backend3.depthwiseConv2D(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad7,dataFormat,dilations,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,DepthwiseConv2dNative2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d6=op2({depthwiseConv2d_:depthwiseConv2d_2});function dilation2d_2(x,filter,strides,pad7,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor2(x,"x","dilation2d"),$filter=convertToTensor2(filter,"filter","dilation2d");assert3($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert3($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert3(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad7,dilations},res=ENGINE2.runKernel(Dilation2D2,inputs,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d2=op2({dilation2d_:dilation2d_2});function getReductionAxes2(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape2(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_2(a,b){let $a=convertToTensor2(a,"a","equal"),$b=convertToTensor2(b,"b","equal");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=backend3=>backend3.equal($a,$b),inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Equal2)}var equal2=op2({equal_:equal_2});function where_2(condition,a,b){let $a=convertToTensor2(a,"a","where"),$b=convertToTensor2(b,"b","where"),$condition=convertToTensor2(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape2($a.shape,$b.shape),$broadcastedA=broadcastTo2($a,broadcastShape),$broadcastedB=broadcastTo2($b,broadcastShape);$condition.rank===1&&assert3($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch2($condition.shape,$broadcastedB.shape,"Error in where: ");let forward=(backend3,save)=>{let res=backend3.select($condition,$broadcastedA,$broadcastedB);return save([$condition]),res},inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE2.runKernelFunc(forward,inputs,null,SelectV22)}var where2=op2({where_:where_2});function zerosLike_2(x){let $x=convertToTensor2(x,"x","zerosLike"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.zerosLike($x),inputs,null,ZerosLike2)}var zerosLike3=op2({zerosLike_:zerosLike_2});function divNoNan_2(a,b){let $a=convertToTensor2(a,"a","div"),$b=convertToTensor2(b,"b","div");[$a,$b]=makeTypesMatch2($a,$b);let divResult=div2($a,$b),zeros10=zerosLike3(divResult),bEqualsZero=equal2($b,zeros10);return where2(bEqualsZero,zeros10,divResult)}var divNoNan2=op2({divNoNan_:divNoNan_2});function dot_2(t1,t2){let $t1=convertToTensor2(t1,"t1","dot"),$t2=convertToTensor2(t2,"t2","dot");assert3(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert3(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape2($t1,[1,-1]),t22D=reshape2($t2,[-1,1]),t1t2=matMul3(t12D,t22D);return reshape2(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape2($t1,[1,-1]),t22D=reshape2($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul3(t12D,t22D);return reshape2(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape2($t2,[-1,1]),t1t2=matMul3($t1,t22D);return reshape2(t1t2,[t1t2.size])}else{let t22D=reshape2($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul3($t1,t22D);return t1t2}}var dot2=op2({dot_:dot_2});function elu_2(x){let $x=convertToTensor2(x,"x","elu"),forward=(backend3,save)=>{let y=backend3.elu($x);return save([y]),y},inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Elu3)}var elu2=op2({elu_:elu_2});function erf_2(x){let $x=convertToTensor2(x,"x","erf");assert3($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast2($x,"float32"));let inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.erf($x);return save([$x]),res},inputs,null,Erf2)}var erf2=op2({erf_:erf_2});function exp_2(x){let $x=convertToTensor2(x,"x","exp"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.exp($x);return save([res]),res},inputs,null,Exp2)}var exp2=op2({exp_:exp_2});function expandDims_2(x,axis=0){let parseAs=null,$x=convertToTensor2(x,"x","expandDims",parseAs);assert3(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let newShape=$x.shape.slice();return axis<0&&(assert3(-($x.rank+1)<=axis,()=>`Axis must be in the interval [${-($x.rank+1)}, ${$x.rank}]`),axis=$x.rank+axis+1),newShape.splice(axis,0,1),reshape2($x,newShape)}var expandDims3=op2({expandDims_:expandDims_2});function expm1_2(x){let $x=convertToTensor2(x,"x","expm1"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.expm1($x);return save([$x]),res},inputs,null,Expm12)}var expm12=op2({expm1_:expm1_2});function tile_2(x,reps){let parseAs=null,$x=convertToTensor2(x,"x","tile",parseAs);assert3($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let forward=(backend3,save)=>{let res=backend3.tile($x,reps);return save([$x]),res},inputsToSave=[$x],inputs={x:$x},attrs={reps};return ENGINE2.runKernelFunc(forward,inputs,null,Tile2,attrs,inputsToSave)}var tile2=op2({tile_:tile_2});function eye_2(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer2([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape2(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile2(expandDims3(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile2(expandDims3(expandDims3(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile2(expandDims3(expandDims3(expandDims3(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye2=op2({eye_:eye_2});function fill2(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE2.runKernelFunc(backend3=>backend3.fill(shape,value,dtype),{},null,Fill2,attrs)}function floor_2(x){let $x=convertToTensor2(x,"x","floor"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.floor($x),inputs,null,Floor2)}var floor2=op2({floor_:floor_2});function collectGatherOpShapeInfo2(x,indices,axis){let dimSize=x.shape[axis],outputShape=[],batchSize=1,sliceSize=1;for(let i=0;i<axis;i++)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=0;i<indices.rank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<x.rank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,dimSize,outputShape}}function gather_2(x,indices,axis=0){let $x=convertToTensor2(x,"x","gather"),$indices=convertToTensor2(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis},forward=(backend3,save)=>{let parsedAxis=parseAxisParam2(axis,$x.shape)[0],shapeInfo=collectGatherOpShapeInfo2($x,$indices,parsedAxis),res=backend3.gather($x,reshape2($indices,[$indices.size]),parsedAxis);return save([$x,$indices]),reshape2(res,shapeInfo.outputShape)};return ENGINE2.runKernelFunc(forward,inputs,null,GatherV22,attrs)}var gather2=op2({gather_:gather_2});function greater_2(a,b){let $a=convertToTensor2(a,"a","greater"),$b=convertToTensor2(b,"b","greater");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=backend3=>backend3.greater($a,$b),inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Greater2)}var greater2=op2({greater_:greater_2});function greaterEqual_2(a,b){let $a=convertToTensor2(a,"a","greaterEqual"),$b=convertToTensor2(b,"b","greaterEqual");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.greaterEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,GreaterEqual2)}var greaterEqual2=op2({greaterEqual_:greaterEqual_2});function imag_2(input2){let $input=convertToTensor2(input2,"input","imag"),forward=backend3=>backend3.imag($input),inputs={input:$input};return ENGINE2.runKernelFunc(forward,inputs,null,Imag2)}var imag2=op2({imag_:imag_2});function isFinite_2(x){let $x=convertToTensor2(x,"x","isFinite"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.isFinite($x),inputs,null,IsFinite2)}var isFinite4=op2({isFinite_:isFinite_2});function isInf_2(x){let $x=convertToTensor2(x,"x","isInf"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.isInf($x),inputs,null,IsInf2)}var isInf3=op2({isInf_:isInf_2});function isNaN_2(x){let $x=convertToTensor2(x,"x","isNaN"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.isNaN($x),inputs,null,IsNan2)}var isNaN4=op2({isNaN_:isNaN_2});function maximum_2(a,b){let $a=convertToTensor2(a,"a","maximum"),$b=convertToTensor2(b,"b","maximum");[$a,$b]=makeTypesMatch2($a,$b),$a.dtype==="bool"&&($a=cast2($a,"int32"),$b=cast2($b,"int32")),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.maximum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Maximum3)}var maximum2=op2({maximum_:maximum_2});function scalar2(value,dtype){if((isTypedArray2(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray2(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");let shape=[],inferredShape=[];return makeTensor2(value,shape,inferredShape,dtype)}function leakyRelu_2(x,alpha=.2){let $x=convertToTensor2(x,"x","leakyRelu");return maximum2(mul2(scalar2(alpha),$x),$x)}var leakyRelu2=op2({leakyRelu_:leakyRelu_2});function less_2(a,b){let $a=convertToTensor2(a,"a","less"),$b=convertToTensor2(b,"b","less");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=backend3=>backend3.less($a,$b),inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Less2)}var less2=op2({less_:less_2});function lessEqual_2(a,b){let $a=convertToTensor2(a,"a","lessEqual"),$b=convertToTensor2(b,"b","lessEqual");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.lessEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,LessEqual2)}var lessEqual2=op2({lessEqual_:lessEqual_2});function localResponseNormalization_2(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor2(x,"x","localResponseNormalization");assert3($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
+               rank ${$x.rank}.`),assert3(isInt2(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let forward=(backend3,save)=>{let y=backend3.localResponseNormalization4D(x4D,depthRadius,bias,alpha,beta);return save([x4D,y]),y},inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE2.runKernelFunc(forward,inputs,null,LRN2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization2=op2({localResponseNormalization_:localResponseNormalization_2});function log_2(x){let $x=convertToTensor2(x,"x","log"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.log($x);return save([$x]),res},inputs,null,Log2)}var log2=op2({log_:log_2});function log1p_2(x){let $x=convertToTensor2(x,"x","log1p"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.log1p($x);return save([$x]),res},inputs,null,Log1p2)}var log1p2=op2({log1p_:log1p_2});function customGrad2(f){return ENGINE2.customGrad(f)}function neg_2(x){let $x=convertToTensor2(x,"x","neg"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.neg($x),inputs,null,Negate2)}var neg2=op2({neg_:neg_2});function softplus_2(x){let $x=convertToTensor2(x,"x","softplus"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.softplus($x);return save([$x]),res},inputs,null,Softplus3)}var softplus2=op2({softplus_:softplus_2});function logSigmoid_2(x){let $x=convertToTensor2(x,"x","logSigmoid"),customOp=customGrad2(x2=>{let value=neg2(softplus2(neg2(x2))),gradFunc=dy=>{let derX=mul2(dy,sigmoid3(neg2(x2)));return derX};return{value,gradFunc}});return customOp($x)}var logSigmoid2=op2({logSigmoid_:logSigmoid_2});function max_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","max"),forward=(backend3,save)=>{let origAxes=parseAxisParam2(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation2(axes,$x.rank),maxInput=$x;permutedAxes!=null&&(maxInput=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,maxInput.rank));let y=backend3.max(maxInput,axes);permutedAxes!=null&&maxInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim2(res.shape,parseAxisParam2(axis,$x.shape));res=reshape2(res,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={reductionIndices:axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Max2,attrs)}var max2=op2({max_:max_2});function sub_2(a,b){let $a=convertToTensor2(a,"a","sub"),$b=convertToTensor2(b,"b","sub");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.subtract($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Sub2)}var sub2=op2({sub_:sub_2});function sum_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","sum");$x.dtype==="bool"&&($x=cast2($x,"int32"));let forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam2(axis,$x.shape),permutation=getAxesPermutation2(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2($x,permutation),reductionAxes=getInnerMostAxes2(reductionAxes.length,$x.rank));let value=backend3.sum(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim2(value.shape,axes);value=reshape2(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Sum2,attrs)}var sum3=op2({sum_:sum_2});function logSoftmax_2(logits,axis=-1){let $logits=convertToTensor2(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);let forward=(backend3,save)=>{let keepDims=!0,xMax=max2(logits,axis,!0),shifted=sub2(logits,xMax),value=sub2(cast2(shifted,"float32"),log2(sum3(exp2(shifted),axis,keepDims)));return save([value]),value},inputs={logits:$logits},attrs={axis};return ENGINE2.runKernelFunc(forward,inputs,null,LogSoftmax3,attrs)}var logSoftmax2=op2({logSoftmax_:logSoftmax_2});function logSumExp_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","logSumExp"),axes=parseAxisParam2(axis,$x.shape),xMax=max2($x,axes,!0),a=sub2($x,xMax),b=exp2(a),c=sum3(b,axes),d=log2(c),res=add4(reshape2(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim2(res.shape,axes);return reshape2(res,newShape)}return res}var logSumExp2=op2({logSumExp_:logSumExp_2});function logicalAnd_2(a,b){let $a=convertToTensor2(a,"a","logicalAnd","bool"),$b=convertToTensor2(b,"b","logicalAnd","bool");assertAndGetBroadcastShape2($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(backend3=>backend3.logicalAnd($a,$b),inputs,null,LogicalAnd2)}var logicalAnd2=op2({logicalAnd_:logicalAnd_2});function logicalNot_2(x){let $x=convertToTensor2(x,"x","logicalNot","bool"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.logicalNot($x),inputs,null,LogicalNot2)}var logicalNot3=op2({logicalNot_:logicalNot_2});function logicalOr_2(a,b){let $a=convertToTensor2(a,"a","logicalOr","bool"),$b=convertToTensor2(b,"b","logicalOr","bool");assertAndGetBroadcastShape2($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(backend3=>backend3.logicalOr($a,$b),inputs,null,LogicalOr2)}var logicalOr2=op2({logicalOr_:logicalOr_2});function logicalXor_2(a,b){let $a=convertToTensor2(a,"a","logicalXor","bool"),$b=convertToTensor2(b,"b","logicalXor","bool");return assertAndGetBroadcastShape2($a.shape,$b.shape),logicalAnd2(logicalOr2(a,b),logicalNot3(logicalAnd2(a,b)))}var logicalXor2=op2({logicalXor_:logicalXor_2});function maxPool_2(x,filterSize,strides,pad7,dimRoundingMode){let $x=convertToTensor2(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo2(x4D.shape,filterSize,strides,1,pad7,dimRoundingMode),y;return convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual2(convInfo.inShape,convInfo.outShape)?y=x4D.clone():y=backend3.maxPool(x4D,convInfo),save([x4D,y]),y},inputs={x:x4D},attrs={filterSize,strides,pad:pad7,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,MaxPool2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool5=op2({maxPool_:maxPool_2});function zeros2(shape,dtype="float32"){if(dtype==="complex64"){let real9=zeros2(shape,"float32"),imag9=zeros2(shape,"float32");return complex2(real9,imag9)}let values=makeZerosTypedArray2(sizeFromShape2(shape),dtype);return ENGINE2.makeTensor(values,shape,dtype)}function ones4(shape,dtype="float32"){if(dtype==="complex64"){let real9=ones4(shape,"float32"),imag9=zeros2(shape,"float32");return complex2(real9,imag9)}let values=makeOnesTypedArray2(sizeFromShape2(shape),dtype);return ENGINE2.makeTensor(values,shape,dtype)}function mean_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","mean"),axes=parseAxisParam2(axis,$x.shape),shapes=computeOutAndReduceShapes2($x.shape,axes),reduceShape=shapes[1],reduceSize=sizeFromShape2(reduceShape),inputs={x:$x},attrs={axis,keepDims},forward=()=>{let reduceSizeScalar=scalar2(reduceSize),xReduce=reduceSizeScalar.dtype===$x.dtype?$x:cast2($x,reduceSizeScalar.dtype),res=div2(xReduce,reduceSizeScalar);return sum3(res,axis,keepDims)},customOp=customGrad2(x2=>{let value=ENGINE2.runKernelFunc(forward,inputs,null,Mean2,attrs),gradFunc=dy=>{let expandedDyShape=x2.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape2(dy,expandedDyShape),derX=div2(mul2(expandedDy,ones4(x2.shape,"float32")),reduceSize);return derX};return{value,gradFunc}});return customOp($x)}var mean2=op2({mean_:mean_2});function min_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","min"),forward=(backend3,save)=>{let origAxes=parseAxisParam2(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation2(axes,$x.rank),minInput=$x;permutedAxes!=null&&(minInput=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank));let y=backend3.min(minInput,axes);permutedAxes!=null&&minInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim2(res.shape,origAxes);res=reshape2(y,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Min2,attrs)}var min2=op2({min_:min_2});function minimum_2(a,b){let $a=convertToTensor2(a,"a","minimum"),$b=convertToTensor2(b,"b","minimum");[$a,$b]=makeTypesMatch2($a,$b),$a.dtype==="bool"&&($a=cast2($a,"int32"),$b=cast2($b,"int32")),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.minimum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Minimum3)}var minimum2=op2({minimum_:minimum_2});function mirrorPad_2(x,paddings,mode){assert3(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor2(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert3(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert3(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert3(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE2.runKernel(MirrorPad2,inputs,attrs)}var mirrorPad3=op2({mirrorPad_:mirrorPad_2});function mod_2(a,b){let $a=convertToTensor2(a,"a","mod"),$b=convertToTensor2(b,"b","mod");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.mod($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Mod2)}var mod2=op2({mod_:mod_2});function square_2(x){let $x=convertToTensor2(x,"x","square"),attrs={},inputsToSave=[$x],outputsToSave=[];return ENGINE2.runKernelFunc((backend3,save)=>(save([$x]),backend3.square($x)),{x:$x},null,"Square",attrs,inputsToSave,outputsToSave)}var square2=op2({square_:square_2});function notEqual_2(a,b){let $a=convertToTensor2(a,"a","notEqual"),$b=convertToTensor2(b,"b","notEqual");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=backend3=>backend3.notEqual($a,$b),inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,NotEqual2)}var notEqual4=op2({notEqual_:notEqual_2});function real_2(input2){let $input=convertToTensor2(input2,"input","real"),forward=backend3=>backend3.real($input),inputs={input:$input};return ENGINE2.runKernelFunc(forward,inputs,null,Real2)}var real2=op2({real_:real_2});function onesLike_2(x){let $x=convertToTensor2(x,"x","onesLike"),forward=(backend3,save)=>{if($x.dtype==="complex64"){let r=onesLike3(real2($x)),i=zerosLike3(imag2($x));return complex2(r,i)}return backend3.onesLike($x)},inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,OnesLike2)}var onesLike3=op2({onesLike_:onesLike_2});function pad_2(x,paddings,constantValue=0){let $x=convertToTensor2(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let forward=(backend3,save)=>(save([$x]),backend3.pad($x,paddings,constantValue)),attrs={paddings,constantValue},inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,PadV22,attrs)}var pad2=op2({pad_:pad_2});function spaceToBatchND_2(x,blockShape,paddings){let $x=convertToTensor2(x,"x","spaceToBatchND");assert3($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert3(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert3($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let forward=backend3=>backend3.spaceToBatchND($x,blockShape,paddings),inputs={x:$x},attrs={blockShape,paddings};return ENGINE2.runKernelFunc(forward,inputs,null,SpaceToBatchND2,attrs)}var spaceToBatchND3=op2({spaceToBatchND_:spaceToBatchND_2});function pool_2(input2,windowShape,poolingType,pad7,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad7===0&&(pad7="valid");let $x=convertToTensor2(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo2(x4D.shape,windowShape,strides,dilations,pad7),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad7==="same"?basePadding=withSpaceToBatchBasePaddings2([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings2([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad7:"valid",convertedX=isDilationOne?x4D:spaceToBatchND3(x4D,dilation,adjustedPadding),forwardOp=poolingType==="avg"?()=>avgPool5(convertedX,windowShape,strides,convertedPad):()=>maxPool5(convertedX,windowShape,strides,convertedPad),y=forwardOp(),res=isDilationOne?y:batchToSpaceND2(y,dilation,adjustedCrops);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings2(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings2(filterShape,dilation){let dilatedFilterShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)),padExtraShape=dilatedFilterShape.map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool2=op2({pool_:pool_2});function pow_2(base2,exp14){let $base=convertToTensor2(base2,"base","pow"),$exp=convertToTensor2(exp14,"exp","pow");[$base,$exp]=makeTypesMatch2($base,$exp);let inputs={a:$base,b:$exp},forward=(backend3,save)=>{let y=backend3.pow($base,$exp);return save([$base,$exp,y]),y};return ENGINE2.runKernelFunc(forward,inputs,null,Pow2)}var pow2=op2({pow_:pow_2});function prelu_2(x,alpha){let $x=convertToTensor2(x,"x","prelu"),$alpha=convertToTensor2(alpha,"alpha","prelu"),forward=(backend3,save)=>{let res=backend3.prelu($x,$alpha);return save([$x,$alpha]),res},inputs={x:$x,alpha:$alpha};return ENGINE2.runKernelFunc(forward,inputs,null,Prelu2)}var prelu2=op2({prelu_:prelu_2});function prod_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","prod");$x.dtype==="bool"&&($x=cast2($x,"int32"));let forward=backend3=>{let axes=parseAxisParam2(axis,$x.shape),permutation=getAxesPermutation2(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2($x,permutation),reductionAxes=getInnerMostAxes2(reductionAxes.length,$x.rank));let value=backend3.prod(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim2(value.shape,axes);value=reshape2(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Prod2,attrs)}var prod2=op2({prod_:prod_2});function tensor1d2(values,dtype){assertNonNull2(values);let inferredShape=inferShape2(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");let shape=null;return makeTensor2(values,shape,inferredShape,dtype)}function range3(start,stop,step9=1,dtype="float32"){if(step9===0)throw new Error("Cannot have a step of zero");let forward=()=>{let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step9<0,decreasingRangePositiveStep=stop<start&&step9>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return zeros2([0],dtype);let numElements=Math.abs(Math.ceil((stop-start)/step9)),values=makeZerosTypedArray2(numElements,dtype);stop<start&&step9===1&&(step9=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d2(values,dtype)},attrs={start,stop,step:step9,dtype};return ENGINE2.runKernelFunc(forward,{},null,Range2,attrs)}function reciprocal_2(x){let $x=convertToTensor2(x,"x","reciprocal"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.reciprocal($x);return save([$x]),res},inputs,null,Reciprocal2)}var reciprocal2=op2({reciprocal_:reciprocal_2});function relu_2(x){let $x=convertToTensor2(x,"x","relu"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast2($x,"int32"):backend3.relu($x)),inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Relu3)}var relu2=op2({relu_:relu_2});function relu6_2(x){let $x=convertToTensor2(x,"x","relu6"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast2($x,"int32"):backend3.relu6($x)),inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Relu63)}var relu62=op2({relu6_:relu6_2});function reverse_2(x,axis){let $x=convertToTensor2(x,"x","reverse"),forward=backend3=>{let axes=parseAxisParam2(axis,$x.shape);if($x.rank===0)return clone2($x);let res=backend3.reverse($x,axes);return reshape2(res,$x.shape)},inputs={x:$x},attrs={dims:axis};return ENGINE2.runKernelFunc(forward,inputs,null,Reverse2,attrs)}var reverse2=op2({reverse_:reverse_2});function round_2(x){let $x=convertToTensor2(x,"x","round"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.round($x),inputs,null,Round2)}var round3=op2({round_:round_2});function rsqrt_2(x){let $x=convertToTensor2(x,"x","rsqrt"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.rsqrt($x);return save([$x]),res},inputs,null,Rsqrt2)}var rsqrt2=op2({rsqrt_:rsqrt_2});function selu_2(x){let $x=convertToTensor2(x,"x","selu"),forward=(backend3,save)=>{let res=backend3.selu($x);return save([$x]),res},inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Selu3)}var selu2=op2({selu_:selu_2});function separableConv2d_2(x,depthwiseFilter,pointwiseFilter,strides,pad7,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor2(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor2(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor2(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert3(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert3($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert3($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert3($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert3($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert3($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d6(x4D,$depthwiseFilter,strides,pad7,dataFormat,dilation),pointwiseStride=1,res=conv2d2(depthwise,$pointwiseFilter,pointwiseStride,"valid",dataFormat);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d3=op2({separableConv2d_:separableConv2d_2});function sign_2(x){let $x=convertToTensor2(x,"x","sign"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.sign($x),inputs,null,Sign2)}var sign2=op2({sign_:sign_2});function sin_2(x){let $x=convertToTensor2(x,"x","sin"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.sin($x);return save([$x]),res},inputs,null,Sin2)}var sin2=op2({sin_:sin_2});function sinh_2(x){let $x=convertToTensor2(x,"x","sinh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.sinh($x);return save([$x]),res},inputs,null,Sinh2)}var sinh2=op2({sinh_:sinh_2});function softmax_2(logits,dim=-1){let $logits=convertToTensor2(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE2.runKernelFunc((backend3,save)=>{let y=backend3.softmax($logits,dim);return save([y]),y},inputs,null,Softmax4,attrs)}var softmax2=op2({softmax_:softmax_2});function fft_2(input2){assert3(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE2.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=input2.as2D(batch,innerDimensionSize),result=backend3.fft(input2D);return result.reshape(input2.shape)},inputs,null,FFT2)}var fft2=op2({fft_:fft_2});function ifft_2(input2){assert3(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE2.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=reshape2(input2,[batch,innerDimensionSize]),result=backend3.ifft(input2D);return reshape2(result,input2.shape)},inputs,null,IFFT2)}var ifft2=op2({ifft_:ifft_2});function irfft_2(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape2(input2,[batch,innerDimensionSize]);ret=ifft2(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape2(real2(input2),[batch,innerDimensionSize]),imagInput=reshape2(imag2(input2),[batch,innerDimensionSize]),realConjugate=reverse2(slice2(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul2(reverse2(slice2(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar2(-1)),r=concat2([realInput,realConjugate],1),i=concat2([imagInput,imagConjugate],1),complexInput=reshape2(complex2(r,i),[outputShape[0],outputShape[1]]);ret=ifft2(complexInput)}if(ret=real2(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape2(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft2=op2({irfft_:irfft_2});function prepareSplitSize2(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert3(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert3(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert3(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}function split_2(x,numOrSizeSplits,axis=0){let $x=convertToTensor2(x,"x","split"),forward=(backend3,_)=>{let $axis=parseAxisParam2(axis,$x.shape)[0],splitSizes=prepareSplitSize2($x,numOrSizeSplits,$axis);return backend3.split($x,splitSizes,$axis)},inputs={x:$x},attr={numOrSizeSplits,axis};return ENGINE2.runKernelFunc(forward,inputs,null,SplitV2,attr)}var split2=op2({split_:split_2});function rfft_2(input2,fftLength){assert3(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice2(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat2([input2,zeros2(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike3(adjustedInput),complexInput=reshape2(complex2(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft2(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real2(ret),imagValues=imag2(ret),realComplexConjugate=split2(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split2(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape2(complex2(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft2=op2({rfft_:rfft_2});function sqrt_2(x){let $x=convertToTensor2(x,"x","sqrt"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.sqrt($x);return save([$x]),res},inputs,null,Sqrt2)}var sqrt2=op2({sqrt_:sqrt_2});function squaredDifference_2(a,b){let $a=convertToTensor2(a,"a","squaredDifference"),$b=convertToTensor2(b,"b","squaredDifference");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.squaredDifference($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE2.runKernelFunc(forward,inputs,null,SquaredDifference2,attrs)}var squaredDifference4=op2({squaredDifference_:squaredDifference_2});function squeeze_2(x,axis){let $x=convertToTensor2(x,"x","squeeze");return reshape2($x,squeezeShape2($x.shape,axis).newShape)}var squeeze2=op2({squeeze_:squeeze_2});function stack_2(tensors,axis=0){let $tensors=convertToTensorArray2(tensors,"tensors","stack");if(assert3($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length===1)return expandDims3($tensors[0],axis);let rank=$tensors[0].rank,shape=$tensors[0].shape,dtype=$tensors[0].dtype;assert3(axis<=rank,()=>"Axis must be <= rank of the tensor"),$tensors.forEach(t=>{assertShapesMatch2(shape,t.shape,"All tensors passed to stack must have matching shapes"),assert3(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=$tensors.map(t=>expandDims3(t,axis));return concat2(expandedTensors,axis)}var stack2=op2({stack_:stack_2});function step_2(x,alpha=0){let $x=convertToTensor2(x,"x","step"),inputs={x:$x},attrs={alpha};return ENGINE2.runKernelFunc(backend3=>backend3.step($x,alpha),inputs,null,Step2,attrs)}var step2=op2({step_:step_2});function stridedSlice_2(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let $x=convertToTensor2(x,"x","stridedSlice"),forward=backend3=>{strides==null&&(strides=new Array(begin.length));let ellipsisAxes=maskToAxes2(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=$x.rank-begin.length,expandAxes=maskToAxes2(newAxisMask),newShape=$x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)}),$x=reshape2($x,newShape);let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes2($x.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=maskToAxes2(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=computeOutShape5(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided)return reshape2(slice2($x,begin,size),outShape);let res=backend3.stridedSlice($x,begin,end,strides);return reshape2(res,outShape)},inputs={x:$x},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE2.runKernelFunc(forward,inputs,null,StridedSlice2,attrs)}var stridedSlice3=op2({stridedSlice_:stridedSlice_2});function tan_2(x){let $x=convertToTensor2(x,"x","tan"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.tan($x);return save([$x]),res},inputs,null,Tan2)}var tan2=op2({tan_:tan_2});function tensor2d2(values,shape,dtype){if(assertNonNull2(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape2(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor2(values,shape,inferredShape,dtype)}function topk_2(x,k=1,sorted=!0){let $x=convertToTensor2(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE2.runKernelFunc(b=>b.topk($x,k,sorted),inputs,null,TopK2,attrs);return{values,indices}}var topk2=op2({topk_:topk_2});function unique_2(x,axis=0){let $x=convertToTensor2(x,"x","unique",null);assert3($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE2.runKernel(Unique2,inputs,attrs);return{values,indices}}var unique2=op2({unique_:unique_2});function unsortedSegmentSum_2(x,segmentIds,numSegments){let $x=convertToTensor2(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor2(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert3(isInt2(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments},forward=(backend3,save)=>{let res=backend3.unsortedSegmentSum($x,$segmentIds,numSegments);return save([$segmentIds]),res};return ENGINE2.runKernelFunc(forward,inputs,null,UnsortedSegmentSum2,attrs)}var unsortedSegmentSum2=op2({unsortedSegmentSum_:unsortedSegmentSum_2});function unstack_2(x,axis=0){let $x=convertToTensor2(x,"x","unstack");assert3(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`),axis<0&&(axis+=$x.shape.length);let inputs={value:$x},attrs={axis},forward=backend3=>backend3.unstack($x,axis);return ENGINE2.runKernelFunc(forward,inputs,null,Unpack2,attrs)}var unstack2=op2({unstack_:unstack_2});function notEqualStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","notEqualStrict"),$b=convertToTensor2(b,"b","notEqualStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual4($a,$b)}function lessStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","lessStrict"),$b=convertToTensor2(b,"b","lessStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in lessStrict: "),less2($a,$b)}function equalStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","equalStrict"),$b=convertToTensor2(b,"b","equalStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in equalStrict: "),equal2($a,$b)}function lessEqualStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","lessEqualStrict"),$b=convertToTensor2(b,"b","lessEqualStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual2($a,$b)}function greaterStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","greaterStrict"),$b=convertToTensor2(b,"b","greaterStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in greaterStrict: "),greater2($a,$b)}function greaterEqualStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","greaterEqualStrict"),$b=convertToTensor2(b,"b","greaterEqualStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual2($a,$b)}var equalStrict2=op2({equalStrict_:equalStrict_2}),greaterEqualStrict2=op2({greaterEqualStrict_:greaterEqualStrict_2}),greaterStrict2=op2({greaterStrict_:greaterStrict_2}),lessEqualStrict2=op2({lessEqualStrict_:lessEqualStrict_2}),lessStrict2=op2({lessStrict_:lessStrict_2}),notEqualStrict2=op2({notEqualStrict_:notEqualStrict_2});function addStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","addStrict"),$b=convertToTensor2(b,"b","addStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in addStrict: "),add4($a,$b)}function subStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","subStrict"),$b=convertToTensor2(b,"b","subStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in subStrict: "),sub2($a,$b)}function powStrict_2(base2,exp14){return deprecationWarn2("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch2(base2.shape,exp14.shape,"Error in powStrict: "),pow2(base2,exp14)}function mulStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","mul"),$b=convertToTensor2(b,"b","mul");return assertShapesMatch2($a.shape,$b.shape,"Error in multiplyStrict: "),mul2($a,$b)}function divStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","div"),$b=convertToTensor2(b,"b","div");return assertShapesMatch2($a.shape,$b.shape,"Error in divideStrict: "),div2($a,$b)}function modStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","modStrict"),$b=convertToTensor2(b,"b","modStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in modStrict: "),mod2($a,$b)}function minimumStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","minimumStrict"),$b=convertToTensor2(b,"b","minimumStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in minimumStrict: "),minimum2($a,$b)}function maximumStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","maximumStrict"),$b=convertToTensor2(b,"b","maximumStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in maximumStrict: "),maximum2($a,$b)}function squaredDifferenceStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","squaredDifferenceStrict"),$b=convertToTensor2(b,"b","squaredDifferenceStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference4($a,$b)}var addStrict2=op2({addStrict_:addStrict_2}),divStrict2=op2({divStrict_:divStrict_2}),maximumStrict2=op2({maximumStrict_:maximumStrict_2}),minimumStrict2=op2({minimumStrict_:minimumStrict_2}),modStrict2=op2({modStrict_:modStrict_2}),mulStrict2=op2({mulStrict_:mulStrict_2}),powStrict2=op2({powStrict_:powStrict_2}),squaredDifferenceStrict2=op2({squaredDifferenceStrict_:squaredDifferenceStrict_2}),subStrict2=op2({subStrict_:subStrict_2});function norm_2(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor2(x,"x","norm");let norm6=normImpl2(x,ord,axis),keepDimsShape=norm6.shape;if(keepDims){let axes=parseAxisParam2(axis,x.shape);keepDimsShape=expandShapeToKeepDim2(norm6.shape,axes)}return reshape2(norm6,keepDimsShape)}function normImpl2(x,p2,axis=null){if(x.rank===0)return abs2(x);if(x.rank!==1&&axis===null)return normImpl2(reshape2(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum3(abs2(x),axis);if(p2===Infinity)return max2(abs2(x),axis);if(p2===-Infinity)return min2(abs2(x),axis);if(p2==="euclidean"||p2===2)return sqrt2(sum3(pow2(abs2(x),scalar2(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max2(sum3(abs2(x),axis[0]),axis[1]-1);if(p2===Infinity)return max2(sum3(abs2(x),axis[1]),axis[0]);if(p2===-Infinity)return min2(sum3(abs2(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt2(sum3(square2(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm2=op2({norm_:norm_2});function enclosingPowerOfTwo2(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow2(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d2(newValues,"float32")}function conv2DBackpropFilter_2(x,dy,filterShape,strides,pad7,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert3(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert3(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert3(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert3(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert3(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=backend3=>{let dilations=1,$dataFormat=convertConv2DDataFormat2(dataFormat),convInfo=computeConv2DInfo2(x4D.shape,filterShape,strides,dilations,pad7,dimRoundingMode,!1,$dataFormat);return backend3.conv2dDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad7,dataFormat,dimRoundingMode,filterShape};return ENGINE2.runKernelFunc(forward,inputs,null,Conv2DBackpropFilter2,attrs)}var conv2DBackpropFilter3=op2({conv2DBackpropFilter_:conv2DBackpropFilter_2});function depthwiseConv2dNativeBackpropFilter_2(x,dy,filterShape,strides,pad7,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo2(x.shape,filterShape,strides,dilations,pad7,dimRoundingMode,!0);return backend3.depthwiseConv2DDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad7,dimRoundingMode,dilations,filterShape};return ENGINE2.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropFilter2,attrs)}var depthwiseConv2dNativeBackpropFilter3=op2({depthwiseConv2dNativeBackpropFilter_:depthwiseConv2dNativeBackpropFilter_2});function depthwiseConv2dNativeBackpropInput_2(xShape,dy,filter,strides,pad7,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo2(xShape,filter.shape,strides,dilations,pad7,dimRoundingMode,!0);return backend3.depthwiseConv2DDerInput(dy4D,filter,convInfo)},inputs={dy:dy4D,filter},attrs={strides,pad:pad7,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE2.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropInput2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput3=op2({depthwiseConv2dNativeBackpropInput_:depthwiseConv2dNativeBackpropInput_2});function hammingWindow_2(windowLength){return cosineWindow2(windowLength,.54,.46)}var hammingWindow2=op2({hammingWindow_:hammingWindow_2});function hannWindow_2(windowLength){return cosineWindow2(windowLength,.5,.5)}var hannWindow2=op2({hannWindow_:hannWindow_2});function frame_2(signal3,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal3.size;)output.push(slice2(signal3,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal3.size;){let padLen=start+frameLength-signal3.size,pad7=concat2([slice2(signal3,start,frameLength-padLen),fill2([padLen],padValue)]);output.push(pad7),start+=frameStep}return output.length===0?tensor2d2([],[0,frameLength]):reshape2(concat2(output),[output.length,frameLength])}var frame2=op2({frame_:frame_2});function stft_2(signal3,frameLength,frameStep,fftLength,windowFn=hannWindow2){fftLength==null&&(fftLength=enclosingPowerOfTwo2(frameLength));let framedSignal=frame2(signal3,frameLength,frameStep),windowedSignal=mul2(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft2(slice2(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat2(output)}var stft2=op2({stft_:stft_2});function cropAndResize_2(image3,boxes,boxInd,cropSize,method,extrapolationValue){let $image=convertToTensor2(image3,"image","cropAndResize"),$boxes=convertToTensor2(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor2(boxInd,"boxInd","cropAndResize","int32");method=method||"bilinear",extrapolationValue=extrapolationValue||0;let numBoxes=$boxes.shape[0];assert3($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert3($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert3($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert3(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert3(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert3(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let forward=backend3=>backend3.cropAndResize($image,$boxes,$boxInd,cropSize,method,extrapolationValue),inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize},res=ENGINE2.runKernelFunc(forward,inputs,null,CropAndResize2,attrs);return res}var cropAndResize3=op2({cropAndResize_:cropAndResize_2});function flipLeftRight_2(image3){let $image=convertToTensor2(image3,"image","flipLeftRight","float32");assert3($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},res=ENGINE2.runKernel(FlipLeftRight2,inputs,{});return res}var flipLeftRight3=op2({flipLeftRight_:flipLeftRight_2});function rotateWithOffset_2(image3,radians,fillValue=0,center=.5){let $image=convertToTensor2(image3,"image","rotateWithOffset","float32");assert3($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center},res=ENGINE2.runKernel(RotateWithOffset2,inputs,attrs);return res}var rotateWithOffset3=op2({rotateWithOffset_:rotateWithOffset_2});function nonMaxSuppSanityCheck2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert3(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert3(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert3(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert3(scores.rank===1,()=>"scores must be a 1D tensor"),assert3(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert3(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor2(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE2.runKernelFunc(b=>b.nonMaxSuppression($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold),{boxes:$boxes,scores:$scores},null,NonMaxSuppressionV32,attrs)}var nonMaxSuppression3=op2({nonMaxSuppression_:nonMaxSuppression_2});function binaryInsert2(arr,element,comparator){let index=binarySearch2(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch2(arr,target,comparator){return binarySearch_2(arr,target,comparator||defaultComparator2)}function defaultComparator2(a,b){return a>b?1:a<b?-1:0}function binarySearch_2(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl3(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0).selectedIndices}function nonMaxSuppressionV4Impl4(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl4(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator2);let scale3=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou3=intersectionOverUnion2(boxes,boxIndex,selectedIndices[j]);if(iou3>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight2(iouThreshold,scale3,iou3),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert2(candidates,candidate,ascendingComparator2))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices:tensor1d2(selectedIndices,"int32")};return returnScoresTensor&&(result.selectedScores=tensor1d2(selectedScores,"float32")),returnValidOutputs&&(result.validOutputs=scalar2(validOutputs,"int32")),result}function intersectionOverUnion2(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight2(iouThreshold,scale3,iou3){let weight=Math.exp(scale3*iou3*iou3);return iou3<=iouThreshold?weight:0}function ascendingComparator2(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor2(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV3Impl3(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionAsync2=nonMaxSuppressionAsync_2;function nonMaxSuppressionWithScore_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor2(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE2.runKernel(NonMaxSuppressionV52,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore2=op2({nonMaxSuppressionWithScore_:nonMaxSuppressionWithScore_2});async function nonMaxSuppressionWithScoreAsync_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor2(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV5Impl4(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionWithScoreAsync2=nonMaxSuppressionWithScoreAsync_2;function nonMaxSuppressionPadded_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor2(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE2.runKernel(NonMaxSuppressionV42,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded2=op2({nonMaxSuppressionPadded_:nonMaxSuppressionPadded_2});async function nonMaxSuppressionPaddedAsync_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor2(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),res=nonMaxSuppressionV4Impl4(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionPaddedAsync2=nonMaxSuppressionPaddedAsync_2;function resizeBilinear_2(images,size,alignCorners=!1){let $images=convertToTensor2(images,"images","resizeBilinear");assert3($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert3(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape2($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,forward=(backend3,save)=>(save([batchImages]),backend3.resizeBilinear(batchImages,newHeight,newWidth,alignCorners)),inputs={images:batchImages},attrs={alignCorners,size},res=ENGINE2.runKernelFunc(forward,inputs,null,ResizeBilinear2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear3=op2({resizeBilinear_:resizeBilinear_2});function resizeNearestNeighbor_2(images,size,alignCorners=!1){let $images=convertToTensor2(images,"images","resizeNearestNeighbor");assert3($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert3(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert3($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape2($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,inputs={images:batchImages},attrs={alignCorners,size},forward=(backend3,save)=>(save([batchImages]),backend3.resizeNearestNeighbor(batchImages,newHeight,newWidth,alignCorners)),res=ENGINE2.runKernelFunc(forward,inputs,null,ResizeNearestNeighbor2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor2=op2({resizeNearestNeighbor_:resizeNearestNeighbor_2});function bandPart_2(a,numLower,numUpper){assert3(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert3(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor2(a,"a","bandPart");assert3($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape2(range3(0,M,1,"int32"),[-1,1]),j=range3(0,N,1,"int32"),ij=sub2(i,j),inBand=logicalAnd2(lessEqual2(ij,scalar2(+numLower,"int32")),greaterEqual2(ij,scalar2(-numUpper,"int32"))),zero=zeros2([M,N],$a.dtype);return reshape2(stack2(unstack2(reshape2($a,[-1,M,N])).map(mat=>where2(inBand,mat,zero))),shape)}var bandPart2=op2({bandPart_:bandPart_2});function gramSchmidt_2(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert3(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert3(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split2(xs,xs.shape[0],0).map(x=>squeeze2(x,[0]));assert3(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE2.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul2(sum3(mul2(ys[j],x)),ys[j]);x=sub2(x,proj)}return div2(x,norm2(x,"euclidean"))}));return inputIsTensor2D?stack2(ys,0):ys}var gramSchmidt2=op2({gramSchmidt_:gramSchmidt_2});function qr_2(x,fullMatrices=!1){if(assert3(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d2(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack2(reshape2(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d2(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape2(stack2(q2ds,0),x.shape),r=reshape2(stack2(r2ds,0),x.shape);return[q,r]}}function qr2d2(x,fullMatrices=!1){return ENGINE2.tidy(()=>{assert3(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye2(m),r=clone2(x),one2D=tensor2d2([[1]],[1,1]),w=clone2(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE2.tidy(()=>{let rjEnd1=slice2(r,[j,j],[m-j,1]),normX=norm2(rjEnd1),rjj=slice2(r,[j,j],[1,1]),s=where2(greater2(rjj,0),tensor2d2([[-1]]),tensor2d2([[1]])),u1=sub2(rjj,mul2(s,normX)),wPre=div2(rjEnd1,u1);wPre.shape[0]===1?w=clone2(one2D):w=concat2([one2D,slice2(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg2(div2(matMul3(s,u1),normX)),rjEndAll=slice2(r,[j,0],[m-j,n]),tauTimesW=mul2(tau,w),wT=transpose2(w);if(j===0)r=sub2(rjEndAll,matMul3(tauTimesW,matMul3(wT,rjEndAll)));else{let rTimesTau=sub2(rjEndAll,matMul3(tauTimesW,matMul3(wT,rjEndAll)));r=concat2([slice2(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose2(tauTimesW),qAllJEnd=slice2(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub2(qAllJEnd,matMul3(matMul3(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub2(qAllJEnd,matMul3(matMul3(qAllJEnd,w),tawTimesWT));q=concat2([slice2(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose2([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice2(q,[0,0],[m,n]),r=slice2(r,[0,0],[n,n])),[q,r]})}var qr2=op2({qr_:qr_2});var Reduction2;(function(Reduction3){Reduction3[Reduction3.NONE=0]="NONE",Reduction3[Reduction3.MEAN=1]="MEAN",Reduction3[Reduction3.SUM=2]="SUM",Reduction3[Reduction3.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction2||(Reduction2={}));function computeWeightedLoss_2(losses3,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor2(losses3,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul2($losses,$weights);if(reduction===Reduction2.NONE)return weightedLoss;if(reduction===Reduction2.SUM)return sum3(weightedLoss);if(reduction===Reduction2.MEAN){if($weights==null)return mean2(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div2(sum3(weightedLoss),sum3($weights));return broadcastFactor>1?div2(result,scalar2(broadcastFactor)):result}}if(reduction===Reduction2.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div2(sum3(weightedLoss),scalar2($losses.size));{let broadcastedWeights=mul2($weights,ones4($losses.shape)),numNonZeros=cast2(sum3(notEqual4(broadcastedWeights,scalar2(0))),"float32");return div2(sum3(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction}`)}var computeWeightedLoss3=op2({computeWeightedLoss_:computeWeightedLoss_2});function absoluteDifference_2(labels,predictions,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","absoluteDifference"),$predictions=convertToTensor2(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","absoluteDifference")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses3=abs2(sub2($labels,$predictions));return computeWeightedLoss3(losses3,$weights,reduction)}var absoluteDifference2=op2({absoluteDifference_:absoluteDifference_2});function cosineDistance_2(labels,predictions,axis,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","cosineDistance"),$predictions=convertToTensor2(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","cosineDistance")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar2(1),losses3=sub2(one,sum3(mul2($labels,$predictions),axis,!0));return computeWeightedLoss3(losses3,$weights,reduction)}var cosineDistance2=op2({cosineDistance_:cosineDistance_2});function hingeLoss_2(labels,predictions,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","hingeLoss"),$predictions=convertToTensor2(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","hingeLoss")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar2(1);$labels=sub2(mul2(scalar2(2),$labels),one);let losses3=relu2(sub2(one,mul2($labels,$predictions)));return computeWeightedLoss3(losses3,$weights,reduction)}var hingeLoss2=op2({hingeLoss_:hingeLoss_2});function huberLoss_2(labels,predictions,weights,delta=1,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","huberLoss"),$predictions=convertToTensor2(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","huberLoss")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar2(delta),error=abs2(sub2($predictions,$labels)),quadratic=minimum2(error,deltaScalar),linear=sub2(error,quadratic),losses3=add4(mul2(scalar2(.5),square2(quadratic)),mul2(deltaScalar,linear));return computeWeightedLoss3(losses3,$weights,reduction)}var huberLoss2=op2({huberLoss_:huberLoss_2});function logLoss_2(labels,predictions,weights,epsilon4=1e-7,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","logLoss"),$predictions=convertToTensor2(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","logLoss")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar2(1),epsilonScalar=scalar2(epsilon4),l13=neg2(mul2($labels,log2(add4($predictions,epsilonScalar)))),l23=mul2(sub2(one,$labels),log2(add4(sub2(one,$predictions),epsilonScalar))),losses3=sub2(l13,l23);return computeWeightedLoss3(losses3,$weights,reduction)}var logLoss2=op2({logLoss_:logLoss_2});function meanSquaredError_2(labels,predictions,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","meanSquaredError"),$predictions=convertToTensor2(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","meanSquaredError")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses3=squaredDifference4($labels,$predictions);return computeWeightedLoss3(losses3,$weights,reduction)}var meanSquaredError4=op2({meanSquaredError_:meanSquaredError_2});function sigmoidCrossEntropyWithLogits_2(labels,logits){let $labels=convertToTensor2(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor2(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch2($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu2($logits),outputXTarget=mul2($logits,$labels),sigmoidOutput=log1p2(exp2(neg2(abs2($logits))));return add4(sub2(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_2(multiClassLabels,logits,weights,labelSmoothing=0,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor2(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor2(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor2(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch2($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar2(labelSmoothing),one=scalar2(1),half=scalar2(.5);$multiClassLabels=add4(mul2($multiClassLabels,sub2(one,labelSmoothingScalar)),mul2(half,labelSmoothingScalar))}let losses3=sigmoidCrossEntropyWithLogits_2($multiClassLabels,$logits);return computeWeightedLoss3(losses3,$weights,reduction)}var sigmoidCrossEntropy2=op2({sigmoidCrossEntropy_:sigmoidCrossEntropy_2});function softmaxCrossEntropyWithLogits_2(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);let customOp=customGrad2((labels2,logits2,save)=>{let keepDims=!0,lse=logSumExp2(logits2,[dim],keepDims),logResult=sub2(cast2(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg2(mul2(logResult,labels2)),value=sum3(costVector,[dim]),gradFunc=(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim2(dy.shape,[dim]);return[mul2(reshape2(dy,dyShape),sub2(cast2(labels3,"float32"),exp2(logResult2))),mul2(reshape2(dy,dyShape),sub2(exp2(logResult2),cast2(labels3,"float32")))]};return{value,gradFunc}});return customOp(labels,logits)}function softmaxCrossEntropy_2(onehotLabels,logits,weights,labelSmoothing=0,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor2(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor2(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor2(weights,"weights","softmaxCrossEntropy")),assertShapesMatch2($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar2(labelSmoothing),one=scalar2(1),numClasses=scalar2($onehotLabels.shape[1]);$onehotLabels=add4(mul2($onehotLabels,sub2(one,labelSmoothingScalar)),div2(labelSmoothingScalar,numClasses))}let losses3=softmaxCrossEntropyWithLogits_2($onehotLabels,$logits);return computeWeightedLoss3(losses3,$weights,reduction)}var softmaxCrossEntropy2=op2({softmaxCrossEntropy_:softmaxCrossEntropy_2});var spectral2={fft:fft2,ifft:ifft2,rfft:rfft2,irfft:irfft2},signal2={hammingWindow:hammingWindow2,hannWindow:hannWindow2,frame:frame2,stft:stft2},image2={flipLeftRight:flipLeftRight3,resizeNearestNeighbor:resizeNearestNeighbor2,resizeBilinear:resizeBilinear3,rotateWithOffset:rotateWithOffset3,cropAndResize:cropAndResize3,nonMaxSuppression:nonMaxSuppression3,nonMaxSuppressionAsync:nonMaxSuppressionAsync2,nonMaxSuppressionWithScore:nonMaxSuppressionWithScore2,nonMaxSuppressionWithScoreAsync:nonMaxSuppressionWithScoreAsync2,nonMaxSuppressionPadded:nonMaxSuppressionPadded2,nonMaxSuppressionPaddedAsync:nonMaxSuppressionPaddedAsync2},linalg2={bandPart:bandPart2,gramSchmidt:gramSchmidt2,qr:qr2},losses2={absoluteDifference:absoluteDifference2,computeWeightedLoss:computeWeightedLoss3,cosineDistance:cosineDistance2,hingeLoss:hingeLoss2,huberLoss:huberLoss2,logLoss:logLoss2,meanSquaredError:meanSquaredError4,sigmoidCrossEntropy:sigmoidCrossEntropy2,softmaxCrossEntropy:softmaxCrossEntropy2};var SELU_SCALEALPHA2=1.7580993408473768,SELU_SCALE2=1.0507009873554805;var absGradConfig2={kernelName:Abs2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,step2(cast2(x,"float32"),-1))}}};var acosGradConfig2={kernelName:Acos2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square2(cast2(x,"float32")),b=sqrt2(sub2(scalar2(1),a));return neg2(div2(dy,b))}}}};var acoshGradConfig2={kernelName:Acosh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt2(sub2(square2(cast2(x,"float32")),1));return div2(dy,a)}}}};var addGradConfig2={kernelName:Add3,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,b.shape)};return{a:derA,b:derB}}};var addNGradConfig2={kernelName:AddN2,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}};var argMaxGradConfig2={kernelName:ArgMax2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike3(x)}}};var argMinGradConfig2={kernelName:ArgMin2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike3(x)}}};var asinGradConfig2={kernelName:Asin2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,sqrt2(sub2(scalar2(1),square2(cast2(x,"float32")))))}}};var asinhGradConfig2={kernelName:Asinh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt2(add4(scalar2(1),square2(cast2(x,"float32"))));return div2(dy,a)}}}};var atan2GradConfig2={kernelName:Atan22,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let d=add4(square2(a),square2(b)),res=mul2(dy,div2(b,d)),reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,a.shape)},derB=()=>{let d=add4(square2(a),square2(b)),res=neg2(mul2(dy,div2(a,d))),reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,b.shape)};return{a:derA,b:derB}}};var atanGradConfig2={kernelName:Atan3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,add4(square2(cast2(x,"float32")),1))}}};var atanhGradConfig2={kernelName:Atanh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,sub2(scalar2(1),square2(cast2(x,"float32"))))}}};function avgPool3dBackprop_2(dy,input2,filterSize,strides,dilations=[1,1,1],pad7,dimRoundingMode){let $dy=convertToTensor2(dy,"dy","avgPool3dBackprop"),$input=convertToTensor2(input2,"input","avgPool3dBackprop"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape2($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape2($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert3(dy5D.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert3(input5D.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=backend3=>{let convInfo=computePool3DInfo2(input5D.shape,filterSize,strides,dilations,pad7,dimRoundingMode);return backend3.avgPool3dBackprop(dy5D,input5D,convInfo)},inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad7,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,AvgPool3DBackprop2,attrs);return reshapedTo5D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dBackprop2=op2({avgPool3dBackprop_:avgPool3dBackprop_2});var avgPool3DGradConfig2={kernelName:AvgPool3D2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad7,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dBackprop2(dy,x,filterSize,strides,$dilations,pad7,dimRoundingMode)}}};function avgPoolBackprop_2(dy,input2,filterSize,strides,pad7){let $dy=convertToTensor2(dy,"dy","avgPoolBackprop"),$input=convertToTensor2(input2,"input","avgPoolBackprop");assert3($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape2($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape2($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert3(dy4D.rank===4,()=>`Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`),assert3(input4D.rank===4,()=>`Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);let forward=backend3=>{let convInfo=computePool2DInfo2(input4D.shape,filterSize,strides,1,pad7);return backend3.avgPoolBackprop(dy4D,input4D,convInfo)},inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad7},res=ENGINE2.runKernelFunc(forward,inputs,null,AvgPoolBackprop2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolBackprop4=op2({avgPoolBackprop_:avgPoolBackprop_2});var avgPoolGradConfig2={kernelName:AvgPool2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad7}=attrs;return{x:()=>avgPoolBackprop4(dy,x,filterSize,strides,pad7)}}};var batchMatMulGradConfig2={kernelName:BatchMatMul2,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul3(dy,b,!1,!0),b:()=>matMul3(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul3(dy,b,!1,!1),b:()=>matMul3(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul3(b,dy,!1,!0),b:()=>matMul3(a,dy,!1,!1)}:{a:()=>matMul3(b,dy,!0,!0),b:()=>matMul3(dy,a,!0,!0)}}};var batchToSpaceNDGradConfig2={kernelName:BatchToSpaceND2,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND3(dy,blockShape,crops)}}};var broadcastToGradConfig2={kernelName:BroadcastTo2,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum3(dy,axes,!0)}}};var castGradConfig2={kernelName:Cast2,gradFunc:dy=>({x:()=>dy.clone()})};var ceilGradConfig2={kernelName:Ceil2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var clipByValueGradConfig2={kernelName:ClipByValue2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where2(logicalAnd2(greaterEqual2(x,clipValueMin),lessEqual2(x,clipValueMax)),dy,zerosLike3(dy))}}};var concatGradConfig2={kernelName:Concat2,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam2(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]),derTensors=split2(dy,sizeSplits,$axis);return derTensors.map(t=>()=>t)}};var conv2DGradConfig2={kernelName:Conv2D3,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad7,dataFormat}=attrs;return assert3(tupleValuesAreOne2(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput4(x4D.shape,dy,$filter,strides,pad7,dataFormat),filter:()=>conv2DBackpropFilter3(x4D,dy,$filter.shape,strides,pad7,dataFormat)}}};var conv2DBackpropInputGradConfig2={kernelName:Conv2DBackpropInput2,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad7,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d2(ddx,filter,strides,pad7,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter3(ddx,dy,filter.shape,strides,pad7,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_2(x,dy,filterShape,strides,pad7){let x5D=x;x.rank===4&&(x5D=reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert3(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert3(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert3(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert3(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert3(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo2(x5D.shape,filterShape,strides,dilations,pad7);return backend3.conv3dDerFilter(x5D,dy5D,convInfo)},inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad7,filterShape};return ENGINE2.runKernelFunc(forward,inputs,null,Conv3DBackpropFilterV22,attrs)}var conv3DBackpropFilter2=op2({conv3DBackpropFilter_:conv3DBackpropFilter_2});var conv3DGradConfig2={kernelName:Conv3D3,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad7}=attrs;assert3(tupleValuesAreOne2(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput2(x5D.shape,dy,$filter,strides,pad7),filter:()=>conv3DBackpropFilter2(x5D,dy,$filter.shape,strides,pad7)}}};var cosGradConfig2={kernelName:Cos2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(neg2(sin2(cast2(x,"float32"))),dy)}}};var coshGradConfig2={kernelName:Cosh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(sinh2(cast2(x,"float32")),dy)}}};var cumsumGradConfig2={kernelName:Cumsum2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse8}=attrs;return{x:()=>{let permutation=getAxesPermutation2([axis],x.rank),out=cumsum2(dy,axis,exclusive,!reverse8);return permutation!=null&&(out=transpose2(out,permutation)),out}}}};var depthwiseConv2dNativeGradConfig2={kernelName:DepthwiseConv2dNative2,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad7,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert3(tupleValuesAreOne2($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert3(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert3(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert3(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert3(eitherStridesOrDilationsAreOne2(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`),{x:()=>depthwiseConv2dNativeBackpropInput3(x.shape,dy,filter,strides,pad7,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter3(x,dy,filter.shape,strides,pad7,dilations,dimRoundingMode)}}};var dilation2dGradConfig2={kernelName:Dilation2D2,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE2.runKernel(Dilation2DBackpropInput2,inputInputs,attrs),filter:()=>ENGINE2.runKernel(Dilation2DBackpropFilter2,filterInputs,attrs)}}};var divGradConfig2={kernelName:Div2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=div2(dy,cast2(b,"float32")),reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),a.shape):res},derB=()=>{let res=mul2(dy,cast2(a,"float32")),reduceAxes=getReductionAxes2(b.shape,outShape);reduceAxes.length>0&&(res=reshape2(sum3(res,reduceAxes),b.shape));let tmp=square2(b);return neg2(div2(res,cast2(tmp,"float32")))};return{a:derA,b:derB}}};var eluGradConfig2={kernelName:Elu3,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,backPropKernelFunc=backend3=>backend3.eluDer(dy,y),inputs={dy,y};return{x:()=>ENGINE2.runKernelFunc(backPropKernelFunc,inputs,null,EluGrad2)}}};var erfGradConfig2={kernelName:Erf2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul2(exp2(neg2(square2(x))),2/Math.sqrt(Math.PI));return{x:()=>mul2(dy,a)}}};var expGradConfig2={kernelName:Exp2,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul2(dy,y)}}};var expm1GradConfig2={kernelName:Expm12,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,exp2(x))}}};var floorGradConfig2={kernelName:Floor2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var floorDivGradConfig2={kernelName:FloorDiv2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=div2(dy,cast2(b,"float32")),reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),a.shape):res},derB=()=>{let res=mul2(dy,cast2(a,"float32")),reduceAxes=getReductionAxes2(b.shape,outShape);reduceAxes.length>0&&(res=reshape2(sum3(res,reduceAxes),b.shape));let tmp=square2(b);return neg2(div2(res,cast2(tmp,"float32")))};return{a:derA,b:derB}}};var fusedBatchNormGradConfig2={kernelName:FusedBatchNorm2,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean7,variance,scale3]=saved,scaleValue=scale3==null?scalar2(1):scale3,reductionAxes=getReductionAxes2(mean7.shape,x.shape),tileShape=[];if(mean7.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}let xMinusMean=sub2(x,mean7),dyTimesScaleValue=mul2(dy,scaleValue),oneOverSqrtVariance=rsqrt2(add4(variance,scalar2(varianceEpsilon))),minusHalfRCube=mul2(mul2(mul2(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar2(-.5)),derX=()=>mean7.rank===1?reshape2(mul2(mul2(dy,tile2(reshape2(oneOverSqrtVariance,[1,1,1,mean7.shape[0]]),tileShape)),scaleValue),x.shape):reshape2(mul2(mul2(dy,oneOverSqrtVariance),scaleValue),x.shape),derMean=()=>{let meanDer=mul2(mul2(oneOverSqrtVariance,scalar2(-1)),dyTimesScaleValue);return mean7.rank===1&&(meanDer=sum3(meanDer,reductionAxes)),reshape2(meanDer,mean7.shape)},derVariance=()=>{let varianceDer=mul2(mul2(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean7.rank===1&&(varianceDer=sum3(varianceDer,reductionAxes)),reshape2(varianceDer,mean7.shape)},derScale=()=>{let xMinusMean2TimesRsqrt=mul2(xMinusMean,oneOverSqrtVariance),scaleDer=mul2(dy,xMinusMean2TimesRsqrt);return mean7.rank===1&&(scaleDer=sum3(scaleDer,reductionAxes)),reshape2(scaleDer,mean7.shape)},derOffset=()=>{let offsetDer=dy;return mean7.rank===1&&(offsetDer=sum3(offsetDer,reductionAxes)),reshape2(offsetDer,mean7.shape)};return{x:derX,mean:derMean,variance:derVariance,scale:derScale,offset:derOffset}}};var gatherGradConfig2={kernelName:GatherV22,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam2(axis,x.shape)[0],derX=()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange2(0,outerDims),innerAxesIndices=arrayRange2(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat2([outerShape,[indicesSize],innerShape]),values=reshape2(dy,valuesShape),reshapedIndices=reshape2(indices,[indicesSize]),transposeDims=arrayConcat2([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose2(values,transposeDims),paramsGrad=unsortedSegmentSum2(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation2(transposeDims);return paramsGrad=transpose2(paramsGrad,invertTransposeDims),paramsGrad};return{x:derX,indices:()=>indices}}};function arrayRange2(start,stop){let result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat2(arrays){let result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}var greaterEqualGradConfig2={kernelName:GreaterEqual2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>zerosLike3(a),b:()=>zerosLike3(b)}}};var identityGradConfig2={kernelName:Identity3,gradFunc:dy=>({x:()=>cast2(dy,"float32")})};var isFiniteGradConfig2={kernelName:IsFinite2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var isInfGradConfig2={kernelName:IsInf2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var isNanGradConfig2={kernelName:IsNan2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var log1pGradConfig2={kernelName:Log1p2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,add4(x,1))}}};var logGradConfig2={kernelName:Log2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,cast2(x,"float32"))}}};var logSoftmaxGradConfig2={kernelName:LogSoftmax3,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax7=exp2(value);return sub2(dy,mul2(sum3(dy,axis,keepDims),softmax7))}}}};function localResponseNormalizationBackprop_2(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let forward=backend3=>backend3.LRNGrad(dy,x,y,depthRadius,bias,alpha,beta),inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE2.runKernelFunc(forward,inputs,null,LRNBackprop2,attrs)}var localResponseNormalizationBackprop2=op2({localResponseNormalizationBackprop_:localResponseNormalizationBackprop_2});var lrnGradConfig2={kernelName:LRN2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop2(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax2(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape2(y,expandShapeToKeepDim2(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape2(dy,expandShapeToKeepDim2(dy.shape,origAxes))),{x:()=>{let dx=mul2(dy,cast2(equal2(xOrig,y),dy.dtype));return dx}}}var maxGradConfig2={kernelName:Max2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam2(reductionIndices,x.shape),maxGrad=gradForMinAndMax2(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}};var maximumGradConfig2={kernelName:Maximum3,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul2(dy,cast2(greaterEqual2(a,b),"float32")),derB=()=>mul2(dy,cast2(less2(a,b),"float32"));return{a:derA,b:derB}}};function maxPool3dBackprop_2(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad7,dimRoundingMode){let $dy=convertToTensor2(dy,"dy","maxPool3dBackprop"),$input=convertToTensor2(input2,"input","maxPool3dBackprop"),$output=convertToTensor2(output,"output","maxPool3dBackprop"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape2($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape2($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape2($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert3(dy5D.rank===5,()=>`Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert3(input5D.rank===5,()=>`Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert3(output5D.rank===5,()=>`Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=backend3=>{let convInfo=computePool3DInfo2(input5D.shape,filterSize,strides,dilations,pad7,dimRoundingMode);return backend3.maxPool3dBackprop(dy5D,input5D,output5D,convInfo)},inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad7,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,MaxPool3DBackprop2,attrs);return reshapedTo5D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dBackprop2=op2({maxPool3dBackprop_:maxPool3dBackprop_2});var maxPool3DGradConfig2={kernelName:MaxPool3D2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad7,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dBackprop2(dy,x,y,filterSize,strides,$dilations,pad7,dimRoundingMode)}}};function maxPoolBackprop_2(dy,input2,output,filterSize,strides,pad7,dimRoundingMode){let $dy=convertToTensor2(dy,"dy","maxPoolBackprop"),$input=convertToTensor2(input2,"input","maxPoolBackprop"),$output=convertToTensor2(output,"output","maxPoolBackprop");assert3($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert3($dy.rank===4,()=>`Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`),assert3($input.rank===4,()=>`Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=backend3=>{let convInfo=computePool2DInfo2($input.shape,filterSize,strides,1,pad7,dimRoundingMode);return backend3.maxPoolBackprop($dy,$input,$output,convInfo)},inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad7,dimRoundingMode};return ENGINE2.runKernelFunc(forward,inputs,null,MaxPoolBackprop2,attrs)}var maxPoolBackprop4=op2({maxPoolBackprop_:maxPoolBackprop_2});var maxPoolGradConfig2={kernelName:MaxPool2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad7}=attrs;return{x:()=>maxPoolBackprop4(dy,x,y,filterSize,strides,pad7)}}};var minGradConfig2={kernelName:Min2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam2(axis,x.shape),minGrad=gradForMinAndMax2(dy,y,x,origAxes);return{x:()=>minGrad.x()}}};var minimumGradConfig2={kernelName:Minimum3,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul2(dy,cast2(lessEqual2(a,b),"float32")),derB=()=>mul2(dy,cast2(greater2(a,b),"float32"));return{a:derA,b:derB}}};var mirrorPadGradConfig2={kernelName:MirrorPad2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice2(dy,begin,x.shape)}}};var modGradConfig2={kernelName:Mod2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0?reshape2(sum3(dy,reduceAxes),a.shape):dy},derB=()=>{let res=mul2(dy,neg2(floor2(div2(a,b)))),reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}};var multiplyGradConfig2={kernelName:Multiply3,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=mul2(dy,cast2(b,"float32")),reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),a.shape):res},derB=()=>{let res=mul2(dy,cast2(a,"float32")),reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}};var negateGradConfig2={kernelName:Negate2,gradFunc:dy=>({x:()=>neg2(dy)})};var oneHotGradConfig2={kernelName:OneHot2,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros2(indices.shape,"float32")}}};var onesLikeGradConfig2={kernelName:OnesLike2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var padV2GradConfig2={kernelName:PadV22,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice2(dy,begin,x.shape)}}};var powGradConfig2={kernelName:Pow2,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp14=b,outShape=assertAndGetBroadcastShape2(base2.shape,exp14.shape),derBase=()=>{let expFloat=cast2(exp14,"float32"),res=mul2(dy,mul2(expFloat,pow2(base2,sub2(expFloat,scalar2(1))))),reduceAxes=getReductionAxes2(base2.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,base2.shape)},derExp=()=>{let condition=greater2(base2,0),logBase=where2(condition,log2(base2),zerosLike3(base2)),res=mul2(dy,mul2(y,logBase)),reduceAxes=getReductionAxes2(exp14.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,exp14.shape)};return{a:derBase,b:derExp}}};var preluGradConfig2={kernelName:Prelu2,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater2(x,0);return{x:()=>where2(mask,dy,mul2(dy,alpha)),alpha:()=>{let res=where2(mask,zerosLike3(dy),mul2(dy,x)),reduceAxes=getReductionAxes2(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,alpha.shape)}}}};var reciprocalGradConfig2={kernelName:Reciprocal2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,neg2(square2(x)))}}};var relu6GradConfig2={kernelName:Relu63,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul2(lessEqual2(x,6),step2(x));return{x:()=>mul2(dy,cast2(mask,"float32"))}}};var reluGradConfig2={kernelName:Relu3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,cast2(step2(x),"float32"))}}};var reshapeGradConfig2={kernelName:Reshape3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape2(dy,x.shape)}}};var resizeBilinearGradConfig2={kernelName:ResizeBilinear2,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeBilinearBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE2.runKernelFunc(backPropKernelFunc,inputs,null,ResizeBilinearGrad2,attrs);return{images:imagesDer}}};var resizeNearestNeighborGradConfig2={kernelName:ResizeNearestNeighbor2,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeNearestNeighborBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE2.runKernelFunc(backPropKernelFunc,inputs,null,ResizeNearestNeighborGrad2,attrs);return{images:imagesDer}}};var reverseGradConfig2={kernelName:Reverse2,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam2(dims,dy.shape);return{x:()=>reverse2(dy,axes)}}};var roundGradConfig2={kernelName:Round2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var rsqrtGradConfig2={kernelName:Rsqrt2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg2(div2(dy,mul2(pow2(x,1.5),2)))}}};var selectV2PoolGradConfig2={kernelName:SelectV22,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast2(zerosLike3(condition),"float32"),t:()=>mul2(dy,cast2(condition,dy.dtype)),e:()=>mul2(dy,cast2(logicalNot3(condition),dy.dtype))}}};var seluGradConfig2={kernelName:Selu3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater2(x,scalar2(0)),scaleAlpha2=scalar2(SELU_SCALEALPHA2),scale3=scalar2(SELU_SCALE2),greaterThanZeroDer=mul2(dy,scale3),lessEqualZeroDer=mul2(mul2(dy,scaleAlpha2),exp2(cast2(x,"float32")));return where2(mask,greaterThanZeroDer,lessEqualZeroDer)}}}};var sigmoidGradConfig2={kernelName:Sigmoid3,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul2(dy,mul2(y,sub2(scalar2(1),y)))}}};var signGradConfig2={kernelName:Sign2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var sinGradConfig2={kernelName:Sin2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(cos2(cast2(x,"float32")),dy)}}};var sinhGradConfig2={kernelName:Sinh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(cosh2(cast2(x,"float32")),dy)}}};var sliceGradConfig2={kernelName:Slice2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams2(x,begin,size),paddings=[];for(let i=0;i<dy.rank;i++)paddings.push([begin_[i],inputShape[i]-begin_[i]-size_[i]]);return{x:()=>pad2(dy,paddings)}}};var softmaxGradConfig2={kernelName:Softmax4,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul2(dy,y);return{logits:()=>sub2(dyTimesY,mul2(sum3(dyTimesY,[dim],keepDims),y))}}};var softplusGradConfig2={kernelName:Softplus3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,sigmoid3(x))}}};var spaceToBatchNDGradConfig2={kernelName:SpaceToBatchND2,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND2(dy,blockShape,paddings)}}};var splitVGradConfig2={kernelName:SplitV2,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat2(dy,axis)}}};var sqrtGradConfig2={kernelName:Sqrt2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,mul2(sqrt2(cast2(x,"float32")),2))}}};var squareGradConfig2={kernelName:Square2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,mul2(cast2(x,"float32"),2))}}};var squaredDifferenceGradConfig2={kernelName:SquaredDifference2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar2(2),derA=()=>mul2(dy,mul2(two,sub2(a,b))),derB=()=>mul2(dy,mul2(two,sub2(b,a)));return{a:derA,b:derB}}};var stepGradConfig2={kernelName:Step2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var subGradConfig2={kernelName:Sub2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(neg2(res),b.shape)};return{a:derA,b:derB}}};var sumGradConfig2={kernelName:Sum2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs,axes=parseAxisParam2(axis,x.shape);axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape2(dy,expandedDyShape),derX=mul2(expandedDy,ones4(x.shape,"float32"));return{x:()=>derX}}};var tanGradConfig2={kernelName:Tan2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,square2(cos2(x)))}}};var tanhGradConfig2={kernelName:Tanh3,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul2(sub2(scalar2(1),square2(y)),dy)}}};var tileGradConfig2={kernelName:Tile2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs,derX=()=>{let xGrad=zerosLike3(x);if(x.rank===1)for(let i=0;i<reps[0];++i)xGrad=add4(xGrad,slice2(dy,[i*x.shape[0]],[x.shape[0]]));else if(x.rank===2)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)xGrad=add4(xGrad,slice2(dy,[i*x.shape[0],j*x.shape[1]],[x.shape[0],x.shape[1]]));else if(x.rank===3)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)xGrad=add4(xGrad,slice2(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2]],[x.shape[0],x.shape[1],x.shape[2]]));else if(x.rank===4)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)for(let l=0;l<reps[3];++l)xGrad=add4(xGrad,slice2(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2],l*x.shape[3]],[x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);return xGrad};return{x:derX}}};var transposeGradConfig2={kernelName:Transpose2,gradFunc:(dy,saved,attrs)=>{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation2(perm);return{x:()=>transpose2(dy,undoPerm)}}};var unpackGradConfig2={kernelName:Unpack2,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack2(dy,axis)}}};var unsortedSegmentSumGradConfig2={kernelName:UnsortedSegmentSum2,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved,derX=()=>gatherDropNegatives2(dy,segmentIds);return{x:derX}}};function gatherDropNegatives2(x,indices){let zeroClippedIndices=maximum2(indices,zerosLike3(indices)),gathered=gather2(x,zeroClippedIndices),isPositive=greaterEqual2(indices,scalar2(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims3(isPositive,i+1);isPositive=logicalAnd2(isPositive,ones4(gathered.shape,"bool"));let zeroSlice=zerosLike3(gathered);return where2(isPositive,gathered,zeroSlice)}var zerosLikeGradConfig2={kernelName:ZerosLike2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var gradConfigs2=[absGradConfig2,acosGradConfig2,acoshGradConfig2,addGradConfig2,addNGradConfig2,argMaxGradConfig2,argMinGradConfig2,asinGradConfig2,asinhGradConfig2,atan2GradConfig2,atanGradConfig2,atanhGradConfig2,avgPool3DGradConfig2,avgPoolGradConfig2,batchMatMulGradConfig2,batchToSpaceNDGradConfig2,broadcastToGradConfig2,castGradConfig2,ceilGradConfig2,clipByValueGradConfig2,concatGradConfig2,conv2DBackpropInputGradConfig2,conv2DGradConfig2,conv3DGradConfig2,cosGradConfig2,coshGradConfig2,cumsumGradConfig2,depthwiseConv2dNativeGradConfig2,dilation2dGradConfig2,divGradConfig2,eluGradConfig2,erfGradConfig2,expGradConfig2,expm1GradConfig2,floorDivGradConfig2,floorGradConfig2,fusedBatchNormGradConfig2,gatherGradConfig2,greaterEqualGradConfig2,identityGradConfig2,isFiniteGradConfig2,isInfGradConfig2,isNanGradConfig2,log1pGradConfig2,logGradConfig2,logSoftmaxGradConfig2,lrnGradConfig2,maxGradConfig2,maxGradConfig2,maximumGradConfig2,maxPool3DGradConfig2,maxPoolGradConfig2,minGradConfig2,minimumGradConfig2,mirrorPadGradConfig2,modGradConfig2,multiplyGradConfig2,negateGradConfig2,oneHotGradConfig2,onesLikeGradConfig2,padV2GradConfig2,padV2GradConfig2,powGradConfig2,preluGradConfig2,reciprocalGradConfig2,relu6GradConfig2,reluGradConfig2,reshapeGradConfig2,resizeBilinearGradConfig2,resizeNearestNeighborGradConfig2,reverseGradConfig2,roundGradConfig2,rsqrtGradConfig2,selectV2PoolGradConfig2,seluGradConfig2,sigmoidGradConfig2,signGradConfig2,sinGradConfig2,sinhGradConfig2,sliceGradConfig2,softmaxGradConfig2,softplusGradConfig2,spaceToBatchNDGradConfig2,spaceToBatchNDGradConfig2,splitVGradConfig2,splitVGradConfig2,sqrtGradConfig2,squaredDifferenceGradConfig2,squareGradConfig2,stepGradConfig2,subGradConfig2,sumGradConfig2,tanGradConfig2,tanhGradConfig2,tileGradConfig2,transposeGradConfig2,unpackGradConfig2,unsortedSegmentSumGradConfig2,zerosLikeGradConfig2];for(let gradientConfig of gradConfigs2)registerGradient2(gradientConfig);Tensor2.prototype.abs=function(){return this.throwIfDisposed(),abs2(this)};Tensor2.prototype.acos=function(){return this.throwIfDisposed(),acos2(this)};Tensor2.prototype.acosh=function(){return this.throwIfDisposed(),acosh2(this)};Tensor2.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict2(this,x)};Tensor2.prototype.add=function(b){return this.throwIfDisposed(),add4(this,b)};Tensor2.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all2(this,axis,keepDims)};Tensor2.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any2(this,axis,keepDims)};Tensor2.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax2(this,axis)};Tensor2.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin2(this,axis)};Tensor2.prototype.asScalar=function(){return this.throwIfDisposed(),assert3(this.size===1,()=>"The array must have only 1 element."),reshape2(this,[])};Tensor2.prototype.asType=function(dtype){return this.throwIfDisposed(),cast2(this,dtype)};Tensor2.prototype.as1D=function(){return this.throwIfDisposed(),reshape2(this,[this.size])};Tensor2.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape2(this,[rows,columns])};Tensor2.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape2(this,[rows,columns,depth])};Tensor2.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape2(this,[rows,columns,depth,depth2])};Tensor2.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape2(this,[rows,columns,depth,depth2,depth3])};Tensor2.prototype.asin=function(){return this.throwIfDisposed(),asin2(this)};Tensor2.prototype.asinh=function(){return this.throwIfDisposed(),asinh2(this)};Tensor2.prototype.atan=function(){return this.throwIfDisposed(),atan3(this)};Tensor2.prototype.atan2=function(b){return this.throwIfDisposed(),atan22(this,b)};Tensor2.prototype.atanh=function(){return this.throwIfDisposed(),atanh2(this)};Tensor2.prototype.avgPool=function(filterSize,strides,pad7,dimRoundingMode){return this.throwIfDisposed(),avgPool5(this,filterSize,strides,pad7,dimRoundingMode)};Tensor2.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND2(this,blockShape,crops)};Tensor2.prototype.batchNorm=function(mean7,variance,offset,scale3,varianceEpsilon){return this.throwIfDisposed(),batchNorm4(this,mean7,variance,offset,scale3,varianceEpsilon)};Tensor2.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo2(this,shape)};Tensor2.prototype.cast=function(dtype){return this.throwIfDisposed(),cast2(this,dtype)};Tensor2.prototype.ceil=function(){return this.throwIfDisposed(),ceil2(this)};Tensor2.prototype.clipByValue=function(min9,max11){return this.throwIfDisposed(),clipByValue2(this,min9,max11)};Tensor2.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor2&&(x=[x]),concat2([this,...x],axis)};Tensor2.prototype.conv1d=function(filter,stride,pad7,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d2(this,filter,stride,pad7,dataFormat,dilation,dimRoundingMode)};Tensor2.prototype.conv2dTranspose=function(filter,outputShape,strides,pad7,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose3(this,filter,outputShape,strides,pad7,dimRoundingMode)};Tensor2.prototype.conv2d=function(filter,strides,pad7,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d2(this,filter,strides,pad7,dataFormat,dilations,dimRoundingMode)};Tensor2.prototype.cos=function(){return this.throwIfDisposed(),cos2(this)};Tensor2.prototype.cosh=function(){return this.throwIfDisposed(),cosh2(this)};Tensor2.prototype.cumsum=function(axis,exclusive,reverse8){return this.throwIfDisposed(),cumsum2(this,axis,exclusive,reverse8)};Tensor2.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace3(this,blockSize,dataFormat)};Tensor2.prototype.depthwiseConv2D=function(filter,strides,pad7,dataFormat,dilations,dimRoundingMode){return deprecationWarn2("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d6(this,filter,strides,pad7,dataFormat,dilations,dimRoundingMode)};Tensor2.prototype.depthwiseConv2d=function(filter,strides,pad7,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d6(this,filter,strides,pad7,dataFormat,dilations,dimRoundingMode)};Tensor2.prototype.dilation2d=function(filter,strides,pad7,dilations,dataFormat){return this.throwIfDisposed(),dilation2d2(this,filter,strides,pad7,dilations,dataFormat)};Tensor2.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan2(this,b)};Tensor2.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict2(this,x)};Tensor2.prototype.div=function(b){return this.throwIfDisposed(),div2(this,b)};Tensor2.prototype.dot=function(b){return this.throwIfDisposed(),dot2(this,b)};Tensor2.prototype.elu=function(){return this.throwIfDisposed(),elu2(this)};Tensor2.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict2(this,x)};Tensor2.prototype.equal=function(b){return this.throwIfDisposed(),equal2(this,b)};Tensor2.prototype.erf=function(){return this.throwIfDisposed(),erf2(this)};Tensor2.prototype.exp=function(){return this.throwIfDisposed(),exp2(this)};Tensor2.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims3(this,axis)};Tensor2.prototype.expm1=function(){return this.throwIfDisposed(),expm12(this)};Tensor2.prototype.fft=function(){return this.throwIfDisposed(),fft2(this)};Tensor2.prototype.flatten=function(){return this.throwIfDisposed(),reshape2(this,[this.size])};Tensor2.prototype.floor=function(){return this.throwIfDisposed(),floor2(this)};Tensor2.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv2(this,b)};Tensor2.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather2(this,indices,axis)};Tensor2.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict2(this,x)};Tensor2.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual2(this,b)};Tensor2.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict2(this,x)};Tensor2.prototype.greater=function(b){return this.throwIfDisposed(),greater2(this,b)};Tensor2.prototype.ifft=function(){return this.throwIfDisposed(),ifft2(this)};Tensor2.prototype.irfft=function(){return this.throwIfDisposed(),irfft2(this)};Tensor2.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite4(this)};Tensor2.prototype.isInf=function(){return this.throwIfDisposed(),isInf3(this)};Tensor2.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN4(this)};Tensor2.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu2(this,alpha)};Tensor2.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict2(this,x)};Tensor2.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual2(this,b)};Tensor2.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict2(this,x)};Tensor2.prototype.less=function(b){return this.throwIfDisposed(),less2(this,b)};Tensor2.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization2(this,depthRadius,bias,alpha,beta)};Tensor2.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid2(this)};Tensor2.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax2(this,axis)};Tensor2.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp2(this,axis,keepDims)};Tensor2.prototype.log=function(){return this.throwIfDisposed(),log2(this)};Tensor2.prototype.log1p=function(){return this.throwIfDisposed(),log1p2(this)};Tensor2.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd2(this,b)};Tensor2.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot3(this)};Tensor2.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr2(this,b)};Tensor2.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor2(this,b)};Tensor2.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul3(this,b,transposeA,transposeB)};Tensor2.prototype.maxPool=function(filterSize,strides,pad7,dimRoundingMode){return this.throwIfDisposed(),maxPool5(this,filterSize,strides,pad7,dimRoundingMode)};Tensor2.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max2(this,axis,keepDims)};Tensor2.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict2(this,x)};Tensor2.prototype.maximum=function(b){return this.throwIfDisposed(),maximum2(this,b)};Tensor2.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean2(this,axis,keepDims)};Tensor2.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min2(this,axis,keepDims)};Tensor2.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict2(this,x)};Tensor2.prototype.minimum=function(b){return this.throwIfDisposed(),minimum2(this,b)};Tensor2.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad3(this,paddings,mode)};Tensor2.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict2(this,x)};Tensor2.prototype.mod=function(b){return this.throwIfDisposed(),mod2(this,b)};Tensor2.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict2(this,x)};Tensor2.prototype.mul=function(b){return this.throwIfDisposed(),mul2(this,b)};Tensor2.prototype.neg=function(){return this.throwIfDisposed(),neg2(this)};Tensor2.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm2(this,ord,axis,keepDims)};Tensor2.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict2(this,x)};Tensor2.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual4(this,b)};Tensor2.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot3(this,depth,onValue,offValue)};Tensor2.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike3(this)};Tensor2.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad2(this,paddings,constantValue)};Tensor2.prototype.pool=function(windowShape,poolingType,padding,dilationRate,strides){return this.throwIfDisposed(),pool2(this,windowShape,poolingType,padding,dilationRate,strides)};Tensor2.prototype.powStrict=function(exp14){return this.throwIfDisposed(),powStrict2(this,exp14)};Tensor2.prototype.pow=function(exp14){return this.throwIfDisposed(),pow2(this,exp14)};Tensor2.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu2(this,alpha)};Tensor2.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod2(this,axis,keepDims)};Tensor2.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal2(this)};Tensor2.prototype.relu=function(){return this.throwIfDisposed(),relu2(this)};Tensor2.prototype.relu6=function(){return this.throwIfDisposed(),relu62(this)};Tensor2.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape2(this,x.shape)};Tensor2.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape2(this,shape)};Tensor2.prototype.resizeBilinear=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeBilinear3(this,newShape2D,alignCorners)};Tensor2.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeNearestNeighbor2(this,newShape2D,alignCorners)};Tensor2.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse2(this,axis)};Tensor2.prototype.rfft=function(){return this.throwIfDisposed(),rfft2(this)};Tensor2.prototype.round=function(){return this.throwIfDisposed(),round3(this)};Tensor2.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt2(this)};Tensor2.prototype.selu=function(){return this.throwIfDisposed(),selu2(this)};Tensor2.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad7,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d3(this,depthwiseFilter,pointwiseFilter,strides,pad7,dilation,dataFormat)};Tensor2.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid3(this)};Tensor2.prototype.sign=function(){return this.throwIfDisposed(),sign2(this)};Tensor2.prototype.sin=function(){return this.throwIfDisposed(),sin2(this)};Tensor2.prototype.sinh=function(){return this.throwIfDisposed(),sinh2(this)};Tensor2.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice2(this,begin,size)};Tensor2.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax2(this,dim)};Tensor2.prototype.softplus=function(){return this.throwIfDisposed(),softplus2(this)};Tensor2.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND3(this,blockShape,paddings)};Tensor2.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split2(this,numOrSizeSplits,axis)};Tensor2.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt2(this)};Tensor2.prototype.square=function(){return this.throwIfDisposed(),square2(this)};Tensor2.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference4(this,b)};Tensor2.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict2(this,x)};Tensor2.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze2(this,axis)};Tensor2.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor2?[this,x]:[this,...x];return stack2(tensorsToBeStacked,axis)};Tensor2.prototype.step=function(alpha){return this.throwIfDisposed(),step2(this,alpha)};Tensor2.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice3(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor2.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict2(this,x)};Tensor2.prototype.sub=function(b){return this.throwIfDisposed(),sub2(this,b)};Tensor2.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum3(this,axis,keepDims)};Tensor2.prototype.tan=function(){return this.throwIfDisposed(),tan2(this)};Tensor2.prototype.tanh=function(){return this.throwIfDisposed(),tanh3(this)};Tensor2.prototype.tile=function(reps){return this.throwIfDisposed(),tile2(this,reps)};Tensor2.prototype.toBool=function(){return this.throwIfDisposed(),cast2(this,"bool")};Tensor2.prototype.toFloat=function(){return this.throwIfDisposed(),cast2(this,"float32")};Tensor2.prototype.toInt=function(){return this.throwIfDisposed(),cast2(this,"int32")};Tensor2.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk2(this,k,sorted)};Tensor2.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose2(this,perm)};Tensor2.prototype.unique=function(axis){return this.throwIfDisposed(),unique2(this,axis)};Tensor2.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum2(this,segmentIds,numSegments)};Tensor2.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack2(this,axis)};Tensor2.prototype.where=function(condition,x){return this.throwIfDisposed(),where2(condition,this,x)};Tensor2.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike3(this)};function imageToSquare(input2,inputSize,centerImage=!1){let{Image,Canvas}=env2.getEnv();if(!(input2 instanceof Image||input2 instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input2),scale3=inputSize/Math.max(dims.height,dims.width),width=scale3*dims.width,height=scale3*dims.height,targetCanvas=createCanvas2({width:inputSize,height:inputSize}),inputCanvas=input2 instanceof Canvas?input2:createCanvasFromMedia(input2),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;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((input2,idx)=>{if(isTensor3D(input2)){this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape;return}if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape.slice(1);return}let canvas=input2 instanceof env2.getEnv().Canvas?input2:createCanvasFromMedia(input2);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 range2(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");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tidy2(()=>{let inputTensors=range2(this.batchSize,0,1).map(batchIdx=>{let input2=this.getInput(batchIdx);if(input2 instanceof Tensor2){let imgTensor=isTensor4D(input2)?input2:input2.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=image2.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input2 instanceof env2.getEnv().Canvas)return browser_exports2.fromPixels(imageToSquare(input2,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input2}`)}),batchTensor=stack2(inputTensors.map(t=>cast2(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3);return batchTensor})}};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");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input2,i)=>{if(!isMediaElement(input2)&&!isTensor3D(input2)&&!isTensor4D(input2))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input2=>isMediaElement(input2)&&awaitMediaLoaded(input2))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input2,detections){let{Canvas}=env2.getEnv(),canvas=input2;if(!(input2 instanceof Canvas)){let netInput=await toNetInput(input2);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height));return boxes.map(({x,y,width,height})=>{let faceImg=createCanvas2({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)),faceTensors=boxes.map(({x,y,width,height})=>slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]));return faceTensors})}async function fetchOrThrow(url,init2){let fetch3=env2.getEnv().fetch,res=await fetch3(url,init2);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),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: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return io_exports.loadWeights(manifest,modelBaseUri)}function matchDimensions(input2,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input2.width=width,input2.height=height,{width,height}}var NeuralNetwork=class{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){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor163){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor163}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:tensor163})=>{this.reassignParamFromPath(path,tensor163.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable4})=>{let tensor163=tensor4(variable4.dataSync());variable4.dispose(),this.reassignParamFromPath(path,tensor163)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor163})=>Array.from(tensor163.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let 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`);let{readFile}=env2.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(filePath2=>readFile(filePath2).then(buf=>buf.buffer))),loadWeights2=io_exports.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights2(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{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");let 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}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};function depthwiseSeparableConv(x,params,stride){return tidy(()=>{let out=separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=add2(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return relu(add2(out1,add2(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=relu(add2(out1,add2(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return relu(add2(out1,add2(out2,add2(out3,out4))))})}function convLayer(x,params,padding="same",withRelu=!1){return tidy(()=>{let out=add2(conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}function extractConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,filterSize,mappedPrefix){let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}function extractFCParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let fc_weights=tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let depthwise_filter=tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return function(prefix){let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return function(originalPath,paramRank,mappedPath){let tensor163=weightMap[originalPath];if(!isTensor(tensor163,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor163}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor163}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return function(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};function fullyConnectedLayer(x,params){return tidy(()=>add2(matMul(x,params.weights),params.bias))}function extractParams3(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),fc=extractFCParams(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap3(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams3(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap3(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{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)}},FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input2){return tidy(()=>softmax(this.runNet(input2)))}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictExpressions(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){let extension={expressions};return Object.assign({},sourceObj,extension)}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){let faceExpressionsArray=Array.isArray(faceExpressions)?faceExpressions:[faceExpressions];faceExpressionsArray.forEach(e=>{let 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");let sorted=expr.asSortedArray(),resultsToDisplay=sorted.filter(expr2=>expr2.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0),drawTextField=new DrawTextField(resultsToDisplay.map(expr2=>`${expr2.expression} (${round2(expr2.probability)})`),anchor);drawTextField.draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims),extension={landmarks,unshiftedLandmarks,alignedRect};return Object.assign({},sourceObj,extension)}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,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)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{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(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let 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){let faceLandmarksArray=Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks];faceLandmarksArray.forEach(f=>{let 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)})}function extractorsFactory3(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams5(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory3(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range2(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),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: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory3(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap5(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory3(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range2(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return add2(conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=maxPool(out,[3,3],[2,2],"same"),out=add2(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv2,[1,1]),out=add2(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(scalar(256)),out=relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range2(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams5(weights,this._numMainBlocks)}};function extractParams7(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap7(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2,pooled=avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input2){return tidy(()=>{let{age,gender}=this.runNet(input2);return{age,gender:softmax(gender)}})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictAgeAndGender(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),ages=unstack(out.age),genders=unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams7(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap7(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale3=inputSize/Math.max(height,width);return{width:width*scale3,height:height*scale3}}),batchSize=inputDimensions.length;return tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>stack([fill([68],fillX,"float32"),fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding2=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding2(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding2(batchIdx,(w,h)=>h<w),landmarkTensors=output.mul(fill([batchSize,136],inputSize,"float32")).sub(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))));return landmarkTensors})}forwardInput(input2){return tidy(()=>{let out=this.runNet(input2);return this.postProcess(out,input2.inputSize,input2.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async detectLandmarks(input2){let netInput=await toNetInput(input2),landmarkTensors=tidy(()=>unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven2(i)),yCoords=landmarksArray.filter((_,i)=>!isEven2(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};function scale2(x,params){return add2(mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=conv2d(x,filters,strides,padding);return out=add2(out,bias),out=scale2(out,params.scale),withRelu?relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}function extractorsFactory5(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),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: ${filterSize}`);return tidy(()=>transpose(tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tensor1d(extractWeights(numWeights)),biases=tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale3=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale3}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams9(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory5(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tidy(()=>transpose(tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return{params,paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale3=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale3}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap9(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory6(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),fc=weightMap.fc;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let 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 disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=add2(out,x),out=relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=avgPool(x,2,2,"valid"),zeros10=zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3],isAdjustShape=pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2];if(isAdjustShape){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=zeros(padShapeX);out=concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=zeros(padShapeY);out=concat([out,zerosH],2)}return pooled=isPad?concat([pooled,zeros10],3):pooled,out=add2(pooled,out),out=relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(150,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(scalar(256)),out=convDown(normalized,params.conv32_down);out=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);let globalAvg=out.mean([1,2]),fullyConnected=matMul(globalAvg,params.fc);return fullyConnected})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async computeFaceDescriptor(input2){let netInput=await toNetInput(input2),faceDescriptorTensors=tidy(()=>unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap9(weightMap)}extractParams(weights){return extractParams9(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){let extension={descriptor};return Object.assign({},sourceObj,extension)}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){let extension={age};return Object.assign({},sourceObj,extension)}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){let extension={gender,genderProbability};return Object.assign({},sourceObj,extension)}function extractorsFactory7(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tensor1d(extractWeights(numChannels)),batch_norm_offset=tensor1d(extractWeights(numChannels)),batch_norm_mean=tensor1d(extractWeights(numChannels)),batch_norm_variance=tensor1d(extractWeights(numChannels));return 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`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),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(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),box_predictor_0={box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1={box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2={box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3={box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4={box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},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){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory7(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),extra_dim=tensor3d(extractWeights(5118*4),[1,5118,4]),output_layer={extra_dim};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${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){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory8(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function pointwiseConvLayer(x,params,strides){return tidy(()=>{let out=conv2d(x,params.filters,strides,"same");return out=add2(out,params.batch_norm_offset),clipByValue(out,0,6)})}var epsilon3=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tidy(()=>{let out=depthwiseConv2d(x,params.filters,strides,"same");return out=batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon3),clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]),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];if(convPairParams.forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function nonMaxSuppression4(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou3=IOU(boxes,c.boxIndex,selected[j]);if(iou3===0)continue;if(c.score*=suppressFunc(iou3),c.score<=scoreThreshold)break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function getCenterCoordinatesAndSizesLayer(x){let vec=unstack(transpose(x,[1,0])),sizes=[sub(vec[2],vec[0]),sub(vec[3],vec[1])],centers=[add2(vec[0],div(sizes[0],scalar(2))),add2(vec[1],div(sizes[1],scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=unstack(transpose(x1,[1,0])),div0_out=div(mul(exp(div(vec[2],scalar(5))),sizes[0]),scalar(2)),add0_out=add2(mul(div(vec[0],scalar(10)),sizes[0]),centers[0]),div1_out=div(mul(exp(div(vec[3],scalar(5))),sizes[1]),scalar(2)),add1_out=add2(mul(div(vec[1],scalar(10)),sizes[1]),centers[1]);return transpose(stack([sub(add0_out,div0_out),sub(add1_out,div1_out),add2(add0_out,div0_out),add2(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tidy(()=>{let batchSize=boxPredictions.shape[0],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]);let scoresAndClasses=sigmoid(slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=unstack(boxes),scoresByBatch=unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}function boxPredictionLayer(x,params){return tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||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}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input2){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(512,!1),"float32"),x=sub(mul(batchTensor,scalar(.007843137718737125)),scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async locateFaces(input2,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input2),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),iouThreshold=.5,indices=nonMaxSuppression4(boxes,scoresData,maxResults,iouThreshold,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[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)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap11(weightMap)}extractParams(weights){return extractParams11(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.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)],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)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME2="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",isNumber3=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(!isNumber3(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=>isNumber3(a.x)&&isNumber3(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(isNumber3)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`)}function leaky(x){return tidy(()=>{let min9=mul(x,scalar(.10000000149011612));return add2(relu(sub(x,min9)),min9)})}function convWithBatchNorm(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=conv2d(out,params.conv.filters,[1,1],"valid"),out=sub(out,params.bn.sub),out=mul(out,params.bn.truediv),out=add2(out,params.conv.bias),leaky(out)})}function depthwiseSeparableConv3(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=add2(out,params.bias),leaky(out)})}function extractorsFactory9(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub30=tensor1d(extractWeights(size)),truediv=tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub30,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams13(weights,config2,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory9(extractWeights,paramMappings),params;if(config2.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config2.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),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: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory10(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub30=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub30,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap13(weightMap,config2){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory10(weightMap,paramMappings),params;if(config2.withSeparableConvs){let 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")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}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={}));var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.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}},TinyYolov2Base2=class 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);return out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv3(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv3(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv3(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input2,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input2,inputSize){return await this.forwardInput(await toNetInput(input2),inputSize)}async detect(input2,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input2),out=await this.forwardInput(netInput,inputSize),out0=tidy(()=>unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]),indices=nonMaxSuppression2(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0),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){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,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){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),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++){let score=sigmoid2(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid2(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid2(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,width2=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,height2=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-width2/2,y=ctY-height2/2,pos={row,col,anchor},{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})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,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((max11,curr)=>max11.classScore>curr.classScore?max11:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config2=Object.assign({},{withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"]},withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0});super(config2)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){let objectDetections=await this.detect(input2,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_NAME2}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function extractAllFacesAndComputeResults(parentResults,input2,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input2 instanceof Tensor?await extractFaceTensors(input2,faceBoxes):await extractFaces(input2,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input2,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input2,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,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)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config2={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config2)}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){let objectDetections=await this.detect(input2,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)}},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},ssdMobilenetv1=(input2,options)=>nets.ssdMobilenetv1.locateFaces(input2,options),tinyFaceDetector=(input2,options)=>nets.tinyFaceDetector.locateFaces(input2,options),tinyYolov23=(input2,options)=>nets.tinyYolov2.locateFaces(input2,options),detectFaceLandmarks=input2=>nets.faceLandmark68Net.detectLandmarks(input2),detectFaceLandmarksTiny=input2=>nets.faceLandmark68TinyNet.detectLandmarks(input2),computeFaceDescriptor=input2=>nets.faceRecognitionNet.computeFaceDescriptor(input2),recognizeFaceExpressions=input2=>nets.faceExpressionNet.predictExpressions(input2),predictAgeAndGender=input2=>nets.ageGenderNet.predictAgeAndGender(input2),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,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)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,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)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{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)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input2){super();this.parentTask=parentTask;this.input=input2}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask,descriptors=await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}));return descriptors.map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input2,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input2;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof Tensor&&f.dispose()),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)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input2,options=new SsdMobilenetv1Options){super();this.input=input2;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input:input2,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input3=>nets.tinyFaceDetector.locateFaces(input3,options):options instanceof SsdMobilenetv1Options?input3=>nets.ssdMobilenetv1.locateFaces(input3,options):options instanceof TinyYolov2Options?input3=>nets.tinyYolov2.locateFaces(input3,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input2)}runAndExtendWithFaceDetections(){return new Promise(async res=>{let detections=await this.run();return res(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async res=>{let detection=await this.run();return res(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input2,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input2,options)}function detectAllFaces(input2,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input2,options)}async function allFacesSsdMobilenetv1(input2,minConfidence){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input2,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input2,forwardParams={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input2,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+Math.pow(diff,2),0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count2=1,createUniqueLabel=()=>`person ${count2++}`;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){let 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(json18){let labeledDescriptors=json18.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json18.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{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)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var version3="0.8.9",node=typeof process!="undefined",browser=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version4={faceapi:version3,node,browser};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,createCanvas2 as createCanvas,createCanvasFromMedia,createFaceDetectionNet,createFaceRecognitionNet,createSsdMobilenetv1,createTinyFaceDetector,createTinyYolov2,detectAllFaces,detectFaceLandmarks,detectFaceLandmarksTiny,detectLandmarks,detectSingleFace,draw_exports as draw,env2 as 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,nonMaxSuppression2 as nonMaxSuppression,normalize,padToSquare,predictAgeAndGender,recognizeFaceExpressions,resizeResults,resolveInput,shuffleArray,sigmoid2 as sigmoid,ssdMobilenetv1,tfjs_esm_exports as tf,tinyFaceDetector,tinyYolov23 as tinyYolov2,toNetInput,utils_exports as utils,validateConfig,version4 as version};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.esm.js.map b/dist/face-api.esm.js.map
index 8b5a8aa..eaff116 100644
--- a/dist/face-api.esm.js.map
+++ b/dist/face-api.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
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"../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/types.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/engine.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/device_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/flags.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/types.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/print.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/progress.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/http.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/io.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/math.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js", 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"../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/squeeze.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/stack.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/step.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/strided_slice.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sub_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sub.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sum.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/tan.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/tile.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/to_bool.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/to_float.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/to_int.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/topk.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/unique.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/unsorted_segment_sum.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/unstack.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/where.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/zeros_like.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts", "../node_modules/@tensorflow/tfjs-core/src/index.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
-  "sourcesContent": ["\"use strict\";\n\n// ref: https://github.com/tc39/proposal-global\nvar getGlobal = function () {\n\t// the only reliable means to get the global object is\n\t// `Function('return this')()`\n\t// However, this causes CSP violations in Chrome apps.\n\tif (typeof self !== 'undefined') { return self; }\n\tif (typeof window !== 'undefined') { return window; }\n\tif (typeof global !== 'undefined') { return global; }\n\tthrow new Error('unable to locate global object');\n}\n\nvar global = getGlobal();\n\nmodule.exports = exports = global.fetch;\n\n// Needed for TypeScript and Webpack.\nif (global.fetch) {\n\texports.default = global.fetch.bind(global);\n}\n\nexports.Headers = global.Headers;\nexports.Request = global.Request;\nexports.Response = global.Response;", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n/**\n * This method asserts whether an object is a Promise instance.\n * @param object\n */\n// tslint:disable-next-line: no-any\nexport function isPromise(object) {\n    //  We chose to not use 'obj instanceOf Promise' for two reasons:\n    //  1. It only reliably works for es6 Promise, not other Promise\n    //  implementations.\n    //  2. It doesn't work with framework that uses zone.js. zone.js monkey patch\n    //  the async calls, so it is possible the obj (patched) is comparing to a\n    //  pre-patched Promise.\n    return object && object.then && typeof object.then === 'function';\n}\n//# sourceMappingURL=util_base.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isPromise } from './util_base';\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (isPromise(flagValue)) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const MirrorPad = 'MirrorPad';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport * as base from './util_base';\nexport * from './util_base';\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = base.flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        base.checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { isPromise } from '../util';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (isPromise(result)) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n                realTensor.dispose();\n                imageTensor.dispose();\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        save([$a, $b]);\n        const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n        const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n        const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n        const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n        const outerDimsA = $a.shape.slice(0, -2);\n        const outerDimsB = $b.shape.slice(0, -2);\n        const batchDimA = util.sizeFromShape(outerDimsA);\n        const batchDimB = util.sizeFromShape(outerDimsB);\n        const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n        util.assert($a.rank >= 2 && $b.rank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the ` +\n            `same or at least one input batch dimension must be 1. Got input ` +\n            `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n        util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n            `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n            `${$b.shape} and transposeA=${transposeA}` +\n            ` and transposeB=${transposeB} must match.`);\n        const outShapeOuterDims = batchDimA > batchDimB ? outerDimsA : outerDimsB;\n        const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n        const a3D = transposeA ?\n            reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n            reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n        const b3D = transposeB ?\n            reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n            reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n        const res3d = backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n        return reshape(res3d, outShape);\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = { transposeA, transposeB };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    const product = matMul(oneHotLabelsT, oneHotPredictions);\n    return cast(product, 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D, filter };\n    const attrs = { pad, strides, inputShape: xShape5D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { customGrad } from '../gradients';\nimport { Mean } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    const forward = () => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === $x.dtype ?\n            $x :\n            cast($x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        return sum(res, axis, keepDims);\n    };\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const value = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Mean, attrs);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MirrorPad } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` using mirror padding.\n *\n * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.\n *\n * ```js\n * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);\n * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * In \"reflect\" mode, the padded regions do not include the borders,\n * while in \"symmetric\" mode the padded regions do include the borders.\n * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,\n * then the output is `[1, 2, 3, 2, 1]` in \"reflect\" mode, and\n * `[1, 2, 3, 3, 2]` in \"symmetric\" mode.\n * If `mode` is \"reflect\" then both `paddings[D, 0]` and `paddings[D, 1]`\n * must be no greater than `x.shape[D] - 1`. If mode is \"symmetric\"\n * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than\n * `x.shape[D]`\n * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction mirrorPad_(x, paddings, mode) {\n    util.assert(mode === 'reflect' || mode === 'symmetric', () => `Invalid mode. Mode must be either reflect or symmetric. ` +\n        `Got ${mode}.`);\n    const $x = convertToTensor(x, 'x', 'mirrorPad');\n    if ($x.rank === 0) {\n        throw new Error('mirrorPad(scalar) is not defined. ' +\n            'Pass non-scalar to mirrorPad');\n    }\n    util.assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. ` +\n        `Got ${paddings.length}.`);\n    const shapeOffset = mode === 'reflect' ? 1 : 0;\n    for (let i = 0; i < $x.rank; i++) {\n        util.assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n        util.assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&\n            paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal ` +\n            `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` +\n            `shape ${$x.shape}`);\n    }\n    const attrs = { paddings, mode };\n    const inputs = { x: $x };\n    return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nexport const mirrorPad = op({ mirrorPad_ });\n//# sourceMappingURL=mirror_pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,\n * refer to `tf.mirrorPad`\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    if ($x.dtype === 'bool') {\n        // bool is not an allowed type for the underlying kernel.\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// 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", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations = [1, 1], dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        return backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dimRoundingMode, dilations, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter, attrs);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations = [1, 1], dimRoundingMode) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computeConv2DInfo(xShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        return backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dimRoundingMode, dilations, inputShape: xShape };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a single 3D image or a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mirrorPad } from './mirror_pad';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, dy: dy5D };\n    const attrs = { strides, pad, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad, dilations, dimRoundingMode),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad, dilations, dimRoundingMode),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return dx;\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const x = saved[0];\n        const y = saved[1];\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n        return {\n            x: () => {\n                return maxGrad['x']();\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n        return {\n            x: () => {\n                return minGrad['x']();\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MirrorPad } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const mirrorPadGradConfig = {\n    kernelName: MirrorPad,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=MirrorPad_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { mirrorPadGradConfig } from './gradients/MirrorPad_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    mirrorPadGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mirrorPad } from '../../ops/mirror_pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mirrorPad = function (paddings, mode) {\n    this.throwIfDisposed();\n    return mirrorPad(this, paddings, mode);\n};\n//# sourceMappingURL=mirror_pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mirror_pad';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context, resourceManager) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context, resourceManager));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor from tensorsMap based on input name.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function getTensor(name, tensorsMap, context, resourceManager) {\n    const [nodeName, index] = parseNodeName(name);\n    if (resourceManager != null) {\n        const tensor = resourceManager.getHashTableHandleByName(nodeName);\n        if (tensor != null) {\n            return tensor;\n        }\n    }\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "export const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "export const json = [\n    {\n        'tfOpName': 'HashTable',\n        'category': 'hash_table',\n        'inputs': [],\n        'attrs': [\n            { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' },\n            {\n                'tfName': 'use_node_name_sharing',\n                'name': 'useNodeNameSharing',\n                'type': 'bool'\n            },\n            { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' },\n            { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' },\n        ]\n    },\n    {\n        'tfOpName': 'HashTableV2',\n        'category': 'hash_table',\n        'inputs': [],\n        'attrs': [\n            { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' },\n            {\n                'tfName': 'use_node_name_sharing',\n                'name': 'useNodeNameSharing',\n                'type': 'bool'\n            },\n            { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' },\n            { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' },\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableImport',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'values', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableImportV2',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'values', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableFind',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableFindV2',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=hash_table.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'MirrorPad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'mode', 'name': 'mode', 'type': 'string' }]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as hashTable from './op_list/hash_table';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation, hashTable\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { keep, scalar, stack, tidy, unstack, util } from '@tensorflow/tfjs-core';\n/**\n * Hashtable contains a set of tensors, which can be accessed by key.\n */\nexport class HashTable {\n    /**\n     * Constructor of HashTable. Creates a hash table.\n     *\n     * @param keyDType `dtype` of the table keys.\n     * @param valueDType `dtype` of the table values.\n     */\n    constructor(keyDType, valueDType) {\n        this.keyDType = keyDType;\n        this.valueDType = valueDType;\n        this.handle = scalar(0);\n        // tslint:disable-next-line: no-any\n        this.tensorMap = new Map();\n        keep(this.handle);\n    }\n    get id() {\n        return this.handle.id;\n    }\n    /**\n     * Dispose the tensors and handle and clear the hashtable.\n     */\n    clearAndClose() {\n        this.tensorMap.forEach(value => value.dispose());\n        this.tensorMap.clear();\n        this.handle.dispose();\n    }\n    /**\n     * The number of items in the hash table.\n     */\n    size() {\n        return this.tensorMap.size;\n    }\n    /**\n     * Replaces the contents of the table with the specified keys and values.\n     * @param keys Keys to store in the hashtable.\n     * @param values Values to store in the hashtable.\n     */\n    async import(keys, values) {\n        this.checkKeyAndValueTensor(keys, values);\n        // We only store the primitive values of the keys, this allows lookup\n        // to be O(1).\n        const $keys = await keys.data();\n        // Clear the hashTable before inserting new values.\n        this.tensorMap.forEach(value => value.dispose());\n        this.tensorMap.clear();\n        return tidy(() => {\n            const $values = unstack(values);\n            const keysLength = $keys.length;\n            const valuesLength = $values.length;\n            util.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ` +\n                `${keysLength} elements, the values has ${valuesLength} ` +\n                `elements.`);\n            for (let i = 0; i < keysLength; i++) {\n                const key = $keys[i];\n                const value = $values[i];\n                keep(value);\n                this.tensorMap.set(key, value);\n            }\n            return this.handle;\n        });\n    }\n    /**\n     * Looks up keys in a hash table, outputs the corresponding values.\n     *\n     * Performs batch lookups, for every element in the key tensor, `find`\n     * stacks the corresponding value into the return tensor.\n     *\n     * If an element is not present in the table, the given `defaultValue` is\n     * used.\n     *\n     * @param keys Keys to look up. Must have the same type as the keys of the\n     *     table.\n     * @param defaultValue The scalar `defaultValue` is the value output for keys\n     *     not present in the table. It must also be of the same type as the\n     *     table values.\n     */\n    async find(keys, defaultValue) {\n        this.checkKeyAndValueTensor(keys, defaultValue);\n        const $keys = await keys.data();\n        return tidy(() => {\n            const result = [];\n            for (let i = 0; i < $keys.length; i++) {\n                const key = $keys[i];\n                const value = this.findWithDefault(key, defaultValue);\n                result.push(value);\n            }\n            return stack(result);\n        });\n    }\n    // tslint:disable-next-line: no-any\n    findWithDefault(key, defaultValue) {\n        const result = this.tensorMap.get(key);\n        return result != null ? result : defaultValue;\n    }\n    checkKeyAndValueTensor(key, value) {\n        if (key.dtype !== this.keyDType) {\n            throw new Error(`Expect key dtype ${this.keyDType}, but got ` +\n                `${key.dtype}`);\n        }\n        if (value.dtype !== this.valueDType) {\n            throw new Error(`Expect value dtype ${this.valueDType}, but got ` +\n                `${value.dtype}`);\n        }\n    }\n}\n//# sourceMappingURL=hash_table.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { HashTable } from '../../executor/hash_table';\nimport { getParamValue } from './utils';\nexport const executeOp = async (node, tensorMap, context, resourceManager) => {\n    switch (node.op) {\n        case 'HashTable':\n        case 'HashTableV2': {\n            const keyDType = getParamValue('keyDType', node, tensorMap, context);\n            const valueDType = getParamValue('valueDType', node, tensorMap, context);\n            const hashTable = new HashTable(keyDType, valueDType);\n            resourceManager.addHashTable(node.name, hashTable);\n            return [hashTable.handle];\n        }\n        case 'LookupTableImport':\n        case 'LookupTableImportV2': {\n            const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n            const keys = getParamValue('keys', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const hashTable = resourceManager.getHashTableById(handle.id);\n            return [await hashTable.import(keys, values)];\n        }\n        case 'LookupTableFind':\n        case 'LookupTableFindV2': {\n            const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n            const keys = getParamValue('keys', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            const hashTable = resourceManager.getHashTableById(handle.id);\n            return [await hashTable.find(keys, defaultValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'hash_table';\n//# sourceMappingURL=hash_table_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'MirrorPad': {\n            return [tfOps.mirrorPad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('mode', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as hashTable from './executors/hash_table_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function executeOp(node, tensorMap, context, resourceManager) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'hash_table':\n                return hashTable.executeOp(node, tensorMap, context, resourceManager);\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (tfc.util.isPromise(value)) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nconst HASH_TABLE_OPS = [\n    'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',\n    'LookupTableFind', 'LookupTableFindV2'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\nexport function isHashTable(node) {\n    return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    /**\n     * Set `ResourceManager` shared by executors of a model.\n     * @param resourceManager: `ResourceManager` of the `GraphModel`.\n     */\n    set resourceManager(resourceManager) {\n        this._resourceManager = resourceManager;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs Optional. output node name from the Tensorflow model, if\n     * no outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context, this._resourceManager);\n                    if (util.isPromise(tensors)) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs Optional. output node name from the Tensorflow model,\n     * if no outputs are specified, the default outputs of the model would be\n     * used. You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     * @param isFunctionExecution Optional. Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param outputNames Optional. output node name from the Tensorflow model,\n     * if no outputs are specified, the default outputs of the model would be\n     * used. You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);\n        // First nodes to execute include inputNodes, weights, and initNodes.\n        const stack = [\n            ...inputNodes, ...this.graph.weights, ...(this._initNodes || [])\n        ].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not in the tensorMap yet, this include\n            // inputNodes and internal initNodes.\n            if (tensorMap[item.node.name] == null) {\n                const tensors = executeOp(item.node, tensorMap, context, this._resourceManager);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (util.isPromise(tensors)) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * Contains global resources of a model.\n */\nexport class ResourceManager {\n    constructor(hashTableNameToHandle = {}, hashTableMap = {}) {\n        this.hashTableNameToHandle = hashTableNameToHandle;\n        this.hashTableMap = hashTableMap;\n    }\n    /**\n     * Register a `HashTable` in the resource manager.\n     *\n     * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,\n     * where id is the table handle tensor's id.\n     *\n     * @param name Op node name that creates the `HashTable`.\n     * @param hashTable The `HashTable` to be added to resource manager.\n     */\n    addHashTable(name, hashTable) {\n        this.hashTableNameToHandle[name] = hashTable.handle;\n        this.hashTableMap[hashTable.id] = hashTable;\n    }\n    /**\n     * Get the table handle by node name.\n     * @param name Op node name that creates the `HashTable`. This name is also\n     *     used in the inputs list of lookup and import `HashTable` ops.\n     */\n    getHashTableHandleByName(name) {\n        return this.hashTableNameToHandle[name];\n    }\n    /**\n     * Get the actual `HashTable` by its handle tensor's id.\n     * @param id The id of the handle tensor.\n     */\n    getHashTableById(id) {\n        return this.hashTableMap[id];\n    }\n    /**\n     * Dispose `ResourceManager`, including its hashTables and tensors in them.\n     */\n    dispose() {\n        for (const key in this.hashTableMap) {\n            this.hashTableMap[key].clearAndClose();\n            delete this.hashTableMap[key];\n        }\n        for (const name in this.hashTableNameToHandle) {\n            this.hashTableNameToHandle[name].dispose();\n            delete this.hashTableNameToHandle[name];\n        }\n    }\n}\n//# sourceMappingURL=resource_manager.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nimport { ResourceManager } from './resource_manager';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n        this.resourceManager = new ResourceManager();\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        // Attach a model-level resourceManager to each executor to share resources,\n        // such as `HashTable`.\n        this.executor.resourceManager = this.resourceManager;\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            // Attach a model-level resourceManager to the initializer, the\n            // hashTables created from when executing the initializer will be stored\n            // in the resourceManager.\n            this.initializer.resourceManager = this.resourceManager;\n            this.initializer.executeAsync({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors and resourceManager.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n        this.resourceManager.dispose();\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (tf.util.isPromise(value)) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        let outId;\n        if (dtype === 'string' && values != null && values.length > 0 &&\n            util.isString(values[0])) {\n            const encodedValues = values.map(d => util.encodeString(d));\n            outId = this.write(encodedValues, shape, dtype);\n        }\n        else {\n            outId = this.write(values, shape, dtype);\n        }\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            // TODO(lina128): Use fill kernel directly once this kernel is\n            // modularized.\n            return tf.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const abs = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: abs,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceil = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceil,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const exp = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: exp,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floor = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floor,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const log = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: log,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrt = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrt,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared functionality among backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { notEqualImpl } from './kernels/NotEqual';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { squaredDifferenceImpl } from './kernels/SquaredDifference';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const elu = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: elu,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nconst preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\nexport function prelu(args) {\n    const { inputs, backend } = args;\n    const { x, alpha } = inputs;\n    assertNotComplex([x, alpha], 'prelu');\n    const aVals = backend.data.get(x.dataId).values;\n    const bVals = backend.data.get(alpha.dataId).values;\n    const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, x.dtype);\n    return backend.makeTensorInfo(resultShape, x.dtype, resultData);\n}\nexport const preluConfig = {\n    kernelName: Prelu,\n    backendName: 'cpu',\n    kernelFunc: prelu,\n};\n//# sourceMappingURL=Prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const relu = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));\nexport const reluConfig = {\n    kernelName: Relu,\n    backendName: 'cpu',\n    kernelFunc: relu,\n};\n//# sourceMappingURL=Relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const relu6 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));\nexport const relu6Config = {\n    kernelName: Relu6,\n    backendName: 'cpu',\n    kernelFunc: relu6,\n};\n//# sourceMappingURL=Relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../kernels/Elu';\nimport { identity } from '../kernels/Identity';\nimport { prelu } from '../kernels/Prelu';\nimport { relu } from '../kernels/Relu';\nimport { relu6 } from '../kernels/Relu6';\nexport function applyActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return identity({ inputs: { x }, backend });\n    }\n    else if (activation === 'relu') {\n        return relu({ inputs: { x }, backend });\n    }\n    else if (activation === 'elu') {\n        return elu({ inputs: { x }, backend });\n    }\n    else if (activation === 'relu6') {\n        return relu6({ inputs: { x }, backend });\n    }\n    else if (activation === 'prelu') {\n        return prelu({ inputs: { x, alpha: preluActivationWeights }, backend });\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\n//# sourceMappingURL=fused_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul, buffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { reshape } from './Reshape';\nexport function batchMatMul(args) {\n    const { inputs, backend, attrs } = args;\n    const { a, b } = inputs;\n    const { transposeA, transposeB } = attrs;\n    assertNotComplex([a, b], 'matMul');\n    const aRank = a.shape.length;\n    const bRank = b.shape.length;\n    const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n    const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n    const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n    const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n    const outerDimsA = a.shape.slice(0, -2);\n    const outerDimsB = b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n    util.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the ` +\n        `same or at least one input batch dimension must be 1. Got input ` +\n        `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n    const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);\n    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +\n        `${b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] :\n        [batchDimA, outerShapeA, innerShapeA];\n    const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] :\n        [batchDimB, innerShapeB, outerShapeB];\n    // The rest of the implementation is designed to operate on rank-3 tensors\n    const a3d = reshape({ inputs: { x: a }, backend, attrs: { shape: a3dShape } });\n    const b3d = reshape({ inputs: { x: b }, backend, attrs: { shape: b3dShape } });\n    const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n    const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n    const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n    const batchDim = Math.max(batchDimA, batchDimB);\n    const a3dValues = backend.data.get(a3d.dataId).values;\n    const b3dValues = backend.data.get(b3d.dataId).values;\n    const a3dStrides = util.computeStrides(a3d.shape);\n    const b3dStrides = util.computeStrides(b3d.shape);\n    const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n        [a3dStrides[0], 1, a3dStrides[1]] :\n        [a3dStrides[0], a3dStrides[1], 1];\n    const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n        [1, b3dStrides[1], b3dStrides[0]] :\n        [b3dStrides[1], 1, b3dStrides[0]];\n    const size = leftDim * rightDim;\n    const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n    const resVals = result.values;\n    const blockSize = backend.blockSize;\n    for (let bi = 0; bi < batchDim; bi++) {\n        for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n            for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                    // for when blockSize doesn't evenly divide the input\n                    const iBlock = Math.min(i0 + blockSize, leftDim);\n                    const jBlock = Math.min(j0 + blockSize, rightDim);\n                    const kBlock = Math.min(k0 + blockSize, sharedDim);\n                    for (let i = i0; i < iBlock; i++) {\n                        for (let j = j0; j < jBlock; j++) {\n                            let sum = 0.0;\n                            for (let k = k0; k < kBlock; k++) {\n                                const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;\n                                const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;\n                                const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];\n                                const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];\n                                sum += aVal * bVal;\n                            }\n                            resVals[bi * size + (i * rightDim + j)] += sum;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    backend.disposeIntermediateTensorInfo(a3d);\n    backend.disposeIntermediateTensorInfo(b3d);\n    // set correct shape on output.\n    return backend.makeTensorInfo(outShape, result.dtype, result.values);\n}\nexport const batchMatMulConfig = {\n    kernelName: BatchMatMul,\n    backendName: 'cpu',\n    kernelFunc: batchMatMul,\n};\n//# sourceMappingURL=BatchMatMul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { _FusedMatMul } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { batchMatMul } from './BatchMatMul';\nexport function _fusedMatMul(args) {\n    const { inputs, backend, attrs } = args;\n    const { a, b, bias, preluActivationWeights } = inputs;\n    const { transposeA, transposeB, activation } = attrs;\n    let current;\n    let addRes;\n    let activationRes;\n    const intermediates = [];\n    const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend });\n    current = matMulRes;\n    if (bias) {\n        addRes = add({ inputs: { a: current, b: bias }, backend });\n        intermediates.push(current);\n        current = addRes;\n    }\n    if (activation) {\n        activationRes =\n            applyActivation(backend, current, activation, preluActivationWeights);\n        intermediates.push(current);\n        current = activationRes;\n    }\n    for (const i of intermediates) {\n        backend.disposeIntermediateTensorInfo(i);\n    }\n    return current;\n}\nexport const _fusedMatMulConfig = {\n    kernelName: _FusedMatMul,\n    backendName: 'cpu',\n    kernelFunc: _fusedMatMul,\n};\n//# sourceMappingURL=_FusedMatMul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acos = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acos,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosh = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acosh,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asin = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asin,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinh = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinh,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atan = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atan,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanh = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanh,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNorm(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNorm,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clip = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clip,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis: $axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis: $axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2D, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode } = attrs;\n    assertNotComplex([x, filter], 'conv2d');\n    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const xBatchStride = xStrides[0];\n    const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];\n    const xColStride = isChannelsLast ? xStrides[2] : 1;\n    const xChannelStride = isChannelsLast ? 1 : xStrides[1];\n    const yBatchStride = y.strides[0];\n    const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n    const yColStride = isChannelsLast ? y.strides[2] : 1;\n    const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xBatchStride;\n        const yOffset1 = b * yBatchStride;\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset2 = yOffset1 + yR * yRowStride;\n            const xRCorner = yR * convInfo.strideHeight - padTop;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const xR = xRCorner + wR * dilationHeight;\n                if (xR < 0 || xR >= convInfo.inHeight) {\n                    continue;\n                }\n                const wOffset1 = wR * filterStrides[0];\n                const xOffset2 = xOffset1 + xR * xRowStride;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const yOffset3 = yOffset2 + yC * yColStride;\n                    const xCCorner = yC * convInfo.strideWidth - padLeft;\n                    for (let wC = 0; wC < filterWidth; ++wC) {\n                        const xC = xCCorner + wC * dilationWidth;\n                        if (xC < 0 || xC >= convInfo.inWidth) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wC * filterStrides[1];\n                        const xOffset3 = xOffset2 + xC * xColStride;\n                        let wOffset3 = wOffset2;\n                        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                            const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                yVals[yOffset3 + d2 * yChannelStride] +=\n                                    xVal * wVals[wOffset3 + d2];\n                            }\n                            wOffset3 += convInfo.outChannels;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, yVals);\n}\nexport const conv2DConfig = {\n    kernelName: Conv2D,\n    backendName: 'cpu',\n    kernelFunc: conv2D\n};\n//# sourceMappingURL=Conv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2DBackpropFilter, TensorBuffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2DBackpropFilter(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, pad, dataFormat, dimRoundingMode, filterShape } = attrs;\n    assertNotComplex([x, dy], 'conv2dBackpropFilter');\n    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad, dimRoundingMode, false /* depthwise */, $dataFormat);\n    const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n    const dW = new TensorBuffer(convInfo.filterShape, 'float32');\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const xVals = backend.data.get(x.dataId).values;\n    const dyVals = backend.data.get(dy.dataId).values;\n    const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n    const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n    for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n        for (let wC = 0; wC < filterWidth; ++wC) {\n            const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n            const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                if (isChannelsLast) {\n                                    dotProd += xBuf.get(b, xR, xC, d1) *\n                                        dyBuf.get(b, yR, yC, d2);\n                                }\n                                else {\n                                    dotProd += xBuf.get(b, d1, xR, xC) *\n                                        dyBuf.get(b, d2, yR, yC);\n                                }\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, d2);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nexport const conv2DBackpropFilterConfig = {\n    kernelName: Conv2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: conv2DBackpropFilter\n};\n//# sourceMappingURL=Conv2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2DBackpropInput, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2DBackpropInput(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { inputShape, strides, pad, dataFormat, dimRoundingMode } = attrs;\n    assertNotComplex([dy, filter], 'conv2dBackpropInput');\n    const filterStrides = util.computeStrides(filter.shape);\n    const dyStrides = util.computeStrides(dy.shape);\n    let $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2] = filterStrides;\n    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n    $dataFormat = convInfo.dataFormat;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const isChannelsLast = $dataFormat === 'channelsLast';\n    const xBatchStride = dx.strides[0];\n    const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n    const xColStride = isChannelsLast ? dx.strides[2] : 1;\n    const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n    const yBatchStride = dyStrides[0];\n    const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n    const yColStride = isChannelsLast ? dyStrides[2] : 1;\n    const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            for (let xR = 0; xR < inHeight; ++xR) {\n                const xRCorner = xR - topPad;\n                const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                for (let xC = 0; xC < inWidth; ++xC) {\n                    const xCCorner = xC - leftPad;\n                    const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                    const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                    let dotProd = 0;\n                    for (let yR = xRMin; yR < yRMax; ++yR) {\n                        const wR = yR * strideHeight - xRCorner;\n                        for (let yC = xCMin; yC < yCMax; ++yC) {\n                            const wC = yC * strideWidth - xCCorner;\n                            const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                            const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                            for (let d2 = 0; d2 < outChannels; ++d2) {\n                                const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                const weight = fltValues[fltOffset + d2];\n                                dotProd += pixel * weight;\n                            }\n                        }\n                    }\n                    const dxOffset = xBatchStride * b + xRowStride * xR +\n                        xColStride * xC + xChannelStride * d1;\n                    dxValues[dxOffset] = dotProd;\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const conv2DBackpropInputConfig = {\n    kernelName: Conv2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: conv2DBackpropInput\n};\n//# sourceMappingURL=Conv2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3D, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dilations } = attrs;\n    assertNotComplex([x, filter], 'conv3d');\n    const convInfo = backend_util.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad);\n    const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;\n    const padFront = padInfo.front;\n    const padLeft = padInfo.left;\n    const padTop = padInfo.top;\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xStrides[0];\n        const yOffset1 = b * y.strides[0];\n        for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n            const yOffset2 = yOffset1 + yF * y.strides[1];\n            const xFCorner = yF * convInfo.strideDepth - padFront;\n            for (let wF = 0; wF < filterDepth; ++wF) {\n                const xF = xFCorner + wF * dilationDepth;\n                if (xF < 0 || xF >= convInfo.inDepth) {\n                    continue;\n                }\n                const wOffset1 = wF * filterStrides[0];\n                const xOffset2 = xOffset1 + xF * xStrides[1];\n                for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                    const yOffset3 = yOffset2 + yR * y.strides[2];\n                    const xRCorner = yR * convInfo.strideHeight - padTop;\n                    for (let wR = 0; wR < filterHeight; ++wR) {\n                        const xR = xRCorner + wR * dilationHeight;\n                        if (xR < 0 || xR >= convInfo.inHeight) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wR * filterStrides[1];\n                        const xOffset3 = xOffset2 + xR * xStrides[2];\n                        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                            const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                            const xCCorner = yC * convInfo.strideWidth - padLeft;\n                            for (let wC = 0; wC < filterWidth; ++wC) {\n                                const xC = xCCorner + wC * dilationWidth;\n                                if (xC < 0 || xC >= convInfo.inWidth) {\n                                    continue;\n                                }\n                                const wOffset3 = wOffset2 + wC * filterStrides[2];\n                                const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                let wOffset4 = wOffset3;\n                                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                    const xVal = xVals[xOffset4 + d1];\n                                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                        yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                    }\n                                    wOffset4 += convInfo.outChannels;\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nexport const conv3DConfig = {\n    kernelName: Conv3D,\n    backendName: 'cpu',\n    kernelFunc: conv3D\n};\n//# sourceMappingURL=Conv3D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3DBackpropFilterV2, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3DBackpropFilterV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, pad, filterShape } = attrs;\n    assertNotComplex([x, dy], 'conv3dBackpropFilterV2');\n    const xStrides = util.computeStrides(x.shape);\n    const dyStrides = util.computeStrides(dy.shape);\n    const convInfo = backend_util.computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad);\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dw = new TensorBuffer(convInfo.filterShape, 'float32');\n    const dwValues = dw.values;\n    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n    const xValues = backend.data.get(x.dataId).values;\n    const [xS0, xS1, xS2, xS3] = xStrides;\n    const frontPad = convInfo.padInfo.front;\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    for (let wF = 0; wF < filterDepth; ++wF) {\n        const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n        const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n        const wOffset1 = wF * dwS0;\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            const wOffset2 = wR * dwS1 + wOffset1;\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                const wOffset3 = wC * dwS2 + wOffset2;\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    const wOffset4 = d1 * dwS3 + wOffset3;\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            const xOffset1 = b * xS0;\n                            const yOffset1 = b * dyS0;\n                            for (let yF = yFMin; yF < yFMax; ++yF) {\n                                const xF = wF + yF * strideDepth - frontPad;\n                                const xOffset2 = xF * xS1 + xOffset1;\n                                const yOffset2 = yF * dyS1 + yOffset1;\n                                for (let yR = yRMin; yR < yRMax; ++yR) {\n                                    const xR = wR + yR * strideHeight - topPad;\n                                    const xOffset3 = xR * xS2 + xOffset2;\n                                    const yOffset3 = yR * dyS2 + yOffset2;\n                                    for (let yC = yCMin; yC < yCMax; ++yC) {\n                                        const xC = wC + yC * strideWidth - leftPad;\n                                        const xOffset4 = xC * xS3 + xOffset3;\n                                        const yOffset4 = yC * dyS3 + yOffset3;\n                                        dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                    }\n                                }\n                            }\n                        }\n                        dwValues[wOffset4 + d2] = dotProd;\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\nexport const conv3DBackpropFilterV2Config = {\n    kernelName: Conv3DBackpropFilterV2,\n    backendName: 'cpu',\n    kernelFunc: conv3DBackpropFilterV2\n};\n//# sourceMappingURL=Conv3DBackpropFilterV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3DBackpropInputV2, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3DBackpropInputV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { pad, strides, inputShape } = attrs;\n    assertNotComplex([dy], 'conv3dBackpropInputV2');\n    const dyStrides = util.computeStrides(dy.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const convInfo = backend_util.computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n    const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n    const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            // Frames of depth\n            for (let xF = 0; xF < inDepth; ++xF) {\n                const xFCorner = xF - frontPad;\n                const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                // Rows as per standard 2d matrix notation\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    // Columns as per standard 2d matrix notation\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yF = xFMin; yF < yFMax; ++yF) {\n                            const wF = yF * strideDepth - xFCorner;\n                            for (let yR = xRMin; yR < yRMax; ++yR) {\n                                const wR = yR * strideHeight - xRCorner;\n                                for (let yC = xCMin; yC < yCMax; ++yC) {\n                                    const wC = yC * strideWidth - xCCorner;\n                                    const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                    const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                        fltS1 * (filterHeight - 1 - wR) +\n                                        fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                    for (let d2 = 0; d2 < outChannels; ++d2) {\n                                        const pixel = dyValues[dyOffset + d2];\n                                        const weight = fltValues[fltOffset + d2];\n                                        dotProd += pixel * weight;\n                                    }\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                            dotProd;\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const conv3DBackpropInputV2Config = {\n    kernelName: Conv3DBackpropInputV2,\n    backendName: 'cpu',\n    kernelFunc: conv3DBackpropInputV2\n};\n//# sourceMappingURL=Conv3DBackpropInputV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cos = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cos,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosh = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: cosh,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNative, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNative(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dilations, dimRoundingMode } = attrs;\n    assertNotComplex([x, filter], 'depthwiseConv2DNative');\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    let $dilations = dilations;\n    if ($dilations == null) {\n        $dilations = [1, 1];\n    }\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n        `1. Got strides ${strides} and dilations '${$dilations}'`);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n    const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;\n    const padLeft = padInfo.left;\n    const padTop = padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xStrides[0];\n        const yOffset1 = b * y.strides[0];\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset2 = yOffset1 + yR * y.strides[1];\n            const xRCorner = yR * convInfo.strideHeight - padLeft;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const xR = xRCorner + wR * dilationHeight;\n                if (xR < 0 || xR >= convInfo.inHeight) {\n                    continue;\n                }\n                const wOffset1 = wR * filterStrides[0];\n                const xOffset2 = xOffset1 + xR * xStrides[1];\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const yOffset3 = yOffset2 + yC * y.strides[2];\n                    const xCCorner = yC * convInfo.strideWidth - padTop;\n                    for (let wC = 0; wC < filterWidth; ++wC) {\n                        const xC = xCCorner + wC * dilationWidth;\n                        if (xC < 0 || xC >= convInfo.inWidth) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wC * filterStrides[1];\n                        const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                        let yOffset4 = yOffset3;\n                        let wOffset3 = wOffset2;\n                        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                            const xVal = xVals[xOffset3 + d1];\n                            for (let q = 0; q < chMul; ++q) {\n                                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                            }\n                            yOffset4 += chMul;\n                            wOffset3 += chMul;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nexport const depthwiseConv2dNativeConfig = {\n    kernelName: DepthwiseConv2dNative,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNative\n};\n//# sourceMappingURL=DepthwiseConv2dNative.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNativeBackpropFilter, TensorBuffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNativeBackpropFilter(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, dilations, pad, dimRoundingMode, filterShape } = attrs;\n    assertNotComplex([x, dy], 'depthwiseConv2dNativeBackpropFilter');\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n    const dW = new TensorBuffer(convInfo.filterShape, 'float32');\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const xVals = backend.data.get(x.dataId).values;\n    const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n    const dyVals = backend.data.get(dy.dataId).values;\n    const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n    for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n        for (let wC = 0; wC < filterWidth; ++wC) {\n            const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n            const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                const d1 = Math.trunc(d2 / chMul);\n                const dm = d2 % chMul;\n                let dotProd = 0;\n                for (let b = 0; b < convInfo.batchSize; ++b) {\n                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                        const xR = wR + yR * strideHeight - topPad;\n                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                            const xC = wC + yC * strideWidth - leftPad;\n                            dotProd += xBuf.get(b, xR, xC, d1) *\n                                dyBuf.get(b, yR, yC, d2);\n                        }\n                    }\n                }\n                dW.set(dotProd, wR, wC, d1, dm);\n            }\n        }\n    }\n    return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nexport const depthwiseConv2dNativeBackpropFilterConfig = {\n    kernelName: DepthwiseConv2dNativeBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNativeBackpropFilter\n};\n//# sourceMappingURL=DepthwiseConv2dNativeBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNativeBackpropInput, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNativeBackpropInput(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { strides, dilations, pad, dimRoundingMode, inputShape } = attrs;\n    assertNotComplex([dy, filter], 'depthwiseConv2DNativeBackpropInput');\n    const dyStrides = util.computeStrides(dy.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const convInfo = backend_util.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2] = dyStrides;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2] = filterStrides;\n    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const chMul = outChannels / inChannels;\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            for (let xR = 0; xR < inHeight; ++xR) {\n                const xRCorner = xR - topPad;\n                const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                for (let xC = 0; xC < inWidth; ++xC) {\n                    const xCCorner = xC - leftPad;\n                    const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                    const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                    let dotProd = 0;\n                    for (let yR = xRMin; yR < yRMax; ++yR) {\n                        const wR = yR * strideHeight - xRCorner;\n                        for (let yC = xCMin; yC < yCMax; ++yC) {\n                            const wC = yC * strideWidth - xCCorner;\n                            const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                            const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                            for (let dm = 0; dm < chMul; ++dm) {\n                                const d2 = d1 * chMul + dm;\n                                const pixel = dyValues[dyOffset + d2];\n                                const weight = fltValues[fltOffset + dm];\n                                dotProd += pixel * weight;\n                            }\n                        }\n                    }\n                    dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const depthwiseConv2dNativeBackpropInputConfig = {\n    kernelName: DepthwiseConv2dNativeBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNativeBackpropInput\n};\n//# sourceMappingURL=DepthwiseConv2dNativeBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erf = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erf,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Fill, util } from '@tensorflow/tfjs-core';\nexport function fill(args) {\n    const { backend, attrs } = args;\n    const { shape, value, dtype } = attrs;\n    const $dtype = dtype || util.inferDtype(value);\n    const values = util.getArrayFromDType($dtype, util.sizeFromShape(shape));\n    fillValues(values, value, $dtype);\n    return backend.makeTensorInfo(shape, $dtype, values);\n}\nexport const fillConfig = {\n    kernelName: Fill,\n    backendName: 'cpu',\n    kernelFunc: fill\n};\nfunction fillValues(values, value, dtype) {\n    if (dtype === 'string') {\n        values.fill(value);\n    }\n    else {\n        values.fill(value);\n    }\n}\n//# sourceMappingURL=Fill.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedConv2D } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { conv2D } from './Conv2D';\nexport function fusedConv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter, bias, preluActivationWeights } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation } = attrs;\n    let result = conv2D({\n        inputs: { x, filter },\n        backend,\n        attrs: { strides, pad, dataFormat, dilations, dimRoundingMode }\n    });\n    if (bias) {\n        const resultOld = result;\n        result = add({ inputs: { a: result, b: bias }, backend });\n        backend.disposeIntermediateTensorInfo(resultOld);\n    }\n    if (activation) {\n        const resultOld = result;\n        result =\n            applyActivation(backend, result, activation, preluActivationWeights);\n        backend.disposeIntermediateTensorInfo(resultOld);\n    }\n    return result;\n}\nexport const fusedConv2DConfig = {\n    kernelName: FusedConv2D,\n    backendName: 'cpu',\n    kernelFunc: fusedConv2D\n};\n//# sourceMappingURL=FusedConv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedDepthwiseConv2D } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { depthwiseConv2dNative } from './DepthwiseConv2dNative';\nexport function fusedDepthwiseConv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter, bias, preluActivationWeights } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation } = attrs;\n    let result = depthwiseConv2dNative({\n        inputs: { x, filter },\n        backend,\n        attrs: { strides, pad, dataFormat, dilations, dimRoundingMode }\n    });\n    if (bias) {\n        const oldResult = result;\n        result = add({ inputs: { a: result, b: bias }, backend });\n        backend.disposeIntermediateTensorInfo(oldResult);\n    }\n    if (activation) {\n        const oldResult = result;\n        result =\n            applyActivation(backend, result, activation, preluActivationWeights);\n        backend.disposeIntermediateTensorInfo(oldResult);\n    }\n    return result;\n}\nexport const fusedDepthwiseConv2DConfig = {\n    kernelName: FusedDepthwiseConv2D,\n    backendName: 'cpu',\n    kernelFunc: fusedDepthwiseConv2D\n};\n//# sourceMappingURL=FusedDepthwiseConv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFinite = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFinite,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInf = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInf,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaN = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaN,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1p = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1p,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNot = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNot,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MirrorPad, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function mirrorPad(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    assertNotComplex(x, 'mirrorPad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const end = paddings.map((p, i) => p[0] + x.shape[i]);\n    const offset = mode === 'reflect' ? 0 : 1;\n    const xVals = backend.data.get(x.dataId).values;\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    for (let i = 0; i < resultSize; i++) {\n        let coords = util.indexToLoc(i, resultRank, resultStrides);\n        for (let i = 0; i < resultRank; i++) {\n            if (coords[i] < start[i]) {\n                coords[i] = start[i] * 2 - coords[i] - offset;\n            }\n            else if (coords[i] >= end[i]) {\n                coords[i] = (end[i] - 1) * 2 - coords[i] + offset;\n            }\n        }\n        coords = coords.map((c, i) => c - start[i]);\n        const inIndex = util.locToIndex(coords, xRank, xStrides);\n        resVals[i] = xVals[inIndex];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const mirrorPadConfig = {\n    kernelName: MirrorPad,\n    backendName: 'cpu',\n    kernelFunc: mirrorPad\n};\n//# sourceMappingURL=MirrorPad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocal = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocal,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const round = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: round,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const selu = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: selu,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoid = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoid,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sign = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: sign,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sin = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sin,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinh = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinh,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplus = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplus,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrt = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrt,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const step = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: step,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tan = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tan,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanh = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanh,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { _fusedMatMulConfig } from './kernels/_FusedMatMul';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchMatMulConfig } from './kernels/BatchMatMul';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { conv2DConfig } from './kernels/Conv2D';\nimport { conv2DBackpropFilterConfig } from './kernels/Conv2DBackpropFilter';\nimport { conv2DBackpropInputConfig } from './kernels/Conv2DBackpropInput';\nimport { conv3DConfig } from './kernels/Conv3D';\nimport { conv3DBackpropFilterV2Config } from './kernels/Conv3DBackpropFilterV2';\nimport { conv3DBackpropInputV2Config } from './kernels/Conv3DBackpropInputV2';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { depthwiseConv2dNativeConfig } from './kernels/DepthwiseConv2dNative';\nimport { depthwiseConv2dNativeBackpropFilterConfig } from './kernels/DepthwiseConv2dNativeBackpropFilter';\nimport { depthwiseConv2dNativeBackpropInputConfig } from './kernels/DepthwiseConv2dNativeBackpropInput';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { fillConfig } from './kernels/Fill';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { fusedConv2DConfig } from './kernels/FusedConv2D';\nimport { fusedDepthwiseConv2DConfig } from './kernels/FusedDepthwiseConv2D';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { mirrorPadConfig } from './kernels/MirrorPad';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { preluConfig } from './kernels/Prelu';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reluConfig } from './kernels/Relu';\nimport { relu6Config } from './kernels/Relu6';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    _fusedMatMulConfig,\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchMatMulConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    conv2DBackpropFilterConfig,\n    conv2DBackpropInputConfig,\n    conv2DConfig,\n    conv3DBackpropFilterV2Config,\n    conv3DBackpropInputV2Config,\n    conv3DConfig,\n    cosConfig,\n    coshConfig,\n    depthwiseConv2dNativeConfig,\n    depthwiseConv2dNativeBackpropFilterConfig,\n    depthwiseConv2dNativeBackpropInputConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    fillConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    fusedConv2DConfig,\n    fusedDepthwiseConv2DConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    mirrorPadConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    preluConfig,\n    realConfig,\n    reciprocalConfig,\n    reluConfig,\n    relu6Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function clearWebGLContext(webGLVersion) {\n    delete contexts[webGLVersion];\n}\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        let batchASnippet = 'rc.x';\n        let batchBSnippet = 'rc.x';\n        if (aShape[0] < bShape[0]) {\n            batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;\n        }\n        else if (bShape[0] < aShape[0]) {\n            batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          int batchA = ${batchASnippet};\n          int batchB = ${batchBSnippet};\n          vec4 a = getMatrixA(batchA, ${aSample});\n          vec4 b = getMatrixB(batchB, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { div, engine, env, max, range, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, {\n            shape,\n            dtype,\n            values,\n            usage: TextureUsage.UPLOAD,\n            refCount: 1,\n            complexParentRefCount: 0\n        });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, {\n            shape,\n            dtype,\n            values,\n            usage: TextureUsage.UPLOAD,\n            refCount: 1,\n            complexParentRefCount: 0\n        });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensorInfos, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensorInfos, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([\n                this.read(complexTensorInfos.real.dataId),\n                this.read(complexTensorInfos.imag.dataId)\n            ]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        // Trying to dispose a textureData that has a 'kept' refCount, e.g. trying\n        // to dispose a tensor whose data bucket is shared with a complex tensor. In\n        // this case we are removing a reference to the textureData, but we\n        // shouldn't actually dispose the texture.\n        if (this.texData.get(dataId).complexParentRefCount > 0) {\n            this.texData.get(dataId).refCount--;\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensorInfos } = this.texData.get(dataId);\n        if (complexTensorInfos != null) {\n            this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--;\n            this.disposeIntermediateTensorInfo(complexTensorInfos.real);\n            this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--;\n            this.disposeIntermediateTensorInfo(complexTensorInfos.imag);\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const batch = Math.max(a.shape[0], b.shape[0]);\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            // TODO(annxingyuan): Call multiply directly as part of batchMatMul\n            // modularization.\n            const product = tf.mul(a3D, b3D);\n            return product.sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, b.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const batch = Math.max(a.shape[0], b.shape[0]);\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, b.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(annxingyuan): Call sub directly as part of softmax kernel\n        // modularization.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, w2Row.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\n/**\n * In WebGL data is stored in GPU textures which can't be efficiently copied, so\n * complex tensors share data with their real and imaginary components. Complex\n * tensors increment the `complexParentRefCount` properties of the underlying\n * data buckets to prevent them from being disposed, as the engine's disposal\n * logic does not account for data sharing by complex tensors.\n *\n * When a complex tensor is disposed, it will explicitly decrease the\n * `complexParentRefCount` properties of its underlying components.\n */\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.texData.get(complexInfo.dataId);\n    const realTensorInfo = identity({ inputs: { x: real }, backend });\n    const realData = backend.texData.get(realTensorInfo.dataId);\n    realData.complexParentRefCount++;\n    const imagTensorInfo = identity({ inputs: { x: imag }, backend });\n    const imagData = backend.texData.get(imagTensorInfo.dataId);\n    imagData.complexParentRefCount++;\n    complex.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'webgl',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, upcastType } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { complex } from '../kernels/Complex';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        if (supportsComplex && a.dtype === 'complex64') {\n            const aData = webglBackend.texData.get(a.dataId);\n            const bData = webglBackend.texData.get(b.dataId);\n            const [real, imag] = [\n                [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n                [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n            ].map(complexParts => {\n                const [aPart, bPart] = complexParts;\n                const aHandle = {\n                    dataId: aPart.dataId,\n                    dtype: aPart.dtype,\n                    shape: a.shape\n                };\n                const bHandle = {\n                    dataId: bPart.dataId,\n                    dtype: bPart.dtype,\n                    shape: b.shape\n                };\n                const program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n                return webglBackend.runWebGLProgram(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n            });\n            const complexOutput = complex({ inputs: { real, imag }, backend: webglBackend });\n            webglBackend.disposeIntermediateTensorInfo(real);\n            webglBackend.disposeIntermediateTensorInfo(imag);\n            // TODO(annxingyuan): Implement CPU forwarding for complex inputs.\n            return complexOutput;\n        }\n        const $dtype = dtype || upcastType(a.dtype, b.dtype);\n        if (webglBackend.shouldExecuteOnCPU([a, b]) && cpuKernelImpl != null) {\n            const aData = webglBackend.texData.get(a.dataId);\n            const bData = webglBackend.texData.get(b.dataId);\n            const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, aData.values, bData.values, $dtype);\n            const out = webglBackend.makeTensorInfo(outShape, $dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n            return out;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') &&\n            packedOpSnippet != null;\n        let program;\n        if (shouldUsePackedProgram) {\n            program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);\n        }\n        else {\n            program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        }\n        return webglBackend.runWebGLProgram(program, [a, b], $dtype);\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nimport { addImplCPU as cpuAdd } from '../kernel_utils/shared';\nconst ADD = 'return a + b;';\nexport const addKernelFunc = binaryKernelFunc({\n    opSnippet: ADD,\n    packedOpSnippet: ADD,\n    supportsComplex: true,\n    cpuKernelImpl: cpuAdd\n});\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'webgl',\n    kernelFunc: addKernelFunc\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2 = binaryKernelFunc({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNorm = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNorm,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst NOT_EQUAL = `return float(a != b);`;\nexport const notEqual = binaryKernelFunc({ opSnippet: NOT_EQUAL, dtype: 'bool' });\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'webgl',\n    kernelFunc: notEqual,\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputData = backend.texData.get(input.dataId);\n    return identity({ inputs: { x: inputData.complexTensorInfos.real }, backend });\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'webgl',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnaryOpProgram } from '../unaryop_gpu';\nconst TO_INT = `return float(int(x));`;\nexport function int(input, backend) {\n    const program = new UnaryOpProgram(input.shape, TO_INT);\n    const output = backend.runWebGLProgram(program, [input], 'int32');\n    return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n//# sourceMappingURL=int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { notEqual } from './NotEqual';\nimport { real } from './Real';\nimport { int } from '../kernel_utils/int';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(annxingyuan): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        return int(x, backend);\n    }\n    if (dtype === 'bool') {\n        const zerosTensorInfo = backend.makeTensorInfo([], 'bool', util.getTypedArrayFromDType('bool', 1));\n        const binaryInputs = { a: x, b: zerosTensorInfo };\n        const result = notEqual({ inputs: binaryInputs, backend });\n        backend.disposeIntermediateTensorInfo(zerosTensorInfo);\n        return result;\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'webgl',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputData = backend.texData.get(input.dataId);\n    return identity({ inputs: { x: inputData.complexTensorInfos.imag }, backend });\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'webgl',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, env, util } from '@tensorflow/tfjs-core';\nimport { ConcatProgram } from '../concat_gpu';\nimport { ConcatPackedProgram } from '../concat_packed_gpu';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concatImpl(inputs, axis, backend) {\n    const dtype = inputs[0].dtype;\n    if (dtype === 'complex64') {\n        const reals = inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concatImpl(reals, axis, backend);\n        const imagConcated = concatImpl(imags, axis, backend);\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    if (inputs.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n        const midIndex = Math.floor(inputs.length / 2);\n        const leftSide = concatImpl(inputs.slice(0, midIndex), axis, backend);\n        const rightSide = concatImpl(inputs.slice(midIndex), axis, backend);\n        const result = concatImpl([leftSide, rightSide], axis, backend);\n        backend.disposeIntermediateTensorInfo(leftSide);\n        backend.disposeIntermediateTensorInfo(rightSide);\n        return result;\n    }\n    if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') &&\n        inputs[0].shape.length > 1) {\n        const program = new ConcatPackedProgram(inputs.map(t => t.shape), axis);\n        return backend.runWebGLProgram(program, inputs, dtype);\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), axis);\n    const tensors2D = inputs.map(x => reshape({\n        inputs: { x },\n        attrs: { shape: [-1, util.sizeFromShape(x.shape.slice(axis))] },\n        backend\n    }));\n    const program = new ConcatProgram(tensors2D.map(t => t.shape));\n    const result = backend.runWebGLProgram(program, tensors2D, dtype);\n    tensors2D.forEach(r => backend.disposeIntermediateTensorInfo(r));\n    const reshapedResult = reshape({ inputs: { x: result }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n}\n//# sourceMappingURL=Concat_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { concatImpl } from './Concat_impl';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    return concatImpl($inputs, $axis, backend);\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'webgl',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cos = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cos,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const div = binaryKernelFunc({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: div,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FFTProgram {\n    constructor(component, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        let opString;\n        if (component === 'real') {\n            opString = 'return real * expR - imag * expI;';\n        }\n        else if (component === 'imag') {\n            opString = 'return real * expI + imag * expR;';\n        }\n        else {\n            throw new Error(`FFT component must be either \"real\" or \"imag\", got ${component}.`);\n        }\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${opString}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FFTProgram } from '../fft_gpu';\nimport { complex } from './Complex';\nimport { reshape } from './Reshape';\nexport function fftImpl(x, inverse, backend) {\n    const xData = backend.texData.get(x.dataId);\n    const inputSize = util.sizeFromShape(x.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = x.shape[x.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({ inputs: { x }, backend, attrs: { shape: [batch, innerDimensionSize] } });\n    const xShape = input2D.shape;\n    const realProgram = new FFTProgram('real', xShape, inverse);\n    const imagProgram = new FFTProgram('imag', xShape, inverse);\n    const inputs = [\n        {\n            dataId: xData.complexTensorInfos.real.dataId,\n            dtype: xData.complexTensorInfos.real.dtype,\n            shape: xShape\n        },\n        {\n            dataId: xData.complexTensorInfos.imag.dataId,\n            dtype: xData.complexTensorInfos.imag.dtype,\n            shape: xShape\n        }\n    ];\n    const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');\n    const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');\n    const complexOutput = complex({ inputs: { real: realPart, imag: imagPart }, backend });\n    backend.disposeIntermediateTensorInfo(realPart);\n    backend.disposeIntermediateTensorInfo(imagPart);\n    const complexOutputReshaped = reshape({ inputs: { x: complexOutput }, backend, attrs: { shape: x.shape } });\n    backend.disposeIntermediateTensorInfo(complexOutputReshaped);\n    return complexOutputReshaped;\n}\n//# sourceMappingURL=FFT_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT } from '@tensorflow/tfjs-core';\nimport { fftImpl } from './FFT_impl';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    return fftImpl(input, false /* inverse */, backend);\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'webgl',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT } from '@tensorflow/tfjs-core';\nimport { fftImpl } from './FFT_impl';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    return fftImpl(input, true /* inverse */, backend);\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'webgl',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class MeanProgram {\n    constructor(reduceInfo, divisor) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `sumValue += dot(values, ones);`;\n        if (divisor != null) {\n            const denominator = 1 / divisor;\n            updateSnippet = `sumValue += dot(values * ${util.isInt(denominator) ? denominator.toPrecision(2) :\n                denominator}, ones);`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return 0.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${updateSnippet}\n        }\n        setOutput(sumValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mean_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { MeanProgram } from '../mean_gpu';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        let program;\n        let previousResult;\n        if (reductionType === 'mean') {\n            program = i === 0 ?\n                new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) :\n                new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });\n        }\n        else {\n            program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        }\n        previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeIntermediateTensorInfo(previousResult);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function meanImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, 'float32', 'mean', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Mean_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Mean, util } from '@tensorflow/tfjs-core';\nimport { meanImpl } from './Mean_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const meanConfig = {\n    kernelName: Mean,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { keepDims, axis } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const meanInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        const intermediates = [];\n        let meanInput = x;\n        if (meanInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(meanInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const meanInputData = webglBackend.texData.get(meanInput.dataId);\n                meanInputData.values = meanInputValues;\n            }\n            else {\n                meanInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            intermediates.push(meanInput);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);\n        const [meanOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(meanInput.shape, axes);\n        let outShape = meanOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(meanOutShape, origAxes);\n        }\n        const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);\n        for (const i of intermediates) {\n            webglBackend.disposeIntermediateTensorInfo(i);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class MirrorPadProgram {\n    constructor(xShape, paddings, mode) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        const offset = mode === 'reflect' ? 0 : 1;\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${offset};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${offset};\n          }\n          setOutput(getX(outC - start));\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${dtype} start = ${dtype}(${start});\n      ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outC = getOutputCoords();\n        for (int i = 0; i < ${rank}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${offset};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};\n          }\n        }\n        ${dtype} coords = outC - start;\n        setOutput(getX(${unpackedCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mirror_pad_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\n/**\n * Example shader code for\n * `mirrorPad(tf.tensor1d([1, 2, 3], 'int32'), [[2, 2]], 'reflect')`\n * ```\n *    const int start = int(2);\n *    const int end = int(5);\n *\n *    void main() {\n *       int outputLoc = getOutputCoords();\n *       vec4 result = vec4(0.);\n *\n *       int rc = outputLoc;\n *\n *       int source = rc;\n *       if (source < start) {\n *         source = start * 2 - source - 0;\n *       } else if (source >= end) {\n *         source = (end - 1) * 2 - source + 0;\n *       }\n *       source -= start;\n *\n *       result[0] = getChannel(getX(source), source);\n *       rc += 1;\n *       if(rc < 6) {\n *          int source = rc;\n *          if (source < start) {\n *            source = start * 2 - source - 0;\n *          } else if (source >= end) {\n *            source = (end - 1) * 2 - source + 0;\n *          }\n *          source -= start;\n *\n *         result[1] = getChannel(getX(source), source);\n *       }\n *\n *       setOutput(result);\n *     }\n * ```\n */\nexport class MirrorPadPackedProgram {\n    constructor(xShape, paddings, mode) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const offset = mode === 'reflect' ? 0 : 1;\n        let mainLoop = '';\n        if (rank === 1) {\n            const padSetup = `\n        ${dtype} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${offset};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${offset};\n        }\n        source -= start;\n      `;\n            mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        else {\n            const padSetup = `\n        ${dtype} source = rc;\n        ${dtype} lt = ${dtype}(lessThan(source, start));\n        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));\n        ${dtype} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${offset}) +\n                gte * ((end - 1) * 2 - source + ${offset});\n        source -= start;\n      `;\n            mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n        rc = outputLoc;\n        ${coords[rank - 2]} += 1;\n        if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {\n          ${padSetup}\n          result[2] = getChannel(getX(${source.join()}), ${innerDims});\n          ${coords[rank - 1]} += 1;\n          if(${cLimit}) {\n            ${padSetup}\n            result[3] = getChannel(getX(${source.join()}), ${innerDims});\n          }\n        }\n      `;\n        }\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mirror_pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, MirrorPad } from '@tensorflow/tfjs-core';\nimport { MirrorPadProgram } from '../mirror_pad_gpu';\nimport { MirrorPadPackedProgram } from '../mirror_pad_packed_gpu';\nexport const mirrorPadKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new MirrorPadPackedProgram(x.shape, paddings, mode) :\n        new MirrorPadProgram(x.shape, paddings, mode);\n    const output = backend.runWebGLProgram(program, [x], x.dtype);\n    return output;\n};\nexport const mirrorPadConfig = {\n    kernelName: MirrorPad,\n    backendName: 'webgl',\n    kernelFunc: mirrorPadKernelFunc,\n};\n//# sourceMappingURL=MirrorPad.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, env, Multiply } from '@tensorflow/tfjs-core';\nimport * as binaryop_complex_gpu from '../binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from '../binaryop_complex_gpu';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { multiplyImplCPU as cpuMultiply } from '../kernel_utils/shared';\nimport { complex } from './Complex';\nconst MUL = 'return a * b;';\nexport function multiply(args) {\n    const { inputs, backend } = args;\n    const { a, b } = inputs;\n    const dtype = backend_util.upcastType(a.dtype, b.dtype);\n    if (a.dtype === 'complex64') {\n        const aData = backend.texData.get(a.dataId);\n        const bData = backend.texData.get(b.dataId);\n        const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n        const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n        const inputs = [\n            {\n                dataId: aData.complexTensorInfos.real.dataId,\n                dtype: aData.complexTensorInfos.real.dtype,\n                shape: a.shape\n            },\n            {\n                dataId: aData.complexTensorInfos.imag.dataId,\n                dtype: aData.complexTensorInfos.imag.dtype,\n                shape: a.shape\n            },\n            {\n                dataId: bData.complexTensorInfos.real.dataId,\n                dtype: bData.complexTensorInfos.real.dtype,\n                shape: b.shape\n            },\n            {\n                dataId: bData.complexTensorInfos.imag.dataId,\n                dtype: bData.complexTensorInfos.imag.dtype,\n                shape: b.shape\n            }\n        ];\n        const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');\n        const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');\n        const complexOutput = complex({ inputs: { real: realPart, imag: imagPart }, backend });\n        backend.disposeIntermediateTensorInfo(realPart);\n        backend.disposeIntermediateTensorInfo(imagPart);\n        // TODO(annxingyuan): CPU forwarding for complex inputs.\n        return complexOutput;\n    }\n    if (backend.shouldExecuteOnCPU([a, b])) {\n        const aData = backend.texData.get(a.dataId);\n        const bData = backend.texData.get(b.dataId);\n        const [outValues, outShape] = cpuMultiply(a.shape, b.shape, aData.values, bData.values, dtype);\n        const out = backend.makeTensorInfo(outShape, dtype);\n        const outData = backend.texData.get(out.dataId);\n        outData.values = outValues;\n        return out;\n    }\n    let program;\n    if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n        program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);\n    }\n    else {\n        program = new BinaryOpProgram(MUL, a.shape, b.shape);\n    }\n    return backend.runWebGLProgram(program, [a, b], dtype);\n}\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'webgl',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sin = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sin,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const square = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: square,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifference = binaryKernelFunc({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifference,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nimport { subImplCPU as cpuSub } from '../kernel_utils/shared';\nconst SUB = 'return a - b;';\nexport const subKernelFunc = binaryKernelFunc({\n    opSnippet: SUB,\n    packedOpSnippet: SUB,\n    supportsComplex: true,\n    cpuKernelImpl: cpuSub\n});\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'webgl',\n    kernelFunc: subKernelFunc\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tan = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tan,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { addConfig } from './kernels/Add';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { meanConfig } from './kernels/Mean';\nimport { mirrorPadConfig } from './kernels/MirrorPad';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { realConfig } from './kernels/Real';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    addConfig,\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    divConfig,\n    fftConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    meanConfig,\n    mirrorPadConfig,\n    multiplyConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    realConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    subConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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"../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
+  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "", "", "// 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", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, "", "// 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", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, "", "// 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", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, "", "// 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", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: 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shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '../dist/tfjs.esm.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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diff --git a/dist/face-api.esm.json b/dist/face-api.esm.json
new file mode 100644
index 0000000..c4210c9
--- /dev/null
+++ b/dist/face-api.esm.json
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+        "src/tinyYolov2/TinyYolov2Options.ts": {
+          "bytesInOutput": 901
+        },
+        "src/tinyYolov2/TinyYolov2Base.ts": {
+          "bytesInOutput": 5791
+        },
+        "src/tinyYolov2/TinyYolov2.ts": {
+          "bytesInOutput": 836
+        },
+        "src/tinyYolov2/index.ts": {
+          "bytesInOutput": 139
+        },
+        "src/tinyFaceDetector/TinyFaceDetectorOptions.ts": {
+          "bytesInOutput": 132
+        },
+        "src/globalApi/ComposableTask.ts": {
+          "bytesInOutput": 155
+        },
+        "src/globalApi/extractFacesAndComputeResults.ts": {
+          "bytesInOutput": 772
+        },
+        "src/tinyFaceDetector/const.ts": {
+          "bytesInOutput": 215
+        },
+        "src/tinyFaceDetector/TinyFaceDetector.ts": {
+          "bytesInOutput": 665
+        },
+        "src/globalApi/nets.ts": {
+          "bytesInOutput": 1532
+        },
+        "src/globalApi/PredictFaceExpressionsTask.ts": {
+          "bytesInOutput": 1703
+        },
+        "src/globalApi/PredictAgeAndGenderTask.ts": {
+          "bytesInOutput": 1797
+        },
+        "src/globalApi/ComputeFaceDescriptorsTasks.ts": {
+          "bytesInOutput": 1419
+        },
+        "src/globalApi/DetectFaceLandmarksTasks.ts": {
+          "bytesInOutput": 1863
+        },
+        "src/globalApi/DetectFacesTasks.ts": {
+          "bytesInOutput": 2229
+        },
+        "src/globalApi/detectFaces.ts": {
+          "bytesInOutput": 228
+        },
+        "src/globalApi/allFaces.ts": {
+          "bytesInOutput": 624
+        },
+        "src/globalApi/index.ts": {
+          "bytesInOutput": 0
+        },
+        "src/euclideanDistance.ts": {
+          "bytesInOutput": 277
+        },
+        "src/globalApi/FaceMatcher.ts": {
+          "bytesInOutput": 1852
+        },
+        "src/tinyFaceDetector/index.ts": {
+          "bytesInOutput": 109
+        },
+        "src/resizeResults.ts": {
+          "bytesInOutput": 824
+        },
+        "package.json": {
+          "bytesInOutput": 20
+        }
+      },
+      "bytes": 1900836
+    }
+  }
+}
diff --git a/dist/face-api.esm.nobundle.js b/dist/face-api.esm.nobundle.js
deleted file mode 100644
index 77821d9..0000000
--- a/dist/face-api.esm.nobundle.js
+++ /dev/null
@@ -1,4635 +0,0 @@
-var __create = Object.create;
-var __defProp = Object.defineProperty;
-var __getProtoOf = Object.getPrototypeOf;
-var __hasOwnProp = Object.prototype.hasOwnProperty;
-var __getOwnPropNames = Object.getOwnPropertyNames;
-var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
-var __markAsModule = (target) => __defProp(target, "__esModule", {value: true});
-var __commonJS = (callback, module) => () => {
-  if (!module) {
-    module = {exports: {}};
-    callback(module.exports, module);
-  }
-  return module.exports;
-};
-var __export = (target, all) => {
-  __markAsModule(target);
-  for (var name in all)
-    __defProp(target, name, {get: all[name], enumerable: true});
-};
-var __exportStar = (target, module, desc) => {
-  __markAsModule(target);
-  if (module && typeof module === "object" || typeof module === "function") {
-    for (let key of __getOwnPropNames(module))
-      if (!__hasOwnProp.call(target, key) && key !== "default")
-        __defProp(target, key, {get: () => module[key], enumerable: !(desc = __getOwnPropDesc(module, key)) || desc.enumerable});
-  }
-  return target;
-};
-var __toModule = (module) => {
-  if (module && module.__esModule)
-    return module;
-  return __exportStar(__defProp(module != null ? __create(__getProtoOf(module)) : {}, "default", {value: module, enumerable: true}), module);
-};
-
-// src/env/isNodejs.ts
-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;
-  }
-});
-
-// src/index.ts
-import * as tf42 from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/draw/index.ts
-var 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
-});
-
-// src/draw/drawContour.ts
-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();
-}
-
-// src/utils/index.ts
-var 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/dist/tf.es2017.js";
-
-// src/classes/Dimensions.ts
-var Dimensions = class {
-  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);
-  }
-};
-
-// src/utils/index.ts
-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;
-}
-
-// src/classes/Point.ts
-var Point = class {
-  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));
-  }
-};
-
-// src/classes/Box.ts
-var Box = class {
-  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`);
-    }
-  }
-  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;
-  }
-  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();
-  }
-};
-
-// src/classes/BoundingBox.ts
-var BoundingBox = class extends Box {
-  constructor(left, top, right, bottom, allowNegativeDimensions = false) {
-    super({left, top, right, bottom}, allowNegativeDimensions);
-  }
-};
-
-// src/classes/ObjectDetection.ts
-var ObjectDetection = class {
-  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});
-  }
-};
-
-// src/classes/FaceDetection.ts
-var FaceDetection = class 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);
-  }
-};
-
-// src/ops/iou.ts
-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);
-}
-
-// src/ops/minBbox.ts
-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);
-}
-
-// src/ops/nonMaxSuppression.ts
-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;
-}
-
-// src/ops/normalize.ts
-import {
-  concat,
-  fill,
-  sub,
-  tidy
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-function normalize(x, meanRgb) {
-  return tidy(() => {
-    const [r, g, b] = meanRgb;
-    const avg_r = fill([...x.shape.slice(0, 3), 1], r, "float32");
-    const avg_g = fill([...x.shape.slice(0, 3), 1], g, "float32");
-    const avg_b = fill([...x.shape.slice(0, 3), 1], b, "float32");
-    const avg_rgb = concat([avg_r, avg_g, avg_b], 3);
-    return sub(x, avg_rgb);
-  });
-}
-
-// src/ops/padToSquare.ts
-import {
-  cast,
-  concat as concat2,
-  fill as fill2,
-  tidy as tidy2
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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, "float32");
-    };
-    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);
-  });
-}
-
-// src/ops/shuffleArray.ts
-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;
-}
-
-// src/ops/index.ts
-function sigmoid(x) {
-  return 1 / (1 + Math.exp(-x));
-}
-function inverseSigmoid(x) {
-  return Math.log(x / (1 - x));
-}
-
-// src/classes/Rect.ts
-var Rect = class extends Box {
-  constructor(x, y, width, height, allowNegativeDimensions = false) {
-    super({x, y, width, height}, allowNegativeDimensions);
-  }
-};
-
-// src/classes/FaceLandmarks.ts
-var relX = 0.5;
-var relY = 0.43;
-var relScale = 0.45;
-var FaceLandmarks = class {
-  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");
-  }
-};
-
-// src/classes/FaceLandmarks5.ts
-var FaceLandmarks5 = class extends FaceLandmarks {
-  getRefPointsForAlignment() {
-    const pts = this.positions;
-    return [
-      pts[0],
-      pts[1],
-      getCenterPoint([pts[3], pts[4]])
-    ];
-  }
-};
-
-// src/classes/FaceLandmarks68.ts
-var FaceLandmarks68 = class 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);
-  }
-};
-
-// src/classes/FaceMatch.ts
-var FaceMatch = class {
-  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)})` : ""}`;
-  }
-};
-
-// src/classes/LabeledBox.ts
-var LabeledBox = class extends Box {
-  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`);
-    }
-  }
-  constructor(box, label) {
-    super(box);
-    this._label = label;
-  }
-  get label() {
-    return this._label;
-  }
-};
-
-// src/classes/LabeledFaceDescriptors.ts
-var LabeledFaceDescriptors = class {
-  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);
-  }
-};
-
-// src/classes/PredictedBox.ts
-var PredictedBox = class extends LabeledBox {
-  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]`);
-    }
-  }
-  constructor(box, label, score, classScore) {
-    super(box, label);
-    this._score = score;
-    this._classScore = classScore;
-  }
-  get score() {
-    return this._score;
-  }
-  get classScore() {
-    return this._classScore;
-  }
-};
-
-// src/factories/WithFaceDetection.ts
-function isWithFaceDetection(obj) {
-  return obj["detection"] instanceof FaceDetection;
-}
-function extendWithFaceDetection(sourceObj, detection) {
-  const extension = {detection};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// src/env/createBrowserEnv.ts
-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
-  };
-}
-
-// src/env/createFileSystem.ts
-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
-  };
-}
-
-// src/env/createNodejsEnv.ts
-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
-  };
-}
-
-// src/env/isBrowser.ts
-function isBrowser() {
-  return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
-}
-
-// src/env/index.ts
-var isNodejs = __toModule(require_isNodejs());
-var 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;
-}
-var env = {
-  getEnv,
-  setEnv,
-  initialize,
-  createBrowserEnv,
-  createFileSystem,
-  createNodejsEnv,
-  monkeyPatch,
-  isBrowser,
-  isNodejs: isNodejs.isNodejs
-};
-initialize();
-
-// src/dom/resolveInput.ts
-function resolveInput(arg) {
-  if (!env.isNodejs() && typeof arg === "string") {
-    return document.getElementById(arg);
-  }
-  return arg;
-}
-
-// src/dom/getContext2dOrThrow.ts
-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;
-}
-
-// src/draw/DrawTextField.ts
-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 = {}));
-var DrawTextFieldOptions = class {
-  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;
-  }
-};
-var DrawTextField = class {
-  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);
-    });
-  }
-};
-
-// src/draw/DrawBox.ts
-var DrawBoxOptions = class {
-  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));
-  }
-};
-var DrawBox = class {
-  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);
-    }
-  }
-};
-
-// src/draw/drawDetections.ts
-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);
-  });
-}
-
-// src/faceExpressionNet/FaceExpressionNet.ts
-import {
-  softmax,
-  tidy as tidy12,
-  unstack
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/dom/isMediaLoaded.ts
-function isMediaLoaded(media) {
-  const {Image, Video} = env.getEnv();
-  return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3;
-}
-
-// src/dom/awaitMediaLoaded.ts
-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);
-  });
-}
-
-// src/dom/bufferToImage.ts
-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);
-  });
-}
-
-// src/dom/getMediaDimensions.ts
-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);
-}
-
-// src/dom/createCanvas.ts
-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;
-}
-
-// src/dom/imageTensorToCanvas.ts
-import {
-  browser,
-  tidy as tidy3
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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;
-}
-
-// src/dom/isMediaElement.ts
-function isMediaElement(input) {
-  const {Image, Canvas, Video} = env.getEnv();
-  return input instanceof Image || input instanceof Canvas || input instanceof Video;
-}
-
-// src/dom/NetInput.ts
-import {
-  Tensor as Tensor2,
-  browser as browser2,
-  cast as cast2,
-  image,
-  stack,
-  tidy as tidy4
-} from "@tensorflow/tfjs-core";
-
-// src/dom/imageToSquare.ts
-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;
-}
-
-// src/dom/NetInput.ts
-var NetInput = class {
-  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;
-    });
-  }
-};
-
-// src/dom/toNetInput.ts
-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));
-}
-
-// src/dom/extractFaces.ts
-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;
-  });
-}
-
-// src/dom/extractFaceTensors.ts
-import {
-  slice3d,
-  tidy as tidy5
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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;
-  });
-}
-
-// src/dom/fetchOrThrow.ts
-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;
-}
-
-// src/dom/fetchImage.ts
-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: ${res.url}`);
-  }
-  return bufferToImage(blob);
-}
-
-// src/dom/fetchJson.ts
-async function fetchJson(uri) {
-  return (await fetchOrThrow(uri)).json();
-}
-
-// src/dom/fetchNetWeights.ts
-async function fetchNetWeights(uri) {
-  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());
-}
-
-// src/dom/loadWeightMap.ts
-import {
-  io
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/common/getModelUris.ts
-function getModelUris(uri, defaultModelName) {
-  const defaultManifestFilename = `${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}`
-  };
-}
-
-// src/dom/loadWeightMap.ts
-async function loadWeightMap(uri, defaultModelName) {
-  const {manifestUri, modelBaseUri} = getModelUris(uri, defaultModelName);
-  let manifest = await fetchJson(manifestUri);
-  return io.loadWeights(manifest, modelBaseUri);
-}
-
-// src/dom/matchDimensions.ts
-function matchDimensions(input, reference, useMediaDimensions = false) {
-  const {width, height} = useMediaDimensions ? getMediaDimensions(reference) : reference;
-  input.width = width;
-  input.height = height;
-  return {width, height};
-}
-
-// src/faceFeatureExtractor/FaceFeatureExtractor.ts
-import {
-  avgPool,
-  cast as cast3,
-  scalar,
-  tidy as tidy9
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/NeuralNetwork.ts
-import {
-  Tensor as Tensor3,
-  Variable,
-  io as io2,
-  tensor
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-var NeuralNetwork = class {
-  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};
-  }
-};
-
-// src/faceFeatureExtractor/denseBlock.ts
-import {
-  add as add2,
-  conv2d,
-  relu,
-  tidy as tidy7
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/common/depthwiseSeparableConv.ts
-import {
-  add,
-  separableConv2d,
-  tidy as tidy6
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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;
-  });
-}
-
-// src/faceFeatureExtractor/denseBlock.ts
-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))));
-  });
-}
-
-// src/common/convLayer.ts
-import {
-  add as add3,
-  conv2d as conv2d2,
-  relu as relu2,
-  tidy as tidy8
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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;
-  });
-}
-
-// src/common/disposeUnusedWeightTensors.ts
-function disposeUnusedWeightTensors(weightMap, paramMappings) {
-  Object.keys(weightMap).forEach((path) => {
-    if (!paramMappings.some((pm) => pm.originalPath === path)) {
-      weightMap[path].dispose();
-    }
-  });
-}
-
-// src/common/extractConvParamsFactory.ts
-import {
-  tensor1d,
-  tensor4d
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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: `${mappedPrefix}/bias`});
-    return {filters, bias};
-  };
-}
-
-// src/common/extractFCParamsFactory.ts
-import {
-  tensor1d as tensor1d2,
-  tensor2d
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-function extractFCParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, mappedPrefix) {
-    const fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-    const fc_bias = tensor1d2(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
-    return {
-      weights: fc_weights,
-      bias: fc_bias
-    };
-  };
-}
-
-// src/common/extractSeparableConvParamsFactory.ts
-import {
-  tensor1d as tensor1d3,
-  tensor4d as tensor4d2
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/common/types.ts
-var SeparableConvParams = class {
-  constructor(depthwise_filter, pointwise_filter, bias) {
-    this.depthwise_filter = depthwise_filter;
-    this.pointwise_filter = pointwise_filter;
-    this.bias = bias;
-  }
-};
-
-// src/common/extractSeparableConvParamsFactory.ts
-function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_filter = tensor4d2(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-    const pointwise_filter = tensor4d2(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-    const bias = tensor1d3(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${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(`${prefix}/bias`, 1);
-    return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-  };
-}
-
-// src/common/extractWeightEntryFactory.ts
-function extractWeightEntryFactory(weightMap, paramMappings) {
-  return function(originalPath, paramRank, mappedPath) {
-    const tensor2 = weightMap[originalPath];
-    if (!isTensor(tensor2, paramRank)) {
-      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
-    }
-    paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-    return tensor2;
-  };
-}
-
-// src/common/extractWeightsFactory.ts
-function extractWeightsFactory(weights) {
-  let remainingWeights = weights;
-  function extractWeights(numWeights) {
-    const ret = remainingWeights.slice(0, numWeights);
-    remainingWeights = remainingWeights.slice(numWeights);
-    return ret;
-  }
-  function getRemainingWeights() {
-    return remainingWeights;
-  }
-  return {
-    extractWeights,
-    getRemainingWeights
-  };
-}
-
-// src/faceFeatureExtractor/extractorsFactory.ts
-function extractorsFactory(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-  function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${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
-  };
-}
-
-// src/faceFeatureExtractor/extractParams.ts
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {dense0, dense1, dense2, dense3}
-  };
-}
-
-// src/common/loadConvParamsFactory.ts
-function loadConvParamsFactory(extractWeightEntry) {
-  return function(prefix) {
-    const filters = extractWeightEntry(`${prefix}/filters`, 4);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {filters, bias};
-  };
-}
-
-// src/faceFeatureExtractor/loadParamsFactory.ts
-function loadParamsFactory(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  function extractDenseBlock3Params(prefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(`${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
-  };
-}
-
-// src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
-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};
-}
-
-// src/faceFeatureExtractor/FaceFeatureExtractor.ts
-var FaceFeatureExtractor = class extends NeuralNetwork {
-  constructor() {
-    super("FaceFeatureExtractor");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("FaceFeatureExtractor - load model before inference");
-    }
-    return tidy9(() => {
-      const batchTensor = cast3(input.toBatchTensor(112, true), "float32");
-      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);
-  }
-};
-
-// src/faceProcessor/FaceProcessor.ts
-import {
-  tidy as tidy11
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/common/fullyConnectedLayer.ts
-import {
-  add as add4,
-  matMul,
-  tidy as tidy10
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-function fullyConnectedLayer(x, params) {
-  return tidy10(() => add4(matMul(x, params.weights), params.bias));
-}
-
-// src/faceProcessor/extractParams.ts
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {fc}
-  };
-}
-
-// src/faceProcessor/extractParamsFromWeigthMap.ts
-function extractParamsFromWeigthMap3(weightMap) {
-  const paramMappings = [];
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractFcParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/weights`, 2);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {weights, bias};
-  }
-  const params = {
-    fc: extractFcParams("fc")
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// src/faceProcessor/util.ts
-function seperateWeightMaps(weightMap) {
-  const featureExtractorMap = {};
-  const classifierMap = {};
-  Object.keys(weightMap).forEach((key) => {
-    const map = key.startsWith("fc") ? classifierMap : featureExtractorMap;
-    map[key] = weightMap[key];
-  });
-  return {featureExtractorMap, classifierMap};
-}
-
-// src/faceProcessor/FaceProcessor.ts
-var FaceProcessor = class extends NeuralNetwork {
-  constructor(_name, faceFeatureExtractor) {
-    super(_name);
-    this._faceFeatureExtractor = faceFeatureExtractor;
-  }
-  get faceFeatureExtractor() {
-    return this._faceFeatureExtractor;
-  }
-  runNet(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error(`${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);
-  }
-};
-
-// src/faceExpressionNet/FaceExpressions.ts
-var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"];
-var FaceExpressions = class {
-  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);
-  }
-};
-
-// src/faceExpressionNet/FaceExpressionNet.ts
-var FaceExpressionNet = class 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;
-  }
-};
-
-// src/factories/WithFaceExpressions.ts
-function isWithFaceExpressions(obj) {
-  return obj["expressions"] instanceof FaceExpressions;
-}
-function extendWithFaceExpressions(sourceObj, expressions) {
-  const extension = {expressions};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// src/draw/drawFaceExpressions.ts
-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);
-  });
-}
-
-// src/factories/WithFaceLandmarks.ts
-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);
-}
-
-// src/draw/DrawFaceLandmarks.ts
-var DrawFaceLandmarksOptions = class {
-  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)";
-  }
-};
-var DrawFaceLandmarks = class {
-  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);
-  });
-}
-
-// src/ageGenderNet/AgeGenderNet.ts
-import {
-  avgPool as avgPool2,
-  softmax as softmax2,
-  tidy as tidy14,
-  unstack as unstack2
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/xception/TinyXception.ts
-import {
-  add as add5,
-  cast as cast4,
-  conv2d as conv2d3,
-  maxPool,
-  relu as relu3,
-  scalar as scalar2,
-  tidy as tidy13
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/xception/extractParams.ts
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {entry_flow, middle_flow, exit_flow}
-  };
-}
-
-// src/xception/extractParamsFromWeigthMap.ts
-function loadParamsFactory3(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  function extractReductionBlockParams(mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(`${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};
-}
-
-// src/xception/TinyXception.ts
-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;
-}
-var TinyXception = class 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 = cast4(input.toBatchTensor(112, true), "float32");
-      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);
-  }
-};
-
-// src/ageGenderNet/extractParams.ts
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {fc: {age, gender}}
-  };
-}
-
-// src/ageGenderNet/extractParamsFromWeigthMap.ts
-function extractParamsFromWeigthMap7(weightMap) {
-  const paramMappings = [];
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractFcParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/weights`, 2);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {weights, bias};
-  }
-  const params = {
-    fc: {
-      age: extractFcParams("fc/age"),
-      gender: extractFcParams("fc/gender")
-    }
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// src/ageGenderNet/types.ts
-var Gender;
-(function(Gender2) {
-  Gender2["FEMALE"] = "female";
-  Gender2["MALE"] = "male";
-})(Gender || (Gender = {}));
-
-// src/ageGenderNet/AgeGenderNet.ts
-var AgeGenderNet = class extends NeuralNetwork {
-  constructor(faceFeatureExtractor = new TinyXception(2)) {
-    super("AgeGenderNet");
-    this._faceFeatureExtractor = faceFeatureExtractor;
-  }
-  get faceFeatureExtractor() {
-    return this._faceFeatureExtractor;
-  }
-  runNet(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error(`${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);
-  }
-};
-
-// src/faceLandmarkNet/FaceLandmark68NetBase.ts
-import {
-  fill as fill3,
-  stack as stack2,
-  tidy as tidy15,
-  unstack as unstack3
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-var FaceLandmark68NetBase = class 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, "float32"), fill3([68], fillY, "float32")], 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, "float32")).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;
-  }
-};
-
-// src/faceLandmarkNet/FaceLandmark68Net.ts
-var FaceLandmark68Net = class extends FaceLandmark68NetBase {
-  constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
-    super("FaceLandmark68Net", faceFeatureExtractor);
-  }
-  getDefaultModelName() {
-    return "face_landmark_68_model";
-  }
-  getClassifierChannelsIn() {
-    return 256;
-  }
-};
-
-// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
-import {
-  avgPool as avgPool3,
-  cast as cast5,
-  scalar as scalar3,
-  tidy as tidy16
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
-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};
-}
-
-// src/faceFeatureExtractor/extractParamsTiny.ts
-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}
-  };
-}
-
-// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
-var TinyFaceFeatureExtractor = class extends NeuralNetwork {
-  constructor() {
-    super("TinyFaceFeatureExtractor");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("TinyFaceFeatureExtractor - load model before inference");
-    }
-    return tidy16(() => {
-      const batchTensor = cast5(input.toBatchTensor(112, true), "float32");
-      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);
-  }
-};
-
-// src/faceLandmarkNet/FaceLandmark68TinyNet.ts
-var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase {
-  constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) {
-    super("FaceLandmark68TinyNet", faceFeatureExtractor);
-  }
-  getDefaultModelName() {
-    return "face_landmark_68_tiny_model";
-  }
-  getClassifierChannelsIn() {
-    return 128;
-  }
-};
-
-// src/faceLandmarkNet/index.ts
-var FaceLandmarkNet = class extends FaceLandmark68Net {
-};
-
-// src/faceRecognitionNet/FaceRecognitionNet.ts
-import {
-  cast as cast6,
-  matMul as matMul2,
-  maxPool as maxPool2,
-  scalar as scalar4,
-  tidy as tidy18,
-  unstack as unstack4
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/faceRecognitionNet/convLayer.ts
-import {
-  add as add7,
-  conv2d as conv2d4,
-  relu as relu4
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/faceRecognitionNet/scaleLayer.ts
-import {
-  add as add6,
-  mul
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-function scale(x, params) {
-  return add6(mul(x, params.weights), params.biases);
-}
-
-// src/faceRecognitionNet/convLayer.ts
-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");
-}
-
-// src/faceRecognitionNet/extractParams.ts
-import {
-  tensor1d as tensor1d4,
-  tensor2d as tensor2d2,
-  tensor4d as tensor4d3,
-  tidy as tidy17,
-  transpose
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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: ${getRemainingWeights().length}`);
-  }
-  const params = {
-    conv32_down,
-    conv32_1,
-    conv32_2,
-    conv32_3,
-    conv64_down,
-    conv64_1,
-    conv64_2,
-    conv64_3,
-    conv128_down,
-    conv128_1,
-    conv128_2,
-    conv256_down,
-    conv256_1,
-    conv256_2,
-    conv256_down_out,
-    fc
-  };
-  return {params, paramMappings};
-}
-
-// src/faceRecognitionNet/extractParamsFromWeigthMap.ts
-function extractorsFactory6(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractScaleLayerParams(prefix) {
-    const weights = extractWeightEntry(`${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};
-}
-
-// src/faceRecognitionNet/residualLayer.ts
-import {
-  add as add8,
-  avgPool as avgPool4,
-  concat as concat3,
-  relu as relu5,
-  zeros
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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;
-}
-
-// src/faceRecognitionNet/FaceRecognitionNet.ts
-var FaceRecognitionNet = class extends NeuralNetwork {
-  constructor() {
-    super("FaceRecognitionNet");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("FaceRecognitionNet - load model before inference");
-    }
-    return tidy18(() => {
-      const batchTensor = cast6(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);
-  }
-};
-
-// src/faceRecognitionNet/index.ts
-function createFaceRecognitionNet(weights) {
-  const net = new FaceRecognitionNet();
-  net.extractWeights(weights);
-  return net;
-}
-
-// src/factories/WithFaceDescriptor.ts
-function extendWithFaceDescriptor(sourceObj, descriptor) {
-  const extension = {descriptor};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// src/factories/WithAge.ts
-function isWithAge(obj) {
-  return typeof obj["age"] === "number";
-}
-function extendWithAge(sourceObj, age) {
-  const extension = {age};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// src/factories/WithGender.ts
-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);
-}
-
-// src/ssdMobilenetv1/SsdMobilenetv1.ts
-import {
-  cast as cast7,
-  mul as mul3,
-  scalar as scalar6,
-  sub as sub3,
-  tidy as tidy24
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/ssdMobilenetv1/extractParams.ts
-import {
-  tensor1d as tensor1d5,
-  tensor3d,
-  tensor4d as tensor4d4
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    params: {
-      mobilenetv1,
-      prediction_layer,
-      output_layer
-    },
-    paramMappings
-  };
-}
-
-// src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
-function extractorsFactory8(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
-    const filters = extractWeightEntry(`${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};
-}
-
-// src/ssdMobilenetv1/mobileNetV1.ts
-import {
-  batchNorm,
-  clipByValue as clipByValue2,
-  depthwiseConv2d,
-  tidy as tidy20
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/ssdMobilenetv1/pointwiseConvLayer.ts
-import {
-  add as add9,
-  clipByValue,
-  conv2d as conv2d5,
-  tidy as tidy19
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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);
-  });
-}
-
-// src/ssdMobilenetv1/mobileNetV1.ts
-var 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
-    };
-  });
-}
-
-// src/ssdMobilenetv1/nonMaxSuppression.ts
-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);
-}
-
-// src/ssdMobilenetv1/outputLayer.ts
-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/dist/tf.es2017.js";
-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
-    };
-  });
-}
-
-// src/ssdMobilenetv1/predictionLayer.ts
-import {
-  concat as concat4,
-  tidy as tidy23
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/ssdMobilenetv1/boxPredictionLayer.ts
-import {
-  reshape as reshape2,
-  tidy as tidy22
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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
-    };
-  });
-}
-
-// src/ssdMobilenetv1/predictionLayer.ts
-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
-    };
-  });
-}
-
-// src/ssdMobilenetv1/SsdMobilenetv1Options.ts
-var SsdMobilenetv1Options = class {
-  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;
-  }
-};
-
-// src/ssdMobilenetv1/SsdMobilenetv1.ts
-var SsdMobilenetv1 = class extends NeuralNetwork {
-  constructor() {
-    super("SsdMobilenetv1");
-  }
-  forwardInput(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error("SsdMobilenetv1 - load model before inference");
-    }
-    return tidy24(() => {
-      const batchTensor = cast7(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);
-  }
-};
-
-// src/ssdMobilenetv1/index.ts
-function createSsdMobilenetv1(weights) {
-  const net = new SsdMobilenetv1();
-  net.extractWeights(weights);
-  return net;
-}
-function createFaceDetectionNet(weights) {
-  return createSsdMobilenetv1(weights);
-}
-var FaceDetectionNet = class extends SsdMobilenetv1 {
-};
-
-// src/tinyYolov2/const.ts
-var IOU_THRESHOLD = 0.4;
-var 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)
-];
-var 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)
-];
-var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
-var DEFAULT_MODEL_NAME = "tiny_yolov2_model";
-var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
-
-// src/tinyYolov2/TinyYolov2Base.ts
-import {
-  cast as cast8,
-  maxPool as maxPool3,
-  scalar as scalar8,
-  softmax as softmax3,
-  tidy as tidy28,
-  unstack as unstack6
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/tinyYolov2/config.ts
-var 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: ${JSON.stringify(config2.meanRgb)}`);
-  }
-}
-
-// src/tinyYolov2/convWithBatchNorm.ts
-import {
-  add as add12,
-  conv2d as conv2d6,
-  mul as mul5,
-  pad,
-  sub as sub5,
-  tidy as tidy26
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/tinyYolov2/leaky.ts
-import {
-  add as add11,
-  mul as mul4,
-  relu as relu6,
-  scalar as scalar7,
-  sub as sub4,
-  tidy as tidy25
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-function leaky(x) {
-  return tidy25(() => {
-    const min = mul4(x, scalar7(0.10000000149011612));
-    return add11(relu6(sub4(x, min)), min);
-  });
-}
-
-// src/tinyYolov2/convWithBatchNorm.ts
-function convWithBatchNorm(x, params) {
-  return tidy26(() => {
-    let out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = conv2d6(out, params.conv.filters, [1, 1], "valid");
-    out = sub5(out, params.bn.sub);
-    out = mul5(out, params.bn.truediv);
-    out = add12(out, params.conv.bias);
-    return leaky(out);
-  });
-}
-
-// src/tinyYolov2/depthwiseSeparableConv.ts
-import {
-  add as add13,
-  pad as pad2,
-  separableConv2d as separableConv2d2,
-  tidy as tidy27
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-function depthwiseSeparableConv3(x, params) {
-  return tidy27(() => {
-    let out = pad2(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = separableConv2d2(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-    out = add13(out, params.bias);
-    return leaky(out);
-  });
-}
-
-// src/tinyYolov2/extractParams.ts
-import {
-  tensor1d as tensor1d6
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-function extractorsFactory9(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  function extractBatchNormParams(size, mappedPrefix) {
-    const sub6 = tensor1d6(extractWeights(size));
-    const truediv = tensor1d6(extractWeights(size));
-    paramMappings.push({paramPath: `${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: ${getRemainingWeights().length}`);
-  }
-  return {params, paramMappings};
-}
-
-// src/tinyYolov2/extractParamsFromWeigthMap.ts
-function extractorsFactory10(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractBatchNormParams(prefix) {
-    const sub6 = extractWeightEntry(`${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};
-}
-
-// src/tinyYolov2/TinyYolov2Options.ts
-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 = {}));
-var TinyYolov2Options = class {
-  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;
-  }
-};
-
-// src/tinyYolov2/TinyYolov2Base.ts
-var TinyYolov2Base2 = class 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 = cast8(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 || TinyYolov2Base2.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);
-  }
-};
-var TinyYolov2Base = TinyYolov2Base2;
-TinyYolov2Base.DEFAULT_FILTER_SIZES = [
-  3,
-  16,
-  32,
-  64,
-  128,
-  256,
-  512,
-  1024,
-  1024
-];
-
-// src/tinyYolov2/TinyYolov2.ts
-var TinyYolov2 = class 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);
-  }
-};
-
-// src/tinyYolov2/index.ts
-function createTinyYolov2(weights, withSeparableConvs = true) {
-  const net = new TinyYolov2(withSeparableConvs);
-  net.extractWeights(weights);
-  return net;
-}
-
-// src/tinyFaceDetector/TinyFaceDetectorOptions.ts
-var TinyFaceDetectorOptions = class extends TinyYolov2Options {
-  constructor() {
-    super(...arguments);
-    this._name = "TinyFaceDetectorOptions";
-  }
-};
-
-// src/globalApi/ComposableTask.ts
-var ComposableTask = class {
-  async then(onfulfilled) {
-    return onfulfilled(await this.run());
-  }
-  async run() {
-    throw new Error("ComposableTask - run is not implemented");
-  }
-};
-
-// src/globalApi/DetectFaceLandmarksTasks.ts
-import {
-  Tensor as Tensor5
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-
-// src/globalApi/extractFacesAndComputeResults.ts
-import {
-  Tensor as Tensor4
-} from "@tensorflow/tfjs/dist/tf.es2017.js";
-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);
-}
-
-// src/tinyFaceDetector/const.ts
-var IOU_THRESHOLD2 = 0.4;
-var 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)
-];
-var MEAN_RGB = [117.001, 114.697, 97.404];
-
-// src/tinyFaceDetector/TinyFaceDetector.ts
-var TinyFaceDetector = class 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);
-  }
-};
-
-// src/globalApi/nets.ts
-var 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()
-};
-var ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options);
-var tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options);
-var tinyYolov23 = (input, options) => nets.tinyYolov2.locateFaces(input, options);
-var detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input);
-var detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input);
-var computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input);
-var recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input);
-var predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input);
-var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url);
-var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url);
-var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url);
-var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url);
-var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url);
-var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url);
-var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url);
-var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url);
-var loadFaceDetectionModel = loadSsdMobilenetv1Model;
-var locateFaces = ssdMobilenetv1;
-var detectLandmarks = detectFaceLandmarks;
-
-// src/globalApi/PredictFaceExpressionsTask.ts
-var PredictFaceExpressionsTaskBase = class extends ComposableTask {
-  constructor(parentTask, input, extractedFaces) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.extractedFaces = extractedFaces;
-  }
-};
-var PredictAllFaceExpressionsTask = class 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);
-  }
-};
-var PredictSingleFaceExpressionsTask = class 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);
-  }
-};
-var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask {
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-};
-var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask {
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-};
-
-// src/globalApi/PredictAgeAndGenderTask.ts
-var PredictAgeAndGenderTaskBase = class extends ComposableTask {
-  constructor(parentTask, input, extractedFaces) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.extractedFaces = extractedFaces;
-  }
-};
-var PredictAllAgeAndGenderTask = class 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);
-  }
-};
-var PredictSingleAgeAndGenderTask = class 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);
-  }
-};
-var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask {
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-};
-var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask {
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-};
-
-// src/globalApi/ComputeFaceDescriptorsTasks.ts
-var ComputeFaceDescriptorsTaskBase = class extends ComposableTask {
-  constructor(parentTask, input) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-  }
-};
-var ComputeAllFaceDescriptorsTask = class 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);
-  }
-};
-var ComputeSingleFaceDescriptorTask = class 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);
-  }
-};
-
-// src/globalApi/DetectFaceLandmarksTasks.ts
-var DetectFaceLandmarksTaskBase = class 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;
-  }
-};
-var DetectAllFaceLandmarksTask = class 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);
-  }
-};
-var DetectSingleFaceLandmarksTask = class 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);
-  }
-};
-
-// src/globalApi/DetectFacesTasks.ts
-var DetectFacesTaskBase = class extends ComposableTask {
-  constructor(input, options = new SsdMobilenetv1Options()) {
-    super();
-    this.input = input;
-    this.options = options;
-  }
-};
-var DetectAllFacesTask = class 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);
-  }
-};
-var DetectSingleFaceTask = class 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);
-  }
-};
-
-// src/globalApi/detectFaces.ts
-function detectSingleFace(input, options = new SsdMobilenetv1Options()) {
-  return new DetectSingleFaceTask(input, options);
-}
-function detectAllFaces(input, options = new SsdMobilenetv1Options()) {
-  return new DetectAllFacesTask(input, options);
-}
-
-// src/globalApi/allFaces.ts
-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();
-}
-var allFaces = allFacesSsdMobilenetv1;
-
-// src/euclideanDistance.ts
-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));
-}
-
-// src/globalApi/FaceMatcher.ts
-var FaceMatcher = class {
-  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);
-  }
-};
-
-// src/tinyFaceDetector/index.ts
-function createTinyFaceDetector(weights) {
-  const net = new TinyFaceDetector();
-  net.extractWeights(weights);
-  return net;
-}
-
-// src/resizeResults.ts
-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;
-}
-
-// package.json
-var version = "0.8.8";
-
-// src/index.ts
-var node = typeof process !== "undefined";
-var browser3 = typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined";
-var 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.esm.nobundle.js.map
diff --git a/dist/face-api.esm.nobundle.js.map b/dist/face-api.esm.nobundle.js.map
deleted file mode 100644
index 38bc591..0000000
--- a/dist/face-api.esm.nobundle.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["../src/env/isNodejs.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/utils/index.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
-  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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diff --git a/dist/face-api.js b/dist/face-api.js
index 430d846..e0effb9 100644
--- a/dist/face-api.js
+++ b/dist/face-api.js
@@ -1,75 +1,82 @@
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UP=Object.freeze({__proto__:null,createScalarValue:ON,toTypedArray:ri,now:sr,fetch:LN,encodeString:od,decodeString:Yu,shuffle:x,clamp:w,nearestLargerEven:k,sum:C,randUniform:$,distSquared:F,assert:_,assertShapesMatch:W,assertNonNull:et,flatten:tt,sizeFromShape:G,isScalarShape:mt,arraysEqual:lt,isInt:gt,tanh:_t,sizeToSquarishShape:Gt,createShuffledIndices:se,rightPad:fe,repeatedTry:_e,inferFromImplicitShape:Ue,parseAxisParam:Vt,squeezeShape:ln,getTypedArrayFromDType:Ce,getArrayFromDType:or,checkConversionForErrors:ti,isValidDtype:ei,hasEncodingLoss:gc,isTypedArray:gn,bytesPerElement:Zb,bytesFromStringArray:rN,isString:us,isBoolean:oN,isNumber:vf,inferDtype:yc,isFunction:ni,nearestDivisor:Tf,computeStrides:Jt,toNestedArray:Dr,makeOnesTypedArray:Qb,makeZerosTypedArray:bc,makeZerosNestedTypedArray:tx,assertNonNegativeIntegerDimensions:ex,locToIndex:Yr,indexToLoc:Yi,isPromise:Ji});class qP{constructor(t,e){this.backendTimer=t,this.logger=e,e==null&&(this.logger=new 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${y}	%c${m}	%c${b}	%c${c}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}}function KP(n,t,e){let r={},o={};for(let p=0;p<t.length;p++)r[t[p].id]=!0;for(let p=0;p<n.length;p++){let f=n[p],m=f.inputs;for(let y in m){let b=m[y],v=!1;for(let T=0;T<t.length;T++)if(r[b.id]){f.outputs.forEach(N=>r[N.id]=!0),v=!0,o[f.id]=!0;break}if(v)break}}let s={};s[e.id]=!0;let c={};for(let p=n.length-1;p>=0;p--){let f=n[p],m=f.inputs;for(let y=0;y<f.outputs.length;y++)if(s[f.outputs[y].id]){for(let b in m)s[m[b].id]=!0,c[f.id]=!0;break}}let l=[];for(let p=0;p<n.length;p++){let f=n[p];if(o[f.id]&&c[f.id]){let m={};for(let b in f.inputs){let v=f.inputs[b];r[v.id]&&(m[b]=v)}let y=Object.assign({},f);y.inputs=m,y.outputs=f.outputs,l.push(y)}}return l}function XP(n,t,e,r){for(let o=t.length-1;o>=0;o--){let s=t[o],c=[];if(s.outputs.forEach(p=>{let f=n[p.id];f!=null?c.push(f):c.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let l=s.gradient(c);for(let p in s.inputs){if(!(p in l))throw new Error(`Cannot backprop through input ${p}. Available gradients found: ${Object.keys(l)}.`);let f=e(()=>l[p]());if(f.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${p} must have 'float32' dtype, but has '${f.dtype}'`);let m=s.inputs[p];if(!lt(f.shape,m.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${p}' has shape '${f.shape}', which does not match the shape of the input '${m.shape}'`);if(n[m.id]==null)n[m.id]=f;else{let y=n[m.id];n[m.id]=r(y,f),y.dispose()}}}}let MN=20,Ju=3,Hx=7;function YP(n,t,e,r){let o=Jt(t),s=JP(n,t,e,o),c=t.length,l=sd(n,t,e,o,s),p=["Tensor"];return r&&(p.push(`  dtype: ${e}`),p.push(`  rank: ${c}`),p.push(`  shape: [${t}]`),p.push("  values:")),p.push(l.map(f=>"    "+f).join(`
-`)),p.join(`
-`)}function JP(n,t,e,r){let o=G(t),s=r[r.length-1],c=new Array(s).fill(0),l=t.length,p=e==="complex64"?Qu(n):n;if(l>1)for(let f=0;f<o/s;f++){let m=f*s;for(let y=0;y<s;y++)c[y]=Math.max(c[y],Zu(p[m+y],0,e).length)}return c}function Zu(n,t,e){let r;return Array.isArray(n)?r=`${parseFloat(n[0].toFixed(Hx))} + ${parseFloat(n[1].toFixed(Hx))}j`:us(n)?r=`'${n}'`:e==="bool"?r=BN(n):r=parseFloat(n.toFixed(Hx)).toString(),fe(r,t)}function BN(n){return n===0?"false":"true"}function sd(n,t,e,r,o,s=!0){let c=e==="complex64"?2:1,l=t[0],p=t.length;if(p===0){if(e==="complex64"){let N=Qu(n);return[Zu(N[0],0,e)]}return e==="bool"?[BN(n[0])]:[n[0].toString()]}if(p===1){if(l>MN){let S=Ju*c,D=Array.from(n.slice(0,S)),I=Array.from(n.slice((l-Ju)*c,l*c));return e==="complex64"&&(D=Qu(D),I=Qu(I)),["["+D.map((P,E)=>Zu(P,o[E],e)).join(", ")+", ..., "+I.map((P,E)=>Zu(P,o[l-Ju+E],e)).join(", ")+"]"]}let N=e==="complex64"?Qu(n):Array.from(n);return["["+N.map((S,D)=>Zu(S,o[D],e)).join(", ")+"]"]}let f=t.slice(1),m=r.slice(1),y=r[0]*c,b=[];if(l>MN){for(let N=0;N<Ju;N++){let S=N*y,D=S+y;b.push(...sd(n.slice(S,D),f,e,m,o,!1))}b.push("...");for(let N=l-Ju;N<l;N++){let S=N*y,D=S+y;b.push(...sd(n.slice(S,D),f,e,m,o,N===l-1))}}else for(let N=0;N<l;N++){let S=N*y,D=S+y;b.push(...sd(n.slice(S,D),f,e,m,o,N===l-1))}let v=p===2?",":"";b[0]="["+b[0]+v;for(let N=1;N<b.length-1;N++)b[N]=" "+b[N]+v;let T=`,
-`;for(let N=2;N<p;N++)T+=`
-`;return b[b.length-1]=" "+b[b.length-1]+"]"+(s?"":T),b}function Qu(n){let t=[];for(let e=0;e<n.length;e+=2)t.push([n[e],n[e+1]]);return t}class hn{constructor(t,e,r){if(this.dtype=e,this.shape=t.slice(),this.size=G(t),r!=null){let o=r.length;_(o===this.size,()=>`Length of values '${o}' does not match the size inferred by the shape '${this.size}'.`)}if(e==="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=r||or(e,this.size),this.strides=Jt(t)}set(t,...e){e.length===0&&(e=[0]),_(e.length===this.rank,()=>`The number of provided coordinates (${e.length}) must match the rank (${this.rank})`);let r=this.locToIndex(e);this.values[r]=t}get(...t){t.length===0&&(t=[0]);let e=0;for(let o of t){if(o<0||o>=this.shape[e]){let s=`Requested out of range element at ${t}.   Buffer shape=${this.shape}`;throw new Error(s)}e++}let r=t[t.length-1];for(let o=0;o<t.length-1;++o)r+=this.strides[o]*t[o];return this.values[r]}locToIndex(t){if(this.rank===0)return 0;if(this.rank===1)return t[0];let e=t[t.length-1];for(let r=0;r<t.length-1;++r)e+=this.strides[r]*t[r];return e}indexToLoc(t){if(this.rank===0)return[];if(this.rank===1)return[t];let e=new Array(this.shape.length);for(let r=0;r<e.length-1;++r)e[r]=Math.floor(t/this.strides[r]),t-=e[r]*this.strides[r];return e[e.length-1]=t,e}get rank(){return this.shape.length}toTensor(){return Mo().makeTensor(this.values,this.shape,this.dtype)}}let Mo=null,$c=null,zN=null;function ZP(n){Mo=n}function QP(n){$c=n}function tO(n){zN=n}class ot{constructor(t,e,r,o){this.kept=!1,this.isDisposedInternal=!1,this.shape=t.slice(),this.dtype=e||"float32",this.size=G(t),this.strides=Jt(t),this.dataId=r,this.id=o,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let t=await this.data();return $c.buffer(this.shape,this.dtype,t)}bufferSync(){return $c.buffer(this.shape,this.dtype,this.dataSync())}async array(){let t=await this.data();return Dr(this.shape,t)}arraySync(){return Dr(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let t=Mo().read(this.dataId);if(this.dtype==="string"){let e=await t;try{return e.map(r=>Yu(r))}catch(r){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return t}dataSync(){this.throwIfDisposed();let t=Mo().readSync(this.dataId);if(this.dtype==="string")try{return t.map(e=>Yu(e))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return t}async bytes(){this.throwIfDisposed();let t=await Mo().read(this.dataId);return this.dtype==="string"?t:new Uint8Array(t.buffer)}dispose(){if(this.isDisposed)return;Mo().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(t=!1){return $c.print(this,t)}clone(){return this.throwIfDisposed(),$c.clone(this)}toString(t=!1){let e=this.dataSync();return YP(e,this.shape,this.dtype,t)}cast(t){return this.throwIfDisposed(),$c.cast(this,t)}variable(t=!0,e,r){return this.throwIfDisposed(),Mo().makeVariable(this,t,e,r)}}Object.defineProperty(ot,Symbol.hasInstance,{value:n=>!!n&&n.data!=null&&n.dataSync!=null&&n.throwIfDisposed!=null});class tp extends ot{constructor(t,e,r,o){super(t.shape,t.dtype,t.dataId,o);this.trainable=e,this.name=r}assign(t){if(t.dtype!==this.dtype)throw new Error(`dtype of the new value (${t.dtype}) and previous value (${this.dtype}) must match`);if(!lt(t.shape,this.shape))throw new Error(`shape of the new value (${t.shape}) and previous value (${this.shape}) must match`);Mo().disposeTensor(this),this.dataId=t.dataId,Mo().incRef(this,null)}dispose(){Mo().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(tp,Symbol.hasInstance,{value:n=>n instanceof ot&&n.assign!=null&&n.assign instanceof Function});(function(n){n.R0="R0",n.R1="R1",n.R2="R2",n.R3="R3",n.R4="R4",n.R5="R5",n.R6="R6"})(i.Rank||(i.Rank={}));var jx;(function(n){n.float32="float32",n.int32="int32",n.bool="int32",n.complex64="complex64"})(jx||(jx={}));var Kx;(function(n){n.float32="float32",n.int32="int32",n.bool="bool",n.complex64="complex64"})(Kx||(Kx={}));var Xx;(function(n){n.float32="float32",n.int32="float32",n.bool="float32",n.complex64="complex64"})(Xx||(Xx={}));var Yx;(function(n){n.float32="complex64",n.int32="complex64",n.bool="complex64",n.complex64="complex64"})(Yx||(Yx={}));let eO={float32:Xx,int32:jx,bool:Kx,complex64:Yx};function Kn(n,t){if(n==="string"||t==="string"){if(n==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${n} with ${t}`)}return eO[n][t]}function id(n){return Kn(n,"int32")}function Je(n,t){if(n.dtype===t.dtype)return[n,t];let e=Kn(n.dtype,t.dtype);return[n.cast(e),t.cast(e)]}function WN(n,t){_(n.dtype===t.dtype,()=>`The dtypes of the first(${n.dtype}) and second(${t.dtype}) input must match`)}function ad(n,t){return t.some(e=>e.id===n.id)}function ps(n){let t=[],e=new Set;return VN(n,t,e),t}function VN(n,t,e){if(n==null)return;if(n instanceof ot){t.push(n);return}if(!nO(n))return;let r=n;for(let o in r){let s=r[o];e.has(s)||(e.add(s),VN(s,t,e))}}function nO(n){return Array.isArray(n)||typeof n=="object"}var rO=Object.freeze({__proto__:null,makeTypesMatch:Je,assertTypesMatch:WN,isTensorInList:ad,getTensorsInContainer:ps});class GN{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}}class Ic{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new GN}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let r=t[e],o=await this.initializeBackend(r).success;if(o){await this.setBackend(r);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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){let{name:t,asyncInit:e}=this.initializeBackendsAndReturnBest();if(e)throw new Error(`The highest priority backend '${t}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(t)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(t){if(!(t in this.registry))if(t in this.registryFactory){let{asyncInit:e}=this.initializeBackend(t);if(e)return null}else return null;return this.registry[t]}findBackendFactory(t){return t in this.registryFactory?this.registryFactory[t].factory:null}registerBackend(t,e,r=1){return t in this.registryFactory?(console.warn(`${t} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[t]={factory:e,priority:r},!0)}async setBackend(t){if(this.registryFactory[t]==null)throw new Error(`Backend name '${t}' not found in registry`);if(this.backendName=t,this.registry[t]==null){this.backendInstance=null;let{success:e,asyncInit:r}=this.initializeBackend(t),o=r?await e:e;if(!o)return!1}return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new qP(this.backendInstance),!0}setupRegisteredKernels(){let t=nd(this.backendName);t.forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){let e=nd(t);e.forEach(r=>{r.disposeFunc!=null&&r.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let r=e.factory();if(r&&!(r instanceof d)&&typeof r.then=="function"){let o=++this.pendingBackendInitId,s=r.then(c=>o<this.pendingBackendInitId?!1:(this.registry[t]=c,this.pendingBackendInit=null,!0)).catch(c=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${t} failed`),console.warn(c.stack||c.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=r,{success:!0,asyncInit:!1}}catch(r){return console.warn(`Initialization of backend ${t} failed`),console.warn(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(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((t,e)=>this.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let r=t[e],{success:o,asyncInit:s}=this.initializeBackend(r);if(s||o)return{name:r,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(t,e){let r=this.state.tensorInfo.get(e),o=r.backend,s=this.readSync(e);o.disposeData(e),r.backend=t,t.move(e,s,r.shape,r.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(t,e){let r=null;if(e==null){if(typeof t!="function")throw new Error("Please provide a function to tidy()");e=t}else{if(typeof t!="string"&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof e!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=t}let o;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,r){t();try{let o=r();return e(),o}catch(o){throw e(),o}}nextTensorId(){return Ic.nextTensorId++}nextVariableId(){return Ic.nextVariableId++}clone(t){let e=this.makeTensorFromDataId(t.dataId,t.shape,t.dtype),r={x:t},o=c=>({x:()=>{let l="float32",p={x:c},f={dtype:l};return X.runKernelFunc(m=>m.cast(c,l),p,null,xc,f)}}),s=[];return this.addTapeNode(this.state.activeScope.name,r,[e],o,s,{}),e}runKernel(t,e,r,o,s){let c=null,l=null;return this.runKernelFunc(c,e,l,t,r,o,s)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,r){let o=this.backend.numDataIds(),s=0;r.forEach(p=>{s+=p.dtype==="complex64"?3:1});let c=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],l=o-e-s-c;if(l>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${l} data ids) after running '${t}'`)}runKernelFunc(t,e,r,o,s,c,l){let p,f=[],m=this.isTapeOn();o==null&&(o=this.state.activeScope!=null?this.state.activeScope.name:"");let y=this.state.numBytes,b=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let v,T=Gx(o,this.backendName),N;if(T!=null)v=()=>{let D=this.backend.numDataIds();N=T.kernelFunc({inputs:e,attrs:s,backend:this.backend});let I=Array.isArray(N)?N:[N];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,D,I);let P=I.map(({dataId:E,shape:L,dtype:B})=>this.makeTensorFromDataId(E,L,B));if(m){let E=this.getTensorsForGradient(o,e,P);if(E==null){l==null&&(l=[]);let L=P.filter((B,q)=>l[q]);E=(c||[]).slice().concat(L)}f=this.saveTensorsForBackwardMode(E)}return P};else{let D=I=>{if(!m)return;f=I.map(P=>this.keep(this.clone(P)))};v=()=>{let I=this.backend.numDataIds();N=this.tidy(()=>t(this.backend,D));let P=Array.isArray(N)?N:[N];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,I,P),P}}let S;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?p=v():(S=this.profiler.profileKernel(o,e,()=>v()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(S),p=S.outputs)}),m&&this.addTapeNode(o,e,p,r,f,s),this.state.profiling&&this.state.activeProfile.kernels.push({name:o,bytesAdded:this.state.numBytes-y,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-b,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(e).map(D=>e[D]!=null?e[D].shape:null),outputShapes:p.map(D=>D.shape),kernelTimeMs:S.timeMs,extraInfo:S.extraInfo}),Array.isArray(N)?p:p[0]}saveTensorsForBackwardMode(t){let e=t.map(r=>this.keep(this.clone(r)));return e}getTensorsForGradient(t,e,r){let o=Ux(t);if(o!=null){let s=o.inputsToSave||[],c=o.outputsToSave||[],l;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an 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c=M(n,"forgetBias","basicLSTMCell"),l=M(t,"lstmKernel","basicLSTMCell"),p=M(e,"lstmBias","basicLSTMCell"),f=M(r,"data","basicLSTMCell"),m=M(o,"c","basicLSTMCell"),y=M(s,"h","basicLSTMCell"),b=Qe([f,y],1),v=ge(b,l),T=Tt(v,p),N=T.shape[0],S=T.shape[1]/4,D=[N,S],I=ce(T,[0,0],D),P=ce(T,[0,S],D),E=ce(T,[0,S*2],D),L=ce(T,[0,S*3],D),B=Tt(nt(Wo(I),Pc(P)),nt(m,Wo(Tt(c,E)))),q=nt(Pc(B),Wo(L));return[B,q]}let oM=j({basicLSTMCell_:rM});function sM(n,t,e){let r=M(n,"x","batchToSpaceND"),o=t.reduce((p,f)=>p*f);_(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),_(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length  ${t.length}`),_(r.shape[0]%o===0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${o}`);let s=p=>p.batchToSpaceND(r,t,e),c={x:r},l={blockShape:t,crops:e};return X.runKernelFunc(s,c,null,ax,l)}let cp=j({batchToSpaceND_:sM});function iM(n){let t;return n.rank===0||n.rank===1?t=Q(n,[1,1,1,n.size]):n.rank===2?t=Q(n,[1,1,n.shape[0],n.shape[1]]):n.rank===3?t=Q(n,[1,n.shape[0],n.shape[1],n.shape[2]]):t=n,t}function aM(n,t,e,r,o,s){s==null&&(s=.001);let c=M(n,"x","batchNorm"),l=M(t,"mean","batchNorm"),p=M(e,"variance","batchNorm"),f;o!=null&&(f=M(o,"scale","batchNorm"));let m;r!=null&&(m=M(r,"offset","batchNorm")),_(l.rank===p.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(m==null||l.rank===m.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(f==null||l.rank===f.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let y=iM(c),b=(S,D)=>(D([y,l,p,f]),S.batchNorm(y,_d(l),_d(p),_d(m),_d(f),s)),v={x:y,scale:f,offset:m,mean:l,variance:p},T={varianceEpsilon:s},N=X.runKernelFunc(b,v,null,ku,T);return Q(N,c.shape)}function _d(n){return 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l=M(n,"x","conv2d"),p=M(t,"filter","conv2d"),f=l,m=!1;l.rank===3&&(m=!0,f=Q(l,[1,l.shape[0],l.shape[1],l.shape[2]])),_(f.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${f.rank}.`),_(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),c!=null&&_(gt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${c} but got pad ${r}.`);let y=o==="NHWC"?f.shape[3]:f.shape[1];_(y===p.shape[2],()=>`Error in conv2d: depth of input (${y}) must match input depth for filter ${p.shape[2]}.`),_(fn(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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QW={fft:wp,ifft:qc,rfft:vp,irfft:qd},t4={hammingWindow:Jz,hannWindow:xC,frame:wC,stft:eW},mi={flipLeftRight:sW,resizeNearestNeighbor:kC,resizeBilinear:TC,rotateWithOffset:aW,cropAndResize:rW,nonMaxSuppression:lW,nonMaxSuppressionAsync:yW,nonMaxSuppressionWithScore:xW,nonMaxSuppressionWithScoreAsync:vW,nonMaxSuppressionPadded:kW,nonMaxSuppressionPaddedAsync:_W},_C={bandPart:IW,gramSchmidt:DW,qr:FW},e4={absoluteDifference:OW,computeWeightedLoss:bs,cosineDistance:MW,hingeLoss:zW,huberLoss:VW,logLoss:UW,meanSquaredError:HW,sigmoidCrossEntropy:XW,softmaxCrossEntropy:ZW};class xs extends sa{minimize(t,e=!1,r){let{value:o,grads:s}=this.computeGradients(t,r);if(r!=null){let c=r.map(l=>({name:l.name,tensor:s[l.name]}));this.applyGradients(c)}else this.applyGradients(s);return Xt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Ww(t,e)}dispose(){this.iterations_!=null&&Xt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Et(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}}Object.defineProperty(xs,Symbol.hasInstance,{value:n=>n.minimize!=null&&n.computeGradients!=null&&n.applyGradients!=null});class Np extends xs{constructor(t,e,r=null){super();this.learningRate=t,this.rho=e,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r],c=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${r}/accum_grad`,variable:rt(()=>re(s).variable(c))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${r}/accum_var`,variable:rt(()=>re(s).variable(c))});let l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;let p=this.accumulatedGrads[o].variable,f=this.accumulatedUpdates[o].variable;rt(()=>{let m=Tt(nt(p,this.rho),nt(Ae(l),1-this.rho)),y=nt(Bt(On(Tt(f,this.epsilon)),On(Tt(p,this.epsilon))),l),b=Tt(nt(f,this.rho),nt(Ae(y),1-this.rho));p.assign(m),f.assign(b);let v=Tt(nt(y,-this.learningRate),s);s.assign(v)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Xt(this.accumulatedGrads.map(t=>t.variable)),Xt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,r=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}}Np.className="Adadelta",vt(Np);class _p extends xs{constructor(t,e=.1){super();this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r];if(this.accumulatedGrads[o]==null){let p=!1;this.accumulatedGrads[o]={originalName:`${r}/accumulator`,variable:rt(()=>Lc(s.shape,this.initialAccumulatorValue).variable(p))}}let c=Array.isArray(t)?t[o].tensor:t[r];if(c==null)return;let l=this.accumulatedGrads[o].variable;rt(()=>{let p=Tt(l,Ae(c));l.assign(p);let f=Tt(nt(Bt(c,On(Tt(p,X.backend.epsilon()))),-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Xt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}}_p.className="Adagrad",vt(_p);class Cp extends xs{constructor(t,e,r,o=null){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],rt(()=>{this.accBeta1=Et(e).variable(),this.accBeta2=Et(r).variable()}),o==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);rt(()=>{let r=Dt(1,this.accBeta1),o=Dt(1,this.accBeta2);e.forEach((s,c)=>{let l=X.registeredVariables[s],p=!1;this.accumulatedFirstMoment[c]==null&&(this.accumulatedFirstMoment[c]={originalName:`${s}/m`,variable:rt(()=>re(l).variable(p))}),this.accumulatedSecondMoment[c]==null&&(this.accumulatedSecondMoment[c]={originalName:`${s}/v`,variable:rt(()=>re(l).variable(p))});let f=Array.isArray(t)?t[c].tensor:t[s];if(f==null)return;let m=this.accumulatedFirstMoment[c].variable,y=this.accumulatedSecondMoment[c].variable,b=Tt(nt(m,this.beta1),nt(f,1-this.beta1)),v=Tt(nt(y,this.beta2),nt(Ae(f),1-this.beta2)),T=Bt(b,r),N=Bt(v,o);m.assign(b),y.assign(v);let S=Tt(nt(Bt(T,Tt(On(N),this.epsilon)),-this.learningRate),l);l.assign(S)}),this.accBeta1.assign(nt(this.accBeta1,this.beta1)),this.accBeta2.assign(nt(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Xt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Xt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),rt(()=>{this.accBeta1.assign(mo(this.beta1,this.iterations_+1)),this.accBeta2.assign(mo(this.beta2,this.iterations_+1))});let e=t.length/2,r=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}}Cp.className="Adam",vt(Cp);class Sp extends xs{constructor(t,e,r,o=null,s=0){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],rt(()=>{this.iteration=Et(0).variable(),this.accBeta1=Et(e).variable()}),o==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);rt(()=>{let r=Dt(1,this.accBeta1),o=Bt(-this.learningRate,Tt(nt(this.iteration,this.decay),1));e.forEach((s,c)=>{let l=X.registeredVariables[s],p=!1;this.accumulatedFirstMoment[c]==null&&(this.accumulatedFirstMoment[c]={originalName:`${s}/m`,variable:re(l).variable(p)}),this.accumulatedWeightedInfNorm[c]==null&&(this.accumulatedWeightedInfNorm[c]={originalName:`${s}/v`,variable:re(l).variable(p)});let f=Array.isArray(t)?t[c].tensor:t[s];if(f==null)return;let m=this.accumulatedFirstMoment[c].variable,y=this.accumulatedWeightedInfNorm[c].variable,b=Tt(nt(m,this.beta1),nt(f,1-this.beta1)),v=nt(y,this.beta2),T=bn(f),N=Jr(v,T);m.assign(b),y.assign(N);let S=Tt(nt(Bt(o,r),Bt(b,Tt(N,this.epsilon))),l);l.assign(S)}),this.iteration.assign(Tt(this.iteration,1)),this.accBeta1.assign(nt(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Xt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Xt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){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(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}}Sp.className="Adamax",vt(Sp);class Xc extends xs{constructor(t){super();this.learningRate=t,this.setLearningRate(t)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=Array.isArray(t)?t[o].tensor:t[r];if(s==null)return;let c=X.registeredVariables[r];rt(()=>{let l=Tt(nt(this.c,s),c);c.assign(l)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Sn(Et(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}}Xc.className="SGD",vt(Xc);class $p extends Xc{constructor(t,e,r=!1){super(t);this.learningRate=t,this.momentum=e,this.useNesterov=r,this.accumulations=[],this.m=Et(this.momentum)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r];if(this.accumulations[o]==null){let p=!1;this.accumulations[o]={originalName:`${r}/momentum`,variable:rt(()=>re(s).variable(p))}}let c=this.accumulations[o].variable,l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;rt(()=>{let p,f=Tt(nt(this.m,c),l);this.useNesterov?p=Tt(nt(this.c,Tt(l,nt(f,this.m))),s):p=Tt(nt(this.c,f),s),c.assign(f),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Xt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}}$p.className="Momentum",vt($p);class Ip extends xs{constructor(t,e=.9,r=0,o=null,s=!1){super();if(this.learningRate=t,this.decay=e,this.momentum=r,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=X.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r],c=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${r}/rms`,variable:rt(()=>re(s).variable(c))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${r}/momentum`,variable:rt(()=>re(s).variable(c))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${r}/mg`,variable:rt(()=>re(s).variable(c))});let l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;let p=this.accumulatedMeanSquares[o].variable,f=this.accumulatedMoments[o].variable;rt(()=>{let m=Tt(nt(p,this.decay),nt(Ae(l),1-this.decay));if(this.centered){let y=this.accumulatedMeanGrads[o].variable,b=Tt(nt(y,this.decay),nt(l,1-this.decay)),v=Bt(nt(l,this.learningRate),On(Dt(m,Tt(Ae(b),this.epsilon)))),T=Tt(nt(f,this.momentum),v);p.assign(m),y.assign(b),f.assign(T);let N=Dt(s,T);s.assign(N)}else{let y=Tt(nt(p,this.decay),nt(Ae(l),1-this.decay)),b=Tt(nt(f,this.momentum),Bt(nt(l,this.learningRate),On(Tt(y,this.epsilon))));p.assign(y),f.assign(b);let v=Dt(s,b);s.assign(v)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Xt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Xt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Xt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,r=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}}Ip.className="RMSProp",vt(Ip);class ma{static sgd(t){return new Xc(t)}static momentum(t,e,r=!1){return new $p(t,e,r)}static rmsprop(t,e=.9,r=0,o=null,s=!1){return new Ip(t,e,r,o,s)}static adam(t=.001,e=.9,r=.999,o=null){return new Cp(t,e,r,o)}static adadelta(t=.001,e=.95,r=null){return new Np(t,e,r)}static adamax(t=.002,e=.9,r=.999,o=null,s=0){return new Sp(t,e,r,o,s)}static adagrad(t,e=.1){return new _p(t,e)}}let ga={sgd:ma.sgd,momentum:ma.momentum,adadelta:ma.adadelta,adagrad:ma.adagrad,rmsprop:ma.rmsprop,adamax:ma.adamax,adam:ma.adam};let n4=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:n=>n())();function rm(){return new Promise(n=>n4(()=>n()))}function uv(n,t,e){let r=e*(typeof n=="number"?n:n[0]),o=t*(typeof n=="number"?n:n[1]);return[r,o]}function Ep(n,t,e,r=!0){let o=[];if(r)o=o.concat(t.slice(0)),o.push(n[0]/e),o=o.concat(n.slice(1));else{o=o.concat(n[0]);let s=t.length;for(let c=0;c<s;++c)o=o.concat([n[c+1]/t[c],t[c]]);o=o.concat(n.slice(s+1))}return o}function Dp(n,t,e=!0){let r=[];if(e){r.push(t);for(let o=t+1;o<n;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o)}else{let o=[],s=[];for(let c=1;c<n;++c)c>=t*2+1||c%2===1?s.push(c):o.push(c);r.push(...o),r.push(0),r.push(...s)}return r}function Ap(n,t,e,r=!0){let o=[];r?o.push(n[0]/e):o.push(n[0]*e);for(let s=1;s<n.length;++s)s<=t.length?r?o.push(t[s-1]*n[s]):o.push(n[s]/t[s-1]):o.push(n[s]);return o}function pv(n,t){let e=[0];for(let r=0;r<t;++r)e.push(n[r][0]);return e}function hv(n,t,e){let r=n.slice(0,1);for(let o=0;o<e;++o)r.push(n[o+1]-t[o][0]-t[o][1]);return r}let om=1.7580993408473768,sm=1.0507009873554805;let fv=.3275911,dv=.254829592,mv=-.284496736,gv=1.421413741,yv=-1.453152027,bv=1.061405429;function Yc(...n){ct().getBool("IS_TEST")||console.warn(...n)}function r4(...n){ct().getBool("IS_TEST")||console.log(...n)}function ws(n,t){if(n.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${n.length}, imag: ${t.length}.`);let e=new Float32Array(n.length*2);for(let r=0;r<e.length;r+=2)e[r]=n[r/2],e[r+1]=t[r/2];return e}function CC(n){let t=new Float32Array(n.length/2),e=new Float32Array(n.length/2);for(let r=0;r<n.length;r+=2)t[r/2]=n[r],e[r/2]=n[r+1];return{real:t,imag:e}}function SC(n){let t=Math.ceil(n.length/4),e=new Float32Array(t),r=new Float32Array(t);for(let o=0;o<n.length;o+=4)e[Math.floor(o/4)]=n[o],r[Math.floor(o/4)]=n[o+1];return{real:e,imag:r}}function $C(n){let t=Math.floor(n.length/4),e=new Float32Array(t),r=new Float32Array(t);for(let o=2;o<n.length;o+=4)e[Math.floor(o/4)]=n[o],r[Math.floor(o/4)]=n[o+1];return{real:e,imag:r}}function xv(n,t){let e=n[t*2],r=n[t*2+1];return{real:e,imag:r}}function IC(n,t,e,r){n[r*2]=t,n[r*2+1]=e}function EC(n,t){let e=new Float32Array(n/2),r=new Float32Array(n/2);for(let o=0;o<Math.ceil(n/2);o++){let s=(t?2:-2)*Math.PI*(o/n);e[o]=Math.cos(s),r[o]=Math.sin(s)}return{real:e,imag:r}}function DC(n,t,e){let r=(e?2:-2)*Math.PI*(n/t),o=Math.cos(r),s=Math.sin(r);return{real:o,imag:s}}function o4(n,t,e){if(t==="complex64"){if(n.dtype==="complex64")return n.clone();let r=xe(n.shape),o=$t(n,"float32"),s=e.complex(o,r);return r.dispose(),o.dispose(),s}if(!gc(n.dtype,t))return X.makeTensorFromDataId(n.dataId,n.shape,t);if(n.dtype==="complex64"){let r=e.real(n),o=$t(r,t);return r.dispose(),o}if(t==="int32")return e.int(n);if(t==="bool"){let r=Et(0,n.dtype),o=e.notEqual(n,r);return r.dispose(),o}else throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${t}`)}function AC(n,t){return X.makeTensorFromDataId(n.dataId,t,n.dtype)}function wv(n,t,e){let r=(t-n)/(e-1),o=bc(e,"float32");o[0]=n;for(let s=1;s<o.length;s++)o[s]=o[s-1]+r;return vr(o,"float32")}var 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Tv(n,t,e){let r=new Array(n.rank).fill(0),o=n.shape.slice();return t.map(s=>{let c=[...o];c[e]=s;let l=ce(n,r,c);return r[e]+=s,l})}function kv(n,t){let e=new Array(n.rank);for(let o=0;o<e.length;o++)e[o]=n.shape[o]*t[o];let r=Se(e,n.dtype);for(let o=0;o<r.values.length;++o){let s=r.indexToLoc(o),c=new Array(n.rank);for(let p=0;p<c.length;p++)c[p]=s[p]%n.shape[p];let l=n.locToIndex(c);r.values[o]=n.values[l]}return r.toTensor()}function Nv(n,t,e,r,o){let s=t[t.length-1],[c,l]=[n.length/s,s],p=Ce(e,c*r),f=Ce("int32",c*r);for(let y=0;y<c;y++){let b=y*l,v=n.subarray(b,b+l),T=[];for(let I=0;I<v.length;I++)T.push({value:v[I],index:I});T.sort((I,P)=>P.value-I.value);let N=y*r,S=p.subarray(N,N+r),D=f.subarray(N,N+r);for(let I=0;I<r;I++)S[I]=T[I].value,D[I]=T[I].index}let m=t.slice();return m[m.length-1]=r,[un(p,m,e),un(f,m,"int32")]}var 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Input received: ${t}`);for(let r=0;r<t.length;r++){let o=t[r],s=e[r];if(s==null)continue;let c=o.rank;if(s.ndim!=null&&c!==s.ndim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${c}`);if(s.maxNDim!=null&&c>s.maxNDim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${c}`);if(s.minNDim!=null&&c<s.minNDim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${c}.`);if(s.dtype!=null&&o.dtype!==s.dtype)throw new Y(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${o.dtype}.`);if(s.axes){let l=o.shape;for(let p in s.axes){let f=Number(p),m=s.axes[p],y=f>=0?l[f]:l[l.length+f];if(m!=null&&[m,null].indexOf(y)===-1)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected axis ${f} of input shape to have value ${m} but got shape ${l}.`)}}if(s.shape!=null)for(let l=0;l<s.shape.length;++l){let p=s.shape[l],f=o.shape[l];if(p!=null&&f!=null&&p!==f)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${o.shape}.`)}}}call(t,e){return t}invokeCallHook(t,e){this._callHook!=null&&this._callHook(t,e)}setCallHook(t){this._callHook=t}clearCallHook(){this._callHook=null}apply(t,e){e=e||{},this.assertNotDisposed();let r=We(t),o=!0;for(let c of r)if(!(c instanceof wo)){o=!1;break}let s=!0;for(let c of r)if(c instanceof wo){s=!1;break}if(o===s)throw new Y("Arguments to apply() must be all SymbolicTensors or all Tensors");return xa(this.name,()=>{if(!this.built){this.assertInputCompatibility(t);let c=[];for(let l of We(t))c.push(l.shape);this.build(hr(c)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(t),s){let c=this.call(t,e),l=We(c),p=[];for(let f of l)r.indexOf(f)!==-1&&(f=f.clone()),p.push(f);if(c=hr(p),this.activityRegularizer!=null)throw new Ut("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}else{let c=JG(t),l=this.computeOutputShape(c),p,f=ZG(t);if(this.warnOnIncompatibleInputShape(Array.isArray(t)?c[0]:c),l!=null&&l.length>0&&Array.isArray(l[0])?p=l.map((m,y)=>new wo(f,m,this,We(t),e,this.name,y)):p=new wo(f,l,this,We(t),e,this.name),this.addInboundNode(t,p,null,null,c,l,e),this._refCount++,this.activityRegularizer!=null)throw new Ut("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return p}})}warnOnIncompatibleInputShape(t){if(this.batchInputShape==null)return;if(t.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(t)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let e=!1;this.batchInputShape.forEach((r,o)=>{r!=null&&t[o]!=null&&t[o]!==r&&(e=!0)}),e&&console.warn(`The shape of the input tensor (${JSON.stringify(t)}) 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 qo(`The layer ${this.name} has never been called and thus has no defined output shape.`);let t=[];for(let e of this.inboundNodes){let r=JSON.stringify(e.outputShapes);t.indexOf(r)===-1&&t.push(r)}if(t.length===1){let e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&e.length===1?e[0]:e}else throw new qo(`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 Qr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return vm(this.weights)}build(t){this.built=!0}getWeights(t=!1){return Hv(t?this.trainableWeights:this.weights)}setWeights(t){rt(()=>{let e=this.weights;if(e.length!==t.length)throw new Y(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${t.length}, but the layer was expecting ${e.length} weights. Provided weights: ${t}...`);if(e.length===0)return;let r=[],o=Hv(e);for(let s=0;s<o.length;++s){let c=o[s],l=e[s],p=t[s];if(!lt(c.shape,p.shape))throw new Y(`Layer weight shape ${c.shape} not compatible with provided weight shape ${p.shape}`);r.push([l,p])}jv(r)})}addWeight(t,e,r,o,s,c,l){if(this._addedWeightNames.indexOf(t)!==-1)throw new Y(`Duplicate weight name ${t} for layer ${this.name}`);this._addedWeightNames.push(t),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(o=je("zeros"));let p=o.apply(e,r),f=new xo(p,r,t,c,l);return p.dispose(),s!=null&&this.addLoss(()=>s.apply(f.read())),c==null&&(c=!0),c?this._trainableWeights.push(f):this._nonTrainableWeights.push(f),f}setFastWeightInitDuringBuild(t){this.fastWeightInitDuringBuild=t}addLoss(t){if(t==null||Array.isArray(t)&&t.length===0)return;t=We(t),this._losses!==void 0&&this._losses!==null&&this.losses.push(...t)}computeOutputShape(t){return t}computeMask(t,e){if(!this.supportsMasking){if(e!=null)if(Array.isArray(e))e.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return e}addInboundNode(t,e,r,o,s,c,l=null){let p=We(t);e=We(e),r=We(r),o=We(o),s=wm(s),c=wm(c);let f=[],m=[],y=[];for(let b of p)f.push(b.sourceLayer),m.push(b.nodeIndex),y.push(b.tensorIndex);new Tm({outboundLayer:this,inboundLayers:f,nodeIndices:m,tensorIndices:y,inputTensors:p,outputTensors:e,inputMasks:r,outputMasks:o,inputShapes:s,outputShapes:c},l);for(let b=0;b<e.length;b++)e[b].sourceLayer=this,e[b].nodeIndex=this.inboundNodes.length-1,e[b].tensorIndex=b}getConfig(){let t={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(t.batchInputShape=this.batchInputShape),this.dtype!=null&&(t.dtype=this.dtype),t}disposeWeights(){return this.weights.forEach(t=>t.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let t=0;return--this._refCount===0&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}}}function JG(n){n=We(n);let t=[];for(let e of n)t.push(e.shape);return hr(t)}function ZG(n){return"float32"}function sS(n,t,e){if((t==null||e!=null&&e>0)&&(t=n.sourceLayer,e=n.nodeIndex),t.inboundNodes.length===0)return[n];{let r=t.inboundNodes[e];if(r.inboundLayers.length===0)return r.inputTensors;{let o=[];for(let s=0;s<r.inboundLayers.length;s++){let c=r.inputTensors[s],l=r.inboundLayers[s],p=r.nodeIndices[s],f=sS(c,l,p);for(let m of f)o.indexOf(m)===-1&&o.push(m)}return o}}}class tl extends ye{constructor(t){super({dtype:t.dtype,name:t.name!=null?t.name:xm("input").toString()});if(t.batchSize==null&&(t.batchSize=null),t.sparse==null&&(t.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=t.sparse,t.inputShape!=null&&t.batchInputShape!=null)throw new Y("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let e=t.batchInputShape;if(e==null){if(t.inputShape==null)throw new Y("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");e=[t.batchSize].concat(t.inputShape)}else if(t.batchSize!=null)throw new Y("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=t.dtype||"float32";this.batchInputShape=e,this.dtype=r,this.inputSpec=[{shape:e}];let o=new wo(this.dtype,this.batchInputShape,this,[],{},this.name);o.nodeIndex=0,o.tensorIndex=0,new Tm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[o],outputTensors:[o],inputMasks:[null],outputMasks:[null],inputShapes:[e],outputShapes:[e]})}apply(t,e){throw new Y(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}tl.className="InputLayer",vt(tl);function iS(n){if(n.batchShape==null&&n.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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Provided ${e} not understood: ${JSON.stringify(n)}`)}function _S(n,t){return NS(n,t,"classWeight")}function ust(n,t){return NS(n,t,"sampleWeight")}async function CS(n,t,e,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(e!=null){let o=rt(()=>{if(n.shape.length===1)return n.clone();if(n.shape.length===2)if(n.shape[1]>1){let l=1;return n.argMax(l)}else{if(n.shape[1]===1)return n.reshape([n.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${n.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${n.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await o.data());Xt(o);let c=[];return s.forEach(l=>{if(e[l]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(n.outputNames)})`);for(let p=0;p<s.length;p++)_(s[p].shape[0]===l,()=>`Batch size mismatch: input ${n.inputNames[p]} has ${s[p].shape[0]}; expected  ${l} based on input ${n.inputNames[0]}.`);for(let p=0;p<c.length;p++)_(c[p].shape[0]===l,()=>`Batch size mismatch: output ${n.outputNames[p]} has ${c[p].shape[0]}; expected  ${l} based on input ${n.inputNames[0]}.`);return{xs:s,ys:c}}function $S(n,t,e){if(e instanceof ot)return[e];if(Array.isArray(e))return _(e.length===t.length,()=>`Received an array of ${e.length} Tensors, but expected ${t.length} to match the ${n} keys ${t}.`),e;{let r=[];for(let o of t){if(e[o]==null)throw new Y(`The feature data generated by the dataset lacks the required ${n} key '${o}'.`);r.push(e[o])}return r}}function EU(n){if(n.length===3)throw new Ut("Validation with sample weights is not implemented yet.");return{xs:n[0],ys:n[1]}}async function DU(n,t,e){let r=e.batchesPerEpoch!=null;if(_(n.optimizer!=null,()=>"You must compile a model before training/testing. 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if(t.metrics!=null){s={};for(let c in t.metrics)s[c]=ba(t.metrics[c])}this.compile({loss:o,metrics:s,optimizer:r})}async save(t,e){if(typeof t=="string"){let f=ew(t);if(f.length===0)throw new Y(`Cannot find any save handlers for URL '${t}'`);if(f.length>1)throw new Y(`Found more than one (${f.length}) save handlers for URL '${t}'`);t=f[0]}if(t.save==null)throw new Y("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Qx(this.getNamedWeights(e)),o=!1,s=null,c=this.toJSON(s,o),l={modelTopology:c,format:WU,generatedBy:`TensorFlow.js tfjs-layers v${Dm}`,convertedBy:null},p=e==null?!1:e.includeOptimizer;if(p&&this.optimizer!=null){l.trainingConfig=this.getTrainingConfig();let f="optimizer",{data:m,specs:y}=await Qx(await this.optimizer.getWeights(),f);r.specs.push(...y),r.data=ud([r.data,m])}if(this.userDefinedMetadata!=null){let 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UU(n,t,e){if(e==null&&(e={}),n.load==null)throw new Y("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await n.load(),o=r.modelTopology;o.model_config!=null&&(o=o.model_config);let s=e.strict==null?!0:e.strict,c=r.weightData!=null&&r.weightSpecs!=null&&s,l=vo(Wp(o),t,c),p=r.trainingConfig;if(p!=null&&l.loadTrainingConfig(p),r.userDefinedMetadata!=null&&l.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new Y("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:f,optimizerWeights:m}=qU(r.weightData,r.weightSpecs);l.loadWeights(f,s),l.optimizer!=null&&m.length>0&&await l.optimizer.setWeights(m),Xt(f),Xt(m.map(y=>y.tensor))}return l}function qU(n,t){let e=ld(n,t),r={},o=[];return t.forEach(s=>{s.group==="optimizer"?o.push({name:s.name,tensor:e[s.name]}):r[s.name]=e[s.name]}),{modelWeights:r,optimizerWeights:o}}class Ta extends ks{constructor(t){super({inputs:[],outputs:[]});if(t=t||{},this.trainable=!0,this.built=!1,this.name=t.name!=null?t.name:xm("sequential_"),t.layers!=null)for(let e of t.layers)this.add(e)}checkShape(t){let e=t.inboundNodes[0].outputTensors[0].shape;if(e.some(r=>r<0))throw new Y(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let e=t instanceof Ta||t instanceof ks,r;if(e){if(r=t,r.outputs.length!==1)throw new Y("All layers in a Sequential model should have a single output tensor. 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compiled before being used.");return this.model.evaluate(t,e,r)}async evaluateDataset(t,e){if(!this.built)throw new Qr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),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(t){this.model.optimizer=t}async fit(t,e,r={}){if(!this.built)throw new Qr("The model needs to be compiled before being used.");return this.model.fit(t,e,r)}async fitDataset(t,e){if(!this.built)throw new Qr("The model needs to be compiled before being used.");return 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this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let r={};r.className=e.getClassName(),r.config=e.getConfig(),t.push(r)}return{name:this.name,layers:t}}}Ta.className="Sequential",vt(Ta);function HU(n){return new ks(n)}function jU(n){return new Ta(n)}function KU(n,t){return t==null&&(t={}),GU(n,t)}function PS(n){return iS(n)}function XU(n,t){Mr.registerCallbackConstructor(n,t)}class kr extends sa{getConfig(){return{}}}class OS extends kr{apply(t,e=1){return kG(t,e)}}OS.className="elu",vt(OS);class LS extends kr{apply(t){return zd(t)}}LS.className="selu",vt(LS);class MS extends kr{apply(t){return Uo(t)}}MS.className="relu",vt(MS);class BS extends kr{apply(t){return rt(()=>ua(6,Uo(t)))}}BS.className="relu6",vt(BS);class zS extends kr{apply(t){return t}}zS.className="linear",vt(zS);class WS extends kr{apply(t){return Wo(t)}}WS.className="sigmoid",vt(WS);class VS extends kr{apply(t){return _G(t)}}VS.className="hardSigmoid",vt(VS);class GS extends kr{apply(t){return 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Received: ${n.length} elements.`);for(let r=0;r<t;++r){let o=n[r];if(!yG(o))throw new Y(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(n)} including a non-integer number ${o}`)}return n}function ko(n,t,e,r,o=1){if(n==null)return n;let s=t+(t-1)*(o-1),c;return e==="same"?c=n:c=n-s+1,Math.floor((c+r-1)/r)}function Am(n,t,e,r){if(n==null)return null;if(r==="valid")n=n*t+bi([e-t,0]);else if(r==="same")n=n*t;else throw new Y(`Unsupport padding mode: ${r}.`);return n}function g0(n,t){return rt(()=>(nn(t),t==="channelsFirst"?Kt(n,[0,2,3,1]):n))}function JS(n,t){return rt(()=>(nn(t),t==="channelsFirst"?Kt(n,[0,2,3,4,1]):n))}function ZS(n,t,e,r=1,o="valid",s,c=1){return rt(()=>{if(s==null&&(s=yo()),nn(s),n.shape.length!==3)throw new Y(`The input of a conv1dWithBias operation should be 3, but is ${n.shape.length} instead.`);if(t.shape.length!==3)throw new Y(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new Y(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(n=Kt(n,[0,2,1])),o==="causal")throw new Ut("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let l=Cd(n,t,r,o==="same"?"same":"valid","NWC",c);return e!=null&&(l=jo(l,e)),l})}function pst(n,t,e=1,r="valid",o,s=1){return rt(()=>(nn(o),ZS(n,t,null,e,r,o,s)))}function hst(n,t,e=[1,1],r="valid",o,s){return rt(()=>(nn(o),y0(n,t,null,e,r,o,s)))}function y0(n,t,e,r=[1,1],o="valid",s,c,l=null){return rt(()=>{if(s==null&&(s=yo()),nn(s),n.rank!==3&&n.rank!==4)throw new Y(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${n.rank}.`);if(t.rank!==3&&t.rank!==4)throw new Y(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${n.rank}.`);let p=g0(n,s);if(o==="causal")throw new Ut("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return p=cv({x:p,filter:t,strides:r,pad:o==="same"?"same":"valid",dilations:c,dataFormat:"NHWC",bias:e,activation:l}),s==="channelsFirst"&&(p=Kt(p,[0,3,1,2])),p})}function fst(n,t,e=[1,1,1],r="valid",o,s){return rt(()=>(nn(o),QS(n,t,null,e,r,o,s)))}function QS(n,t,e,r=[1,1,1],o="valid",s,c){return rt(()=>{if(s==null&&(s=yo()),nn(s),n.rank!==4&&n.rank!==5)throw new Y(`conv3dWithBias expects input to be of rank 4 or 5, but received ${n.rank}.`);if(t.rank!==4&&t.rank!==5)throw new Y(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${n.rank}.`);let l=JS(n,s);if(o==="causal")throw new Ut("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return l=Fw(l,t,r,o==="same"?"same":"valid","NDHWC",c),e!=null&&(l=jo(l,e)),s==="channelsFirst"&&(l=Kt(l,[0,4,1,2,3])),l})}class Fm extends ye{constructor(t,e){super(e);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Fm.verifyArgs(e),this.rank=t,$n(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ut(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=rl(e.kernelSize,t,"kernelSize"),this.strides=rl(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,Lr(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,nn(this.dataFormat),this.activation=ki(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Tn(e.biasConstraint),this.biasRegularizer=Ke(e.biasRegularizer),this.activityRegularizer=Ke(e.activityRegularizer),this.dilationRate=rl(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new Y(`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 Y(`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 Y(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(Or("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!$v(t.kernelSize,"number",1,3))throw new Y(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ti(this.activation),useBias:this.useBias,biasInitializer:rn(this.biasInitializer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),biasConstraint:vn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}class ol extends Fm{constructor(t,e){super(t,e);this.kernel=null,ol.verifyArgs(e),this.filters=e.filters,$n(this.filters,"filters"),this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Tn(e.kernelConstraint),this.kernelRegularizer=Ke(e.kernelRegularizer)}build(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Y(`The channel dimension of the input should be defined. Found ${t[e]}`);let r=t[e],o=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",o,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:{[e]:r}}],this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r,o=this.bias==null?null:this.bias.read(),s=GC(this.activation.getClassName());if(s!=null&&this.rank===2)r=y0(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)r=ZS(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=y0(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=QS(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ut("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(t){t=Re(t);let e=[],r=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<r.length;++s){let c=ko(r[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(c)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:rn(this.kernelInitializer),kernelRegularizer:Pe(this.kernelRegularizer),kernelConstraint:vn(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new Y(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}}class sl extends ol{constructor(t){super(2,t);sl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!$v(t.kernelSize,"number",1,2))throw new Y(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}}sl.className="Conv2D",vt(sl);class qp extends ol{constructor(t){super(3,t);qp.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new Y(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}}qp.className="Conv3D",vt(qp);class b0 extends sl{constructor(t){super(t);if(this.inputSpec=[new In({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new Y(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Re(t),t.length!==4)throw new Y("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Y("The channel dimension of the inputs should be defined. Found `None`.");let r=t[e],o=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new In({ndim:4,axes:{[e]:r}})],this.built=!0}call(t,e){return rt(()=>{let r=Qt(t);if(r.shape.length!==4)throw new Y(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let o=r.shape,s=o[0],c,l;this.dataFormat==="channelsFirst"?(c=2,l=3):(c=1,l=2);let p=o[c],f=o[l],m=this.kernelSize[0],y=this.kernelSize[1],b=this.strides[0],v=this.strides[1],T=Am(p,b,m,this.padding),N=Am(f,v,y,this.padding),S=[s,T,N,this.filters];this.dataFormat!=="channelsLast"&&(r=Kt(r,[0,2,3,1]));let D=Sd(r,this.kernel.read(),S,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(D=Kt(D,[0,3,1,2])),this.bias!=null&&(D=jo(D,this.bias.read(),this.dataFormat)),this.activation!=null&&(D=this.activation.apply(D)),D})}computeOutputShape(t){t=Re(t);let e=t.slice(),r,o,s;this.dataFormat==="channelsFirst"?(r=1,o=2,s=3):(r=3,o=1,s=2);let c=this.kernelSize[0],l=this.kernelSize[1],p=this.strides[0],f=this.strides[1];return e[r]=this.filters,e[o]=Am(e[o],p,c,this.padding),e[s]=Am(e[s],f,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}}b0.className="Conv2DTranspose",vt(b0);class t$ extends ol{constructor(t,e){super(t,e);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new Y("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new Y("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new Y(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ke(e.depthwiseRegularizer),this.depthwiseConstraint=Tn(e.depthwiseConstraint),this.pointwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ke(e.pointwiseRegularizer),this.pointwiseConstraint=Tn(e.pointwiseConstraint)}build(t){if(t=Re(t),t.length<this.rank+2)throw new Y(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new Y(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let r=t[e],o=this.kernelSize.concat([r,this.depthMultiplier]),s=[];for(let l=0;l<this.rank;++l)s.push(1);s.push(r*this.depthMultiplier,this.filters);let c=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,c,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,c,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,c,this.biasConstraint):this.bias=null,this.inputSpec=[new In({ndim:this.rank+2,axes:{[e]:r}})],this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r;if(this.rank===1)throw new Ut("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Kt(t,[0,2,3,1])),r=Jw(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=jo(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=Kt(r,[0,3,1,2])),r})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=rn(this.depthwiseInitializer),t.pointwiseInitializer=rn(this.pointwiseInitializer),t.depthwiseRegularizer=Pe(this.depthwiseRegularizer),t.pointwiseRegularizer=Pe(this.pointwiseRegularizer),t.depthwiseConstraint=vn(this.depthwiseConstraint),t.pointwiseConstraint=vn(this.pointwiseConstraint),t}}t$.className="SeparableConv";class x0 extends t${constructor(t){super(2,t)}}x0.className="SeparableConv2D",vt(x0);class Hp extends ol{constructor(t){super(1,t);Hp.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!$v(t.kernelSize,"number",1,1))throw new Y(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}}Hp.className="Conv1D",vt(Hp);class w0 extends ye{constructor(t){super(t);typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return rt(()=>{if(t=Qt(t),this.dataFormat==="channelsLast"){let r=lm(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return lm(r,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=lm(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return lm(r,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}w0.className="Cropping2D",vt(w0);class v0 extends ye{constructor(t){super(t);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],r=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,r]}else{let e=t[1]==null?null:this.size[0]*t[1],r=t[2]==null?null:this.size[1]*t[2];return[t[0],e,r,t[3]]}}call(t,e){return rt(()=>{let r=Qt(t),o=r.shape;if(this.dataFormat==="channelsFirst"){r=Kt(r,[0,2,3,1]);let s=this.size[0]*o[2],c=this.size[1]*o[3],l=r.resizeNearestNeighbor([s,c]);return Kt(l,[0,3,1,2])}else{let s=this.size[0]*o[1],c=this.size[1]*o[2];return r.resizeNearestNeighbor([s,c])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}v0.className="UpSampling2D",vt(v0);function ZU(n,t,e=[1,1],r="valid",o,s){return rt(()=>{o==null&&(o=yo()),nn(o);let c=g0(n,o);if(n.rank!==4)throw new Y(`Input for depthwiseConv2d is required to be 4-D, but is instead ${n.rank}-D`);if(t.rank!==4)throw new Y(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return c=ca(c,t,e,r==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(c=Kt(c,[0,3,1,2])),c})}class T0 extends Fm{constructor(t){super(2,t);this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=je(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Tn(t.depthwiseConstraint),this.depthwiseRegularizer=Ke(t.depthwiseRegularizer)}build(t){if(t=Re(t),t.length<4)throw new Y(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new Y(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let r=t[e],o=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r=ZU(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=jo(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=ko(e,this.kernelSize[0],this.padding,this.strides[0]),c=ko(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,c]:[t[0],s,c,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=rn(this.depthwiseInitializer),t.depthwiseRegularizer=Pe(this.depthwiseRegularizer),t.depthwiseConstraint=vn(this.depthwiseRegularizer),t}}T0.className="DepthwiseConv2D",vt(T0);function e$(n,t,e,r){if(Array.isArray(n)){if(t!=null||e!=null)throw new Y("When inputs is an array, neither initialState or constants should be provided");r!=null&&(e=n.slice(n.length-r,n.length),n=n.slice(0,n.length-r)),n.length>1&&(t=n.slice(1,n.length)),n=n[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:n,initialState:t,constants:e}}function n$(n,t,e,r=!1,o,s,c=!1,l=!1){return rt(()=>{let p=t.shape.length;if(p<3)throw new Y(`Input should be at least 3D, but is ${p}D.`);let f=[1,0].concat(bo(2,p));if(t=Kt(t,f),s!=null)throw new Ut("The rnn() functoin of the deeplearn.js backend does not support constants yet.");c&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===p-1&&(o=lr(o,-1)),o=Kt(o,f)),r&&(t=Pr(t,0),o!=null&&(o=Pr(o,0)));let m=[],y,b=e,v=t.shape[0],T=go(t),N;o!=null&&(N=go(o));for(let D=0;D<v;++D){let I=T[D],P=rt(()=>n(I,b));if(o==null)y=P[0],b=P[1];else{let E=rt(()=>{let L=N[D],B=qn(L).sub(L),q=P[0].mul(L).add(b[0].mul(B)),H=b.map((Z,J)=>P[1][J].mul(L).add(Z.mul(B)));return{output:q,newStates:H}});y=E.output,b=E.newStates}l&&m.push(y)}let S;if(l){let D=1;S=pr(m,D)}return[y,S,b]})}class No extends ye{constructor(t){super(t);let e;if(t.cell==null)throw new Y("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Om({cells:t.cell}):e=t.cell,e.stateSize==null)throw new Y("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new In({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return bo(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){qv(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let r=e[0],o;if(this.returnSequences?o=[t[0],t[1],r]:o=[t[0],r],this.returnState){let s=[];for(let c of e)s.push([t[0],c]);return[o].concat(s)}else return o}computeMask(t,e){return rt(()=>{Array.isArray(e)&&(e=e[0]);let r=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[r].concat(o)}else return r})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let r=0;r<t;++r)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){let e=null;if(this.numConstants!=null)throw new Ut("Constants support is not implemented in RNN yet.");qv(t)&&(t=t[0]),t=t;let r=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new In({shape:[r,null,...o]});let s=[t[0]].concat(t.slice(2));if(e!=null)throw new Ut("Constants support is not implemented in RNN yet.");this.cell.build(s);let c;if(Array.isArray(this.cell.stateSize)?c=this.cell.stateSize:c=[this.cell.stateSize],this.stateSpec!=null){if(!lt(this.stateSpec.map(l=>l.shape[l.shape.length-1]),c))throw new Y(`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=c.map(l=>new In({shape:[null,l]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){rt(()=>{if(!this.stateful)throw new qo("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new Y("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(o=>xe([r,o])):this.states_=[xe([r,this.cell.stateSize])];else if(t==null)Xt(this.states_),this.keptStates!=null&&(Xt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>xe([r,o])):this.states_[0]=xe([r,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Y(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Xt(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],c=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,l=[r,c];if(!lt(s.shape,l))throw new Y(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Sn(o.clone()))})}apply(t,e){let r=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=e$(t,r,o,this.numConstants);t=s.inputs,r=s.initialState,o=s.constants;let c=[],l=[];if(r!=null){e.initialState=r,c=c.concat(r),this.stateSpec=[];for(let f of r)this.stateSpec.push(new In({shape:f.shape}));l=l.concat(this.stateSpec)}o!=null&&(e.constants=o,c=c.concat(o),this.numConstants=o.length);let p=c[0]instanceof wo;if(p){let f=[t].concat(c),m=this.inputSpec.concat(l),y=this.inputSpec;this.inputSpec=m;let b=super.apply(f,e);return this.inputSpec=y,b}else return super.apply(t,e)}call(t,e){return rt(()=>{let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Qt(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let c=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==c)throw new Y(`RNN Layer has ${c} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let l={training:o},p=(T,N)=>{let S=this.cell.call([T].concat(N),l);return[S[0],S.slice(1)]},f=n$(p,t,s,this.goBackwards,r,null,this.unroll,this.returnSequences),m=f[0],y=f[1],b=f[2];this.stateful&&this.resetStates(b,o);let v=this.returnSequences?y:m;return this.returnState?[v].concat(b):v})}getInitialState(t){return rt(()=>{let e=xe(t.shape);return e=zt(e,[1,2]),e=Lp(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?Ov(e,[1,r]):e):this.cell.stateSize>1?[Ov(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===No.className&&(e.cell={className:this.cell.getClassName(),config:r}),Object.assign({},r,t,e)}static fromConfig(t,e,r={}){let o=e.cell,s=vo(o,r);return new t(Object.assign(e,{cell:s}))}}No.className="RNN",vt(No);class il extends ye{}class Rm extends il{constructor(t){super(t);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,$n(this.units,"units"),this.activation=ki(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Qc([1,bi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Qc([1,bi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Re(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return rt(()=>{if(t=t,t.length!==2)throw new Y(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let r=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ni({ones:()=>qn(t),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ni({ones:()=>qn(r),rate:this.recurrentDropout,training:o}));let s,c=this.dropoutMask,l=this.recurrentDropoutMask;c!=null?s=Ho(nt(t,c),this.kernel.read()):s=Ho(t,this.kernel.read()),this.bias!=null&&(s=jo(s,this.bias.read())),l!=null&&(r=nt(r,l));let p=Tt(s,Ho(r,this.recurrentKernel.read()));return this.activation!=null&&(p=this.activation.apply(p)),[p,p]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:Ti(this.activation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},t,e)}}Rm.className="SimpleRNNCell",vt(Rm);class k0 extends No{constructor(t){t.cell=new Rm(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}}k0.className="SimpleRNN",vt(k0);class Pm extends il{constructor(t){super(t);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",t.resetAfter)throw new Y("GRUCell does not support reset_after parameter set to true.");this.units=t.units,$n(this.units,"units"),this.activation=ki(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=ki(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Qc([1,bi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Qc([1,bi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Re(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return rt(()=>{if(t=t,t.length!==2)throw new Y(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ni({ones:()=>qn(t),rate:this.dropout,training:r,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ni({ones:()=>qn(o),rate:this.recurrentDropout,training:r,count:3}));let s=this.dropoutMask,c=this.recurrentDropoutMask,l,p,f;0<this.dropout&&this.dropout<1&&(t=nt(t,s[0]));let m=Ho(t,this.kernel.read());this.useBias&&(m=jo(m,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=nt(o,c[0]));let y=this.recurrentKernel.read(),[b,v]=Tr(y,[2*this.units,this.units],y.rank-1),T=Ho(o,b),[N,S,D]=Tr(m,3,m.rank-1),[I,P]=Tr(T,2,T.rank-1);l=this.recurrentActivation.apply(Tt(N,I)),p=this.recurrentActivation.apply(Tt(S,P));let E=Ho(nt(p,o),v);f=this.activation.apply(Tt(D,E));let L=Tt(nt(l,o),nt(Tt(1,tn(l)),f));return[L,L]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:Ti(this.activation),recurrentActivation:Ti(this.recurrentActivation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},t,e)}}Pm.className="GRUCell",vt(Pm);class N0 extends No{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Pm(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}N0.className="GRU",vt(N0);class jp extends il{constructor(t){super(t);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=t.units,$n(this.units,"units"),this.activation=ki(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=ki(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Qc([1,bi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Qc([1,bi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Re(t);let r=t[t.length-1];this.kernel=this.addWeight("kernel",[r,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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,c=this.units;o=new(e=class extends eo{apply(p,f){let m=s.apply([c]),y=new pm().apply([c]),b=s.apply([c*2]);return ZC(ZC(m,y),b)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return rt(()=>{let r=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new Y(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ni({ones:()=>qn(t),rate:this.dropout,training:r,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ni({ones:()=>qn(o),rate:this.recurrentDropout,training:r,count:4}));let c=this.dropoutMask,l=this.recurrentDropoutMask,p,f,m,y;0<this.dropout&&this.dropout<1&&(t=nt(t,c[0]));let b=Ho(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=nt(o,l[0])),b=Tt(b,Ho(o,this.recurrentKernel.read())),this.useBias&&(b=jo(b,this.bias.read()));let[v,T,N,S]=Tr(b,4,b.rank-1);p=this.recurrentActivation.apply(v),f=this.recurrentActivation.apply(T),m=Tt(nt(f,s),nt(p,this.activation.apply(N))),y=this.recurrentActivation.apply(S);let D=nt(y,this.activation.apply(m));return[D,D,m]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:Ti(this.activation),recurrentActivation:Ti(this.recurrentActivation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},t,e)}}jp.className="LSTMCell",vt(jp);class _0 extends No{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new jp(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}_0.className="LSTM",vt(_0);class Om extends il{constructor(t){super(t);this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return rt(()=>{t=t;let r=t.slice(1),o=[];for(let l of this.cells.slice().reverse())Array.isArray(l.stateSize)?o.push(r.splice(0,l.stateSize.length)):o.push(r.splice(0,1));o.reverse();let s=[],c;for(let l=0;l<this.cells.length;++l){let p=this.cells[l];r=o[l],l===0?c=[t[0]].concat(r):c=[c[0]].concat(r),c=p.call(c,e),s.push(c.slice(1))}r=[];for(let l of s.slice().reverse())r.push(...l);return[c[0]].concat(r)})}build(t){qv(t)&&(t=t[0]),t=t;let e;this.cells.forEach((r,o)=>{xa(`RNNCell_${o}`,()=>{r.build(t),Array.isArray(r.stateSize)?e=r.stateSize[0]:e=r.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),r=this.cells.map(e),o={cells:r};return Object.assign({},t,o)}static fromConfig(t,e,r={}){let o=[];for(let s of e.cells)o.push(vo(s,r));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let r of this.cells)e.push(...r.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Hv(t)}setWeights(t){let e=[];for(let r of this.cells){let o=r.weights.length,s=t.splice(o);for(let c=0;c<r.weights.length;++c)e.push([r.weights[c],s[c]])}jv(e)}}Om.className="StackedRNNCells",vt(Om);function Ni(n){let{ones:t,rate:e,training:r=!1,count:o=1}=n,s=()=>tS(t(),e),c=()=>Bp(s,t,r);if(!o||o<=1)return Sn(c().clone());let l=Array(o).fill(void 0).map(c);return l.map(p=>Sn(p.clone()))}var QU=function(n,t){var e={};for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&t.indexOf(r)<0&&(e[r]=n[r]);if(n!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,r=Object.getOwnPropertySymbols(n);o<r.length;o++)t.indexOf(r[o])<0&&Object.prototype.propertyIsEnumerable.call(n,r[o])&&(e[r[o]]=n[r[o]]);return e};class dst extends il{}class r$ extends No{constructor(t){if(t.unroll)throw new Ut("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new Ut("It is not possible at the moment to stack convolutional cells.");super(t);this.inputSpec=[new In({ndim:5})]}call(t,e){return rt(()=>{if(this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new Y("ConvRNN2D cell does not support constants");let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return rt(()=>{let{stateSize:e}=this.cell,r=t.shape,o=this.computeSingleOutputShape(r),s=[o[0],...o.slice(2)],c=xe(s);return Array.isArray(e)?Array(e.length).fill(c):[c]})}resetStates(t,e=!1){rt(()=>{if(!this.stateful)throw new qo("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,o=this.computeSingleOutputShape(r),s=[o[0],...o.slice(2)],c=r[0];if(c==null)throw new Y("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(()=>xe(s)):this.states_=[xe(s)];else if(t==null)Xt(this.states_),this.keptStates!=null&&(Xt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xe(s)):this.states_[0]=xe(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Y(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Xt(this.states_);for(let l=0;l<this.states_.length;++l){let p=t[l],f=s;if(!lt(p.shape,f))throw new Y(`State ${l} is incompatible with layer ${this.name}: expected shape=${f}, received shape=${p.shape}`);this.states_[l]=p}}this.states_=this.states_.map(l=>Sn(l.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:r,kernelSize:o,padding:s,strides:c,dilationRate:l}=this.cell,p=e==="channelsFirst",f=t[p?3:2],m=t[p?4:3],y=ko(f,o[0],s,c[0],l[0]),b=ko(m,o[1],s,c[1],l[1]),v=[...t.slice(0,2),...p?[r,y,b]:[y,b,r]];return v}}r$.className="ConvRNN2D";class Lm extends jp{constructor(t){let{filters:e,kernelSize:r,strides:o,padding:s,dataFormat:c,dilationRate:l}=t;super(Object.assign({},t,{units:e}));this.filters=e,$n(this.filters,"filters"),this.kernelSize=rl(r,2,"kernelSize"),this.kernelSize.forEach(p=>$n(p,"kernelSize")),this.strides=rl(o||1,2,"strides"),this.strides.forEach(p=>$n(p,"strides")),this.padding=s||"valid",Lr(this.padding),this.dataFormat=c||"channelsLast",nn(this.dataFormat),this.dilationRate=rl(l||1,2,"dilationRate"),this.dilationRate.forEach(p=>$n(p,"dilationRate"))}build(t){var e;t=Re(t);let r=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[r]==null)throw new Y(`The channel dimension of the input should be defined. Found ${t[r]}`);let o=t[r],s=4,c=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",c,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let l=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",l,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let p;if(this.unitForgetBias){let f=this.biasInitializer,m=this.filters;p=new(e=class extends eo{apply(b,v){let T=f.apply([m]),N=fo([m]),S=f.apply([m*2]);return Pv([T,N,S])}},e.className="CustomInit",e)}else p=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,p,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return rt(()=>{if(t.length!==3)throw new Y(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training||!1,o=t[0],s=t[1],c=t[2],l=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ni({ones:()=>qn(o),rate:this.dropout,training:r,count:l}));let p=this.dropoutMask,f=(kt,Nt,At)=>!Nt||!Nt[At]?kt:nt(Nt[At],kt),m=f(o,p,0),y=f(o,p,1),b=f(o,p,2),v=f(o,p,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ni({ones:()=>qn(s),rate:this.recurrentDropout,training:r,count:l}));let T=this.recurrentDropoutMask,N=f(s,T,0),S=f(s,T,1),D=f(s,T,2),I=f(s,T,3),P=3,[E,L,B,q]=Tr(this.kernel.read(),l,P),[H,Z,J,it]=this.useBias?Tr(this.bias.read(),l):[null,null,null,null];m=this.inputConv(m,E,H,this.padding),y=this.inputConv(y,L,Z,this.padding),b=this.inputConv(b,B,J,this.padding),v=this.inputConv(v,q,it,this.padding);let[pt,ht,dt,ft]=Tr(this.recurrentKernel.read(),l,P);N=this.recurrentConv(N,pt),S=this.recurrentConv(S,ht),D=this.recurrentConv(D,dt),I=this.recurrentConv(I,ft);let ut=this.recurrentActivation.apply(Tt(m,N)),bt=this.recurrentActivation.apply(Tt(y,S)),yt=Tt(nt(bt,c),nt(ut,this.activation.apply(Tt(b,D)))),xt=nt(this.recurrentActivation.apply(Tt(v,I)),this.activation.apply(yt));return[xt,xt,yt]})}getConfig(){let t=super.getConfig(),{units:e}=t,r=QU(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},r,o)}inputConv(t,e,r,o){let s=gs(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?jo(s,r,this.dataFormat):s}recurrentConv(t,e){let r=1;return gs(t,e,r,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Lm.className="ConvLSTM2DCell",vt(Lm);class C0 extends r${constructor(t){let e=new Lm(t);super(Object.assign({},t,{cell:e}))}static fromConfig(t,e){return new t(e)}}C0.className="ConvLSTM2D",vt(C0);class Mm extends ye{constructor(t){super(t);this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,r=[];for(let o=0;o<this.noiseShape.length;++o)r.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return r}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(r),c=Bp(()=>tS(r,this.rate,s,this.seed),()=>r,o);return c}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}}Mm.className="Dropout",vt(Mm);class S0 extends Mm{constructor(t){super(t);this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}}S0.className="SpatialDropout1D",vt(S0);class $0 extends ye{constructor(t){super(t);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,$n(this.units,"units"),this.activation=ki(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Tn(t.kernelConstraint),this.biasConstraint=Tn(t.biasConstraint),this.kernelRegularizer=Ke(t.kernelRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.activityRegularizer=Ke(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Re(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Re(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=GC(this.activation.getClassName()),s;return o!=null?s=Ho(r,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Ho(r,this.kernel.read()),this.bias!=null&&(s=jo(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:Ti(this.activation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),biasConstraint:vn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}$0.className="Dense",vt($0);class I0 extends ye{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Re(t);for(let e of t.slice(1))if(e==null)throw new Y(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],yi(t,1)]}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(this.dataFormat==="channelsFirst"&&r.rank>1){let o=[0];for(let s=2;s<r.rank;++s)o.push(s);o.push(1),r=r.transpose(o)}return TG(r)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}}I0.className="Flatten",vt(I0);class E0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.activation=ki(t.activation)}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);return this.activation.apply(r)})}getConfig(){let t={activation:Ti(this.activation)},e=super.getConfig();return Object.assign(t,e),t}}E0.className="Activation",vt(E0);class D0 extends ye{constructor(t){super(t);this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return rt(()=>(t=Qt(t),wG(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}}D0.className="RepeatVector",vt(D0);class A0 extends ye{constructor(t){super(t);this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let r="Total size of new array must be unchanged.",o=e.slice(),s=1,c=null;for(let p=0;p<o.length;++p){let f=o[p];if(this.isUnknown(f))if(c===null)c=p;else throw new Y("Can only specifiy one unknown dimension.");else s*=f}let l=yi(t);if(c!==null){if(s===0||l%s!==0)throw new Y(r);o[c]=l/s}else if(l!==s)throw new Y(r);return o}computeOutputShape(t){let e=!1;for(let r=0;r<t.length;++r)if(this.isUnknown(t[r])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=r.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return r.reshape(s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}}A0.className="Reshape",vt(A0);class F0 extends ye{constructor(t){super(t);if(t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=bo(1,t.dims.length+1);if(!lt(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new In({ndim:this.dims.length+1})]}computeOutputShape(t){t=Re(t);let e=t.slice();return this.dims.forEach((r,o)=>{e[o+1]=t[r]}),e}call(t,e){return Kt(Qt(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}}F0.className="Permute",vt(F0);class R0 extends ye{constructor(t){super(t==null?{}:t);this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let r=Qt(t),o=-1;return op(hi(r,this.maskValue),o)}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=-1,s=!0,c=op(hi(r,this.maskValue),o,s),l=r.mul(c.asType(r.dtype));return l})}}R0.className="Masking",vt(R0);class P0 extends ye{constructor(t){super(t);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(We(t.inputLength))}this.inputDim=t.inputDim,$n(this.inputDim,"inputDim"),this.outputDim=t.outputDim,$n(this.outputDim,"outputDim"),this.embeddingsInitializer=je(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ke(t.embeddingsRegularizer),this.activityRegularizer=Ke(t.activityRegularizer),this.embeddingsConstraint=Tn(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return rt(()=>this.maskZero?(t=Qt(t),hi(t,re(t))):null)}computeOutputShape(t){if(t=Re(t),this.inputLength==null)return[...t,this.outputDim];let e=We(this.inputLength);if(e.length!==t.length-1)throw new Y(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let r=0;for(let o=0;o<e.length;++o){let s=e[o],c=t[o+1];if(s!=null&&c!=null&&s!==c)throw new Y(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);s==null&&(e[r]=c),r++}}return[t[0],...e,this.outputDim]}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);r.dtype!=="int32"&&(r=Op(r,"int32"));let o=QC(this.embeddings.read(),r.as1D());return o.reshape(Re(this.computeOutputShape(r.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:rn(this.embeddingsInitializer),embeddingsRegularizer:Pe(this.embeddingsRegularizer),activityRegularizer:Pe(this.activityRegularizer),embeddingsConstraint:vn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}}P0.className="Embedding",vt(P0);class ka extends ye{constructor(t){super(t||{});this.supportsMasking=!0}mergeFunction(t){throw new Ut}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let r=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],c=e[o];if(s==null||c==null||s<0||c<0)r.push(null);else if(s===1)r.push(c);else if(c===1)r.push(s);else{if(s!==c)throw new Y("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));r.push(s)}}return r}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Re(t)]),t=t,t.length<2)throw new Y(`A merge layer should be called on an Array of at least 2 inputs. Got ${t.length} input(s).`);let e=[];for(let s of t)s!=null&&s[0]!==null&&e.push(s[0]);if(e=gi(e),e.length>1)throw new Y(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let r=t[0]==null?null:t[0].slice(1);for(let s=1;s<t.length;++s){let c=t[s]==null?null:t[s].slice(1);r=this.computeElementwiseOpOutputShape(r,c)}let o=t.map(s=>s.length);t.indexOf(null)===-1&&gi(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return rt(()=>{if(t=t,this.reshapeRequired){let r=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=bi(o);for(let c of t){let l=c.rank;for(let p=0;p<s-l;++p)c=Lp(c,1);r.push(c)}return this.mergeFunction(r)}else{let s=!1;for(let p of t){let f=p.rank;if(f==null){let m=p.shape,y=m[0],b=m.slice(1).concat([y]),v=p.reshape([y].concat(yi(m.slice(1))));v=Kt(v,[1,0]),v=v.reshape(b),r.push(v),s=!0}else if(f>1){let m=bo(1,f).concat([0]);r.push(Kt(p,m)),s=!0}else r.push(p)}let c=this.mergeFunction(r),l=c.rank;if(s){if(l==null){let p=c.shape,f=p.length,m=p[f-1],y=[m].concat(p.slice(0,p.length-1));c=Kt(c.reshape([-1,m]),[1,0]).reshape(y)}else if(l>1){let p=[l-1].concat(bo(0,l-1));c=Kt(c,p)}}return c}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o<t.length;++o){let s=t[o]==null?null:t[o].slice(1);e=this.computeElementwiseOpOutputShape(e,s)}let r=[];for(let o of t)o!=null&&o[0]!==null&&r.push(o[0]);return r=gi(r),r.length===1?e=r.concat(e):e=[null].concat(e),e}computeMask(t,e){return rt(()=>{if(e==null)return null;if(!Array.isArray(e))throw new Y("`mask` should be an Array");if(!Array.isArray(t))throw new Y("`inputs` should be an Array");if(e.length!==t.length)throw new Y(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:lr(o,0));let r=e[0];for(let o=1;o<e.length-1;++o)r=Zr(r,e[o]);return r})}}class Kp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Tt(e,t[r]);return e})}}Kp.className="Add",vt(Kp);function mst(n){if(Array.isArray(n)){let t=new Kp({});return t.apply(n)}else return new Kp(n)}class Xp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=nt(e,t[r]);return e})}}Xp.className="Multiply",vt(Xp);function gst(n){if(Array.isArray(n)){let t=new Xp({});return t.apply(n)}else return new Xp(n)}class Yp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Tt(e,t[r]);return nt(1/t.length,e)})}}Yp.className="Average",vt(Yp);function yst(n){if(Array.isArray(n)){let t=new Yp({});return t.apply(n)}else return new Yp(n)}class Jp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=Jr(e,t[r]);return e})}}Jp.className="Maximum",vt(Jp);function bst(n){if(Array.isArray(n)){let t=new Jp({});return t.apply(n)}else return new Jp(n)}class Zp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=ua(e,t[r]);return e})}}Zp.className="Minimum",vt(Zp);function xst(n){if(Array.isArray(n)){let t=new Zp({});return t.apply(n)}else return new Zp(n)}class Qp extends ka{constructor(t){super(t);this.DEFAULT_AXIS=-1,t==null&&(t={}),this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){if(!(Array.isArray(t)&&Array.isArray(t[0]))||t.length===1)throw new Y("A `Concatenate` layer should be called on a list of at least 2 inputs");t=t;let e=!0;for(let o of t)if(o!=null){e=!1;break}if(e)return;let r=[];for(let o=0;o<t.length;++o){let s=t[o].slice();s.splice(this.axis,1);let c=!1;for(let l of r)if(lt(l,s)){c=!0;break}c||r.push(s)}if(r.length>1)throw new Y("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return rt(()=>Pv(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new Y("A `Concatenate` layer should be called on a list of inputs.");let e=t,r=e[0].slice(),o=this.axis<0?r.length+this.axis:this.axis;for(let s of e.slice(1)){if(r[o]==null||s[o]==null){r[o]=null;break}r[o]+=s[o]}return r}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new Y("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new Y("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new Y(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return rt(()=>{let r=!0;if(e.forEach(c=>{if(c!=null){r=!1;return}}),r)return null;let o=[];for(let c=0;c<t.length;++c)e[c]==null?o.push(qn(t[c]).asType("bool")):e[c].rank<t[c].rank?o.push(lr(e[c],-1)):o.push(e[c]);let s=Qe(o,this.axis);return vd(s,-1,!1)})}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}}Qp.className="Concatenate",vt(Qp);function wst(n){if(Array.isArray(n)){let t=new Qp({});return t.apply(n)}else return new Qp(n)}function th(n,t){for(;n<0;)n+=t;return n}function tq(n,t,e){if(n.shape.length>3||t.shape.length>3)throw new Ut("batchDot is not implemented for tensors of 4D or higher rank yet");if(_(n.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${n.shape.length}`),_(n.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),n.dtype==="complex64"||t.dtype==="complex64")throw new Ut("batchDot is not implemented for complex64-type Tensors yet.");let r=n.shape.length,o=t.shape.length;e==null&&(e=[r-1,o-2]);let s=e;return rt(()=>{let c;if(r>o){c=r-o;let p=[];for(let f=0;f<c;++f)p.push(1);t=t.reshape(t.shape.concat(p))}else if(o>r){c=o-r;let p=[];for(let f=0;f<c;++f)p.push(1);n=n.reshape(n.shape.concat(p))}else c=0;let l;if(n.shape.length===2&&t.shape.length===2)s[0]===s[1]?l=n.mul(t).sum(s[0]):l=n.transpose([1,0]).mul(t).sum(s[1]);else{let p=s[0]!==n.shape.length-1,f=s[1]===t.shape.length-1;l=n.matMul(t,p,f)}if(c>0){let p;r>o?p=r+o-3:p=r-1;let f=[];for(let m=p;m<p+c;++m)f.push(m);l=l.squeeze(f)}return l.shape.length===1&&(l=l.expandDims(1)),l})}class O0 extends ka{constructor(t){super(t);this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){_(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],r=t[1];if(e.length>3||r.length>3)throw new Ut("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,r);if(e[o[0]]!==r[o[1]])throw new Y(`Dimension incompatibility: ${e[o[0]]} !== ${r[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new Y(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],r=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,c)=>th(s,t[c].shape.length)):o=[th(this.axes,e.shape.length),th(this.axes,r.shape.length)],this.normalize&&(e=km(e,o[0]),r=km(r,o[1])),tq(e,r,o)}interpretAxes(t,e){let r;return Array.isArray(this.axes)?r=this.axes:r=[th(this.axes,t.length),th(this.axes,e.length)],r}computeOutputShape(t){_(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),r=t[1].slice();if(e.length>3||r.length>3)throw new Ut("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,r);e.splice(o[0],1),r.splice(o[1],1),r.splice(0,1);let s=e.concat(r);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}}O0.className="Dot",vt(O0);class L0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=()=>um(r.shape,0,this.stddev).add(r),s=Bp(o,()=>r,e.training||!1);return s})}}L0.className="GaussianNoise",vt(L0);class M0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(this.rate>0&&this.rate<1){let o=()=>{let s=Math.sqrt(this.rate/(1-this.rate));return r.mul(um(r.shape,1,s))};return Bp(o,()=>r,e.training||!1)}return r})}}M0.className="GaussianDropout",vt(M0);class B0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||Qt(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return rt(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(t),o=()=>{let s=Qt(t),c=1.6732632423543772,l=1.0507009873554805,p=-c*l,f=ys(fa(r),this.rate);f=Op(f,"float32");let m=((1-this.rate)*(1+this.rate*p**2))**-.5,y=-m*p*this.rate,b=s.mul(f).add(f.add(-1).mul(p));return b.mul(m).add(y)};return Bp(o,()=>Qt(t),e.training||!1)}return t})}}B0.className="AlphaDropout",vt(B0);function eh(n,t,e,r,o,s=.001){let c;if(n.rank===2)c=$_(n,t,e,r,o,s);else if(n.rank===3)c=I_(n,t,e,r,o,s);else if(n.rank===4)c=E_(n,t,e,r,o,s);else throw new Ut(`batchNormalization is not implemented for array of rank ${n.rank} yet`);return c}function eq(n,t,e,r,o=.001){return rt(()=>{let s=Ld(n,r),c=s.mean,l=s.variance,p=eh(n,c,l,e,t,o);return[p,c,l]})}function nq(n,t,e,r,o=.001){return rt(()=>{let s=Ld(n,r),c=s.mean,l=s.variance,p=[];for(let T of bo(0,n.rank))r.indexOf(T)!==-1?p.push(1):p.push(n.shape[T]);let f=c.reshape(p),m=l.reshape(p),y=t==null?null:t.reshape(p),b=e==null?null:e.reshape(p),v=eh(n,f,m,b,y,o);return[v,c,l]})}function rq(n,t,e,r,o=.001){return lt(r.slice().sort(),bo(0,n.rank-1))?eq(n,t,e,r,o):nq(n,t,e,r,o)}class z0 extends ye{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=je(t.betaInitializer||"zeros"),this.gammaInitializer=je(t.gammaInitializer||"ones"),this.movingMeanInitializer=je(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=je(t.movingVarianceInitializer||"ones"),this.betaConstraint=Tn(t.betaConstraint),this.gammaConstraint=Tn(t.gammaConstraint),this.betaRegularizer=Ke(t.betaRegularizer),this.gammaRegularizer=Ke(t.gammaRegularizer)}build(t){t=Re(t);let e=this.axis>=0?this.axis:this.axis+t.length,r=t[e];if(r==null)throw new Y(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new In({ndim:t.length,axes:{[e]:r}})];let o=[r];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return rt(()=>{let r=e.training==null?!1:e.training,o=Qt(t),s=o.shape,c=s.length,l=bo(0,c),p=this.axis>=0?this.axis:this.axis+c;l.splice(p,1);let f=ya(1,c);f[p]=s[p];let m=l.slice();m.sort();let y=!lt(m,bo(0,c).slice(0,c-1)),b=()=>{if(y){let I=this.movingMean.read().reshape(f),P=this.movingVariance.read().reshape(f),E=this.center?this.beta.read().reshape(f):null,L=this.scale?this.gamma.read().reshape(f):null;return eh(o,I,P,E,L,this.epsilon)}else return eh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return b();let[v,T,N]=rq(o,this.gamma.read(),this.beta.read(),l,this.epsilon),S=(I,P,E)=>{rt(()=>{let L=1-E,B=I.read(),q=B.sub(P).mul(L);I.write(B.sub(q))})},D=()=>{S(this.movingMean,T,this.momentum),S(this.movingVariance,N,this.momentum)};return D(),v})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:rn(this.betaInitializer),gammaInitializer:rn(this.gammaInitializer),movingMeanInitializer:rn(this.movingMeanInitializer),movingVarianceInitializer:rn(this.movingVarianceInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer),betaConstraint:vn(this.betaConstraint),gammaConstraint:vn(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}}z0.className="BatchNormalization",vt(z0);class W0 extends ye{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected 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r=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let r=Qt(t),o=r.shape,s=o.length;return rt(()=>{let c=!0,{mean:l,variance:p}=Ld(r,this.axis,c),f=ya(1,s);for(let N of this.axis)f[N]=o[N];let m=N=>N!=null&&N.shape.length!==s&&this.axis!==[s-1]?N.reshape(f):N,y=m(this.gamma.read()),b=m(this.beta.read()),v=[],T=[];for(let N=0;N<s;++N)this.axis.indexOf(N)!==-1?(v.push(o[N]),T.push(1)):(v.push(1),T.push(o[N]));return l=l.tile(v),p=p.tile(v),y=y.tile(T),b=b.tile(T),eh(r,l,p,b,y,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:rn(this.betaInitializer),gammaInitializer:rn(this.gammaInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}}W0.className="LayerNormalization",vt(W0);function vst(n,t){return rt(()=>{if(n.rank!==3)throw new Y(`temporalPadding expects input tensor to be 3-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new Y(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);let e=[[0,0],t,[0,0]];return Go(n,e)})}function oq(n,t,e){return rt(()=>{if(n.rank!==4)throw new Y(`temporalPadding expects input tensor to be 4-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new Y("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=yo()),e!=="channelsLast"&&e!=="channelsFirst")throw new Y(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);r=t.padding[1]}this.padding=[e,r]}this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){t=Re(t);let e,r;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?r=t[3]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],t[1],e,r]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?r=t[2]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],e,r,t[3]])}call(t,e){return rt(()=>oq(Qt(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}V0.className="ZeroPadding2D",vt(V0);function Bm(n,t,e,r,o,s){return rt(()=>{nn(o),HC(s),Lr(r),e==null&&(e=[1,1]),r==null&&(r="valid"),o==null&&(o=yo()),s==null&&(s="max"),n=g0(n,o);let c,l=r==="same"?"same":"valid";return s==="max"?c=mp(n,t,e,l):c=ap(n,t,e,l),o==="channelsFirst"&&(c=Kt(c,[0,3,1,2])),c})}function o$(n,t,e,r,o,s){return rt(()=>{nn(o),HC(s),Lr(r),e==null&&(e=[1,1,1]),r==null&&(r="valid"),o==null&&(o=yo()),s==null&&(s="max"),n=JS(n,o);let c,l=r==="same"?"same":"valid";return s==="max"?c=Gw(n,t,e,l):c=Ew(n,t,e,l),o==="channelsFirst"&&(c=Kt(c,[0,4,1,2,3])),c})}class s$ extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new Y(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if($n(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new Y(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,Lr(this.padding),this.inputSpec=[new In({ndim:3})]}computeOutputShape(t){t=Re(t);let e=ko(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return rt(()=>{this.invokeCallHook(t,e),t=Lp(Qt(t),2);let r=this.poolingFunction(Qt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return fi(r,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}}class G0 extends s${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),Bm(t,e,r,o,s,"max")}}G0.className="MaxPooling1D",vt(G0);class U0 extends s${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),Bm(t,e,r,o,s,"avg")}}U0.className="AveragePooling1D",vt(U0);class i$ extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new Y(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];$n(this.poolSize,"poolSize"),$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),Lr(this.padding),this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=ko(e,this.poolSize[0],this.padding,this.strides[0]),r=ko(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r]:[t[0],e,r,t[3]]}call(t,e){return rt(()=>(this.invokeCallHook(t,e),this.poolingFunction(Qt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class q0 extends i${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),Bm(t,e,r,o,s,"max")}}q0.className="MaxPooling2D",vt(q0);class H0 extends i${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),Bm(t,e,r,o,s,"avg")}}H0.className="AveragePooling2D",vt(H0);class a$ extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new Y(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];$n(this.poolSize,"poolSize"),$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),Lr(this.padding),this.inputSpec=[new In({ndim:5})]}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=ko(e,this.poolSize[0],this.padding,this.strides[0]),r=ko(r,this.poolSize[1],this.padding,this.strides[1]),o=ko(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r,o]:[t[0],e,r,o,t[4]]}call(t,e){return rt(()=>(this.invokeCallHook(t,e),this.poolingFunction(Qt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class j0 extends a${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),o$(t,e,r,o,s,"max")}}j0.className="MaxPooling3D",vt(j0);class K0 extends a${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),o$(t,e,r,o,s,"avg")}}K0.className="AveragePooling3D",vt(K0);class c$ extends ye{constructor(t){super(t);this.inputSpec=[new In({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Ut}}class X0 extends c${constructor(t){super(t||{})}call(t,e){return rt(()=>{let r=Qt(t);return en(r,1)})}}X0.className="GlobalAveragePooling1D",vt(X0);class Y0 extends c${constructor(t){super(t||{})}call(t,e){return rt(()=>{let r=Qt(t);return ur(r,1)})}}Y0.className="GlobalMaxPooling1D",vt(Y0);class l$ extends ye{constructor(t){super(t);this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Ut}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class J0 extends l${call(t,e){return rt(()=>{let r=Qt(t);return this.dataFormat==="channelsLast"?en(r,[1,2]):en(r,[2,3])})}}J0.className="GlobalAveragePooling2D",vt(J0);class Z0 extends l${call(t,e){return rt(()=>{let r=Qt(t);return this.dataFormat==="channelsLast"?ur(r,[1,2]):ur(r,[2,3])})}}Z0.className="GlobalMaxPooling2D",vt(Z0);class u$ extends ye{constructor(t){super(t);this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,r={}){let o=e.layer,s=vo(o,r);delete e.layer;let c={layer:s};return Object.assign(c,e),new t(c)}}class Q0 extends u${constructor(t){super(t);this.supportsMasking=!0}build(t){if(t=Re(t),t.length<3)throw new Y(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Re(t);let e=[t[0]].concat(t.slice(2)),r=this.layer.computeOutputShape(e),o=t[1];return[r[0],o].concat(r.slice(1))}call(t,e){return rt(()=>{t=Qt(t);let r=(c,l)=>{let p=Qt(this.layer.call(c,e));return[p,[]]},o=n$(r,t,[],!1,null,null,!1,!0),s=o[1];return s})}}Q0.className="TimeDistributed",vt(Q0);function sq(n){Jc(dG,"BidirectionalMergeMode",n)}let iq="concat";class t1 extends u${constructor(t){super(t);let e=t.layer.getConfig(),r={};r.className=t.layer.getClassName(),r.config=e,this.forwardLayer=vo(r),e.goBackwards=!(e.goBackwards===!0);let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=vo(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?iq:t.mergeMode,sq(this.mergeMode),t.weights)throw new Ut("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,r=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,r)),this.backwardLayer.setWeights(t.slice(r))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let r,o,s;return this.returnState&&(s=e.slice(1)),r=e[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,o=[r]):this.mergeMode==null?o=[r,r.slice()]:o=[r],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[r].concat(s).concat(s.slice()):hr(o)}apply(t,e){let r=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=e$(t,r,o,this.numConstants);if(t=s.inputs,r=s.initialState,o=s.constants,Array.isArray(t)&&(r=t.slice(1),t=t[0]),(r==null||r.length===0)&&o==null)return super.apply(t,e);let c=[],l=[];if(r!=null){let f=r.length;if(f%2>0)throw new Y("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=r,c.push(...r);let m=r.map(y=>new In({shape:y.shape}));this.forwardLayer.stateSpec=m.slice(0,f/2),this.backwardLayer.stateSpec=m.slice(f/2),l.push(...m)}if(o!=null)throw new Ut("Support for constants in Bidirectional layers is not implemented yet.");let p=c[0]instanceof wo;for(let f of c)if(f instanceof wo!==p)throw new Y("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(p){let f=[t].concat(c),m=this.inputSpec.concat(l),y=this.inputSpec;this.inputSpec=m;let b=super.apply(f,e);return this.inputSpec=y,b}else return super.apply(t,e)}call(t,e){return rt(()=>{let r=e.initialState,o,s;if(r==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let p=r.slice(0,r.length/2),f=r.slice(r.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:p})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:f}))}let c;this.returnState&&(Array.isArray(o)&&(c=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=Pr(s,1));let l;return 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oj=Object.freeze({__proto__:null,json:rj});let 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ij=Object.freeze({__proto__:null,json:sj});let aj=[{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"}]}];var cj=Object.freeze({__proto__:null,json:aj});let lj=[{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}]}];var uj=Object.freeze({__proto__:null,json:lj});let pj=[{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}]}];var hj=Object.freeze({__proto__:null,json:pj});let fj=[{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}]}];var dj=Object.freeze({__proto__:null,json:fj});let mj=[{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"}]}];var gj=Object.freeze({__proto__:null,json:mj});let yj=[{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}]}];var bj=Object.freeze({__proto__:null,json:yj});let xj=[{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}]}];var wj=Object.freeze({__proto__:null,json:xj});let vj=[{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:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{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:[]}];var Tj=Object.freeze({__proto__:null,json:vj});class w${static get Instance(){return this._instance||(this._instance=new this)}constructor(){let t=[UH,HH,KH,YH,ZH,tj,nj,uj,cj,oj,hj,dj,gj,bj,wj,Tj,ij],e=[].concat(...t.map(r=>r.json));this.opMappers=e.reduce((r,o)=>(r[o.tfOpName]=o,r),{})}transformGraph(t,e={}){let r=t.node,o=[],s=[],c=[],l=r.reduce((N,S)=>(N[S.name]=this.mapNode(S),S.op.startsWith("Placeholder")?o.push(N[S.name]):S.op==="Const"?s.push(N[S.name]):(S.input==null||S.input.length===0)&&c.push(N[S.name]),N),{}),p=[],f=[],m={},y={};e!=null&&(m=this.mapSignatureEntries(e.inputs),y=this.mapSignatureEntries(e.outputs));let b=Object.keys(l);b.forEach(N=>{let S=l[N];S.inputNames.forEach(D=>{let[I]=Ns(D);S.inputs.push(l[I]),l[I].children.push(S)})}),Object.keys(y).length===0?b.forEach(N=>{let S=l[N];S.children.length===0&&f.push(S)}):Object.keys(y).forEach(N=>{let[S]=Ns(N),D=l[S];D!=null&&(D.signatureKey=y[N],f.push(D))}),Object.keys(m).length>0?Object.keys(m).forEach(N=>{let[S]=Ns(N),D=l[S];D&&(D.signatureKey=m[N],p.push(D))}):p=o;let v={};t.library!=null&&t.library.function!=null&&(v=t.library.function.reduce((N,S)=>(N[S.signature.name]=this.mapFunction(S),N),{}));let T={nodes:l,inputs:p,outputs:f,weights:s,placeholders:o,signature:e,functions:v};return c.length>0&&(T.initNodes=c),T}mapSignatureEntries(t){return Object.keys(t||{}).reduce((e,r)=>(e[t[r].name]=r,e),{})}mapNode(t){let e=x$(t.op)||this.opMappers[t.op]||{};t.attr==null&&(t.attr={});let r={name:t.name,op:t.op,category:e.category,inputNames:(t.input||[]).map(o=>o.startsWith("^")?o.substr(1):o),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:t.attr};return 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0&&!!s.tfDeprecatedName&&(l=m1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape":l=p1(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=p1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape[]":l=d1(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=d1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype":l=l1(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=l1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype[]":l=u1(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=u1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"func":l=T$(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=T$(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${s.type} for op: ${t.op}`)}return o[s.name]={value:l,type:c},o},{})),r}mapFunction(t){let e=t.nodeDef,r=[],o=[],s={};e!=null&&(s=e.reduce((y,b)=>(y[b.name]=this.mapNode(b),b.op==="Const"&&o.push(y[b.name]),y),{}));let c=[],l=[];t.signature.inputArg.forEach(y=>{let[b]=Ns(y.name),v={name:b,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:c1(y.type),type:"dtype"}},children:[]};v.signatureKey=y.name,c.push(v),s[b]=v});let p=Object.keys(s);p.forEach(y=>{let b=s[y];b.inputNames.forEach(v=>{let[T]=Ns(v);b.inputs.push(s[T]),s[T].children.push(b)})});let f=t.ret;t.signature.outputArg.forEach(y=>{let[b,v]=Ns(f[y.name]),T=s[b];T!=null&&(T.defaultOutput=v,l.push(T))});let m=this.mapArgsToSignature(t);return{nodes:s,inputs:c,outputs:l,weights:o,placeholders:r,signature:m}}mapArgsToSignature(t){return{methodName:t.signature.name,inputs:t.signature.inputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r),e),{}),outputs:t.signature.outputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r,t.ret),e),{})}}mapArgToTensorInfo(t,e){let r=t.name;return e!=null&&(r=e[r]),{name:r,dtype:t.type}}}function kj(n){let t=ct().global;if(typeof t.atob!="undefined")return t.atob(n);if(typeof Buffer!="undefined")return new Buffer(n,"base64").toString();throw new Error("Unable to decode base64 in this environment. 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-          because the value dtype is ${e.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=e.shape),no(this.elementShape,e.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${t}.`),r.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been read.`);if(r.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been written.`);r.tensor=e,Sn(e),r.written=!0,this.tensors[t]=r}writeMany(t,e){if(t.length!==e.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${t.length} is not the same as tensors size: ${e.length}.`);t.forEach((r,o)=>this.write(r,e[o]))}gather(t,e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${e}`);if(t)t=t.slice(0,this.size());else{t=[];for(let o=0;o<this.size();o++)t.push(o)}if(t.length===0)return un([],[0].concat(this.elementShape));let r=this.readMany(t);return no(this.elementShape,r[0].shape,"TensorArray shape mismatch: "),pr(r,0)}concat(t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${t}`);if(this.size()===0)return un([],[0].concat(this.elementShape));let e=[];for(let o=0;o<this.size();o++)e.push(o);let r=this.readMany(e);return no(this.elementShape,r[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${r[0].shape})`),Qe(r,0)}scatter(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(!this.dynamicSize&&r>=this.maxSize)throw new Error(`Max index must be < array size (${r}  vs. ${this.maxSize})`);this.writeMany(t,go(e,0))}split(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);let r=0,o=t.map(p=>(r+=p,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
+
+  /*
+  Face-API
+  homepage: <https://github.com/vladmandic/face-api>
+  author: <https://github.com/vladmandic>'
+  */
+
+var faceapi=(()=>{var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all6)=>{__markAsModule(target);for(var name in all6)__defProp(target,name,{get:all6[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module),require_isNodejs=__commonJS((exports,module)=>{__export(exports,{isNodejs:()=>isNodejs3});function isNodejs3(){return typeof global=="object"&&!0&&typeof module!="undefined"&&typeof process!="undefined"&&!!process.version}}),require_browser2=__commonJS(()=>{}),src_exports={};__export(src_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:()=>createCanvas2,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:()=>env2,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:()=>nonMaxSuppression2,normalize:()=>normalize,padToSquare:()=>padToSquare,predictAgeAndGender:()=>predictAgeAndGender,recognizeFaceExpressions:()=>recognizeFaceExpressions,resizeResults:()=>resizeResults,resolveInput:()=>resolveInput,shuffleArray:()=>shuffleArray,sigmoid:()=>sigmoid2,ssdMobilenetv1:()=>ssdMobilenetv1,tf:()=>tfjs_esm_exports,tinyFaceDetector:()=>tinyFaceDetector,tinyYolov2:()=>tinyYolov23,toNetInput:()=>toNetInput,utils:()=>utils_exports,validateConfig:()=>validateConfig,version:()=>version4});var 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t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(state6.x&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor4096=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(state6.X&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports3,module)=>{(function(pool62,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten52(options.entropy?[seed,tostring(pool62)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool62),(options.pass||callback||function(prng2,seed2,is_math_call,state6){return state6&&(state6.S&&copy(state6,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten52(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten52(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser2=global2.navigator,plugins=browser2&&browser2.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool62)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool62),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS2((exports3,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_string_decoder=__commonJS2(()=>{}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports3,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer11=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert32(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert32(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir&&(scriptDirectory=_scriptDir),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert32(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:165,maximum:165+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert32(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert32(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack92=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack92===0&&(stack92=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack92!==0&&stackRestore(stack92),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer12){GROWABLE_HEAP_I8().set(array2,buffer12)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer11,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer11=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer11=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer11=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer11.byteLength,updateGlobalBufferAndViews(buffer11),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks3){for(;callbacks3.length>0;){var callback=callbacks3.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert32(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports5=instance.exports;if(Module.asm=exports5,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports4=Module.instantiateWasm(info,receiveInstance);return exports4}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=12976,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack92=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack92),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer11.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests())return;for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),context.initExtensionsDone)return;context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer12=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer12,0)),buffer12.length=0):buffer12.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert32(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 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cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,endPtr=idx;heap[endPtr]&&!(endPtr>=endIdx);)++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder)return UTF8Decoder.decode(heap.subarray(idx,endPtr));for(var str="";idx<endPtr;){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer12){HEAP8.set(array2,buffer12)}var buffer11,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer11=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks3){for(;callbacks3.length>0;){var callback=callbacks3.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={env:asmLibraryArg,wasi_snapshot_preview1:asmLibraryArg};function receiveInstance(instance,module2){var exports5=instance.exports;Module.asm=exports5,wasmMemory=exports5.memory,updateGlobalBufferAndViews(wasmMemory.buffer),removeRunDependency("wasm-instantiate")}addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports4=Module.instantiateWasm(info,receiveInstance);return exports4}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer12=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer12,0)),buffer12.length=0):buffer12.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){for(var num=0,i=0;i<iovcnt;i++){for(var ptr=HEAP32[iov+i*8>>2],len=HEAP32[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,HEAPU8[ptr+j]);num+=len}return HEAP32[pnum>>2]=num,0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}var asmLibraryArg={emscripten_notify_memory_growth:_emscripten_notify_memory_growth,fd_close:_fd_close,fd_seek:_fd_seek,fd_write:_fd_write,proc_exit:_proc_exit,roundf:_roundf},asm=createWasm();Module.asm=asm;var 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calledRun;Module.then=function(func2){if(calledRun)func2(Module);else{var old=Module.onRuntimeInitialized;Module.onRuntimeInitialized=function(){old&&old(),func2(Module)}}return Module};function ExitStatus(status){this.name="ExitStatus",this.message="Program terminated with exit("+status+")",this.status=status}var calledMain=!1;dependenciesFulfilled=function runCaller(){calledRun||run(),calledRun||(dependenciesFulfilled=runCaller)};function callMain(args){var entryFunction=Module.__start;try{entryFunction();var ret=0;exit(ret,!0)}catch(e){if(e instanceof ExitStatus)return;if(e=="unwind"){noExitRuntime=!0;return}else{var toLog=e;e&&typeof e=="object"&&e.stack&&(toLog=[e,e.stack]),err("exception thrown: "+toLog),quit_(1,e)}}finally{calledMain=!0}}function run(args){if(args=args||arguments_,runDependencies>0)return;if(preRun(),runDependencies>0)return;function doRun(){if(calledRun)return;if(calledRun=!0,Module.calledRun=!0,ABORT)return;initRuntime(),preMain(),Module.onRuntimeInitialized&&Module.onRuntimeInitialized(),shouldRunNow&&callMain(args),postRun()}Module.setStatus?(Module.setStatus("Running..."),setTimeout(function(){setTimeout(function(){Module.setStatus("")},1),doRun()},1)):doRun()}Module.run=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0)return;noExitRuntime||(ABORT=!0,EXITSTATUS=status,exitRuntime(),Module.onExit&&Module.onExit(status)),quit_(status,new ExitStatus(status))}if(Module.preInit)for(typeof Module.preInit=="function"&&(Module.preInit=[Module.preInit]);Module.preInit.length>0;)Module.preInit.pop()();var shouldRunNow=!0;return Module.noInitialRun&&(shouldRunNow=!1),noExitRuntime=!0,run(),WasmBackendModule2}}();typeof exports3=="object"&&typeof module=="object"?module.exports=WasmBackendModule:typeof define=="function"&&define.amd?define([],function(){return WasmBackendModule}):typeof 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urlParams=getQueryParams(this.global.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in urlParams){let keyValues=urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");keyValues.forEach(keyValue=>{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment15){ENV=environment15}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolBackprop="AvgPoolBackprop",AvgPool3D="AvgPool3D",AvgPool3DBackprop="AvgPool3DBackprop",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",Div="Div",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNBackprop="LRNBackprop",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolBackprop="MaxPoolBackprop",MaxPool3D="MaxPool3D",MaxPool3DBackprop="MaxPool3DBackprop",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multiply="Multiply",Negate="Negate",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",SelectV2="SelectV2",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config2]=value,[backend3]=key.split("_");backend3===backendName&&result.push(config2)}return result}function registerKernel(config2){let{kernelName,backendName}=config2,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config2)}function registerGradient(config2){let{kernelName}=config2;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config2)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){let kernels=getKernelsForBackend(registeredBackendName);kernels.forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}let kernelProfile={kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")};return kernelProfile}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape2,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape2.length;i++){let node2=tape2[i],nodeInputs=node2.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node2.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node2.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape2.length-1;i>=0;i--){let node2=tape2[i],nodeInputs=node2.inputs;for(let j=0;j<node2.outputs.length;j++)if(tensorsLeadToY[node2.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node2.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape2.length;i++){let node2=tape2[i];if(nodesFromX[node2.id]&&nodesToY[node2.id]){let prunedInputs={};for(let inputName in node2.inputs){let nodeInput=node2.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node2);prunedNode.inputs=prunedInputs,prunedNode.outputs=node2.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy22,add33){for(let i=filteredTape.length-1;i>=0;i--){let node2=filteredTape[i],dys=[];if(node2.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node2.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);let inputGradients=node2.gradient(dys);for(let inputName in node2.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy22(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node2.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add33(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
+`)),lines.join(`
+`)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){let offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString(val,pad11,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`:isString(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),rightPad(valStr,pad11)}function boolNumToString(v){return v===0?"false":"true"}function subTensorToString(vals,shape,dtype,strides,padPerCol,isLast=!0){let storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){let complexTuple=createComplexTuples(vals);return[valToString(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}let sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
+`;for(let i=2;i<rank;i++)newLineSep+=`
+`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){if(this.isDisposed)return;trackerFn().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank22){Rank22.R0="R0",Rank22.R1="R1",Rank22.R2="R2",Rank22.R3="R3",Rank22.R4="R4",Rank22.R5="R5",Rank22.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap22){UpcastInt32AndMap22.float32="float32",UpcastInt32AndMap22.int32="int32",UpcastInt32AndMap22.bool="int32",UpcastInt32AndMap22.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap22){UpcastBoolAndMap22.float32="float32",UpcastBoolAndMap22.int32="int32",UpcastBoolAndMap22.bool="bool",UpcastBoolAndMap22.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap22){UpcastFloat32AndMap22.float32="float32",UpcastFloat32AndMap22.int32="float32",UpcastFloat32AndMap22.bool="float32",UpcastFloat32AndMap22.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap22){UpcastComplex64AndMap22.float32="complex64",UpcastComplex64AndMap22.int32="complex64",UpcastComplex64AndMap22.bool="complex64",UpcastComplex64AndMap22.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor168,tensorList){return tensorList.some(x=>x.id===tensor168.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container2,list,seen){if(container2==null)return;if(container2 instanceof Tensor){list.push(container2);return}if(!isIterable(container2))return;let iterable=container2;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV52){this.ENV=ENV52,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],success=await this.initializeBackend(backendName).success;if(success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName),result=asyncInit?await success:success;if(!result)return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){let kernels=getKernelsForBackend(this.backendName);kernels.forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){let kernels=getKernelsForBackend(backendName);kernels.forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend3=registryFactoryEntry.factory();if(backend3&&!(backend3 instanceof KernelBackend)&&typeof backend3.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend3.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend3,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend3,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend3,backend3.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend3=>backend3.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3,kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(({dataId,shape,dtype})=>this.makeTensorFromDataId(dataId,shape,dtype));if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{let saveFunc=tensors=>{if(!isTapeOn)return;saved=tensors.map(tensor168=>this.keep(this.clone(tensor168)))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){let saved=tensors.map(tensor168=>this.keep(this.clone(tensor168)));return saved}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend3){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend3=backend3||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend3.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend3),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend3){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend3),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend3){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend3||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId),refCount=info.refCount;refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor168=this.state.activeScope.track[i];!tensor168.kept&&!tensorsToTrackInParentSet.has(tensor168.id)&&tensor168.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor168=>{!tensor168.kept&&tensor168.scopeId===oldScope.id&&this.track(tensor168)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node2=>{for(let tensor168 of node2.saved)tensor168.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.readSync(dataId)}read(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment15=new Environment(ns);ns._tfengine=new Engine(environment15)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.add(a,b);return save([a,b]),res},inputs,null,Add)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype==null)return;if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let skipTypedArray=!0,values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],skipTypedArray);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);let tensors=arg;return tensors.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName),parseAsDtype)}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real82,imag82){let $real=convertToTensor(real82,"real","complex"),$imag=convertToTensor(imag82,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let forward=backend3=>backend3.complex($real,$imag),inputs={real:$real,imag:$imag};return ENGINE.runKernelFunc(forward,inputs,null,Complex)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor4(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor168=>tensor168.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer11,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer11.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer11.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer11.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer11.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real82=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real82.length;i++)real82[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor4(real82,shape,"float32"),imageTensor=tensor4(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor4(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer11){if(useNodeBuffer)return Buffer.from(buffer11).toString("base64");let buf=new Uint8Array(buffer11),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer11=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer11.set([s.charCodeAt(i)],i);return buffer11.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer11=>{totalByteLength+=buffer11.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer11=>{temp.set(new Uint8Array(buffer11),offset),offset+=buffer11.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)===0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer11=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer11);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer11)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[],routers=handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters;return routers.forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),modelStore=modelTx.objectStore(MODEL_STORE_NAME),getRequest=modelStore.get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME),putModelRequest=modelStore.put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),store=tx.objectStore(INFO_STORE_NAME),getAllInfoRequest=store.getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME),deleteModelRequest=modelStore.delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology21=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{return this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology21),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata})),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology21=JSON.parse(this.LS.getItem(this.keys.topology));if(topology21==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology21;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,out.userDefinedMetadata=metadata.userDefinedMetadata}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}},URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url),manager=ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);return manager.removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){let deleteSource=!1;return cloneModelInternal(sourceURL,destURL,deleteSource)}async function moveModel(sourceURL,destURL){let deleteSource=!0;return cloneModelInternal(sourceURL,destURL,deleteSource)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}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}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernelFunc(backend3=>backend3.cast($x,dtype),inputs,null,Cast,attrs)}var cast=op({cast_});function clone_(x){let $x=convertToTensor(x,"x","clone",null),forward=()=>ENGINE.makeTensorFromDataId($x.dataId,$x.shape,$x.dtype),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Identity)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest},modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1&&resolve({modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy,userDefinedMetadata:modelJSON.userDefinedMetadata})},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,responses=loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction),bufferPromises=responses.map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1,buffers=loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction);return buffers}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){let fetchWeights=fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}),loadWeights2=weightsLoaderFactory(fetchWeights);return loadWeights2(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
+Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer11=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer11,groupBufferOffset),groupBufferOffset+=buffer11.byteLength}let weightsEntries=groupWeightsToFetch[i];weightsEntries.forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata,weightsManifest};init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;if(weightsManifest!=null){let results=await this.loadWeights(weightsManifest);[weightSpecs,weightData]=results}let artifacts={modelTopology,weightSpecs,weightData,userDefinedMetadata,generatedBy,convertedBy,format},initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){if(arguments.length===1){let isModelArtifacts=modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null;return isModelArtifacts?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts}))}else return console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig})}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function reshape_(x,shape){let $x=convertToTensor(x,"x","reshape",null),inputs={x:$x},attrs={shape},forward=(backend3,save)=>(shape=inferFromImplicitShape(shape,$x.size),assert($x.size===sizeFromShape(shape),()=>"new shape and old shape must have the same number of elements."),save([$x]),backend3.reshape($x,shape));return ENGINE.runKernelFunc(forward,inputs,null,Reshape,attrs)}var reshape=op({reshape_});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{save([$a,$b]);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;assert($a.rank>=2&&$b.rank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`),assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShapeOuterDims=batchDimA>batchDimB?outerDimsA:outerDimsB,outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),res3d=backend3.batchMatMul(a3D,b3D,transposeA,transposeB);return reshape(res3d,outShape)},inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernelFunc(forward,inputs,null,BatchMatMul,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let $indices=convertToTensor(indices,"indices","oneHot","int32"),outShape=[...$indices.shape,depth],forward=(backend3,save)=>(save([$indices]),reshape(backend3.oneHot(reshape($indices,[$indices.size]),depth,onValue,offValue),outShape)),inputs={indices:$indices},attrs={depth,onValue,offValue};return ENGINE.runKernelFunc(forward,inputs,null,OneHot,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernelFunc(backend3=>backend3.transpose($x,perm),inputs,null,Transpose,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}let kernel=getKernel(FromPixels,ENGINE.backendName);if(kernel!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}let outShape=[height,width,numChannels];return tensor3d(values,outShape,"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor168,indices){if(tensor168.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor168.rank}.`);if(indices.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indices.rank-1]>tensor168.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank-1]} vs. ${tensor168.rank}`);if(tensor168.size===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor168.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor168.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensor168.rank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor168.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export2(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config2){return new cls(config2)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon32){return epsilon32==null&&(epsilon32=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon32))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`);for(let i=0;i<expectedFlat.length;++i){let a=actualFlat[i],e=expectedFlat[i];if(!predicate(a,e))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp132=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp132,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon32){if(epsilon32==null&&(epsilon32=testEpsilon()),!areClose(a,e,epsilon32))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon32){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon32)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}var version="2.7.0";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine15(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container2){let tensors=getTensorsInContainer(container2);tensors.forEach(tensor168=>tensor168.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend2(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.add($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Add)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.floorDiv($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,FloorDiv)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let forward=(backend3,save)=>{let res=backend3.realDivide($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE.runKernelFunc(forward,inputs,null,Div,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.multiply($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Multiply)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>(save([$x]),$x.dtype==="complex64"?backend3.complexAbs($x):backend3.abs($x)),inputs,null,Abs)}var abs=op({abs_});function acos_(x){let $x=convertToTensor(x,"x","acos"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.acos($x);return save([$x]),res},inputs,null,Acos)}var acos=op({acos_});function acosh_(x){let $x=convertToTensor(x,"x","acosh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.acosh($x);return save([$x]),res},inputs,null,Acosh)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let forward=(backend3,save)=>{let res=backend3.addN($tensors);return save($tensors),res},inputs=$tensors;return ENGINE.runKernelFunc(forward,inputs,null,AddN)}var addN=op({addN_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function all_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","all","bool"),forward=backend3=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank);permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let res=backend3.all($x,axes);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,origAxes);return reshape(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,All,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","any","bool"),forward=backend3=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank);permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let res=backend3.any($x,axes);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,origAxes);return reshape(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Any,attrs)}var any=op({any_});function argMax_(x,axis=0){let $x=convertToTensor(x,"x","argMax"),forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam(axis,$x.shape),permutedAxes=getAxesPermutation(axes,$x.rank);return permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank)),backend3.argMax($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,ArgMax,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let $x=convertToTensor(x,"x","argMin"),forward=(backend3,save)=>{save([$x]),axis==null&&(axis=0);let axes=parseAxisParam(axis,$x.shape),permutedAxes=getAxesPermutation(axes,$x.rank);return permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank)),backend3.argMin($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,ArgMin,attrs)}var argMin=op({argMin_});function asin_(x){let $x=convertToTensor(x,"x","asin"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.asin($x);return save([$x]),res},inputs,null,Asin)}var asin=op({asin_});function asinh_(x){let $x=convertToTensor(x,"x","asinh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.asinh($x);return save([$x]),res},inputs,null,Asinh)}var asinh=op({asinh_});function atan_(x){let $x=convertToTensor(x,"x","atan"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.atan($x);return save([$x]),res},inputs,null,Atan)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.atan2($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Atan2)}var atan2=op({atan2_});function atanh_(x){let $x=convertToTensor(x,"x","atanh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.atanh($x);return save([$x]),res},inputs,null,Atanh)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad11,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad11,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad11,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad11,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad11,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad11,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad11,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad11,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad11,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad11,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=conditionalRound((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert(isInt(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=conditionalRound((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputDepths),()=>`The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);let outputRows=conditionalRound((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert(isInt(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad11,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad11=="number"){let padType=pad11===0?"VALID":"NUMBER";padInfo={top:pad11,bottom:pad11,left:pad11,right:pad11,type:padType};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad11,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad11==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad11==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad11=="object"){let top=dataFormat==="channelsLast"?pad11[1][0]:pad11[2][0],bottom=dataFormat==="channelsLast"?pad11[1][1]:pad11[2][1],left=dataFormat==="channelsLast"?pad11[2][0]:pad11[3][0],right=dataFormat==="channelsLast"?pad11[2][1]:pad11[3][1],padType=top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT";padInfo={top,bottom,left,right,type:padType},outHeight=conditionalRound((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=conditionalRound((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad11}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad11,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad11=="number"){let padType=pad11===0?"VALID":"NUMBER";padInfo={top:pad11,bottom:pad11,left:pad11,right:pad11,front:pad11,back:pad11,type:padType};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad11,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad11==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad11==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad11}`);return{padInfo,outDepth,outHeight,outWidth}}function conditionalRound(value,roundingMode){if(!roundingMode)return value;switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function avgPool_(x,filterSize,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo(x4D.shape,filterSize,strides,1,pad11,dimRoundingMode);return save([x4D]),convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual(convInfo.inShape,convInfo.outShape)?x4D.clone():backend3.avgPool(x4D,convInfo)},inputs={x:x4D},attrs={filterSize,strides,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad11,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1,1]);let convInfo=computePool3DInfo(x5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode,dataFormat);return save([x5D]),backend3.avgPool3d(x5D,convInfo)},inputs={x:x5D},attrs={filterSize,strides,pad:pad11,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool3D,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat");$tensors[0].dtype==="complex64"&&$tensors.forEach(tensor168=>{if(tensor168.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor168.dtype}. `)});let forward=(backend3,save)=>{let $axis=parseAxisParam(axis,$tensors[0].shape)[0],outShape=computeOutShape2($tensors.map(t=>t.shape),$axis);if(sizeFromShape(outShape)===0)return tensor4([],outShape);if($tensors=$tensors.filter(t=>t.size>0),$tensors.length===1)return $tensors[0];let shapes=$tensors.map(t=>t.shape);assertParamsConsistent(shapes,$axis);let res=backend3.concat($tensors,$axis);return save($tensors),res},inputs=$tensors,attr={axis};return ENGINE.runKernelFunc(forward,inputs,null,Concat,attr)}var concat=op({concat_});function sigmoid_(x){let $x=convertToTensor(x,"x","sigmoid"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sigmoid($x);return save([res]),res},inputs,null,Sigmoid)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice");if($x.rank===0)throw new Error("Slicing scalar is not possible");let forward=(backend3,save)=>{let[begin_,size_]=parseSliceParams($x,begin,size);return assertParamsValid($x,begin_,size_),save([$x]),backend3.slice($x,begin_,size_)},inputs={x:$x},attrs={begin,size};return ENGINE.runKernelFunc(forward,inputs,null,Slice,attrs)}var slice=op({slice_});function tanh_(x){let $x=convertToTensor(x,"x","tanh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let y=backend3.tanh($x);return save([y]),y},inputs,null,Tanh)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod52=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod52===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod52}`);let forward=backend3=>backend3.batchToSpaceND($x,blockShape,crops),inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernelFunc(forward,inputs,null,BatchToSpaceND,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean7,variance,offset,scale22,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let x4D=xAs4D($x),forward=(backend3,save)=>(save([x4D,$mean,$variance,$scale]),backend3.batchNorm(x4D,as1DOr4D($mean),as1DOr4D($variance),as1DOr4D($offset),as1DOr4D($scale),varianceEpsilon)),inputs={x:x4D,scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernelFunc(forward,inputs,null,FusedBatchNorm,attrs);return reshape(res,$x.shape)}function as1DOr4D(x){return x==null?null:x.rank===0?reshape(x,[x.size]):x.rank===1?x:x.rank===2?reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean7,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean7,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean7,variance,offset,scale22,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale22!=null&&($scale=convertToTensor(scale22,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);let axes=reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0);if(axes.length===0)return clone(input2);let forward=backend3=>backend3.tile(input2,reps),inputs={x:input2},attrs={shape,inputShape};return ENGINE.runKernelFunc(forward,inputs,null,BroadcastTo,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let $x=convertToTensor(x,"x","ceil"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.ceil($x),inputs,null,Ceil)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.clip($x,clipValueMin,clipValueMax);return save([$x]),res},inputs,null,ClipByValue,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let forward=(backend3,save)=>{let $dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2d(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,Conv2D,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad11,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),strides=[1,stride],dilations=[1,dilation],conv2dDataFormat="NHWC",res=conv2d(input4D,filter4D,strides,pad11,conv2dDataFormat,dilations,dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad11,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let dilations=1,$dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(xShape4D,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2dDerInput(dy4D,filter,convInfo);return save([dy4D,filter]),res2},inputs={dy:dy4D,filter},attrs={strides,pad:pad11,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernelFunc(forward,inputs,null,Conv2DBackpropInput,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad11,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad11,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let forward=(backend3,save)=>{let convInfo=computeConv3DInfo(x5D.shape,$filter.shape,strides,dilations,pad11),res2=backend3.conv3d(x5D,$filter,convInfo);return save([x5D,$filter]),res2},inputs={x:x5D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,Conv3D,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad11){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo(xShape5D,filter.shape,strides,dilations,pad11);return backend3.conv3dDerInput(dy5D,filter,convInfo)},inputs={dy:dy5D,filter},attrs={pad:pad11,strides,inputShape:xShape5D},res=ENGINE.runKernelFunc(forward,inputs,null,Conv3DBackpropInputV2,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad11){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad11)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let $x=convertToTensor(x,"x","cos"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.cos($x);return save([$x]),res},inputs,null,Cos)}var cos=op({cos_});function cosh_(x){let $x=convertToTensor(x,"x","cosh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.cosh($x);return save([$x]),res},inputs,null,Cosh)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse12=!1){let $x=convertToTensor(x,"x","cumsum"),forward=(backend3,save)=>{let permutation=getAxesPermutation([axis],$x.rank),permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation));let permutedAxis=getInnerMostAxes(1,$x.rank)[0],value=backend3.cumsum(permutedX,permutedAxis,exclusive,reverse12);if(save([$x]),permutation!=null){let reversePermutation=getUndoAxesPermutation(permutation);value=transpose(value,reversePermutation)}return value},inputs={x:$x},attrs={axis,exclusive,reverse:reverse12};return ENGINE.runKernelFunc(forward,inputs,null,Cumsum,attrs)}var cumsum=op({cumsum_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
+    ${$x.shape}`),assert(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputWidth} and ${blockSize} for depthToSpace with input shape
+        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let forward=backend3=>backend3.depthToSpace($x,blockSize,dataFormat),inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernelFunc(forward,inputs,null,DepthToSpace,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!0),res2=backend3.depthwiseConv2D(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNative,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let $x=convertToTensor(x,"x","diag"),forward=backend3=>{let flat=reshape($x,[$x.size]),result=backend3.diag(flat),outShape=[...x.shape,...x.shape];return reshape(result,outShape)},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Diag)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad11,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1,b=outShape[outShape.length-1-i]||1;b>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.equal($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Equal)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let forward=(backend3,save)=>{let res=backend3.select($condition,$broadcastedA,$broadcastedB);return save([$condition]),res},inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernelFunc(forward,inputs,null,SelectV2)}var where=op({where_});function zerosLike_(x){let $x=convertToTensor(x,"x","zerosLike"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.zerosLike($x),inputs,null,ZerosLike)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros10=zerosLike(divResult),bEqualsZero=equal($b,zeros10);return where(bEqualsZero,zeros10,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul($t1,t22D);return t1t2}}var dot=op({dot_});function elu_(x){let $x=convertToTensor(x,"x","elu"),forward=(backend3,save)=>{let y=backend3.elu($x);return save([y]),y},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Elu)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.erf($x);return save([$x]),res},inputs,null,Erf)}var erf=op({erf_});function exp_(x){let $x=convertToTensor(x,"x","exp"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.exp($x);return save([res]),res},inputs,null,Exp)}var exp=op({exp_});function expandDims_(x,axis=0){let parseAs=null,$x=convertToTensor(x,"x","expandDims",parseAs);assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let newShape=$x.shape.slice();return axis<0&&(assert(-($x.rank+1)<=axis,()=>`Axis must be in the interval [${-($x.rank+1)}, ${$x.rank}]`),axis=$x.rank+axis+1),newShape.splice(axis,0,1),reshape($x,newShape)}var expandDims=op({expandDims_});function expm1_(x){let $x=convertToTensor(x,"x","expm1"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.expm1($x);return save([$x]),res},inputs,null,Expm1)}var expm1=op({expm1_});function tile_(x,reps){let parseAs=null,$x=convertToTensor(x,"x","tile",parseAs);assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let forward=(backend3,save)=>{let res=backend3.tile($x,reps);return save([$x]),res},inputsToSave=[$x],inputs={x:$x},attrs={reps};return ENGINE.runKernelFunc(forward,inputs,null,Tile,attrs,inputsToSave)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernelFunc(backend3=>backend3.fill(shape,value,dtype),{},null,Fill,attrs)}function floor_(x){let $x=convertToTensor(x,"x","floor"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.floor($x),inputs,null,Floor)}var floor=op({floor_}),segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis){let dimSize=x.shape[axis],outputShape=[],batchSize=1,sliceSize=1;for(let i=0;i<axis;i++)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=0;i<indices.rank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<x.rank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,dimSize,outputShape}}function gather_(x,indices,axis=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis},forward=(backend3,save)=>{let parsedAxis=parseAxisParam(axis,$x.shape)[0],shapeInfo=collectGatherOpShapeInfo($x,$indices,parsedAxis),res=backend3.gather($x,reshape($indices,[$indices.size]),parsedAxis);return save([$x,$indices]),reshape(res,shapeInfo.outputShape)};return ENGINE.runKernelFunc(forward,inputs,null,GatherV2,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.greater($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Greater)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.greaterEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,GreaterEqual)}var greaterEqual=op({greaterEqual_});function imag_(input2){let $input=convertToTensor(input2,"input","imag"),forward=backend3=>backend3.imag($input),inputs={input:$input};return ENGINE.runKernelFunc(forward,inputs,null,Imag)}var imag=op({imag_});function isFinite_(x){let $x=convertToTensor(x,"x","isFinite"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isFinite($x),inputs,null,IsFinite)}var isFinite2=op({isFinite_});function isInf_(x){let $x=convertToTensor(x,"x","isInf"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isInf($x),inputs,null,IsInf)}var isInf=op({isInf_});function isNaN_(x){let $x=convertToTensor(x,"x","isNaN"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isNaN($x),inputs,null,IsNan)}var isNaN2=op({isNaN_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.maximum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Maximum)}var maximum=op({maximum_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");let shape=[],inferredShape=[];return makeTensor(value,shape,inferredShape,dtype)}function leakyRelu_(x,alpha=.2){let $x=convertToTensor(x,"x","leakyRelu");return maximum(mul(scalar(alpha),$x),$x)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.less($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Less)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.lessEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,LessEqual)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernelFunc(backend3=>backend3.linspace(start,stop,num),{},null,LinSpace,attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
+               rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let forward=(backend3,save)=>{let y=backend3.localResponseNormalization4D(x4D,depthRadius,bias,alpha,beta);return save([x4D,y]),y},inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernelFunc(forward,inputs,null,LRN,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let $x=convertToTensor(x,"x","log"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.log($x);return save([$x]),res},inputs,null,Log)}var log=op({log_});function log1p_(x){let $x=convertToTensor(x,"x","log1p"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.log1p($x);return save([$x]),res},inputs,null,Log1p)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad",null),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads",null),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable32=>!variable32.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable32=>variable32.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){let numNullGradients=grads2.filter(g=>g==null).length;if(numNullGradients>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
+    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let $x=convertToTensor(x,"x","neg"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.neg($x),inputs,null,Negate)}var neg=op({neg_});function softplus_(x){let $x=convertToTensor(x,"x","softplus"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.softplus($x);return save([$x]),res},inputs,null,Softplus)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid"),customOp=customGrad(x2=>{let value=neg(softplus(neg(x2))),gradFunc=dy=>{let derX=mul(dy,sigmoid(neg(x2)));return derX};return{value,gradFunc}});return customOp($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","max"),forward=(backend3,save)=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank),maxInput=$x;permutedAxes!=null&&(maxInput=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,maxInput.rank));let y=backend3.max(maxInput,axes);permutedAxes!=null&&maxInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim(res.shape,parseAxisParam(axis,$x.shape));res=reshape(res,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Max,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.subtract($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Sub)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam(axis,$x.shape),permutation=getAxesPermutation(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation),reductionAxes=getInnerMostAxes(reductionAxes.length,$x.rank));let value=backend3.sum(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim(value.shape,axes);value=reshape(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Sum,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);let forward=(backend3,save)=>{let keepDims=!0,xMax=max(logits,axis,!0),shifted=sub(logits,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),value},inputs={logits:$logits},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,LogSoftmax,attrs)}var logSoftmax=op({logSoftmax_});function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernelFunc(backend3=>backend3.logicalAnd($a,$b),inputs,null,LogicalAnd)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let $x=convertToTensor(x,"x","logicalNot","bool"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.logicalNot($x),inputs,null,LogicalNot)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernelFunc(backend3=>backend3.logicalOr($a,$b),inputs,null,LogicalOr)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo(x4D.shape,filterSize,strides,1,pad11,dimRoundingMode),y;return convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual(convInfo.inShape,convInfo.outShape)?y=x4D.clone():y=backend3.maxPool(x4D,convInfo),save([x4D,y]),y},inputs={x:x4D},attrs={filterSize,strides,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad11,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1,1]);let convInfo=computePool3DInfo(x5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode,dataFormat),y=backend3.maxPool3d(x5D,convInfo);return save([x5D,y]),y},inputs={x:x5D},attrs={filterSize,strides,pad:pad11,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool3D,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad11,includeBatchInIndex=!1){let $x=convertToTensor(x,"x","maxPoolWithArgmax"),inputs={x:$x},attrs={filterSize,strides,pad:pad11,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real82=zeros(shape,"float32"),imag82=zeros(shape,"float32");return complex(real82,imag82)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real82=ones2(shape,"float32"),imag82=zeros(shape,"float32");return complex(real82,imag82)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function mean_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","mean"),axes=parseAxisParam(axis,$x.shape),shapes=computeOutAndReduceShapes($x.shape,axes),reduceShape=shapes[1],reduceSize=sizeFromShape(reduceShape),inputs={x:$x},attrs={axis,keepDims},forward=()=>{let reduceSizeScalar=scalar(reduceSize),xReduce=reduceSizeScalar.dtype===$x.dtype?$x:cast($x,reduceSizeScalar.dtype),res=div(xReduce,reduceSizeScalar);return sum2(res,axis,keepDims)},customOp=customGrad(x2=>{let value=ENGINE.runKernelFunc(forward,inputs,null,Mean,attrs),gradFunc=dy=>{let expandedDyShape=x2.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=div(mul(expandedDy,ones2(x2.shape,"float32")),reduceSize);return derX};return{value,gradFunc}});return customOp($x)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","min"),forward=(backend3,save)=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank),minInput=$x;permutedAxes!=null&&(minInput=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let y=backend3.min(minInput,axes);permutedAxes!=null&&minInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim(res.shape,origAxes);res=reshape(y,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Min,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.minimum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Minimum)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.mod($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Mod)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={},inputsToSave=[$x],outputsToSave=[];return ENGINE.runKernelFunc((backend3,save)=>(save([$x]),backend3.square($x)),{x:$x},null,"Square",attrs,inputsToSave,outputsToSave)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let logits2D=origRank===1?reshape($logits,[1,-1]):$logits,res=ENGINE.runKernelFunc(backend3=>backend3.multinomial(logits2D,normalized,numSamples,seed),{logits2D});return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.notEqual($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,NotEqual)}var notEqual=op({notEqual_});function real_(input2){let $input=convertToTensor(input2,"input","real"),forward=backend3=>backend3.real($input),inputs={input:$input};return ENGINE.runKernelFunc(forward,inputs,null,Real)}var real=op({real_});function onesLike_(x){let $x=convertToTensor(x,"x","onesLike"),forward=(backend3,save)=>{if($x.dtype==="complex64"){let r=onesLike(real($x)),i=zerosLike(imag($x));return complex(r,i)}return backend3.onesLike($x)},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,OnesLike)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let forward=(backend3,save)=>(save([$x]),backend3.pad($x,paddings,constantValue)),attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,PadV2,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let forward=backend3=>backend3.spaceToBatchND($x,blockShape,paddings),inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernelFunc(forward,inputs,null,SpaceToBatchND,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad11,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad11===0&&(pad11="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad11),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad11==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad11:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),forwardOp=poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad),y=forwardOp(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let dilatedFilterShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)),padExtraShape=dilatedFilterShape.map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp132){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp132,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp},forward=(backend3,save)=>{let y=backend3.pow($base,$exp);return save([$base,$exp,y]),y};return ENGINE.runKernelFunc(forward,inputs,null,Pow)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),forward=(backend3,save)=>{let res=backend3.prelu($x,$alpha);return save([$x,$alpha]),res},inputs={x:$x,alpha:$alpha};return ENGINE.runKernelFunc(forward,inputs,null,Prelu)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let forward=backend3=>{let axes=parseAxisParam(axis,$x.shape),permutation=getAxesPermutation(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation),reductionAxes=getInnerMostAxes(reductionAxes.length,$x.rank));let value=backend3.prod(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim(value.shape,axes);value=reshape(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Prod,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_}),seedrandom=__toModule2(require_seedrandom2()),MPRandGauss=class{constructor(mean7,stdDeviation,dtype,truncated,seed){this.mean=mean7,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul64=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul64,resultY=this.mean+this.stdDev*v2*mul64,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min82=0,max102=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min82,this.range=max102-min82,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min82} - ${max102} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean7=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean7,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");let shape=null;return makeTensor(values,shape,inferredShape,dtype)}function range(start,stop,step9=1,dtype="float32"){if(step9===0)throw new Error("Cannot have a step of zero");let forward=()=>{let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step9<0,decreasingRangePositiveStep=stop<start&&step9>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return zeros([0],dtype);let numElements=Math.abs(Math.ceil((stop-start)/step9)),values=makeZerosTypedArray(numElements,dtype);stop<start&&step9===1&&(step9=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d(values,dtype)},attrs={start,stop,step:step9,dtype};return ENGINE.runKernelFunc(forward,{},null,Range,attrs)}function reciprocal_(x){let $x=convertToTensor(x,"x","reciprocal"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.reciprocal($x);return save([$x]),res},inputs,null,Reciprocal)}var reciprocal=op({reciprocal_});function relu_(x){let $x=convertToTensor(x,"x","relu"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast($x,"int32"):backend3.relu($x)),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Relu)}var relu=op({relu_});function relu6_(x){let $x=convertToTensor(x,"x","relu6"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast($x,"int32"):backend3.relu6($x)),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Relu6)}var relu6=op({relu6_});function reverse_(x,axis){let $x=convertToTensor(x,"x","reverse"),forward=backend3=>{let axes=parseAxisParam(axis,$x.shape);if($x.rank===0)return clone($x);let res=backend3.reverse($x,axes);return reshape(res,$x.shape)},inputs={x:$x},attrs={dims:axis};return ENGINE.runKernelFunc(forward,inputs,null,Reverse,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let $x=convertToTensor(x,"x","round"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.round($x),inputs,null,Round)}var round=op({round_});function rsqrt_(x){let $x=convertToTensor(x,"x","rsqrt"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.rsqrt($x);return save([$x]),res},inputs,null,Rsqrt)}var rsqrt=op({rsqrt_});function selu_(x){let $x=convertToTensor(x,"x","selu"),forward=(backend3,save)=>{let res=backend3.selu($x);return save([$x]),res},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Selu)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad11,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad11,dataFormat,dilation),pointwiseStride=1,res=conv2d(depthwise,$pointwiseFilter,pointwiseStride,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer11=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer11.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer11.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let $x=convertToTensor(x,"x","sign"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.sign($x),inputs,null,Sign)}var sign=op({sign_});function sin_(x){let $x=convertToTensor(x,"x","sin"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sin($x);return save([$x]),res},inputs,null,Sin)}var sin=op({sin_});function sinh_(x){let $x=convertToTensor(x,"x","sinh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sinh($x);return save([$x]),res},inputs,null,Sinh)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernelFunc((backend3,save)=>{let y=backend3.softmax($logits,dim);return save([y]),y},inputs,null,Softmax,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=input2.as2D(batch,innerDimensionSize),result=backend3.fft(input2D);return result.reshape(input2.shape)},inputs,null,FFT)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=reshape(input2,[batch,innerDimensionSize]),result=backend3.ifft(input2D);return reshape(result,input2.shape)},inputs,null,IFFT)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}function split_(x,numOrSizeSplits,axis=0){let $x=convertToTensor(x,"x","split"),forward=(backend3,_)=>{let $axis=parseAxisParam(axis,$x.shape)[0],splitSizes=prepareSplitSize($x,numOrSizeSplits,$axis);return backend3.split($x,splitSizes,$axis)},inputs={x:$x},attr={numOrSizeSplits,axis};return ENGINE.runKernelFunc(forward,inputs,null,SplitV,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let $x=convertToTensor(x,"x","sqrt"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sqrt($x);return save([$x]),res},inputs,null,Sqrt)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.squaredDifference($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE.runKernelFunc(forward,inputs,null,SquaredDifference,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack");if(assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length===1)return expandDims($tensors[0],axis);let rank=$tensors[0].rank,shape=$tensors[0].shape,dtype=$tensors[0].dtype;assert(axis<=rank,()=>"Axis must be <= rank of the tensor"),$tensors.forEach(t=>{assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=$tensors.map(t=>expandDims(t,axis));return concat(expandedTensors,axis)}var stack=op({stack_});function step_(x,alpha=0){let $x=convertToTensor(x,"x","step"),inputs={x:$x},attrs={alpha};return ENGINE.runKernelFunc(backend3=>backend3.step($x,alpha),inputs,null,Step,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let $x=convertToTensor(x,"x","stridedSlice"),forward=backend3=>{strides==null&&(strides=new Array(begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=$x.rank-begin.length,expandAxes=maskToAxes(newAxisMask),newShape=$x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)}),$x=reshape($x,newShape);let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes($x.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided)return reshape(slice($x,begin,size),outShape);let res=backend3.stridedSlice($x,begin,end,strides);return reshape(res,outShape)},inputs={x:$x},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernelFunc(forward,inputs,null,StridedSlice,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let $x=convertToTensor(x,"x","tan"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.tan($x);return save([$x]),res},inputs,null,Tan)}var tan=op({tan_});function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernelFunc(b=>b.topk($x,k,sorted),inputs,null,TopK,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean7=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean7,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique",null);assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments},forward=(backend3,save)=>{let res=backend3.unsortedSegmentSum($x,$segmentIds,numSegments);return save([$segmentIds]),res};return ENGINE.runKernelFunc(forward,inputs,null,UnsortedSegmentSum,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`),axis<0&&(axis+=$x.shape.length);let inputs={value:$x},attrs={axis},forward=backend3=>backend3.unstack($x,axis);return ENGINE.runKernelFunc(forward,inputs,null,Unpack,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor168,mask,axis){let $tensor=convertToTensor(tensor168,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor168!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp132){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp132.shape,"Error in powStrict: "),pow(base2,exp132)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm52=normImpl(x,ord,axis),keepDimsShape=norm52.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm52.shape,axes)}return reshape(norm52,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step9,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step9!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step9,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let forward=backend3=>backend3.scatterND($indices,$updates,shape),inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernelFunc(forward,inputs,null,ScatterNd,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernelFunc(backend3=>backend3.sparseToDense($sparseIndices,$sparseValues,outputShape,$defaultValue),inputs,null,SparseToDense,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),$x=convertToTensor(x,"x","gatherND"),forward=backend3=>backend3.gatherND($x,$indices),inputs={params:$x,indices:$indices};return ENGINE.runKernelFunc(forward,inputs,null,GatherNd)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor4(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_,fused_ops_exports={};__export2(fused_ops_exports,{conv2d:()=>conv2d5,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad11,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let dilations=1,$dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(x4D.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat);return backend3.conv2dDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad11,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,Conv2DBackpropFilter,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>{let gradientMode=gradientDepth>0;return!gradientMode||activation2==="linear"};function fusedConv2d_({x,filter,strides,pad:pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad11,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad11),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad11),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},forward=backend3=>{let res=backend3.fusedConv2d({input:x4D,filter:$filter,convInfo,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return res},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2};if(bias==null){let customOp=customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedConv2D,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOp(x4D,$filter)}else{let customOpWithBias=customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedConv2D,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOpWithBias(x4D,$filter,$bias)}}var conv2d5=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad11,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo(x.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!0);return backend3.depthwiseConv2DDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad11,dimRoundingMode,dilations,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropFilter,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad11,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo(xShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0);return backend3.depthwiseConv2DDerInput(dy4D,filter,convInfo)},inputs={dy:dy4D,filter},attrs={strides,pad:pad11,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropInput,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad11,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad11,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad11,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},forward=backend3=>{let res=backend3.fusedDepthwiseConv2D({input:x4D,filter:$filter,convInfo,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return res},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2};if(bias==null){let customOp=customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedDepthwiseConv2D,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOp(x4D,$filter)}else{let customOpWithBias=customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedDepthwiseConv2D,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOpWithBias(x4D,$filter,$bias)}}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},forward=backend3=>{let y=backend3.fusedBatchMatMul({a:a3D,b:b3D,transposeA,transposeB,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return y},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2};if(bias==null){let customOp=customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,_FusedMatMul,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}});return customOp(a3D,b3D)}else{let customOpWithBias=customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,_FusedMatMul,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}});return customOpWithBias(a3D,b3D,$bias)}}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal22,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal22.size;)output.push(slice(signal22,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal22.size;){let padLen=start+frameLength-signal22.size,pad11=concat([slice(signal22,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad11),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal22,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal22,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method,extrapolationValue){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32");method=method||"bilinear",extrapolationValue=extrapolationValue||0;let numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let forward=backend3=>backend3.cropAndResize($image,$boxes,$boxInd,cropSize,method,extrapolationValue),inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize},res=ENGINE.runKernelFunc(forward,inputs,null,CropAndResize,attrs);return res}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},res=ENGINE.runKernel(FlipLeftRight,inputs,{});return res}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center},res=ENGINE.runKernel(RotateWithOffset,inputs,attrs);return res}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernelFunc(b=>b.nonMaxSuppression($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold),{boxes:$boxes,scores:$scores},null,NonMaxSuppressionV3,attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0).selectedIndices}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale22=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou3=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou3>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale22,iou3),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices:tensor1d(selectedIndices,"int32")};return returnScoresTensor&&(result.selectedScores=tensor1d(selectedScores,"float32")),returnValidOutputs&&(result.validOutputs=scalar(validOutputs,"int32")),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale22,iou3){let weight=Math.exp(scale22*iou3*iou3);return iou3<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),res=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,forward=(backend3,save)=>(save([batchImages]),backend3.resizeBilinear(batchImages,newHeight,newWidth,alignCorners)),inputs={images:batchImages},attrs={alignCorners,size},res=ENGINE.runKernelFunc(forward,inputs,null,ResizeBilinear,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,inputs={images:batchImages},attrs={alignCorners,size},forward=(backend3,save)=>(save([batchImages]),backend3.resizeNearestNeighbor(batchImages,newHeight,newWidth,alignCorners)),res=ENGINE.runKernelFunc(forward,inputs,null,ResizeNearestNeighbor,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction22){Reduction22[Reduction22.NONE=0]="NONE",Reduction22[Reduction22.MEAN=1]="MEAN",Reduction22[Reduction22.SUM=2]="SUM",Reduction22[Reduction22.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses8,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses8,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses8=abs(sub($labels,$predictions));return computeWeightedLoss(losses8,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses8=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses8,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses8=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses8,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses8=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses8,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon32=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon32),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses8=sub(l13,l23);return computeWeightedLoss(losses8,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses8=squaredDifference($labels,$predictions);return computeWeightedLoss(losses8,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses8=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses8,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);let customOp=customGrad((labels2,logits2,save)=>{let keepDims=!0,lse=logSumExp(logits2,[dim],keepDims),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2)),value=sum2(costVector,[dim]),gradFunc=(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]};return{value,gradFunc}});return customOp(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses8=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses8,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon32=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon32,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables5=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.rho,config2.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables5=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon32=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon32,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon,config2.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);varNames.forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config2){return new cls(config2.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.momentum,config2.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon32,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon32==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables5=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables5.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.decay,config2.momentum,config2.epsilon,config2.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon32=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon32,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon32=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon32)}static adadelta(learningRate=.001,rho=.95,epsilon32=null){return new AdadeltaOptimizer(learningRate,rho,epsilon32)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon32=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon32,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,linspaceImpl:()=>linspaceImpl,log:()=>log6,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod52,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod52),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2===1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod52,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod52):reshapedPermuted.push(inputShape[0]*prod52);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log6(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real82,imag82){if(real82.length!==imag82.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real82.length}, imag: ${imag82.length}.`);let result=new Float32Array(real82.length*2);for(let i=0;i<result.length;i+=2)result[i]=real82[i/2],result[i+1]=imag82[i/2];return result}function splitRealAndImagArrays(complex11){let real82=new Float32Array(complex11.length/2),imag82=new Float32Array(complex11.length/2);for(let i=0;i<complex11.length;i+=2)real82[i/2]=complex11[i],imag82[i/2]=complex11[i+1];return{real:real82,imag:imag82}}function complexWithEvenIndex(complex11){let len=Math.ceil(complex11.length/4),real82=new Float32Array(len),imag82=new Float32Array(len);for(let i=0;i<complex11.length;i+=4)real82[Math.floor(i/4)]=complex11[i],imag82[Math.floor(i/4)]=complex11[i+1];return{real:real82,imag:imag82}}function complexWithOddIndex(complex11){let len=Math.floor(complex11.length/4),real82=new Float32Array(len),imag82=new Float32Array(len);for(let i=2;i<complex11.length;i+=4)real82[Math.floor(i/4)]=complex11[i],imag82[Math.floor(i/4)]=complex11[i+1];return{real:real82,imag:imag82}}function getComplexWithIndex(complex11,index){let real82=complex11[index*2],imag82=complex11[index*2+1];return{real:real82,imag:imag82}}function assignToTypedArray(data,real82,imag82,index){data[index*2]=real82,data[index*2+1]=imag82}function exponents(n,inverse){let real82=new Float32Array(n/2),imag82=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real82[i]=Math.cos(x),imag82[i]=Math.sin(x)}return{real:real82,imag:imag82}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real82=Math.cos(x),imag82=Math.sin(x);return{real:real82,imag:imag82}}function castTensor(x,dtype,backend3){if(dtype==="complex64"){if(x.dtype==="complex64")return x.clone();let zerosTensor=zeros(x.shape),floatX=cast(x,"float32"),result=backend3.complex(floatX,zerosTensor);return zerosTensor.dispose(),floatX.dispose(),result}if(!hasEncodingLoss(x.dtype,dtype))return ENGINE.makeTensorFromDataId(x.dataId,x.shape,dtype);if(x.dtype==="complex64"){let real82=backend3.real(x),result=cast(real82,dtype);return real82.dispose(),result}if(dtype==="int32")return backend3.int(x);if(dtype==="bool"){let zero=scalar(0,x.dtype),result=backend3.notEqual(x,zero);return zero.dispose(),result}else throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}function reshapeTensor(x,shape){return ENGINE.makeTensorFromDataId(x.dataId,shape,x.dtype)}function linspaceImpl(start,stop,num){let step9=(stop-start)/(num-1),values=makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d(values,"float32")}var kernel_impls_exports={};__export2(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,split:()=>split5,tile:()=>tile4,topkImpl:()=>topkImpl,whereImpl:()=>whereImpl});function split5(x,sizeSplits,axis){let begin=new Array(x.rank).fill(0),size=x.shape.slice();return sizeSplits.map(s=>{let sliceSize=[...size];sliceSize[axis]=s;let sliceT=slice(x,begin,sliceSize);return begin[axis]+=s,sliceT})}function tile4(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result.toTensor()}function topkImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[tensor4(allTopKVals,outputShape,xDtype),tensor4(allTopKIndices,outputShape,"int32")]}var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}},acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}},acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}},addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)};return{a:derA,b:derB}}},addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}},argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}},asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}},atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)};return{a:derA,b:derB}}},atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}},atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dBackprop_(dy,input2,filterSize,strides,dilations=[1,1,1],pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dBackprop"),$input=convertToTensor(input2,"input","avgPool3dBackprop"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool3DInfo(input5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode);return backend3.avgPool3dBackprop(dy5D,input5D,convInfo)},inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool3DBackprop,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dBackprop=op({avgPool3dBackprop_}),avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dBackprop(dy,x,filterSize,strides,$dilations,pad11,dimRoundingMode)}}};function avgPoolBackprop_(dy,input2,filterSize,strides,pad11){let $dy=convertToTensor(dy,"dy","avgPoolBackprop"),$input=convertToTensor(input2,"input","avgPoolBackprop");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);let forward=backend3=>{let convInfo=computePool2DInfo(input4D.shape,filterSize,strides,1,pad11);return backend3.avgPoolBackprop(dy4D,input4D,convInfo)},inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad11},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPoolBackprop,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolBackprop=op({avgPoolBackprop_}),avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad11}=attrs;return{x:()=>avgPoolBackprop(dy,x,filterSize,strides,pad11)}}},batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}},batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}},broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}},castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})},ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})},clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}},concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]),derTensors=split(dy,sizeSplits,$axis);return derTensors.map(t=>()=>t)}},conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad11,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad11,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad11,dataFormat)}}},conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad11,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad11,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad11,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad11){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo(x5D.shape,filterShape,strides,dilations,pad11);return backend3.conv3dDerFilter(x5D,dy5D,convInfo)},inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad11,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,Conv3DBackpropFilterV2,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_}),conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad11}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad11),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad11)}}},cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}},coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}},cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse12}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse12);return permutation!=null&&(out=transpose(out,permutation)),out}}}},depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad11,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad11,dilations,dimRoundingMode)}}},dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}},divGradConfig={kernelName:Div,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))};return{a:derA,b:derB}}},eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,backPropKernelFunc=backend3=>backend3.eluDer(dy,y),inputs={dy,y};return{x:()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,EluGrad)}}},erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}},expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}},expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}},floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})},floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))};return{a:derA,b:derB}}},fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean7,variance,scale22]=saved,scaleValue=scale22==null?scalar(1):scale22,reductionAxes=getReductionAxes(mean7.shape,x.shape),tileShape=[];if(mean7.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}let xMinusMean=sub(x,mean7),dyTimesScaleValue=mul(dy,scaleValue),oneOverSqrtVariance=rsqrt(add2(variance,scalar(varianceEpsilon))),minusHalfRCube=mul(mul(mul(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar(-.5)),derX=()=>mean7.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean7.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),derMean=()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean7.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean7.shape)},derVariance=()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean7.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean7.shape)},derScale=()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean7.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean7.shape)},derOffset=()=>{let offsetDer=dy;return mean7.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean7.shape)};return{x:derX,mean:derMean,variance:derVariance,scale:derScale,offset:derOffset}}},gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0],derX=()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad};return{x:derX,indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat(arrays){let result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}var greaterEqualGradConfig={kernelName:GreaterEqual,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}},identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})},isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})},isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})},isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})},log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}},logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}},logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax62=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax62))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let forward=backend3=>backend3.LRNGrad(dy,x,y,depthRadius,bias,alpha,beta),inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernelFunc(forward,inputs,null,LRNBackprop,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_}),lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape(y,expandShapeToKeepDim(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape(dy,expandShapeToKeepDim(dy.shape,origAxes))),{x:()=>{let dx=mul(dy,cast(equal(xOrig,y),dy.dtype));return dx}}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}},maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul(dy,cast(greaterEqual(a,b),"float32")),derB=()=>mul(dy,cast(less(a,b),"float32"));return{a:derA,b:derB}}};function maxPool3dBackprop_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dBackprop"),$input=convertToTensor(input2,"input","maxPool3dBackprop"),$output=convertToTensor(output,"output","maxPool3dBackprop"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool3DInfo(input5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode);return backend3.maxPool3dBackprop(dy5D,input5D,output5D,convInfo)},inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool3DBackprop,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dBackprop=op({maxPool3dBackprop_}),maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dBackprop(dy,x,y,filterSize,strides,$dilations,pad11,dimRoundingMode)}}};function maxPoolBackprop_(dy,input2,output,filterSize,strides,pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolBackprop"),$input=convertToTensor(input2,"input","maxPoolBackprop"),$output=convertToTensor(output,"output","maxPoolBackprop");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool2DInfo($input.shape,filterSize,strides,1,pad11,dimRoundingMode);return backend3.maxPoolBackprop($dy,$input,$output,convInfo)},inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad11,dimRoundingMode};return ENGINE.runKernelFunc(forward,inputs,null,MaxPoolBackprop,attrs)}var maxPoolBackprop=op({maxPoolBackprop_}),maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad11}=attrs;return{x:()=>maxPoolBackprop(dy,x,y,filterSize,strides,pad11)}}},minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}},minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul(dy,cast(lessEqual(a,b),"float32")),derB=()=>mul(dy,cast(greater(a,b),"float32"));return{a:derA,b:derB}}},mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},derB=()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}},multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}},negateGradConfig={kernelName:Negate,gradFunc:dy=>({x:()=>neg(dy)})},oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}},onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},padV2GradConfig={kernelName:PadV2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},powGradConfig={kernelName:Pow,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp132=b,outShape=assertAndGetBroadcastShape(base2.shape,exp132.shape),derBase=()=>{let expFloat=cast(exp132,"float32"),res=mul(dy,mul(expFloat,pow(base2,sub(expFloat,scalar(1))))),reduceAxes=getReductionAxes(base2.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,base2.shape)},derExp=()=>{let condition=greater(base2,0),logBase=where(condition,log(base2),zerosLike(base2)),res=mul(dy,mul(y,logBase)),reduceAxes=getReductionAxes(exp132.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,exp132.shape)};return{a:derBase,b:derExp}}},preluGradConfig={kernelName:Prelu,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha)),alpha:()=>{let res=where(mask,zerosLike(dy),mul(dy,x)),reduceAxes=getReductionAxes(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,alpha.shape)}}}},reciprocalGradConfig={kernelName:Reciprocal,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,neg(square(x)))}}},relu6GradConfig={kernelName:Relu6,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul(lessEqual(x,6),step(x));return{x:()=>mul(dy,cast(mask,"float32"))}}},reluGradConfig={kernelName:Relu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,cast(step(x),"float32"))}}},reshapeGradConfig={kernelName:Reshape,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape(dy,x.shape)}}},resizeBilinearGradConfig={kernelName:ResizeBilinear,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeBilinearBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,ResizeBilinearGrad,attrs);return{images:imagesDer}}},resizeNearestNeighborGradConfig={kernelName:ResizeNearestNeighbor,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeNearestNeighborBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,ResizeNearestNeighborGrad,attrs);return{images:imagesDer}}},reverseGradConfig={kernelName:Reverse,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam(dims,dy.shape);return{x:()=>reverse(dy,axes)}}},roundGradConfig={kernelName:Round,gradFunc:dy=>({x:()=>zerosLike(dy)})},rsqrtGradConfig={kernelName:Rsqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg(div(dy,mul(pow(x,1.5),2)))}}},selectV2PoolGradConfig={kernelName:SelectV2,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast(zerosLike(condition),"float32"),t:()=>mul(dy,cast(condition,dy.dtype)),e:()=>mul(dy,cast(logicalNot(condition),dy.dtype))}}},seluGradConfig={kernelName:Selu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale22=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale22),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}},sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}},signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})},sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}},sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}},sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let i=0;i<dy.rank;i++)paddings.push([begin_[i],inputShape[i]-begin_[i]-size_[i]]);return{x:()=>pad(dy,paddings)}}},softmaxGradConfig={kernelName:Softmax,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul(dy,y);return{logits:()=>sub(dyTimesY,mul(sum2(dyTimesY,[dim],keepDims),y))}}},softplusGradConfig={kernelName:Softplus,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,sigmoid(x))}}},spaceToBatchNDGradConfig={kernelName:SpaceToBatchND,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND(dy,blockShape,paddings)}}},splitVGradConfig={kernelName:SplitV,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat(dy,axis)}}},sqrtGradConfig={kernelName:Sqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,mul(sqrt(cast(x,"float32")),2))}}},squareGradConfig={kernelName:Square,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,mul(cast(x,"float32"),2))}}},squaredDifferenceGradConfig={kernelName:SquaredDifference,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar(2),derA=()=>mul(dy,mul(two,sub(a,b))),derB=()=>mul(dy,mul(two,sub(b,a)));return{a:derA,b:derB}}},stepGradConfig={kernelName:Step,gradFunc:dy=>({x:()=>zerosLike(dy)})},subGradConfig={kernelName:Sub,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)};return{a:derA,b:derB}}},sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs,axes=parseAxisParam(axis,x.shape);axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}},tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}},tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}},tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs,derX=()=>{let xGrad=zerosLike(x);if(x.rank===1)for(let i=0;i<reps[0];++i)xGrad=add2(xGrad,slice(dy,[i*x.shape[0]],[x.shape[0]]));else if(x.rank===2)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1]],[x.shape[0],x.shape[1]]));else if(x.rank===3)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2]],[x.shape[0],x.shape[1],x.shape[2]]));else if(x.rank===4)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)for(let l=0;l<reps[3];++l)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2],l*x.shape[3]],[x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);return xGrad};return{x:derX}}},transposeGradConfig={kernelName:Transpose,gradFunc:(dy,saved,attrs)=>{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}},unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}},unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved,derX=()=>gatherDropNegatives(dy,segmentIds);return{x:derX}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims(isPositive,i+1);isPositive=logicalAnd(isPositive,ones2(gathered.shape,"bool"));let zeroSlice=zerosLike(gathered);return where(isPositive,gathered,zeroSlice)}var zerosLikeGradConfig={kernelName:ZerosLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negateGradConfig,oneHotGradConfig,onesLikeGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectV2PoolGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad11,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean7,variance,offset,scale22,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean7,variance,offset,scale22,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min82,max102){return this.throwIfDisposed(),clipByValue(this,min82,max102)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad11,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad11,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad11,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse12){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse12)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad11,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad11,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad11,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding2,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding2,dilationRate,strides)};Tensor.prototype.powStrict=function(exp132){return this.throwIfDisposed(),powStrict(this,exp132)};Tensor.prototype.pow=function(exp132){return this.throwIfDisposed(),pow(this,exp132)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad11,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad11,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon(){return _epsilon==null&&(_epsilon=backend2().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;i<numValues;i++)newArray=newArray.concat(value);return newArray}else{let newArray=new Array(numValues);return newArray.fill(value),newArray}}function assert2(val,message){if(!val)throw new AssertionError(message)}function count(array2,refernce){let counter=0;for(let item of array2)item===refernce&&counter++;return counter}function singletonOrArray(xs){return xs.length===1?xs[0]:xs}function toList(x){return Array.isArray(x)?x:[x]}function toSnakeCase(name){let intermediate=name.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2"),insecure=intermediate.replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return insecure[0]!=="_"?insecure:"private"+insecure}function toCamelCase(identifier){return identifier.length<=1||identifier.indexOf("_")===-1?identifier:identifier.replace(/[_]+(\w|$)/g,(m,p1)=>p1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config2){if(config2==null||typeof config2!="object")return;if(Array.isArray(config2))config2.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config2);for(let field of fields){let value=config2[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config2[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config2=identifier;if(config2.className==null||config2.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config2)}.
+'className' and 'config' must set.`);let className=config2.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config2.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config2.config);let returnObj=fromConfig(cls,config2.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config2.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique5(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value==null)return;if(values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult,f2=(...args)=>{let now22=util_exports.now();return now22-lastTime<waitMs||(lastTime=now22,lastResult=f(...args)),lastResult};return f2}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let className=identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config2={className,config:{}};return deserializeConstraint(config2)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config2){return new MinMaxNorm(config2)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones8,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros9});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod52=1;for(let i=begin;i<end;++i)prod52*=array2[i];return prod52}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min6(array2){return min(toArray1D(array2)).dataSync()[0]}function max8(array2){return max(toArray1D(array2)).dataSync()[0]}function range4(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast48(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile8(y,[1,n,1])})}function flatten3(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile8(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean7=0,stddev=1,dtype,seed){return randomNormal(shape,mean7,stddev,dtype,seed)}function dot5(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather7(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square24(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu6(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training5=!1){return training5?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale22=this.scale;if(this.mode==="fanIn"?scale22/=Math.max(1,fanIn):this.mode==="fanOut"?scale22/=Math.max(1,fanOut):scale22/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale22);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale22);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={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 deserializeInitializer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config2={};return config2.className=className,config2.config={},deserializeInitializer(config2)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros9(){return new Zeros}function ones8(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config2){return new VarianceScaling(config2)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add31,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d5,conv2d:()=>conv2d10,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d3,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot6,dropout:()=>dropout3,elu:()=>elu7,embedding:()=>embedding,flatten:()=>flatten4,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum9,minimum:()=>minimum7,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu6,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape87,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax4,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),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(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{let variable32=variableAndValue[0];variable32.write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,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 name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`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 AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}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(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec==null)continue;let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape==null)return;if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) 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 AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node2 of this.inboundNodes){let shapeString=JSON.stringify(node2.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`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 RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses8){if(losses8==null||Array.isArray(losses8)&&losses8.length===0)return;losses8=toList(losses8),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses8)}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config2={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config2.batchInputShape=this.batchInputShape),this.dtype!=null&&(config2.dtype=this.dtype),config2}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount===0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor168,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor168.sourceLayer,nodeIndex=tensor168.nodeIndex),layer.inboundNodes.length===0)return[tensor168];{let node2=layer.inboundNodes[nodeIndex];if(node2.inboundLayers.length===0)return node2.inputTensors;{let sourceTensors=[];for(let i=0;i<node2.inboundLayers.length;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config2){if(config2.batchShape==null&&config2.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config2.batchShape!=null&&config2.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config2.batchShape;config2.shape!=null&&batchShape==null&&(batchShape=[null].concat(config2.shape));let dtype=config2.dtype;dtype==null&&(dtype="float32");let inputLayer2=new InputLayer({batchInputShape:batchShape,name:config2.name,dtype,sparse:config2.sparse}),outputs=inputLayer2.inboundNodes[0].outputTensors;return outputs[0]}async function resolveScalarsInLogs(logs5){if(logs5==null)return;let promises=[],keys=[],scalarsToDispose=[];for(let key in logs5){let value=logs5[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs5[keys[i]]=values[i][0];dispose(scalarsToDispose)}}function disposeTensorsInLogs(logs5){if(logs5==null)return;for(let key in logs5){let value=logs5[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs5){}async onEpochEnd(epoch,logs5){}async onBatchBegin(batch,logs5){}async onBatchEnd(batch,logs5){}async onTrainBegin(logs5){}async onTrainEnd(logs5){}setModel(model2){}},CallbackList=class{constructor(callbacks3,queueLength=10){callbacks3==null&&(callbacks3=[]),this.callbacks=callbacks3,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs5)}async onEpochEnd(epoch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs5)}async onBatchBegin(batch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs5)}async onBatchEnd(batch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs5)}async onTrainBegin(logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onTrainBegin(logs5)}async onTrainEnd(logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onTrainEnd(logs5)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs5){logs5==null&&(logs5={});let batchSize=logs5.size==null?0:logs5.size;this.seen+=batchSize;for(let key in logs5){let value=logs5[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs5){if(logs5!=null)for(let key of this.params.metrics){if(this.totals[key]==null)continue;typeof this.totals[key]=="number"?logs5[key]=this.totals[key]/this.seen:tidy(()=>{let log102=mul(div(1,this.seen),this.totals[key]);logs5[key]=log102,this.totals[key].dispose(),keep(logs5[key])})}}},History=class extends BaseCallback{async onTrainBegin(logs5){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs5){logs5==null&&(logs5={}),this.epoch.push(epoch);for(let key in logs5)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs5[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n){let tensorToDispose=this.history[keys[n]][indices[n]];tensorToDispose.dispose(),this.history[keys[n]][indices[n]]=values[n][0]}}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs5){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.yield(epoch,batch,logs5))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs5){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs5),await this.epochBegin(epoch,logs5))}async onEpochEnd(epoch,logs5){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.epochEnd(epoch,logs5))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs5){this.batchBegin!=null&&(await resolveScalarsInLogs(logs5),await this.batchBegin(batch,logs5))}async onBatchEnd(batch,logs5){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.batchEnd(batch,logs5))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs5)),await Promise.all(ps)}async onTrainBegin(logs5){this.trainBegin!=null&&(await resolveScalarsInLogs(logs5),await this.trainBegin(logs5))}async onTrainEnd(logs5){this.trainEnd!=null&&(await resolveScalarsInLogs(logs5),await this.trainEnd(logs5))}};function standardizeCallbacks(callbacks3,yieldEvery){if(callbacks3==null&&(callbacks3={}),callbacks3 instanceof BaseCallback)return[callbacks3];if(Array.isArray(callbacks3)&&callbacks3[0]instanceof BaseCallback)return callbacks3;let callbackConfigs=toList(callbacks3);return callbackConfigs.map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors){let constructors=CallbackConstructorRegistry.constructors[+levelName];constructors.forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks3,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks3!=null&&actualCallbacks.push(...callbacks3),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config2,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square24(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm52=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm52)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square24(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square24(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square24(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg202=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg202,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten3(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast48(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast48(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys){if(typeof key!="string")return!1;if(!plainObjectCheck(x[key]))return!1}return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node2 of layer.inboundNodes)if(nodes.indexOf(node2)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node2 of layer.inboundNodes){if(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node2)===-1)continue;for(let i=0;i<node2.inboundLayers.length;++i){let inboundLayer=node2.inboundLayers[i].name,inboundLayerIndex=node2.nodeIndices[i],inboundTensorIndex=node2.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.7.0";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training5=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training5||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(srcLayer instanceof InputLayer)continue;let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training5||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training5||dispose(tensorsToDispose)}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack92=[],marks=[];for(stack92.push(fetch3);stack92.length>0;){let top=stack92[stack92.length-1];if(visited.has(top.name)){stack92.pop();continue}let topIsMarked=marks[marks.length-1]===stack92.length-1;if(top.inputs.length===0||topIsMarked)stack92.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack92.length-1);for(let input2 of top.inputs){if(recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),visited.has(input2.name))continue;stack92.push(input2)}}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique5(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique5(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor168,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor168.sourceLayer,nodeIndex=tensor168.nodeIndex,tensorIndex=tensor168.tensorIndex);let node2=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node2)!==-1)throw new RuntimeError(`The tensor ${tensor168.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node2)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node2)===-1&&nodesInProgress2.push(node2);let numInboundLayers=node2.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node2.inputTensors[i],layer2=node2.inboundLayers[i],nodeIndex2=node2.nodeIndices[i],tensorIndex2=node2.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node2);nodesInProgress2.indexOf(node2)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node2),1);nodesInDecreasingDepth.push(node2)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node2 of reversedNodesInDecreasingDepth){nodeIDToNode[node2.id]=node2,node2.id in nodesDepths||(nodesDepths[node2.id]=0);let depth=nodesDepths[node2.id],previousDepth=layersDepths[node2.outboundLayer.id]==null?0:layersDepths[node2.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node2.outboundLayer.id]=depth,layerIDToLayer[node2.outboundLayer.id]=node2.outboundLayer,nodesDepths[node2.id]=depth;for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node2 of nodesByDepth[depth]){let layer=node2.outboundLayer;if(layer!=null){for(let x of node2.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node2.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container2 of this.internalContainerRefs)result.numDisposedVariables+=container2.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("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 weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/"),shortenNameArray=tokens.slice(0,-2).concat([tokens[tokens.length-1]]);validatedName=shortenNameArray.join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;j<node2.inboundLayers.length;j++){let inboundLayer=node2.inboundLayers[j],nodeIndex2=node2.nodeIndices[j],tensorIndex=node2.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node2);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node2 of nodes){let layer=node2.outboundLayer,referenceInputTensors=node2.inputTensors,referenceOutputTensors=node2.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node2.callArgs!=null&&(kwargs=node2.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor168,mask]=tensorMap[x.id];outputShapes.push(tensor168.shape),outputTensors.push(tensor168),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses8=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses8.push(...layer.calculateLosses())}return losses8})}getConfig(){let config2={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node2=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node2.callArgs)try{JSON.stringify(node2.callArgs),kwargs=node2.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node2.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node2.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node2.inboundLayers.length;i++){let inboundLayer=node2.inboundLayers[i],nodeIndex=node2.nodeIndices[i],tensorIndex=node2.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config2.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}config2.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}return config2.outputLayers=modelOutputs,config2}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config2.customObjects!=null?config2.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer;let inboundNodesData=layerData.inboundNodes;inboundNodesData.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config2.name,layersFromConfig=config2.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config2.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layer=createdLayers[layerName],layerOutputTensors=layer.inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config2.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layer=createdLayers[layerName],layerOutputTensors=layer.inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("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(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses8,sampleWeights){return mul(losses8,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=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 ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset5,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks3=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks3,verbose,args.epochs,null,null,getStepsPerEpoch(dataset5,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset5.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset5.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset5,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset5.size)&&(stepsPerEpoch=dataset5.size),stepsPerEpoch}function isDatasetObject(dataset5){return typeof dataset5.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset5,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset5)?dataset5:await dataset5.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather7(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks3,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range4(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks3,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks3=standardizeCallbacks(args.callbacks,args.yieldEvery),out=await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks3,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null);return out}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor168=tensors[i];if(tensor168.rank===1)outs.push(expandDims2(tensor168,1));else{if(tensor168.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor168)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors==null)return;let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor168=tensors[name];oldTensorIds.indexOf(tensor168.id)===-1&&tensorsToDispose.push(tensor168)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique5(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique5(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss==null)continue;if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);let theLosses=args.loss;lossFunctions=theLosses.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let outputMetrics=nestedMetrics[i],handleMetrics=metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else{let metricFn=get2(metric);weightedMetricFn=metricFn,metricName=metricNamePrefix+getLossOrMetricName(metric)}let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}};handleMetrics(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){if(this.collectedTrainableWeights==null)return;this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset5,args){return this.makeTestFunction(),evaluateDataset(this,dataset5,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor168,i)=>{tensor168==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchOuts=tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)});batchOuts.forEach((batchOut,i)=>outsBatches[i].push(batchOut))}return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i],lossFn=this.feedLossFns[i];lossFn===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!==0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range4(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;if(count(outLabels,label)>1){let dupIndex=count(outLabels.slice(0,i),label);newLabel+=`_${dupIndex}`}dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=lossFunction(targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables5=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0,totalLossValue=this.optimizer_.minimize(totalLossFunction,returnCost,variables5);return[totalLossValue].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset5,args){return fitDataset(this,dataset5,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],trainFunction=this.makeTrainFunction(),losses8=trainFunction(inputs.concat(targets)),lossValues=[];for(let loss of losses8){let v=await loss.data();lossValues.push(v[0])}return dispose(losses8),singletonOrArray(lossValues)}getNamedWeights(config2){let namedWeights=[],trainableOnly=config2!=null&&config2.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i){if(trainableOnly&&!weights[i].trainable)continue;namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]})}return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer7){this.optimizer_!==optimizer7&&(this.optimizer_=optimizer7,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses8=this.loss;for(let outputName of outputNames)if(typeof losses8[outputName]=="string")lossNames[outputName]=toSnakeCase(losses8[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer7=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer:optimizer7})}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config2)),returnString=!1,unusedArg=null,modelConfig=this.toJSON(unusedArg,returnString),modelArtifacts={modelTopology:modelConfig,format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null},includeOptimizer=config2==null?!1:config2.includeOptimizer;if(includeOptimizer&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer11,specs){let name2Tensor=io_exports.decodeWeights(buffer11,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){let shape=layer.inboundNodes[0].outputTensors[0].shape;if(shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("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(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))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(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),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{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),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 LayersModel({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(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset5,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset5,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),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(optimizer7){this.model.optimizer=optimizer7}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset5,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset5,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config2 instanceof Array){if(!(config2[0].className!=null)||config2[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config2}else util_exports.assert(config2.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config2.layers,delete config2.layers,extraModelConfig=config2;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let customObjects2,layer=deserialize(conf,customObjects2,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config2){return new Sequential(config2)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config2){return Input(config2)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu6(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config2={};return config2.className="linear",config2.config={},deserializeActivation(config2)}if(typeof identifier=="string"){let config2={};return config2.className=identifier,config2.config={},deserializeActivation(config2)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square24(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config2){return new cls({l1:config2.l1,l2:config2.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let className=identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config2={className,config:{}};return deserializeRegularizer(config2)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config2={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast48(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding2,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding2==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding2){if(dimSize==null)return null;if(padding2==="valid")dimSize=dimSize*strideSize+max8([kernelSize-strideSize,0]);else if(padding2==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding2}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding2="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding2==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding2="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding2==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding2="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding2==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`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 ValueError(`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 ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config2={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,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:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config2={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config2=super.getConfig();return delete config2.dilationRate,config2}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config2){super(rank,config2);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config2.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config2.kernelInitializer!=null||config2.kernelRegularizer!=null||config2.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config2.padding!=null&&config2.padding!=="same"&&config2.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config2.padding)}`);this.depthMultiplier=config2.depthMultiplier==null?1:config2.depthMultiplier,this.depthwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config2.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config2.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config2.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config2.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.kernelInitializer,delete config2.kernelRegularizer,delete config2.kernelConstraint,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config2.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config2}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.dataFormat,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config2={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=input2.resizeNearestNeighbor([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return input2.resizeNearestNeighbor([height,width])}})}getConfig(){let config2={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding2="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding2==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config2=super.getConfig();return config2.depthMultiplier=this.depthMultiplier,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config2}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range4(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state6,i)=>stepOutputs[1][i].mul(stepMask).add(state6.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;if(needPerStepOutputs){let axis=1;outputs=stack(perStepOutputs,axis)}return[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range4(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`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=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training5=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("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(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training5===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state6=>keep(state6.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state6 of initialState)this.stateSpec.push(new InputSpec({shape:state6.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length);let isTensor2=additionalInputs[0]instanceof SymbolicTensor;if(isTensor2){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training:training5},step9=(inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},rnnOutputs=rnn(step9,inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training5);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile8(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile8(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config2={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config2.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config2.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cellConfig=config2.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config2,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.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(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training5=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training:training5}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot5(mul(inputs,dpMask),this.kernel.read()):h=dot5(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot5(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config2)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return new cls(config2)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);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",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,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(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training5=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot5(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot5(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot5(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config2)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);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=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,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 biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training5=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot5(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot5(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config2)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),cellConfigs=this.cells.map(getCellConfig),config2={cells:cellConfigs};return Object.assign({},baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cells=[];for(let cellConfig of config2.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones92,rate,training:training5=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones92(),rate),createMask=()=>inTrainPhase(droppedInputs,ones92,training5);if(!count2||count2<=1)return keep(createMask().clone());let masks=Array(count2).fill(void 0).map(createMask);return masks.map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t},ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training5=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],batchSize=inputShape[0];if(batchSize==null)throw new ValueError("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(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training5?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state6=>keep(state6.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding:padding2,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding2,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding2,strides[1],dilationRate[1]),outShape=[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]];return outShape}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding:padding2,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding2||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training5=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training:training5,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config2={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config2)}inputConv(x,w,b,padding2){let out=conv2d(x,w,this.strides,padding2||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){let strides=1;return conv2d(x,w,strides,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config2){return new cls(config2)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training5=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2),output=inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training5);return output}return inputs})}getConfig(){let config2={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,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]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot5(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot5(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config2={};this.dataFormat!=null&&(config2.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config2={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config2={n:this.n},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!==0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config2={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range4(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config2={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={maskValue:this.maskValue};return Object.assign(config2,baseConfig),config2}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims),output=input2.mul(booleanMask.asType(input2.dtype));return output})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);input2.dtype!=="int32"&&(input2=cast48(input2,"int32"));let output=gather7(this.embeddings.read(),input2.as1D());return output.reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config2={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique5(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique5(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max8(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range4(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range4(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique5(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(allNoneShape)return;let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config2={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config2={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={stddev:this.stddev};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),noised=()=>randomNormal2(input2.shape,0,this.stddev).add(input2),output=inTrainPhase(noised,()=>input2,kwargs.training||!1);return output})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.rate>0&&this.rate<1){let noised=()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))};return inTrainPhase(noised,()=>input2,kwargs.training||!1)}return input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs),droppedInputs=()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale22=1.0507009873554805,alphaP=-alpha*scale22,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast48(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate,x=input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));return x.mul(a).add(b)};return inTrainPhase(droppedInputs,()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean7,variance,beta,gamma,epsilon32=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean7,variance,beta,gamma,epsilon32);else if(x.rank===3)out=batchNorm3d(x,mean7,variance,beta,gamma,epsilon32);else if(x.rank===4)out=batchNorm4d(x,mean7,variance,beta,gamma,epsilon32);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean7=meanAndVariance.mean,variance=meanAndVariance.variance,normed=batchNormalization(x,mean7,variance,beta,gamma,epsilon32);return[normed,mean7,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean7=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range4(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean7.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape),normed=batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon32);return[normed,mean7,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range4(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon32)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training5=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range4(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range4(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training5)return normalizeInference();let[normedTraining,mean7,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable32,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable32.read(),updateDelta=origValue.sub(value).mul(decay);variable32.write(origValue.sub(updateDelta))})},updateMovingMeanAndVariance=()=>{doMovingAverage(this.movingMean,mean7,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)};return updateMovingMeanAndVariance(),normedTraining})}getConfig(){let config2={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.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(let axis of this.axis)if(!Number.isInteger(axis))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=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.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]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique5(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean7,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale22=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean7=mean7.tile(momentsTiling),variance=variance.tile(momentsTiling),scale22=scale22.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean7,variance,offset,scale22,this.epsilon)})}getConfig(){let config2={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding2,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding2==null&&(padding2=[[1,1],[1,1]]),padding2.length!==2||padding2[0].length!==2||padding2[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding2[0],padding2[1]]:pattern=[[0,0],padding2[0],padding2[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config2={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding2,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding2),strides==null&&(strides=[1,1]),padding2==null&&(padding2="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding2==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding2,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding2),strides==null&&(strides=[1,1,1]),padding2==null&&(padding2="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding2==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool3d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool3d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config2={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}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(weights){this.layer.setWeights(weights)}getConfig(){let config2={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config2,customObjects={}){let layerConfig=config2.layer,layer=deserialize(layerConfig,customObjects);delete config2.layer;let newConfig={layer};return Object.assign(newConfig,config2),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let step9=(inputs2,states)=>{let output=getExactlyOneTensor(this.layer.call(inputs2,kwargs));return[output,[]]},rnnOutputs=rnn(step9,inputs,[],!1,null,null,!1,!0),y=rnnOutputs[1];return y})}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=!(layerConfig.goBackwards===!0);let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state6=>new InputSpec({shape:state6.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor168 of additionalInputs)if(tensor168 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let states=this.forwardLayer.states,stateMask=states.map(state6=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config2={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static fromConfig(cls,config2){let rnnLayer=deserialize(config2.layer);if(delete config2.layer,config2.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config2;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu7(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu6(args){return new PReLU(args)}function softmax4(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d5(args){return new Conv1D(args)}function conv2d10(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d3(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten4(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape87(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add31(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum9(args){return new Maximum2(args)}function minimum7(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot6(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config2){return new L1L2(config2)}function l12(config2){return l1(config2)}function l22(config2){return l2(config2)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less7(currVal,prevVal){return currVal<prevVal}function greater11(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less7:this.mode==="max"?this.monitorFunc=greater11:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater11:this.monitorFunc=less7,this.monitorFunc===less7&&(this.minDelta*=-1)}async onTrainBegin(logs5){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less7?Infinity:-Infinity}async onEpochEnd(epoch,logs5){await resolveScalarsInLogs(logs5);let current=this.getMonitorValue(logs5);if(current==null)return;this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0))}async onTrainEnd(logs5){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs5){logs5==null&&(logs5={});let monitorValue=logs5[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs5)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node2,tensorMap,context,resourceManager){let inputParam=node2.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node2.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors"){let inputs=node2.inputNames.slice(start,end);return inputs.map(name=>getTensor(name,tensorMap,context,resourceManager))}let tensor168=getTensor(node2.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor168.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor168.shape,data)}let attrParam=node2.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor168=resourceManager.getHashTableHandleByName(nodeName);if(tensor168!=null)return tensor168}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");if(parts.length===1)return[name,0];let nodeName=parts[0];return[nodeName,Number(parts[parts.length-1])]}function getPadding(node2,tensorMap,context){let pad11=getParamValue("pad",node2,tensorMap,context);if(pad11==="explicit"){pad11=getParamValue("explicitPaddings",node2,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad11[i*2],explicitPadding[i][1]=pad11[i*2+1];return explicitPadding}return pad11}function cloneTensor(tensor168){return tensor168.kept?tensor168:clone(tensor168)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var 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json17=[{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:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{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:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops692=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops692.map(op22=>op22.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op.startsWith("Placeholder")?placeholders.push(map[node2.name]):node2.op==="Const"?weights.push(map[node2.name]):(node2.input==null||node2.input.length===0)&&initNodes.push(map[node2.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node2=nodes[key];node2.children.length===0&&outputs.push(node2)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2!=null&&(node2.signatureKey=outputNodeNameToKey[name],outputs.push(node2))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node2=nodes[nodeName];node2&&(node2.signatureKey=inputNodeNameToKey[name],inputs.push(node2))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node2){let mapper=getRegisteredOp(node2.op)||this.opMappers[node2.op]||{};node2.attr==null&&(node2.attr={});let newNode={name:node2.name,op:node2.op,category:mapper.category,inputNames:(node2.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node2.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node2.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node2.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node2.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node2.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node2)=>(map[node2.name]=this.mapNode(node2),node2.op==="Const"&&weights.push(map[node2.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node2={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node2.signatureKey=arg.name,inputs.push(node2),nodes[nodeName]=node2});let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node2=nodes[key];node2.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node2.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node2)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node2=nodes[nodeName];node2!=null&&(node2.defaultOutput=index,outputs.push(node2))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){typeof value=="string"&&(value=DataType[value]);switch(value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){return shape.unknownRank?void 0:shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node2,tensorMap,context){this.node=node2,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node2.inputNames.map(name=>this.getInput(name)),node2.rawAttrs!=null&&(this.attrs=Object.keys(node2.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node2,tensorMap,context)=>{switch(node2.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node2,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp2=(node2,tensorMap,context)=>{switch(node2.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node2,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node2,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node2,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node2,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node2,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node2,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node2,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node2,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node2,tensorMap,context),getParamValue("imag",node2,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node2,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node2,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node2,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node2,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node2,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node2,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node2,tensorMap,context))];case"Log":return[log(getParamValue("x",node2,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node2,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node2,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node2,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node2,tensorMap,context))];case"Real":return[real(getParamValue("x",node2,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node2,tensorMap,context))];case"Round":return[round(getParamValue("x",node2,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node2,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node2,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node2,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node2,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node2,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node2,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node2,tensorMap,context))];case"Square":return[square(getParamValue("x",node2,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node2,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node2,tensorMap,context))];case"Relu6":case"ClipByValue":return[clipByValue(getParamValue("x",node2,tensorMap,context),getParamValue("clipValueMin",node2,tensorMap,context),getParamValue("clipValueMax",node2,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node2.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node2,tensorMap,context),getParamValue("axes",node2,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor168=>{(keepIds==null||!keepIds.has(tensor168.tensor.id))&&tensor168.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor168){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
+          because the value dtype is ${tensor168.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor168.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor168.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor168,keep(tensor168),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i<this.size();i++)indices.push(i)}if(indices.length===0)return tensor4([],[0].concat(this.elementShape));let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,"TensorArray shape mismatch: "),stack(tensors,0)}concat(dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);if(this.size()===0)return tensor4([],[0].concat(this.elementShape));let indices=[];for(let i=0;i<this.size();i++)indices.push(i);let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`),concat(tensors,0)}scatter(indices,tensor168){if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor168.dtype}`);if(indices.length!==tensor168.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor168.shape[0]}`);let maxIndex=Math.max(...indices);if(!this.dynamicSize&&maxIndex>=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor168,0))}split(length,tensor168){if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor168.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor168.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${r}, and tensor's shape is: ${e.shape}`);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${t.length}), and the TensorArray is not marked as dynamically resizeable`);let s=r===0?0:e.size/r,c=[];rt(()=>{e=Q(e,[1,r,s]);for(let p=0;p<t.length;++p){let f=p===0?0:o[p-1],m=[0,f,0],y=[1,t[p],s];c[p]=Q(ce(e,m,y),this.elementShape)}return c});let l=[];for(let p=0;p<t.length;p++)l[p]=p;this.writeMany(l,c)}}class al{constructor(t,e,r,o=-1){this.tensors=t,this.elementShape=e,this.elementDtype=r,t!=null&&t.forEach(s=>{if(r!==s.dtype)throw new Error(`Invalid data types; op elements ${r}, but list elements ${s.dtype}`);no(e,s.shape,"TensorList shape mismatch: "),Sn(s)}),this.idTensor=Et(0),this.maxNumElements=o,Sn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new al([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.id))&&e.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(t,e,r=-1){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(r!==-1&&this.tensors.length!==r)throw new Error(`Operation expected a list with ${r} elements but got a list with ${this.tensors.length} elements.`);return no(t,this.elementShape,"TensorList shape mismatch: "),rt(()=>{let o=this.tensors.map(s=>Q(s,t));return pr(o,0)})}popBack(t,e){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let r=this.tensors.pop();return no(r.shape,t,"TensorList shape mismatch: "),Q(r,t)}pushBack(t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(no(t.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Sn(t),this.tensors.push(t)}resize(t){if(t<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${t}`);if(this.maxNumElements!==-1&&t>this.maxNumElements)throw new Error(`TensorListResize input size ${t} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=t}getItem(t,e,r){if(r!==this.elementDtype)throw new Error(`Invalid data types; op elements ${r}, but list elements ${this.elementDtype}`);if(t<0||t>this.tensors.length)throw new Error(`Trying to access element ${t} in a list with ${this.tensors.length} elements.`);if(this.tensors[t]==null)throw new Error(`element at index ${t} is null.`);return no(this.tensors[t].shape,e,"TensorList shape mismatch: "),this.tensors[t]}setItem(t,e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(t<0||this.maxNumElements!==-1&&t>=this.maxNumElements)throw new Error(`Trying to set element ${t} in a list with max ${this.maxNumElements} elements.`);no(this.elementShape,e.shape,"TensorList shape mismatch: "),Sn(e),this.tensors[t]=e}gather(t,e,r){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);return no(this.elementShape,r,"TensorList shape mismatch: "),t=t.slice(0,this.size()),t.length===0?un([],[0].concat(this.elementShape)):rt(()=>{let o=t.map(s=>Q(this.tensors[s],r));return pr(o,0)})}concat(t,e){if(!!t&&t!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${t}`);return no(this.elementShape,e,"TensorList shape mismatch: "),this.size()===0?un([],[0].concat(this.elementShape)):rt(()=>{let r=this.tensors.map(o=>Q(o,e));return Qe(r,0)})}}function Ij(n,t,e){let r=n.dtype;if(n.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${n.shape}`);if(n.dtype!==e)throw new Error(`Invalid data types; op elements ${n.dtype}, but list elements ${e}`);let o=n.shape.slice(1);no(o,t,"TensorList shape mismatch: ");let s=go(n);return new al(s,t,r)}function Ej(n,t,e){return new al([],n,t,e)}function Dj(n,t,e,r){if(t.length!==n.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${n.shape[0]}`);let o=Math.max(...t);if(r!=null&&r!==-1&&o>=r)throw new Error(`Max index must be < array size (${o}  vs. ${r})`);let s=new al([],e,n.dtype,r),c=go(n,0);return t.forEach((l,p)=>{s.setItem(l,c[p])}),s}function Aj(n,t,e){let r=0,o=t.map(p=>(r+=p,r));if(r!==n.shape[0])throw new Error(`Expected sum of lengths to be equal to
+        ${totalLength}, and tensor's shape is: ${tensor168.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor168.size/totalLength,tensors=[];tidy(()=>{tensor168=reshape(tensor168,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices2=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors[i]=reshape(slice(tensor168,indices2,sizes),this.elementShape)}return tensors});let indices=[];for(let i=0;i<length.length;i++)indices[i]=i;this.writeMany(indices,tensors)}},TensorList=class{constructor(tensors,elementShape,elementDtype,maxNumElements=-1){this.tensors=tensors,this.elementShape=elementShape,this.elementDtype=elementDtype,tensors!=null&&tensors.forEach(tensor168=>{if(elementDtype!==tensor168.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor168.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor168.shape,"TensorList shape mismatch: "),keep(tensor168)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor168=>{(keepIds==null||!keepIds.has(tensor168.id))&&tensor168.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor168=>reshape(tensor168,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor168=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor168.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor168,elementShape)}pushBack(tensor168){if(tensor168.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor168.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor168),this.tensors.push(tensor168)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor168){if(tensor168.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor168.shape,"TensorList shape mismatch: "),keep(tensor168),this.tensors[elementIndex]=tensor168}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor4([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor4([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor168,elementShape,elementDtype){let dtype=tensor168.dtype;if(tensor168.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor168.shape}`);if(tensor168.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${elementDtype}`);let outputShape=tensor168.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor168);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor168,indices,elementShape,numElements){if(indices.length!==tensor168.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor168.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);let list=new TensorList([],elementShape,tensor168.dtype,numElements),tensors=unstack(tensor168,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split9(tensor168,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor168.shape[0])throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
-        ${r}, and tensor's shape is: ${n.shape}`);let s=r===0?0:n.size/r,c=rt(()=>{let p=[];n=Q(n,[1,r,s]);for(let f=0;f<t.length;++f){let m=f===0?0:o[f-1],y=[0,m,0],b=[1,t[f],s];p[f]=Q(ce(n,y,b),e)}return n.dispose(),p}),l=new al([],e,n.dtype,t.length);for(let p=0;p<c.length;p++)l.setItem(p,c[p]);return l}let Fj=async(n,t,e)=>{switch(n.op){case"If":case"StatelessIf":{let r=A("thenBranch",n,t,e),o=A("elseBranch",n,t,e),s=A("cond",n,t,e),c=A("args",n,t,e),l=await s.data();return l[0]?e.functionMap[r].executeFunctionAsync(c,e.tensorArrayMap,e.tensorListMap):e.functionMap[o].executeFunctionAsync(c,e.tensorArrayMap,e.tensorListMap)}case"While":case"StatelessWhile":{let r=A("body",n,t,e),o=A("cond",n,t,e),s=A("args",n,t,e),c=await e.functionMap[o].executeFunctionAsync(s,e.tensorArrayMap,e.tensorListMap),l=s.map(m=>m.id),p=await c[0].data();c.forEach(m=>{!m.kept&&l.indexOf(m.id)===-1&&m.dispose()});let f=s;for(;p[0];){let m=f;f=await e.functionMap[r].executeFunctionAsync(f,e.tensorArrayMap,e.tensorListMap);let y=f.map(v=>v.id);m.forEach(v=>{!v.kept&&l.indexOf(v.id)===-1&&y.indexOf(v.id)===-1&&v.dispose()});let b=await e.functionMap[o].executeFunctionAsync(f,e.tensorArrayMap,e.tensorListMap);p=await b[0].data(),b.forEach(v=>{!v.kept&&l.indexOf(v.id)===-1&&y.indexOf(v.id)===-1&&v.dispose()})}return f}case"LoopCond":{let r=A("pred",n,t,e);return[_s(r)]}case"Switch":{let r=A("pred",n,t,e),o=A("data",n,t,e);return o.kept||(o=_s(o)),(await r.data())[0]?[void 0,o]:[o,void 0]}case"Merge":{let r=n.inputNames.find(o=>dr(o,t,e)!==void 0);if(r){let o=dr(r,t,e);return[_s(o)]}return}case"Enter":{let r=A("frameName",n,t,e),o=A("tensor",n,t,e);return e.enterFrame(r),[_s(o)]}case"Exit":{let r=A("tensor",n,t,e);return e.exitFrame(),[_s(r)]}case"NextIteration":{let r=A("tensor",n,t,e);return e.nextIteration(),[_s(r)]}case"TensorArrayV3":{let r=A("size",n,t,e),o=A("dtype",n,t,e),s=A("elementShape",n,t,e),c=A("dynamicSize",n,t,e),l=A("clearAfterRead",n,t,e),p=A("identicalElementShapes",n,t,e),f=A("name",n,t,e),m=new $j(f,o,r,s,p,c,l);return e.addTensorArray(m),[m.idTensor,Et(1)]}case"TensorArrayWriteV3":{let r=A("tensorArrayId",n,t,e),o=A("index",n,t,e),s=A("tensor",n,t,e),c=e.getTensorArray(r.id);return c.write(o,s),[c.idTensor]}case"TensorArrayReadV3":{let r=A("tensorArrayId",n,t,e),o=A("index",n,t,e),s=e.getTensorArray(r.id);return[s.read(o)]}case"TensorArrayGatherV3":{let r=A("tensorArrayId",n,t,e),o=A("indices",n,t,e),s=A("dtype",n,t,e),c=e.getTensorArray(r.id);return[c.gather(o,s)]}case"TensorArrayScatterV3":{let r=A("tensorArrayId",n,t,e),o=A("indices",n,t,e),s=A("tensor",n,t,e),c=e.getTensorArray(r.id);return c.scatter(o,s),[c.idTensor]}case"TensorArrayConcatV3":{let r=A("tensorArrayId",n,t,e),o=e.getTensorArray(r.id),s=A("dtype",n,t,e);return[o.concat(s)]}case"TensorArraySplitV3":{let r=A("tensorArrayId",n,t,e),o=A("tensor",n,t,e),s=A("lengths",n,t,e),c=e.getTensorArray(r.id);return c.split(s,o),[c.idTensor]}case"TensorArraySizeV3":{let r=A("tensorArrayId",n,t,e),o=e.getTensorArray(r.id);return[Et(o.size(),"int32")]}case"TensorArrayCloseV3":{let r=A("tensorArrayId",n,t,e),o=e.getTensorArray(r.id);return o.clearAndClose(),[o.idTensor]}case"TensorListSetItem":{let r=A("tensorListId",n,t,e),o=A("index",n,t,e),s=A("tensor",n,t,e),c=e.getTensorList(r.id);return c.setItem(o,s),[c.idTensor]}case"TensorListGetItem":{let r=A("tensorListId",n,t,e),o=A("index",n,t,e),s=A("elementShape",n,t,e),c=A("elementDType",n,t,e),l=e.getTensorList(r.id);return[l.getItem(o,s,c)]}case"TensorListScatterV2":case"TensorListScatter":{let r=A("indices",n,t,e),o=A("tensor",n,t,e),s=A("elementShape",n,t,e),c=A("numElements",n,t,e),l=Dj(o,r,s,c);return e.addTensorList(l),[l.idTensor]}case"TensorListReserve":{let r=A("elementShape",n,t,e),o=A("elementDType",n,t,e),s=A("numElements",n,t,e),c=Ej(r,o,s);return e.addTensorList(c),[c.idTensor]}case"TensorListGather":{let r=A("tensorListId",n,t,e),o=A("indices",n,t,e),s=A("elementShape",n,t,e),c=A("elementDType",n,t,e),l=e.getTensorList(r.id);return[l.gather(o,c,s)]}case"TensorListStack":{let r=A("tensorListId",n,t,e),o=A("elementShape",n,t,e),s=A("elementDType",n,t,e),c=A("numElements",n,t,e),l=e.getTensorList(r.id);return[l.stack(o,s,c)]}case"TensorListFromTensor":{let r=A("tensor",n,t,e),o=A("elementShape",n,t,e),s=A("elementDType",n,t,e),c=Ij(r,o,s);return e.addTensorList(c),[c.idTensor]}case"TensorListConcat":{let r=A("tensorListId",n,t,e),o=e.getTensorList(r.id),s=A("dtype",n,t,e),c=A("elementShape",n,t,e);return[o.concat(s,c)]}case"TensorListPushBack":{let r=A("tensorListId",n,t,e),o=A("tensor",n,t,e),s=e.getTensorList(r.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let r=A("tensorListId",n,t,e),o=A("elementShape",n,t,e),s=A("elementDType",n,t,e),c=e.getTensorList(r.id);return[c.popBack(o,s)]}case"TensorListSplit":{let r=A("tensor",n,t,e),o=A("elementShape",n,t,e),s=A("lengths",n,t,e),c=Aj(r,s,o);return e.addTensorList(c),[c.idTensor]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},_st="control";function N$(n,t,e){let[r,o]=A("fusedOps",n,t,e),s=r==="biasadd",c=o==="prelu",l=r==="fusedbatchnorm",p=A("numArgs",n,t,e);if(s){if(c&&p!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!c&&p!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let f=A("strides",n,t,e),m=Vm(n,t,e),y=A("dataFormat",n,t,e).toUpperCase(),b=A("dilations",n,t,e),[v,T]=A("args",n,t,e);return{stride:f,pad:m,dataFormat:y,dilations:b,biasArg:v,preluArg:T,activationFunc:o}}let Rj=(n,t,e)=>{switch(n.op){case"Conv1D":{let r=A("stride",n,t,e),o=A("pad",n,t,e),s=A("dataFormat",n,t,e).toUpperCase(),c=A("dilation",n,t,e);return[Cd(A("x",n,t,e),A("filter",n,t,e),r,o,s,c)]}case"Conv2D":{let r=A("strides",n,t,e),o=Vm(n,t,e),s=A("dataFormat",n,t,e).toUpperCase(),c=A("dilations",n,t,e);return[gs(A("x",n,t,e),A("filter",n,t,e),[r[1],r[2]],o,s,[c[1],c[2]])]}case"_FusedConv2D":{let{stride:r,pad:o,dataFormat:s,dilations:c,biasArg:l,preluArg:p,activationFunc:f}=N$(n,t,e);return[cv({x:A("x",n,t,e),filter:A("filter",n,t,e),strides:[r[1],r[2]],pad:o,dataFormat:s,dilations:[c[1],c[2]],bias:l,activation:f,preluActivationWeights:p})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:o,dataFormat:s,dilations:c,biasArg:l,preluArg:p,activationFunc:f}=N$(n,t,e);return[bC({x:A("x",n,t,e),filter:A("filter",n,t,e),strides:[r[1],r[2]],pad:o,dataFormat:s,dilations:[c[1],c[2]],bias:l,activation:f,preluActivationWeights:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=A("outputShape",n,t,e),o=A("strides",n,t,e),s=Vm(n,t,e);return[Sd(A("x",n,t,e),A("filter",n,t,e),r,[o[1],o[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=A("strides",n,t,e),o=Vm(n,t,e),s=A("dilations",n,t,e),c=A("dataFormat",n,t,e).toUpperCase();return[ca(A("input",n,t,e),A("filter",n,t,e),[r[1],r[2]],o,c,[s[1],s[2]])]}case"Conv3D":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("dataFormat",n,t,e).toUpperCase(),c=A("dilations",n,t,e);return[Fw(A("x",n,t,e),A("filter",n,t,e),[r[1],r[2],r[3]],o,s,[c[1],c[2],c[3]])]}case"AvgPool":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e);return[ap(A("x",n,t,e),[s[1],s[2]],[r[1],r[2]],o)]}case"MaxPool":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e);return[mp(A("x",n,t,e),[s[1],s[2]],[r[1],r[2]],o)]}case"MaxPoolWithArgmax":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e),c=A("includeBatchInIndex",n,t,e),{result:l,indexes:p}=U_(A("x",n,t,e),[s[1],s[2]],[r[1],r[2]],o,c);return[l,p]}case"AvgPool3D":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e);return[Ew(A("x",n,t,e),[s[1],s[2],s[3]],[r[1],r[2],r[3]],o)]}case"MaxPool3D":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e);return[Gw(A("x",n,t,e),[s[1],s[2],s[3]],[r[1],r[2],r[3]],o)]}case"Dilation2D":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("dilations",n,t,e),c=r[1],l=r[2],p=s[1],f=s[2];return[Pw(A("x",n,t,e),A("filter",n,t,e),[c,l],o,[p,f],"NHWC")]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Cst="convolution";let Pj=(n,t,e)=>{switch(n.op){case"Fill":{let r=A("shape",n,t,e),o=A("dtype",n,t,e),s=A("value",n,t,e);return[Lc(r,s,o)]}case"LinSpace":{let r=A("start",n,t,e),o=A("stop",n,t,e),s=A("num",n,t,e);return[W_(r,o,s)]}case"Multinomial":{let r=A("logits",n,t,e),o=A("numSamples",n,t,e),s=A("seed",n,t,e);return[q_(r,o,s)]}case"OneHot":{let 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Oj=async(n,t,e)=>{switch(n.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:o,maxOutputSize:s,iouThreshold:c,scoreThreshold:l,softNmsSigma:p}=g1(n,t,e),f=await mi.nonMaxSuppressionWithScoreAsync(r,o,s,c,l,p);return[f.selectedIndices,f.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:o,maxOutputSize:s,iouThreshold:c,scoreThreshold:l}=g1(n,t,e),p=A("padToMaxOutputSize",n,t,e),f=await mi.nonMaxSuppressionPaddedAsync(r,o,s,c,l,p);return[f.selectedIndices,f.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:o,maxOutputSize:s,iouThreshold:c,scoreThreshold:l}=g1(n,t,e);return[await mi.nonMaxSuppressionAsync(r,o,s,c,l)]}case"Where":{let r=$t(A("condition",n,t,e),"bool"),o=[await ov(r)];return r.dispose(),o}case"ListDiff":return j_(A("x",n,t,e),A("y",n,t,e));default:throw TypeError(`Node type ${n.op} is not implemented`)}},$st="dynamic";let Lj=(n,t,e)=>{switch(n.op){case"TopKV2":{let r=A("x",n,t,e),o=A("k",n,t,e),s=A("sorted",n,t,e),c=nv(r,o,s);return[c.values,c.indices]}case"Unique":{let r=A("x",n,t,e),o=Hd(r);return[o.values,o.indices]}case"UniqueV2":{let r=A("x",n,t,e),o=A("axis",n,t,e),s=Hd(r,o);return[s.values,s.indices]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ist="evaluation";let Mj=(n,t,e)=>{switch(n.op){case"Const":return t[n.name];case"PlaceholderWithDefault":let r=A("default",n,t,e);return[dr(n.name,t,e)||r];case"Placeholder":return[dr(n.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let f=A("x",n,t,e);return[_s(f)]}case"IdentityN":return A("x",n,t,e).map(f=>_s(f));case"Snapshot":let o=A("x",n,t,e);return[_s(o)];case"Shape":return[vr(A("x",n,t,e).shape,"int32")];case"ShapeN":return A("x",n,t,e).map(f=>vr(f.shape));case"Size":return[Et(A("x",n,t,e).size,"int32")];case"Rank":return[Et(A("x",n,t,e).rank,"int32")];case"NoOp":return[Et(1)];case"Print":let s=A("x",n,t,e),c=A("data",n,t,e),l=A("message",n,t,e),p=A("summarize",n,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(l);for(let f=0;f<c.length;f++)console.log(Array.prototype.slice.call(c[f].dataSync()).slice(0,p));return[s];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Est="graph";class Bj{constructor(t,e){this.keyDType=t,this.valueDType=e,this.handle=Et(0),this.tensorMap=new Map,Sn(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(t=>t.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),rt(()=>{let o=go(e),s=r.length,c=o.length;_(s===c,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${c} elements.`);for(let l=0;l<s;l++){let p=r[l],f=o[l];Sn(f),this.tensorMap.set(p,f)}return this.handle})}async find(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return rt(()=>{let o=[];for(let s=0;s<r.length;s++){let c=r[s],l=this.findWithDefault(c,e);o.push(l)}return pr(o)})}findWithDefault(t,e){let r=this.tensorMap.get(t);return r!=null?r:e}checkKeyAndValueTensor(t,e){if(t.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${t.dtype}`);if(e.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`)}}let zj=async(n,t,e,r)=>{switch(n.op){case"HashTable":case"HashTableV2":{let o=A("keyDType",n,t,e),s=A("valueDType",n,t,e),c=new Bj(o,s);return r.addHashTable(n.name,c),[c.handle]}case"LookupTableImport":case"LookupTableImportV2":{let o=A("tableHandle",n,t,e,r),s=A("keys",n,t,e),c=A("values",n,t,e),l=r.getHashTableById(o.id);return[await l.import(s,c)]}case"LookupTableFind":case"LookupTableFindV2":{let o=A("tableHandle",n,t,e,r),s=A("keys",n,t,e),c=A("defaultValue",n,t,e),l=r.getHashTableById(o.id);return[await l.find(s,c)]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Dst="hash_table";let Wj=(n,t,e)=>{switch(n.op){case"ResizeBilinear":{let r=A("images",n,t,e),o=A("size",n,t,e),s=A("alignCorners",n,t,e);return[mi.resizeBilinear(r,[o[0],o[1]],s)]}case"ResizeNearestNeighbor":{let r=A("images",n,t,e),o=A("size",n,t,e),s=A("alignCorners",n,t,e);return[mi.resizeNearestNeighbor(r,[o[0],o[1]],s)]}case"CropAndResize":{let r=A("image",n,t,e),o=A("boxes",n,t,e),s=A("boxInd",n,t,e),c=A("cropSize",n,t,e),l=A("method",n,t,e),p=A("extrapolationValue",n,t,e);return[mi.cropAndResize(r,o,s,c,l,p)]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ast="image";let Vj=(n,t,e)=>{switch(n.op){case"Equal":return[ho(A("a",n,t,e),A("b",n,t,e))];case"NotEqual":return[hi(A("a",n,t,e),A("b",n,t,e))];case"Greater":return[Rr(A("a",n,t,e),A("b",n,t,e))];case"GreaterEqual":return[ys(A("a",n,t,e),A("b",n,t,e))];case"Less":return[fp(A("a",n,t,e),A("b",n,t,e))];case"LessEqual":return[pi(A("a",n,t,e),A("b",n,t,e))];case"LogicalAnd":return[Zr(A("a",n,t,e),A("b",n,t,e))];case"LogicalNot":return[dp(A("a",n,t,e))];case"LogicalOr":return[Pd(A("a",n,t,e),A("b",n,t,e))];case"Select":case"SelectV2":return[Yn(A("condition",n,t,e),A("a",n,t,e),A("b",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Fst="logical";let Gj=(n,t,e)=>{switch(n.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[ge(A("a",n,t,e),A("b",n,t,e),A("transposeA",n,t,e),A("transposeB",n,t,e))];case"Transpose":return[Kt(A("x",n,t,e),A("perm",n,t,e))];case"_FusedMatMul":let[r,o]=A("fusedOps",n,t,e),s=r==="biasadd",c=o==="prelu",l=A("numArgs",n,t,e);if(s){if(c&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!c&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,f]=A("args",n,t,e);return[Qd({a:A("a",n,t,e),b:A("b",n,t,e),transposeA:A("transposeA",n,t,e),transposeB:A("transposeB",n,t,e),bias:p,activation:o,preluActivationWeights:f})];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Rst="matrices";let Uj=(n,t,e)=>{switch(n.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[aa(A("x",n,t,e),A("mean",n,t,e),A("variance",n,t,e),A("offset",n,t,e),A("scale",n,t,e),A("epsilon",n,t,e))];case"FusedBatchNormV3":return[aa(A("x",n,t,e),A("mean",n,t,e),A("variance",n,t,e),A("offset",n,t,e),A("scale",n,t,e),A("epsilon",n,t,e))];case"LRN":return[zw(A("x",n,t,e),A("radius",n,t,e),A("bias",n,t,e),A("alpha",n,t,e),A("beta",n,t,e))];case"Softmax":return[da(A("x",n,t,e))];case"LogSoftmax":return[Rd(A("x",n,t,e))];case"SparseToDense":return[sv(A("sparseIndices",n,t,e),A("outputShape",n,t,e),A("sparseValues",n,t,e),A("defaultValue",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Pst="normalization";let qj=(n,t,e)=>{switch(n.op){case"Max":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[ur(A("x",n,t,e),r,o)]}case"Mean":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[en(A("x",n,t,e),r,o)]}case"Min":{let 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r=A("axis",n,t,e),o=A("x",n,t,e),s=A("indices",n,t,e);return[Bc(o,$t(s,"int32"),r)]}case"ReverseV2":case"Reverse":{let r=A("axis",n,t,e),o=A("x",n,t,e);return[Pr(o,r)]}case"Slice":{let r=A("begin",n,t,e),o=A("size",n,t,e);return[ce(A("x",n,t,e),r,o)]}case"StridedSlice":{let r=A("begin",n,t,e),o=A("end",n,t,e),s=A("strides",n,t,e),c=A("beginMask",n,t,e),l=A("endMask",n,t,e),p=A("ellipsisMask",n,t,e),f=A("newAxisMask",n,t,e),m=A("shrinkAxisMask",n,t,e),y=A("x",n,t,e);return[tv(y,r,o,s,c,l,p,f,m)]}case"Pack":return rt(()=>{let r=A("axis",n,t,e),o=A("tensors",n,t,e),s=o[0].shape,c=fi(o[0]).shape,l=o.map(p=>{let f=lt(p.shape,s);if(!f&&!lt(fi(p).shape,c))throw new Error("the input tensors shape does not match");return f?p:Q(p,s)});return[pr(l,r)]});case"Unpack":{let r=A("axis",n,t,e),o=A("tensor",n,t,e);return go(o,r)}case"Tile":{let r=A("reps",n,t,e);return[ui(A("x",n,t,e),r)]}case"Split":case"SplitV":{let r=A("axis",n,t,e),o=A("numOrSizeSplits",n,t,e),s=A("x",n,t,e);return 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r=A("axis",n,t,e);return[fi(A("x",n,t,e),r)]}case"Reshape":return[Q(A("x",n,t,e),A("shape",n,t,e))];case"MirrorPad":return[Uw(A("x",n,t,e),A("padding",n,t,e),A("mode",n,t,e))];case"PadV2":case"Pad":return[Go(A("x",n,t,e),A("padding",n,t,e),A("constantValue",n,t,e))];case"SpaceToBatchND":{let r=A("blockShape",n,t,e),o=A("paddings",n,t,e);return[gp(A("x",n,t,e),r,o)]}case"BatchToSpaceND":{let r=A("blockShape",n,t,e),o=A("crops",n,t,e);return[cp(A("x",n,t,e),r,o)]}case"DepthToSpace":{let r=A("blockSize",n,t,e),o=A("dataFormat",n,t,e).toUpperCase();return[Rw(A("x",n,t,e),r,o)]}case"BroadcastTo":return[lp(A("x",n,t,e),A("shape",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Bst="transformation";function _$(n,t,e,r){let o=((s,c,l)=>{switch(s.category){case"arithmetic":return rt(()=>_j(s,c,l));case"basic_math":return rt(()=>Cj(s,c,l));case"control":return Fj(s,c,l);case"convolution":return rt(()=>Rj(s,c,l));case"creation":return rt(()=>Pj(s,c,l));case"dynamic":return Oj(s,c,l);case"evaluation":return rt(()=>Lj(s,c,l));case"image":return rt(()=>Wj(s,c,l));case"graph":return rt(()=>Mj(s,c,l));case"logical":return rt(()=>Vj(s,c,l));case"matrices":return rt(()=>Gj(s,c,l));case"normalization":return rt(()=>Uj(s,c,l));case"reduction":return rt(()=>qj(s,c,l));case"slice_join":return rt(()=>Hj(s,c,l));case"spectral":return rt(()=>jj(s,c,l));case"transformation":return rt(()=>Kj(s,c,l));case"hash_table":return zj(s,c,l,r);case"custom":let p=x$(s.op);if(p&&p.customExecutor)return p.customExecutor(new Nj(s,c,l));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(n,t,e);return Ji(o)?o.then(s=>[].concat(s)):[].concat(o)}class C${constructor(t={},e={},r={},o={}){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=r,this.functionMap=o,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let r=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(r))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}}function S$(n,t,e,r){let o=new Set,s=[],c=null,l=null,p=new Set,f=Object.keys(n).map(b=>Nr(b)[0]),m=[];r!=null&&(m=r.map(b=>Nr(b.name)[0]));let y=[...t];for(;y.length>0;){let b=y.pop();if(($$(b)||Qj(b)||t6(b))&&(c==null&&(c=b,l=c.children.map(v=>v.name).filter(v=>o.has(v)))),o.add(b.name),e[b.name]!=null)continue;if(f.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){s.push(b.name);continue}b.inputs.forEach(v=>{if(p.has(v.name))return;p.add(v.name),y.push(v)})}return{inputs:n,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:c,syncInputs:l}}function Xj(n,t,e){let{usedNodes:r,inputs:o}=e,s=[],c=Object.keys(o).map(m=>Nr(m)[0]).map(m=>n.nodes[m]),l=n.initNodes;c.forEach(m=>{r.has(m.name)&&s.push(m)}),n.weights.forEach(m=>{r.has(m.name)&&s.push(m)}),l!=null&&l.forEach(m=>{r.has(m.name)&&s.push(m)});let p=new Set,f=[];for(;s.length>0;){let m=s.pop();p.add(m.name),t[m.name]||f.push(m),m.children.forEach(y=>{!p.has(y.name)&&r.has(y.name)&&y.inputs.every(b=>p.has(b.name))&&s.push(y)})}return f}let Yj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Jj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Zj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function $$(n){return Yj.indexOf(n.op)>=0}function Qj(n){return Jj.indexOf(n.op)>=0}function t6(n){return Zj.indexOf(n.op)>=0}class Gm{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(r=>{this._functionExecutorMap[r]=new Gm(t.functions[r],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(t){let e=Object.keys(t).map(r=>t[r].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let r=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return r.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let r=S$(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:c}=r;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${c}]`);if(o.length>0){let l=e.map(f=>f.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${p}]. Missing the following inputs: [${o}]`)}return Xj(this.graph,this.weightMap,r)}execute(t,e){t=this.mapInputs(t);let r=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=r.map(y=>this.graph.nodes[Nr(y)[0]]),s=e.map(y=>Nr(y)[0]),c=s.map(y=>this.graph.nodes[y]);c.length===0&&(c=this._outputs);let l=this.getCompilationKey(o,c),p=this.compiledMap.get(l);p==null&&(p=this.compile(t,c),this.compiledMap.set(l,p));let f={},m={};return rt(()=>{let y=new C$(this.weightMap,f,m,this.functionExecutorMap),b=Object.assign({},this.weightMap);Object.keys(t).forEach(N=>{let[S,D]=Nr(N),I=[];I[D]=t[N],b[S]=I});let v=this.getFrozenTensorIds(b),T={};for(let N=0;N<p.length;N++){let S=p[N];if(!b[S.name]){let D=_$(S,b,y,this._resourceManager);if(Ji(D))throw new Error(`The execution of the op '${S.op}' returned a promise. 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You can use model.execute() instead.");let I=p.filter(P=>!$$(P)&&!dr(P.name,T,e)).map(P=>P.name);if(I.length>0){let P="";throw y!=null&&(P=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${b}]`),new Error(`Cannot compute the outputs [${I}] from the provided inputs [${s}]. 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m}processChildNodes(t,e,r,o,s,c){t.children.forEach(l=>{let[p]=Ns(l.name,r);if(s[p]||!c.has(l.name))return;l.op==="Merge"?l.inputNames.some(f=>!!dr(f,o,r))&&(s[p]=!0,e.push({contexts:r.currentContext,node:l})):l.inputNames.every(f=>!!dr(f,o,r))&&(s[p]=!0,e.push({contexts:r.currentContext,node:l}))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let r=t[e],[o]=Nr(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let c=s.attrParams.shape.value,l=c.length===r.shape.length&&r.shape.every((p,f)=>c[f]===-1||c[f]===p);_(l,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${c}], but was [${r.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&_(r.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(t){let e={};for(let r in t)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let o=this._signature.inputs[r];e[o.name]=t[r]}else e[r]=t[r];return e}checkInputs(t){let e=Object.keys(t).filter(r=>{let[o]=Nr(r);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[e]!=null){let r=this._signature.outputs[e];return r.name}return e},{})}checkOutputs(t){t.forEach(e=>{let[r]=Nr(e);if(!this.graph.nodes[r])throw new Error(`The output '${e}' is not found in the graph`)})}}class e6{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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save(t,e){if(typeof t=="string"){let r=ew(t);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${t}'`);t=r[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}predict(t,e){return this.execute(t,this.outputNodes)}normalizeInputs(t){if(!(t instanceof ot)&&!Array.isArray(t))return t;if(t=Array.isArray(t)?t:[t],t.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${t.length} input tensors.`);return this.inputNodes.reduce((e,r,o)=>(e[r]=t[o],e),{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}execute(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=this.executor.execute(t,e);return r.length>1?r:r[0]}async executeAsync(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=await this.executor.executeAsync(t,e);return r.length>1?r:r[0]}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,r)=>(e[r]=[t[r]],e),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function o6(n,t={}){if(n==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(n.load==null&&(n.endsWith("/")||(n=n+"/"),n=`${n}${r6}${n6}`));let e=new I$(n,t);return await e.load(),e}let E$="2.7.0";function s6(n,t){return Um(n,t)}function Um(n,t,e=new Map,r=new Set){if(n==null)return null;if(r.has(n))throw new Error("Circular references are not supported.");if(e.has(n))return e.get(n);let o=t(n);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(cl(n)){let s=Array.isArray(n)?[]:{};r.add(n);for(let c in n){let l=n[c],p=Um(l,t,e,r);s[c]=p}return r.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return e.set(n,o.value),o.value}function i6(n,t=A$){return D$(n,t)}function D$(n,t,e=new Set){let r=n[0];if(e.has(r))throw new Error("Circular references are not supported.");let o=t(n);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a 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R${constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),r=this.get(e);return this.set(e,this.pop()),r}}class qm extends R${constructor(){super(qm.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),r=this.length();for(let o=0;o<r;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}}qm.INITIAL_CAPACITY=32;function P$(n){return new h6(n)}function zst(n){let t=n;return nh(()=>({value:t++,done:!1}))}function nh(n){return new f6(n)}function O$(n,t){return new M$(n,t)}function Wst(n,t,e){return O$(nh(n).take(t),e)}function p6(n,t=_i.FAIL){return new T6(n,t)}class En{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],r=await t.next();for(;!r.done;)e.push(r.value),r=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),r=t(e.value);for(;!e.done&&r;)e=await this.next(),r=t(e.value)}handleErrors(t){return new w6(this,t)}filter(t){return new b6(this,t)}map(t){return new x6(this,t)}mapAsync(t){return new L$(this,t)}serialMapAsync(t){return new L$(this,t).serial()}flatmap(t){return new v6(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new y6(this,t,e)}columnMajorBatch(t,e=!0,r=A$){let o=this.rowMajorBatch(t,e);return o.map(s=>i6(s,r))}concatenate(t,e){return new M$(P$([this,t]),e)}take(t){return t<0||t==null?this:new g6(this,t)}skip(t){return t<0||t==null?this:new m6(this,t)}prefetch(t){return new B$(this,t)}shuffle(t,e){return new k6(this,t,e)}serial(){return new d6(this)}}class h6 extends En{constructor(t){super();this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:l6(t),done:!1}}}class f6 extends En{constructor(t){super();this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}}class d6 extends En{constructor(t){super();this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class m6 extends En{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;Xt(t.value)}return this.upstream.next()}}class g6 extends En{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class y6 extends En{constructor(t,e,r=!0){super();this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}}class b6 extends En{constructor(t,e){super();this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Xt(t.value)}}}class x6 extends En{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=ps(t.value),r=this.transform(t.value),o=ps(r);for(let s of e)ad(s,o)||s.dispose();return{value:r,done:!1}}}class w6 extends En{constructor(t,e){super();this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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(t){if(!this.handler(t))return{value:null,done:!0}}}}class L$ extends En{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=ps(t.value),r=await this.transform(t.value),o=ps(r);for(let s of e)ad(s,o)||s.dispose();return{value:r,done:!1}}}class y1 extends En{constructor(){super();this.outputQueue=new qm,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class v6 extends y1{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=ps(t.value),r=this.transform(t.value),o=ps(r);this.outputQueue.pushAll(r);for(let s of e)ad(s,o)||s.dispose();return!0}}class M$ extends En{constructor(t,e){super();this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){let t="TODO: fill in upstream of chained summaries";return`${t} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}}var _i;(function(n){n[n.FAIL=0]="FAIL",n[n.SHORTEST=1]="SHORTEST",n[n.LONGEST=2]="LONGEST"})(_i||(_i={}));class T6 extends En{constructor(t,e=_i.FAIL){super();this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){let t="TODO: fill in upstream of zip summaries";return`{${t}} -> Zip`}async nextState(t){await t;let e=0,r=0;function o(c){if(c instanceof En){let l=c.next();return{value:l.then(p=>(e++,p.done&&r++,p.value)),recurse:!1}}else return{value:null,recurse:!0}}let s=await F$(this.iterators,o);if(e===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case _i.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case _i.SHORTEST:return{value:null,done:!0};case _i.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class B$ extends En{constructor(t,e){super();this.upstream=t,this.bufferSize=e,this.buffer=new R$(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}}class k6 extends B${constructor(t,e,r){super(t,e);this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=Uc(r||sr().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}}class ll{constructor(){this.size=null}batch(t,e=!0){let r=this;_(t>0,()=>`batchSize needs to be positive, but it is
-      ${t}`);let o;return this.size===Infinity||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),_r(async()=>(await r.iterator()).columnMajorBatch(t,e,C6),o)}concatenate(t){let e=this,r;return this.size===Infinity||t.size===Infinity?r=Infinity:this.size!=null&&t.size!=null?r=this.size+t.size:r=null,_r(async()=>(await e.iterator()).concatenate(await t.iterator()),r)}filter(t){let e=this,r;return this.size===Infinity?r=Infinity:r=null,_r(async()=>(await e.iterator()).filter(o=>rt(()=>t(o))),r)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return _r(async()=>(await e.iterator()).map(r=>rt(()=>t(r))),this.size)}mapAsync(t){let e=this;return _r(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return _r(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,r;return this.size!=null&&t>0?r=this.size*t:t===0?r=0:this.size!=null&&(t===void 0||t<0)?r=Infinity:r=null,_r(async()=>{let o=nh(async()=>({value:await e.iterator(),done:!1}));return O$(o.take(t))},r)}skip(t){let e=this,r;return this.size!=null&&t>=0&&this.size>=t?r=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?r=0:r=null,_r(async()=>(await e.iterator()).skip(t),r)}shuffle(t,e,r=!0){if(t==null||t<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 (${this.size} elements)`);let o=this,s=Uc(e||sr().toString());return _r(async()=>{let c=s.int32();return r&&(c+=s.int32()),(await o.iterator()).shuffle(t,c.toString())},this.size)}take(t){let e=this,r;return this.size!=null&&this.size>t?r=t:this.size!=null&&this.size<=t?r=this.size:r=null,_r(async()=>(await e.iterator()).take(t),r)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}ll.MAX_BUFFER_SIZE=1e4;function _r(n,t=null){return new class extends ll{constructor(){super(...arguments);this.size=t}async iterator(){return n()}}}function N6(n){return _r(async()=>P$(n),n.length)}function _6(n){if(!cl(n))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(n))for(let e=0;e<n.length;e++)t=t==null?n[e].size:Math.min(t,n[e].size);else if(n instanceof Object)for(let e in n)t=t==null?n[e].size:Math.min(t,n[e].size);return _r(async()=>{let e=await F$(n,r=>{if(r instanceof ll)return{value:r.iterator(),recurse:!1};if(cl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return p6(e,_i.SHORTEST)},t)}function C6(n){if(n===null)return null;let t=n[0];if(a6(t)){let e=S6(n);return{value:e,recurse:!1}}return{value:null,recurse:!0}}function S6(n){if(n.length===0)throw new Error("Can't make a batch of zero elements.");return n[0]instanceof ot?pr(n):un(n)}class z$ extends ll{constructor(t){super();this.input=t}async iterator(){let t=await this.input.iterator(),e=t.decodeUTF8(),r=e.split(`
-`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o));return r}}let Hm='"',rh=Symbol("out"),W$=Symbol("field"),jm=Symbol("quote"),b1=Symbol("quoteafterquote"),V$=Symbol("quoteinquote");class G$ extends ll{constructor(t,e){super();this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new z$(t),e||(e={}),this.hasHeader=!(e.hasHeader===!1),this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(_(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&_(t.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 ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),r=Object.keys(e).filter(o=>e[o]>1);if(_(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs)for(let o of Object.keys(this.columnConfigs)){let s=this.fullColumnNames.indexOf(o);if(s===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await this.base.iterator(),e=await t.next();if(e.done)throw new Error("No data was found for CSV parsing.");let r=e.value,o=this.parseRow(r,!1);return o}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),r={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let c=this.fullColumnNames[s],l=this.columnConfigs?this.columnConfigs[c]:null;if(this.configuredColumnsOnly&&!l)continue;{let p=e[s],f=null;if(p==="")if(l&&l.default!==void 0)f=l.default;else{if(l&&(l.required||l.isLabel))throw new Error(`Required column ${c} is empty in this line: ${t}`);f=void 0}else{let m=Number(p);if(isNaN(m))l&&l.dtype==="bool"?f=this.getBoolean(p):f=p;else if(!l||!l.dtype)f=m;else switch(l.dtype){case"float32":f=m;break;case"int32":f=Math.floor(m);break;case"bool":f=this.getBoolean(p);break;default:f=m}}l&&l.isLabel?o[c]=f:r[c]=f}}return Object.keys(o).length===0?r:{xs:r,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let r=[],o=0,s=t.length,c=rh;for(let l=0;l<s;l++)switch(c){case rh:switch(t.charAt(l)){case Hm:o=l+1,c=jm;break;case this.delimiter:if(o=l+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),c=rh;break;default:c=W$,o=l;break}break;case W$:switch(t.charAt(l)){case this.delimiter:r.push(t.substring(o,l)),c=rh,o=l+1;break;default:}break;case jm:switch(t.charAt(l)){case Hm:c=b1;break;default:}break;case b1:switch(t.charAt(l)){case this.delimiter:r.push(t.substring(o,l-1)),c=rh,o=l+1;break;case Hm:c=jm;break;default:c=V$;break}break;case V$:switch(t.charAt(l)){case Hm:c=jm;break;default:}break;default:}if(c===b1?r.push(t.substring(o,s-1)):r.push(t.substring(o)),e&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}}class x1 extends En{constructor(t){super();this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=!(t.includeSpectrogram===!1),this.includeWaveform=t.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(t={}){if(ct().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let e=new x1(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,r=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(r.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(r.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],r=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,r=new Float32Array(t.length*e);return t.forEach((o,s)=>r.set(o,s*e)),r}getTensorFromAudioDataArray(t,e){let r=new Float32Array(G(e));return r.set(t,r.length-t.length),un(r,e)}}class w1 extends En{constructor(t,e){super();if(this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=vr([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-r)/2,c=(1-o)/2,l=s+r,p=o+c;this.cropBox=di([c,s,p,l],[1,4])}else this.cropBox=di([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(ct().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let r=new w1(t,e);return await r.start(),r}async start(){this.webcamConfig.facingMode&&_(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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=p_(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return rt(()=>{let e=t.toFloat().expandDims(0),r;r=mi.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=r.shape;return r.reshape(o.slice(1))})}async capture(){return(await this.next()).value}stop(){let t=this.stream.getTracks();t.forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class U${}class q$ extends En{split(t){return new $6(this,t)}}class $6 extends q${constructor(t,e){super();this.upstream=t,this.impl=new I6(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class I6 extends y1{constructor(t,e){super();this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let r of e.slice(0,-1))this.outputQueue.push(r);return this.carryover=e[e.length-1],!0}}class E6 extends En{decodeUTF8(){return new D6(this)}}class D6 extends q${constructor(t){super();this.upstream=t,this.impl=new A6(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class A6 extends y1{constructor(t){super();if(this.upstream=t,ct().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=require("string_decoder");this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let r;return ct().get("IS_BROWSER")?r=this.decoder.decode(e,{stream:!0}):r=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(r),!0}}class H$ extends E6{constructor(t,e={}){super();this.file=t,this.options=e,_(t instanceof Uint8Array||(ct().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};let t=new Promise((e,r)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=l=>{let p=s.result;if(p instanceof ArrayBuffer&&(p=new Uint8Array(p)),!(p instanceof Uint8Array))return r(new TypeError("FileReader returned unknown type."));e(p)},s.onabort=l=>r(new Error("Aborted")),s.onerror=l=>r(new Error(l.type));let c=this.file.slice(this.offset,o);s.readAsArrayBuffer(c)}this.offset=o});return{value:await t,done:!1}}}async function F6(n,t={}){let e,r;typeof n=="string"?e=n:(e=n.url,r=R6(n));let o=await LN(e,r);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new H$(s,t)}else throw new Error(o.statusText)}let R6=n=>{let t={method:n.method,headers:n.headers,body:n.body,mode:n.mode,credentials:n.credentials,cache:n.cache,redirect:n.redirect,referrer:n.referrer,integrity:n.integrity};return t};function j$(n){return typeof n=="string"&&n.substr(0,7)==="file://"}class K$ extends U${constructor(t,e={}){super();this.input=t,this.options=e}async iterator(){if(j$(this.input)&&ct().get("IS_NODE")){let t=require("fs");this.input=t.readFileSync(this.input.substr(7))}return new H$(this.input,this.options)}}class X$ extends U${constructor(t,e={}){super();this.url=t,this.fileOptions=e}async iterator(){return j$(this.url)?new K$(this.url,this.fileOptions).iterator():F6(this.url,this.fileOptions)}}function P6(n,t={}){return new G$(new X$(n),t)}function O6(n){let t=nh(n);return _r(async()=>t)}function L6(n){return _r(async()=>{let t=await n();return nh(()=>t.next())})}async function M6(n,t){return w1.create(n,t)}async function B6(n){return x1.create(n)}let Y$="2.7.0";var z6=Object.freeze({__proto__:null,array:N6,Dataset:ll,zip:_6,CSVDataset:G$,TextLineDataset:z$,csv:P6,func:O6,generator:L6,microphone:B6,webcam:M6,FileDataSource:K$,URLDataSource:X$,version_data:Y$});function Ct(n,t){Array.isArray(n)||(n=[n]),n.forEach(e=>{e!=null&&_(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}let W6=tm,V6=Tv,G6=kv,U6=Nv,q6=jd;class H6 extends d{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new h(this,ds())}write(t,e,r){this.firstUse&&(this.firstUse=!1,ct().get("IS_NODE")&&Yc(`
+        ${totalLength}, and tensor's shape is: ${tensor168.shape}`);let elementPerRow=totalLength===0?0:tensor168.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor168=reshape(tensor168,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor168,indices,sizes),elementShape)}return tensor168.dispose(),tensors2}),list=new TensorList([],elementShape,tensor168.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node2,tensorMap,context)=>{switch(node2.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node2,tensorMap,context),elseFunc=getParamValue("elseBranch",node2,tensorMap,context),cond=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context),condValue=await cond.data();return condValue[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node2,tensorMap,context),condFunc=getParamValue("cond",node2,tensorMap,context),args=getParamValue("args",node2,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor168=>tensor168.id),condValue=await condResult[0].data();condResult.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&tensor168.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor168=>tensor168.id);origResult.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&resultIds.indexOf(tensor168.id)===-1&&tensor168.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&resultIds.indexOf(tensor168.id)===-1&&tensor168.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node2,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node2.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node2,tensorMap,context),data=getParamValue("tensor",node2,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node2,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node2,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node2,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node2,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node2,tensorMap,context),name=getParamValue("name",node2,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context),readTensorArray=context.getTensorArray(readId.id);return[readTensorArray.read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),gatherDtype=getParamValue("dtype",node2,tensorMap,context),gatherTensorArray=context.getTensorArray(gatherId.id);return[gatherTensorArray.gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node2,tensorMap,context),scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node2,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node2,tensorMap,context),splitTensor=getParamValue("tensor",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node2,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node2,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),index=getParamValue("index",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),readIndex=getParamValue("index",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node2,tensorMap,context),scatterTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":{let elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node2,tensorMap,context),gatherIndices=getParamValue("indices",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),tensorList=context.getTensorList(gatherId.id);return[tensorList.gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),numElements=getParamValue("numElements",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor168=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDtype=getParamValue("elementDType",node2,tensorMap,context),tensorList=fromTensor(tensor168,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node2,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),writeTensor=getParamValue("tensor",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),elementDType=getParamValue("elementDType",node2,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node2,tensorMap,context),elementShape=getParamValue("elementShape",node2,tensorMap,context),lengths=getParamValue("lengths",node2,tensorMap,context),tensorList=split9(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node2,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node2,tensorMap,context),pad11=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context);return{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}}var executeOp4=(node2,tensorMap,context)=>{switch(node2.op){case"Conv1D":{let stride=getParamValue("stride",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node2,tensorMap,context);return[conv1d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),stride,pad11,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getPadding(node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad11,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad11,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}=fusedConvAndDepthWiseParams(node2,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node2,tensorMap,context),filter:getParamValue("filter",node2,tensorMap,context),strides:[stride[1],stride[2]],pad:pad11,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node2,tensorMap,context),stride=getParamValue("strides",node2,tensorMap,context),pad11=getPadding(node2,tensorMap,context);return[conv2dTranspose(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),shape,[stride[1],stride[2]],pad11)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getPadding(node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2]],pad11,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node2,tensorMap,context);return[conv3d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[stride[1],stride[2],stride[3]],pad11,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11)]}case"MaxPool":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node2,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[avgPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad11)]}case"MaxPool3D":{let stride=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),kernelSize=getParamValue("kernelSize",node2,tensorMap,context);return[maxPool3d(getParamValue("x",node2,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad11)]}case"Dilation2D":{let strides=getParamValue("strides",node2,tensorMap,context),pad11=getParamValue("pad",node2,tensorMap,context),dilations=getParamValue("dilations",node2,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node2,tensorMap,context),getParamValue("filter",node2,tensorMap,context),[strideHeight,strideWidth],pad11,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp5=(node2,tensorMap,context)=>{switch(node2.op){case"Fill":{let shape=getParamValue("shape",node2,tensorMap,context),dtype=getParamValue("dtype",node2,tensorMap,context),value=getParamValue("value",node2,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),num=getParamValue("num",node2,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node2,tensorMap,context),numSamples=getParamValue("numSamples",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node2,tensorMap,context),depth=getParamValue("depth",node2,tensorMap,context),onValue=getParamValue("onValue",node2,tensorMap,context),offValue=getParamValue("offValue",node2,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node2,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node2,tensorMap,context),getParamValue("minval",node2,tensorMap,context),getParamValue("maxval",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"Range":{let start=getParamValue("start",node2,tensorMap,context),stop=getParamValue("stop",node2,tensorMap,context),step9=getParamValue("step",node2,tensorMap,context);return[range(start,stop,step9,getParamValue("dtype",node2,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node2,tensorMap,context),mean7=getParamValue("mean",node2,tensorMap,context),stdDev=getParamValue("stdDev",node2,tensorMap,context),seed=getParamValue("seed",node2,tensorMap,context);return[truncatedNormal(shape,mean7,stdDev,getParamValue("dtype",node2,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function nmsParams(node2,tensorMap,context){let boxes=getParamValue("boxes",node2,tensorMap,context),scores=getParamValue("scores",node2,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node2,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node2,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node2,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node2,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node2,tensorMap,context)=>{switch(node2.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node2,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node2,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node2,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node2,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node2,tensorMap,context),getParamValue("y",node2,tensorMap,context));default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp7=(node2,tensorMap,context)=>{switch(node2.op){case"TopKV2":{let x=getParamValue("x",node2,tensorMap,context),k=getParamValue("k",node2,tensorMap,context),sorted=getParamValue("sorted",node2,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node2,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp8=(node2,tensorMap,context)=>{switch(node2.op){case"Const":return tensorMap[node2.name];case"PlaceholderWithDefault":let def=getParamValue("default",node2,tensorMap,context);return[getTensor(node2.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node2.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node2,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node2,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node2,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node2,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node2,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node2,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node2,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node2,tensorMap,context),data=getParamValue("data",node2,tensorMap,context),message=getParamValue("message",node2,tensorMap,context),summarize=getParamValue("summarize",node2,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node2,tensorMap,context,resourceManager)=>{switch(node2.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node2,tensorMap,context),valueDType=getParamValue("valueDType",node2,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node2.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context),hashTable2=resourceManager.getHashTableById(handle.id);return[await hashTable2.import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node2,tensorMap,context,resourceManager),keys=getParamValue("keys",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context),hashTable2=resourceManager.getHashTableById(handle.id);return[await hashTable2.find(keys,defaultValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp10=(node2,tensorMap,context)=>{switch(node2.op){case"ResizeBilinear":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context),alignCorners=getParamValue("alignCorners",node2,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners)]}case"CropAndResize":{let image3=getParamValue("image",node2,tensorMap,context),boxes=getParamValue("boxes",node2,tensorMap,context),boxInd=getParamValue("boxInd",node2,tensorMap,context),cropSize=getParamValue("cropSize",node2,tensorMap,context),method=getParamValue("method",node2,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node2,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp11=(node2,tensorMap,context)=>{switch(node2.op){case"Equal":return[equal(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Less":return[less(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node2,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node2,tensorMap,context),getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp12=(node2,tensorMap,context)=>{switch(node2.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node2,tensorMap,context),getParamValue("b",node2,tensorMap,context),getParamValue("transposeA",node2,tensorMap,context),getParamValue("transposeB",node2,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node2,tensorMap,context),getParamValue("perm",node2,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node2,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node2,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node2,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node2,tensorMap,context),b:getParamValue("b",node2,tensorMap,context),transposeA:getParamValue("transposeA",node2,tensorMap,context),transposeB:getParamValue("transposeB",node2,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp13=(node2,tensorMap,context)=>{switch(node2.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node2,tensorMap,context),getParamValue("mean",node2,tensorMap,context),getParamValue("variance",node2,tensorMap,context),getParamValue("offset",node2,tensorMap,context),getParamValue("scale",node2,tensorMap,context),getParamValue("epsilon",node2,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node2,tensorMap,context),getParamValue("radius",node2,tensorMap,context),getParamValue("bias",node2,tensorMap,context),getParamValue("alpha",node2,tensorMap,context),getParamValue("beta",node2,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node2,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node2,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node2,tensorMap,context),getParamValue("outputShape",node2,tensorMap,context),getParamValue("sparseValues",node2,tensorMap,context),getParamValue("defaultValue",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp14=(node2,tensorMap,context)=>{switch(node2.op){case"Max":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[max(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[mean(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[min(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[sum2(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[all(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[any(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMax(getParamValue("x",node2,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node2,tensorMap,context);return[argMin(getParamValue("x",node2,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node2,tensorMap,context),keepDims=getParamValue("keepDims",node2,tensorMap,context);return[prod(getParamValue("x",node2,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node2,tensorMap,context),exclusive=getParamValue("exclusive",node2,tensorMap,context),reverse12=getParamValue("reverse",node2,tensorMap,context);return[cumsum(getParamValue("x",node2,tensorMap,context),axis,exclusive,reverse12)]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp15=(node2,tensorMap,context)=>{switch(node2.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node2,tensorMap,context),axis=getParamValue("axis",node2,tensorMap,context),inputs=getParamValue("tensors",node2,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"GatherV2":case"Gather":{let axis=getParamValue("axis",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis)]}case"ReverseV2":case"Reverse":{let axis=getParamValue("axis",node2,tensorMap,context),input2=getParamValue("x",node2,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node2,tensorMap,context),size=getParamValue("size",node2,tensorMap,context);return[slice(getParamValue("x",node2,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node2,tensorMap,context),end=getParamValue("end",node2,tensorMap,context),strides=getParamValue("strides",node2,tensorMap,context),beginMask=getParamValue("beginMask",node2,tensorMap,context),endMask=getParamValue("endMask",node2,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node2,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node2,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node2,tensorMap,context),tensor168=getParamValue("x",node2,tensorMap,context);return[stridedSlice(tensor168,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node2,tensorMap,context),tensors=getParamValue("tensors",node2,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor168=>{let sameShape=util_exports.arraysEqual(tensor168.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor168).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor168:reshape(tensor168,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node2,tensorMap,context),tensor168=getParamValue("tensor",node2,tensorMap,context);return unstack(tensor168,axis)}case"Tile":{let reps=getParamValue("reps",node2,tensorMap,context);return[tile(getParamValue("x",node2,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node2,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node2,tensorMap,context),tensor168=getParamValue("x",node2,tensorMap,context);return split(tensor168,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node2,tensorMap,context),values=getParamValue("values",node2,tensorMap,context),shape=getParamValue("shape",node2,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node2,tensorMap,context),indices=getParamValue("indices",node2,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node2,tensorMap,context),shape=getParamValue("outputShape",node2,tensorMap,context),sparseValues=getParamValue("sparseValues",node2,tensorMap,context),defaultValue=getParamValue("defaultValue",node2,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp16=(node2,tensorMap,context)=>{switch(node2.op){case"FFT":return[fft(getParamValue("x",node2,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node2,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node2,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}},executeOp17=(node2,tensorMap,context)=>{switch(node2.op){case"Cast":return[cast(getParamValue("x",node2,tensorMap,context),getParamValue("dtype",node2,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node2,tensorMap,context);return[expandDims(getParamValue("x",node2,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node2,tensorMap,context);return[squeeze(getParamValue("x",node2,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("mode",node2,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node2,tensorMap,context),getParamValue("padding",node2,tensorMap,context),getParamValue("constantValue",node2,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),paddings=getParamValue("paddings",node2,tensorMap,context);return[spaceToBatchND(getParamValue("x",node2,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node2,tensorMap,context),crops=getParamValue("crops",node2,tensorMap,context);return[batchToSpaceND(getParamValue("x",node2,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node2,tensorMap,context),dataFormat=getParamValue("dataFormat",node2,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node2,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node2,tensorMap,context),getParamValue("shape",node2,tensorMap,context))];default:throw TypeError(`Node type ${node2.op} is not implemented`)}};function executeOp18(node2,tensorMap,context,resourceManager){let value=((node22,tensorMap2,context2)=>{switch(node22.category){case"arithmetic":return tidy(()=>executeOp(node22,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node22,tensorMap2,context2));case"control":return executeOp3(node22,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node22,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node22,tensorMap2,context2));case"dynamic":return executeOp6(node22,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node22,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node22,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node22,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node22,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node22,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node22,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node22,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node22,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node22,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node22,tensorMap2,context2));case"hash_table":return executeOp9(node22,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node22.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node22,tensorMap2,context2));throw TypeError(`Custom op ${node22.op} is not registered.`);default:throw TypeError(`Unknown op '${node22.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node2,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),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++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node2=>parseNodeName(node2.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node2=frontier.pop();if((isControlFlow(node2)||isDynamicShape(node2)||isHashTable(node2))&&dynamicNode==null&&(dynamicNode=node2,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node2.name),weightMap[node2.name]!=null)continue;if(inputNodeNames.indexOf(node2.name)!==-1)continue;if(initNodeNames.indexOf(node2.name)!==-1)continue;if(node2.inputs.length===0){missingInputs.push(node2.name);continue}node2.inputs.forEach(input2=>{if(seen.has(input2.name))return;seen.add(input2.name),frontier.push(input2)})}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node2=>{usedNodes.has(node2.name)&&frontier.push(node2)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node2=frontier.pop();seen.add(node2.name),weightMap[node2.name]||orderedNodes.push(node2),node2.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node2){return CONTROL_FLOW_OPS.indexOf(node2.op)>=0}function isDynamicShape(node2){return DYNAMIC_SHAPE_OPS.indexOf(node2.op)>=0}function isHashTable(node2){return HASH_TABLE_OPS.indexOf(node2.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],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(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor168=>tensor168.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node2=>({name:node2.name,shape:node2.attrParams.shape?node2.attrParams.shape.value:void 0,dtype:node2.attrParams.dtype?node2.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node2=>node2.signatureKey||node2.name)}get outputNodes(){return this._outputs.map(node2=>{let name=node2.signatureKey||node2.name;return node2.defaultOutput?`${name}:${node2.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node2=>node2.name).sort(),sortedOutputs=outputs.map(node2=>node2.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node2=orderedNodes[i];if(!tensorsMap[node2.name]){let tensors=executeOp18(node2,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node2.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node2.name]=tensors,this.checkTensorForDisposal(node2.name,node2,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor168=>tensor168.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node2,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){if(node2.category==="control"||outputNames.indexOf(nodeName)!==-1)return;tensorMap[nodeName].forEach(tensor168=>{tensor168!=null&&(intermediateTensorConsumerCount[tensor168.id]=(intermediateTensorConsumerCount[tensor168.id]||0)+node2.children.length)}),node2.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor168=>{if(tensor168&&!tensorsToKeep.has(tensor168.id)){let count2=intermediateTensorConsumerCount[tensor168.id];count2===1?(tensor168.dispose(),delete intermediateTensorConsumerCount[tensor168.id]):count2!=null&&intermediateTensorConsumerCount[tensor168.id]--}})}})}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{let tensorArray=tensorMap[key];tensorArray.forEach(tensor168=>{tensor168&&!tensor168.isDisposed&&!keepIds.has(tensor168.id)&&tensor168.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor168,index)=>(map[this.inputs[index].name]=tensor168,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack92=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node2=>({node:node2,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack92.length>0;){let promises=this.processStack(inputNodes,stack92,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node2=>!isControlFlow(node2)&&!getTensor(node2.name,tensorsMap,context)).map(node2=>node2.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack92,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack92.length>0;){let item=stack92.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack92,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack92,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack92,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node2,stack92,context,tensorMap,added,usedNodes){node2.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);if(added[nodeName]||!usedNodes.has(childNode.name))return;childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack92.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack92.push({contexts:context.currentContext,node:childNode}))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor168=>tensor168.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node2=this.graph.nodes[nodeName];if(node2.attrParams.shape&&node2.attrParams.shape.value){let shape=node2.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node2.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node2.attrParams.dtype&&node2.attrParams.dtype.value&&util_exports.assert(input2.dtype===node2.attrParams.dtype.value,()=>`The dtype of dict['${node2.name}'] provided in model.execute(dict) must be ${node2.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor168=this._signature.inputs[inputName];result[tensor168.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null){let tensor168=this._signature.outputs[name];return tensor168.name}return name},{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature={};this.artifacts.userDefinedMetadata!=null&&(signature=this.artifacts.userDefinedMetadata.signature),this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config2){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version6="2.7.0",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version8,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule2(require_seedrandom2()),seedrandom2=__toModule2(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}let result=deepMapInternal(input2,mapFn,seen);return result}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container2){return deepMap(container2,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){let rowBatches=this.rowMajorBatch(batchSize,smallLastBatch);return rowBatches.map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){let upstreamSummaries="TODO: fill in upstream of chained summaries";return`${upstreamSummaries} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){let upstreamSummaries="TODO: fill in upstream of zip summaries";return`{${upstreamSummaries}} -> Zip`}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container2){if(container2 instanceof LazyIterator){let result=container2.next();return{value:result.then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}}else return{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max102){return Math.floor(this.random()*max102)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
+      ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset5){let base2=this,size;return this.size===Infinity||dataset5.size===Infinity?size=Infinity:this.size!=null&&dataset5.size!=null?size=this.size+dataset5.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset5.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size<count2||count2===void 0||count2<0)?size=0:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<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)`);let base2=this,random=seedrandom3.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}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()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i<datasets.length;i++)size=size==null?datasets[i].size:Math.min(size,datasets[i].size);else if(datasets instanceof Object)for(let ds in datasets)size=size==null?datasets[ds].size:Math.min(size,datasets[ds].size);return datasetFromIteratorFn(async()=>{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];if(canTensorify(exampleRow)){let value=batchConcat(rows);return{value,recurse:!1}}return{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor4(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){let inputIterator=await this.input.iterator(),utf8Iterator=inputIterator.decodeUTF8(),lineIterator=utf8Iterator.split(`
+`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line));return lineIterator}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=!(csvConfig.hasHeader===!1),this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.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 ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs)for(let key of Object.keys(this.columnConfigs)){let index=this.fullColumnNames.indexOf(key);if(index===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let iter=await this.base.iterator(),firstElement=await iter.next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value,headers=this.parseRow(firstLine,!1);return headers}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config2=this.columnConfigs?this.columnConfigs[key]:null;if(this.configuredColumnsOnly&&!config2)continue;{let value=values[i],parsedValue=null;if(value==="")if(config2&&config2.default!==void 0)parsedValue=config2.default;else{if(config2&&(config2.required||config2.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config2&&config2.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config2||!config2.dtype)parsedValue=valueAsNum;else switch(config2.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config2&&config2.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}},MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))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=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=!(microphoneConfig.includeSpectrogram===!1),this.includeWaveform=microphoneConfig.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(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!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}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.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 spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},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(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor4(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.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(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){let tracks=this.stream.getTracks();tracks.forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.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};let chunk=new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice21=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice21)}this.offset=end});return{value:await chunk,done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>{let init2={method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity};return init2};function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version8="2.7.0",seedrandom4=__toModule2(require_seedrandom2());function assertNotComplex(tensor168,opName){Array.isArray(tensor168)||(tensor168=[tensor168]),tensor168.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl,split10=kernel_impls_exports.split,tile9=kernel_impls_exports.tile,topkImpl2=kernel_impls_exports.topkImpl,whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine15())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
 ============================
 Hi there \u{1F44B}. 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.
-============================`));let o={};return this.data.set(o,{values:t,dtype:r,refCount:1}),o}makeTensorInfo(t,e,r){let o;if(e==="string"&&r!=null&&r.length>0&&us(r[0])){let s=r.map(c=>od(c));o=this.write(s,t,e)}else o=this.write(r,t,e);return{dataId:o,shape:t,dtype:e}}incRef(t){let e=this.data.get(t);e.refCount++}decRef(t){if(this.data.has(t)){let e=this.data.get(t);e.refCount--}}move(t,e,r,o){this.data.set(t,{values:e,dtype:o,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(t){return this.readSync(t)}readSync(t){let{dtype:e,complexTensorInfos:r}=this.data.get(t);if(e==="complex64"){let o=this.readSync(r.real.dataId),s=this.readSync(r.imag.dataId);return ws(o,s)}return this.data.get(t).values}bufferSync(t){let e=this.readSync(t.dataId),r=e;if(t.dtype==="string")try{r=e.map(o=>Yu(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Se(t.shape,t.dtype,r)}makeOutput(t,e,r){let o=this.write(t,e,r);return ds().makeTensorFromDataId(o,e,r,this)}disposeData(t){if(this.data.has(t)){let{complexTensorInfos:e}=this.data.get(t);e!=null&&(this.disposeData(e.real.dataId),this.disposeData(e.imag.dataId)),this.data.delete(t)}}disposeIntermediateTensorInfo(t){let e=t.dataId;if(this.data.has(e)){let r=this.data.get(e);r.refCount--,r.refCount<1&&this.disposeData(e)}}async time(t){let e=sr();t();let r=sr()-e;return{kernelMs:r}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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Ct([t,e],"atan2"),this.broadcastedBinaryOp(t,e,t.dtype,(r,o)=>Math.atan2(r,o))}tile(t,e){return Ct(t,"tile"),G6(this.bufferSync(t),e)}gather(t,e,r){Ct([t,e],"gather");let o=t.shape.slice(),s=this.readSync(e.dataId);o[r]=s.length;let c=Se(o,t.dtype),l=this.bufferSync(t);for(let p=0;p<c.size;++p){let f=c.indexToLoc(p),m=f.slice();m[r]=s[f[r]];let y=l.locToIndex(m);c.values[p]=l.values[y]}return c.toTensor()}batchToSpaceND(t,e,r){Ct([t],"batchToSpaceND");let o=e.reduce((m,y)=>m*y),s=Ep(t.shape,e,o),c=Dp(s.length,e.length),l=Ap(t.shape,e,o),p=pv(r,e.length),f=hv(l,r,e.length);return Kt(t.reshape(s),c).reshape(l).slice(p,f)}pool3d(t,e,r){Ct(t,"pool3d");let o=e.strideDepth,s=e.strideHeight,c=e.strideWidth,l=e.dilationDepth,p=e.dilationHeight,f=e.dilationWidth,m=e.effectiveFilterDepth,y=e.effectiveFilterHeight,b=e.effectiveFilterWidth,v=e.padInfo.front,T=e.padInfo.top,N=e.padInfo.left,S=r==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,D=this.readSync(t.dataId),I=Se(e.outShape,t.dtype),P=I.values,E=e.outShape[1]*e.outShape[2]*e.outShape[3]*e.outShape[4],L=e.outShape[2]*e.outShape[3]*e.outShape[4],B=e.outShape[3]*e.outShape[4],q=e.outShape[4];for(let H=0;H<e.batchSize;++H){let Z=H*E,J=H*t.strides[0];for(let it=0;it<e.inChannels;++it)for(let pt=0;pt<e.outDepth;++pt){let ht=pt*o-v,dt=ht;for(;dt<0;)dt+=l;let ft=Math.min(e.inDepth,m+ht),ut=Z+pt*L;for(let bt=0;bt<e.outHeight;++bt){let yt=bt*s-T,xt=yt;for(;xt<0;)xt+=p;let kt=Math.min(e.inHeight,y+yt),Nt=ut+bt*B;for(let At=0;At<e.outWidth;++At){let It=At*c-N,St=It;for(;St<0;)St+=f;let Lt=Math.min(e.inWidth,b+It),Ht=Nt+At*q,oe=S,de=0,ie=0;for(let pe=dt;pe<ft;pe+=l){let 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ut=0;ut<v;ut+=m){let bt=(it+ut)/l;if(bt<0||bt>=o.outHeight||Math.floor(bt)!==bt)continue;for(let yt=0;yt<T;yt+=y){let xt=(pt+yt)/p;if(xt<0||xt>=o.outWidth||Math.floor(xt)!==xt)continue;let kt=b*v*T-1-P.get(L,ft,bt,xt,B),Nt=dt*v*T+ut*T+yt,At=kt===Nt?1:0;if(At===0)continue;let It=E.get(L,ft,bt,xt,B);ht+=It*At}}}I.set(ht,L,q,H,Z,B)}return I.toTensor()}resizeBilinear(t,e,r,o){Ct(t,"resizeBilinear");let[s,c,l,p]=t.shape,f=this.readSync(t.dataId),m=new Float32Array(G([s,e,r,p])),y=[o&&e>1?c-1:c,o&&r>1?l-1:l],b=[o&&e>1?e-1:e,o&&r>1?r-1:r],v=0,T=y[0]/b[0],N=y[1]/b[1];for(let S=0;S<s;S++)for(let D=0;D<e;D++){let I=T*D,P=Math.floor(I),E=I-P,L=Math.min(c-1,Math.ceil(I)),B=S*t.strides[0]+P*t.strides[1],q=S*t.strides[0]+L*t.strides[1];for(let H=0;H<r;H++){let Z=N*H,J=Math.floor(Z),it=Z-J,pt=Math.min(l-1,Math.ceil(Z)),ht=B+J*t.strides[2],dt=q+J*t.strides[2],ft=B+pt*t.strides[2],ut=q+pt*t.strides[2];for(let bt=0;bt<p;bt++){let yt=f[ht+bt],xt=f[dt+bt],kt=f[ft+bt],Nt=f[ut+bt],At=yt+(kt-yt)*it,It=xt+(Nt-xt)*it,St=At+(It-At)*E;m[v++]=St}}}return un(m,[s,e,r,p])}resizeBilinearBackprop(t,e,r){Ct([t,e],"resizeBilinearBackprop");let[o,s,c,l]=e.shape,[,p,f]=t.shape,m=new Float32Array(o*s*c*l),y=[r&&p>1?s-1:s,r&&f>1?c-1:c],b=[r&&p>1?p-1:p,r&&f>1?f-1:f],v=y[0]/b[0],T=y[1]/b[1],N=this.readSync(t.dataId),S=0;for(let D=0;D<o;D++){let I=D*e.strides[0];for(let P=0;P<p;P++){let E=P*v,L=Math.floor(E),B=Math.min(Math.ceil(E),s-1),q=I+L*e.strides[1],H=I+B*e.strides[1],Z=E-L,J=1-Z;for(let it=0;it<f;it++){let pt=it*T,ht=Math.floor(pt),dt=Math.min(Math.ceil(pt),c-1),ft=pt-ht,ut=1-ft,bt=q+ht*e.strides[2],yt=q+dt*e.strides[2],xt=H+ht*e.strides[2],kt=H+dt*e.strides[2],Nt=J*ut,At=J*ft,It=Z*ut,St=Z*ft;for(let Lt=0;Lt<l;Lt++){let Ht=N[S++];m[bt+Lt]+=Ht*Nt,m[yt+Lt]+=Ht*At,m[xt+Lt]+=Ht*It,m[kt+Lt]+=Ht*St}}}}return jc(m,[o,c,s,l],e.dtype)}resizeNearestNeighbor(t,e,r,o){Ct(t,"resizeNearestNeighbor");let[s,c,l,p]=t.shape,f=this.readSync(t.dataId),m=new Float32Array(s*e*r*p),y=[o&&e>1?c-1:c,o&&r>1?l-1:l],b=[o&&e>1?e-1:e,o&&r>1?r-1:r],v=y[0]/b[0],T=y[1]/b[1],N=0;for(let S=0;S<s;S++){let D=S*t.strides[0];for(let I=0;I<e;I++){let P=v*I,E=Math.min(c-1,o?Math.round(P):Math.floor(P)),L=D+E*t.strides[1];for(let B=0;B<r;B++){let q=T*B,H=Math.min(l-1,o?Math.round(q):Math.floor(q)),Z=L+H*t.strides[2];for(let J=0;J<p;J++){let it=f[Z+J];m[N++]=it}}}}return un(m,[s,e,r,p],t.dtype)}resizeNearestNeighborBackprop(t,e,r){Ct([t,e],"resizeNearestNeighborBackprop");let[o,s,c,l]=e.shape,[,p,f]=t.shape,m=new Float32Array(o*s*c*l),y=this.readSync(t.dataId),b=[r&&p>1?s-1:s,r&&f>1?c-1:c],v=[r&&p>1?p-1:p,r&&f>1?f-1:f],T=b[0]/v[0],N=b[1]/v[1],S=1/T,D=1/N,I=Math.ceil(S)*2+2,P=Math.ceil(D)*2+2;for(let E=0;E<o;E++){let L=E*e.strides[0];for(let B=0;B<s;B++){let q=L+B*e.strides[1],H=Math.floor(B*S),Z=Math.floor(H-I/2);for(let J=0;J<c;J++){let it=q+J*e.strides[2],pt=Math.floor(J*D),ht=Math.floor(pt-P/2);for(let dt=0;dt<l;dt++){let ft=0;for(let ut=0;ut<I;ut++){let bt=ut+Z;if(bt<0||bt>=p)continue;let yt=L+bt*t.strides[1],xt=bt*T,kt=Math.min(s-1,r?Math.round(xt):Math.floor(xt));if(B!==kt)continue;for(let Nt=0;Nt<P;Nt++){let At=Nt+ht;if(At<0||At>=f)continue;let It=yt+At*t.strides[2],St=At*N,Lt=Math.min(c-1,r?Math.round(St):Math.floor(St));J===Lt&&(ft+=y[It+dt])}}m[it+dt]=ft}}}}return jc(m,e.shape,e.dtype)}localResponseNormalization4D(t,e,r,o,s){Ct(t,"localResponseNormalization4D");let c=t.shape[3],l=c-1,p=this.readSync(t.dataId),f=t.size,m=new Float32Array(f);function y(b){let v=b%c,T=b-v+Math.max(0,v-e),N=b-v+Math.min(v+e,l),S=0;for(;T<=N;T++){let D=p[T];S+=D*D}return S}for(let b=0;b<f;b++){let v=y(b),T=p[b]*Math.pow(r+o*v,-s);m[b]=T}return jc(m,t.shape)}LRNGrad(t,e,r,o,s,c,l){Ct(t,"LRNGrad");let p=t.shape[3],f=this.readSync(t.dataId),m=this.readSync(e.dataId),y=this.readSync(r.dataId),b=new Float32Array(t.size),v=t.size;for(let T=0;T<v;T++){let N=T%p,S=T-N+Math.max(0,N-o),D=T-N+Math.min(p,N+o+1),I=0;for(let P=S;P<D;P++)I+=Math.pow(m[P],2);I=c*I+s;for(let P=S;P<D;P++){let E=-2*c*l*m[P]*y[T]/I;T===P&&(E+=Math.pow(I,-l)),E*=f[T],b[P]+=E}}return jc(b,t.shape)}multinomial(t,e,r,o){Ct(t,"multinomial");let s=e?t:da(t),c=s.shape[0],l=s.shape[1],p=xe([c,r],"int32"),f=this.readSync(p.dataId),m=this.readSync(s.dataId);for(let y=0;y<c;++y){let b=y*l,v=new Float32Array(l-1);v[0]=m[b];for(let S=1;S<v.length;++S)v[S]=v[S-1]+m[b+S];let T=Uc(o.toString()),N=y*r;for(let S=0;S<r;++S){let D=T();f[N+S]=v.length;for(let I=0;I<v.length;I++)if(D<v[I]){f[N+S]=I;break}}}return p}oneHot(t,e,r,o){Ct(t,"oneHot");let s=new Float32Array(t.size*e);s.fill(o);let c=this.readSync(t.dataId);for(let l=0;l<t.size;++l)c[l]>=0&&c[l]<e&&(s[l*e+c[l]]=r);return di(s,[t.size,e],"int32")}nonMaxSuppression(t,e,r,o,s){Ct(t,"nonMaxSuppression");let c=this.readSync(t.dataId),l=this.readSync(e.dataId);return W6(c,l,r,o,s)}depthToSpace(t,e,r){_(r==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${r}`),_(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`);let o=t.shape[0],s=t.shape[1],c=t.shape[2],l=t.shape[3],p=s*e,f=c*e,m=l/(e*e),y=this.readSync(t.dataId),b=new Float32Array(o*p*f*m),v=0;for(let T=0;T<o;++T)for(let N=0;N<p;++N){let S=Math.floor(N/e),D=N%e;for(let I=0;I<f;++I){let P=Math.floor(I/e),E=I%e,L=(D*e+E)*m;for(let B=0;B<m;++B){let q=B+L,H=q+l*(P+c*(S+s*T));b[v++]=y[H]}}}return jc(b,[o,p,f,m])}broadcastedBinaryOp(t,e,r,o){let s=le(t.shape,e.shape),c=Se(s,r),l=this.readSync(t.dataId),p=this.readSync(e.dataId),f=la(t.shape,s),m=la(e.shape,s),y=c.values;if(f.length+m.length===0)for(let b=0;b<y.length;++b)y[b]=o(l[b%l.length],p[b%p.length]);else{let b=this.bufferSync(t),v=this.bufferSync(e);for(let T=0;T<y.length;++T){let N=c.indexToLoc(T),S=N.slice(-t.rank);f.forEach(E=>S[E]=0);let D=b.locToIndex(S),I=N.slice(-e.rank);m.forEach(E=>I[E]=0);let P=v.locToIndex(I);y[T]=o(l[D],p[P])}}return c.toTensor()}split(t,e,r){return V6(t,e,r)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(t,e,r,o,s,c){let[l,p,f,m]=t.shape,y=e.shape[0],[b,v]=o,T=Se([y,b,v,m],"float32"),N=this.readSync(e.dataId),S=this.readSync(r.dataId),D=this.readSync(t.dataId),I=t.strides,P=T.strides;for(let E=0;E<y;E++){let L=E*4,B=N[L],q=N[L+1],H=N[L+2],Z=N[L+3],J=S[E];if(J>=l)continue;let it=b>1?(H-B)*(p-1)/(b-1):0,pt=v>1?(Z-q)*(f-1)/(v-1):0;for(let ht=0;ht<b;ht++){let dt=b>1?B*(p-1)+ht*it:.5*(B+H)*(p-1);if(dt<0||dt>p-1){for(let ft=0;ft<v;ft++)for(let ut=0;ut<m;ut++){let bt=ut+ft*P[2]+ht*P[1]+E*P[0];T.values[bt]=c}continue}if(s==="bilinear"){let ft=Math.floor(dt),ut=Math.ceil(dt),bt=dt-ft;for(let yt=0;yt<v;yt++){let xt=v>1?q*(f-1)+yt*pt:.5*(q+Z)*(f-1);if(xt<0||xt>f-1){for(let It=0;It<m;It++){let St=It+yt*P[2]+ht*P[1]+E*P[0];T.values[St]=c}continue}let kt=Math.floor(xt),Nt=Math.ceil(xt),At=xt-kt;for(let It=0;It<m;It++){let St=It+kt*I[2]+ft*I[1]+J*I[0],Lt=D[St];St=It+Nt*I[2]+ft*I[1]+J*I[0];let Ht=D[St];St=It+kt*I[2]+ut*I[1]+J*I[0];let oe=D[St];St=It+Nt*I[2]+ut*I[1]+J*I[0];let de=D[St],ie=Lt+(Ht-Lt)*At,we=oe+(de-oe)*At;St=It+yt*P[2]+ht*P[1]+E*P[0],T.values[St]=ie+(we-ie)*bt}}}else for(let ft=0;ft<v;++ft){let ut=v>1?q*(f-1)+ft*pt:.5*(q+Z)*(f-1);if(ut<0||ut>f-1){for(let xt=0;xt<m;xt++){let kt=xt+ft*P[2]+ht*P[1]+E*P[0];T.values[kt]=c}continue}let bt=Math.round(ut),yt=Math.round(dt);for(let xt=0;xt<m;xt++){let kt=xt+bt*I[2]+yt*I[1]+J*I[0],Nt=xt+ft*P[2]+ht*P[1]+E*P[0];T.values[Nt]=D[kt]}}}}return T.toTensor()}sparseToDense(t,e,r,o){let{sliceRank:s,numUpdates:c,sliceSize:l,strides:p,outputSize:f}=Fc(e,t,r),m=!1;return this.scatter(t,e,r,f,l,c,s,p,o,m)}gatherND(t,e){let r=e.shape,o=r[r.length-1],[s,c,l,p]=dd(t,e);if(c===0)return un([],s,t.dtype);let f=new hn([c,l],t.dtype),m=this.readSync(e.dataId),y=this.readSync(t.dataId);for(let b=0;b<c;b++){let v=[],T=0;for(let N=0;N<o;N++){let S=m[b*o+N];T+=S*p[N],v.push(S)}if(T<0||T>=t.size/l)throw new Error(`Invalid indices: ${v} does not 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Co(n){let{inputs:t,backend:e}=n,{real:r,imag:o}=t,s=e.data.get(r.dataId).values,c=e.data.get(o.dataId).values,l=e.makeTensorInfo(r.shape,"complex64"),p=e.data.get(l.dataId);return p.complexTensorInfos={real:e.makeTensorInfo(r.shape,"float32",s),imag:e.makeTensorInfo(o.shape,"float32",c)},l}let X6={kernelName:Sf,backendName:"cpu",kernelFunc:Co};function Na(n){let{inputs:t,backend:e}=n,{x:r}=t;return e.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}let Y6={kernelName:Nu,backendName:"cpu",kernelFunc:Na};function oh(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.data.get(r.dataId).complexTensorInfos.real,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}let J6={kernelName:Hf,backendName:"cpu",kernelFunc:oh};function sh(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{dtype:s}=r;if(s==="complex64"){if(o.dtype==="complex64")return Na({inputs:{x:o},backend:e});let 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rI=pl(n=>Math.log(n)),lK=hl($u,rI),uK={kernelName:$u,backendName:"cpu",kernelFunc:lK};function oI(n,t,e,r){let o=Ce(r,G(e));for(let s=0;s<o.length;++s){let c=s*t,l=n[c];for(let p=0;p<t;++p){let f=n[c+p];f>l&&(l=f)}o[s]=l}return o}let sI=Ci((n,t)=>n*t),pK=v1((n,t,e,r)=>({real:n*e-t*r,imag:n*r+t*e})),iI=ul(Tc,sI,pK),hK={kernelName:Tc,backendName:"cpu",kernelFunc:iI};let aI=Ci((n,t)=>n!==t?1:0),fK=ul(Fu,aI,null,"bool"),dK={kernelName:Fu,backendName:"cpu",kernelFunc:fK};let cI=pl(n=>1/Math.sqrt(n)),mK=hl(Bu,cI),gK={kernelName:Bu,backendName:"cpu",kernelFunc:mK};function lI(n,t,e,r,o){let s=fw(r,t,e),c=G(e),l=Jt(r);if(s){let f=dw(t,l);return n.subarray(f,f+c)}let p=Ce(o,c);for(let f=0;f<c;++f){let m=e.length,y=Jt(e),b=Yi(f,m,y),v=b.map((N,S)=>N+t[S]),T=Yr(v,r.length,l);p[f]=n[T]}return p}function T1(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{begin:s,size:c}=r;Ct(o,"slice");let[l,p]=yd(o,s,c);hw(o,l,p);let f=e.data.get(o.dataId).values,m=lI(f,l,p,o.shape,o.dtype);return 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y=s.slice();y[1]=Object.keys(c).length;let b=new hn(y,r);f.forEach((T,N)=>{for(let S=0;S<s[0];S++)for(let D=0;D<s[2];D++)b.set(p.get(S,T,D),S,N,D)});let v=e.slice();return v[o]=y[1],{outputValues:b.values,outputShape:v,indices:l}}var TK=Object.freeze({__proto__:null,simpleAbsImpl:J$,addImpl:Z$,ceilImpl:Q$,expImpl:tI,expm1Impl:eI,floorImpl:nI,logImpl:rI,maxImpl:oI,multiplyImpl:sI,notEqualImpl:aI,rsqrtImpl:cI,sliceImpl:lI,squaredDifferenceImpl:uI,subImpl:pI,transposeImpl:k1,uniqueImpl:fI});let kK="2.7.0";bw("cpu",()=>new H6,1);let dI=Ee(bu,n=>n>=0?n:Math.exp(n)-1),NK={kernelName:bu,backendName:"cpu",kernelFunc:dI};let _K=Ci((n,t)=>n<0?t*n:n);function mI(n){let{inputs:t,backend:e}=n,{x:r,alpha:o}=t;Ct([r,o],"prelu");let s=e.data.get(r.dataId).values,c=e.data.get(o.dataId).values,[l,p]=_K(r.shape,o.shape,s,c,r.dtype);return e.makeTensorInfo(p,r.dtype,l)}let CK={kernelName:qf,backendName:"cpu",kernelFunc:mI};let gI=Ee(Pu,n=>Math.max(0,n)),SK={kernelName:Pu,backendName:"cpu",kernelFunc:gI};let yI=Ee(Lu,n=>Math.min(Math.max(0,n),6)),$K={kernelName:Lu,backendName:"cpu",kernelFunc:yI};function N1(n,t,e,r){if(e==="linear")return Na({inputs:{x:t},backend:n});if(e==="relu")return gI({inputs:{x:t},backend:n});if(e==="elu")return dI({inputs:{x:t},backend:n});if(e==="relu6")return yI({inputs:{x:t},backend:n});if(e==="prelu")return mI({inputs:{x:t,alpha:r},backend:n});throw new Error(`Activation ${e} has not been implemented for the CPU backend.`)}function Ko(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{shape:s}=r,c=G(o.shape),l=Ue(s,c),p=G(l);_(c===p,()=>`The new shape (${l}) has ${p} elements and the old shape (${o.shape}) has ${c} elements. 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bt=(ht+ut)/y;if(bt<0||bt>=m.outHeight||Math.floor(bt)!==bt)continue;for(let yt=0;yt<I;yt+=S){let xt=(dt+yt)/b;if(xt<0||xt>=m.outWidth||Math.floor(xt)!==xt)continue;let kt=H.get(Z,bt,xt,J);ft+=kt}}L.set(ft*B,Z,it,pt,J)}return e.makeTensorInfo(L.shape,L.dtype,L.values)}let KK={kernelName:_f,backendName:"cpu",kernelFunc:jK};function XK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,scale:s,offset:c,mean:l,variance:p}=t;_(l.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||l.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(s==null||l.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ct([o,l,p,s,c],"batchNorm");let{varianceEpsilon:f}=r;f==null&&(f=.001);let m=e.data.get(o.dataId).values,y=e.data.get(l.dataId).values,b=e.data.get(p.dataId).values,v=s?e.data.get(s.dataId).values:new 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p=l.map(v=>v.shape);if(Nd(p,s),l[0].dtype==="complex64"){let v=l.map(I=>oh({inputs:{input:I},backend:e})),T=l.map(I=>Km({inputs:{input:I},backend:e})),N=ah({inputs:v,backend:e,attrs:{axis:s}}),S=ah({inputs:T,backend:e,attrs:{axis:s}}),D=Co({inputs:{real:N,imag:S},backend:e});return v.forEach(I=>e.disposeIntermediateTensorInfo(I)),T.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(N),e.disposeIntermediateTensorInfo(S),D}let f=l.map(v=>{let T=G(v.shape.slice(s)),N=[-1,T];return Ko({inputs:{x:v},backend:e,attrs:{shape:N}})});c=ms(f.map(v=>v.shape),1);let m=Ce(l[0].dtype,G(c));if(f[0].shape[0]===1){let v=0;f.forEach(T=>{let N=e.data.get(T.dataId).values,S=G(T.shape);m.set(N,v),v+=S})}else{let v=0;f.forEach(T=>{let N=e.data.get(T.dataId).values,S=0;for(let D=0;D<T.shape[0];++D){let I=D*c[1]+v;for(let P=0;P<T.shape[1];++P)m[I+P]=N[S++]}v+=T.shape[1]})}let y=ms(l.map(v=>v.shape),s),b=e.makeTensorInfo(y,t[0].dtype,m);return f.forEach(v=>e.disposeIntermediateTensorInfo(v)),b}let t5={kernelName:gu,backendName:"cpu",kernelFunc:ah};function wI(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dataFormat:p,dilations:f,dimRoundingMode:m}=r;Ct([o,s],"conv2d");let y=li(p),b=Un(o.shape,s.shape,c,f,l,m,!1,y),v=b.filterHeight,T=b.filterWidth,N=b.dilationHeight,S=b.dilationWidth,D=b.padInfo.left,I=b.padInfo.top,P=b.dataFormat==="channelsLast",E=new hn(b.outShape,o.dtype),L=Jt(o.shape),B=Jt(s.shape),q=L[0],H=P?L[1]:L[2],Z=P?L[2]:1,J=P?1:L[1],it=E.strides[0],pt=P?E.strides[1]:E.strides[2],ht=P?E.strides[2]:1,dt=P?1:E.strides[1],ft=e.data.get(o.dataId).values,ut=e.data.get(s.dataId).values,bt=E.values;for(let yt=0;yt<b.batchSize;++yt){let xt=yt*q,kt=yt*it;for(let Nt=0;Nt<b.outHeight;++Nt){let At=kt+Nt*pt,It=Nt*b.strideHeight-I;for(let St=0;St<v;++St){let Lt=It+St*N;if(Lt<0||Lt>=b.inHeight)continue;let Ht=St*B[0],oe=xt+Lt*H;for(let de=0;de<b.outWidth;++de){let ie=At+de*ht,we=de*b.strideWidth-D;for(let pe=0;pe<T;++pe){let Xe=we+pe*S;if(Xe<0||Xe>=b.inWidth)continue;let on=Ht+pe*B[1],sn=oe+Xe*Z,Fe=on;for(let Ye=0;Ye<b.inChannels;++Ye){let Le=ft[sn+Ye*J];for(let Mn=0;Mn<b.outChannels;++Mn)bt[ie+Mn*dt]+=Le*ut[Fe+Mn];Fe+=b.outChannels}}}}}}return e.makeTensorInfo(E.shape,E.dtype,bt)}let e5={kernelName:$f,backendName:"cpu",kernelFunc:wI};function n5(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,pad:l,dataFormat:p,dimRoundingMode:f,filterShape:m}=r;Ct([o,s],"conv2dBackpropFilter");let y=li(p),b=Un(o.shape,m,c,1,l,f,!1,y),{strideHeight:v,strideWidth:T,filterHeight:N,filterWidth:S}=b,D=b.dataFormat==="channelsLast",I=new hn(b.filterShape,"float32"),P=b.padInfo.left,E=b.padInfo.top,L=e.data.get(o.dataId).values,B=e.data.get(s.dataId).values,q=new hn(o.shape,o.dtype,L),H=new hn(s.shape,s.dtype,B);for(let Z=0;Z<N;++Z){let J=Math.max(0,Math.ceil((E-Z)/v)),it=Math.min(b.outHeight,(b.inHeight+E-Z)/v);for(let pt=0;pt<S;++pt){let ht=Math.max(0,Math.ceil((P-pt)/T)),dt=Math.min(b.outWidth,(b.inWidth+P-pt)/T);for(let ft=0;ft<b.inChannels;++ft)for(let ut=0;ut<b.outChannels;++ut){let bt=0;for(let yt=0;yt<b.batchSize;++yt)for(let xt=J;xt<it;++xt){let kt=Z+xt*v-E;for(let Nt=ht;Nt<dt;++Nt){let At=pt+Nt*T-P;D?bt+=q.get(yt,kt,At,ft)*H.get(yt,xt,Nt,ut):bt+=q.get(yt,ft,kt,At)*H.get(yt,ut,xt,Nt)}}I.set(bt,Z,pt,ft,ut)}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let r5={kernelName:lx,backendName:"cpu",kernelFunc:n5};function o5(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{inputShape:c,strides:l,pad:p,dataFormat:f,dimRoundingMode:m}=r;Ct([o,s],"conv2dBackpropInput");let y=Jt(s.shape),b=Jt(o.shape),v=li(f),T=Un(c,s.shape,l,1,p,m,!1,v),N=new hn(T.inShape,"float32"),S=N.values,D=e.data.get(o.dataId).values,I=e.data.get(s.dataId).values,[P,E,L]=y,{batchSize:B,filterHeight:q,filterWidth:H,inChannels:Z,inHeight:J,inWidth:it,outChannels:pt,outHeight:ht,outWidth:dt,strideHeight:ft,strideWidth:ut}=T;v=T.dataFormat;let bt=q-1-T.padInfo.top,yt=H-1-T.padInfo.left,xt=v==="channelsLast",kt=N.strides[0],Nt=xt?N.strides[1]:N.strides[2],At=xt?N.strides[2]:1,It=xt?1:N.strides[1],St=b[0],Lt=xt?b[1]:b[2],Ht=xt?b[2]:1,oe=xt?1:b[1];for(let de=0;de<B;++de)for(let ie=0;ie<Z;++ie)for(let we=0;we<J;++we){let pe=we-bt,Xe=Math.max(0,Math.ceil(pe/ft)),on=Math.min(ht,(q+pe)/ft);for(let sn=0;sn<it;++sn){let Fe=sn-yt,Ye=Math.max(0,Math.ceil(Fe/ut)),Le=Math.min(dt,(H+Fe)/ut),Mn=0;for(let Bn=Xe;Bn<on;++Bn){let So=Bn*ft-pe;for(let zr=Ye;zr<Le;++zr){let Sa=zr*ut-Fe,$o=St*de+Lt*Bn+Ht*zr,Yo=P*(q-1-So)+E*(H-1-Sa)+L*ie;for(let $i=0;$i<pt;++$i){let Ii=D[$o+oe*$i],Ei=I[Yo+$i];Mn+=Ii*Ei}}}let Ds=kt*de+Nt*we+At*sn+It*ie;S[Ds]=Mn}}return e.makeTensorInfo(N.shape,N.dtype,N.values)}let s5={kernelName:If,backendName:"cpu",kernelFunc:o5};function i5(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dilations:p}=r;Ct([o,s],"conv3d");let f=ai(o.shape,s.shape,c,p,l),{filterDepth:m,filterHeight:y,filterWidth:b,dilationDepth:v,dilationHeight:T,dilationWidth:N,padInfo:S}=f,D=S.front,I=S.left,P=S.top,E=new hn(f.outShape,o.dtype),L=e.data.get(o.dataId).values,B=e.data.get(s.dataId).values,q=E.values,H=Jt(o.shape),Z=Jt(s.shape);for(let J=0;J<f.batchSize;++J){let it=J*H[0],pt=J*E.strides[0];for(let ht=0;ht<f.outDepth;++ht){let dt=pt+ht*E.strides[1],ft=ht*f.strideDepth-D;for(let ut=0;ut<m;++ut){let bt=ft+ut*v;if(bt<0||bt>=f.inDepth)continue;let yt=ut*Z[0],xt=it+bt*H[1];for(let kt=0;kt<f.outHeight;++kt){let Nt=dt+kt*E.strides[2],At=kt*f.strideHeight-P;for(let It=0;It<y;++It){let St=At+It*T;if(St<0||St>=f.inHeight)continue;let Lt=yt+It*Z[1],Ht=xt+St*H[2];for(let oe=0;oe<f.outWidth;++oe){let de=Nt+oe*f.outChannels,ie=oe*f.strideWidth-I;for(let we=0;we<b;++we){let pe=ie+we*N;if(pe<0||pe>=f.inWidth)continue;let Xe=Lt+we*Z[2],on=Ht+pe*f.inChannels,sn=Xe;for(let Fe=0;Fe<f.inChannels;++Fe){let Ye=L[on+Fe];for(let Le=0;Le<f.outChannels;++Le)q[de+Le]+=Ye*B[sn+Le];sn+=f.outChannels}}}}}}}}return e.makeTensorInfo(E.shape,E.dtype,E.values)}let a5={kernelName:Ef,backendName:"cpu",kernelFunc:i5};function c5(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,pad:l,filterShape:p}=r;Ct([o,s],"conv3dBackpropFilterV2");let f=Jt(o.shape),m=Jt(s.shape),y=ai(o.shape,p,c,1,l),b=y.strideDepth,v=y.strideHeight,T=y.strideWidth,N=y.filterDepth,S=y.filterHeight,D=y.filterWidth,I=new hn(y.filterShape,"float32"),P=I.values,[E,L,B,q]=I.strides,H=e.data.get(s.dataId).values,[Z,J,it,pt]=m,ht=e.data.get(o.dataId).values,[dt,ft,ut,bt]=f,yt=y.padInfo.front,xt=y.padInfo.left,kt=y.padInfo.top;for(let Nt=0;Nt<N;++Nt){let At=Math.max(0,Math.ceil((yt-Nt)/b)),It=Math.min(y.outDepth,(y.inDepth+yt-Nt)/b),St=Nt*E;for(let Lt=0;Lt<S;++Lt){let Ht=Math.max(0,Math.ceil((kt-Lt)/v)),oe=Math.min(y.outHeight,(y.inHeight+kt-Lt)/v),de=Lt*L+St;for(let ie=0;ie<D;++ie){let we=Math.max(0,Math.ceil((xt-ie)/T)),pe=Math.min(y.outWidth,(y.inWidth+xt-ie)/T),Xe=ie*B+de;for(let on=0;on<y.inChannels;++on){let sn=on*q+Xe;for(let Fe=0;Fe<y.outChannels;++Fe){let Ye=0;for(let Le=0;Le<y.batchSize;++Le){let Mn=Le*dt,Ds=Le*Z;for(let Bn=At;Bn<It;++Bn){let So=Nt+Bn*b-yt,zr=So*ft+Mn,Sa=Bn*J+Ds;for(let $o=Ht;$o<oe;++$o){let Yo=Lt+$o*v-kt,$i=Yo*ut+zr,Ii=$o*it+Sa;for(let Ei=we;Ei<pe;++Ei){let Nl=ie+Ei*T-xt,V1=Nl*bt+$i,G1=Ei*pt+Ii;Ye+=ht[V1+on]*H[G1+Fe]}}}}P[sn+Fe]=Ye}}}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let l5={kernelName:ux,backendName:"cpu",kernelFunc:c5};function u5(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{pad:c,strides:l,inputShape:p}=r;Ct([o],"conv3dBackpropInputV2");let f=Jt(o.shape),m=Jt(s.shape),y=ai(p,s.shape,l,1,c),b=new hn(y.inShape,"float32"),v=b.values,[T,N,S,D]=b.strides,I=e.data.get(o.dataId).values,[P,E,L,B]=f,q=e.data.get(s.dataId).values,[H,Z,J,it]=m,{batchSize:pt,filterDepth:ht,filterHeight:dt,filterWidth:ft,inChannels:ut,inDepth:bt,inHeight:yt,inWidth:xt,outChannels:kt,outDepth:Nt,outHeight:At,outWidth:It,strideDepth:St,strideHeight:Lt,strideWidth:Ht}=y,oe=ht-1-y.padInfo.front,de=dt-1-y.padInfo.top,ie=ft-1-y.padInfo.left;for(let we=0;we<pt;++we)for(let pe=0;pe<ut;++pe)for(let Xe=0;Xe<bt;++Xe){let on=Xe-oe,sn=Math.max(0,Math.ceil(on/St)),Fe=Math.min(Nt,(ht+on)/St);for(let Ye=0;Ye<yt;++Ye){let Le=Ye-de,Mn=Math.max(0,Math.ceil(Le/Lt)),Ds=Math.min(At,(dt+Le)/Lt);for(let Bn=0;Bn<xt;++Bn){let So=Bn-ie,zr=Math.max(0,Math.ceil(So/Ht)),Sa=Math.min(It,(ft+So)/Ht),$o=0;for(let Yo=sn;Yo<Fe;++Yo){let $i=Yo*St-on;for(let Ii=Mn;Ii<Ds;++Ii){let Ei=Ii*Lt-Le;for(let Nl=zr;Nl<Sa;++Nl){let V1=Nl*Ht-So,G1=P*we+E*Yo+L*Ii+B*Nl,cQ=H*(ht-1-$i)+Z*(dt-1-Ei)+J*(ft-1-V1)+it*pe;for(let cg=0;cg<kt;++cg){let lQ=I[G1+cg],uQ=q[cQ+cg];$o+=lQ*uQ}}}}v[T*we+N*Xe+S*Ye+D*Bn+pe]=$o}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}let p5={kernelName:px,backendName:"cpu",kernelFunc:u5};let h5=Ee(wc,n=>Math.cos(n)),f5={kernelName:wc,backendName:"cpu",kernelFunc:h5};let d5=Ee(yu,n=>Math.cosh(n)),m5={kernelName:yu,backendName:"cpu",kernelFunc:d5};function vI(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dilations:p,dimRoundingMode:f}=r;Ct([o,s],"depthwiseConv2DNative");let m=Jt(o.shape),y=Jt(s.shape),b=p;b==null&&(b=[1,1]),_(fn(c,b),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${c} and dilations '${b}'`);let v=Un(o.shape,s.shape,c,b,l,f,!0),{filterHeight:T,filterWidth:N,dilationHeight:S,dilationWidth:D,padInfo:I}=v,P=I.left,E=I.top,L=v.outChannels/v.inChannels,B=new hn(v.outShape,o.dtype),q=e.data.get(o.dataId).values,H=e.data.get(s.dataId).values,Z=B.values;for(let J=0;J<v.batchSize;++J){let it=J*m[0],pt=J*B.strides[0];for(let ht=0;ht<v.outHeight;++ht){let dt=pt+ht*B.strides[1],ft=ht*v.strideHeight-P;for(let ut=0;ut<T;++ut){let bt=ft+ut*S;if(bt<0||bt>=v.inHeight)continue;let yt=ut*y[0],xt=it+bt*m[1];for(let kt=0;kt<v.outWidth;++kt){let Nt=dt+kt*B.strides[2],At=kt*v.strideWidth-E;for(let It=0;It<N;++It){let St=At+It*D;if(St<0||St>=v.inWidth)continue;let Lt=yt+It*y[1],Ht=xt+St*v.inChannels,oe=Nt,de=Lt;for(let ie=0;ie<v.inChannels;++ie){let we=q[Ht+ie];for(let pe=0;pe<L;++pe)Z[oe+pe]+=we*H[de+pe];oe+=L,de+=L}}}}}}return e.makeTensorInfo(B.shape,B.dtype,B.values)}let g5={kernelName:Df,backendName:"cpu",kernelFunc:vI};function y5(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,dilations:l,pad:p,dimRoundingMode:f,filterShape:m}=r;Ct([o,s],"depthwiseConv2dNativeBackpropFilter");let y=Un(o.shape,m,c,l,p,f,!0),{strideHeight:b,strideWidth:v,filterHeight:T,filterWidth:N}=y,S=new hn(y.filterShape,"float32"),D=y.padInfo.left,I=y.padInfo.top,P=y.outChannels/y.inChannels,E=e.data.get(o.dataId).values,L=new hn(o.shape,o.dtype,E),B=e.data.get(s.dataId).values,q=new hn(s.shape,s.dtype,B);for(let H=0;H<T;++H){let Z=Math.max(0,Math.ceil((I-H)/b)),J=Math.min(y.outHeight,(y.inHeight+I-H)/b);for(let it=0;it<N;++it){let pt=Math.max(0,Math.ceil((D-it)/v)),ht=Math.min(y.outWidth,(y.inWidth+D-it)/v);for(let dt=0;dt<y.outChannels;++dt){let ft=Math.trunc(dt/P),ut=dt%P,bt=0;for(let yt=0;yt<y.batchSize;++yt)for(let xt=Z;xt<J;++xt){let kt=H+xt*b-I;for(let Nt=pt;Nt<ht;++Nt){let At=it+Nt*v-D;bt+=L.get(yt,kt,At,ft)*q.get(yt,xt,Nt,dt)}}S.set(bt,H,it,ft,ut)}}}return e.makeTensorInfo(S.shape,S.dtype,S.values)}let b5={kernelName:fx,backendName:"cpu",kernelFunc:y5};function x5(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{strides:c,dilations:l,pad:p,dimRoundingMode:f,inputShape:m}=r;Ct([o,s],"depthwiseConv2DNativeBackpropInput");let y=Jt(o.shape),b=Jt(s.shape),v=Un(m,s.shape,c,l,p,f,!0),T=new hn(v.inShape,"float32"),N=T.values,[S,D,I]=T.strides,P=e.data.get(o.dataId).values,[E,L,B]=y,q=e.data.get(s.dataId).values,[H,Z,J]=b,{batchSize:it,filterHeight:pt,filterWidth:ht,inChannels:dt,inHeight:ft,inWidth:ut,outChannels:bt,outHeight:yt,outWidth:xt,strideHeight:kt,strideWidth:Nt}=v,At=pt-1-v.padInfo.top,It=ht-1-v.padInfo.left,St=bt/dt;for(let Lt=0;Lt<it;++Lt)for(let Ht=0;Ht<dt;++Ht)for(let oe=0;oe<ft;++oe){let de=oe-At,ie=Math.max(0,Math.ceil(de/kt)),we=Math.min(yt,(pt+de)/kt);for(let pe=0;pe<ut;++pe){let Xe=pe-It,on=Math.max(0,Math.ceil(Xe/Nt)),sn=Math.min(xt,(ht+Xe)/Nt),Fe=0;for(let Ye=ie;Ye<we;++Ye){let Le=Ye*kt-de;for(let Mn=on;Mn<sn;++Mn){let Ds=Mn*Nt-Xe,Bn=E*Lt+L*Ye+B*Mn,So=H*(pt-1-Le)+Z*(ht-1-Ds)+J*Ht;for(let zr=0;zr<St;++zr){let Sa=Ht*St+zr,$o=P[Bn+Sa],Yo=q[So+zr];Fe+=$o*Yo}}}N[S*Lt+D*oe+I*pe+Ht]=Fe}}return e.makeTensorInfo(T.shape,T.dtype,T.values)}let w5={kernelName:dx,backendName:"cpu",kernelFunc:x5};let v5={kernelName:Af,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o}=n,{strides:s,pad:c,dilations:l}=e,p=t,f=p.data.get(r.dataId).values,m=r.shape.length,y=p.data.get(o.dataId).values,b=o.shape.length,{batchSize:v,inHeight:T,inWidth:N,inChannels:S,outHeight:D,outWidth:I,padInfo:P,strideHeight:E,strideWidth:L,filterHeight:B,filterWidth:q,dilationHeight:H,dilationWidth:Z,outShape:J}=Td(r.shape,o.shape,s,c,"NHWC",l),it=G(J),pt=J.length,ht=or(r.dtype,it);for(let ft=0;ft<v;++ft)for(let ut=0;ut<D;++ut){let bt=ut*E-P.top;for(let yt=0;yt<I;++yt){let xt=yt*L-P.left;for(let kt=0;kt<S;++kt){let Nt=Number.MIN_SAFE_INTEGER;for(let It=0;It<B;++It){let St=bt+It*H;if(St>=0&&St<T)for(let Lt=0;Lt<q;++Lt){let Ht=xt+Lt*Z;if(Ht>=0&&Ht<N){let oe=Yr([ft,St,Ht,kt],m,Jt(r.shape)),de=Yr([It,Lt,kt],b,Jt(o.shape)),ie=f[oe]+y[de];ie>Nt&&(Nt=ie)}}}let At=Yr([ft,ut,yt,kt],pt,Jt(J));ht[At]=Nt}}}let dt=p.write(ri(ht,r.dtype),J,r.dtype);return{dataId:dt,shape:J,dtype:r.dtype}}};let T5={kernelName:Rf,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o,dy:s}=n,{strides:c,pad:l,dilations:p}=e,f=t,m=Dr(r.shape,f.data.get(r.dataId).values),y=Dr(o.shape,f.data.get(o.dataId).values),{batchSize:b,inHeight:v,inWidth:T,inChannels:N,outHeight:S,outWidth:D,padInfo:I,strideHeight:P,strideWidth:E,filterHeight:L,filterWidth:B,dilationHeight:q,dilationWidth:H,outShape:Z}=Td(r.shape,o.shape,c,l,"NHWC",p);_(s.rank===Z.length,()=>`Error in ${Rf}, dy must have the same rank as output ${Z.length}, but got ${s.rank}`);let J=Dr(Z,f.data.get(s.dataId).values),it=tx(o.shape,o.dtype);for(let ht=0;ht<b;++ht)for(let dt=0;dt<S;++dt){let ft=dt*P-I.top;for(let ut=0;ut<D;++ut){let bt=ut*E-I.left;for(let yt=0;yt<N;++yt){let xt=Number.MIN_SAFE_INTEGER,kt=0,Nt=0;for(let At=0;At<L;++At){let It=ft+At*q;if(It>=0&&It<v)for(let St=0;St<B;++St){let Lt=bt+St*H;if(Lt>=0&&Lt<T){let Ht=m[ht][It][Lt][yt]+y[At][St][yt];Ht>xt&&(xt=Ht,kt=At,Nt=St)}}}it[kt][Nt][yt]+=J[ht][dt][ut][yt]}}}let pt=f.write(ri(it,r.dtype),o.shape,o.dtype);return{dataId:pt,shape:o.shape,dtype:o.dtype}}};let k5={kernelName:Ff,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o,dy:s}=n,{strides:c,pad:l,dilations:p}=e,f=t,m=Dr(r.shape,f.data.get(r.dataId).values),y=Dr(o.shape,f.data.get(o.dataId).values),{batchSize:b,inHeight:v,inWidth:T,inChannels:N,outHeight:S,outWidth:D,padInfo:I,strideHeight:P,strideWidth:E,filterHeight:L,filterWidth:B,dilationHeight:q,dilationWidth:H,outShape:Z}=Td(r.shape,o.shape,c,l,"NHWC",p);_(s.rank===Z.length,()=>`Error in ${Ff}, dy must have the same rank as output ${Z.length}, but got ${s.rank}`);let J=Dr(Z,f.data.get(s.dataId).values),it=tx(r.shape,r.dtype);for(let ht=0;ht<b;++ht)for(let dt=0;dt<S;++dt){let ft=dt*P-I.top;for(let ut=0;ut<D;++ut){let bt=ut*E-I.left;for(let yt=0;yt<N;++yt){let xt=Number.MIN_SAFE_INTEGER,kt=ft<0?0:ft,Nt=bt<0?0:bt;for(let At=0;At<L;++At){let It=ft+At*q;if(It>=0&&It<v)for(let St=0;St<B;++St){let Lt=bt+St*H;if(Lt>=0&&Lt<T){let Ht=m[ht][It][Lt][yt]+y[At][St][yt];Ht>xt&&(xt=Ht,kt=It,Nt=Lt)}}}it[ht][kt][Nt][yt]+=J[ht][dt][ut][yt]}}}let pt=f.write(ri(it,r.dtype),r.shape,r.dtype);return{dataId:pt,shape:r.shape,dtype:r.dtype}}};let N5=Ci((n,t)=>n/t),_5=ul(vc,N5),C1={kernelName:vc,backendName:"cpu",kernelFunc:_5};let C5=fv,S5=dv,$5=mv,I5=gv,E5=yv,D5=bv,A5=Ee(xu,n=>{let t=Math.sign(n),e=Math.abs(n),r=1/(1+C5*e);return t*(1-((((D5*r+E5)*r+I5)*r+$5)*r+S5)*r*Math.exp(-e*e))}),F5={kernelName:xu,backendName:"cpu",kernelFunc:A5};function TI(n,t,e){let r=n.shape,o=r[0],s=r[1],c=e.data.get(n.dataId),l=c.complexTensorInfos.real,p=c.complexTensorInfos.imag,f=[o,s],m=G(f),y=Ce("float32",m),b=Ce("float32",m);for(let S=0;S<o;S++){let D=T1({inputs:{x:l},backend:e,attrs:{begin:[S,0],size:[1,s]}}),I=T1({inputs:{x:p},backend:e,attrs:{begin:[S,0],size:[1,s]}}),P=Co({inputs:{real:D,imag:I},backend:e}),{real:E,imag:L}=R5(P,t,e),B=ws(E,L);for(let q=0;q<s;q++){let H=xv(B,q);y[S*s+q]=H.real,b[S*s+q]=H.imag}e.disposeIntermediateTensorInfo(D),e.disposeIntermediateTensorInfo(I),e.disposeIntermediateTensorInfo(P)}let v=e.makeTensorInfo(f,"float32",y),T=e.makeTensorInfo(f,"float32",b),N=Co({inputs:{real:v,imag:T},backend:e});return e.disposeIntermediateTensorInfo(v),e.disposeIntermediateTensorInfo(T),N}function R5(n,t,e){let r=G(n.shape),o=e.data.get(n.dataId),s=e.data.get(o.complexTensorInfos.real.dataId).values,c=e.data.get(o.complexTensorInfos.imag.dataId).values;if(P5(r)){let l=S1(s,c,r,t,e),p=[n.shape[0],n.shape[1]];if(t){let f=e.makeTensorInfo(p,"float32",l.real),m=e.makeTensorInfo(p,"float32",l.imag),y=e.makeTensorInfo([],"float32",ON(r,"float32")),b=Na({inputs:{x:y},backend:e}),v=C1.kernelFunc({inputs:{a:f,b:y},backend:e}),T=C1.kernelFunc({inputs:{a:m,b},backend:e}),N=e.data.get(v.dataId).values,S=e.data.get(T.dataId).values;return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v),e.disposeIntermediateTensorInfo(T),{real:N,imag:S}}return l}else{let l=ws(s,c),p=O5(l,r,t);return CC(p)}}function P5(n){return(n&n-1)===0}function S1(n,t,e,r,o){if(e===1)return{real:n,imag:t};let 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Due to automatic garbage collection, the true allocated memory may be less."]}}stridedSlice(x,begin,end,strides){assertNotComplex(x,"stridedSlice");let outShape=slice_util_exports.computeOutShape(begin,end,strides);if(outShape.some(axis=>axis===0))return tensor4([],outShape);let buffer11=buffer(outShape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<buffer11.size;i++){let loc=buffer11.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];buffer11.set(xBuf.get(...newLoc),...loc)}return buffer11.toTensor()}diag(x){let xVals=this.readSync(x.dataId),buffer11=buffer([x.size,x.size],x.dtype),vals=buffer11.values;for(let i=0;i<xVals.length;i++)vals[i*x.size+i]=xVals[i];return buffer11.toTensor()}unstack(x,axis){let num=x.shape[axis],outShape=new Array(x.rank-1),outIndex=0;for(let i=0;i<x.rank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let begin=new Array(x.rank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++)begin[axis]=i,res[i]=slice(x,begin,size).reshape(outShape);return res}reverse(x,axis){assertNotComplex(x,"reverse");let buffer11=buffer(x.shape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<buffer11.size;i++){let outLoc=buffer11.indexToLoc(i),inLoc=outLoc.slice();axis.forEach(ax=>inLoc[ax]=x.shape[ax]-1-inLoc[ax]),buffer11.set(xBuf.get(...inLoc),...outLoc)}return buffer11.toTensor()}neg(x){return assertNotComplex(x,"neg"),mul(scalar(-1),x)}addN(tensors){assertNotComplex(tensors,"addN");let vals=tensors.map(t=>this.readSync(t.dataId)),result=buffer(tensors[0].shape,tensors[0].dtype),resultVals=result.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<resultVals.length;j++)resultVals[j]+=currVals[j]}return result.toTensor()}softmax(logits,dim){let axes=util_exports.parseAxisParam([dim],logits.shape),maxLogit=max(logits,axes),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),a=sub(logits,maxLogit.reshape(expandedShape)),b=exp(a),sumExp=this.sum(b,axes).reshape(expandedShape);return div(b,sumExp)}pow(a,b){return assertNotComplex([a,b],"pow"),this.broadcastedBinaryOp(a,b,a.dtype,(aValue,bValue)=>Math.pow(aValue,bValue))}floorDiv(a,b){assertNotComplex([a,b],"floorDiv");let op22=(a6,b2)=>Math.floor(a6/b2),outputDtype="int32";return this.broadcastedBinaryOp(a,b,outputDtype,op22)}sum(x,axes){assertNotComplex(x,"sum"),backend_util_exports.assertAxesAreInnerMostDims("sum",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),resultDtype=upcastType(x.dtype,"int32"),result=zeros(outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum29=0;for(let j=0;j<reduceSize;++j)sum29+=aVals[offset+j];vals[i]=sum29}return result}prod(x,axes){assertNotComplex(x,"sum");let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),resultDtype=upcastType(x.dtype,"int32"),result=zeros(outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,prod52=1;for(let j=0;j<reduceSize;++j)prod52*=aVals[offset+j];vals[i]=prod52}return result}unsortedSegmentSum(x,segmentIds,numSegments){assertNotComplex(x,"unsortedSegmentSum");let res=[],numIters=x.rank-segmentIds.rank;for(let i=0;i<numIters;++i)segmentIds=segmentIds.expandDims(i+1);for(let i=0;i<numSegments;++i){let segmentId=scalar(i,"int32"),mask=equal(segmentId,segmentIds).asType("float32"),sum29=mask.mul(x).sum(0);res.push(sum29)}return stack(res)}argMin(x,axis){assertNotComplex(x,"argMin");let axes=[axis];backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min82=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min82&&(min82=value,minIndex=j)}vals[i]=minIndex}return result}argMax(x,axis){assertNotComplex(x,"argMax");let axes=[axis];backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max102=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max102&&(max102=value,maxIndex=j)}vals[i]=maxIndex}return result}cumsum(x,axis,exclusive,reverse12){if(assertNotComplex(x,"cumsum"),axis!==x.rank-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank-1} but got axis=${axis}`);let resultDtype=upcastType(x.dtype,"int32"),result=zeros(x.shape,resultDtype),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId),finalDim=x.shape[x.rank-1],indexAdjuster=reverse12?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}return result}equal(a,b){return assertNotComplex([a,b],"equal"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal===bVal?1:0)}notEqual(a,b){return assertNotComplex([a,b],"notEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal!==bVal?1:0)}less(a,b){return assertNotComplex([a,b],"less"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal<bVal?1:0)}lessEqual(a,b){return assertNotComplex([a,b],"lessEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal<=bVal?1:0)}greater(a,b){return assertNotComplex([a,b],"greater"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal>bVal?1:0)}greaterEqual(a,b){return assertNotComplex([a,b],"greaterEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal>=bVal?1:0)}logicalAnd(a,b){return assertNotComplex([a,b],"logicalAnd"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal&&bVal)}logicalOr(a,b){return assertNotComplex([a,b],"logicalOr"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal||bVal)}select(condition,a,b){assertNotComplex([condition,a,b],"select");let values=this.readSync(condition.dataId),aValues=this.readSync(a.dataId),bValues=this.readSync(b.dataId),result=zeros(a.shape,upcastType(a.dtype,b.dtype)),newValues=this.readSync(result.dataId),index=0,offset=condition.rank===0||condition.rank>1||a.rank===1?1:util_exports.sizeFromShape(a.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=aValues[i]:newValues[index++]=bValues[i];return result}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}topk(x,k,sorted){assertNotComplex(x,"topk");let xVals=this.readSync(x.dataId);return topkImpl2(xVals,x.shape,x.dtype,k,sorted)}min(x,axes){assertNotComplex(x,"min"),backend_util_exports.assertAxesAreInnerMostDims("min",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min82=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min82&&(min82=value)}vals[i]=min82}return result}minimum(a,b){return assertNotComplex([a,b],"minimum"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>Math.min(aVal,bVal))}mod(a,b){return assertNotComplex([a,b],"mod"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>{let rem=aVal%bVal;return aVal<0&&bVal<0||aVal>=0&&bVal>=0?rem:(rem+bVal)%bVal})}maximum(a,b){return assertNotComplex([a,b],"maximum"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>Math.max(aVal,bVal))}all(x,axes){assertNotComplex(x,"all"),backend_util_exports.assertAxesAreInnerMostDims("all",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all52=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all52=all52&&value}vals[i]=all52}return result}any(x,axes){assertNotComplex(x,"any"),backend_util_exports.assertAxesAreInnerMostDims("any",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}return result}squaredDifference(a,b){return assertNotComplex([a,b],"squaredDifference"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>{let diff=aVal-bVal;return diff*diff})}eluDer(dy,y){assertNotComplex([dy,y],"eluDer");let resultValues=new Float32Array(y.size),values=this.readSync(y.dataId),dyValues=this.readSync(dy.dataId);for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return this.makeOutput(resultValues,y.shape,"float32")}atan2(a,b){return assertNotComplex([a,b],"atan2"),this.broadcastedBinaryOp(a,b,a.dtype,(aValue,bValue)=>Math.atan2(aValue,bValue))}tile(x,reps){return assertNotComplex(x,"tile"),tile9(this.bufferSync(x),reps)}gather(x,indices,axis){assertNotComplex([x,indices],"gather");let newShape=x.shape.slice(),indicesValues=this.readSync(indices.dataId);newShape[axis]=indicesValues.length;let result=buffer(newShape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<result.size;++i){let newLoc=result.indexToLoc(i),originalLoc=newLoc.slice();originalLoc[axis]=indicesValues[newLoc[axis]];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result.toTensor()}batchToSpaceND(x,blockShape,crops){assertNotComplex([x],"batchToSpaceND");let prod52=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod52),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod52),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length);return transpose(x.reshape(reshaped),permuted).reshape(reshapedPermuted).slice(sliceBeginCoords,sliceSize)}pool3d(x,convInfo,poolType){assertNotComplex(x,"pool3d");let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,xValues=this.readSync(x.dataId),output=buffer(convInfo.outShape,x.dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*x.strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*x.strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*x.strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*x.strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output.toTensor()}avgPool3d(x,convInfo){return assertNotComplex(x,"avgPool3d"),this.pool3d(x,convInfo,"avg").toFloat()}avgPool3dBackprop(dy,x,convInfo){assertNotComplex([dy,x],"avgPool3dBackprop");let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=this.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth)continue;for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow)continue;for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let pixel=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel);dotProd+=pixel}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return dx.toTensor()}maxPool3d(x,convInfo){return assertNotComplex(x,"maxPool3d"),this.pool3d(x,convInfo,"max").toFloat()}maxPool3dPositions(x,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=this.bufferSync(x);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions.toTensor()}maxPool3dBackprop(dy,x,y,convInfo){assertNotComplex([x,y],"maxPool3dBackprop");let maxPositions=this.maxPool3dPositions(x,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),maxPosBuf=this.bufferSync(maxPositions),dyBuf=this.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth)continue;for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow)continue;for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;if(mask===0)continue;let pixel=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel);dotProd+=pixel*mask}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return dx.toTensor()}resizeBilinear(x,newHeight,newWidth,alignCorners){assertNotComplex(x,"resizeBilinear");let[batch,oldHeight,oldWidth,numChannels]=x.shape,xValues=this.readSync(x.dataId),result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow=effectiveRowSizeRatio*r,sourceRowFloor=Math.floor(sourceFracRow),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*x.strides[0]+sourceRowFloor*x.strides[1],botRowOffset=b*x.strides[0]+sourceRowCeil*x.strides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=effectiveColSizeRatio*c,sourceColFloor=Math.floor(sourceFracCol),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*x.strides[2],botLeftOffset=botRowOffset+sourceColFloor*x.strides[2],topRightOffset=topRowOffset+sourceColCeil*x.strides[2],botRightOffest=botRowOffset+sourceColCeil*x.strides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return tensor4(result,[batch,newHeight,newWidth,numChannels])}resizeBilinearBackprop(dy,x,alignCorners){assertNotComplex([dy,x],"resizeBilinearBackprop");let[batch,xHeight,xWidth,depth]=x.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=this.readSync(dy.dataId),offset=0;for(let b=0;b<batch;b++){let bOffset=b*x.strides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*x.strides[1],bottomDxROffset=bOffset+bottomDxRIndex*x.strides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*x.strides[2],topRightRCOffset=topDxROffset+rightDxCIndex*x.strides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*x.strides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*x.strides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return tensor4d(output,[batch,xWidth,xHeight,depth],x.dtype)}resizeNearestNeighbor(x,newHeight,newWidth,alignCorners){assertNotComplex(x,"resizeNearestNeighbor");let[batch,oldHeight,oldWidth,numChannels]=x.shape,xValues=this.readSync(x.dataId),output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*x.strides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow)),rowOffset=batchOffset+sourceNearestRow*x.strides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol)),colOffset=rowOffset+sourceNearestCol*x.strides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return tensor4(output,[batch,newHeight,newWidth,numChannels],x.dtype)}resizeNearestNeighborBackprop(dy,x,alignCorners){assertNotComplex([dy,x],"resizeNearestNeighborBackprop");let[batch,xHeight,xWidth,depth]=x.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=this.readSync(dy.dataId),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*x.strides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*x.strides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*x.strides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(dyR<0||dyR>=yHeight)continue;let dyROffset=batchOffset+dyR*dy.strides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r!==sourceNearestRow)continue;for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(dyC<0||dyC>=yWidth)continue;let dyCOffset=dyROffset+dyC*dy.strides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}output[colOffset+d]=accum}}}}return tensor4d(output,x.shape,x.dtype)}localResponseNormalization4D(x,depthRadius,bias,alpha,beta){assertNotComplex(x,"localResponseNormalization4D");let channels=x.shape[3],maxD=channels-1,xValues=this.readSync(x.dataId),size=x.size,result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum29=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum29+=z*z}return sum29}for(let offset=0;offset<size;offset++){let sum29=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum29,-beta);result[offset]=val}return tensor4d(result,x.shape)}LRNGrad(dy,inputImage,outputImage,depthRadius,bias,alpha,beta){assertNotComplex(dy,"LRNGrad");let channels=dy.shape[3],dyValues=this.readSync(dy.dataId),inputImageValues=this.readSync(inputImage.dataId),outputImageValues=this.readSync(outputImage.dataId),result=new Float32Array(dy.size),size=dy.size;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm52=0;for(let k=depthBegin;k<depthEnd;k++)norm52+=Math.pow(inputImageValues[k],2);norm52=alpha*norm52+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*inputImageValues[k]*outputImageValues[offset]/norm52;offset===k&&(dyi+=Math.pow(norm52,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return tensor4d(result,dy.shape)}multinomial(logits,normalized,numSamples,seed){assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax(logits),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],res=zeros([batchSize,numSamples],"int32"),resVals=this.readSync(res.dataId),probVals=this.readSync(probabilities.dataId);for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return res}oneHot(indices,depth,onValue,offValue){assertNotComplex(indices,"oneHot");let res=new Float32Array(indices.size*depth);res.fill(offValue);let indicesVal=this.readSync(indices.dataId);for(let event=0;event<indices.size;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return tensor2d(res,[indices.size,depth],"int32")}nonMaxSuppression(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){assertNotComplex(boxes,"nonMaxSuppression");let boxesVals=this.readSync(boxes.dataId),scoresVals=this.readSync(scores.dataId);return nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold)}depthToSpace(x,blockSize,dataFormat){util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=this.readSync(x.dataId),result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inD=d+offsetD,inputIdx=inD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return tensor4d(result,[batchSize,outputHeight,outputWidth,outputDepth])}broadcastedBinaryOp(a,b,dtype,op22){let newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),result=buffer(newShape,dtype),aVals=this.readSync(a.dataId),bVals=this.readSync(b.dataId),aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),resVals=result.values;if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resVals.length;++i)resVals[i]=op22(aVals[i%aVals.length],bVals[i%bVals.length]);else{let aBuf=this.bufferSync(a),bBuf=this.bufferSync(b);for(let i=0;i<resVals.length;++i){let loc=result.indexToLoc(i),aLoc=loc.slice(-a.rank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=aBuf.locToIndex(aLoc),bLoc=loc.slice(-b.rank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=bBuf.locToIndex(bLoc);resVals[i]=op22(aVals[aIndex],bVals[bIndex])}}return result.toTensor()}split(x,sizeSplits,axis){return split10(x,sizeSplits,axis)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(images,boxes,boxIndex,cropSize,method,extrapolationValue){let[batch,imageHeight,imageWidth,numChannels]=images.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=this.readSync(boxes.dataId),boxIndVals=this.readSync(boxIndex.dataId),imageVals=this.readSync(images.dataId),inStride=images.strides,outStride=output.strides;for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(bInd>=batch)continue;let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}return output.toTensor()}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1;return this.scatter(sparseIndices,sparseValues,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices)}gatherND(x,indices){let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(x,indices);if(numSlices===0)return tensor4([],resultShape,x.dtype);let buffer11=new TensorBuffer([numSlices,sliceSize],x.dtype),indicesData=this.readSync(indices.dataId),xData=this.readSync(x.dataId);for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=x.size/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);for(let k=0;k<sliceSize;k++)buffer11.values[i*sliceSize+k]=xData[flattenIndex*sliceSize+k]}return buffer11.toTensor().reshape(resultShape)}scatterND(indices,updates,shape){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),defaultValue=scalar(0),sumDupeIndices=!0;return this.scatter(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices)}onesLike(x){if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");return fill(x.shape,1,x.dtype)}zerosLike(x){let values=util_exports.getArrayFromDType(x.dtype,util_exports.sizeFromShape(x.shape));return this.makeOutput(values,x.shape,x.dtype)}linspace(start,stop,num){return backend_util_exports.linspaceImpl(start,stop,num)}scatter(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=this.readSync(indices.dataId),updatesData=this.readSync(updates.dataId);if(outputSize===0)return tensor4([],shape,updates.dtype);let buffer11=new TensorBuffer(flattenShape,updates.dtype);buffer11.values.fill(this.readSync(defaultValue.dataId)[0]);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?buffer11.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:buffer11.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return buffer11.toTensor().reshape(shape)}},shared_exports={};__export2(shared_exports,{addImpl:()=>addImpl,ceilImpl:()=>ceilImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,multiplyImpl:()=>multiplyImpl,notEqualImpl:()=>notEqualImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,subImpl:()=>subImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs9=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape));if(x.dtype!=="complex64"){let values=cpuBackend.data.get(x.dataId).values;resultValues=simpleAbsImpl(values)}else{let complexVals=cpuBackend.data.get(x.dataId),real82=complexVals.complexTensorInfos.real,imag82=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real82.dataId).values,imagVals=cpuBackend.data.get(imag82.dataId).values;for(let i=0;i<realVals.length;i++){let real9=realVals[i],imag9=imagVals[i];resultValues[i]=Math.hypot(real9,imag9)}}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs9};function createSimpleBinaryKernelImpl(op22){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op22(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op22(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex9(args){let{inputs,backend:backend3}=args,{real:real82,imag:imag82}=inputs,realVals=backend3.data.get(real82.dataId).values,imagVals=backend3.data.get(imag82.dataId).values,complexInfo=backend3.makeTensorInfo(real82.shape,"complex64"),complex11=backend3.data.get(complexInfo.dataId);return complex11.complexTensorInfos={real:backend3.makeTensorInfo(real82.shape,"float32",realVals),imag:backend3.makeTensorInfo(imag82.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex9};function identity2(args){let{inputs,backend:backend3}=args,{x}=inputs;return backend3.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,real82=backend3.data.get(input2.dataId).complexTensorInfos.real,realVal=backend3.data.get(real82.dataId).values;return backend3.makeTensorInfo(real82.shape,real82.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real6};function cast49(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend3});let zerosTensor=zeros(x.shape),floatX=cast49({inputs:{x},backend:backend3,attrs:{dtype:"float32"}}),result=complex9({inputs:{real:floatX,imag:zerosTensor},backend:backend3});return zerosTensor.dispose(),backend3.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real6({inputs:{input:x},backend:backend3}),result=cast49({inputs:{x:realPart},backend:backend3,attrs:{dtype}});return backend3.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend3});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend3.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend3.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend3.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend3.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast49};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend3})=>{let{a,b}=inputs,cpuBackend=backend3;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend3})=>{let{a,b}=inputs,cpuBackend=backend3;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast49({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast49({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex9({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op22){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op22(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op22(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add32=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add32};function createSimpleUnaryImpl(op22){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op22(values[i],attrs);return newValues}}function unaryKernelFunc(name,op22,dtype){return({inputs,attrs,backend:backend3})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend3,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op22(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend3})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend3,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil4=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil4},expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp12=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp12},expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm14=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm14},floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor6=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor6},logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log9=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log9};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max102=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max102&&(max102=value)}vals[i]=max102}return vals}var multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2},notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2},rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt5=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt5};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return vals.subarray(flatOffset,flatOffset+length)}let outVals=util_exports.getTypedArrayFromDType(dtype,length);for(let i=0;i<length;++i){let rank=size.length,strides=util_exports.computeStrides(size),loc=util_exports.indexToLoc(i,rank,strides),xLoc=loc.map((idx,j)=>idx+begin[j]),xIndex=util_exports.locToIndex(xLoc,shape.length,xStrides);outVals[i]=vals[xIndex]}return outVals}function slice19(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend3.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend3.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice19},squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2},subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub34=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub34};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version10="2.7.0";registerBackend("cpu",()=>new MathBackendCPU,1);var elu8=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu8},preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu7(args){let{inputs,backend:backend3}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend3.data.get(x.dataId).values,bVals=backend3.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend3.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu7},relu9=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu9},relu66=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu66};function applyActivation2(backend3,x,activation2,preluActivationWeights){if(activation2==="linear")return identity2({inputs:{x},backend:backend3});if(activation2==="relu")return relu9({inputs:{x},backend:backend3});if(activation2==="elu")return elu8({inputs:{x},backend:backend3});if(activation2==="relu6")return relu66({inputs:{x},backend:backend3});if(activation2==="prelu")return prelu7({inputs:{x,alpha:preluActivationWeights},backend:backend3});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape88(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend3.incRef(x.dataId);let xData=backend3.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real82=xData.complexTensorInfos.real,imag82=xData.complexTensorInfos.imag;real82.shape=$shape,imag82.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape88};function batchMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShapeOuterDims=batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2),outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape88({inputs:{x:a},backend:backend3,attrs:{shape:a3dShape}}),b3d=reshape88({inputs:{x:b},backend:backend3,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend3.data.get(a3d.dataId).values,b3dValues=backend3.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend3.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum29=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum29+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum29}}return backend3.disposeIntermediateTensorInfo(a3d),backend3.disposeIntermediateTensorInfo(b3d),backend3.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2}=attrs,current,addRes,activationRes,intermediates=[],matMulRes=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend3});current=matMulRes,bias&&(addRes=add32({inputs:{a:current,b:bias},backend:backend3}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend3,current,activation2,preluActivationWeights),intermediates.push(current),current=activationRes);for(let i of intermediates)backend3.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul},acos4=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos4},acosh4=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh4},asin4=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin4},asinh4=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh4},atan5=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan5},atanh4=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh4};function pool5(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function avgPool2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend3});else{let xValues=backend3.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer11=pool5(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend3.makeTensorInfo(convInfo.outShape,x.dtype,buffer11.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPoolBackprop2(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolBackprop");let{filterSize,strides,pad:pad11}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend3.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR)continue;for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let pixel=dyBuf.get(b,dyR,dyC,d);dotProd+=pixel}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolBackpropConfig={kernelName:AvgPoolBackprop,backendName:"cpu",kernelFunc:avgPoolBackprop2};function batchNorm2(args){let{inputs,backend:backend3,attrs}=args,{x,scale:scale22,offset,mean:mean7,variance}=inputs;util_exports.assert(mean7.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean7.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale22==null||mean7.shape.length===scale22.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean7,variance,scale22,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend3.data.get(x.dataId).values,mVals=backend3.data.get(mean7.dataId).values,varVals=backend3.data.get(variance.dataId).values,sVals=scale22?backend3.data.get(scale22.dataId).values:new Float32Array([1]),offVals=offset?backend3.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend3.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2},clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip};function imag6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,imag82=backend3.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend3.data.get(imag82.dataId).values;return backend3.makeTensorInfo(imag82.shape,imag82.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag6};function concat17(args){let{inputs,backend:backend3,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend3.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real6({inputs:{input:t},backend:backend3})),imags=$inputs.map(t=>imag6({inputs:{input:t},backend:backend3})),realConcated=concat17({inputs:reals,backend:backend3,attrs:{axis:$axis}}),imagConcated=concat17({inputs:imags,backend:backend3,attrs:{axis:$axis}}),result=complex9({inputs:{real:realConcated,imag:imagConcated},backend:backend3});return reals.forEach(r=>backend3.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend3.disposeIntermediateTensorInfo(i)),backend3.disposeIntermediateTensorInfo(realConcated),backend3.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis)),shape=[-1,innerSize];return reshape88({inputs:{x:t},backend:backend3,attrs:{shape}})});outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let outVals=util_exports.getTypedArrayFromDType($inputs[0].dtype,util_exports.sizeFromShape(outShape));if(inputs2D[0].shape[0]===1){let offset=0;inputs2D.forEach(t=>{let val=backend3.data.get(t.dataId).values,size=util_exports.sizeFromShape(t.shape);outVals.set(val,offset),offset+=size})}else{let colOffset=0;inputs2D.forEach(t=>{let tVals=backend3.data.get(t.dataId).values,tIdx=0;for(let row=0;row<t.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<t.shape[1];++col)outVals[resIdx+col]=tVals[tIdx++]}colOffset+=t.shape[1]})}let finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend3.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend3.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat17};function conv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,pad:pad11,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad11,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend3.data.get(x.dataId).values,dyVals=backend3.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend3.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad11,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad11,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend3.data.get(dy.dataId).values,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad11),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(xF<0||xF>=convInfo.inDepth)continue;let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,pad:pad11,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad11),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend3.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xF=wF+yF*strideDepth-frontPad,xOffset2=xF*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad,xOffset3=xR*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad,xOffset4=xC*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend3.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{pad:pad11,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad11),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2},cos6=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos6},cosh5=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh5};function depthwiseConv2dNative(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad11,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad11,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend3.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend3.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend3.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad11,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2},dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}let dataId=cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype);return{dataId,shape:outShape,dtype:x.dtype}}},dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients8=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients8[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}let dataId=cpuBackend.write(util_exports.toTypedArray(gradients8,x.dtype),filter.shape,filter.dtype);return{dataId,shape:filter.shape,dtype:filter.dtype}}},dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients8=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients8[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}let dataId=cpuBackend.write(util_exports.toTypedArray(gradients8,x.dtype),x.shape,x.dtype);return{dataId,shape:x.shape,dtype:x.dtype}}},divImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div35=binaryKernelFunc(Div,divImpl),divConfig={kernelName:Div,backendName:"cpu",kernelFunc:div35},p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf4=unaryKernelFunc(Erf,xi=>{let sign52=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign52*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf4};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice19({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice19({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex9({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real82,imag:imag82}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real82,imag82);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex9({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=divConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=divConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)===0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex9({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex9({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex9({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex9({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex9({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add32({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub34({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real6({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real6({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag6({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag6({inputs:{input:subPart},backend:cpuBackend}),$real=concat17({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat17({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real82=0,imag82=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real82+=term.real*e.real-term.imag*e.imag,imag82+=term.real*e.imag+term.imag*e.real}inverse&&(real82/=size,imag82/=size),backend_util_exports.assignToTypedArray(ret,real82,imag82,r)}return ret}function fft6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape88({inputs:{x:input2},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend3),resultReshaped=reshape88({inputs:{x:result},backend:backend3,attrs:{shape:input2.shape}});return backend3.disposeIntermediateTensorInfo(input2D),backend3.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft6};function fill5(args){let{backend:backend3,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend3.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill5};function fillValues(values,value,dtype){dtype==="string",values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,cpuBackend=backend3,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}let dataId=cpuBackend.write(output,image3.shape,image3.dtype);return{dataId,shape:image3.shape,dtype:image3.dtype}}};function fusedConv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2}=attrs,result=conv2D({inputs:{x,filter},backend:backend3,attrs:{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add32({inputs:{a:result,b:bias},backend:backend3}),backend3.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend3,result,activation2,preluActivationWeights),backend3.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend3,attrs:{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add32({inputs:{a:result,b:bias},backend:backend3}),backend3.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend3,result,activation2,preluActivationWeights),backend3.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function ifft6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape88({inputs:{x:input2},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend3),resultReshaped=reshape88({inputs:{x:result},backend:backend3,attrs:{shape:input2.shape}});return backend3.disposeIntermediateTensorInfo(input2D),backend3.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft6},isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3},isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2},isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3},log1p5=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p5},logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2},maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend3,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes);outShape=newShape}return{dataId,shape:outShape,dtype:x.dtype}}};function maxPool2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend3});else{let xValues=backend3.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer11=pool5(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend3.makeTensorInfo(convInfo.outShape,x.dtype,buffer11.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPoolBackprop2(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolBackprop");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),xValues=backend3.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend3.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR)continue;for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;if(mask===0)continue;let pixel=dyBuf.get(b,dyR,dyC,d);dotProd+=pixel*mask}}dx.set(dotProd,b,dxR,dxC,d)}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolBackpropConfig={kernelName:MaxPoolBackprop,backendName:"cpu",kernelFunc:maxPoolBackprop2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool5(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{filterSize,strides,pad:pad11,includeBatchInIndex}=attrs,cpuBackend=backend3;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad11),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function mirrorPad2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend3.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}let outId=backend3.write(resVals,outShape,x.dtype);return{dataId:outId,shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2},nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl,nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs,cpuBackend=backend3;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=cpuBackend.data.get(boxes.dataId).values,scoresVals=cpuBackend.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[selectedIndices,validOutputs]}},nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl,nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs,cpuBackend=backend3;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=cpuBackend.data.get(boxes.dataId).values,scoresVals=cpuBackend.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[selectedIndices,selectedScores]}};function padV2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend3.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let coords2=util_exports.indexToLoc(i,xRank,xStrides),outCoords=coords2.map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}let outId=backend3.write(resVals,outShape,x.dtype);return{dataId:outId,shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2},reciprocal4=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal4},rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend3,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}let dataId=cpuBackend.write(output,image3.shape,image3.dtype);return{dataId,shape:image3.shape,dtype:image3.dtype}}},round4=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2===0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round4},scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu5=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu5},sigmoid7=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid7},sign4=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign4},sin5=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin5},sinh5=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh5},epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus5=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus5};function transpose18(args){let{inputs,attrs,backend:backend3}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend3.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape),dataId=backend3.write(result,newShape,x.dtype);return{dataId,shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose18};function spaceToBatchND2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod52=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend3,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod52,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod52,!1),reshapeInputs={x:paddedX},reshapeAttrs={shape:reshapedPaddedShape},paddedXReshaped=reshape88({inputs:reshapeInputs,backend:backend3,attrs:reshapeAttrs}),transposeInputs={x:paddedXReshaped},transposeAttrs={perm:permutedReshapedPaddedPermutation},paddedXT=transpose18({inputs:transposeInputs,backend:backend3,attrs:transposeAttrs}),resultReshapeInputs={x:paddedXT},resultReshapeAttrs={shape:flattenShape},result=reshape88({inputs:resultReshapeInputs,backend:backend3,attrs:resultReshapeAttrs});return backend3.disposeIntermediateTensorInfo(paddedX),backend3.disposeIntermediateTensorInfo(paddedXReshaped),backend3.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2},sqrt13=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt13},squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend3})=>{let{x}=inputs,cpuBackend=backend3;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}let dataId=cpuBackend.write(newValues,x.shape,x.dtype);return{dataId,shape:x.shape,dtype:x.dtype}}},step8=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step8},tan4=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan4},tanh6=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh6};function unique6(args){let{inputs,attrs,backend:backend3}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend3.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend3.makeTensorInfo(outputShape,x.dtype,outputValues),backend3.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique6},kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,asinConfig,asinhConfig,atanConfig,atanhConfig,avgPoolConfig,avgPoolBackpropConfig,batchMatMulConfig,batchNormConfig,castConfig,ceilConfig,clipConfig,complexConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,divConfig,eluConfig,erfConfig,expConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,logConfig,log1pConfig,logicalNotConfig,maxPoolConfig,maxPoolBackpropConfig,maxPoolWithArgmaxConfig,maxConfig,mirrorPadConfig,multiplyConfig,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,padV2Config,preluConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softplusConfig,spaceToBatchNDConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,subConfig,tanConfig,tanhConfig,transposeConfig,uniqueConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
+`),pad11=shaderLines.length.toString().length+2,linesWithLineNumbers=shaderLines.map((line,lineNumber2)=>util_exports.rightPad((lineNumber2+1).toString(),pad11)+line),maxLineLength=0;for(let i=0;i<linesWithLineNumbers.length;i++)maxLineLength=Math.max(linesWithLineNumbers[i].length,maxLineLength);let beforeErrorLines=linesWithLineNumbers.slice(0,lineNumber-1),errorLine=linesWithLineNumbers.slice(lineNumber-1,lineNumber),afterErrorLines=linesWithLineNumbers.slice(lineNumber);console.log(beforeErrorLines.join(`
+`)),console.log(shaderInfoLog.split(`
+`)[0]),console.log(`%c ${util_exports.rightPad(errorLine[0],maxLineLength)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(afterErrorLines.join(`
+`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer11=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer11}function createStaticIndexBuffer(gl,data){let buffer11=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer11}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max102=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max102+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer11,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1],isScalar=shape.length===0||shape.length===1&&shape[0]===1;return isScalar||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2){let squeezeResult=util_exports.squeezeShape(logShape);logShape=squeezeResult.newShape}let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2===0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2))return!0;if(!shape1.length||!shape2.length)return!0;if(shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols)return!0;if(isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){let ext=gl.getExtension(extensionName);return ext!=null}function isWebGLVersionEnabled(webGLVersion){try{let gl=getWebGLContext(webGLVersion);if(gl!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;let isFrameBufferComplete=createFloatTextureAndBindToFramebuffer(gl);return isFrameBufferComplete}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1;if(!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}let isFrameBufferComplete=createFloatTextureAndBindToFramebuffer(gl);return isFrameBufferComplete}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){if(webGLVersion!==2)return!1;let gl=getWebGLContext(webGLVersion),isEnabled=gl.fenceSync!=null;return isEnabled}function assertNotComplex2(tensor168,opName){Array.isArray(tensor168)||(tensor168=[tensor168]),tensor168.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{let useUniforms=ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return useUniforms?4:0});ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});var{simpleAbsImpl:simpleAbsImplCPU,addImpl:addImplCPU,ceilImpl:ceilImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,multiplyImpl:multiplyImplCPU,rsqrtImpl:rsqrtImplCPU,sliceImpl:sliceImplCPU,subImpl:subImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports,AddNProgram=class{constructor(outputShape,shapes){this.outputShape=[],this.outputShape=outputShape,this.variableNames=shapes.map((_,i)=>`T${i}`);let snippets=[];this.variableNames.forEach(variable32=>{snippets.push(`float v${variable32} = get${variable32}AtOutCoords();`)});let operation211=this.variableNames.map(variable32=>`v${variable32}`).join(" + ");this.userCode=`
       void main() {
-        ${r.join(`
+        ${snippets.join(`
         `)}
 
-        float result = ${o};
+        float result = ${operation211};
         setOutput(result);
       }
-    `}}class KX{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,c)=>`T${c}`);let r=[];this.variableNames.forEach(s=>{r.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
+    `}},AddNPackedProgram=class{constructor(outputShape,shapes){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape,this.variableNames=shapes.map((_,i)=>`T${i}`);let snippets=[];this.variableNames.forEach(variable32=>{snippets.push(`vec4 v${variable32} = get${variable32}AtOutCoords();`)});let operation211=this.variableNames.map(variable32=>`v${variable32}`).join(" + ");this.userCode=`
       void main() {
-        ${r.join(`
+        ${snippets.join(`
         `)}
 
-        vec4 result = ${o};
+        vec4 result = ${operation211};
         setOutput(result);
       }
-    `}}class XX{constructor(t,e,r){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:c}=t;r||this.variableNames.push("bestIndicesA"),this.outputShape=[s,c];let l=e==="max"?">":"<",p=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}},ArgMinMaxProgram=class{constructor(reduceInfo,op22,firstPass){this.variableNames=["A"];let{windowSize,batchSize,outSize}=reduceInfo;firstPass||this.variableNames.push("bestIndicesA"),this.outputShape=[batchSize,outSize];let compOp=op22==="max"?">":"<",indexSnippet=firstPass?"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 * ${o};
+        int inOffset = outIdx * ${windowSize};
 
         int bestIndex = inOffset;
         float bestValue = getA(batch, bestIndex);
 
-        for (int i = 0; i < ${o}; i++) {
-          int inIdx = ${p};
+        for (int i = 0; i < ${windowSize}; i++) {
+          int inIdx = ${indexSnippet};
           float candidate = getA(batch, inIdx);
-          if (candidate ${l} bestValue) {
+          if (candidate ${compOp} bestValue) {
             bestValue = candidate;
             bestIndex = inIdx;
           }
         }
         setOutput(float(bestIndex));
       }
-    `}}function EI(n,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${n}.${e}`)}function Jn(n,t){return t===1?[n]:EI(n,t)}function YX(n,t){if(n===1)return"rc";let e="";for(let r=0;r<n;r++)e+=t[r],r<n-1&&(e+=",");return e}function Zn(){let n,t,e,r,o,s,c,l,p,f;return ct().getNumber("WEBGL_VERSION")===2?(n="#version 300 es",t="in",e="out",r="in",o="texture",s="outputColor",c="out vec4 outputColor;",l=`
+    `}};function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}function getGlslDifferences(){let version19,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version19="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -80,7 +87,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       }
 
       #define isnan(value) isnan_custom(value)
-    `,p="",f=`
+    `,defineSpecialInf="",defineRound=`
       #define round(value) newRound(value)
       int newRound(float value) {
         return int(floor(value + 0.5));
@@ -89,7 +96,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 newRound(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `):(n="",t="attribute",e="varying",r="varying",o="texture2D",s="gl_FragColor",c="",l=`
+    `):(version19="",attribute="attribute",varyingVs="varying",varyingFs="varying",texture2D="texture2D",output="gl_FragColor",defineOutput="",defineSpecialNaN=`
       #define isnan(value) isnan_custom(value)
       bool isnan_custom(float val) {
         return (val > 0. || val < 1. || val == 0.) ? false : true;
@@ -97,7 +104,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       bvec4 isnan_custom(vec4 val) {
         return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
       }
-    `,p=`
+    `,defineSpecialInf=`
       uniform float INFINITY;
 
       bool isinf(float val) {
@@ -106,7 +113,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       bvec4 isinf(vec4 val) {
         return equal(abs(val), vec4(INFINITY));
       }
-    `,f=`
+    `,defineRound=`
       int round(float value) {
         return int(floor(value + 0.5));
       }
@@ -114,11 +121,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:n,attribute:t,varyingVs:e,varyingFs:r,texture2D:o,output:s,defineOutput:c,defineSpecialNaN:l,defineSpecialInf:p,defineRound:f}}function _a(n,t,e="index"){let r=Jt(t);return r.map((o,s)=>{let c=`int ${n[s]} = ${e} / ${o}`,l=s===r.length-1?`int ${n[s+1]} = ${e} - ${n[s]} * ${o}`:`index -= ${n[s]} * ${o}`;return`${c}; ${l};`}).join("")}function eg(n){return n.length===1?`${n[0]}`:`vec${n.length}(${n.join(",")})`}function Jst(n,t){if(n.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${n.length} and ${t.length}`);let e=[],r=Math.floor(n.length/4),o=n.length%4;for(let s=0;s<r;s++){let c=n.slice(s*4,s*4+4),l=t.slice(s*4,s*4+4);e.push(`${eg(c)}, ${eg(l)}`)}if(o!==0){let s=n.slice(r*4),c=t.slice(r*4);s.length===1&&(s=s.map(l=>`float(${l})`),c=c.map(l=>`float(${l})`)),e.push(`${eg(s)}, ${eg(c)}`)}return e.map((s,c)=>`dot(${s})`).join("+")}function P1(n){let t=Jt(n).map(e=>e.toString());return`
+    `),{version:version19,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound}}function getLogicalCoordinatesFromFlatIndex(coords2,shape,index="index"){let strides=util_exports.computeStrides(shape);return strides.map((stride,i)=>{let line1=`int ${coords2[i]} = ${index} / ${stride}`,line2=i===strides.length-1?`int ${coords2[i+1]} = ${index} - ${coords2[i]} * ${stride}`:`index -= ${coords2[i]} * ${stride}`;return`${line1}; ${line2};`}).join("")}function getFlatIndexFrom3D(shape){let strides=util_exports.computeStrides(shape).map(d=>d.toString());return`
   int getFlatIndex(ivec3 coords) {
-    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
+    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
   }
-`}let DI=`
+`}var ENCODE_FLOAT_SNIPPET=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -157,27 +164,27 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`;let{getBroadcastDims:AI}=vv;function JX(n,t,e,r){let o=[];n.forEach(T=>{let N=G(T.shapeInfo.logicalShape);T.shapeInfo.isUniform?o.push(`uniform float ${T.name}${N>1?`[${N}]`:""};`):(o.push(`uniform sampler2D ${T.name};`),o.push(`uniform int offset${T.name};`))});let s=o.join(`
-`),c=n.map(T=>ZX(T,t,r)).join(`
-`),l=t.texShape,p=Zn(),f=eY(p),m,y,b=oY(p);t.isPacked?(m=QX(t.logicalShape,l),y=rY(p)):(m=tY(t.logicalShape,l),y=nY(p)),r&&(b+=cY);let v=[b,f,y,s,m,c,e].join(`
-`);return v}function gl(n){let t=n.shapeInfo.logicalShape;switch(t.length){case 0:return wY(n);case 1:return TY(n);case 2:return NY(n);case 3:return CY(n);case 4:return $Y(n);case 5:return IY(n);case 6:return EY(n);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function FI(n){let t=n.shapeInfo.logicalShape;switch(t.length){case 0:return xY(n);case 1:return vY(n);case 2:return kY(n);case 3:return _Y(n);default:return SY(n)}}function ZX(n,t,e=!1){let r="";e?r+=FI(n):r+=gl(n);let o=n.shapeInfo.logicalShape,s=t.logicalShape;return o.length<=s.length&&(e?r+=DY(n,t):r+=AY(n,t)),r}function QX(n,t){switch(n.length){case 0:return RI();case 1:return lY(n,t);case 2:return yY(n,t);case 3:return pY(n,t);default:return fY(n,t)}}function tY(n,t){switch(n.length){case 0:return RI();case 1:return uY(n,t);case 2:return bY(n,t);case 3:return hY(n,t);case 4:return dY(n,t);case 5:return mY(n,t);case 6:return gY(n,t);default:throw new Error(`${n.length}-D output sampling is not yet supported`)}}function eY(n){return`
+`,{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
+`),inputSamplingSnippet=inputsInfo.map(x=>getInputSamplingSnippet(x,outputShape,usesPackedTextures)).join(`
+`),outTexShape=outputShape.texShape,glsl=getGlslDifferences(),floatTextureSampleSnippet=getFloatTextureSampleSnippet(glsl),outputSamplingSnippet,floatTextureSetOutputSnippet,shaderPrefix=getShaderPrefix(glsl);outputShape.isPacked?(outputSamplingSnippet=getPackedOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRGBASnippet(glsl)):(outputSamplingSnippet=getOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRSnippet(glsl)),usesPackedTextures&&(shaderPrefix+=SHADER_PACKED_PREFIX);let source=[shaderPrefix,floatTextureSampleSnippet,floatTextureSetOutputSnippet,inputPrefixSnippet,outputSamplingSnippet,inputSamplingSnippet,userCode].join(`
+`);return source}function getSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getSamplerScalar(inInfo);case 1:return getSampler1D(inInfo);case 2:return getSampler2D(inInfo);case 3:return getSampler3D(inInfo);case 4:return getSampler4D(inInfo);case 5:return getSampler5D(inInfo);case 6:return getSampler6D(inInfo);default:throw new Error(`${shape.length}-D input sampling is not yet supported`)}}function getPackedSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getPackedSamplerScalar(inInfo);case 1:return getPackedSampler1D(inInfo);case 2:return getPackedSampler2D(inInfo);case 3:return getPackedSampler3D(inInfo);default:return getPackedSamplerND(inInfo)}}function getInputSamplingSnippet(inInfo,outShapeInfo,usesPackedTextures=!1){let res="";usesPackedTextures?res+=getPackedSamplerFromInInfo(inInfo):res+=getSamplerFromInInfo(inInfo);let inShape=inInfo.shapeInfo.logicalShape,outShape=outShapeInfo.logicalShape;return inShape.length<=outShape.length&&(usesPackedTextures?res+=getPackedSamplerAtOutputCoords(inInfo,outShapeInfo):res+=getSamplerAtOutputCoords(inInfo,outShapeInfo)),res}function getPackedOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(outShape,outTexShape);case 2:return getOutputPacked2DCoords(outShape,outTexShape);case 3:return getOutputPacked3DCoords(outShape,outTexShape);default:return getOutputPackedNDCoords(outShape,outTexShape)}}function getOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(outShape,outTexShape);case 2:return getOutput2DCoords(outShape,outTexShape);case 3:return getOutput3DCoords(outShape,outTexShape);case 4:return getOutput4DCoords(outShape,outTexShape);case 5:return getOutput5DCoords(outShape,outTexShape);case 6:return getOutput6DCoords(outShape,outTexShape);default:throw new Error(`${outShape.length}-D output sampling is not yet supported`)}}function getFloatTextureSampleSnippet(glsl){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
-      return ${n.texture2D}(textureSampler, uv).r;
+      return ${glsl.texture2D}(textureSampler, uv).r;
     }
-  `}function nY(n){return`
+  `}function getFloatTextureSetRSnippet(glsl){return`
     void setOutput(float val) {
-      ${n.output} = vec4(val, 0, 0, 0);
+      ${glsl.output} = vec4(val, 0, 0, 0);
     }
-  `}function rY(n){return`
+  `}function getFloatTextureSetRGBASnippet(glsl){return`
     void setOutput(vec4 val) {
-      ${n.output} = val;
+      ${glsl.output} = val;
     }
-  `}function oY(n){let t=`${n.version}
+  `}function getShaderPrefix(glsl){let SHADER_PREFIX=`${glsl.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
-    ${n.varyingFs} vec2 resultUV;
-    ${n.defineOutput}
+    ${glsl.varyingFs} vec2 resultUV;
+    ${glsl.defineOutput}
     const vec2 halfCR = vec2(0.5, 0.5);
 
     struct ivec5
@@ -200,9 +207,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     };
 
     uniform float NAN;
-    ${n.defineSpecialNaN}
-    ${n.defineSpecialInf}
-    ${n.defineRound}
+    ${glsl.defineSpecialNaN}
+    ${glsl.defineSpecialInf}
+    ${glsl.defineRound}
 
     int imod(int x, int y) {
       return x - y * (x / y);
@@ -227,10 +234,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${sY}
-    ${iY}
-    ${aY}
-  `;return t}let sY=`
+    ${SAMPLE_1D_SNIPPET}
+    ${SAMPLE_2D_SNIPPET}
+    ${SAMPLE_3D_SNIPPET}
+  `;return SHADER_PREFIX}var SAMPLE_1D_SNIPPET=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -242,7 +249,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,iY=`
+`,SAMPLE_2D_SNIPPET=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -250,7 +257,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,aY=`
+`,SAMPLE_3D_SNIPPET=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -259,7 +266,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,cY=`
+`,SHADER_PACKED_PREFIX=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -270,503 +277,503 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function RI(){return`
+`;function getOutputScalarCoords(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function lY(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return e[0]===1?`
+  `}function getOutputPacked1DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];return packedTexShape[0]===1?`
       int getOutputCoords() {
-        return 2 * int(resultUV.x * ${e[1]}.0);
+        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
       }
-    `:e[1]===1?`
+    `:packedTexShape[1]===1?`
       int getOutputCoords() {
-        return 2 * int(resultUV.y * ${e[0]}.0);
+        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
       }
     `:`
     int getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
-      return 2 * (resTexRC.x * ${e[1]} + resTexRC.y);
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
     }
-  `}function uY(n,t){return t[0]===1?`
+  `}function getOutput1DCoords(shape,texShape){return texShape[0]===1?`
       int getOutputCoords() {
-        return int(resultUV.x * ${t[1]}.0);
+        return int(resultUV.x * ${texShape[1]}.0);
       }
-    `:t[1]===1?`
+    `:texShape[1]===1?`
       int getOutputCoords() {
-        return int(resultUV.y * ${t[0]}.0);
+        return int(resultUV.y * ${texShape[0]}.0);
       }
     `:`
     int getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${t[0]}, ${t[1]}));
-      return resTexRC.x * ${t[1]} + resTexRC.y;
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      return resTexRC.x * ${texShape[1]} + resTexRC.y;
     }
-  `}function pY(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[2]/2),o=r*Math.ceil(n[1]/2);return`
+  `}function getOutputPacked3DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texelsInLogicalRow=Math.ceil(shape[2]/2),texelsInBatch=texelsInLogicalRow*Math.ceil(shape[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
 
-      int b = index / ${o};
-      index -= b * ${o};
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
 
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
 
       return ivec3(b, r, c);
     }
-  `}function hY(n,t){let e=_a(["r","c","d"],n);return`
+  `}function getOutput3DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      ${e}
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
       return ivec3(r, c, d);
     }
-  `}function fY(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[n.length-1]/2),o=r*Math.ceil(n[n.length-2]/2),s=o,c="",l="b, r, c";for(let p=2;p<n.length-1;p++)s*=n[n.length-p-1],c=`
-      int b${p} = index / ${s};
-      index -= b${p} * ${s};
-    `+c,l=`b${p}, `+l;return`
-    ivec${n.length} getOutputCoords() {
+  `}function getOutputPackedNDCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texelsInLogicalRow=Math.ceil(shape[shape.length-1]/2),texelsInBatch=texelsInLogicalRow*Math.ceil(shape[shape.length-2]/2),texelsInBatchN=texelsInBatch,batches="",coords2="b, r, c";for(let b=2;b<shape.length-1;b++)texelsInBatchN*=shape[shape.length-b-1],batches=`
+      int b${b} = index / ${texelsInBatchN};
+      index -= b${b} * ${texelsInBatchN};
+    `+batches,coords2=`b${b}, `+coords2;return`
+    ivec${shape.length} getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
 
-      ${c}
+      ${batches}
 
-      int b = index / ${o};
-      index -= b * ${o};
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
 
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
 
-      return ivec${n.length}(${l});
+      return ivec${shape.length}(${coords2});
     }
-  `}function dY(n,t){let e=_a(["r","c","d","d2"],n);return`
+  `}function getOutput4DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2"],shape);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      ${e}
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
       return ivec4(r, c, d, d2);
     }
-  `}function mY(n,t){let e=_a(["r","c","d","d2","d3"],n);return`
+  `}function getOutput5DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],shape);return`
     ivec5 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
-                             ${t[1]}));
+      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
+                             ${texShape[1]}));
 
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
 
-      ${e}
+      ${coordsFromIndexSnippet}
 
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function gY(n,t){let e=_a(["r","c","d","d2","d3","d4"],n);return`
+  `}function getOutput6DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],shape);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
 
-      ${e}
+      ${coordsFromIndexSnippet}
 
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function yY(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(lt(n,t))return`
+  `}function getOutputPacked2DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];if(util_exports.arraysEqual(shape,texShape))return`
       ivec2 getOutputCoords() {
-        return 2 * ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
+        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
       }
-    `;let r=Math.ceil(n[1]/2);return`
+    `;let texelsInLogicalRow=Math.ceil(shape[1]/2);return`
     ivec2 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
 
-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
 
       return ivec2(r, c);
     }
-  `}function bY(n,t){return lt(n,t)?`
+  `}function getOutput2DCoords(shape,texShape){return util_exports.arraysEqual(shape,texShape)?`
       ivec2 getOutputCoords() {
-        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
+        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
       }
-    `:n[1]===1?`
+    `:shape[1]===1?`
       ivec2 getOutputCoords() {
         ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(${t[0]}, ${t[1]}));
-        int index = resTexRC.x * ${t[1]} + resTexRC.y;
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
         return ivec2(index, 0);
       }
-    `:n[0]===1?`
+    `:shape[0]===1?`
       ivec2 getOutputCoords() {
         ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(${t[0]}, ${t[1]}));
-        int index = resTexRC.x * ${t[1]} + resTexRC.y;
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
         return ivec2(0, index);
       }
     `:`
     ivec2 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      int r = index / ${n[1]};
-      int c = index - r * ${n[1]};
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      int r = index / ${shape[1]};
+      int c = index - r * ${shape[1]};
       return ivec2(r, c);
     }
-  `}function Ca(n){return`offset${n}`}function xY(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=Zn();return`
-    vec4 ${e}() {
-      return ${r.texture2D}(${t}, halfCR);
+  `}function getFlatOffsetUniformName(texName){return`offset${texName}`}function getPackedSamplerScalar(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),glsl=getGlslDifferences();return`
+    vec4 ${funcName}() {
+      return ${glsl.texture2D}(${texName}, halfCR);
     }
-  `}function wY(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`float ${e}() {return ${t};}`;let[r,o]=n.shapeInfo.texShape;if(r===1&&o===1)return`
-      float ${e}() {
-        return sampleTexture(${t}, halfCR);
+  `}function getSamplerScalar(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1);if(inputInfo.shapeInfo.isUniform)return`float ${funcName}() {return ${texName};}`;let[texNumR,texNumC]=inputInfo.shapeInfo.texShape;if(texNumR===1&&texNumC===1)return`
+      float ${funcName}() {
+        return sampleTexture(${texName}, halfCR);
       }
-    `;let[s,c]=n.shapeInfo.texShape,l=Ca(t);return`
-    float ${e}() {
-      vec2 uv = uvFromFlat(${s}, ${c}, ${l});
-      return sampleTexture(${t}, uv);
+    `;let[tNumR,tNumC]=inputInfo.shapeInfo.texShape,offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}() {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
+      return sampleTexture(${texName}, uv);
     }
-  `}function vY(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=n.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=Zn();return`
-    vec4 ${e}(int index) {
+  `}function getPackedSampler1D(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],glsl=getGlslDifferences();return`
+    vec4 ${funcName}(int index) {
       vec2 uv = packedUVfrom1D(
-        ${o[0]}, ${o[1]}, index);
-      return ${s.texture2D}(${t}, uv);
+        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
+      return ${glsl.texture2D}(${texName}, uv);
     }
-  `}function TY(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`
-      float ${e}(int index) {
-        ${yl(n)}
+  `}function getSampler1D(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1);if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int index) {
+        ${getUniformSampler(inputInfo)}
       }
-    `;let r=n.shapeInfo.texShape,o=r[0],s=r[1];if(s===1&&o===1)return`
-      float ${e}(int index) {
-        return sampleTexture(${t}, halfCR);
+    `;let texShape=inputInfo.shapeInfo.texShape,tNumR=texShape[0],tNumC=texShape[1];if(tNumC===1&&tNumR===1)return`
+      float ${funcName}(int index) {
+        return sampleTexture(${texName}, halfCR);
       }
-    `;let c=Ca(t);return s===1?`
-      float ${e}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${c}) + 0.5) / ${o}.0);
-        return sampleTexture(${t}, uv);
+    `;let offset=getFlatOffsetUniformName(texName);return tNumC===1?`
+      float ${funcName}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `:o===1?`
-      float ${e}(int index) {
-        vec2 uv = vec2((float(index + ${c}) + 0.5) / ${s}.0, 0.5);
-        return sampleTexture(${t}, uv);
+    `:tNumR===1?`
+      float ${funcName}(int index) {
+        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
+        return sampleTexture(${texName}, uv);
       }
     `:`
-    float ${e}(int index) {
-      vec2 uv = uvFromFlat(${o}, ${s}, index + ${c});
-      return sampleTexture(${t}, uv);
+    float ${funcName}(int index) {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
     }
-  `}function kY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape,s=o[0],c=o[1],l=Zn();if(o!=null&&lt(t,o))return`
-      vec4 ${r}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${s}.0);
+  `}function getPackedSampler2D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1],glsl=getGlslDifferences();if(texShape!=null&&util_exports.arraysEqual(shape,texShape))return`
+      vec4 ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
 
-        return ${l.texture2D}(${e}, uv);
+        return ${glsl.texture2D}(${texName}, uv);
       }
-    `;let p=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],f=Math.ceil(t[1]/2);return`
-    vec4 ${r}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${f}, ${p[0]}, ${p[1]}, row, col);
-      return ${l.texture2D}(${e}, uv);
+    `;let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],valuesPerRow=Math.ceil(shape[1]/2);return`
+    vec4 ${funcName}(int row, int col) {
+      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
     }
-  `}function NY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape;if(o!=null&&lt(t,o)){let y=o[0],b=o[1];return`
-    float ${r}(int row, int col) {
-      vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
-      return sampleTexture(${e}, uv);
+  `}function getSampler2D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape;if(texShape!=null&&util_exports.arraysEqual(shape,texShape)){let texNumR2=texShape[0],texNumC2=texShape[1];return`
+    float ${funcName}(int row, int col) {
+      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);
+      return sampleTexture(${texName}, uv);
     }
-  `}let{newShape:s,keptDims:c}=ln(t),l=s;if(l.length<t.length){let y=bl(n,l),b=["row","col"];return`
-      ${gl(y)}
-      float ${r}(int row, int col) {
-        return ${r}(${xl(b,c)});
+  `}let{newShape,keptDims}=util_exports.squeezeShape(shape),squeezedShape=newShape;if(squeezedShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["row","col"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
       }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col) {
-        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
-        ${yl(n)}
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col) {
+        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
+        ${getUniformSampler(inputInfo)}
       }
-    `;let p=o[0],f=o[1],m=Ca(e);return f===1?`
-    float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2(0.5, (index + 0.5) / ${p}.0);
-      return sampleTexture(${e}, uv);
+    `;let texNumR=texShape[0],texNumC=texShape[1],offset=getFlatOffsetUniformName(texName);return texNumC===1?`
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
     }
-  `:p===1?`
-    float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2((index + 0.5) / ${f}.0, 0.5);
-      return sampleTexture(${e}, uv);
+  `:texNumR===1?`
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
+      return sampleTexture(${texName}, uv);
     }
   `:`
-  float ${r}(int row, int col) {
+  float ${funcName}(int row, int col) {
     // Explicitly use integer operations as dot() only works on floats.
-    int index = row * ${t[1]} + col + ${m};
-    vec2 uv = uvFromFlat(${p}, ${f}, index);
-    return sampleTexture(${e}, uv);
+    int index = row * ${shape[1]} + col + ${offset};
+    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+    return sampleTexture(${texName}, uv);
   }
-`}function _Y(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape,s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(t[0]===1){let y=t.slice(1),b=[1,2],v=bl(n,y),T=["b","row","col"];return`
-        ${FI(v)}
-        vec4 ${r}(int b, int row, int col) {
-          return ${r}(${xl(T,b)});
+`}function getPackedSampler3D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];if(shape[0]===1){let squeezedShape=shape.slice(1),keptDims=[1,2],newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["b","row","col"];return`
+        ${getPackedSamplerFromInInfo(newInputInfo)}
+        vec4 ${funcName}(int b, int row, int col) {
+          return ${funcName}(${getSqueezedParams(params,keptDims)});
         }
-      `}let c=s[0],l=s[1],p=Math.ceil(t[2]/2),f=p*Math.ceil(t[1]/2),m=Zn();return`
-    vec4 ${r}(int b, int row, int col) {
+      `}let texNumR=packedTexShape[0],texNumC=packedTexShape[1],valuesPerRow=Math.ceil(shape[2]/2),texelsInBatch=valuesPerRow*Math.ceil(shape[1]/2),glsl=getGlslDifferences();return`
+    vec4 ${funcName}(int b, int row, int col) {
       vec2 uv = packedUVfrom3D(
-        ${c}, ${l}, ${f}, ${p}, b, row, col);
-      return ${m.texture2D}(${e}, uv);
+        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
     }
-  `}function CY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[1]*t[2],s=t[2],{newShape:c,keptDims:l}=ln(t),p=c;if(p.length<t.length){let T=bl(n,p),N=["row","col","depth"];return`
-        ${gl(T)}
-        float ${r}(int row, int col, int depth) {
-          return ${r}(${xl(N,l)});
+  `}function getSampler3D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride0=shape[1]*shape[2],stride1=shape[2],{newShape,keptDims}=util_exports.squeezeShape(shape),squeezedShape=newShape;if(squeezedShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["row","col","depth"];return`
+        ${getSamplerFromInInfo(newInputInfo)}
+        float ${funcName}(int row, int col, int depth) {
+          return ${funcName}(${getSqueezedParams(params,keptDims)});
         }
-      `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth) {
+      `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
-                          vec3(${o}, ${s}, 1)));
-        ${yl(n)}
+                          vec3(${stride0}, ${stride1}, 1)));
+        ${getUniformSampler(inputInfo)}
       }
-    `;let f=n.shapeInfo.texShape,m=f[0],y=f[1],b=n.shapeInfo.flatOffset;if(y===o&&b==null)return`
-        float ${r}(int row, int col, int depth) {
+    `;let texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1],flatOffset=inputInfo.shapeInfo.flatOffset;if(texNumC===stride0&&flatOffset==null)return`
+        float ${funcName}(int row, int col, int depth) {
           float texR = float(row);
-          float texC = dot(vec2(col, depth), vec2(${s}, 1));
+          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
           vec2 uv = (vec2(texC, texR) + halfCR) /
-                     vec2(${y}.0, ${m}.0);
-          return sampleTexture(${e}, uv);
+                     vec2(${texNumC}.0, ${texNumR}.0);
+          return sampleTexture(${texName}, uv);
         }
-      `;if(y===s&&b==null)return`
-    float ${r}(int row, int col, int depth) {
-      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
+      `;if(texNumC===stride1&&flatOffset==null)return`
+    float ${funcName}(int row, int col, int depth) {
+      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
       float texC = float(depth);
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}.0, ${m}.0);
-      return sampleTexture(${e}, uv);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
     }
-  `;let v=Ca(e);return`
-      float ${r}(int row, int col, int depth) {
+  `;let offset=getFlatOffsetUniformName(texName);return`
+      float ${funcName}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
-        int index = row * ${o} + col * ${s} + depth + ${v};
-        vec2 uv = uvFromFlat(${m}, ${y}, index);
-        return sampleTexture(${e}, uv);
+        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
+        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+        return sampleTexture(${texName}, uv);
       }
-  `}function SY(n){let t=n.shapeInfo.logicalShape,e=t.length,r=n.name,o="get"+r.charAt(0).toUpperCase()+r.slice(1),s=n.shapeInfo.texShape,c=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],l=c[0],p=c[1],f=Math.ceil(t[e-1]/2),m=f*Math.ceil(t[e-2]/2),y="int b, int row, int col",b=`b * ${m} + (row / 2) * ${f} + (col / 2)`;for(let T=2;T<e-1;T++)y=`int b${T}, `+y,m*=t[e-T-1],b=`b${T} * ${m} + `+b;let v=Zn();return`
-    vec4 ${o}(${y}) {
-      int index = ${b};
-      int texR = index / ${p};
-      int texC = index - texR * ${p};
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${l});
-      return ${v.texture2D}(${r}, uv);
+  `}function getPackedSamplerND(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,rank=shape.length,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texNumR=packedTexShape[0],texNumC=packedTexShape[1],valuesPerRow=Math.ceil(shape[rank-1]/2),texelsInBatch=valuesPerRow*Math.ceil(shape[rank-2]/2),params="int b, int row, int col",index=`b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;for(let b=2;b<rank-1;b++)params=`int b${b}, `+params,texelsInBatch*=shape[rank-b-1],index=`b${b} * ${texelsInBatch} + `+index;let glsl=getGlslDifferences();return`
+    vec4 ${funcName}(${params}) {
+      int index = ${index};
+      int texR = index / ${texNumC};
+      int texC = index - texR * ${texNumC};
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
+      return ${glsl.texture2D}(${texName}, uv);
     }
-  `}function $Y(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[3],s=t[2]*o,c=t[1]*s,{newShape:l,keptDims:p}=ln(t);if(l.length<t.length){let T=bl(n,l),N=["row","col","depth","depth2"];return`
-      ${gl(T)}
-      float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${xl(N,p)});
+  `}function getSampler4D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride2=shape[3],stride1=shape[2]*stride2,stride0=shape[1]*stride1,{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
       }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth, int depth2) {
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
-                          vec4(${c}, ${s}, ${o}, 1)));
-        ${yl(n)}
+                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
+        ${getUniformSampler(inputInfo)}
       }
-    `;let f=n.shapeInfo.flatOffset,m=n.shapeInfo.texShape,y=m[0],b=m[1];if(b===c&&f==null)return`
-      float ${r}(int row, int col, int depth, int depth2) {
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
         float texR = float(row);
         float texC =
             dot(vec3(col, depth, depth2),
-                vec3(${s}, ${o}, 1));
+                vec3(${stride1}, ${stride2}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${b}.0, ${y}.0);
-        return sampleTexture(${e}, uv);
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;if(b===o&&f==null)return`
-      float ${r}(int row, int col, int depth, int depth2) {
+    `;if(texNumC===stride2&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
         float texR = dot(vec3(row, col, depth),
-                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
+                         vec3(${shape[1]*shape[2]}, ${shape[2]}, 1));
         float texC = float(depth2);
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${b}.0, ${y}.0);
-        return sampleTexture(${e}, uv);
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;let v=Ca(e);return`
-    float ${r}(int row, int col, int depth, int depth2) {
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${c} + col * ${s} +
-          depth * ${o} + depth2;
-      vec2 uv = uvFromFlat(${y}, ${b}, index + ${v});
-      return sampleTexture(${e}, uv);
+      int index = row * ${stride0} + col * ${stride1} +
+          depth * ${stride2} + depth2;
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
     }
-  `}function IY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,c=t[2]*s,l=t[1]*c,{newShape:p,keptDims:f}=ln(t);if(p.length<t.length){let N=bl(n,p),S=["row","col","depth","depth2","depth3"];return`
-      ${gl(N)}
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        return ${r}(${xl(S,f)});
+  `}function getSampler5D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride3=shape[4],stride2=shape[3]*stride3,stride1=shape[2]*stride2,stride0=shape[1]*stride1,{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2","depth3"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
       }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
         float index = dot(
           vec4(row, col, depth, depth2),
-          vec4(${l}, ${c}, ${s}, ${o})) +
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
           depth3;
-        ${yl(n)}
+        ${getUniformSampler(inputInfo)}
       }
-    `;let m=n.shapeInfo.flatOffset,y=n.shapeInfo.texShape,b=y[0],v=y[1];if(v===l&&m==null)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-                         vec4(${c}, ${s}, ${o}, 1));
+                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${v}.0, ${b}.0);
-        return sampleTexture(${e}, uv);
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;if(v===o&&m==null)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
+    `;if(texNumC===stride3&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
         float texR = dot(
           vec4(row, col, depth, depth2),
-          vec4(${t[1]*t[2]*t[3]},
-               ${t[2]*t[3]}, ${t[3]}, 1));
+          vec4(${shape[1]*shape[2]*shape[3]},
+               ${shape[2]*shape[3]}, ${shape[3]}, 1));
         int texC = depth3;
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${v}.0, ${b}.0);
-        return sampleTexture(${e}, uv);
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;let T=Ca(e);return`
-    float ${r}(int row, int col, int depth, int depth2, int depth3) {
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${l} + col * ${c} + depth * ${s} +
-          depth2 * ${o} + depth3 + ${T};
-      vec2 uv = uvFromFlat(${b}, ${v}, index);
-      return sampleTexture(${e}, uv);
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
     }
-  `}function EY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=ln(t);if(o.length<t.length){let S=bl(n,o),D=["row","col","depth","depth2","depth3","depth4"];return`
-      ${gl(S)}
-      float ${r}(int row, int col, int depth,
+  `}function getSampler6D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2","depth3","depth4"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${r}(${xl(D,s)});
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
       }
-    `}let c=t[5],l=t[4]*c,p=t[3]*l,f=t[2]*p,m=t[1]*f;if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth,
+    `}let stride4=shape[5],stride3=shape[4]*stride4,stride2=shape[3]*stride3,stride1=shape[2]*stride2,stride0=shape[1]*stride1;if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
         int index = round(dot(
           vec4(row, col, depth, depth2),
-          vec4(${m}, ${f}, ${p}, ${l})) +
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
           dot(
             vec2(depth3, depth4),
-            vec2(${c}, 1)));
-        ${yl(n)}
+            vec2(${stride4}, 1)));
+        ${getUniformSampler(inputInfo)}
       }
-    `;let y=n.shapeInfo.flatOffset,b=n.shapeInfo.texShape,v=b[0],T=b[1];if(T===m&&y==null)return`
-      float ${r}(int row, int col, int depth,
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${f}, ${p}, ${l}, ${c})) +
+          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
                float(depth4);
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${T}.0, ${v}.0);
-        return sampleTexture(${e}, uv);
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;if(T===c&&y==null)return`
-      float ${r}(int row, int col, int depth,
+    `;if(texNumC===stride4&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         float texR = dot(vec4(row, col, depth, depth2),
-          vec4(${t[1]*t[2]*t[3]*t[4]},
-               ${t[2]*t[3]*t[4]},
-               ${t[3]*t[4]},
-               ${t[4]})) + float(depth3);
+          vec4(${shape[1]*shape[2]*shape[3]*shape[4]},
+               ${shape[2]*shape[3]*shape[4]},
+               ${shape[3]*shape[4]},
+               ${shape[4]})) + float(depth3);
         int texC = depth4;
         vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${T}.0, ${v}.0);
-        return sampleTexture(${e}, uv);
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
       }
-    `;let N=Ca(e);return`
-    float ${r}(int row, int col, int depth,
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(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 * ${m} + col * ${f} + depth * ${p} +
-          depth2 * ${l} + depth3 * ${c} + depth4 + ${N};
-      vec2 uv = uvFromFlat(${v}, ${T}, index);
-      return sampleTexture(${e}, uv);
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
     }
-  `}function yl(n){let t=n.name,e=G(n.shapeInfo.logicalShape);return e<2?`return ${t};`:`
-    for (int i = 0; i < ${e}; i++) {
+  `}function getUniformSampler(inputInfo){let texName=inputInfo.name,inSize=util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);return inSize<2?`return ${texName};`:`
+    for (int i = 0; i < ${inSize}; i++) {
       if (i == index) {
-        return ${t}[i];
+        return ${texName}[i];
       }
     }
-  `}function DY(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),o="get"+r+"AtOutCoords",s=n.shapeInfo.logicalShape.length,c=t.logicalShape.length,l=AI(n.shapeInfo.logicalShape,t.logicalShape),p=Oe(c),f=c-s,m,y=["x","y","z","w","u","v"];s===0?m="":c<2&&l.length>=1?m="coords = 0;":m=l.map(I=>`coords.${y[I+f]} = 0;`).join(`
-`);let b="";c<2&&s>0?b="coords":b=n.shapeInfo.logicalShape.map((I,P)=>`coords.${y[P+f]}`).join(", ");let v="return outputValue;",T=G(n.shapeInfo.logicalShape),N=T===1,S=G(t.logicalShape),D=S===1;if(s===1&&!N&&!D)v=`
+  `}function getPackedSamplerAtOutputCoords(inputInfo,outShapeInfo){let texName=inputInfo.name,texFuncSnippet=texName.charAt(0).toUpperCase()+texName.slice(1),funcName="get"+texFuncSnippet+"AtOutCoords",inRank=inputInfo.shapeInfo.logicalShape.length,outRank=outShapeInfo.logicalShape.length,broadcastDims=getBroadcastDims2(inputInfo.shapeInfo.logicalShape,outShapeInfo.logicalShape),type=getCoordsDataType(outRank),rankDiff=outRank-inRank,coordsSnippet,fields=["x","y","z","w","u","v"];inRank===0?coordsSnippet="":outRank<2&&broadcastDims.length>=1?coordsSnippet="coords = 0;":coordsSnippet=broadcastDims.map(d=>`coords.${fields[d+rankDiff]} = 0;`).join(`
+`);let unpackedCoordsSnippet="";outRank<2&&inRank>0?unpackedCoordsSnippet="coords":unpackedCoordsSnippet=inputInfo.shapeInfo.logicalShape.map((s,i)=>`coords.${fields[i+rankDiff]}`).join(", ");let output="return outputValue;",inSize=util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape),isInputScalar=inSize===1,outSize=util_exports.sizeFromShape(outShapeInfo.logicalShape),isOutputScalar=outSize===1;if(inRank===1&&!isInputScalar&&!isOutputScalar)output=`
       return vec4(outputValue.xy, outputValue.xy);
-    `;else if(N&&!D)c===1?v=`
+    `;else if(isInputScalar&&!isOutputScalar)outRank===1?output=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
-      `:v=`
+      `:output=`
         return vec4(outputValue.x);
-      `;else if(l.length){let I=s-2,P=s-1;l.indexOf(I)>-1&&l.indexOf(P)>-1?v="return vec4(outputValue.x);":l.indexOf(I)>-1?v="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":l.indexOf(P)>-1&&(v="return vec4(outputValue.xx, outputValue.zz);")}return`
-    vec4 ${o}() {
-      ${p} coords = getOutputCoords();
-      ${m}
-      vec4 outputValue = get${r}(${b});
-      ${v}
+      `;else if(broadcastDims.length){let rows=inRank-2,cols=inRank-1;broadcastDims.indexOf(rows)>-1&&broadcastDims.indexOf(cols)>-1?output="return vec4(outputValue.x);":broadcastDims.indexOf(rows)>-1?output="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":broadcastDims.indexOf(cols)>-1&&(output="return vec4(outputValue.xx, outputValue.zz);")}return`
+    vec4 ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
+      ${output}
     }
-  `}function AY(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),o="get"+r+"AtOutCoords",s=t.texShape,c=n.shapeInfo.texShape,l=n.shapeInfo.logicalShape.length,p=t.logicalShape.length;if(!n.shapeInfo.isUniform&&l===p&&n.shapeInfo.flatOffset==null&&lt(c,s))return`
-      float ${o}() {
-        return sampleTexture(${e}, resultUV);
+  `}function getSamplerAtOutputCoords(inputInfo,outShapeInfo){let texName=inputInfo.name,texFuncSnippet=texName.charAt(0).toUpperCase()+texName.slice(1),funcName="get"+texFuncSnippet+"AtOutCoords",outTexShape=outShapeInfo.texShape,inTexShape=inputInfo.shapeInfo.texShape,inRank=inputInfo.shapeInfo.logicalShape.length,outRank=outShapeInfo.logicalShape.length;if(!inputInfo.shapeInfo.isUniform&&inRank===outRank&&inputInfo.shapeInfo.flatOffset==null&&util_exports.arraysEqual(inTexShape,outTexShape))return`
+      float ${funcName}() {
+        return sampleTexture(${texName}, resultUV);
       }
-    `;let f=Oe(p),m=AI(n.shapeInfo.logicalShape,t.logicalShape),y=p-l,b,v=["x","y","z","w","u","v"];l===0?b="":p<2&&m.length>=1?b="coords = 0;":b=m.map(N=>`coords.${v[N+y]} = 0;`).join(`
-`);let T="";return p<2&&l>0?T="coords":T=n.shapeInfo.logicalShape.map((N,S)=>`coords.${v[S+y]}`).join(", "),`
-    float ${o}() {
-      ${f} coords = getOutputCoords();
-      ${b}
-      return get${r}(${T});
+    `;let type=getCoordsDataType(outRank),broadcastDims=getBroadcastDims2(inputInfo.shapeInfo.logicalShape,outShapeInfo.logicalShape),rankDiff=outRank-inRank,coordsSnippet,fields=["x","y","z","w","u","v"];inRank===0?coordsSnippet="":outRank<2&&broadcastDims.length>=1?coordsSnippet="coords = 0;":coordsSnippet=broadcastDims.map(d=>`coords.${fields[d+rankDiff]} = 0;`).join(`
+`);let unpackedCoordsSnippet="";return outRank<2&&inRank>0?unpackedCoordsSnippet="coords":unpackedCoordsSnippet=inputInfo.shapeInfo.logicalShape.map((s,i)=>`coords.${fields[i+rankDiff]}`).join(", "),`
+    float ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      return get${texFuncSnippet}(${unpackedCoordsSnippet});
     }
-  `}function Oe(n){if(n<=1)return"int";if(n===2)return"ivec2";if(n===3)return"ivec3";if(n===4)return"ivec4";if(n===5)return"ivec5";if(n===6)return"ivec6";throw Error(`GPU for rank ${n} is not yet supported`)}function bl(n,t){let e=JSON.parse(JSON.stringify(n));return e.shapeInfo.logicalShape=t,e}function xl(n,t){return t.map(e=>n[e]).join(", ")}class FY{constructor(t,e,r,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,_(t.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],c=Math.ceil(s/e);this.outputShape=t.slice(0,-1),c>1&&this.outputShape.push(c),o||this.variableNames.push("bestIndicesA");let l=this.outputShape,p=l.length,f=Oe(p),m=Jn("coords",p),y,b;if(c===1){b=p+1;let H=Oe(b);y=`
-        ${H} sourceLocR = ${H}(${m.join()}, 0);
-        ++${m[p-1]};
-        ${H} sourceLocG = ${H}(${m.join()}, 0);
-        ++${m[p-2]};
-        ${H} sourceLocA = ${H}(${m.join()}, 0);
-        --${m[p-1]};
-        ${H} sourceLocB = ${H}(${m.join()}, 0);
-        --${m[p-2]};`}else b=p,y=`
-        ${f} sourceLocR = coords;
-        ++${m[p-1]};
-        ${f} sourceLocG = coords;
-        ++${m[p-2]};
-        ${f} sourceLocA = coords;
-        --${m[p-1]};
-        ${f} sourceLocB = coords;
-        --${m[p-2]};`;let v=["x","y","z","w","u","v"].slice(0,b),T="."+v[b-1],N=v.map(H=>"int "+H),S=Jn("sourceLocR",b-1).concat("inIdx.r"),D=Jn("sourceLocG",b-1).concat("inIdx.g"),I=Jn("sourceLocB",b-1).concat("inIdx.b"),P=Jn("sourceLocA",b-1).concat("inIdx.a"),E=r==="max"?"greaterThan":"lessThan",L=o?"":`
-          inIdx = round(vec4(getBestIndicesAChannel(${S.join()}),
-                             getBestIndicesAChannel(${D.join()}),
-                             getBestIndicesAChannel(${I.join()}),
-                             getBestIndicesAChannel(${P.join()})));`,B=`vec4(
-            getAChannel(${S.join()}),
-            hasNextCol ? getAChannel(${D.join()}) : 0.,
-            hasNextRow ? getAChannel(${I.join()}) : 0.,
-            hasNextRow && hasNextCol ? getAChannel(${P.join()}) : 0.)`,q=o?"":`
-      float getBestIndicesAChannel(${N.join()}) {
-        return getChannel(getBestIndicesA(${v.join()}),
-                                          vec2(${v.slice(-2).join()}));
+  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}var ArgMinMaxPackedProgram=class{constructor(shape,windowSize,op22,firstPass){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,util_exports.assert(shape.length>2,()=>`Packed arg${op22.charAt(0).toUpperCase()+op22.slice(1)} supports only inputs with rank above 2.`);let inSize=shape[shape.length-1],outSize=Math.ceil(inSize/windowSize);this.outputShape=shape.slice(0,-1),outSize>1&&this.outputShape.push(outSize),firstPass||this.variableNames.push("bestIndicesA");let outShape=this.outputShape,rank=outShape.length,dtype=getCoordsDataType(rank),coords2=getChannels("coords",rank),sourceLocSetup,sourceRank;if(outSize===1){sourceRank=rank+1;let sourceLocDType=getCoordsDataType(sourceRank);sourceLocSetup=`
+        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0);
+        ++${coords2[rank-1]};
+        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0);
+        ++${coords2[rank-2]};
+        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0);
+        --${coords2[rank-1]};
+        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0);
+        --${coords2[rank-2]};`}else sourceRank=rank,sourceLocSetup=`
+        ${dtype} sourceLocR = coords;
+        ++${coords2[rank-1]};
+        ${dtype} sourceLocG = coords;
+        ++${coords2[rank-2]};
+        ${dtype} sourceLocA = coords;
+        --${coords2[rank-1]};
+        ${dtype} sourceLocB = coords;
+        --${coords2[rank-2]};`;let channels=["x","y","z","w","u","v"].slice(0,sourceRank),inChannel="."+channels[sourceRank-1],intChannels=channels.map(x=>"int "+x),srcRCoords=getChannels("sourceLocR",sourceRank-1).concat("inIdx.r"),srcGCoords=getChannels("sourceLocG",sourceRank-1).concat("inIdx.g"),srcBCoords=getChannels("sourceLocB",sourceRank-1).concat("inIdx.b"),srcACoords=getChannels("sourceLocA",sourceRank-1).concat("inIdx.a"),compOp=op22==="max"?"greaterThan":"lessThan",fetchCandidateIdx=firstPass?"":`
+          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
+                             getBestIndicesAChannel(${srcGCoords.join()}),
+                             getBestIndicesAChannel(${srcBCoords.join()}),
+                             getBestIndicesAChannel(${srcACoords.join()})));`,fetchValue=`vec4(
+            getAChannel(${srcRCoords.join()}),
+            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
+            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`,getBestIndicesAChannelSnippet=firstPass?"":`
+      float getBestIndicesAChannel(${intChannels.join()}) {
+        return getChannel(getBestIndicesA(${channels.join()}),
+                                          vec2(${channels.slice(-2).join()}));
       }`;this.userCode=`
-      float getAChannel(${N.join()}) {
-        return getChannel(getA(${v.join()}),
-                               vec2(${v.slice(-2).join()}));
+      float getAChannel(${intChannels.join()}) {
+        return getChannel(getA(${channels.join()}),
+                               vec2(${channels.slice(-2).join()}));
       }
-      ${q}
+      ${getBestIndicesAChannelSnippet}
       void main() {
-        ${f} coords = getOutputCoords();
-        bool hasNextCol = ${m[p-1]} < ${l[p-1]-1};
-        bool hasNextRow = ${m[p-2]} < ${l[p-2]-1};
-        ${y}
-        ivec4 srcIdx = ivec4(sourceLocR${T}, sourceLocG${T},
-          sourceLocB${T}, sourceLocA${T}) * ${e};
+        ${dtype} coords = getOutputCoords();
+        bool hasNextCol = ${coords2[rank-1]} < ${outShape[rank-1]-1};
+        bool hasNextRow = ${coords2[rank-2]} < ${outShape[rank-2]-1};
+        ${sourceLocSetup}
+        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
+          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
         ivec4 inIdx = srcIdx;
         vec4 bestIndex = vec4(inIdx);
-        vec4 bestValue = ${B};
+        vec4 bestValue = ${fetchValue};
 
-        for (int i = 0; i < ${e}; i++) {
+        for (int i = 0; i < ${windowSize}; i++) {
           inIdx = srcIdx;
-          ${L}
-          vec4 candidate = ${B};
+          ${fetchCandidateIdx}
+          vec4 candidate = ${fetchValue};
           bvec4 nan = isnan(candidate);
           bvec4 replace = bvec4(
-            vec4(${E}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
+            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
 
           bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                            replace.y  ? candidate.y : bestValue.y,
@@ -777,9 +784,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(bestIndex);
       }
-    `}}class RY{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=t.dilationHeight,l=t.dilationWidth,p=t.effectiveFilterHeight,f=t.effectiveFilterWidth,m=p-1-t.padInfo.top,y=f-1-t.padInfo.left,b=1/(e*r);this.userCode=`
-      const ivec2 pads = ivec2(${m}, ${y});
-      const float avgMultiplier = float(${b});
+    `}},AvgPool2DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,avgMultiplier=1/(filterHeight*filterWidth);this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -793,20 +800,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         // 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 < ${p};
-            wR += ${c}) {
-          float dyR = float(dyRCorner + wR) / ${o}.0;
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
           }
           int idyR = int(dyR);
 
-          for (int wC = 0; wC < ${f};
-            wC+= ${l}) {
-            float dyC = float(dyCCorner + wC) / ${s}.0;
+          for (int wC = 0; wC < ${effectiveFilterWidth};
+            wC+= ${dilationWidth}) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                 fract(dyC) > 0.0) {
               continue;
             }
@@ -819,9 +826,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(dotProd);
       }
-    `}}class PY{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,o=t.filterWidth,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=t.dilationDepth,f=t.dilationHeight,m=t.dilationWidth,y=t.effectiveFilterDepth,b=t.effectiveFilterHeight,v=t.effectiveFilterWidth,T=y-1-t.padInfo.front,N=b-1-t.padInfo.top,S=v-1-t.padInfo.left,D=1/(e*r*o);this.userCode=`
-      const ivec3 pads = ivec3(${T}, ${N}, ${S});
-      const float avgMultiplier = float(${D});
+    `}},AvgPool3DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy"],this.outputShape=convInfo.inShape;let filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,avgMultiplier=1/(filterDepth*filterHeight*filterWidth);this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -838,30 +845,30 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${y};
-            wD += ${p}) {
-          float dyD = float(dyDCorner + wD) / ${s}.0;
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
 
-          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
             continue;
           }
           int idyD = int(dyD);
 
-          for (int wR = 0; wR < ${b};
-              wR += ${f}) {
-            float dyR = float(dyRCorner + wR) / ${c}.0;
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                 fract(dyR) > 0.0) {
               continue;
             }
             int idyR = int(dyR);
 
-            for (int wC = 0; wC < ${v};
-                wC += ${m}) {
-              float dyC = float(dyCCorner + wC) / ${l}.0;
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                   fract(dyC) > 0.0) {
                 continue;
               }
@@ -875,10 +882,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(dotProd);
       }
-    `}}let PI=`
+    `}},CHECK_NAN_SNIPPET=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,OY=`
+`,INT_DIV=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -888,7 +895,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   } else {
     return NAN;
   }
-`,LY=`
+`,POW=`
 if(a < 0.0 && floor(b) < b){
   return NAN;
 }
@@ -897,14 +904,14 @@ if (b == 0.0) {
 }
 return (round(mod(b, 2.0)) != 1) ?
     pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,Zst="return (a - b) * (a - b);",MY="return float(a == b);",BY="return float(a < b);",zY="return float(a <= b);",WY="return float(a > b);",VY="return float(a >= b);",GY="return float(a >= 1.0 && b >= 1.0);",UY="return float(a >= 1.0 || b >= 1.0);",qY=PI+`
+`,EQUAL="return float(a == b);",LESS="return float(a < b);",LESS_EQUAL="return float(a <= b);",GREATER="return float(a > b);",GREATER_EQUAL="return float(a >= b);",LOGICAL_AND="return float(a >= 1.0 && b >= 1.0);",LOGICAL_OR="return float(a >= 1.0 || b >= 1.0);",MAX=CHECK_NAN_SNIPPET+`
   return max(a, b);
-`,HY=PI+`
+`,MIN=CHECK_NAN_SNIPPET+`
   return min(a, b);
-`,jY=`if (b == 0.0) return NAN;
-  return mod(a, b);`,KY="return (b >= 1.0) ? a : a * (b + 1.0);",OI="return (a < 0.) ? b * a : a;";class Hn{constructor(t,e,r){this.variableNames=["A","B"],this.outputShape=le(e,r),this.userCode=`
+`,MOD=`if (b == 0.0) return NAN;
+  return mod(a, b);`,ELU_DER="return (b >= 1.0) ? a : a * (b + 1.0);",PRELU="return (a < 0.) ? b * a : a;",BinaryOpProgram=class{constructor(op22,aShape,bShape){this.variableNames=["A","B"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
       float binaryOperation(float a, float b) {
-        ${t}
+        ${op22}
       }
 
       void main() {
@@ -912,12 +919,12 @@ return (round(mod(b, 2.0)) != 1) ?
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}}let ng=`
+    `}},CHECK_NAN_SNIPPET2=`
   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;
-`,XY=`
+`,INT_DIV2=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -938,7 +945,7 @@ return (round(mod(b, 2.0)) != 1) ?
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,YY=`
+`,POW2=`
   // 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);
@@ -952,71 +959,69 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+ng+`
+  `+CHECK_NAN_SNIPPET2+`
   return result;
-`,LI=`
+`,PRELU2=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`,JY=`
+`,ELU_DER2=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,ZY=`
+`,EQUAL2=`
   return vec4(equal(a, b));
-`,Qst=`
-  return vec4(notEqual(a, b));
-`,QY=`
+`,LESS2=`
   return vec4(lessThan(a, b));
-`,t7=`
+`,LESS_EQUAL2=`
   return vec4(lessThanEqual(a, b));
-`,e7=`
+`,GREATER2=`
   return vec4(greaterThan(a, b));
-`,n7=`
+`,GREATER_EQUAL2=`
   return vec4(greaterThanEqual(a, b));
-`,r7=`
+`,LOGICAL_AND2=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,o7=`
+`,LOGICAL_OR2=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,s7=`
+`,MAX2=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+ng+`
+  `+CHECK_NAN_SNIPPET2+`
   return result;
-`,i7=`
+`,MIN2=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+ng+`
+  `+CHECK_NAN_SNIPPET2+`
   return result;
-`,a7=`
+`,MOD2=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+ng+`
+  `+CHECK_NAN_SNIPPET2+`
   return result;
-`;class Ss{constructor(t,e,r,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=le(e,r);let s=this.outputShape.length,c="";if(o)if(s===0||G(this.outputShape)===1)c=`
+`,BinaryOpPackedProgram=class{constructor(op22,aShape,bShape,checkOutOfBounds=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape);let rank=this.outputShape.length,checkOutOfBoundsString="";if(checkOutOfBounds)if(rank===0||util_exports.sizeFromShape(this.outputShape)===1)checkOutOfBoundsString=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
-        `;else{let l=Oe(s);if(c=`
-          ${l} coords = getOutputCoords();
-        `,s===1)c+=`
+        `;else{let dtype=getCoordsDataType(rank);if(checkOutOfBoundsString=`
+          ${dtype} coords = getOutputCoords();
+        `,rank===1)checkOutOfBoundsString+=`
             result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
             result.z = 0.;
             result.w = 0.;
-          `;else{let p=Jn("coords",s);c+=`
+          `;else{let channels=getChannels("coords",rank);checkOutOfBoundsString+=`
             bool nextRowOutOfBounds =
-              (${p[s-2]} + 1) >= ${this.outputShape[s-2]};
+              (${channels[rank-2]} + 1) >= ${this.outputShape[rank-2]};
             bool nextColOutOfBounds =
-              (${p[s-1]} + 1) >= ${this.outputShape[s-1]};
+              (${channels[rank-1]} + 1) >= ${this.outputShape[rank-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) {
-        ${t}
+        ${op22}
       }
 
       void main() {
@@ -1024,11 +1029,11 @@ return (round(mod(b, 2.0)) != 1) ?
         vec4 b = getBAtOutCoords();
 
         vec4 result = binaryOperation(a, b);
-        ${c}
+        ${checkOutOfBoundsString}
 
         setOutput(result);
       }
-    `}}class c7{constructor(t){this.variableNames=["A"],this.outputShape=t,this.userCode=`
+    `}},ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1041,7 +1046,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(t,e){return(r,o)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class l7{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=`
+    `}getCustomSetupFunc(min82,max102){return(gpgpu,webGLProgram)=>{this.minLoc==null&&(this.minLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"minVal"),this.maxLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"maxVal")),gpgpu.gl.uniform1f(this.minLoc,min82),gpgpu.gl.uniform1f(this.maxLoc,max102)}}},ClipPackedProgram=class{constructor(aShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=aShape,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1055,7 +1060,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(t,e){return(r,o)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class u7{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=`
+    `}getCustomSetupFunc(min82,max102){return(gpgpu,webGLProgram)=>{this.minLoc==null&&(this.minLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"minVal"),this.maxLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"maxVal")),gpgpu.gl.uniform1f(this.minLoc,min82),gpgpu.gl.uniform1f(this.maxLoc,max102)}}},ComplexAbsProgram=class{constructor(shape){this.variableNames=["real","imag"],this.outputShape=shape,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1068,7 +1073,7 @@ return (round(mod(b, 2.0)) != 1) ?
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}}class p7{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,c=t.dataFormat==="channelsLast";this.userCode=`
+    `}},Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -1080,22 +1085,22 @@ return (round(mod(b, 2.0)) != 1) ?
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int b = 0; b < ${t.batchSize}; b++) {
-          for (int yR = 0; yR < ${t.outHeight}; yR++) {
-            int xR = wR + yR * ${e} - ${o};
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int yC = 0; yC < ${t.outWidth}; yC++) {
-              int xC = wC + yC * ${r} - ${s};
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
 
-              if (xC < 0 || xC >= ${t.inWidth}) {
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                 continue;
               }
 
-              if (${c}) {
+              if (${isChannelsLast}) {
                 float dyValue = getDy(b, yR, yC, d2);
                 float xValue = getX(b, xR, xC, d1);
                 dotProd += (xValue * dyValue);
@@ -1110,45 +1115,45 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class h7{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=t.dataFormat==="channelsLast",l=e-1-t.padInfo.top,p=r-1-t.padInfo.left,f=c?1:2,m=c?2:3,y=c?3:1;this.userCode=`
-      const ivec2 pads = ivec2(${l}, ${p});
+    `}},Conv2DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,isChannelsLast=convInfo.dataFormat==="channelsLast",padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left,rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords[0];
-        int d1 = coords[${y}];
+        int d1 = coords[${channelDim}];
 
-        ivec2 dyCorner = ivec2(coords[${f}], coords[${m}]) - pads;
+        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - 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 < ${e}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${o}.0;
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
           }
           int idyR = int(dyR);
 
-          int wRPerm = ${e} - 1 - wR;
+          int wRPerm = ${filterHeight} - 1 - wR;
 
-          for (int wC = 0; wC < ${r}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${s}.0;
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                 fract(dyC) > 0.0) {
               continue;
             }
             int idyC = int(dyC);
 
-            int wCPerm = ${r} - 1 - wC;
+            int wCPerm = ${filterWidth} - 1 - wC;
 
-            for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
+            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
 
-              if (${c}) {
+              if (${isChannelsLast}) {
                 float xValue = getDy(batch, idyR, idyC, d2);
                 float wValue = getW(wRPerm, wCPerm, d1, d2);
                 dotProd += xValue * wValue;
@@ -1163,7 +1168,7 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class f7{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,r=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,c=t.padInfo.top,l=t.padInfo.left;this.userCode=`
+    `}},Conv3DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -1174,25 +1179,25 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float dotProd = 0.0;
 
-        for (int b = 0; b < ${t.batchSize}; b++) {
-          for (int yF = 0; yF < ${t.outDepth}; yF++) {
-            int xF = wF + yF * ${e} - ${s};
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
+            int xF = wF + yF * ${strideDepth} - ${padFront};
 
-            if (xF < 0 || xF >= ${t.inDepth}) {
+            if (xF < 0 || xF >= ${convInfo.inDepth}) {
               continue;
             }
 
-            for (int yR = 0; yR < ${t.outHeight}; yR++) {
-              int xR = wR + yR * ${r} - ${c};
+            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+              int xR = wR + yR * ${strideHeight} - ${padTop};
 
-              if (xR < 0 || xR >= ${t.inHeight}) {
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                 continue;
               }
 
-              for (int yC = 0; yC < ${t.outWidth}; yC++) {
-                int xC = wC + yC * ${o} - ${l};
+              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+                int xC = wC + yC * ${strideWidth} - ${padLeft};
 
-                if (xC < 0 || xC >= ${t.inWidth}) {
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                   continue;
                 }
 
@@ -1205,8 +1210,8 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class d7{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,o=t.filterWidth,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=e-1-t.padInfo.front,f=r-1-t.padInfo.top,m=o-1-t.padInfo.left;this.userCode=`
-      const ivec3 pads = ivec3(${p}, ${f}, ${m});
+    `}},Conv3DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padFront=filterDepth-1-convInfo.padInfo.front,padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -1220,39 +1225,39 @@ return (round(mod(b, 2.0)) != 1) ?
         int dyCCorner = dyCorner.z;
 
         float dotProd = 0.0;
-        for (int wF = 0; wF < ${e}; wF++) {
-          float dyF = float(dyFCorner + wF) / ${s}.0;
+        for (int wF = 0; wF < ${filterDepth}; wF++) {
+          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;
 
-          if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
+          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
             continue;
           }
           int idyF = int(dyF);
 
-          int wFPerm = ${e} - 1 - wF;
+          int wFPerm = ${filterDepth} - 1 - wF;
 
-          for (int wR = 0; wR < ${r}; wR++) {
-            float dyR = float(dyRCorner + wR) / ${c}.0;
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
               fract(dyR) > 0.0) {
               continue;
             }
             int idyR = int(dyR);
 
-            int wRPerm = ${r} - 1 - wR;
+            int wRPerm = ${filterHeight} - 1 - wR;
 
-            for (int wC = 0; wC < ${o}; wC++) {
-              float dyC = float(dyCCorner + wC) / ${l}.0;
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                   fract(dyC) > 0.0) {
                 continue;
               }
               int idyC = int(dyC);
 
-              int wCPerm = ${o} - 1 - wC;
+              int wCPerm = ${filterWidth} - 1 - wC;
 
-              for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
+              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
                 float xValue = getDy(batch, idyF, idyR, idyC, d2);
                 float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                 dotProd += xValue * wValue;
@@ -1262,30 +1267,30 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class m7{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,c=t.outChannels/t.inChannels;this.userCode=`
+    `}},DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.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 * ${c} + dm;
+        int d2 = d1 * ${channelMul} + dm;
 
         float dotProd = 0.0;
 
         // TO DO: Vec4 over the batch size
-        for (int b = 0; b < ${t.batchSize}; b++) {
-          for (int yR = 0; yR < ${t.outHeight}; yR++) {
-            int xR = wR + yR * ${e} - ${o};
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int yC = 0; yC < ${t.outWidth}; yC++) {
-              int xC = wC + yC * ${r} - ${s};
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
 
-              if (xC < 0 || xC >= ${t.inWidth}) {
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                 continue;
               }
 
@@ -1297,8 +1302,8 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class g7{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=e-1-t.padInfo.top,l=r-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=`
-      const ivec2 pads = ivec2(${c}, ${l});
+    `}},DepthwiseConv2DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -1310,30 +1315,30 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float dotProd = 0.0;
 
-        for (int wR = 0; wR < ${e}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${o}.0;
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
           }
           int idyR = int(dyR);
 
-          int wRPerm = ${e} - 1 - wR;
+          int wRPerm = ${filterHeight} - 1 - wR;
 
-          for (int wC = 0; wC < ${r}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${s}.0;
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                 fract(dyC) > 0.0) {
               continue;
             }
             int idyC = int(dyC);
 
-            int wCPerm = ${r} - 1 - wC;
+            int wCPerm = ${filterWidth} - 1 - wC;
 
             // TO DO: Vec4 over the channelMul
-            for (int dm = 0; dm < ${p}; dm++) {
-              int d2 = d1 * ${p} + dm;
+            for (int dm = 0; dm < ${channelMul}; dm++) {
+              int d2 = d1 * ${channelMul} + dm;
               float xValue = getDy(batch, idyR, idyC, d2);
               float wValue = getW(wRPerm, wCPerm, d1, dm);
               dotProd += xValue * wValue;
@@ -1342,47 +1347,47 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class MI{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let s=t.padInfo.top,c=t.padInfo.left,l=t.strideHeight,p=t.strideWidth,f=t.dilationHeight,m=t.dilationWidth,y=t.filterHeight,b=t.filterWidth,v=Math.floor(t.inChannels/4)*4,T=t.inChannels%4,N=t.dataFormat==="channelsLast",S=N?1:2,D=N?2:3,I=N?3:1,P="",E="";r&&(o?P=`float activation(float a) {
+    `}},Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:P=`
+          ${activation2}
+        }`:activationSnippet=`
           float activation(float x) {
-            ${r}
+            ${activation2}
           }
-        `,E="result = activation(result);");let L=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${P}
+        `,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
 
-      const ivec2 strides = ivec2(${l}, ${p});
-      const ivec2 pads = ivec2(${s}, ${c});
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords[0];
-        int d2 = coords[${I}];
+        int d2 = coords[${channelDim}];
 
         ivec2 xRCCorner =
-            ivec2(coords[${S}], coords[${D}]) * strides - pads;
+            ivec2(coords[${rowDim}], coords[${colDim}]) * 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 < ${y}; wR++) {
-          int xR = xRCorner + wR * ${f};
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * ${dilationHeight};
 
-          if (xR < 0 || xR >= ${t.inHeight}) {
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
             continue;
           }
 
-          for (int wC = 0; wC < ${b}; wC++) {
-            int xC = xCCorner + wC * ${m};
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * ${dilationWidth};
 
-            if (xC < 0 || xC >= ${t.inWidth}) {
+            if (xC < 0 || xC >= ${convInfo.inWidth}) {
               continue;
             }
 
-            for (int d1 = 0; d1 < ${v}; d1 += 4) {
+            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
               vec4 wValues = vec4(
                 getW(wR, wC, d1, d2),
                 getW(wR, wC, d1 + 1, d2),
@@ -1390,7 +1395,7 @@ return (round(mod(b, 2.0)) != 1) ?
                 getW(wR, wC, d1 + 3, d2)
               );
 
-              if (${N}) {
+              if (${isChannelsLast}) {
                 vec4 xValues = vec4(
                   getX(batch, xR, xC, d1),
                   getX(batch, xR, xC, d1 + 1),
@@ -1409,57 +1414,57 @@ return (round(mod(b, 2.0)) != 1) ?
               }
             }
 
-            if (${T===1}) {
+            if (${inputDepthVec4Remainder===1}) {
 
-              if (${N}) {
+              if (${isChannelsLast}) {
                 dotProd +=
-                    getX(batch, xR, xC, ${v}) *
-                    getW(wR, wC, ${v}, d2);
+                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
               } else {
                 dotProd +=
-                    getX(batch, ${v}, xR, xC) *
-                    getW(wR, wC, ${v}, d2);
+                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
               }
 
-            } else if (${T===2}) {
+            } else if (${inputDepthVec4Remainder===2}) {
               vec2 wValues = vec2(
-                getW(wR, wC, ${v}, d2),
-                getW(wR, wC, ${v} + 1, d2)
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
               );
 
-              if (${N}) {
+              if (${isChannelsLast}) {
                 vec2 xValues = vec2(
-                  getX(batch, xR, xC, ${v}),
-                  getX(batch, xR, xC, ${v} + 1)
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
                 );
                 dotProd += dot(xValues, wValues);
               } else {
                 vec2 xValues = vec2(
-                  getX(batch, ${v}, xR, xC),
-                  getX(batch, ${v} + 1, xR, xC)
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
                 );
                 dotProd += dot(xValues, wValues);
               }
 
-            } else if (${T===3}) {
+            } else if (${inputDepthVec4Remainder===3}) {
               vec3 wValues = vec3(
-                getW(wR, wC, ${v}, d2),
-                getW(wR, wC, ${v} + 1, d2),
-                getW(wR, wC, ${v} + 2, d2)
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
               );
 
-              if (${N}) {
+              if (${isChannelsLast}) {
                 vec3 xValues = vec3(
-                  getX(batch, xR, xC, ${v}),
-                  getX(batch, xR, xC, ${v} + 1),
-                  getX(batch, xR, xC, ${v} + 2)
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
                 );
                 dotProd += dot(xValues, wValues);
               } else {
                 vec3 xValues = vec3(
-                  getX(batch, ${v}, xR, xC),
-                  getX(batch, ${v} + 1, xR, xC),
-                  getX(batch, ${v} + 2, xR, xC)
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
                 );
                 dotProd += dot(xValues, wValues);
               }
@@ -1469,13 +1474,13 @@ return (round(mod(b, 2.0)) != 1) ?
         }
 
         float result = dotProd;
-        ${L}
-        ${E}
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
         setOutput(result);
       }
-    `}}class y7{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,r=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=t.dilationDepth,f=t.dilationHeight,m=t.dilationWidth,y=t.filterDepth,b=t.filterHeight,v=t.filterWidth,T=Math.floor(t.inChannels/4)*4,N=t.inChannels%4;this.userCode=`
-      const ivec3 strides = ivec3(${s}, ${c}, ${l});
-      const ivec3 pads = ivec3(${e}, ${r}, ${o});
+    `}},Conv3DProgram=class{constructor(convInfo){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4;this.userCode=`
+      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -1491,28 +1496,28 @@ return (round(mod(b, 2.0)) != 1) ?
         // y(yF, yR, yC, d2). ? = to be determined. : = across all
         // values in that axis.
         float dotProd = 0.0;
-        for (int wF = 0; wF < ${y}; wF++) {
-          int xF = xFCorner + wF * ${p};
+        for (int wF = 0; wF < ${filterDepth}; wF++) {
+          int xF = xFCorner + wF * ${dilationDepth};
 
-          if (xF < 0 || xF >= ${t.inDepth}) {
+          if (xF < 0 || xF >= ${convInfo.inDepth}) {
             continue;
           }
 
-          for (int wR = 0; wR < ${b}; wR++) {
-            int xR = xRCorner + wR * ${f};
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            int xR = xRCorner + wR * ${dilationHeight};
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int wC = 0; wC < ${v}; wC++) {
-              int xC = xCCorner + wC * ${m};
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              int xC = xCCorner + wC * ${dilationWidth};
 
-              if (xC < 0 || xC >= ${t.inWidth}) {
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                 continue;
               }
 
-              for (int d1 = 0; d1 < ${T}; d1 += 4) {
+              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
                 vec4 xValues = vec4(
                   getX(batch, xF, xR, xC, d1),
                   getX(batch, xF, xR, xC, d1 + 1),
@@ -1529,30 +1534,30 @@ return (round(mod(b, 2.0)) != 1) ?
                 dotProd += dot(xValues, wValues);
               }
 
-              if (${N===1}) {
+              if (${inputDepthVec4Remainder===1}) {
                 dotProd +=
-                  getX(batch, xF, xR, xC, ${T}) *
-                  getW(wF, wR, wC, ${T}, d2);
-              } else if (${N===2}) {
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
+              } else if (${inputDepthVec4Remainder===2}) {
                 vec2 xValues = vec2(
-                  getX(batch, xF, xR, xC, ${T}),
-                  getX(batch, xF, xR, xC, ${T} + 1)
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
                 );
                 vec2 wValues = vec2(
-                  getW(wF, wR, wC, ${T}, d2),
-                  getW(wF, wR, wC, ${T} + 1, d2)
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
                 );
                 dotProd += dot(xValues, wValues);
-              } else if (${N===3}) {
+              } else if (${inputDepthVec4Remainder===3}) {
                 vec3 xValues = vec3(
-                  getX(batch, xF, xR, xC, ${T}),
-                  getX(batch, xF, xR, xC, ${T} + 1),
-                  getX(batch, xF, xR, xC, ${T} + 2)
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
                 );
                 vec3 wValues = vec3(
-                  getW(wF, wR, wC, ${T}, d2),
-                  getW(wF, wR, wC, ${T} + 1, d2),
-                  getW(wF, wR, wC, ${T} + 2, d2)
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
                 );
                 dotProd += dot(xValues, wValues);
               }
@@ -1561,26 +1566,26 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class BI{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let s=t.inHeight,c=t.inWidth,l=t.padInfo.top,p=t.padInfo.left,f=t.strideHeight,m=t.strideWidth,y=t.dilationHeight,b=t.dilationWidth,v=t.filterHeight,T=t.filterWidth,N=t.outChannels/t.inChannels,S="",D="";r&&(o?S=`float activation(float a) {
+    `}},DepthwiseConv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let xNumRows=convInfo.inHeight,xNumCols=convInfo.inWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,channelMul=convInfo.outChannels/convInfo.inChannels,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:S=`
+          ${activation2}
+        }`:activationSnippet=`
           float activation(float x) {
-            ${r}
+            ${activation2}
           }
-        `,D="result = activation(result);");let I=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${S}
+        `,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
 
-      const ivec2 strides = ivec2(${f}, ${m});
-      const ivec2 pads = ivec2(${l}, ${p});
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
-        int d1 = d2 / ${N};
-        int q = d2 - d1 * ${N};
+        int d1 = d2 / ${channelMul};
+        int q = d2 - d1 * ${channelMul};
 
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
@@ -1589,17 +1594,17 @@ return (round(mod(b, 2.0)) != 1) ?
         // ? = 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 < ${v}; wR++) {
-          int xR = xRCorner + wR * ${y};
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * ${dilationHeight};
 
-          if (xR < 0 || xR >= ${s}) {
+          if (xR < 0 || xR >= ${xNumRows}) {
             continue;
           }
 
-          for (int wC = 0; wC < ${T}; wC++) {
-            int xC = xCCorner + wC * ${b};
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * ${dilationWidth};
 
-            if (xC < 0 || xC >= ${c}) {
+            if (xC < 0 || xC >= ${xNumCols}) {
               continue;
             }
 
@@ -1610,139 +1615,139 @@ return (round(mod(b, 2.0)) != 1) ?
         }
 
         float result = dotProd;
-        ${I}
-        ${D}
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
         setOutput(result);
       }
-    `}}class zI{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;let s=t.inHeight,c=t.inWidth,l=t.padInfo.top,p=t.padInfo.left,f=t.strideHeight,m=t.strideWidth,y=t.dilationHeight,b=t.dilationWidth,v=t.filterHeight,T=t.filterWidth,N=T,S="int xR; int xC; int xCOffset;";for(let E=0;E<v;E++)for(let L=0;L<T;L++)S+=`
-          vec4 xTexelR${E}C${L*2} = vec4(0.);
-          vec4 wR${E}C${L} = vec4(0.);
-          vec4 xR${E}C${L} = vec4(0.);`;for(let E=0;E<v;E++)for(let L=0;L<N;L++){let B=L*2;if(S+=`
-          xR = xRCorner + ${E*y};
-          xC = xCCorner + ${B*b};
-        `,m===1){if(B<T&&(p%2===1?S+=`
+    `}},DepthwiseConvPacked2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=convInfo.outShape;let xNumRows=convInfo.inHeight,xNumCols=convInfo.inWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,texelsAcross=filterWidth,mainLoop="int xR; int xC; int xCOffset;";for(let r=0;r<filterHeight;r++)for(let c=0;c<filterWidth;c++)mainLoop+=`
+          vec4 xTexelR${r}C${c*2} = vec4(0.);
+          vec4 wR${r}C${c} = vec4(0.);
+          vec4 xR${r}C${c} = vec4(0.);`;for(let r=0;r<filterHeight;r++)for(let texelC=0;texelC<texelsAcross;texelC++){let c=texelC*2;if(mainLoop+=`
+          xR = xRCorner + ${r*dilationHeight};
+          xC = xCCorner + ${c*dilationWidth};
+        `,strideWidth===1){if(c<filterWidth&&(padLeft%2===1?mainLoop+=`
                 xCOffset = xC + 1;
-                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${c}) {
-                  xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
+                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
 
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${c}) {
-                    xTexelR${E}C${B}.zw = vec2(0.);
+                  if(xCOffset + 1 >= ${xNumCols}) {
+                    xTexelR${r}C${c}.zw = vec2(0.);
                   }
                 } else {
-                  xTexelR${E}C${B} = vec4(0.);
+                  xTexelR${r}C${c} = vec4(0.);
                 }
 
                 xCOffset = xC + 1 - 2;
-                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${c}) {
+                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                   vec4 previous = getX(batch, xR, xCOffset, d1);
 
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${c}) {
+                  if(xCOffset + 1 >= ${xNumCols}) {
                     previous.zw = vec2(0.);
                   }
 
-                  xR${E}C${B} = vec4(previous.zw, xTexelR${E}C${B}.xy);
+                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);
                 } else {
-                  xR${E}C${B} = vec4(0, 0, xTexelR${E}C${B}.xy);
+                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);
                 }
-              `:S+=`
-                if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${c}) {
-                  xTexelR${E}C${B} = getX(batch, xR, xC, d1);
+              `:mainLoop+=`
+                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
                 } else {
-                  xTexelR${E}C${B} = vec4(0.);
+                  xTexelR${r}C${c} = vec4(0.);
                 }
 
-                xR${E}C${B} = xTexelR${E}C${B};
-              `,B+1<T)){let q=p%2===0?k(b):b;b%2===0&&p%2===1||b%2!==0&&p%2!==1?(S+=`
-                  xCOffset = xC + ${p%2} + ${q};
+                xR${r}C${c} = xTexelR${r}C${c};
+              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2===0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2===0&&padLeft%2===1||dilationWidth%2!==0&&padLeft%2!==1?(mainLoop+=`
+                  xCOffset = xC + ${padLeft%2} + ${nextTexelOffset};
 
-                  if(xR >= 0 && xR < ${s} &&
-                    xCOffset >= 0 && xCOffset < ${c}) {
-                    xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
+                  if(xR >= 0 && xR < ${xNumRows} &&
+                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                    xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                   }
-                `,b>1&&(S+=`
+                `,dilationWidth>1&&(mainLoop+=`
                     xCOffset -= 2;
-                    if(xR >= 0 && xR < ${s} &&
-                      xCOffset >= 0 && xCOffset < ${c}) {
-                      xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
+                    if(xR >= 0 && xR < ${xNumRows} &&
+                      xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
                     } else {
-                      xTexelR${E}C${B} = vec4(0.);
+                      xTexelR${r}C${c} = vec4(0.);
                     }
-                  `),S+=`
-                  xR${E}C${B+1} = vec4(
-                    xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.xy);
-                `):S+=`
-                  xCOffset = xC + ${q};
+                  `),mainLoop+=`
+                  xR${r}C${c+1} = vec4(
+                    xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.xy);
+                `):mainLoop+=`
+                  xCOffset = xC + ${nextTexelOffset};
 
-                  if(xR >= 0 && xR < ${s} &&
-                    xCOffset >= 0 && xCOffset < ${c}) {
-                    xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
+                  if(xR >= 0 && xR < ${xNumRows} &&
+                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                    xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                   }
 
-                  xR${E}C${B+1} = xTexelR${E}C${B+2};
-                `}}else B<T&&(S+=`
-              if(xR >= 0 && xR < ${s}) {
-            `,p%2===1?(S+=`
-                xCOffset = xC + 1 - ${m};
-                if(xCOffset >= 0 && xCOffset < ${c}) {
-                  xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
+                  xR${r}C${c+1} = xTexelR${r}C${c+2};
+                `}}else c<filterWidth&&(mainLoop+=`
+              if(xR >= 0 && xR < ${xNumRows}) {
+            `,padLeft%2===1?(mainLoop+=`
+                xCOffset = xC + 1 - ${strideWidth};
+                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
                 } else {
-                  xTexelR${E}C${B} = vec4(0.);
+                  xTexelR${r}C${c} = vec4(0.);
                 }
 
-                if(xC + 1 >= 0 && xC + 1 < ${c}) {
-                  xTexelR${E}C${B+2} = getX(batch, xR, xC + 1, d1);
+                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {
+                  xTexelR${r}C${c+2} = getX(batch, xR, xC + 1, d1);
                 } else {
-                  xTexelR${E}C${B+2} = vec4(0.);
+                  xTexelR${r}C${c+2} = vec4(0.);
                 }
 
-                xR${E}C${B} = vec4(
-                  xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.zw);
-              `,B+1<T&&(S+=`
+                xR${r}C${c} = vec4(
+                  xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.zw);
+              `,c+1<filterWidth&&(mainLoop+=`
                   vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${m};
-                  if(xCOffset >= 0 && xCOffset < ${c}) {
+                  xCOffset = xC + 1 + ${strideWidth};
+                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
-                  xR${E}C${B+1} = vec4(xTexelR${E}C${B+2}.xy, final.xy);
-                `)):(S+=`
-                if(xC >= 0 && xC < ${c}) {
-                  xTexelR${E}C${B} = getX(batch, xR, xC, d1);
+                  xR${r}C${c+1} = vec4(xTexelR${r}C${c+2}.xy, final.xy);
+                `)):(mainLoop+=`
+                if(xC >= 0 && xC < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
                 } else {
-                  xTexelR${E}C${B} = vec4(0.);
+                  xTexelR${r}C${c} = vec4(0.);
                 }
 
-                xCOffset = xC + ${m};
-                if(xCOffset >= 0 && xCOffset < ${c}) {
-                  xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
+                xCOffset = xC + ${strideWidth};
+                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                 } else {
-                  xTexelR${E}C${B+2} = vec4(0.);
+                  xTexelR${r}C${c+2} = vec4(0.);
                 }
 
-                xR${E}C${B} = vec4(
-                  xTexelR${E}C${B}.xy, xTexelR${E}C${B+2}.xy);
-              `,B+1<T&&(S+=`
-                  xR${E}C${B+1} = vec4(
-                    xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.zw);
-                `)),S+="}");B<T&&(S+=`
-            vec4 wTexelR${E}C${B} = getW(${E}, ${B}, d1, q);
-            wR${E}C${B} = vec4(wTexelR${E}C${B}.xz, wTexelR${E}C${B}.xz);
-          `,B+1<T&&(S+=`
-              vec4 wTexelR${E}C${B+1} = getW(${E}, ${B+1}, d1, q);
-              wR${E}C${B+1} =
-                vec4(wTexelR${E}C${B+1}.xz, wTexelR${E}C${B+1}.xz);`))}for(let E=0;E<v;E++)for(let L=0;L<T;L++)S+=`dotProd += xR${E}C${L} * wR${E}C${L};`;let D="",I="";r&&(o?D=`vec4 activation(vec4 a) {
+                xR${r}C${c} = vec4(
+                  xTexelR${r}C${c}.xy, xTexelR${r}C${c+2}.xy);
+              `,c+1<filterWidth&&(mainLoop+=`
+                  xR${r}C${c+1} = vec4(
+                    xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.zw);
+                `)),mainLoop+="}");c<filterWidth&&(mainLoop+=`
+            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);
+            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);
+          `,c+1<filterWidth&&(mainLoop+=`
+              vec4 wTexelR${r}C${c+1} = getW(${r}, ${c+1}, d1, q);
+              wR${r}C${c+1} =
+                vec4(wTexelR${r}C${c+1}.xz, wTexelR${r}C${c+1}.xz);`))}for(let r=0;r<filterHeight;r++)for(let c=0;c<filterWidth;c++)mainLoop+=`dotProd += xR${r}C${c} * wR${r}C${c};`;let activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:D=`vec4 activation(vec4 x) {
-          ${r}
-        }`,I="result = activation(result);");let P=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${D}
+          ${activation2}
+        }`:activationSnippet=`vec4 activation(vec4 x) {
+          ${activation2}
+        }`,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
 
-      const ivec2 strides = ivec2(${f}, ${m});
-      const ivec2 pads = ivec2(${l}, ${p});
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
 
@@ -1757,16 +1762,16 @@ return (round(mod(b, 2.0)) != 1) ?
 
         vec4 dotProd = vec4(0.);
 
-        ${S}
+        ${mainLoop}
 
         vec4 result = dotProd;
-        ${P}
-        ${I}
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
         setOutput(result);
       }
-    `}}class b7{constructor(t,e,r,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[c,l,p,f]=t,[m]=e,[y,b]=r;this.outputShape=[m,y,b,f];let v=o==="bilinear"?1:0,[T,N]=[`${l-1}.0`,`${p-1}.0`],[S,D,I]=y>1?[`${(l-1)/(y-1)}`,"(y2-y1) * height_ratio",`y1*${T} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${T}`],[P,E,L]=b>1?[`${(p-1)/(b-1)}`,"(x2-x1) * width_ratio",`x1*${N} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${N}`];this.userCode=`
-      const float height_ratio = float(${S});
-      const float width_ratio = float(${P});
+    `}},CropAndResizeProgram=class{constructor(imageShape,boxShape,cropSize,method,extrapolationValue){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[batch,imageHeight,imageWidth,depth]=imageShape,[numBoxes]=boxShape,[cropHeight,cropWidth]=cropSize;this.outputShape=[numBoxes,cropHeight,cropWidth,depth];let methodId=method==="bilinear"?1:0,[inputHeightFloat,inputWidthFloat]=[`${imageHeight-1}.0`,`${imageWidth-1}.0`],[heightRatio,heightScale,inY]=cropHeight>1?[`${(imageHeight-1)/(cropHeight-1)}`,"(y2-y1) * height_ratio",`y1*${inputHeightFloat} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${inputHeightFloat}`],[widthRatio,widthScale,inX]=cropWidth>1?[`${(imageWidth-1)/(cropWidth-1)}`,"(x2-x1) * width_ratio",`x1*${inputWidthFloat} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${inputWidthFloat}`];this.userCode=`
+      const float height_ratio = float(${heightRatio});
+      const float width_ratio = float(${widthRatio});
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -1782,26 +1787,26 @@ return (round(mod(b, 2.0)) != 1) ?
 
         // get image in batch index
         int bInd = round(getBoxInd(b));
-        if(bInd < 0 || bInd >= ${c}) {
+        if(bInd < 0 || bInd >= ${batch}) {
           return;
         }
 
-        float height_scale = ${D};
-        float width_scale = ${E};
+        float height_scale = ${heightScale};
+        float width_scale = ${widthScale};
 
-        float in_y = ${I};
-        if( in_y < 0.0 || in_y > ${T} ) {
-          setOutput(float(${s}));
+        float in_y = ${inY};
+        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
+          setOutput(float(${extrapolationValue}));
           return;
         }
-        float in_x = ${L};
-        if( in_x < 0.0 || in_x > ${N} ) {
-          setOutput(float(${s}));
+        float in_x = ${inX};
+        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
+          setOutput(float(${extrapolationValue}));
           return;
         }
 
         vec2 sourceFracIndexCR = vec2(in_x,in_y);
-        if(${v} == 1) {
+        if(${methodId} == 1) {
           // Compute the four integer indices.
           ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
           ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
@@ -1825,30 +1830,30 @@ return (round(mod(b, 2.0)) != 1) ?
           setOutput(newValue);
         }
       }
-    `}}class WI{constructor(t,e,r){this.variableNames=["x"],this.outputShape=t;let o=t.length,s=e?"0.0":`getX(${VI(o,"coords")})`,c=t[t.length-1],l="",p="";e?(l=r?`end != ${c-1}`:"end != 0",p=r?"end + 1":"end - 1"):(l=r?`end + pow2 < ${c}`:"end >= pow2",p=r?"end + pow2":"end - pow2"),this.userCode=`
+    `}},CumSumProgram=class{constructor(shape,exclusive,reverse12){this.variableNames=["x"],this.outputShape=shape;let rank=shape.length,val=exclusive?"0.0":`getX(${getCoords(rank,"coords")})`,length=shape[shape.length-1],condition="",idxString="";exclusive?(condition=reverse12?`end != ${length-1}`:"end != 0",idxString=reverse12?"end + 1":"end - 1"):(condition=reverse12?`end + pow2 < ${length}`:"end >= pow2",idxString=reverse12?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
-        ${Oe(o)} coords = getOutputCoords();
-        int end = ${GI(o,"coords")};
-        float val = ${s};
+        ${getCoordsDataType(rank)} coords = getOutputCoords();
+        int end = ${getFinalCoord(rank,"coords")};
+        float val = ${val};
         int pow2 = int(pow(2.0, index));
-        if (${l}) {
-          int idx = ${p};
-          ${GI(o,"coords")} = idx;
-          val += getX(${VI(o,"coords")});
+        if (${condition}) {
+          int idx = ${idxString};
+          ${getFinalCoord(rank,"coords")} = idx;
+          val += getX(${getCoords(rank,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(t){return(e,r)=>{this.index==null&&(this.index=e.getUniformLocation(r,"index")),e.gl.uniform1f(this.index,t)}}}function VI(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.x, ${t}.y`;if(n===3)return`${t}.x, ${t}.y, ${t}.z`;if(n===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}function GI(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.y`;if(n===3)return`${t}.z`;if(n===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}class x7{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ch.DENSE;let e=uh(t),r=Zn();this.outputShape=t,this.userCode=`
+    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}var DecodeMatrixProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=PackingScheme.DENSE;let texShape=getDenseTexShape(outputShape),glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${_a(["r","c","d"],t)}
+        ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],outputShape)}
         return ivec3(r, c, d);
       }
 
       void main() {
         ivec2 resTexRC = ivec2(resultUV.yx *
-          vec2(${e[0]}, ${e[1]}));
-        int index = 4 * (resTexRC.x * ${e[1]} + resTexRC.y);
+          vec2(${texShape[0]}, ${texShape[1]}));
+        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);
 
         vec4 result = vec4(0.);
 
@@ -1858,18 +1863,18 @@ return (round(mod(b, 2.0)) != 1) ?
           result[i] = getA(rc.x, rc.y, rc.z);
         }
 
-        ${r.output} = result;
+        ${glsl.output} = result;
       }
-    `}}class w7{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ch.DENSE;let e=uh(t),r=Zn();this.outputShape=t,this.userCode=`
+    `}},DecodeMatrixPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=PackingScheme.DENSE;let texShape=getDenseTexShape(outputShape),glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${_a(["r","c","d"],t)}
+        ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],outputShape)}
         return ivec3(r, c, d);
       }
 
       void main() {
         ivec2 resTexRC = ivec2(resultUV.yx *
-          vec2(${e[0]}, ${e[1]}));
-        int index = 4 * (resTexRC.x * ${e[1]} + resTexRC.y);
+          vec2(${texShape[0]}, ${texShape[1]}));
+        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);
 
         vec4 result = vec4(0.);
 
@@ -1879,9 +1884,9 @@ return (round(mod(b, 2.0)) != 1) ?
           result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
         }
 
-        ${r.output} = result;
+        ${glsl.output} = result;
       }
-    `}}class v7{constructor(t,e,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=r,this.userCode=`
+    `}},DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -1889,40 +1894,40 @@ return (round(mod(b, 2.0)) != 1) ?
       int w = ${this.getWidthCoordString()};
       int d = ${this.getDepthCoordString()};
 
-      int in_h = h / ${e};
-      int offset_h = imod(h, ${e});
-      int in_w = w / ${e};
-      int offset_w = imod(w, ${e});
-      int offset_d = (offset_h * ${e} + offset_w) *
+      int in_h = h / ${blockSize};
+      int offset_h = imod(h, ${blockSize});
+      int in_w = w / ${blockSize};
+      int offset_w = imod(w, ${blockSize});
+      int offset_d = (offset_h * ${blockSize} + 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 T7{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
+  `}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)"}},DiagProgram=class{constructor(size){this.variableNames=["X"],this.outputShape=[size,size],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}}class k7{constructor(t){this.variableNames=["A"],this.outTexUsage=Br.DOWNLOAD;let e=Zn();this.outputShape=t,this.userCode=`
-      ${DI}
+    `}},EncodeFloatProgram=class{constructor(outputShape){this.variableNames=["A"],this.outTexUsage=TextureUsage.DOWNLOAD;let glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
+      ${ENCODE_FLOAT_SNIPPET}
 
       void main() {
         float x = getAAtOutCoords();
-        ${e.output} = encode_float(x);
+        ${glsl.output} = encode_float(x);
       }
-    `}}class N7{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Br.DOWNLOAD;let e=Zn();this.outputShape=t,this.userCode=`
-      ${DI}
+    `}},EncodeFloatPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=TextureUsage.DOWNLOAD;let glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
+      ${ENCODE_FLOAT_SNIPPET}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
-        ${e.output} = encode_float(x);
+        ${glsl.output} = encode_float(x);
       }
-    `}}class _7{constructor(t,e,r=!1){this.variableNames=["A"];let o=Zn(),[s,c]=e;this.outputShape=t;let l="result";r&&(l="floor(result * 255. + 0.5)"),this.userCode=`
-      ${P1(t)}
+    `}},EncodeMatrixProgram=class{constructor(outputShape,texShape,inputIsUnsignedByte=!1){this.variableNames=["A"];let glsl=getGlslDifferences(),[height,width]=texShape;this.outputShape=outputShape;let output="result";inputIsUnsignedByte&&(output="floor(result * 255. + 0.5)"),this.userCode=`
+      ${getFlatIndexFrom3D(outputShape)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1932,10 +1937,10 @@ return (round(mod(b, 2.0)) != 1) ?
 
         flatIndex = idiv(flatIndex, 4, 1.);
 
-        int r = flatIndex / ${c};
-        int c = imod(flatIndex, ${c});
-        vec2 uv = (vec2(c, r) + halfCR) / vec2(${c}.0, ${s}.0);
-        vec4 values = ${o.texture2D}(A, uv);
+        int r = flatIndex / ${width};
+        int c = imod(flatIndex, ${width});
+        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
+        vec4 values = ${glsl.texture2D}(A, uv);
 
         float result;
 
@@ -1949,38 +1954,38 @@ return (round(mod(b, 2.0)) != 1) ?
           result = values[3];
         }
 
-        ${o.output} = vec4(${l}, 0., 0., 0.);
+        ${glsl.output} = vec4(${output}, 0., 0., 0.);
       }
-    `}}class C7{constructor(t,e,r=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let o=Zn(),[s,c]=e;this.outputShape=t;let l="",p="result";r&&(p="floor(result * 255. + 0.5)");for(let f=0;f<=1;f++)for(let m=0;m<=1;m++){let y=f*2+m;l+=`
+    `}},EncodeMatrixPackedProgram=class{constructor(outputShape,texShape,inputIsUnsignedByte=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let glsl=getGlslDifferences(),[height,width]=texShape;this.outputShape=outputShape;let mainLoop="",output="result";inputIsUnsignedByte&&(output="floor(result * 255. + 0.5)");for(let row=0;row<=1;row++)for(let col=0;col<=1;col++){let channel=row*2+col;mainLoop+=`
           localCoords = coords;
-          if(localCoords[2] + ${m} < ${t[2]}) {
-            localCoords[2] += ${m};
-            if(localCoords[1] + ${f} < ${t[1]}) {
-              localCoords[1] += ${f};
+          if(localCoords[2] + ${col} < ${outputShape[2]}) {
+            localCoords[2] += ${col};
+            if(localCoords[1] + ${row} < ${outputShape[1]}) {
+              localCoords[1] += ${row};
 
               flatIndex = getFlatIndex(localCoords);
               offset = imod(flatIndex, 4);
 
               flatIndex = idiv(flatIndex, 4, 1.);
 
-              r = flatIndex / ${c};
-              c = imod(flatIndex, ${c});
-              uv = (vec2(c, r) + halfCR) / vec2(${c}.0, ${s}.0);
-              values = ${o.texture2D}(A, uv);
+              r = flatIndex / ${width};
+              c = imod(flatIndex, ${width});
+              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
+              values = ${glsl.texture2D}(A, uv);
 
               if(offset == 0) {
-                result[${y}] = values[0];
+                result[${channel}] = values[0];
               } else if(offset == 1) {
-                result[${y}] = values[1];
+                result[${channel}] = values[1];
               } else if(offset == 2) {
-                result[${y}] = values[2];
+                result[${channel}] = values[2];
               } else {
-                result[${y}] = values[3];
+                result[${channel}] = values[3];
               }
             }
           }
         `}this.userCode=`
-      ${P1(t)}
+      ${getFlatIndexFrom3D(outputShape)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1991,66 +1996,66 @@ return (round(mod(b, 2.0)) != 1) ?
         vec2 uv;
         vec4 values;
 
-        ${l}
+        ${mainLoop}
 
-        ${o.output} = ${p};
+        ${glsl.output} = ${output};
       }
-    `}}class S7{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.outputShape=t,this.userCode=`
+    `}},FillProgram=class{constructor(shape,value){this.outputShape=[],this.variableNames=["x"],this.outputShape=shape,this.userCode=`
       uniform float value;
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}getCustomSetupFunc(t){return(e,r)=>{this.valueLoc==null&&(this.valueLoc=e.getUniformLocationNoThrow(r,"value")),e.gl.uniform1f(this.valueLoc,t)}}}class $7{constructor(t,e,r){this.variableNames=["A","indices"];let o=t.slice();o[r]=e,this.outputShape=o,this.rank=o.length;let s=Oe(this.rank),c=I7(t,r);this.userCode=`
+    `}getCustomSetupFunc(value){return(gpgpu,webGLProgram)=>{this.valueLoc==null&&(this.valueLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"value")),gpgpu.gl.uniform1f(this.valueLoc,value)}}},GatherProgram=class{constructor(aShape,indicesLength,axis){this.variableNames=["A","indices"];let outputShape=aShape.slice();outputShape[axis]=indicesLength,this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),sourceCoords=getSourceCoords2(aShape,axis);this.userCode=`
       void main() {
-        ${s} resRC = getOutputCoords();
-        setOutput(getA(${c}));
+        ${dtype} resRC = getOutputCoords();
+        setOutput(getA(${sourceCoords}));
       }
-    `}}function I7(n,t){let e=n.length;if(e>4)throw Error(`Gather for rank ${e} is not yet supported`);if(e===1)return"int(getIndices(resRC))";let r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let s=0;s<n.length;s++)s===t?o.push(`int(getIndices(${r[s]}))`):o.push(`${r[s]}`);return o.join()}class E7{constructor(t,e,r){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=r;let o=Oe(e.length),s=Oe(r.length),c=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
-        ${o} strides = ${o}(${this.strides});
+    `}};function getSourceCoords2(aShape,axis){let rank=aShape.length;if(rank>4)throw Error(`Gather for rank ${rank} is not yet supported`);if(rank===1)return"int(getIndices(resRC))";let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w"],sourceCoords=[];for(let i=0;i<aShape.length;i++)i===axis?sourceCoords.push(`int(getIndices(${currentCoords[i]}))`):sourceCoords.push(`${currentCoords[i]}`);return sourceCoords.join()}var GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${stridesType} strides = ${stridesType}(${this.strides});
          void main() {
-          ${s} coords = getOutputCoords();
+          ${dtype} coords = getOutputCoords();
           int flattenIndex = 0;
           for (int j = 0; j < ${this.sliceDim}; j++) {
             int index = round(getIndices(coords[0], j));
-            flattenIndex += index * ${c};
+            flattenIndex += index * ${strideString};
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}}function D7(n){let t=Zn(),e=`${t.version}
+      `}};function createVertexShader2(gl){let glsl=getGlslDifferences(),vertexShaderSource=`${glsl.version}
     precision highp float;
-    ${t.attribute} vec3 clipSpacePos;
-    ${t.attribute} vec2 uv;
-    ${t.varyingVs} vec2 resultUV;
+    ${glsl.attribute} vec3 clipSpacePos;
+    ${glsl.attribute} vec2 uv;
+    ${glsl.varyingVs} vec2 resultUV;
 
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return uX(n,e)}function A7(n){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return gX(n,t)}function F7(n){let t=new Uint16Array([0,1,2,2,1,3]);return yX(n,t)}function hh(n,t,e,r,o,s){xX(t,e);let c=bX(n),l=n.TEXTURE_2D;return Rt(n,()=>n.bindTexture(l,c)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_MIN_FILTER,n.NEAREST)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_MAG_FILTER,n.NEAREST)),Rt(n,()=>n.texImage2D(l,0,r,t,e,0,o,s,null)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null)),c}function UI(n){return n.internalFormatFloat}function R7(n,t,e,r){let[o,s]=lh(t,e);return hh(n,o,s,UI(r),r.textureFormatFloat,n.FLOAT)}function qI(n){return n.internalFormatHalfFloat}function P7(n,t,e,r){let[o,s]=lh(t,e);return hh(n,o,s,qI(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function HI(n){return n.downloadTextureFormat}function O7(n,t,e,r){let[o,s]=lh(t,e);return hh(n,o,s,HI(r),n.RGBA,n.UNSIGNED_BYTE)}function jI(n){return n.internalFormatPackedFloat}function L7(n,t,e,r){let[o,s]=fl(t,e);return hh(n,o,s,jI(r),n.RGBA,n.FLOAT)}function KI(n){return n.internalFormatPackedHalfFloat}function M7(n,t,e,r){let[o,s]=fl(t,e);return hh(n,o,s,KI(r),n.RGBA,r.textureTypeHalfFloat)}function B7(n,t,e){let r=0,o=3*4,s=3*4+2*4;Rt(n,()=>n.bindBuffer(n.ARRAY_BUFFER,e));let c=CI(n,t,"clipSpacePos",e,3,s,r);return c&&CI(n,t,"uv",e,2,s,o)}function z7(n,t,e,r,o,s){Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t));let c,l,p;o instanceof Uint8Array?(c=new Uint8Array(e*r*4),l=n.UNSIGNED_BYTE,p=n.RGBA):(c=new Float32Array(e*r*4),l=n.FLOAT,p=s.internalFormatPackedFloat),c.set(o),Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,p,e,r,0,n.RGBA,l,c)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function W7(n,t,e){Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t)),e.data instanceof Uint8Array?Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,e.width,e.height,0,n.RGBA,n.UNSIGNED_BYTE,e.data)):Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,n.RGBA,n.UNSIGNED_BYTE,e)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function V7(n,t,e,r){let o=n.createBuffer();Rt(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,o));let s=4,c=4,l=s*c*t*e;return Rt(n,()=>n.bufferData(n.PIXEL_PACK_BUFFER,l,n.STREAM_READ)),Rt(n,()=>n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,0)),Rt(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,null)),o}function G7(n,t,e){let r=n,o=new Float32Array(e);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,o),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),o}function U7(n,t,e,r){let[o,s]=lh(t,e),c=4,l=new Uint8Array(rX(t*e,c));return Rt(n,()=>n.readPixels(0,0,o,s,r.downloadTextureFormat,n.UNSIGNED_BYTE,l)),new Float32Array(l.buffer)}function q7(n,t,e,r,o,s,c,l){let p=n,f=new Float32Array(oX(s,c));return p.bindBuffer(p.PIXEL_PACK_BUFFER,t),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,f),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),f}function H7(n,t,e){let r=new Float32Array(t*e*4);return Rt(n,()=>n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,r)),r}class j7{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let e=ct().getNumber("WEBGL_VERSION");t!=null?(this.gl=t,tX(e,t)):this.gl=Xo(e);let r="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(ct().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",c="OES_texture_half_float";if(this.textureFloatExtension=Xm(this.gl,s),ro(this.gl,c))this.textureHalfFloatExtension=Xm(this.gl,c);else if(ct().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(r),ro(this.gl,o))this.colorBufferHalfFloatExtension=Xm(this.gl,o);else if(ct().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(r="EXT_color_buffer_float",ro(this.gl,r))this.colorBufferFloatExtension=this.gl.getExtension(r);else if(ro(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=A7(this.gl),this.indexBuffer=F7(this.gl),this.framebuffer=wX(this.gl),this.textureConfig=I1(this.gl,this.textureHalfFloatExtension)}get debug(){return ct().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.");let t=this.gl;Rt(t,()=>t.finish()),Rt(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),Rt(t,()=>t.deleteFramebuffer(this.framebuffer)),Rt(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),Rt(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),Rt(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),R7(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),P7(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),O7(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),W7(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,r,o){this.throwIfDisposed(),z7(this.gl,t,e,r,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),M7(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),L7(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(SI(this.gl,this.framebuffer),this.outputTexture=null),Rt(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,r){return this.downloadMatrixDriver(t,()=>U7(this.gl,e,r,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,r,o,s,c){return q7(this.gl,t,e,r,o,s,c,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return G7(this.gl,t,e)}createBufferFromTexture(t,e,r){this.bindTextureToFrameBuffer(t);let o=V7(this.gl,e,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,r;if(ct().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),r=()=>{let c=o.clientWaitSync(s,0,0);return c===o.ALREADY_SIGNALED||c===o.CONDITION_SATISFIED},e=s}else ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),r=()=>this.isQueryAvailable(e,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):r=()=>!0;return{query:e,isFencePassed:r}}downloadMatrixFromPackedTexture(t,e,r){return this.downloadMatrixDriver(t,()=>H7(this.gl,e,r))}createProgram(t){this.throwIfDisposed();let e=this.gl,r=pX(e,t),o=D7(e),s=dX(e);return Rt(e,()=>e.attachShader(s,o)),Rt(e,()=>e.attachShader(s,r)),mX(e,s),this.debug&&E1(e,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=B7(e,this.program,this.vertexBuffer)),s}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&Rt(this.gl,()=>this.gl.deleteProgram(t))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&E1(this.gl,this.program),Rt(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,r=!0){return this.throwIfDisposed(),r?TX(this.gl,t,e):kX(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),Rt(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,r){this.throwIfDisposed(),this.throwIfNoProgram(),NX(this.gl,t,e,r)}setOutputMatrixTexture(t,e,r){this.setOutputMatrixTextureDriver(t,r,e)}setOutputPackedMatrixTexture(t,e,r){this.throwIfDisposed();let[o,s]=fl(e,r);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,r,o){this.setOutputMatrixWriteRegionDriver(r,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,r,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&E1(this.gl,this.program),Ym(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;this.debug&&this.debugValidate(),Rt(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Rt(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Xm(this.gl,ct().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(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=r.createQuery();return r.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,r=this.getQueryTimerExtensionWebGL2();e.endQuery(r.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await _e(()=>this.disposed||this.isQueryAvailable(t,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let r=this.gl,o=r.getQueryParameter(t,r.QUERY_RESULT);return o/1e6}else{let r=this.getQueryTimerExtensionWebGL1(),o=r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT);return o/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let r=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=r.getQueryParameter(t,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),o=r.getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=K7(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:r}=this.itemsToPoll[e];r()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;_e(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(t){this.throwIfDisposed(),D1(this.gl,t,this.framebuffer),this.debug&&Ym(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(D1(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ym(this.gl)):SI(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let r=e();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(t,e,r){this.throwIfDisposed();let o=this.gl;D1(o,t,this.framebuffer),this.debug&&Ym(o),this.outputTexture=t,Rt(o,()=>o.viewport(0,0,e,r)),Rt(o,()=>o.scissor(0,0,e,r))}setOutputMatrixWriteRegionDriver(t,e,r,o){this.throwIfDisposed(),Rt(this.gl,()=>this.gl.scissor(t,e,r,o))}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 K7(n){let t=0;for(;t<n.length;++t){let e=n[t]();if(!e)break}return t-1}function X7(n,t,e,r){let o=t.userCode,s=e.map((v,T)=>{let N={logicalShape:v.shape,texShape:v.isUniform?null:v.texData.texShape,isUniform:v.isUniform,isPacked:v.isUniform?!1:v.texData.isPacked,flatOffset:null};return v.texData!=null&&v.texData.slice!=null&&v.texData.slice.flatOffset>0&&(N.flatOffset=v.texData.slice.flatOffset),{name:t.variableNames[T],shapeInfo:N}}),c=s.map(v=>v.shapeInfo),l={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},p=JX(s,l,o,t.packedInputs),f=n.createProgram(p),m=null,y=n.getUniformLocation(f,"NAN",!1);ct().getNumber("WEBGL_VERSION")===1&&(m=n.getUniformLocation(f,"INFINITY",!1));let b={};for(let v=0;v<t.variableNames.length;v++){let T=t.variableNames[v],N=!1;b[T]=n.getUniformLocation(f,T,N),b[`offset${T}`]=n.getUniformLocation(f,`offset${T}`,N)}return{program:t,source:p,webGLProgram:f,uniformLocations:b,inShapeInfos:c,outShapeInfo:l,infLoc:m,nanLoc:y}}function XI(n,t){if(n.length!==t.length)throw Error(`Binary was compiled with ${n.length} inputs, but was executed with ${t.length} inputs`);n.forEach((e,r)=>{let o=e.logicalShape,s=t[r],c=s.shape;if(!lt(o,c))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${c} must match`);if(e.isUniform&&s.isUniform)return;let l=e.texShape,p=s.isUniform?null:s.texData.texShape;if(!lt(l,p))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${l} and ${p} must match`)})}function Y7(n,t,e,r,o){XI(t.inShapeInfos,e),XI([t.outShapeInfo],[r]);let s=r.texData.texture,c=r.texData.texShape;r.texData.isPacked?n.setOutputPackedMatrixTexture(s,c[0],c[1]):n.setOutputMatrixTexture(s,c[0],c[1]),n.setProgram(t.webGLProgram),ct().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&n.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&n.gl.uniform1f(t.nanLoc,NaN),e.forEach((l,p)=>{let f=t.program.variableNames[p],m=t.uniformLocations[f],y=t.uniformLocations[`offset${f}`];if(m==null)return;if(l.isUniform){if(G(l.shape)<2)n.gl.uniform1f(m,l.uniformValues[0]);else{let b=l.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),n.gl.uniform1fv(m,b)}return}l.texData.slice!=null&&y!=null&&n.gl.uniform1i(y,l.texData.slice.flatOffset),n.setInputMatrixTexture(l.texData.texture,m,p)}),o!=null&&o(n,t.webGLProgram),n.executeProgram()}function J7(n,t,e){let r="";t.concat(e).forEach(c=>{let l=c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0,p=c.isUniform?"uniform":c.texData.texShape;r+=`${c.shape}_${p}_${l}`});let o=n.userCode,s=n.constructor.name;return s+="_"+r+"_"+o,s}class Z7{constructor(t,e,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let{filterWidth:o,inChannels:s,strideWidth:c,strideHeight:l,padInfo:p,outWidth:f,dilationWidth:m,dilationHeight:y,dataFormat:b}=r,{left:v,top:T}=p,N=s*o,S=Zn(),D=b==="channelsLast",I=D?0:1,P=D?1:2,E="";for(let L=0;L<=1;L++)for(let B=0;B<=1;B++)E+=`
-          blockIndex = rc.y + ${B};
-          pos = rc.x + ${L};
+    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer));let success=bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset);return success&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer11=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11));let bytesPerFloat=4,valuesPerTexel=4,bufferSizeBytes=bytesPerFloat*valuesPerTexel*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer11}function downloadFloat32MatrixFromBuffer(gl,buffer11,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer11,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().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(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().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(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().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.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer11,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer11,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer11,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer11,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().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(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl,timeElapsedNanos=gl2.getQueryParameter(query,gl2.QUERY_RESULT);return timeElapsedNanos/1e6}else{let ext=this.getQueryTimerExtensionWebGL1(),timeElapsedNanos=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT);return timeElapsedNanos/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){if(this.itemsToPoll.push({isDoneFn,resolveFn}),this.itemsToPoll.length>1)return;util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}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 linearSearchLastTrue(arr){let i=0;for(;i<arr.length;++i){let isDone=arr[i]();if(!isDone)break}return i-1}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(s.isUniform&&input2.isUniform)return;let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc==null)return;if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var Im2ColPackedProgram=class{constructor(outputShape,inputShape,convInfo){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let{filterWidth,inChannels,strideWidth,strideHeight,padInfo,outWidth,dilationWidth,dilationHeight,dataFormat}=convInfo,{left,top}=padInfo,itemsPerBlockRow=inChannels*filterWidth,glsl=getGlslDifferences(),isChannelsLast=dataFormat==="channelsLast",rowDim=isChannelsLast?0:1,colDim=isChannelsLast?1:2,unrolled="";for(let row=0;row<=1;row++)for(let col=0;col<=1;col++)unrolled+=`
+          blockIndex = rc.y + ${col};
+          pos = rc.x + ${row};
 
-          if(blockIndex < ${t[1]} && pos < ${t[0]}) {
-            offsetY = int(blockIndex / (${f})) * ${l} - ${T};
-            d0 = offsetY + ${y} * (pos / ${N});
+          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {
+            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};
+            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});
 
-            if(d0 < ${e[I]} && d0 >= 0) {
+            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {
 
-              offsetX = int(mod(float(blockIndex), ${f}.) * ${c}. - ${v}.);
-              d1 = offsetX + ${m} * (int(mod(float(pos), ${N}.) / ${s}.));
+              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);
+              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));
 
-              if(d1 < ${e[P]} && d1 >= 0) {
+              if(d1 < ${inputShape[colDim]} && d1 >= 0) {
 
-                ch = int(mod(float(pos), ${s}.));
+                ch = int(mod(float(pos), ${inChannels}.));
 
-                if (${D}) {
+                if (${isChannelsLast}) {
                   innerDims = vec2(d1, ch);
-                  result[${L*2+B}] = getChannel(
+                  result[${row*2+col}] = getChannel(
                     getA(d0, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 } else {
                   innerDims = vec2(d0, d1);
-                  result[${L*2+B}] = getChannel(
+                  result[${row*2+col}] = getChannel(
                     getA(ch, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 }
@@ -2066,11 +2071,11 @@ return (round(mod(b, 2.0)) != 1) ?
         int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
         vec2 innerDims;
 
-        ${E}
+        ${unrolled}
 
-        ${S.output} = result;
+        ${glsl.output} = result;
       }
-    `}}class Q7{constructor(t,e,r,o,s){this.variableNames=["x"],this.outputShape=[];let c=e,l=t[3]-1;this.outputShape=t;let p,f=`float(${r}) + float(${o}) * sum`;s===.5?p=`inversesqrt(${f})`:s===1?p=`1.0/(${f})`:p=`exp(log(${f}) * float(-${s}));`,this.userCode=`
+    `}},LRNProgram=class{constructor(xShape,radius,bias,alpha,beta){this.variableNames=["x"],this.outputShape=[];let rad=radius,maxD=xShape[3]-1;this.outputShape=xShape;let powOperator,basis=`float(${bias}) + float(${alpha}) * sum`;beta===.5?powOperator=`inversesqrt(${basis})`:beta===1?powOperator=`1.0/(${basis})`:powOperator=`exp(log(${basis}) * float(-${beta}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -2079,17 +2084,17 @@ return (round(mod(b, 2.0)) != 1) ?
         int d = coords[3];
         float x = getX(b, r, c, d);
         float sum = 0.0;
-        for (int j = -${c}; j <= ${c}; j++) {
+        for (int j = -${rad}; j <= ${rad}; j++) {
           int idx = d + j;
-          if (idx >= 0 && idx <=  ${l}) {
+          if (idx >= 0 && idx <=  ${maxD}) {
             float z = getX(b, r, c, idx);
             sum += z * z;
           }
         }
-        float val = x * ${p};
+        float val = x * ${powOperator};
         setOutput(val);
       }
-    `}}class tJ{constructor(t,e,r,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=r,this.alpha=o,this.beta=s,this.userCode=`
+    `}},LRNGradProgram=class{constructor(inputShape,depthRadius,bias,alpha,beta){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=inputShape,this.depth=inputShape[3],this.depthRadius=depthRadius,this.bias=bias,this.alpha=alpha,this.beta=beta,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -2098,9 +2103,9 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float result = 0.0;
         for (int d = 0; d < ${this.depth}; ++d) {
-          int depthBegin = int(max(0.0, float(d - ${e})));
+          int depthBegin = int(max(0.0, float(d - ${depthRadius})));
           int depthEnd = int(min(float(${this.depth}),
-              float(d + ${e} + 1)));
+              float(d + ${depthRadius} + 1)));
 
           const int MIN_DEPTH_BEGIN = 0;
           const int MAX_DEPTH_END = ${this.depth};
@@ -2118,19 +2123,19 @@ return (round(mod(b, 2.0)) != 1) ?
             }
           }
 
-          norm = float(${o}) * norm + float(${r});
+          norm = float(${alpha}) * norm + float(${bias});
 
           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(${o})
-                * float(${s})
+              float dyi = -2.0 * float(${alpha})
+                * float(${beta})
                 * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                 / norm;
               if (k == d) {
-                dyi += pow(norm, -1.0 * ${s});
+                dyi += pow(norm, -1.0 * ${beta});
               }
               if (k == coords[3]) {
                 dyi *= getDy(b, r, c, d);
@@ -2144,7 +2149,7 @@ return (round(mod(b, 2.0)) != 1) ?
       }
       setOutput(result);
       }
-    `}}class eJ{constructor(t,e,r,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let c=e,l=t[3]-1;this.outputShape=t;let p,f=`float(${r}) + float(${o}) * sum`;s===.5?p=`inversesqrt(${f})`:s===1?p=`1.0/(${f})`:p=`exp(log(${f}) * float(-${s}));`,this.userCode=`
+    `}},LRNPackedProgram=class{constructor(xShape,radius,bias,alpha,beta){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let rad=radius,maxD=xShape[3]-1;this.outputShape=xShape;let powOperator,basis=`float(${bias}) + float(${alpha}) * sum`;beta===.5?powOperator=`inversesqrt(${basis})`:beta===1?powOperator=`1.0/(${basis})`:powOperator=`exp(log(${basis}) * float(-${beta}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -2168,7 +2173,7 @@ return (round(mod(b, 2.0)) != 1) ?
             getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
         );
 
-        int firstChannel = d - ${c};
+        int firstChannel = d - ${rad};
         vec2 cache = vec2(0.);
         if(firstChannel >= 0){
           vec4 firstChannelFrag = getX(b, r, c, firstChannel);
@@ -2179,10 +2184,10 @@ return (round(mod(b, 2.0)) != 1) ?
         }
 
         ivec2 depth = ivec2(d, d + 1);
-        for (int j = - ${c}; j <= ${c}; j++) {
+        for (int j = - ${rad}; j <= ${rad}; j++) {
           ivec2 idx = depth + j;
           bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
-          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${l}));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));
 
           bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
           bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
@@ -2203,11 +2208,11 @@ return (round(mod(b, 2.0)) != 1) ?
             sum += z * z;
           }
         }
-        vec4 result = xAtOutputCoords * ${p};
+        vec4 result = xAtOutputCoords * ${powOperator};
         setOutput(result);
       }
-    `}}class nJ{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,r=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=s-1-t.padInfo.top,p=c-1-t.padInfo.left,f=s*c-1;this.userCode=`
-      const ivec2 pads = ivec2(${l}, ${p});
+    `}},MaxPool2DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy","maxPos"],this.outputShape=convInfo.inShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,lastIndex=effectiveFilterHeight*effectiveFilterWidth-1;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -2221,30 +2226,30 @@ return (round(mod(b, 2.0)) != 1) ?
         // 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 < ${s};
-          wR += ${o}) {
-          float dyR = float(dyRCorner + wR) / ${e}.0;
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+          wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
           }
           int idyR = int(dyR);
 
-          for (int wC = 0; wC < ${c}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${r}.0;
+          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                 fract(dyC) > 0.0) {
               continue;
             }
             int idyC = int(dyC);
 
             float dyValue = getDy(b, idyR, idyC, d);
-            int maxPosValue = ${f} - int(getMaxPos(b, idyR, idyC, d));
+            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));
 
             // Get the current value, check it against the value from the
             // position matrix.
-            int curPosValue = wR * ${c} + wC;
+            int curPosValue = wR * ${effectiveFilterWidth} + wC;
             float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
 
             dotProd += dyValue * mask;
@@ -2252,8 +2257,8 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class rJ{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,r=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,c=t.dilationHeight,l=t.dilationWidth,p=t.effectiveFilterDepth,f=t.effectiveFilterHeight,m=t.effectiveFilterWidth,y=p-1-t.padInfo.front,b=f-1-t.padInfo.top,v=m-1-t.padInfo.left,T=p*f*m-1;this.userCode=`
-      const ivec3 pads = ivec3(${y}, ${b}, ${v});
+    `}},MaxPool3DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy","maxPos"],this.outputShape=convInfo.inShape;let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,lastIndex=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1;this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -2270,44 +2275,44 @@ return (round(mod(b, 2.0)) != 1) ?
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
 
-        for (int wD = 0; wD < ${p};
-           wD += ${s}) {
-          float dyD = float(dyDCorner + wD) / ${e}.0;
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+           wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
 
-          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
             continue;
           }
           int idyD = int(dyD);
 
-          for (int wR = 0; wR < ${f};
-              wR += ${c}) {
-            float dyR = float(dyRCorner + wR) / ${r}.0;
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
 
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                 fract(dyR) > 0.0) {
               continue;
             }
             int idyR = int(dyR);
 
-            for (int wC = 0; wC < ${m};
-                wC += ${l}) {
-              float dyC = float(dyCCorner + wC) / ${o}.0;
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
 
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                   fract(dyC) > 0.0) {
                 continue;
               }
               int idyC = int(dyC);
 
               float dyValue = getDy(batch, idyD, idyR, idyC, ch);
-              int maxPosValue = ${T} -
+              int maxPosValue = ${lastIndex} -
                   int(getMaxPos(batch, idyD, idyR, idyC, ch));
 
               // Get the current value, check it against the value from the
               // position matrix.
               int curPosValue =
-                  wD * ${f} * ${m} +
-                  wR * ${m} + wC;
+                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                  wR * ${effectiveFilterWidth} + wC;
               float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
 
               dotProd += dyValue * mask;
@@ -2316,28 +2321,28 @@ return (round(mod(b, 2.0)) != 1) ?
         }
         setOutput(dotProd);
       }
-    `}}class O1{constructor(t,e,r,o=!1,s=!1,c=!1,l=null,p=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r;let f=o?t[1]:t[2],m=Math.ceil(f/2),y=o?"i * 2, rc.y":"rc.y, i * 2",b=s?"rc.z, i * 2":"i * 2, rc.z",v=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],T=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],N="",S="";l&&(p?N=`vec4 activation(vec4 a) {
+    `}},MatMulPackedProgram=class{constructor(aShape,bShape,outputShape,transposeA=!1,transposeB=!1,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let sharedDim=transposeA?aShape[1]:aShape[2],sharedDimensionPacked=Math.ceil(sharedDim/2),aSample=transposeA?"i * 2, rc.y":"rc.y, i * 2",bSample=transposeB?"rc.z, i * 2":"i * 2, rc.z",aSwizzle=transposeA?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],bSwizzle=transposeB?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${l}
-        }`:N=`vec4 activation(vec4 x) {
-          ${l}
-        }`,S="result = activation(result);");let D=c?"result += getBiasAtOutCoords();":"";c&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights");let I="rc.x",P="rc.x";t[0]<e[0]?I=`int(min(float(rc.x), ${t[0]-1}.))`:e[0]<t[0]&&(P=`int(min(float(rc.x), ${e[0]-1}.))`),this.userCode=`
-      ${N}
+          ${activation2}
+        }`:activationSnippet=`vec4 activation(vec4 x) {
+          ${activation2}
+        }`,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights");let batchASnippet="rc.x",batchBSnippet="rc.x";aShape[0]<bShape[0]?batchASnippet=`int(min(float(rc.x), ${aShape[0]-1}.))`:bShape[0]<aShape[0]&&(batchBSnippet=`int(min(float(rc.x), ${bShape[0]-1}.))`),this.userCode=`
+      ${activationSnippet}
 
-      const float sharedDimension = ${m}.0;
+      const float sharedDimension = ${sharedDimensionPacked}.0;
 
       vec4 dot2x2ARowBCol(ivec3 rc) {
         vec4 result = vec4(0);
-        for (int i = 0; i < ${m}; i++) {
-          int batchA = ${I};
-          int batchB = ${P};
-          vec4 a = getMatrixA(batchA, ${y});
-          vec4 b = getMatrixB(batchB, ${b});
+        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
+          int batchA = ${batchASnippet};
+          int batchB = ${batchBSnippet};
+          vec4 a = getMatrixA(batchA, ${aSample});
+          vec4 b = getMatrixB(batchB, ${bSample});
 
           // These swizzled products need to be separately added.
           // See: https://github.com/tensorflow/tfjs/issues/1735
-          result += (${v[0]} * ${T[0]});
-          result += (${v[1]} * ${T[1]});
+          result += (${aSwizzle[0]} * ${bSwizzle[0]});
+          result += (${aSwizzle[1]} * ${bSwizzle[1]});
         }
         return result;
       }
@@ -2346,13 +2351,13 @@ return (round(mod(b, 2.0)) != 1) ?
         ivec3 rc = getOutputCoords();
         vec4 result = dot2x2ARowBCol(rc);
 
-        ${D}
+        ${addBiasSnippet}
 
-        ${S}
+        ${applyActivationSnippet}
 
         setOutput(result);
       }
-    `}}class oJ{constructor(t,e,r){this.variableNames=["probs"],this.outputShape=[t,r],this.userCode=`
+    `}},MultinomialProgram=class{constructor(batchSize,numOutcomes,numSamples){this.variableNames=["probs"],this.outputShape=[batchSize,numSamples],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -2362,7 +2367,7 @@ return (round(mod(b, 2.0)) != 1) ?
         float r = random(seed);
         float cdf = 0.0;
 
-        for (int i = 0; i < ${e-1}; i++) {
+        for (int i = 0; i < ${numOutcomes-1}; i++) {
           cdf += getProbs(batch, i);
 
           if (r < cdf) {
@@ -2372,96 +2377,96 @@ return (round(mod(b, 2.0)) != 1) ?
         }
 
         // If no other event happened, last event happened.
-        setOutput(float(${e-1}));
+        setOutput(float(${numOutcomes-1}));
       }
-    `}getCustomSetupFunc(t){return(e,r)=>{this.seedLoc==null&&(this.seedLoc=e.getUniformLocation(r,"seed")),e.gl.uniform1f(this.seedLoc,t)}}}class sJ{constructor(t,e,r,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
+    `}getCustomSetupFunc(seed){return(gpgpu,webGLProgram)=>{this.seedLoc==null&&(this.seedLoc=gpgpu.getUniformLocation(webGLProgram,"seed")),gpgpu.gl.uniform1f(this.seedLoc,seed)}}},OneHotProgram=class{constructor(numIndices,depth,onValue,offValue){this.variableNames=["indices"],this.outputShape=[numIndices,depth],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
-        setOutput(mix(float(${o}), float(${r}),
+        setOutput(mix(float(${offValue}), float(${onValue}),
                       float(index == coords.y)));
       }
-    `}}class iJ{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;let e=t.length;if(e===0)this.userCode=`
+    `}},PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let r=Jn("rc",e),o=Oe(e),s=cJ(e,t,r),c=lJ(e,t[t.length-1],t[t.length-2],r),l=uJ(t,r);this.userCode=`
+      `;else{let channels=getChannels("rc",rank),dtype=getCoordsDataType(rank),outOfBoundsCondition=getOutOfBoundsCondition(rank,outputShape,channels),setup38=getSetup(rank,outputShape[outputShape.length-1],outputShape[outputShape.length-2],channels),output=getOutput(outputShape,channels);this.userCode=`
         void main() {
-          ${o} rc = getOutputCoords();
+          ${dtype} rc = getOutputCoords();
 
-          if(${s}) {
+          if(${outOfBoundsCondition}) {
             setOutput(vec4(0));
           } else {
-            ${c}
+            ${setup38}
 
-            setOutput(vec4(${l}));
+            setOutput(vec4(${output}));
           }
         }
-      `}}}function aJ(n,t){let e=[];for(let r=0;r<=1;r++)for(let o=0;o<=1;o++){let s=`${r===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let c=2;c<n;c++)s=`${t[t.length-1-c]},`+s;e.push(s)}return e}function cJ(n,t,e){if(n===1)return`rc > ${t[0]}`;let r="";for(let o=n-2;o<n;o++)r+=`${e[o]} >= ${t[o]}`,o<n-1&&(r+="||");return r}function lJ(n,t,e,r){if(n===1)return"";let o=r.slice(-2);return`
-    int r = ${o[0]};
-    int c = ${o[1]};
+      `}}};function getSourceCoordsArr(rank,dims){let coords2=[];for(let row=0;row<=1;row++)for(let col=0;col<=1;col++){let coord=`${row===0?"r":"rp1"}, ${col===0?"c":"cp1"}`;for(let d=2;d<rank;d++)coord=`${dims[dims.length-1-d]},`+coord;coords2.push(coord)}return coords2}function getOutOfBoundsCondition(rank,shape,dims){if(rank===1)return`rc > ${shape[0]}`;let cond="";for(let i=rank-2;i<rank;i++)cond+=`${dims[i]} >= ${shape[i]}`,i<rank-1&&(cond+="||");return cond}function getSetup(rank,cols,rows,dims){if(rank===1)return"";let innerDims=dims.slice(-2);return`
+    int r = ${innerDims[0]};
+    int c = ${innerDims[1]};
     int rp1 = r + 1;
     int cp1 = c + 1;
 
-    bool cEdge = cp1 >= ${t};
-    bool rEdge = rp1 >= ${e};
-  `}function uJ(n,t){let e=n.length,r=aJ(e,t);return e===1?`getA(rc),
-            rc + 1 >= ${n[0]} ? 0. : getA(rc + 1),
-            0, 0`:`getA(${r[0]}),
-          cEdge ? 0. : getA(${r[1]}),
-          rEdge ? 0. : getA(${r[2]}),
-          rEdge || cEdge ? 0. : getA(${r[3]})`}class pJ{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((f,m)=>f[0]+t[m]+f[1]);let o=t.length,s=Oe(o),c=e.map(f=>f[0]).join(","),l=e.map((f,m)=>f[0]+t[m]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
-        int start = ${c};
-        int end = ${l};
+    bool cEdge = cp1 >= ${cols};
+    bool rEdge = rp1 >= ${rows};
+  `}function getOutput(shape,dims){let rank=shape.length,sourceCoords=getSourceCoordsArr(rank,dims);return rank===1?`getA(rc),
+            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),
+            0, 0`:`getA(${sourceCoords[0]}),
+          cEdge ? 0. : getA(${sourceCoords[1]}),
+          rEdge ? 0. : getA(${sourceCoords[2]}),
+          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var PadProgram=class{constructor(xShape,paddings,constantValue){this.variableNames=["x"],this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,type=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),unpackedCoords=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,rank);if(rank===1){this.userCode=`
+        int start = ${start};
+        int end = ${end};
 
         void main() {
           int outC = getOutputCoords();
           if (outC < start || outC >= end) {
-            setOutput(float(${r}));
+            setOutput(float(${constantValue}));
           } else {
             setOutput(getX(outC - start));
           }
         }
       `;return}this.userCode=`
-      ${s} start = ${s}(${c});
-      ${s} end = ${s}(${l});
+      ${type} start = ${type}(${start});
+      ${type} end = ${type}(${end});
 
       void main() {
-        ${s} outC = getOutputCoords();
+        ${type} outC = getOutputCoords();
         if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
-          setOutput(float(${r}));
+          setOutput(float(${constantValue}));
         } else {
-          ${s} coords = outC - start;
-          setOutput(getX(${p}));
+          ${type} coords = outC - start;
+          setOutput(getX(${unpackedCoords}));
         }
       }
-    `}}class hJ{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((N,S)=>N[0]+t[S]+N[1]);let o=t.length,s=Oe(o),c=e.map(N=>N[0]).join(","),l=e.map((N,S)=>N[0]+t[S]).join(","),p=Jn("rc",o),f=Jn("source",o),m=`${p[o-1]} < ${this.outputShape[o-1]}`,y=o===1?"source":`vec2(${f.slice(-2).join()})`,b=[`${s} rc = outputLoc;`,`${p[o-1]} += 1;
-       if(${m}) {
-      `,o===1?"":`}
+    `}},PadPackedProgram=class{constructor(xShape,paddings,constantValue){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),coords2=getChannels("rc",rank),source=getChannels("source",rank),cLimit=`${coords2[rank-1]} < ${this.outputShape[rank-1]}`,innerDims=rank===1?"source":`vec2(${source.slice(-2).join()})`,componentSetup=[`${dtype} rc = outputLoc;`,`${coords2[rank-1]} += 1;
+       if(${cLimit}) {
+      `,rank===1?"":`}
        rc = outputLoc;
-       ${p[o-2]} += 1;
-       if(${p[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":`  ${p[o-1]} += 1;
-         if(${m}) {`],v=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",T="";for(let N=0,S=o===1?2:4;N<S;N++)T+=`
-        ${b[N]}
-        if (${v}) {
-          result[${N}] = float(${r});
+       ${coords2[rank-2]} += 1;
+       if(${coords2[rank-2]} < ${this.outputShape[rank-2]}) {`,rank===1?"":`  ${coords2[rank-1]} += 1;
+         if(${cLimit}) {`],paddingArea=rank===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",mainLoop="";for(let i=0,j=rank===1?2:4;i<j;i++)mainLoop+=`
+        ${componentSetup[i]}
+        if (${paddingArea}) {
+          result[${i}] = float(${constantValue});
         } else {
-          ${s} source = rc - start;
-          result[${N}] = getChannel(getX(${f.join()}), ${y});
+          ${dtype} source = rc - start;
+          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
         }
-      `;T+=o===1?"} ":"}}",this.userCode=`
-      const ${s} start = ${s}(${c});
-      const ${s} end = ${s}(${l});
+      `;mainLoop+=rank===1?"} ":"}}",this.userCode=`
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
 
       void main() {
-        ${s} outputLoc = getOutputCoords();
+        ${dtype} outputLoc = getOutputCoords();
         vec4 result = vec4(0.);
-        ${T}
+        ${mainLoop}
         setOutput(result);
       }
-    `}}class fh{constructor(t,e,r,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let c=t.filterWidth,l=t.strideHeight,p=t.strideWidth,f=t.dilationHeight,m=t.dilationWidth,y=t.effectiveFilterHeight,b=t.effectiveFilterWidth,v=t.padInfo.top,T=t.padInfo.left;this.outputShape=t.outShape;let N=e==="avg",S=`((batch  * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,D=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,I="0.0";if(N||(I="-1.0 / 1e-20"),r){let H=">=";this.userCode=`
-        const ivec2 strides = ivec2(${l}, ${p});
-        const ivec2 pads = ivec2(${v}, ${T});
+    `}},Pool2DProgram=class{constructor(convInfo,poolType,computePositions,flattenPositions=!1,includeBatchInIndex=!1){if(this.variableNames=["x"],poolType==="avg"&&computePositions)throw new Error("Cannot compute positions for average pool.");let filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.outputShape=convInfo.outShape;let isAvgPool=poolType==="avg",batchFlattenPositionStr=`((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`,flattenPositionStr=`(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`,initializationValue="0.0";if(isAvgPool||(initializationValue="-1.0 / 1e-20"),computePositions){let compareOp2=">=";this.userCode=`
+        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+        const ivec2 pads = ivec2(${padTop}, ${padLeft});
 
         void main() {
           ivec4 coords = getOutputCoords();
@@ -2479,19 +2484,19 @@ return (round(mod(b, 2.0)) != 1) ?
           int minMaxPosition = 0;
           float avgValue = 0.0;
 
-          for (int wR = 0; wR < ${y};
-              wR += ${f}) {
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
             int xR = xRCorner + wR;
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int wC = 0; wC < ${b};
-                wC += ${m}) {
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
               int xC = xCCorner + wC;
 
-              if (xC < 0 || xC >= ${t.inWidth}) {
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                 continue;
               }
 
@@ -2501,31 +2506,31 @@ return (round(mod(b, 2.0)) != 1) ?
               // use the current value.
               float currMinMaxValue = mix(
                   value, minMaxValue, minMaxValueFound);
-              if (value ${H} currMinMaxValue) {
+              if (value ${compareOp2} currMinMaxValue) {
                 minMaxValue = value;
                 minMaxValueFound = 1.0;
-                minMaxPosition = ${o?s?S:D:`wR * ${b} + wC`};
+                minMaxPosition = ${flattenPositions?includeBatchInIndex?batchFlattenPositionStr:flattenPositionStr:`wR * ${effectiveFilterWidth} + wC`};
               }
             }
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let P="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / count");let L=Math.floor(c/4)*4,B=c%4,q=`
-      if (${N}) {
+      `;return}let compareOp="max",returnValue=`${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;poolType==="avg"&&(returnValue="avgValue / count");let filterWidthNearestVec4=Math.floor(filterWidth/4)*4,filterWidthVec4Remainder=filterWidth%4,updateSnippet=`
+      if (${isAvgPool}) {
         avgValue += dot(values, ones);
       } else {
-        minMaxValue = ${P}(values, minMaxValue);
+        minMaxValue = ${compareOp}(values, minMaxValue);
       }
     `;this.userCode=`
-      const ivec2 strides = ivec2(${l}, ${p});
-      const ivec2 pads = ivec2(${v}, ${T});
-      const float initializationValue = ${I};
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
       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 >= ${t.inWidth}) {
+        if (xC < 0 || xC >= ${convInfo.inWidth}) {
           return initializationValue;
         }
         count += 1.0;
@@ -2543,33 +2548,33 @@ return (round(mod(b, 2.0)) != 1) ?
 
         // max/min x(?, ?, d) to get y(yR, yC, d).
         // ? = to be determined
-        vec4 minMaxValue = vec4(${I});
+        vec4 minMaxValue = vec4(${initializationValue});
         float avgValue = 0.0;
         count = 0.0;
 
-        for (int wR = 0; wR < ${y};
-            wR += ${f}) {
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
           int xR = xRCorner + wR;
 
-          if (xR < 0 || xR >= ${t.inHeight}) {
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
             continue;
           }
 
-          for (int wC = 0; wC < ${L}; wC += 4) {
-            int xC = xCCorner + wC * ${m};
+          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+            int xC = xCCorner + wC * ${dilationWidth};
 
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${m}, d),
-              getValue(batch, xR, xC + 2 * ${m}, d),
-              getValue(batch, xR, xC + 3 * ${m}, d)
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
+              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
             );
 
-            ${q}
+            ${updateSnippet}
           }
 
-          int xC = xCCorner + ${L};
-          if (${B===1}) {
+          int xC = xCCorner + ${filterWidthNearestVec4};
+          if (${filterWidthVec4Remainder===1}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               initializationValue,
@@ -2577,33 +2582,33 @@ return (round(mod(b, 2.0)) != 1) ?
               initializationValue
             );
 
-            ${q}
-          } else if (${B===2}) {
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder===2}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${m}, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
               initializationValue,
               initializationValue
             );
 
-            ${q}
-          } else if (${B===3}) {
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder===3}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${m}, d),
-              getValue(batch, xR, xC + 2 * ${m}, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
               initializationValue
             );
 
-            ${q}
+            ${updateSnippet}
           }
         }
-        setOutput(${E});
+        setOutput(${returnValue});
       }
-    `}}class L1{constructor(t,e,r,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let c=t.filterWidth,l=t.strideDepth,p=t.strideHeight,f=t.strideWidth,m=t.dilationDepth,y=t.dilationHeight,b=t.dilationWidth,v=t.effectiveFilterDepth,T=t.effectiveFilterHeight,N=t.effectiveFilterWidth,S=t.padInfo.front,D=t.padInfo.top,I=t.padInfo.left;this.outputShape=t.outShape;let P=e==="avg",E="0.0";if(P||(E="-1.0 / 1e-20"),r){let J=">=";this.userCode=`
+    `}},Pool3DProgram=class{constructor(convInfo,poolType,computePositions,flattenPositions=!1,includeBatchInIndex=!1){if(this.variableNames=["x"],poolType==="avg"&&computePositions)throw new Error("Cannot compute positions for average pool.");let filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.outputShape=convInfo.outShape;let isAvgPool=poolType==="avg",initializationValue="0.0";if(isAvgPool||(initializationValue="-1.0 / 1e-20"),computePositions){let compareOp2=">=";this.userCode=`
         const ivec3 strides =
-            ivec3(${l}, ${p}, ${f});
-        const ivec3 pads = ivec3(${S}, ${D}, ${I});
+            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
 
         void main() {
           ivec5 coords = getOutputCoords();
@@ -2621,27 +2626,27 @@ return (round(mod(b, 2.0)) != 1) ?
           float minMaxValueFound = 0.0;
           int minMaxPosition = 0;
 
-          for (int wD = 0; wD < ${v};
-              wD += ${m}) {
+          for (int wD = 0; wD < ${effectiveFilterDepth};
+              wD += ${dilationDepth}) {
             int xD = xDCorner + wD;
 
-            if (xD < 0 || xD >= ${t.inDepth}) {
+            if (xD < 0 || xD >= ${convInfo.inDepth}) {
               continue;
             }
 
-            for (int wR = 0; wR < ${T};
-                wR += ${y}) {
+            for (int wR = 0; wR < ${effectiveFilterHeight};
+                wR += ${dilationHeight}) {
               int xR = xRCorner + wR;
 
-              if (xR < 0 || xR >= ${t.inHeight}) {
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                 continue;
               }
 
-              for (int wC = 0; wC < ${N};
-                  wC += ${b}) {
+              for (int wC = 0; wC < ${effectiveFilterWidth};
+                  wC += ${dilationWidth}) {
                 int xC = xCCorner + wC;
 
-                if (xC < 0 || xC >= ${t.inWidth}) {
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                   continue;
                 }
 
@@ -2651,34 +2656,34 @@ return (round(mod(b, 2.0)) != 1) ?
                 // use the current value.
                 float currMinMaxValue = mix(
                     value, minMaxValue, minMaxValueFound);
-                if (value ${J} currMinMaxValue) {
+                if (value ${compareOp2} currMinMaxValue) {
                   minMaxValue = value;
                   minMaxValueFound = 1.0;
-                  minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${T} * ${N} +
-                      wR * ${N} + wC`};
+                  minMaxPosition = ${flattenPositions?includeBatchInIndex?`(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`:`((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`:`wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                      wR * ${effectiveFilterWidth} + wC`};
                 }
               }
             }
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let L="max",B=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(B="avgValue / count");let q=Math.floor(c/4)*4,H=c%4,Z=`
-      if (${P}) {
+      `;return}let compareOp="max",returnValue=`${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;poolType==="avg"&&(returnValue="avgValue / count");let filterWidthNearestVec4=Math.floor(filterWidth/4)*4,filterWidthVec4Remainder=filterWidth%4,updateSnippet=`
+      if (${isAvgPool}) {
         avgValue += dot(values, ones);
       } else {
-        minMaxValue = ${L}(values, minMaxValue);
+        minMaxValue = ${compareOp}(values, minMaxValue);
       }
     `;this.userCode=`
       const ivec3 strides =
-        ivec3(${l}, ${p}, ${f});
-      const ivec3 pads = ivec3(${S}, ${D}, ${I});
-      const float initializationValue = ${E};
+        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
       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 >= ${t.inWidth}) {
+        if (xC < 0 || xC >= ${convInfo.inWidth}) {
           return initializationValue;
         }
         count += 1.0;
@@ -2697,41 +2702,41 @@ return (round(mod(b, 2.0)) != 1) ?
 
         // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
         // ? = to be determined
-        vec4 minMaxValue = vec4(${E});
+        vec4 minMaxValue = vec4(${initializationValue});
         float avgValue = 0.0;
         count = 0.0;
 
-        for (int wD = 0; wD < ${v};
-            wD += ${m}) {
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
           int xD = xDCorner + wD;
 
-          if (xD < 0 || xD >= ${t.inDepth}) {
+          if (xD < 0 || xD >= ${convInfo.inDepth}) {
             continue;
           }
 
-          for (int wR = 0; wR < ${T};
-            wR += ${y}) {
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
             int xR = xRCorner + wR;
 
-            if (xR < 0 || xR >= ${t.inHeight}) {
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
               continue;
             }
 
-            for (int wC = 0; wC < ${q}; wC += 4) {
-              int xC = xCCorner + wC * ${b};
+            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+              int xC = xCCorner + wC * ${dilationWidth};
 
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${b}, ch),
-                getValue(batch, xD, xR, xC + 2 * ${b}, ch),
-                getValue(batch, xD, xR, xC + 3 * ${b}, ch)
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
               );
 
-              ${Z}
+              ${updateSnippet}
             }
 
-            int xC = xCCorner + ${q};
-            if (${H===1}) {
+            int xC = xCCorner + ${filterWidthNearestVec4};
+            if (${filterWidthVec4Remainder===1}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 initializationValue,
@@ -2739,57 +2744,57 @@ return (round(mod(b, 2.0)) != 1) ?
                 initializationValue
               );
 
-              ${Z}
-            } else if (${H===2}) {
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder===2}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${b}, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                 initializationValue,
                 initializationValue
               );
 
-              ${Z}
-            } else if (${H===3}) {
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder===3}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${b}, ch),
-                getValue(batch, xD, xR, xC + 2 * ${b}, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
                 initializationValue
               );
 
-              ${Z}
+              ${updateSnippet}
             }
           }
-          setOutput(${B});
+          setOutput(${returnValue});
         }
       }
-    `}}class YI{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:o,inSize:s,outSize:c}=t;this.outputShape=[o,c];let l="0.0",p="";e==="prod"?l="1.0":e==="min"?(l="1.0 / 1e-20",p="min"):e==="max"&&(l="-1.0 / 1e-20",p="max");let f=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?f="sumValue":e==="prod"?f="prodValue":e==="all"?f="allValue":e==="any"&&(f="anyValue");let m=Math.floor(r/4)*4,y=r%4,b=`
-      if (${e==="sum"}) {
+    `}},ReduceProgram=class{constructor(reduceInfo,reduceType){this.variableNames=["x"];let{windowSize,batchSize,inSize,outSize}=reduceInfo;this.outputShape=[batchSize,outSize];let initializationValue="0.0",compareOp="";reduceType==="prod"?initializationValue="1.0":reduceType==="min"?(initializationValue="1.0 / 1e-20",compareOp="min"):reduceType==="max"&&(initializationValue="-1.0 / 1e-20",compareOp="max");let returnValue=`${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;reduceType==="sum"?returnValue="sumValue":reduceType==="prod"?returnValue="prodValue":reduceType==="all"?returnValue="allValue":reduceType==="any"&&(returnValue="anyValue");let windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
+      if (${reduceType==="sum"}) {
         sumValue += dot(values, ones);
-      } else if (${e==="prod"}) {
+      } else if (${reduceType==="prod"}) {
         vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
         prodValue *= tmp[0] * tmp[1];
       } else {
-        minMaxValue = ${p}(values, minMaxValue);
+        minMaxValue = ${compareOp}(values, minMaxValue);
       }
-    `,v="vec4";e==="all"?(l="1.0",b=`
+    `,vecType="vec4";reduceType==="all"?(initializationValue="1.0",updateSnippet=`
         bool reducedAllValue = all(values);
         float floatedReducedAllValue = float(reducedAllValue);
         allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
-      `,v="bvec4"):e==="any"&&(l="0.0",b=`
+      `,vecType="bvec4"):reduceType==="any"&&(initializationValue="0.0",updateSnippet=`
         bool reducedAnyValue = any(values);
         float floatedReducedAnyValue = float(reducedAnyValue);
         anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
-      `,v="bvec4");let T="";s%r>0&&(T=`
-        if (inIdx < 0 || inIdx >= ${s}) {
+      `,vecType="bvec4");let checkOutOfBounds="";inSize%windowSize>0&&(checkOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
           return initializationValue;
         }
       `),this.userCode=`
-      const float initializationValue = ${l};
+      const float initializationValue = ${initializationValue};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
-        ${T}
+        ${checkOutOfBounds}
         return getX(batch, inIdx);
       }
 
@@ -2797,71 +2802,71 @@ return (round(mod(b, 2.0)) != 1) ?
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int outIdx = coords[1];
-        int inOffset = outIdx * ${r};
+        int inOffset = outIdx * ${windowSize};
 
-        vec4 minMaxValue = vec4(${l});
+        vec4 minMaxValue = vec4(${initializationValue});
         float prodValue = 1.0;
         float sumValue = 0.0;
         float allValue = 1.0;
         float anyValue = 0.0;
 
-        for (int i = 0; i < ${m}; i += 4) {
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
           int inIdx = inOffset + i;
-          ${v} values = ${v}(
+          ${vecType} values = ${vecType}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
             getValue(batch, inIdx + 2),
             getValue(batch, inIdx + 3)
           );
 
-          ${b}
+          ${updateSnippet}
         }
 
-        int inIdx = inOffset + ${m};
-        if (${y===1}) {
-          ${v} values = ${v}(
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===1}) {
+          ${vecType} values = ${vecType}(
             getValue(batch, inIdx),
             initializationValue,
             initializationValue,
             initializationValue
           );
 
-          ${b}
-        } else if (${y===2}) {
-          ${v} values = ${v}(
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===2}) {
+          ${vecType} values = ${vecType}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
             initializationValue,
             initializationValue
           );
 
-          ${b}
-        } else if (${y===3}) {
-          ${v} values = ${v}(
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===3}) {
+          ${vecType} values = ${vecType}(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
             getValue(batch, inIdx + 2),
             initializationValue
           );
 
-          ${b}
+          ${updateSnippet}
         }
-        setOutput(${f});
+        setOutput(${returnValue});
       }
-    `}}class JI{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let r="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),r+=`
-        ${s}
-        ${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
+    `}},ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2===1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
+        ${thisRC}
+        ${i>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[${o}] =
+          result[${i}] =
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
-        ${o>0?"}":""}
+        ${i>0?"}":""}
       `}this.userCode=`
-      ${fJ(e)}
-      ${P1(t)}
+      ${getReshapedInputCoords(inputShape)}
+      ${getFlatIndexFrom3D(outputShape)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -2869,19 +2874,19 @@ return (round(mod(b, 2.0)) != 1) ?
         vec4 result = vec4(0.);
 
         ivec3 thisRC;
-        int rows = ${t[1]};
-        int cols = ${t[2]};
+        int rows = ${outputShape[1]};
+        int cols = ${outputShape[2]};
 
-        ${r}
+        ${mainLoop}
 
         setOutput(result);
       }
-    `}}function fJ(n){let t=_a(["r","c","d"],n);return`
+    `}};function getReshapedInputCoords(shape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape);return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t}
+      ${coordsFromIndexSnippet}
       return ivec3(r, c, d);
     }
-  `}class dJ{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,o,s]=e.shape,[,c,l]=t.shape,p=[r&&c>1?o-1:o,r&&l>1?s-1:s],f=[r&&c>1?c-1:c,r&&l>1?l-1:l],m=p[0]/f[0],y=p[1]/f[1],b=1/m,v=1/y,T=Math.ceil(b)*2+2,N=Math.ceil(v)*2+2;this.userCode=`
+  `}var ResizeBilinearBackpropProgram=class{constructor(dy,x,alignCorners){this.variableNames=["dy"],this.outputShape=[],this.outputShape=x.shape;let[,xHeight,xWidth]=x.shape,[,yHeight,yWidth]=dy.shape,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -2891,14 +2896,14 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${m});
-        const float widthScale = float(${y});
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
 
-        const float invHeightScale = float(${b});
-        const float invWidthScale = float(${v});
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
 
-        const int winHeight = int(${T});
-        const int winWidth = int(${N});
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
 
         // Compute bounds for where in dy we will look
         float startRLerp = floor(float(r) * invHeightScale);
@@ -2912,7 +2917,7 @@ return (round(mod(b, 2.0)) != 1) ?
           int dyR = dyROffset + startDyR;
 
           // Guard against the window exceeding the bounds of dy
-          if (dyR < 0 || dyR >= ${c}) {
+          if (dyR < 0 || dyR >= ${yHeight}) {
             continue;
           }
 
@@ -2920,19 +2925,19 @@ return (round(mod(b, 2.0)) != 1) ?
             int dyC = dyCOffset + startDyC;
 
             // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${l}) {
+            if (dyC < 0 || dyC >= ${yWidth}) {
               continue;
             }
 
             float dxR = float(dyR) * heightScale;
             int topDxRIndex = int(floor(dxR));
-            int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));
+            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight-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), ${s-1}.0));
+            int rightDxCIndex = int(min(ceil(dxC), ${xWidth-1}.0));
             float dxCLerp = dxC - float(leftDxCIndex);
             float inverseDxCLerp = 1.0 - dxCLerp;
 
@@ -2962,11 +2967,11 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(accumulator);
       }
-    `}}class mJ{constructor(t,e,r,o){this.variableNames=["A"],this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r];this.userCode=`
+    `}},ResizeBilinearProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${f[0]/m[0]},
-          ${f[1]/m[1]});
-      const vec2 inputShapeRC = vec2(${c}.0, ${l}.0);
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -2995,13 +3000,13 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(newValue);
       }
-    `}}class gJ{constructor(t,e,r,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r];this.userCode=`
+    `}},ResizeBilinearPackedProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
-          ${f[0]/m[0]},
-          ${f[1]/m[1]},
-          ${f[1]/m[1]});
-      const vec3 inputShapeRC = vec3(${c}.0, ${l}.0,
-                                     ${l}.0);
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
+                                     ${oldWidth}.0);
 
       float getAValue(int b, int r, int c, int d) {
         return getChannel(getA(b, r, c, d), vec2(c, d));
@@ -3023,8 +3028,8 @@ return (round(mod(b, 2.0)) != 1) ?
           min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
 
         // Should we calculate next column and row elements in 2x2 packed cell.
-        bool hasNextCol = d < ${p-1};
-        bool hasNextRow = coords.z < ${r-1};
+        bool hasNextCol = d < ${depth-1};
+        bool hasNextRow = coords.z < ${newWidth-1};
 
         // In parallel, construct four corners for all four components in
         // packed 2x2 cell.
@@ -3072,7 +3077,7 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(newValue);
       }
-    `}}class yJ{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,o,s]=e.shape,[,c,l]=t.shape,p=[r&&c>1?o-1:o,r&&l>1?s-1:s],f=[r&&c>1?c-1:c,r&&l>1?l-1:l],m=p[0]/f[0],y=p[1]/f[1],b=1/m,v=1/y,T=Math.ceil(b)*2+2,N=Math.ceil(v)*2+2;this.userCode=`
+    `}},ResizeNearestNeigborBackpropProgram=class{constructor(dy,x,alignCorners){this.variableNames=["dy"],this.outputShape=[],this.outputShape=x.shape;let[,xHeight,xWidth]=x.shape,[,yHeight,yWidth]=dy.shape,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3082,14 +3087,14 @@ return (round(mod(b, 2.0)) != 1) ?
 
         float accumulator = 0.0;
 
-        const float heightScale = float(${m});
-        const float widthScale = float(${y});
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
 
-        const float invHeightScale = float(${b});
-        const float invWidthScale = float(${v});
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
 
-        const int winHeight = int(${T});
-        const int winWidth = int(${N});
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
 
         // Compute bounds for where in dy we will look
         float startRLerp = floor(float(r) * invHeightScale);
@@ -3103,7 +3108,7 @@ return (round(mod(b, 2.0)) != 1) ?
           int dyR = dyROffset + startDyR;
 
           // Guard against the window exceeding the bounds of dy
-          if (dyR < 0 || dyR >= ${c}) {
+          if (dyR < 0 || dyR >= ${yHeight}) {
             continue;
           }
 
@@ -3111,26 +3116,26 @@ return (round(mod(b, 2.0)) != 1) ?
             int dyC = dyCOffset + startDyC;
 
             // Guard against the window exceeding the bounds of dy
-            if (dyC < 0 || dyC >= ${l}) {
+            if (dyC < 0 || dyC >= ${yWidth}) {
               continue;
             }
 
             float sourceFracRow =
-              float(${p[0]}) *
-                (float(dyR) / float(${f[0]}));
+              float(${effectiveXSize[0]}) *
+                (float(dyR) / float(${effectiveYSize[0]}));
 
             float sourceFracCol =
-                float(${p[1]}) *
-                  (float(dyC) / float(${f[1]}));
+                float(${effectiveXSize[1]}) *
+                  (float(dyC) / float(${effectiveYSize[1]}));
 
             int sourceNearestRow = int(min(
-                float(int(${o}) - 1),
-                ${r} ? float(round(sourceFracRow)) :
+                float(int(${xHeight}) - 1),
+                ${alignCorners} ? float(round(sourceFracRow)) :
                                   float(floor(sourceFracRow))));
 
             int sourceNearestCol = int(min(
-                float(int(${s}) - 1),
-                ${r} ? float(round(sourceFracCol)) :
+                float(int(${xWidth}) - 1),
+                ${alignCorners} ? float(round(sourceFracCol)) :
                                   float(floor(sourceFracCol))));
 
             if (r == sourceNearestRow && c == sourceNearestCol) {
@@ -3142,11 +3147,11 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(accumulator);
       }
-    `}}class bJ{constructor(t,e,r,o){this.variableNames=["A"],this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r],y=o?"0.5":"0.0";this.userCode=`
+    `}},ResizeNearestNeighborProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],roundBase=alignCorners?"0.5":"0.0";this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${f[0]/m[0]},
-          ${f[1]/m[1]});
-      const vec2 inputShapeRC = vec2(${c}.0, ${l}.0);
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
 
       void main() {
         ivec4 coords = getOutputCoords();
@@ -3159,90 +3164,90 @@ return (round(mod(b, 2.0)) != 1) ?
 
         // Compute the coordinators of nearest neighbor point.
         ivec2 sourceNearestRC = ivec2(
-          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${y})));
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
 
         float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
 
         setOutput(newValue);
       }
-    `}}class xJ{constructor(t,e){this.variableNames=["x"];let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=t,r===1){this.userCode=`
+    `}},ReverseProgram=class{constructor(xShape,axis){this.variableNames=["x"];let rank=xShape.length;if(rank>4)throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);if(this.outputShape=xShape,rank===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
-          setOutput(getX(${t[0]} - coord - 1));
+          setOutput(getX(${xShape[0]} - coord - 1));
         }
-      `;return}let o=l=>e.indexOf(l)!==-1&&t[l]!==1?`${t[l]} - coords[${l}] - 1`:`coords[${l}]`,s=t.map((l,p)=>o(p)).join(","),c=Oe(r);this.userCode=`
+      `;return}let getInCoord=i=>axis.indexOf(i)!==-1&&xShape[i]!==1?`${xShape[i]} - coords[${i}] - 1`:`coords[${i}]`,inCoords=xShape.map((_,i)=>getInCoord(i)).join(","),type=getCoordsDataType(rank);this.userCode=`
       void main() {
-        ${c} coords = getOutputCoords();
-        setOutput(getX(${s}));
+        ${type} coords = getOutputCoords();
+        setOutput(getX(${inCoords}));
       }
-    `}}class wJ{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=t;let o=Jn("rc",r),s=`${o[r-1]} + 1 < ${this.outputShape[r-1]}`,c=`${o[r-2]} + 1 < ${this.outputShape[r-2]}`,l=Oe(r);r===1?this.userCode=`
+    `}},ReversePackedProgram=class{constructor(xShape,axis){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let rank=xShape.length;if(rank>4)throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);this.outputShape=xShape;let channels=getChannels("rc",rank),nextColumn=`${channels[rank-1]} + 1 < ${this.outputShape[rank-1]}`,nextRow=`${channels[rank-2]} + 1 < ${this.outputShape[rank-2]}`,type=getCoordsDataType(rank);rank===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
-          result.r = getChannel(getX(${t[0]} - rc - 1),
-            ${t[0]} - rc - 1);
-          if(${s}){
-              result.g = getChannel(getX(${t[0]} - (rc  + 1) - 1),
-                ${t[0]} - (rc  + 1) - 1);
+          result.r = getChannel(getX(${xShape[0]} - rc - 1),
+            ${xShape[0]} - rc - 1);
+          if(${nextColumn}){
+              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
+                ${xShape[0]} - (rc  + 1) - 1);
           }
           setOutput(result);
         }
       `:this.userCode=`
         void main() {
-          ${l} rc = getOutputCoords();
+          ${type} rc = getOutputCoords();
           vec4 result = vec4(0.);
-          result.r = ${p(o.slice())};
-          if(${s}){
-            result.g = ${f(o.slice())};
+          result.r = ${getR(channels.slice())};
+          if(${nextColumn}){
+            result.g = ${getG(channels.slice())};
           }
-          if(${c}) {
-            result.b = ${m(o.slice())};
-            if(${s}) {
-              result.a = ${y(o.slice())};
+          if(${nextRow}) {
+            result.b = ${getB(channels.slice())};
+            if(${nextColumn}) {
+              result.a = ${getA(channels.slice())};
             }
           }
           setOutput(result);
         }
-    `;function p(T){return b(T)}function f(T){return T[r-1]="("+T[r-1]+" + 1)",b(T)}function m(T){return T[r-2]="("+T[r-2]+" + 1)",b(T)}function y(T){return T[r-1]="("+T[r-1]+" + 1)",T[r-2]="("+T[r-2]+" + 1)",b(T)}function b(T){let N=t.map((I,P)=>v(P,T)),S=N.join(","),D=N.slice(-2).join(",");return`getChannel(getX(${S}), vec2(${D}))`}function v(T,N){return e.indexOf(T)!==-1&&t[T]!==1?`${t[T]} - ${N[T]} - 1`:`${N[T]}`}}}class ZI{constructor(t,e,r,o,s,c,l=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=c;let p=Oe(s.length),f=Oe(c.length),m="";r===1?m="i":r===2&&(m="i, j");let y=`getIndices(${m})`,b="";o===1?b="i":o===2&&(b="i, coords[1]");let v=`getUpdates(${b})`,T=e>1?"strides[j]":"strides";this.userCode=`
-        ${p} strides = ${p}(${s});
+    `;function getR(channels2){return getChannel(channels2)}function getG(channels2){return channels2[rank-1]="("+channels2[rank-1]+" + 1)",getChannel(channels2)}function getB(channels2){return channels2[rank-2]="("+channels2[rank-2]+" + 1)",getChannel(channels2)}function getA(channels2){return channels2[rank-1]="("+channels2[rank-1]+" + 1)",channels2[rank-2]="("+channels2[rank-2]+" + 1)",getChannel(channels2)}function getChannel(channels2){let inCoordsArray=xShape.map((_,i)=>getInCoord(i,channels2)),inCoords=inCoordsArray.join(","),innerDims=inCoordsArray.slice(-2).join(",");return`getChannel(getX(${inCoords}), vec2(${innerDims}))`}function getInCoord(i,channels1){return axis.indexOf(i)!==-1&&xShape[i]!==1?`${xShape[i]} - ${channels1[i]} - 1`:`${channels1[i]}`}}},ScatterProgram=class{constructor(updateSize,sliceDim,indicesRank,updatesRank,strides,shape,summingDupeIndex=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),indicesString="";indicesRank===1?indicesString="i":indicesRank===2&&(indicesString="i, j");let indicesSnippet=`getIndices(${indicesString})`,updatesString="";updatesRank===1?updatesString="i":updatesRank===2&&(updatesString="i, coords[1]");let updatesSnippet=`getUpdates(${updatesString})`,strideString=sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${stridesType} strides = ${stridesType}(${strides});
 
         void main() {
-          ${f} coords = getOutputCoords();
+          ${dtype} coords = getOutputCoords();
           float sum = 0.0;
           bool found = false;
-          for (int i = 0; i < ${t}; i++) {
+          for (int i = 0; i < ${updateSize}; i++) {
             int flattenedIndex = 0;
-            for (int j = 0; j < ${e}; j++) {
-              int index = round(${y});
-              flattenedIndex += index * ${T};
+            for (int j = 0; j < ${sliceDim}; j++) {
+              int index = round(${indicesSnippet});
+              flattenedIndex += index * ${strideString};
             }
             if (flattenedIndex == coords[0]) {
-              sum += ${v};
+              sum += ${updatesSnippet};
               found = true;
             }
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}}class vJ{constructor(t,e){this.variableNames=["x","segmentIds"];let r=t.windowSize,o=t.batchSize,s=t.inSize,c=t.numSegments,l=c*Math.ceil(s/r);this.outputShape=[o,l];let p="0.0",f="sumValue",m=Math.floor(r/4)*4,y=r%4,b=`
+      `}},SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
         sumValue += dot(values, segFilter);
-    `,v="";s%r>0&&(v=`
-        if (inIdx < 0 || inIdx >= ${s}) {
+    `,checkValueOutOfBounds="";inSize%windowSize>0&&(checkValueOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
           return initializationValue;
         }
-      `);let T="";s%r>0&&(T=`
-        if (inIdx < 0 || inIdx >= ${s}) {
+      `);let checkSegmentIdOutOfBounds="";inSize%windowSize>0&&(checkSegmentIdOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
           return -1.0;
         }
       `),this.userCode=`
-      const float initializationValue = ${p};
+      const float initializationValue = ${initializationValue};
 
       float getValue(int batch, int inIdx) {
-        ${v}
+        ${checkValueOutOfBounds}
         return getX(batch, inIdx);
       }
 
       float getSegmentIdAtIndex(int inIdx) {
-        ${T}
+        ${checkSegmentIdOutOfBounds}
         return getSegmentIds(inIdx);
       }
 
@@ -3251,12 +3256,12 @@ return (round(mod(b, 2.0)) != 1) ?
         int batch = coords[0];
         int outIdx = coords[1];
         int inOffset = int(floor(float(outIdx) / float(
-          ${c})) * float(${r}));
-        int currentSeg = int(mod(float(outIdx), float(${c})));
+          ${numSegments})) * float(${windowSize}));
+        int currentSeg = int(mod(float(outIdx), float(${numSegments})));
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${m}; i += 4) {
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -3272,11 +3277,11 @@ return (round(mod(b, 2.0)) != 1) ?
             int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
           );
 
-          ${b}
+          ${updateSnippet}
         }
 
-        int inIdx = inOffset + ${m};
-        if (${y===1}) {
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===1}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             initializationValue,
@@ -3293,8 +3298,8 @@ return (round(mod(b, 2.0)) != 1) ?
             0
           );
 
-          ${b}
-        } else if (${y===2}) {
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -3309,8 +3314,8 @@ return (round(mod(b, 2.0)) != 1) ?
               0
           );
 
-          ${b}
-        } else if (${y===3}) {
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
@@ -3325,77 +3330,77 @@ return (round(mod(b, 2.0)) != 1) ?
             0
           );
 
-          ${b}
+          ${updateSnippet}
         }
-        setOutput(${f});
+        setOutput(${returnValue});
       }
-    `}}class TJ{constructor(t,e,r){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)s="resRC",o="resRC";else{let l=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],f=[];for(let m=0;m<e.length;m++)f.push(`${l[m]}`),m<t&&p.push(`${l[m]}`);o=p.join(),s=f.join()}let c=Oe(r);this.userCode=`
+    `}},SelectProgram=class{constructor(cRank,shape,rank){this.variableNames=["c","a","b"],this.outputShape=shape;let cCoords,abCoords;if(rank>4)throw Error(`Where for rank ${rank} is not yet supported`);if(rank===1)abCoords="resRC",cCoords="resRC";else{let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w"],cCoordVars=[],abCoordVars=[];for(let i=0;i<shape.length;i++)abCoordVars.push(`${currentCoords[i]}`),i<cRank&&cCoordVars.push(`${currentCoords[i]}`);cCoords=cCoordVars.join(),abCoords=abCoordVars.join()}let dtype=getCoordsDataType(rank);this.userCode=`
       void main() {
-        ${c} resRC = getOutputCoords();
-        float cVal = getC(${o});
+        ${dtype} resRC = getOutputCoords();
+        float cVal = getC(${cCoords});
         if (cVal >= 1.0) {
-          setOutput(getA(${s}));
+          setOutput(getA(${abCoords}));
         } else {
-          setOutput(getB(${s}));
+          setOutput(getB(${abCoords}));
         }
       }
-    `}}class kJ{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Oe(this.rank),r=`uniform int start[${this.rank}];`,o=NJ(this.rank),s,c=t.map((l,p)=>`sourceLoc.${M1[p]} = start[${p}] + coords.${M1[p]};`);s=`
-        ${e} sourceLoc;
-        ${e} coords = getOutputCoords();
-        ${c.join(`
+    `}},SliceProgram=class{constructor(destSize){this.variableNames=["source"],this.outputShape=destSize,this.rank=destSize.length;let dtype=getCoordsDataType(this.rank),uniformPart=`uniform int start[${this.rank}];`,sourceCoords=getCoords2(this.rank),body,coordSum=destSize.map((_,i)=>`sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`);body=`
+        ${dtype} sourceLoc;
+        ${dtype} coords = getOutputCoords();
+        ${coordSum.join(`
 `)}
       `,this.userCode=`
-      ${r}
+      ${uniformPart}
       void main() {
-        ${s}
-        setOutput(getSource(${o}));
+        ${body}
+        setOutput(getSource(${sourceCoords}));
       }
-    `}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}let M1=["x","y","z","w","u","v"];function NJ(n){if(n===1)return"sourceLoc";if(n<=6)return M1.slice(0,n).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${n} is not yet supported`)}class _J{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;let e=Oe(this.rank),r=Jn("coords",this.rank),o=Jn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,c=`getChannel(getSource(${o.join()}), ${s})`,l=`
-      result.x = ${c};
-      if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
-        ++${o[this.rank-1]};
-        result.y = ${c};
-        --${o[this.rank-1]};
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{if(this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null))return;gpgpu.gl.uniform1iv(this.startLoc,start)}}},coords=["x","y","z","w","u","v"];function getCoords2(rank){if(rank===1)return"sourceLoc";if(rank<=6)return coords.slice(0,rank).map(x=>"sourceLoc."+x).join(",");throw Error(`Slicing for rank ${rank} is not yet supported`)}var SlicePackedProgram=class{constructor(destSize){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=destSize,this.rank=destSize.length;let dtype=getCoordsDataType(this.rank),coords2=getChannels("coords",this.rank),sourceLoc=getChannels("sourceLoc",this.rank),innerDims=this.rank===1?"sourceLoc":`vec2(${sourceLoc.slice(-2).join()})`,getChannel=`getChannel(getSource(${sourceLoc.join()}), ${innerDims})`,upperRow=`
+      result.x = ${getChannel};
+      if (++${coords2[this.rank-1]} < ${destSize[this.rank-1]}) {
+        ++${sourceLoc[this.rank-1]};
+        result.y = ${getChannel};
+        --${sourceLoc[this.rank-1]};
       }
-    `,p=this.rank===1?"":`
-      --${r[this.rank-1]};
-      if (++${r[this.rank-2]} < ${t[this.rank-2]}) {
-        ++${o[this.rank-2]};
-        result.z = ${c};
-        if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
-          ++${o[this.rank-1]};
-          result.w = ${c};
+    `,lowerRow=this.rank===1?"":`
+      --${coords2[this.rank-1]};
+      if (++${coords2[this.rank-2]} < ${destSize[this.rank-2]}) {
+        ++${sourceLoc[this.rank-2]};
+        result.z = ${getChannel};
+        if (++${coords2[this.rank-1]} < ${destSize[this.rank-1]}) {
+          ++${sourceLoc[this.rank-1]};
+          result.w = ${getChannel};
         }
       }
-    `,f=this.rank<=4?`sourceLoc = coords +
-            ${e}(${t.map((m,y)=>`start[${y}]`).join()});`:t.map((m,y)=>`${o[y]} = ${r[y]} + start[${y}];`).join(`
+    `,sourceLocSetup=this.rank<=4?`sourceLoc = coords +
+            ${dtype}(${destSize.map((_,i)=>`start[${i}]`).join()});`:destSize.map((_,i)=>`${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join(`
 `);this.userCode=`
       uniform int start[${this.rank}];
       void main() {
-        ${e} coords = getOutputCoords();
-        ${e} sourceLoc;
-        ${f}
+        ${dtype} coords = getOutputCoords();
+        ${dtype} sourceLoc;
+        ${sourceLocSetup}
         vec4 result = vec4(0.);
-        ${l}
-        ${p}
+        ${upperRow}
+        ${lowerRow}
         setOutput(result);
       }
-    `}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}class CJ{constructor(t,e,r){this.variableNames=["x"],this.outputShape=r;let o=r.length,s=Oe(r.length),c=Oe(r.length),l="";if(o===1)l="coords * strides + begin";else{let p=0;l=r.map((f,m)=>(p++,r.length===1?`coords * strides[${m}] + begin[${m}]`:`coords[${p-1}] * strides[${m}] + begin[${m}]`)).join(",")}this.userCode=`
-      ${s} begin = ${s}(${t});
-      ${s} strides = ${s}(${e});
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{if(this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null))return;gpgpu.gl.uniform1iv(this.startLoc,start)}}},StridedSliceProgram=class{constructor(begin,strides,size){this.variableNames=["x"],this.outputShape=size;let rank=size.length,inputDtype=getCoordsDataType(size.length),dtype=getCoordsDataType(size.length),newCoords="";if(rank===1)newCoords="coords * strides + begin";else{let outputAxis=0;newCoords=size.map((_,i)=>(outputAxis++,size.length===1?`coords * strides[${i}] + begin[${i}]`:`coords[${outputAxis-1}] * strides[${i}] + begin[${i}]`)).join(",")}this.userCode=`
+      ${inputDtype} begin = ${inputDtype}(${begin});
+      ${inputDtype} strides = ${inputDtype}(${strides});
 
       void main() {
-        ${c} coords = getOutputCoords();
-        setOutput(getX(${l}));
+        ${dtype} coords = getOutputCoords();
+        setOutput(getX(${newCoords}));
       }
-    `}}class SJ{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,r){let o=tE(e,r),s=eE(t,o,r);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let c=QI(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=c,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let l;return o===Ln.PACKED_2X2_FLOAT32?l=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Ln.PACKED_2X2_FLOAT16?l=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Ln.UNPACKED_FLOAT32?l=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Ln.UNPACKED_FLOAT16?l=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Ln.PACKED_4X1_UNSIGNED_BYTE&&(l=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(l),this.numUsedTextures++,this._numBytesAllocated+=c,this.log(),l}releaseTexture(t,e,r,o){if(this.freeTextures==null)return;let s=tE(r,o),c=eE(e,s,o);c in this.freeTextures||(this.freeTextures[c]=[]);let l=QI(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),p=ct().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t),this._numBytesAllocated-=l):(this.freeTextures[c].push(t),this.numFreeTextures++,this._numBytesFree+=l),this.numUsedTextures--;let f=this.usedTextures[c],m=f.indexOf(t);if(m<0)throw new Error("Cannot release a texture that was never provided by this texture manager");f.splice(m,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function $J(n,t){let e=n;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===n.RGBA)return 16;if(t===e.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function QI(n,t,e,r,o){let s=IJ(t,r),c;if(o){let[p,f]=fl(n[0],n[1]);c=p*f}else{let[p,f]=lh(n[0],n[1]);c=p*f}let l=$J(e,s);return c*l}function IJ(n,t){switch(n){case Ln.PACKED_2X2_FLOAT32:return jI(t);case Ln.PACKED_2X2_FLOAT16:return KI(t);case Ln.UNPACKED_FLOAT32:return UI(t);case Ln.UNPACKED_FLOAT16:return qI(t);case Ln.PACKED_4X1_UNSIGNED_BYTE:return HI(t);default:throw new Error(`Unknown physical texture type ${n}`)}}function EJ(n){return ct().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?n?Ln.PACKED_2X2_FLOAT32:Ln.UNPACKED_FLOAT32:n?Ln.PACKED_2X2_FLOAT16:Ln.UNPACKED_FLOAT16}function tE(n,t){if(n===Br.UPLOAD)return Ln.PACKED_2X2_FLOAT32;if(n===Br.RENDER||n==null)return EJ(t);if(n===Br.DOWNLOAD||n===Br.PIXELS)return Ln.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${n}`)}function eE(n,t,e){return`${n[0]}_${n[1]}_${t}_${e}`}class DJ{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let c=0;c<r.length;c++)r[c]=t[c]*e[c];this.outputShape=r,this.rank=r.length;let o=Oe(this.rank),s=AJ(t);this.userCode=`
+    `}},TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures==null)return;let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}log(){if(!this.logEnabled)return;let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}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(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F)return 16;if(internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement22=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement22}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var TileProgram=class{constructor(aShape,reps){this.variableNames=["A"];let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[i]*reps[i];this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),sourceCoords=getSourceCoords3(aShape);this.userCode=`
       void main() {
-        ${o} resRC = getOutputCoords();
-        setOutput(getA(${s}));
+        ${dtype} resRC = getOutputCoords();
+        setOutput(getA(${sourceCoords}));
       }
-    `}}function AJ(n){let t=n.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${n[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let o=0;o<n.length;o++)r.push(`imod(${e[o]}, ${n[o]})`);return r.join()}class ue{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.userCode=`
+    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
       float unaryOperation(float x) {
-        ${e}
+        ${opSnippet}
       }
 
       void main() {
@@ -3404,22 +3409,22 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(y);
       }
-    `}}let $s="if (isnan(x)) return x;",FJ="return x;",nE="return abs(x);",rE=$s+`
+    `}},CHECK_NAN_SNIPPET3="if (isnan(x)) return x;",LINEAR="return x;",ABS="return abs(x);",RELU=CHECK_NAN_SNIPPET3+`
   return (x < 0.0) ? 0.0 : x;
-`,oE=$s+`
+`,RELU6=CHECK_NAN_SNIPPET3+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,sE="return (x >= 0.0) ? x : (exp(x) - 1.0);",RJ=`
+`,ELU2="return (x >= 0.0) ? x : (exp(x) - 1.0);",SELU=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
-  float scaleAlpha = ${om};
-  float scale = ${sm};
+  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};
+  float scale = ${backend_util_exports.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`;function PJ(n=0){return $s+`
-    return x > 0.0 ? 1.0 : float(${n});
-  `}let iE="return -x;",aE="return ceil(x);",cE="return floor(x);",OJ=`
+`;function STEP(alpha=0){return CHECK_NAN_SNIPPET3+`
+    return x > 0.0 ? 1.0 : float(${alpha});
+  `}var NEG="return -x;",CEIL="return ceil(x);",FLOOR="return floor(x);",SIGN=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,LJ="return float(isnan(x));",MJ="return float(isinf(x));",BJ="return float(!isnan(x) && !isinf(x));",zJ=`
+`,IS_NAN="return float(isnan(x));",IS_INF="return float(isinf(x));",IS_FINITE="return float(!isnan(x) && !isinf(x));",ROUND=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3434,8 +3439,8 @@ return (round(mod(b, 2.0)) != 1) ?
       return base + 1.0;
     }
   }
-`,lE="return exp(x);",uE="return exp(x) - 1.0;",WJ=`if (x < 0.0) return NAN;
-  return log(x);`,VJ="return log(1.0 + x);",GJ="return sqrt(x);",UJ="return inversesqrt(x);",qJ="return 1.0 / (1.0 + exp(-1.0 * x));",HJ=`
+`,EXP="return exp(x);",EXPM1="return exp(x) - 1.0;",LOG=`if (x < 0.0) return NAN;
+  return log(x);`,LOG1P="return log(1.0 + x);",SQRT="return sqrt(x);",RSQRT="return inversesqrt(x);",SIGMOID="return 1.0 / (1.0 + exp(-1.0 * x));",SOFTPLUS=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3455,47 +3460,47 @@ return (round(mod(b, 2.0)) != 1) ?
     result = log(exp_x + 1.0);
   }
   return result;
-`,jJ=$s+`
+`,ASIN=CHECK_NAN_SNIPPET3+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,KJ=$s+`
+`,ACOS=CHECK_NAN_SNIPPET3+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,XJ=$s+`
+`,ATAN=CHECK_NAN_SNIPPET3+`
   return atan(x);
-`,YJ=`
+`,SINH=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,JJ=`
+`,COSH=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,ZJ=`
+`,TANH=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,QJ=$s+"return log(x + sqrt(x * x + 1.0));",tZ=$s+`
+`,ASINH=CHECK_NAN_SNIPPET3+"return log(x + sqrt(x * x + 1.0));",ACOSH=CHECK_NAN_SNIPPET3+`
   if (x < 1.0) return NAN;
-  return log(x + sqrt(x * x - 1.0));`,eZ=$s+`
+  return log(x + sqrt(x * x - 1.0));`,ATANH=CHECK_NAN_SNIPPET3+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,nZ=`
+  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ERF=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
-  float p = ${fv};
-  float a1 = ${dv};
-  float a2 = ${mv};
-  float a3 = ${gv};
-  float a4 = ${yv};
-  float a5 = ${bv};
+  float p = ${backend_util_exports.ERF_P};
+  float a1 = ${backend_util_exports.ERF_A1};
+  float a2 = ${backend_util_exports.ERF_A2};
+  float a3 = ${backend_util_exports.ERF_A3};
+  float a4 = ${backend_util_exports.ERF_A4};
+  float a5 = ${backend_util_exports.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));
-`,rZ="return 1.0 / x;",oZ="return float(!(x >= 1.0));",rg="return x;";let sZ="return x;",iZ=`
+`,RECIPROCAL="return 1.0 / x;",LOGICAL_NOT="return float(!(x >= 1.0));",CLONE="return x;",LINEAR2="return x;",LOG2=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -3504,7 +3509,7 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,pE=`
+`,RELU2=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3514,7 +3519,7 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,hE=`
+`,RELU62=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3524,7 +3529,7 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,fE=`
+`,ELU3=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3533,9 +3538,9 @@ return (round(mod(b, 2.0)) != 1) ?
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`;class dh{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=`
+`,UnaryOpPackedProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=aShape,this.userCode=`
       vec4 unaryOperation(vec4 x) {
-        ${e}
+        ${opSnippet}
       }
 
       void main() {
@@ -3544,104 +3549,104 @@ return (round(mod(b, 2.0)) != 1) ?
 
         setOutput(y);
       }
-    `}}class aZ{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;let e=t.length,r=Jn("rc",e),o=Oe(e),s=YX(e,r),c=r.slice(-2),l=e<=1?"rc":`vec2(${c.join(",")})`;this.userCode=`
+    `}},UnpackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=outputShape;let rank=outputShape.length,channels=getChannels("rc",rank),dtype=getCoordsDataType(rank),sourceCoords=getSourceCoords(rank,channels),innerDims=channels.slice(-2),coords2=rank<=1?"rc":`vec2(${innerDims.join(",")})`;this.userCode=`
       void main() {
-        ${o} rc = getOutputCoords();
-        vec4 packedInput = getA(${s});
+        ${dtype} rc = getOutputCoords();
+        vec4 packedInput = getA(${sourceCoords});
 
-        setOutput(getChannel(packedInput, ${l}));
+        setOutput(getChannel(packedInput, ${coords2}));
       }
-    `}}let{segment_util:dE}=vv,cZ=Tv,lZ=kv,uZ=Nv,pZ=jd,hZ=1e-7,fZ=1e-4,og={};function dZ(n){return n in og||(og[n]={}),og[n]}function sg(n,t=!1){if(n==="linear")return t?sZ:FJ;if(n==="relu")return t?pE:rE;if(n==="elu")return t?fE:sE;if(n==="relu6")return t?hE:oE;if(n==="prelu")return t?LI:OI;throw new Error(`Activation ${n} has not been implemented for the WebGL backend.`)}let mZ=128,gZ=600;function yZ(){return ct().global.screen==null?1024:ct().global.screen.height*ct().global.screen.width*window.devicePixelRatio*gZ/1024/1024}let mE=1e3;class bZ extends d{constructor(t){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,!ct().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(t==null){let e=Xo(ct().getNumber("WEBGL_VERSION"));this.binaryCache=dZ(ct().getNumber("WEBGL_VERSION")),this.gpgpu=new j7(e),this.canvas=e.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=t,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=t.gl.canvas;this.textureManager=new SJ(this.gpgpu),this.numMBBeforeWarning=yZ(),this.texData=new h(this,ds())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(t,e,r){if((ct().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ct().getBool("DEBUG"))&&this.checkNumericalProblems(t),r==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={};return this.texData.set(o,{shape:e,dtype:r,values:t,usage:Br.UPLOAD,refCount:1,complexParentRefCount:0}),o}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,r,o){if(ct().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:r,dtype:o,values:e,usage:Br.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(t){let e=t.dataId;if(this.texData.has(e)){let r=this.texData.get(e);r.refCount--,r.refCount<1&&this.disposeData(e)}}readSync(t){let e=this.texData.get(t),{values:r,dtype:o,complexTensorInfos:s,slice:c,shape:l,isPacked:p}=e;if(c!=null){let b;p?b=new dh(l,rg):b=new ue(l,rg);let v=this.runWebGLProgram(b,[{dataId:t,shape:l,dtype:o}],o),T=this.readSync(v.dataId);return this.disposeIntermediateTensorInfo(v),T}if(r!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return r;let f=this.activeTimers!=null,m;f&&(m=sr());let y;if(o==="complex64"){let b=this.readSync(s.real.dataId),v=this.readSync(s.imag.dataId);y=ws(b,v)}else y=this.getValuesFromTexture(t);return f&&(this.downloadWaitMs+=sr()-m),this.convertAndCacheOnCPU(t,y)}async read(t){if(this.pendingRead.has(t)){let T=this.pendingRead.get(t);return new Promise(N=>T.push(N))}let e=this.texData.get(t),{values:r,shape:o,slice:s,dtype:c,complexTensorInfos:l,isPacked:p}=e;if(s!=null){let T;p?T=new dh(o,rg):T=new ue(o,rg);let N=this.runWebGLProgram(T,[{dataId:t,shape:o,dtype:c}],c),S=this.read(N.dataId);return this.disposeIntermediateTensorInfo(N),S}if(r!=null)return this.convertAndCacheOnCPU(t);if(!ct().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ct().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let f=null,m;if(c!=="complex64"&&ct().get("WEBGL_BUFFER_SUPPORTED")){m=this.decode(t);let T=this.texData.get(m.dataId);f=this.gpgpu.createBufferFromTexture(T.texture,...uh(o))}this.pendingRead.set(t,[]),c!=="complex64"&&await this.gpgpu.createAndWaitForFence();let y;if(c==="complex64"){let T=await Promise.all([this.read(l.real.dataId),this.read(l.imag.dataId)]),N=T[0],S=T[1];y=ws(N,S)}else if(f==null)y=this.getValuesFromTexture(t);else{let T=G(o);y=this.gpgpu.downloadFloat32MatrixFromBuffer(f,T)}m!=null&&this.disposeIntermediateTensorInfo(m);let b=this.convertAndCacheOnCPU(t,y),v=this.pendingRead.get(t);return this.pendingRead.delete(t),v.forEach(T=>T(b)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t),this.pendingDeletes--),b}checkNumericalProblems(t){if(t==null)return;for(let e=0;e<t.length;e++){let r=t[e];if(!cX(r))throw ct().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:r,isPacked:o}=this.texData.get(t),s=G(e);if(ct().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let b=this.decode(t),v=this.texData.get(b.dataId),T=this.gpgpu.downloadMatrixFromPackedTexture(v.texture,...uh(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(b),T}let c=ct().getBool("WEBGL_PACK")&&o===!0,l=c?A1(e):e,p=c?new N7(l):new k7(l),f=this.runWebGLProgram(p,[{shape:l,dtype:r,dataId:t}],"float32"),m=this.texData.get(f.dataId),y=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(m.texture,m.texShape[0],m.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(f),y}async time(t){let e=this.activeTimers,r=[],o=!1;this.programTimersStack==null?(this.programTimersStack=r,o=!0):this.activeTimers.push(r),this.activeTimers=r,t();let s=tt(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),c=tt(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let l={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);l.kernelMs=C(p),l.getExtraProfileInfo=()=>p.map((f,m)=>({name:c[m],ms:f})).map(f=>`${f.name}: ${f.ms}`).join(", ")}else l.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,l}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:sr(),endMs:null}}endTimer(t){return ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=sr(),t)}async getQueryTime(t){if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t){if(this.pendingDisposal.has(t))return;if(this.pendingRead.has(t)){this.pendingDisposal.add(t),this.pendingDeletes++;return}if(!this.texData.has(t))return;if(this.texData.get(t).complexParentRefCount>0){this.texData.get(t).refCount--;return}this.releaseGPUData(t);let{complexTensorInfos:e}=this.texData.get(t);e!=null&&(this.texData.get(e.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(e.real),this.texData.get(e.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(e.imag)),this.texData.delete(t)}releaseGPUData(t){let{texture:e,dtype:r,texShape:o,usage:s,isPacked:c,slice:l}=this.texData.get(t),p=l&&l.origDataId||t,f=this.dataRefCount.get(p);f>1?this.dataRefCount.set(p,f-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,r),this.textureManager.releaseTexture(e,o,s,c)));let m=this.texData.get(t);m.texture=null,m.texShape=null,m.isPacked=!1,m.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture}getDataInfo(t){return this.texData.get(t)}getCPUBackend(){return ct().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=ds().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(t,e=mZ){let r=this.getCPUBackend();return!this.warnedAboutCPUBackend&&r==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. 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y.reshape(r)}gatherND(t,e){let r=e.shape,o=r[r.length-1],[s,c,l,p]=dd(t,e),f=e.reshape([c,o]),m=t.reshape([t.size/l,l]),y=new E7(o,p,[c,l]),b=this.compileAndRun(y,[m,f]);return b.reshape(s)}fill(t,e,r){if(r=r||yc(e),r==="string"){let o=or(r,G(t));return o.fill(e),ds().makeTensor(o,t,r,this)}else{let o=new S7(t,e),s=o.getCustomSetupFunc(e);return this.compileAndRun(o,[],r,s)}}onesLike(t){if(t.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(t.shape,1,t.dtype)}zerosLike(t){return this.fill(t.shape,t.dtype==="string"?"":0,t.dtype)}linspace(t,e,r){return wv(t,e,r)}makeTensorInfo(t,e,r){let o=this.write(r,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,r){let{dataId:o}=this.makeTensorInfo(t,e,r);return ds().makeTensorFromDataId(o,t,e,this)}unpackTensor(t){let e=new aZ(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new iJ(t.shape),r=!0;return this.runWebGLProgram(e,[t],t.dtype,null,r)}packedReshape(t,e){let r=[dl(t.shape),...ml(t.shape)],o={dtype:t.dtype,shape:r,dataId:t.dataId},s=[dl(e),...ml(e)],c=new JI(s,r),l=!0,p=this.runWebGLProgram(c,[o],t.dtype,null,l);return{dataId:p.dataId,shape:e,dtype:p.dtype}}decode(t){let e=this.texData.get(t),{isPacked:r,shape:o,dtype:s}=e,c=A1(o),l;r?l=new w7(c):l=new x7(c);let p=!0,f=this.runWebGLProgram(l,[{shape:c,dtype:s,dataId:t}],s,null,p);return{dtype:s,shape:o,dataId:f.dataId}}runWebGLProgram(t,e,r,o,s=!1){let c=this.makeTensorInfo(t.outputShape,r),l=this.texData.get(c.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===ch.DENSE){let N=uh(t.outputShape);l.texShape=N.map(S=>S*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),G(c.shape)===0)return l.values=Ce(c.dtype,0),c;let p=[],f=e.map(N=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let S=this.texData.get(N.dataId);if(S.texture==null){if(!t.packedInputs&&G(N.shape)<=ct().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:N.shape,texData:null,isUniform:!0,uniformValues:S.values};t.packedInputs&&(S.isPacked=!0,S.shape=N.shape)}else if(!!S.isPacked!==!!t.packedInputs)N=S.isPacked?this.unpackTensor(N):this.packTensor(N),p.push(N),S=this.texData.get(N.dataId);else if(S.isPacked&&!Zm(S.shape,N.shape)){let D=N,I=N.shape;N.shape=S.shape,N=this.packedReshape(N,I),p.push(N),S=this.texData.get(N.dataId),D.shape=I}return this.uploadToGPU(N.dataId),{shape:N.shape,texData:S,isUniform:!1}});this.uploadToGPU(c.dataId);let m={shape:c.shape,texData:l,isUniform:!1},y=J7(t,f,m),b=this.getAndSaveBinary(y,()=>X7(this.gpgpu,t,f,m)),v=this.activeTimers!=null,T;if(v&&(T=this.startTimer()),Y7(this.gpgpu,b,f,m,o),p.forEach(N=>this.disposeIntermediateTensorInfo(N)),v&&(T=this.endTimer(T),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(T)})),!ct().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let N=this.unpackTensor(c);return this.disposeIntermediateTensorInfo(c),N}return c}compileAndRun(t,e,r,o,s=!1){r=r||e[0].dtype;let c=this.runWebGLProgram(t,e,r,o,s);return ds().makeTensorFromDataId(c.dataId,c.shape,c.dtype)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!ct().getBool("IS_TEST")){let t=Object.keys(this.binaryCache);t.forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})}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=rt(()=>{if(!ct().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=ct().getBool("DEBUG");ct().set("DEBUG",!1);let e=this.abs(Et(1e-8)).dataSync()[0];if(ct().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hZ:fZ}uploadToGPU(t){let e=this.texData.get(t),{shape:r,dtype:o,values:s,texture:c,usage:l,isPacked:p}=e;if(c!=null)return;let f=this.activeTimers!=null,m;f&&(m=sr());let y=e.texShape;if(y==null&&(y=CX(r,p),e.texShape=y),s!=null){let b=A1(r),v,T=y[1],N=y[0],S=s instanceof Uint8Array;p?([T,N]=fl(y[0],y[1]),v=new C7(b,[N,T],S)):v=new _7(b,[N,T],S);let D=this.makeTensorInfo([N,T],o);S?this.texData.get(D.dataId).usage=Br.PIXELS:this.texData.get(D.dataId).usage=Br.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(D.dataId),T,N,s);let I=!0,P=this.runWebGLProgram(v,[D],o,null,I),E=this.texData.get(P.dataId);e.texture=E.texture,e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,this.disposeIntermediateTensorInfo(D),this.texData.delete(P.dataId),e.values=null,f&&(this.uploadWaitMs+=sr()-m)}else{let b=this.acquireTexture(y,l,o,p);e.texture=b}}convertAndCacheOnCPU(t,e){let r=this.texData.get(t),{dtype:o}=r;return this.releaseGPUData(t),e!=null&&(r.values=xZ(e,o)),r.values}acquireTexture(t,e,r,o){if(this.numBytesInGPU+=this.computeBytes(t,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*Zb(e)}tryRunOnCpuOrThrow(t,e){if(this.shouldExecuteOnCPU(t))try{return e()}catch(r){if(ct().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function xZ(n,t){if(t==="float32"||t==="complex64")return n;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(n.length):new Uint8Array(n.length);for(let r=0;r<e.length;++r)e[r]=Math.round(n[r]);return e}else throw new Error(`Unknown dtype ${t}`)}let wZ="2.7.0";function vZ(){ct().set("WEBGL_FORCE_F16_TEXTURES",!0)}Jx()&&bw("webgl",()=>new bZ,2);let tit={forceHalfFloat:vZ};function Is(n){let{inputs:t,backend:e}=n,{x:r}=t;return e.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}let TZ={kernelName:Nu,backendName:"webgl",kernelFunc:Is};function wl(n){let{inputs:t,backend:e}=n,{real:r,imag:o}=t,s=e.makeTensorInfo(r.shape,"complex64"),c=e.texData.get(s.dataId),l=Is({inputs:{x:r},backend:e}),p=e.texData.get(l.dataId);p.complexParentRefCount++;let f=Is({inputs:{x:o},backend:e}),m=e.texData.get(f.dataId);return m.complexParentRefCount++,c.complexTensorInfos={real:l,imag:f},s}let kZ={kernelName:Sf,backendName:"webgl",kernelFunc:wl};let gE="if (isnan(x)) return x;",NZ=`
+    `}},{segment_util:segment_util2}=backend_util_exports,split11=kernel_impls_exports.split,tile10=kernel_impls_exports.tile,topkImpl3=kernel_impls_exports.topkImpl,whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}function mapActivationToShaderProgram(activation2,packed=!1){if(activation2==="linear")return packed?LINEAR2:LINEAR;if(activation2==="relu")return packed?RELU2:RELU;if(activation2==="elu")return packed?ELU3:ELU2;if(activation2==="relu6")return packed?RELU62:RELU6;if(activation2==="prelu")return packed?PRELU2:PRELU;throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`)}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){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,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine15())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice21,shape,isPacked}=texData;if(slice21!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice21,dtype,complexTensorInfos,isPacked}=texData;if(slice21!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer11=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer11=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer11==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer11,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}checkNumericalProblems(values){if(values==null)return;for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(this.pendingDisposal.has(dataId))return;if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(!this.texData.has(dataId))return;if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice21}=this.texData.get(dataId),key=slice21&&slice21.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine15().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!this.warnedAboutCPUBackend&&cpuBackend==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),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}slice(x,begin,size){if(this.shouldExecuteOnCPU([x])){let outValues=sliceImplCPU(this.texData.get(x.dataId).values,begin,size,x.shape,x.dtype);return this.makeOutput(size,x.dtype,outValues)}if(util_exports.sizeFromShape(size)===0)return tensor4([],size,x.dtype);let{isPacked}=this.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,begin,size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram(size):new SliceProgram(size),customSetup=program.getCustomSetupFunc(begin);return this.compileAndRun(program,[x],null,customSetup)}return this.uploadToGPU(x.dataId),this.shallowSlice(x,begin,size)}shallowSlice(x,begin,size){let xTexData=this.texData.get(x.dataId),t=this.makeOutput(size,x.dtype),newTexData=this.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,x.strides);xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=this.dataRefCount.get(newTexData.slice.origDataId)||1;return this.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}stridedSlice(x,begin,end,strides){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.stridedSlice(x,begin,end,strides));if(cpuRes)return cpuRes;let outShape=slice_util_exports.computeOutShape(begin,end,strides);if(outShape.some(axis=>axis===0))return tensor4([],outShape);let program=new StridedSliceProgram(begin,strides,outShape);return this.compileAndRun(program,[x])}reverse(x,axis){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ReversePackedProgram(x.shape,axis):new ReverseProgram(x.shape,axis);return this.compileAndRun(program,[x])}neg(x){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.neg(x));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,NEG,x.dtype);let program=new UnaryOpProgram(x.shape,NEG);return this.compileAndRun(program,[x])}batchMatMul(a,b,transposeA,transposeB){let outerShapeA=transposeA?a.shape[2]:a.shape[1],outerShapeB=transposeB?b.shape[1]:b.shape[2],sharedDim=transposeA?a.shape[1]:a.shape[2],batch=Math.max(a.shape[0],b.shape[0]);if((outerShapeA===1||outerShapeB===1)&&sharedDim>MATMUL_SHARED_DIM_THRESHOLD){transposeA&&(a=transpose(a,[0,2,1])),transposeB&&(b=transpose(b,[0,2,1]));let a3D=outerShapeB===1?a:a.as3D(batch,sharedDim,1),axis=outerShapeB===1?2:1,b3D=outerShapeB===1?b.as3D(batch,1,sharedDim):b,product=mul(a3D,b3D);return product.sum(axis,!0)}let dtype=upcastType(a.dtype,b.dtype),program=new MatMulPackedProgram(a.shape,b.shape,[batch,outerShapeA,outerShapeB],transposeA,transposeB);return this.compileAndRun(program,[a,b],dtype)}fusedBatchMatMul({a,b,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}){let outerShapeA=transposeA?a.shape[2]:a.shape[1],outerShapeB=transposeB?b.shape[1]:b.shape[2],batch=Math.max(a.shape[0],b.shape[0]),dtype=upcastType(a.dtype,b.dtype),hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,program=new MatMulPackedProgram(a.shape,b.shape,[batch,outerShapeA,outerShapeB],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[a,b];return bias&&inputs.push(bias),preluActivationWeights&&inputs.push(preluActivationWeights),this.compileAndRun(program,inputs,dtype)}localResponseNormalization4D(x,radius,bias,alpha,beta){let program=env().getBool("WEBGL_PACK_NORMALIZATION")?new LRNPackedProgram(x.shape,radius,bias,alpha,beta):new LRNProgram(x.shape,radius,bias,alpha,beta);return this.compileAndRun(program,[x])}LRNGrad(dy,inputImage,outputImage,depthRadius,bias,alpha,beta){let program=new LRNGradProgram(inputImage.shape,depthRadius,bias,alpha,beta);return this.compileAndRun(program,[inputImage,outputImage,dy])}tile(x,reps){if(x.dtype==="string"){let data=this.readSync(x.dataId),decodedData=data.map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData);return tile10(buf,reps)}let program=new TileProgram(x.shape,reps);return this.compileAndRun(program,[x])}pad(x,paddings,constantValue){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PadPackedProgram(x.shape,paddings,constantValue):new PadProgram(x.shape,paddings,constantValue);return this.compileAndRun(program,[x])}gather(x,indices,axis){let cpuRes=this.tryRunOnCpuOrThrow([x,indices],()=>this.cpuBackend.gather(x,indices,axis));if(cpuRes)return cpuRes;let program=new GatherProgram(x.shape,indices.size,axis);return this.compileAndRun(program,[x,indices])}batchToSpaceND(x,blockShape,crops){util_exports.assert(x.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod52=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod52),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod52),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length);return transpose(x.reshape(reshaped),permuted).reshape(reshapedPermuted).slice(sliceBeginCoords,sliceSize)}spaceToBatchND(x,blockShape,paddings){util_exports.assert(x.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod52=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=x.pad(completePaddings),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod52,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod52,!1),paddedXT=transpose(paddedX.reshape(reshapedPaddedShape),permutedReshapedPaddedPermutation);return reshape(paddedXT,flattenShape)}reduce(x,reduceType,dtype){let batchSize=x.shape[0],inSize=x.shape[1],windowSize=backend_util_exports.computeOptimalWindowSize(inSize),outSize=Math.ceil(inSize/windowSize),reduceInfo={windowSize,inSize,batchSize,outSize},program=new ReduceProgram(reduceInfo,reduceType),output=this.compileAndRun(program,[x],dtype);return output.shape[1]===1?output:this.reduce(output,reduceType,dtype)}argReduce(x,reduceType,bestIndicesA=null){let batchSize=x.shape[0],inSize=x.shape[1];bestIndicesA!=null&&(batchSize=bestIndicesA.shape[0],inSize=bestIndicesA.shape[1]);let windowSize=backend_util_exports.computeOptimalWindowSize(inSize),reduceInfo={windowSize,inSize,batchSize,outSize:Math.ceil(inSize/windowSize)},program=new ArgMinMaxProgram(reduceInfo,reduceType,bestIndicesA==null),inputs=[x];bestIndicesA!=null&&inputs.push(bestIndicesA);let output=this.compileAndRun(program,inputs,"int32");return output.shape[1]===1?output:this.argReduce(x,reduceType,output)}argReducePacked(x,reduceType,bestIndicesA=null){let inShape=bestIndicesA!=null?bestIndicesA.shape:x.shape,inSize=inShape[inShape.length-1],windowSize=backend_util_exports.computeOptimalWindowSize(inSize),program=new ArgMinMaxPackedProgram(inShape,windowSize,reduceType,bestIndicesA==null),inputs=bestIndicesA==null?[x]:[x,bestIndicesA],output=this.compileAndRun(program,inputs,"int32");return output.rank===x.rank?this.argReducePacked(x,reduceType,output):output}sum(x,axes){backend_util_exports.assertAxesAreInnerMostDims("sum",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize),outputDType=sumOutType(x.dtype);return this.reduce(a2D,"sum",outputDType).reshape(outShape)}prod(x,axes){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.prod(x,axes));if(cpuRes)return cpuRes;let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize),outputDType=sumOutType(x.dtype);return this.reduce(a2D,"prod",outputDType).reshape(outShape)}unsortedSegmentSum(x,segmentIds,numSegments){let axis=0,permutation=backend_util_exports.getAxesPermutation([axis],x.rank),permutedX=x;permutation!=null&&(permutedX=transpose(x,permutation),axis=backend_util_exports.getInnerMostAxes(1,x.rank)[0]);let outShape=segment_util2.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=permutedX.as2D(-1,inSize),outputDType=sumOutType(x.dtype),result=this.segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments).reshape(outShape);return permutation!=null&&(result=transpose(result,backend_util_exports.getUndoAxesPermutation(permutation))),result}segOpCompute(x,segOpType,segmentIds,dtype,numSegments){let batchSize=x.shape[0],inSize=x.shape[1],windowSize=segment_util2.segOpComputeOptimalWindowSize(inSize,numSegments),segOpInfo={windowSize,inSize,batchSize,numSegments},program=new SegmentOpProgram(segOpInfo,segOpType),output=this.compileAndRun(program,[x,segmentIds],dtype);return output.shape[1]===numSegments?output:(segmentIds=range(0,numSegments).tile([inSize/windowSize]),this.segOpCompute(output,segOpType,segmentIds,dtype,numSegments))}argMinMaxReduce(x,axis,reduceType){let axes=[axis];if(backend_util_exports.assertAxesAreInnerMostDims("arg"+reduceType.charAt(0).toUpperCase()+reduceType.slice(1),axes,x.rank),!env().getBool("WEBGL_PACK_REDUCE")||x.rank<=2){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.argReduce(a2D,reduceType).reshape(outShape)}return this.argReducePacked(x,reduceType)}argMin(x,axis){return this.argMinMaxReduce(x,axis,"min")}argMax(x,axis){return this.argMinMaxReduce(x,axis,"max")}cumsum(x,axis,exclusive,reverse12){if(axis!==x.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank-1} but got axis=${axis}`);let size=x.shape[axis],result=x;for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(x.shape,!1,reverse12),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=this.compileAndRun(program,[result],result.dtype,customSetup),prevResult.dispose()}if(exclusive){let program=new CumSumProgram(x.shape,exclusive,reverse12),prevResult=result;result=this.compileAndRun(program,[result]),prevResult.dispose()}return result}equal(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,EQUAL2,"bool");let program=new BinaryOpProgram(EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}less(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.less(a,b));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LESS2,"bool");let program=new BinaryOpProgram(LESS,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}lessEqual(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LESS_EQUAL2,"bool");let program=new BinaryOpProgram(LESS_EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}greater(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.greater(a,b));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,GREATER2,"bool");let program=new BinaryOpProgram(GREATER,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}greaterEqual(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,GREATER_EQUAL2,"bool");let program=new BinaryOpProgram(GREATER_EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}logicalNot(x){let program=new UnaryOpProgram(x.shape,LOGICAL_NOT);return this.compileAndRun(program,[x])}logicalAnd(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LOGICAL_AND2,"bool");let program=new BinaryOpProgram(LOGICAL_AND,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}logicalOr(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LOGICAL_OR2,"bool");let program=new BinaryOpProgram(LOGICAL_OR,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}select(condition,a,b){let program=new SelectProgram(condition.rank,a.shape,a.rank);return this.compileAndRun(program,[condition,a,b],upcastType(a.dtype,b.dtype))}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}topk(x,k,sorted){let xVals=x.dataSync();return topkImpl3(xVals,x.shape,x.dtype,k,sorted)}min(x,axes){backend_util_exports.assertAxesAreInnerMostDims("min",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"min",a2D.dtype).reshape(outShape)}minimum(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.minimum(a,b));if(cpuRes)return cpuRes;let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MIN2,a.shape,b.shape):new BinaryOpProgram(MIN,a.shape,b.shape);return this.compileAndRun(program,[a,b])}mod(a,b){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MOD2,a.shape,b.shape):new BinaryOpProgram(MOD,a.shape,b.shape);return this.compileAndRun(program,[a,b])}maximum(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.maximum(a,b));if(cpuRes)return cpuRes;let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MAX2,a.shape,b.shape):new BinaryOpProgram(MAX,a.shape,b.shape);return this.compileAndRun(program,[a,b])}all(x,axes){backend_util_exports.assertAxesAreInnerMostDims("all",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"all",a2D.dtype).reshape(outShape)}any(x,axes){backend_util_exports.assertAxesAreInnerMostDims("any",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"any",a2D.dtype).reshape(outShape)}floorDiv(a,b){let op22=INT_DIV,outputDtype="int32";if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,INT_DIV2,outputDtype);let program=new BinaryOpProgram(op22,a.shape,b.shape);return this.compileAndRun(program,[a,b],outputDtype)}packedUnaryOp(x,op22,dtype){let program=new UnaryOpPackedProgram(x.shape,op22);return this.compileAndRun(program,[x],dtype)}packedBinaryOp(a,b,op22,dtype,checkOutOfBounds=!1){let program=new BinaryOpPackedProgram(op22,a.shape,b.shape,checkOutOfBounds);return this.compileAndRun(program,[a,b],dtype)}makeComplexComponentTensorInfo(complexTensor,complexPart){return{dataId:complexPart.dataId,dtype:complexPart.dtype,shape:complexTensor.shape}}addN(tensors){if(tensors.length===1)return tensors[0];if(tensors.length>env().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(tensors.length/2),leftSide=this.addN(tensors.slice(0,midIndex)),rightSide=this.addN(tensors.slice(midIndex));return this.addN([leftSide,rightSide])}let dtype=tensors.map(t=>t.dtype).reduce((d1,d2)=>upcastType(d1,d2)),shapes=tensors.map(t=>t.shape),usePackedOp=env().getBool("WEBGL_PACK"),program=usePackedOp?new AddNPackedProgram(tensors[0].shape,shapes):new AddNProgram(tensors[0].shape,shapes);return this.compileAndRun(program,tensors,dtype)}pow(a,b){let usePackedOp=env().getBool("WEBGL_PACK_BINARY_OPERATIONS"),program=usePackedOp?new BinaryOpPackedProgram(POW2,a.shape,b.shape):new BinaryOpProgram(POW,a.shape,b.shape),dtype=upcastType(a.dtype,b.dtype);return this.compileAndRun(program,[a,b],dtype)}ceil(x){if(this.shouldExecuteOnCPU([x])){let outValues=ceilImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,CEIL,x.dtype);let program=new UnaryOpProgram(x.shape,CEIL);return this.compileAndRun(program,[x])}floor(x){if(this.shouldExecuteOnCPU([x])){let outValues=floorImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,FLOOR,x.dtype);let program=new UnaryOpProgram(x.shape,FLOOR);return this.compileAndRun(program,[x])}sign(x){let program=new UnaryOpProgram(x.shape,SIGN);return this.compileAndRun(program,[x])}isNaN(x){let program=new UnaryOpProgram(x.shape,IS_NAN);return this.compileAndRun(program,[x],"bool")}isInf(x){let program=new UnaryOpProgram(x.shape,IS_INF);return this.compileAndRun(program,[x],"bool")}isFinite(x){let program=new UnaryOpProgram(x.shape,IS_FINITE);return this.compileAndRun(program,[x],"bool")}round(x){let program=new UnaryOpProgram(x.shape,ROUND);return this.compileAndRun(program,[x])}exp(x){if(this.shouldExecuteOnCPU([x])){let outValues=expImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,EXP,x.dtype);let program=new UnaryOpProgram(x.shape,EXP);return this.compileAndRun(program,[x])}expm1(x){if(this.shouldExecuteOnCPU([x])){let outValues=expm1ImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,EXPM1,x.dtype);let program=new UnaryOpProgram(x.shape,EXPM1);return this.compileAndRun(program,[x])}softmax(logits,dim){let axes=util_exports.parseAxisParam([dim],logits.shape),maxLogit=max(logits,axes),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),a=sub(logits,maxLogit.reshape(expandedShape)),b=this.exp(a),sumExp=this.sum(b,axes).reshape(expandedShape);return div(b,sumExp)}log(x){if(this.shouldExecuteOnCPU([x])){let outValues=logImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,LOG2,x.dtype);let program=new UnaryOpProgram(x.shape,LOG);return this.compileAndRun(program,[x])}log1p(x){let program=new UnaryOpProgram(x.shape,LOG1P);return this.compileAndRun(program,[x])}sqrt(x){let program=new UnaryOpProgram(x.shape,SQRT);return this.compileAndRun(program,[x])}rsqrt(x){if(this.shouldExecuteOnCPU([x])){let outValues=rsqrtImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}let program=new UnaryOpProgram(x.shape,RSQRT);return this.compileAndRun(program,[x])}reciprocal(x){let program=new UnaryOpProgram(x.shape,RECIPROCAL);return this.compileAndRun(program,[x])}relu(x){let program;return env().getBool("WEBGL_PACK")?program=new UnaryOpPackedProgram(x.shape,RELU2):program=new UnaryOpProgram(x.shape,RELU),this.compileAndRun(program,[x])}relu6(x){let program;return env().getBool("WEBGL_PACK")?program=new UnaryOpPackedProgram(x.shape,RELU62):program=new UnaryOpProgram(x.shape,RELU6),this.compileAndRun(program,[x])}prelu(x,alpha){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(PRELU2,x.shape,alpha.shape):new BinaryOpProgram(PRELU,x.shape,alpha.shape);return this.compileAndRun(program,[x,alpha])}elu(x){if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ELU3,x.dtype);let program=new UnaryOpProgram(x.shape,ELU2);return this.compileAndRun(program,[x])}eluDer(dy,y){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(ELU_DER2,dy.shape,y.shape):new BinaryOpProgram(ELU_DER,dy.shape,y.shape);return this.compileAndRun(program,[dy,y])}selu(x){let program=new UnaryOpProgram(x.shape,SELU);return this.compileAndRun(program,[x])}clip(x,min82,max102){let program;env().getBool("WEBGL_PACK_CLIP")?program=new ClipPackedProgram(x.shape):program=new ClipProgram(x.shape);let customSetup=program.getCustomSetupFunc(min82,max102);return this.compileAndRun(program,[x],null,customSetup)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}complexAbs(x){let xData=this.texData.get(x.dataId),program=new ComplexAbsProgram(x.shape),inputs=[this.makeComplexComponentTensorInfo(x,xData.complexTensorInfos.real),this.makeComplexComponentTensorInfo(x,xData.complexTensorInfos.imag)];return this.compileAndRun(program,inputs)}sigmoid(x){let program=new UnaryOpProgram(x.shape,SIGMOID);return this.compileAndRun(program,[x])}softplus(x){let program=new UnaryOpProgram(x.shape,SOFTPLUS);return this.compileAndRun(program,[x])}asin(x){let program=new UnaryOpProgram(x.shape,ASIN);return this.compileAndRun(program,[x])}acos(x){let program=new UnaryOpProgram(x.shape,ACOS);return this.compileAndRun(program,[x])}atan(x){let program=new UnaryOpProgram(x.shape,ATAN);return this.compileAndRun(program,[x])}sinh(x){let program=new UnaryOpProgram(x.shape,SINH);return this.compileAndRun(program,[x])}cosh(x){let program=new UnaryOpProgram(x.shape,COSH);return this.compileAndRun(program,[x])}tanh(x){let program=new UnaryOpProgram(x.shape,TANH);return this.compileAndRun(program,[x])}asinh(x){let program=new UnaryOpProgram(x.shape,ASINH);return this.compileAndRun(program,[x])}acosh(x){let program=new UnaryOpProgram(x.shape,ACOSH);return this.compileAndRun(program,[x])}atanh(x){let program=new UnaryOpProgram(x.shape,ATANH);return this.compileAndRun(program,[x])}erf(x){let program=new UnaryOpProgram(x.shape,ERF);return this.compileAndRun(program,[x])}step(x,alpha){let program=new UnaryOpProgram(x.shape,STEP(alpha));return this.compileAndRun(program,[x])}conv2dByMatMul(x,filter,convInfo,bias,activation2,preluActivationWeights){let xShape=x.shape,xTexData=this.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!==0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape2=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped2=reshape(x,[1,targetShape2,convInfo.inChannels]),filterReshaped2=reshape(filter,[1,convInfo.inChannels,convInfo.outChannels]),result=this.fusedBatchMatMul({a:xReshaped2,b:filterReshaped2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights});return reshape(result,convInfo.outShape)}let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape(filter,[1,convInfo.inChannels,convInfo.outChannels]),pointwiseConv=this.fusedBatchMatMul({a:xReshaped,b:filterReshaped,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}),pointwiseConvTexData=this.texData.get(pointwiseConv.dataId);return util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,engine15().makeTensorFromDataId(pointwiseConv.dataId,convInfo.outShape,pointwiseConv.dtype)}conv2dWithIm2Row(x,filter,convInfo,bias,activation2,preluActivationWeights){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,xSqueezed=x.squeeze([0]),w2Row=filter.reshape([1,sharedDim,-1]),im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=this.compileAndRun(im2ColProgram,[xSqueezed]).reshape([1,x2ColShape[0],x2ColShape[1]]),hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2Col.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[im2Col,w2Row];bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights);let product=this.compileAndRun(matmulProgram,inputs);return isChannelsLast?product.reshape([1,outHeight,outWidth,convInfo.outChannels]):product.reshape([1,convInfo.outChannels,outHeight,outWidth])}fusedConv2d({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))return this.conv2dByMatMul(input2,filter,convInfo,bias,activation2,preluActivationWeights);if(env().getBool("WEBGL_CONV_IM2COL")&&input2.shape[0]===1)return this.conv2dWithIm2Row(input2,filter,convInfo,bias,activation2,preluActivationWeights);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[input2,filter];return bias&&inputs.push(bias),preluActivationWeights&&inputs.push(preluActivationWeights),this.compileAndRun(program,inputs)}conv2d(x,filter,convInfo){if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))return this.conv2dByMatMul(x,filter,convInfo);if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)return this.conv2dWithIm2Row(x,filter,convInfo);let program=new Conv2DProgram(convInfo);return this.compileAndRun(program,[x,filter])}conv2dDerInput(dy,filter,convInfo){let program=new Conv2DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}conv2dDerFilter(x,dy,convInfo){let program=new Conv2DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}fusedDepthwiseConv2D({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){let shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,inputs=[input2,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null;hasBias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights);let program;return shouldPackDepthwiseConv?(program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),this.compileAndRun(program,inputs)):(program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),this.compileAndRun(program,inputs))}depthwiseConv2D(x,filter,convInfo){let program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1?(program=new DepthwiseConvPacked2DProgram(convInfo),this.compileAndRun(program,[x,filter])):(program=new DepthwiseConv2DProgram(convInfo),this.compileAndRun(program,[x,filter]))}depthwiseConv2DDerInput(dy,filter,convInfo){let program=new DepthwiseConv2DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}depthwiseConv2DDerFilter(x,dy,convInfo){let program=new DepthwiseConv2DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}conv3d(x,filter,convInfo){let program=new Conv3DProgram(convInfo);return this.compileAndRun(program,[x,filter])}conv3dDerInput(dy,filter,convInfo){let program=new Conv3DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}conv3dDerFilter(x,dy,convInfo){let program=new Conv3DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}unstack(x,axis){let num=x.shape[axis],outShape=new Array(x.rank-1),outIndex=0;for(let i=0;i<x.rank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let begin=new Array(x.rank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++)begin[axis]=i,res[i]=this.slice(x,begin,size).reshape(outShape);return res}avgPool3d(x,convInfo){let program=new Pool3DProgram(convInfo,"avg",!1);return this.compileAndRun(program,[x],"float32")}avgPool3dBackprop(dy,x,convInfo){let avgPool3dBackpropProgram=new AvgPool3DBackpropProgram(convInfo);return this.compileAndRun(avgPool3dBackpropProgram,[dy],x.dtype)}maxPool3d(x,convInfo){let program=new Pool3DProgram(convInfo,"max",!1);return this.compileAndRun(program,[x],"float32")}maxPool3dBackprop(dy,x,y,convInfo){let getPositions=!0,maxPool3dPositionsProgram=new Pool3DProgram(convInfo,"max",getPositions),maxPool3dPositions=this.compileAndRun(maxPool3dPositionsProgram,[x]),maxPool3dBackPropProgram=new MaxPool3DBackpropProgram(convInfo),result=this.compileAndRun(maxPool3dBackPropProgram,[dy,maxPool3dPositions],x.dtype);return maxPool3dPositions.dispose(),result}resizeBilinear(x,newHeight,newWidth,alignCorners){let program=env().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ResizeBilinearPackedProgram(x.shape,newHeight,newWidth,alignCorners):new ResizeBilinearProgram(x.shape,newHeight,newWidth,alignCorners);return this.compileAndRun(program,[x],"float32")}resizeBilinearBackprop(dy,x,alignCorners){let program=new ResizeBilinearBackpropProgram(dy,x,alignCorners);return this.compileAndRun(program,[dy])}resizeNearestNeighbor(x,newHeight,newWidth,alignCorners){let program=new ResizeNearestNeighborProgram(x.shape,newHeight,newWidth,alignCorners);return this.compileAndRun(program,[x])}resizeNearestNeighborBackprop(dy,x,alignCorners){let program=new ResizeNearestNeigborBackpropProgram(dy,x,alignCorners);return this.compileAndRun(program,[dy])}multinomial(logits,normalized,numSamples,seed){let probs=normalized?logits:softmax(logits),batchSize=probs.shape[0],numOutcomes=probs.shape[1],program=new MultinomialProgram(batchSize,numOutcomes,numSamples),customSetup=program.getCustomSetupFunc(seed);return this.compileAndRun(program,[probs],"int32",customSetup)}oneHot(indices,depth,onValue,offValue){let program=new OneHotProgram(indices.size,depth,onValue,offValue);return this.compileAndRun(program,[indices])}diag(x){let program=new DiagProgram(x.size);return this.compileAndRun(program,[x])}cropAndResize(image3,boxes,boxIndex,cropSize,method,extrapolationValue){let program=new CropAndResizeProgram(image3.shape,boxes.shape,cropSize,method,extrapolationValue);return this.compileAndRun(program,[image3,boxes,boxIndex],"float32")}depthToSpace(x,blockSize,dataFormat){util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],program=new DepthToSpaceProgram(outputShape,blockSize,dataFormat);return this.compileAndRun(program,[x])}split(x,sizeSplits,axis){return split11(x,sizeSplits,axis)}scatterND(indices,updates,shape){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),flattenShape=[outputSize/sliceSize,sliceSize],flattenIndices=indices.reshape([numUpdates,sliceRank]),flattenX=updates.reshape([numUpdates,sliceSize]);if(outputSize===0)return backend_util_exports.reshapeTensor(tensor4([]),shape);let defaultValue=scalar(0),program=new ScatterProgram(numUpdates,sliceRank,flattenIndices.rank,flattenX.rank,strides,flattenShape),res=this.compileAndRun(program,[flattenX,flattenIndices,defaultValue]);return res.reshape(shape)}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){let{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.rank,sparseValues.rank,strides,[outputSize,1],sumDupeIndices),res=this.compileAndRun(program,[sparseValues,sparseIndices,defaultValue]);return res.reshape(outputShape)}gatherND(x,indices){let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(x,indices),flattenIndices=indices.reshape([numSlices,sliceRank]),flattenX=x.reshape([x.size/sliceSize,sliceSize]),program=new GatherNDProgram(sliceRank,strides,[numSlices,sliceSize]),res=this.compileAndRun(program,[flattenX,flattenIndices]);return res.reshape(resultShape)}fill(shape,value,dtype){if(dtype=dtype||util_exports.inferDtype(value),dtype==="string"){let values=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(shape));return values.fill(value),engine15().makeTensor(values,shape,dtype,this)}else{let program=new FillProgram(shape,value),customSetup=program.getCustomSetupFunc(value);return this.compileAndRun(program,[],dtype,customSetup)}}onesLike(x){if(x.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(x.shape,1,x.dtype)}zerosLike(x){return this.fill(x.shape,x.dtype==="string"?"":0,x.dtype)}linspace(start,stop,num){return backend_util_exports.linspaceImpl(start,stop,num)}makeTensorInfo(shape,dtype,values){let dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine15().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!==!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine15().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!env().getBool("IS_TEST")){let allKeys=Object.keys(this.binaryCache);allKeys.forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]})}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=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture!=null)return;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}tryRunOnCpuOrThrow(inputs,fn){if(this.shouldExecuteOnCPU(inputs))try{return fn()}catch(e){if(env().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version12="2.7.0";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);function identity3(args){let{inputs,backend:backend3}=args,{x}=inputs;return backend3.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig2={kernelName:Identity,backendName:"webgl",kernelFunc:identity3};function complex10(args){let{inputs,backend:backend3}=args,{real:real82,imag:imag82}=inputs,complexInfo=backend3.makeTensorInfo(real82.shape,"complex64"),complex11=backend3.texData.get(complexInfo.dataId),realTensorInfo=identity3({inputs:{x:real82},backend:backend3}),realData=backend3.texData.get(realTensorInfo.dataId);realData.complexParentRefCount++;let imagTensorInfo=identity3({inputs:{x:imag82},backend:backend3}),imagData=backend3.texData.get(imagTensorInfo.dataId);return imagData.complexParentRefCount++,complex11.complexTensorInfos={real:realTensorInfo,imag:imagTensorInfo},complexInfo}var complexConfig2={kernelName:Complex,backendName:"webgl",kernelFunc:complex10},CHECK_NAN_SNIPPET_UNARY="if (isnan(x)) return x;",CHECK_NAN_SNIPPET_BINARY=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,_Z=`
+`,CHECK_NAN_SNIPPET_BINARY_PACKED=`
   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 ig(n){return({inputs:t,backend:e})=>{let{x:r}=t,o=e,s=new ue(r.shape,n);return o.runWebGLProgram(s,[r],r.dtype)}}function vl({opSnippet:n,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:r=!1,cpuKernelImpl:o,dtype:s}){return({inputs:c,backend:l})=>{let{a:p,b:f}=c,m=l;if(r&&p.dtype==="complex64"){let T=m.texData.get(p.dataId),N=m.texData.get(f.dataId),[S,D]=[[T.complexTensorInfos.real,N.complexTensorInfos.real],[T.complexTensorInfos.imag,N.complexTensorInfos.imag]].map(P=>{let[E,L]=P,B={dataId:E.dataId,dtype:E.dtype,shape:p.shape},q={dataId:L.dataId,dtype:L.dtype,shape:f.shape},H=new Hn(n,p.shape,f.shape);return m.runWebGLProgram(H,[B,q],Kn(E.dtype,L.dtype))}),I=wl({inputs:{real:S,imag:D},backend:m});return m.disposeIntermediateTensorInfo(S),m.disposeIntermediateTensorInfo(D),I}let y=s||Kn(p.dtype,f.dtype);if(m.shouldExecuteOnCPU([p,f])&&o!=null){let T=m.texData.get(p.dataId),N=m.texData.get(f.dataId),[S,D]=o(p.shape,f.shape,T.values,N.values,y),I=m.makeTensorInfo(D,y),P=m.texData.get(I.dataId);return P.values=S,I}let b=ct().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,v;return b?v=new Ss(t,p.shape,f.shape,e):v=new Hn(n,p.shape,f.shape),m.runWebGLProgram(v,[p,f],y)}}let yE="return a + b;",CZ=vl({opSnippet:yE,packedOpSnippet:yE,supportsComplex:!0,cpuKernelImpl:PX}),SZ={kernelName:Zi,backendName:"webgl",kernelFunc:CZ};let $Z=NZ+`
+`;function unaryKernelFunc2(opSnippet){return({inputs,backend:backend3})=>{let{x}=inputs,webglBackend=backend3,program=new UnaryOpProgram(x.shape,opSnippet);return webglBackend.runWebGLProgram(program,[x],x.dtype)}}function binaryKernelFunc2({opSnippet,packedOpSnippet,checkOutOfBounds=!1,supportsComplex=!1,cpuKernelImpl,dtype}){return({inputs,backend:backend3})=>{let{a,b}=inputs,webglBackend=backend3;if(supportsComplex&&a.dtype==="complex64"){let aData=webglBackend.texData.get(a.dataId),bData=webglBackend.texData.get(b.dataId),[real82,imag82]=[[aData.complexTensorInfos.real,bData.complexTensorInfos.real],[aData.complexTensorInfos.imag,bData.complexTensorInfos.imag]].map(complexParts=>{let[aPart,bPart]=complexParts,aHandle={dataId:aPart.dataId,dtype:aPart.dtype,shape:a.shape},bHandle={dataId:bPart.dataId,dtype:bPart.dtype,shape:b.shape},program2=new BinaryOpProgram(opSnippet,a.shape,b.shape);return webglBackend.runWebGLProgram(program2,[aHandle,bHandle],upcastType(aPart.dtype,bPart.dtype))}),complexOutput=complex10({inputs:{real:real82,imag:imag82},backend:webglBackend});return webglBackend.disposeIntermediateTensorInfo(real82),webglBackend.disposeIntermediateTensorInfo(imag82),complexOutput}let $dtype=dtype||upcastType(a.dtype,b.dtype);if(webglBackend.shouldExecuteOnCPU([a,b])&&cpuKernelImpl!=null){let aData=webglBackend.texData.get(a.dataId),bData=webglBackend.texData.get(b.dataId),[outValues,outShape]=cpuKernelImpl(a.shape,b.shape,aData.values,bData.values,$dtype),out=webglBackend.makeTensorInfo(outShape,$dtype),outData=webglBackend.texData.get(out.dataId);return outData.values=outValues,out}let shouldUsePackedProgram=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&packedOpSnippet!=null,program;return shouldUsePackedProgram?program=new BinaryOpPackedProgram(packedOpSnippet,a.shape,b.shape,checkOutOfBounds):program=new BinaryOpProgram(opSnippet,a.shape,b.shape),webglBackend.runWebGLProgram(program,[a,b],$dtype)}}var ADD="return a + b;",addKernelFunc=binaryKernelFunc2({opSnippet:ADD,packedOpSnippet:ADD,supportsComplex:!0,cpuKernelImpl:addImplCPU}),addConfig2={kernelName:Add,backendName:"webgl",kernelFunc:addKernelFunc},ATAN2=CHECK_NAN_SNIPPET_BINARY+`
   return atan(a, b);
-`,IZ=`
+`,ATAN2_PACKED=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+_Z+`
+  `+CHECK_NAN_SNIPPET_BINARY_PACKED+`
   return result;
-`,EZ=vl({opSnippet:$Z,packedOpSnippet:IZ}),DZ={kernelName:Nf,backendName:"webgl",kernelFunc:EZ};function AZ(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;ph(o,"avgPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Xn(o.shape,s,c,f,l,p);if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))return Is({inputs:{x:o},backend:e});let y=new fh(m,"avg",!1);return e.runWebGLProgram(y,[o],"float32")}let FZ={kernelName:fu,backendName:"webgl",kernelFunc:AZ};function RZ(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s}=t,c=s;ph([o,s],"avgPoolBackprop");let{filterSize:l,strides:p,pad:f}=r,m=Xn(c.shape,l,p,1,f),y=new RY(m);return e.runWebGLProgram(y,[o],c.dtype)}let PZ={kernelName:_f,backendName:"webgl",kernelFunc:RZ};class OZ{constructor(t,e,r,o,s,c){this.outputShape=[],this.variableNames=["x","mean","variance"],le(t,e),le(t,r);let l="0.0";o!=null&&(le(t,o),this.variableNames.push("offset"),l="getOffsetAtOutCoords()");let p="1.0";s!=null&&(le(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
+`,atan25=binaryKernelFunc2({opSnippet:ATAN2,packedOpSnippet:ATAN2_PACKED}),atan2Config={kernelName:Atan2,backendName:"webgl",kernelFunc:atan25};function avgPool3(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex2(x,"avgPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))return identity3({inputs:{x},backend:backend3});let avgPoolProgram=new Pool2DProgram(convInfo,"avg",!1);return backend3.runWebGLProgram(avgPoolProgram,[x],"float32")}var avgPoolConfig2={kernelName:AvgPool,backendName:"webgl",kernelFunc:avgPool3};function avgPoolBackprop3(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex2([dy,input2],"avgPoolBackprop");let{filterSize,strides,pad:pad11}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11),avgPoolBackpropProgram=new AvgPool2DBackpropProgram(convInfo);return backend3.runWebGLProgram(avgPoolBackpropProgram,[dy],x.dtype)}var avgPoolBackpropConfig2={kernelName:AvgPoolBackprop,backendName:"webgl",kernelFunc:avgPoolBackprop3},BatchNormProgram=class{constructor(xShape,meanShape,varianceShape,offsetShape,scaleShape,varianceEpsilon){this.outputShape=[],this.variableNames=["x","mean","variance"],backend_util_exports.assertAndGetBroadcastShape(xShape,meanShape),backend_util_exports.assertAndGetBroadcastShape(xShape,varianceShape);let offsetSnippet="0.0";offsetShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,offsetShape),this.variableNames.push("offset"),offsetSnippet="getOffsetAtOutCoords()");let scaleSnippet="1.0";scaleShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,scaleShape),this.variableNames.push("scale"),scaleSnippet="getScaleAtOutCoords()"),this.outputShape=xShape,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
         float variance = getVarianceAtOutCoords();
-        float offset = ${l};
-        float scale = ${p};
-        float inv = scale * inversesqrt(variance + float(${c}));
+        float offset = ${offsetSnippet};
+        float scale = ${scaleSnippet};
+        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}}class LZ{constructor(t,e,r,o,s,c){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],le(t,e),le(t,r);let l="vec4(0.0)";o!=null&&(le(t,o),this.variableNames.push("offset"),l="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(le(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
+    `}},BatchNormPackedProgram=class{constructor(xShape,meanShape,varianceShape,offsetShape,scaleShape,varianceEpsilon){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],backend_util_exports.assertAndGetBroadcastShape(xShape,meanShape),backend_util_exports.assertAndGetBroadcastShape(xShape,varianceShape);let offsetSnippet="vec4(0.0)";offsetShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,offsetShape),this.variableNames.push("offset"),offsetSnippet="getOffsetAtOutCoords()");let scaleSnippet="vec4(1.0)";scaleShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,scaleShape),this.variableNames.push("scale"),scaleSnippet="getScaleAtOutCoords()"),this.outputShape=xShape,this.userCode=`
       void main() {
-        vec4 offset = ${l};
-        vec4 scale = ${p};
+        vec4 offset = ${offsetSnippet};
+        vec4 scale = ${scaleSnippet};
 
         vec4 x = getXAtOutCoords();
         vec4 mean = getMeanAtOutCoords();
         vec4 variance = getVarianceAtOutCoords();
 
-        vec4 inv = scale * inversesqrt(variance + vec4(${c}));
+        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));
 
         setOutput((x - mean) * inv + offset);
       }
-    `}}let MZ=({inputs:n,backend:t,attrs:e})=>{let{x:r,mean:o,variance:s,offset:c,scale:l}=n;_(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||o.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=e;p==null&&(p=.001);let f=[r,o,s],m=null;c!=null&&(m=c.shape,f.push(c));let y=null;l!=null&&(y=l.shape,f.push(l));let b=ct().getBool("WEBGL_PACK_NORMALIZATION")?new LZ(r.shape,o.shape,s.shape,m,y,p):new OZ(r.shape,o.shape,s.shape,m,y,p),v=t.runWebGLProgram(b,f,f[0].dtype);return v},BZ={kernelName:ku,backendName:"webgl",kernelFunc:MZ};let zZ="return float(a != b);",bE=vl({opSnippet:zZ,dtype:"bool"}),WZ={kernelName:Fu,backendName:"webgl",kernelFunc:bE};function B1(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.texData.get(r.dataId);return Is({inputs:{x:o.complexTensorInfos.real},backend:e})}let VZ={kernelName:Hf,backendName:"webgl",kernelFunc:B1};let GZ="return float(int(x));";function UZ(n,t){let e=new ue(n.shape,GZ),r=t.runWebGLProgram(e,[n],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function z1(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{dtype:s}=r;if(s==="complex64"){if(o.dtype==="complex64")return Is({inputs:{x:o},backend:e});let c=xe(o.shape),l=z1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),p=wl({inputs:{real:l,imag:c},backend:e});return c.dispose(),e.disposeIntermediateTensorInfo(l),p}if(o.dtype==="complex64"){let c=B1({inputs:{input:o},backend:e}),l=z1({inputs:{x:c},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(c),l}if(!gc(o.dtype,s)){let c=Is({inputs:{x:o},backend:e});return{dataId:c.dataId,shape:c.shape,dtype:s}}if(s==="int32")return UZ(o,e);if(s==="bool"){let c=e.makeTensorInfo([],"bool",Ce("bool",1)),l={a:o,b:c},p=bE({inputs:l,backend:e});return e.disposeIntermediateTensorInfo(c),p}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}let qZ={kernelName:xc,backendName:"webgl",kernelFunc:z1};class HZ{constructor(t){this.outputShape=[],this.outputShape=ms(t,1),this.variableNames=t.map((c,l)=>`T${l}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let c=1;c<e.length;c++)e[c]=e[c-1]+t[c][1];let r=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let c=1;c<e.length;c++){let l=e[c-1];r.push(`else if (yC < ${e[c]}) setOutput(getT${c}(yR, yC-${l}));`)}let o=e.length,s=e[e.length-1];r.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
+    `}},batchNorm3=({inputs,backend:backend3,attrs})=>{let{x,mean:mean7,variance,offset,scale:scale22}=inputs;util_exports.assert(mean7.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean7.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale22==null||mean7.shape.length===scale22.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let finalInputs=[x,mean7,variance],offsetShape=null;offset!=null&&(offsetShape=offset.shape,finalInputs.push(offset));let scaleShape=null;scale22!=null&&(scaleShape=scale22.shape,finalInputs.push(scale22));let program=env().getBool("WEBGL_PACK_NORMALIZATION")?new BatchNormPackedProgram(x.shape,mean7.shape,variance.shape,offsetShape,scaleShape,varianceEpsilon):new BatchNormProgram(x.shape,mean7.shape,variance.shape,offsetShape,scaleShape,varianceEpsilon),output=backend3.runWebGLProgram(program,finalInputs,finalInputs[0].dtype);return output},batchNormConfig2={kernelName:FusedBatchNorm,backendName:"webgl",kernelFunc:batchNorm3},NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputData=backend3.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend3})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real7},TO_INT="return float(int(x));";function int(input2,backend3){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend3.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast50(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend3});let zerosTensor=zeros(x.shape),floatX=cast50({inputs:{x},backend:backend3,attrs:{dtype:"float32"}}),result=complex10({inputs:{real:floatX,imag:zerosTensor},backend:backend3});return zerosTensor.dispose(),backend3.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real7({inputs:{input:x},backend:backend3}),result=cast50({inputs:{x:realPart},backend:backend3,attrs:{dtype}});return backend3.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend3});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend3);if(dtype==="bool"){let zerosTensorInfo=backend3.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),binaryInputs={a:x,b:zerosTensorInfo},result=notEqual3({inputs:binaryInputs,backend:backend3});return backend3.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast50},ConcatProgram=class{constructor(shapes){this.outputShape=[],this.outputShape=backend_util_exports.computeOutShape(shapes,1),this.variableNames=shapes.map((_,i)=>`T${i}`);let offsets=new Array(shapes.length-1);offsets[0]=shapes[0][1];for(let i=1;i<offsets.length;i++)offsets[i]=offsets[i-1]+shapes[i][1];let snippets=[`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<offsets.length;i++){let shift=offsets[i-1];snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`)}let lastIndex=offsets.length,lastShift=offsets[offsets.length-1];snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
         int yC = coords.y;
 
-        ${r.join(`
+        ${snippets.join(`
         `)}
       }
-    `}}class jZ{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=ms(t,e);let r=this.outputShape,o=r.length,s=Oe(o),c=Jn("coords",o),l=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((N,S)=>`T${S}`);let p=new Array(t.length-1);p[0]=t[0][e];for(let N=1;N<p.length;N++)p[N]=p[N-1]+t[N][e];let f=l[e],m=l.slice(-2),y=l.join(),b=`if (${f} < ${p[0]}) {
+    `}},ConcatPackedProgram=class{constructor(shapes,axis){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=backend_util_exports.computeOutShape(shapes,axis);let shape=this.outputShape,rank=shape.length,dtype=getCoordsDataType(rank),coords2=getChannels("coords",rank),channels=["x","y","z","w","u","v"].slice(0,rank);this.variableNames=shapes.map((_,i)=>`T${i}`);let offsets=new Array(shapes.length-1);offsets[0]=shapes[0][axis];for(let i=1;i<offsets.length;i++)offsets[i]=offsets[i-1]+shapes[i][axis];let channel=channels[axis],lastChannels=channels.slice(-2),allChannels=channels.join(),getValueSnippet=`if (${channel} < ${offsets[0]}) {
         return getChannel(
-            getT0(${y}), vec2(${m.join()}));
-        }`;for(let N=1;N<p.length;N++){let S=p[N-1];b+=`
-        if (${f} < ${p[N]}  && ${f} >= ${p[N-1]}) {
+            getT0(${allChannels}), vec2(${lastChannels.join()}));
+        }`;for(let i=1;i<offsets.length;i++){let shift2=offsets[i-1];getValueSnippet+=`
+        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i-1]}) {
           return getChannel(
-            getT${N}(${ag(l,f,S)}),
-            vec2(${ag(m,f,S)}));
-        }`}let v=p.length,T=p[p.length-1];b+=`
+            getT${i}(${shiftedChannels(channels,channel,shift2)}),
+            vec2(${shiftedChannels(lastChannels,channel,shift2)}));
+        }`}let lastIndex=offsets.length,shift=offsets[offsets.length-1];getValueSnippet+=`
         return getChannel(
-          getT${v}(${ag(l,f,T)}),
-          vec2(${ag(m,f,T)}));`,this.userCode=`
-      float getValue(${l.map(N=>"int "+N)}) {
-        ${b}
+          getT${lastIndex}(${shiftedChannels(channels,channel,shift)}),
+          vec2(${shiftedChannels(lastChannels,channel,shift)}));`,this.userCode=`
+      float getValue(${channels.map(x=>"int "+x)}) {
+        ${getValueSnippet}
       }
 
       void main() {
-        ${s} coords = getOutputCoords();
-        vec4 result = vec4(getValue(${c}), 0., 0., 0.);
+        ${dtype} coords = getOutputCoords();
+        vec4 result = vec4(getValue(${coords2}), 0., 0., 0.);
 
-        ${c[o-1]} = ${c[o-1]} + 1;
-        if (${c[o-1]} < ${r[o-1]}) {
-          result.g = getValue(${c});
+        ${coords2[rank-1]} = ${coords2[rank-1]} + 1;
+        if (${coords2[rank-1]} < ${shape[rank-1]}) {
+          result.g = getValue(${coords2});
         }
 
-        ${c[o-2]} = ${c[o-2]} + 1;
-        if (${c[o-2]} < ${r[o-2]}) {
-          result.a = getValue(${c});
+        ${coords2[rank-2]} = ${coords2[rank-2]} + 1;
+        if (${coords2[rank-2]} < ${shape[rank-2]}) {
+          result.a = getValue(${coords2});
         }
 
-        ${c[o-1]} = ${c[o-1]} - 1;
-        if (${c[o-2]} < ${r[o-2]} &&
-            ${c[o-1]} < ${r[o-1]}) {
-          result.b = getValue(${c});
+        ${coords2[rank-1]} = ${coords2[rank-1]} - 1;
+        if (${coords2[rank-2]} < ${shape[rank-2]} &&
+            ${coords2[rank-1]} < ${shape[rank-1]}) {
+          result.b = getValue(${coords2});
         }
         setOutput(result);
       }
-    `}}function ag(n,t,e){let r=n.indexOf(t),o=n.map((s,c)=>c===r?`${s} - ${e}`:s);return o.join()}function xE(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.texData.get(r.dataId);return Is({inputs:{x:o.complexTensorInfos.imag},backend:e})}let KZ={kernelName:Mf,backendName:"webgl",kernelFunc:xE};function XZ(n,t,e){let r=[dl(n.shape),...ml(n.shape)],o={dtype:n.dtype,shape:r,dataId:n.dataId},s=[dl(t),...ml(t)],c=new JI(s,r),l=!0,p=e.runWebGLProgram(c,[o],n.dtype,null,l);return{dataId:p.dataId,shape:t,dtype:p.dtype}}function Es(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{shape:s}=r,c=e,l=G(o.shape),p=Ue(s,l),f=G(p);_(l===f,()=>`The new shape (${p}) has ${f} elements and the old shape (${o.shape}) has ${l} elements. The new shape and old shape must have the same number of elements.`);let m=c.texData.get(o.dataId);return m.isPacked&&!Zm(o.shape,p)&&!(m.texture!==null&&Zm(m.shape,p))?XZ(o,p,c):(c.incRef(o.dataId),{dataId:o.dataId,shape:p,dtype:o.dtype})}let YZ={kernelName:Ou,backendName:"webgl",kernelFunc:Es};function Tl(n,t,e){let r=n[0].dtype;if(r==="complex64"){let f=n.map(T=>B1({inputs:{input:T},backend:e})),m=n.map(T=>xE({inputs:{input:T},backend:e})),y=Tl(f,t,e),b=Tl(m,t,e),v=wl({inputs:{real:y,imag:b},backend:e});return f.forEach(T=>e.disposeIntermediateTensorInfo(T)),m.forEach(T=>e.disposeIntermediateTensorInfo(T)),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),v}if(n.length>ct().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let f=Math.floor(n.length/2),m=Tl(n.slice(0,f),t,e),y=Tl(n.slice(f),t,e),b=Tl([m,y],t,e);return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(y),b}if(ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&n[0].shape.length>1){let f=new jZ(n.map(m=>m.shape),t);return e.runWebGLProgram(f,n,r)}let o=ms(n.map(f=>f.shape),t),s=n.map(f=>Es({inputs:{x:f},attrs:{shape:[-1,G(f.shape.slice(t))]},backend:e})),c=new HZ(s.map(f=>f.shape)),l=e.runWebGLProgram(c,s,r);s.forEach(f=>e.disposeIntermediateTensorInfo(f));let p=Es({inputs:{x:l},attrs:{shape:o},backend:e});return e.disposeIntermediateTensorInfo(l),p}function JZ(n){let{inputs:t,backend:e,attrs:r}=n,{axis:o}=r,s=Vt(o,t[0].shape)[0],c=ms(t.map(f=>f.shape),s);if(G(c)===0)return e.makeTensorInfo(c,t[0].dtype,[]);let l=t.filter(f=>G(f.shape)>0);if(l.length===1)return l[0];let p=l.map(f=>f.shape);return Nd(p,s),Tl(l,s,e)}let ZZ={kernelName:gu,backendName:"webgl",kernelFunc:JZ};let QZ=gE+`
+    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel),res=channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c);return res.join()}function imag7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputData=backend3.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend3})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag7};function packedReshape(input2,afterShape,backend3){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=backend3.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}function reshape90(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{shape}=attrs,webglBackend=backend3,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);let xTexData=webglBackend.texData.get(x.dataId);return xTexData.isPacked&&!isReshapeFree(x.shape,$shape)&&!(xTexData.texture!==null&&isReshapeFree(xTexData.shape,$shape))?packedReshape(x,$shape,webglBackend):(webglBackend.incRef(x.dataId),{dataId:x.dataId,shape:$shape,dtype:x.dtype})}var reshapeConfig2={kernelName:Reshape,backendName:"webgl",kernelFunc:reshape90};function concatImpl(inputs,axis,backend3){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real7({inputs:{input:t},backend:backend3})),imags=inputs.map(t=>imag7({inputs:{input:t},backend:backend3})),realConcated=concatImpl(reals,axis,backend3),imagConcated=concatImpl(imags,axis,backend3),result2=complex10({inputs:{real:realConcated,imag:imagConcated},backend:backend3});return reals.forEach(r=>backend3.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend3.disposeIntermediateTensorInfo(i)),backend3.disposeIntermediateTensorInfo(realConcated),backend3.disposeIntermediateTensorInfo(imagConcated),result2}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl(inputs.slice(0,midIndex),axis,backend3),rightSide=concatImpl(inputs.slice(midIndex),axis,backend3),result2=concatImpl([leftSide,rightSide],axis,backend3);return backend3.disposeIntermediateTensorInfo(leftSide),backend3.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend3.runWebGLProgram(program2,inputs,dtype)}let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),tensors2D=inputs.map(x=>reshape90({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend3})),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend3.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend3.disposeIntermediateTensorInfo(r));let reshapedResult=reshape90({inputs:{x:result},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(result),reshapedResult}function concat18(args){let{inputs,backend:backend3,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend3.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl($inputs,$axis,backend3)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat18},COS=CHECK_NAN_SNIPPET_UNARY+`
   return cos(x);
-`,t9=ig(QZ),e9={kernelName:wc,backendName:"webgl",kernelFunc:t9};let n9=`
+`,cos7=unaryKernelFunc2(COS),cosConfig2={kernelName:Cos,backendName:"webgl",kernelFunc:cos7},DIV=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,r9=`
+return a / b;`,DIV_PACKED=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3659,21 +3664,21 @@ return a / b;`,r9=`
   }
 
   return result;
-`,o9=vl({opSnippet:n9,packedOpSnippet:r9,checkOutOfBounds:!0}),s9={kernelName:vc,backendName:"webgl",kernelFunc:o9};class wE{constructor(t,e,r){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,c=r?`${o}.0`:"1.0",l;if(t==="real")l="return real * expR - imag * expI;";else if(t==="imag")l="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
-      const float exponentMultiplier = ${s};
+`,div36=binaryKernelFunc2({opSnippet:DIV,packedOpSnippet:DIV_PACKED,checkOutOfBounds:!0}),divConfig2={kernelName:Div,backendName:"webgl",kernelFunc:div36},FFTProgram=class{constructor(component,inputShape,inverse){this.variableNames=["real","imag"];let innerDim=inputShape[1];this.outputShape=inputShape;let exponentMultiplierSnippet=inverse?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,resultDenominator=inverse?`${innerDim}.0`:"1.0",opString;if(component==="real")opString="return real * expR - imag * expI;";else if(component==="imag")opString="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);this.userCode=`
+      const float exponentMultiplier = ${exponentMultiplierSnippet};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
-        ${l}
+        ${opString}
       }
 
       float mulMatDFT(int batch, int index) {
-        float indexRatio = float(index) / float(${o});
+        float indexRatio = float(index) / float(${innerDim});
         float exponentMultiplierTimesIndexRatio =
             exponentMultiplier * indexRatio;
 
         float result = 0.0;
 
-        for (int i = 0; i < ${o}; i++) {
+        for (int i = 0; i < ${innerDim}; i++) {
           // x = (-2|2 * PI / N) * index * i;
           float x = exponentMultiplierTimesIndexRatio * float(i);
           float expR = cos(x);
@@ -3682,7 +3687,7 @@ return a / b;`,r9=`
           float imag = getImag(batch, i);
 
           result +=
-              unaryOpComplex(real, expR, imag, expI) / ${c};
+              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
         }
 
         return result;
@@ -3692,29 +3697,29 @@ return a / b;`,r9=`
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}}function vE(n,t,e){let r=e.texData.get(n.dataId),o=G(n.shape),s=n.shape[n.shape.length-1],c=o/s,l=Es({inputs:{x:n},backend:e,attrs:{shape:[c,s]}}),p=l.shape,f=new wE("real",p,t),m=new wE("imag",p,t),y=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:p},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:p}],b=e.runWebGLProgram(f,y,"float32"),v=e.runWebGLProgram(m,y,"float32"),T=wl({inputs:{real:b,imag:v},backend:e});e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v);let N=Es({inputs:{x:T},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(N),N}function i9(n){let{inputs:t,backend:e}=n,{input:r}=t;return vE(r,!1,e)}let a9={kernelName:Pf,backendName:"webgl",kernelFunc:i9};class c9{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
+    `}};function fftImpl2(x,inverse,backend3){let xData=backend3.texData.get(x.dataId),inputSize=util_exports.sizeFromShape(x.shape),innerDimensionSize=x.shape[x.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape90({inputs:{x},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),xShape=input2D.shape,realProgram=new FFTProgram("real",xShape,inverse),imagProgram=new FFTProgram("imag",xShape,inverse),inputs=[{dataId:xData.complexTensorInfos.real.dataId,dtype:xData.complexTensorInfos.real.dtype,shape:xShape},{dataId:xData.complexTensorInfos.imag.dataId,dtype:xData.complexTensorInfos.imag.dtype,shape:xShape}],realPart=backend3.runWebGLProgram(realProgram,inputs,"float32"),imagPart=backend3.runWebGLProgram(imagProgram,inputs,"float32"),complexOutput=complex10({inputs:{real:realPart,imag:imagPart},backend:backend3});backend3.disposeIntermediateTensorInfo(realPart),backend3.disposeIntermediateTensorInfo(imagPart);let complexOutputReshaped=reshape90({inputs:{x:complexOutput},backend:backend3,attrs:{shape:x.shape}});return backend3.disposeIntermediateTensorInfo(complexOutputReshaped),complexOutputReshaped}function fft7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs;return fftImpl2(input2,!1,backend3)}var fftConfig2={kernelName:FFT,backendName:"webgl",kernelFunc:fft7},FlipLeftRightProgram=class{constructor(imageShape){this.variableNames=["Image"],this.outputShape=[];let imageWidth=imageShape[2];this.outputShape=imageShape,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
 
-          int coordX = ${e} - x;
+          int coordX = ${imageWidth} - x;
           float outputValue;
-          if(coordX >= 0 && coordX < ${e}) {
+          if(coordX >= 0 && coordX < ${imageWidth}) {
             outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
           } else {
             outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
           }
           setOutput(outputValue);
         }
-    `}}let l9={kernelName:Of,backendName:"webgl",kernelFunc:({inputs:n,backend:t})=>{let{image:e}=n,r=t,o=new c9(e.shape),s=r.runWebGLProgram(o,[e],e.dtype);return s}};class u9{constructor(t){this.variableNames=["A"];let e=Zn(),[r,o]=t;this.outputShape=t,this.userCode=`
+    `}},flipLeftRightConfig2={kernelName:FlipLeftRight,backendName:"webgl",kernelFunc:({inputs,backend:backend3})=>{let{image:image3}=inputs,webglBackend=backend3,program=new FlipLeftRightProgram(image3.shape),output=webglBackend.runWebGLProgram(program,[image3],image3.dtype);return output}},FromPixelsProgram=class{constructor(outputShape){this.variableNames=["A"];let glsl=getGlslDifferences(),[height,width]=outputShape;this.outputShape=outputShape,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
         int texC = coords[1];
         int depth = coords[2];
-        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${r}.0);
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);
 
-        vec4 values = ${e.texture2D}(A, uv);
+        vec4 values = ${glsl.texture2D}(A, uv);
         float value;
         if (depth == 0) {
           value = values.r;
@@ -3728,7 +3733,7 @@ return a / b;`,r9=`
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}}class p9{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Zn(),[r,o]=t;this.outputShape=t,this.userCode=`
+    `}},FromPixelsPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let glsl=getGlslDifferences(),[height,width]=outputShape;this.outputShape=outputShape,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3743,8 +3748,8 @@ return a / b;`,r9=`
             depth = coords[2] + col;
 
             vec2 uv = (vec2(texC, texR) + halfCR) /
-                       vec2(${o}.0, ${r}.0);
-            vec4 values = ${e.texture2D}(A, uv);
+                       vec2(${width}.0, ${height}.0);
+            vec4 values = ${glsl.texture2D}(A, uv);
             float value;
             if (depth == 0) {
               value = values.r;
@@ -3760,17 +3765,17 @@ return a / b;`,r9=`
           }
         }
 
-        ${e.output} = result;
+        ${glsl.output} = result;
       }
-    `}}let h9={kernelName:Jf,backendName:"webgl",kernelFunc:f9},kl;function f9(n){let{inputs:t,backend:e,attrs:r}=n,{pixels:o}=t,{numChannels:s}=r,c=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,l=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[p,f]=c?[o.videoWidth,o.videoHeight]:[o.width,o.height],m=[f,p],y=[f,p,s];(l||c)&&(kl==null&&(kl=document.createElement("canvas").getContext("2d")),kl.canvas.width=p,kl.canvas.height=f,kl.drawImage(o,0,0,p,f),o=kl.canvas);let b=e.makeTensorInfo(m,"int32");e.texData.get(b.dataId).usage=Br.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(b.dataId),o);let v=ct().getBool("WEBGL_PACK")?new p9(y):new u9(y),T=e.runWebGLProgram(v,[b],"int32");return e.disposeData(b.dataId),T}function d9(n){let{inputs:t,backend:e}=n,{input:r}=t;return vE(r,!0,e)}let m9={kernelName:Lf,backendName:"webgl",kernelFunc:d9};class TE{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:o,inSize:s,outSize:c}=t;this.outputShape=[o,c];let l=Math.floor(r/4)*4,p=r%4,f="sumValue += dot(values, ones);";if(e!=null){let y=1/e;f=`sumValue += dot(values * ${gt(y)?y.toPrecision(2):y}, ones);`}let m="";s%r>0&&(m=`
-        if (inIdx < 0 || inIdx >= ${s}) {
+    `}},fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend3,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend3.makeTensorInfo(texShape,"int32");backend3.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend3.gpgpu.uploadPixelDataToTexture(backend3.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend3.runWebGLProgram(program,[tempPixelHandle],"int32");return backend3.disposeData(tempPixelHandle.dataId),res}function ifft7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs;return fftImpl2(input2,!0,backend3)}var ifftConfig2={kernelName:IFFT,backendName:"webgl",kernelFunc:ifft7},MeanProgram=class{constructor(reduceInfo,divisor){this.variableNames=["x"];let{windowSize,batchSize,inSize,outSize}=reduceInfo;this.outputShape=[batchSize,outSize];let windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet="sumValue += dot(values, ones);";if(divisor!=null){let denominator=1/divisor;updateSnippet=`sumValue += dot(values * ${util_exports.isInt(denominator)?denominator.toPrecision(2):denominator}, ones);`}let checkOutOfBounds="";inSize%windowSize>0&&(checkOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
           return 0.0;
         }
       `),this.userCode=`
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float getValue(int batch, int inIdx) {
-        ${m}
+        ${checkOutOfBounds}
         return getX(batch, inIdx);
       }
 
@@ -3778,11 +3783,11 @@ return a / b;`,r9=`
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int outIdx = coords[1];
-        int inOffset = outIdx * ${r};
+        int inOffset = outIdx * ${windowSize};
 
         float sumValue = 0.0;
 
-        for (int i = 0; i < ${l}; i += 4) {
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
           int inIdx = inOffset + i;
           vec4 values = vec4(
             getValue(batch, inIdx),
@@ -3791,141 +3796,141 @@ return a / b;`,r9=`
             getValue(batch, inIdx + 3)
           );
 
-          ${f}
+          ${updateSnippet}
         }
 
-        int inIdx = inOffset + ${l};
-        if (${p===1}) {
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===1}) {
           vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
 
-          ${f}
-        } else if (${p===2}) {
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===2}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1), 0.0, 0.0);
 
-          ${f}
-        } else if (${p===3}) {
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===3}) {
           vec4 values = vec4(
             getValue(batch, inIdx),
             getValue(batch, inIdx + 1),
             getValue(batch, inIdx + 2), 0.0);
 
-          ${f}
+          ${updateSnippet}
         }
         setOutput(sumValue);
       }
-    `}}function g9(n){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:n[1],r=pp(e);t.push({inSize:e,windowSize:r,outSize:Math.ceil(e/r)})}return t}function kE(n,t,e,r){let o=g9(n.shape),s=n;for(let c=0;c<o.length;c++){let{inSize:l,windowSize:p,outSize:f}=o[c],m,y;e==="mean"?m=c===0?new TE({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f},l):new TE({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f}):m=new YI({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f},e),y=s,s=r.runWebGLProgram(m,[s],t),y.dataId!==n.dataId&&r.disposeIntermediateTensorInfo(y)}return s}function y9(n,t,e,r){let o=G(t),s=G(n.shape),c=s/o,l=Es({inputs:{x:n},attrs:{shape:[c,o]},backend:r}),p=kE(l,n.dtype,"max",r),f=Es({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),f}class b9{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let c=0;c<r.length;c++)r[c]=t[e[c]];this.outputShape=r,this.rank=r.length;let o=Oe(this.rank),s=x9(e);this.userCode=`
+    `}};function getReductionStages(inShape){let stages=[];for(;stages.length===0||stages[stages.length-1].outSize!==1;){let outSize=stages.length?stages[stages.length-1].outSize:inShape[1],windowSize=backend_util_exports.computeOptimalWindowSize(outSize);stages.push({inSize:outSize,windowSize,outSize:Math.ceil(outSize/windowSize)})}return stages}function reduce(x,dtype,reductionType,backend3){let reductionStages=getReductionStages(x.shape),result=x;for(let i=0;i<reductionStages.length;i++){let{inSize,windowSize,outSize}=reductionStages[i],program,previousResult;reductionType==="mean"?program=i===0?new MeanProgram({windowSize,inSize,batchSize:x.shape[0],outSize},inSize):new MeanProgram({windowSize,inSize,batchSize:x.shape[0],outSize}):program=new ReduceProgram({windowSize,inSize,batchSize:x.shape[0],outSize},reductionType),previousResult=result,result=backend3.runWebGLProgram(program,[result],dtype),previousResult.dataId!==x.dataId&&backend3.disposeIntermediateTensorInfo(previousResult)}return result}function maxImpl2(x,reduceShape,outShape,backend3){let inSize=util_exports.sizeFromShape(reduceShape),xSize=util_exports.sizeFromShape(x.shape),batchSize=xSize/inSize,reshapedInput=reshape90({inputs:{x},attrs:{shape:[batchSize,inSize]},backend:backend3}),reduced=reduce(reshapedInput,x.dtype,"max",backend3),reshapedOutput=reshape90({inputs:{x:reduced},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(reshapedInput),backend3.disposeIntermediateTensorInfo(reduced),reshapedOutput}var TransposeProgram=class{constructor(aShape,newDim){this.variableNames=["A"];let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[newDim[i]];this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),switched=getSwitchedCoords(newDim);this.userCode=`
     void main() {
-      ${o} resRC = getOutputCoords();
-      setOutput(getA(${s}));
+      ${dtype} resRC = getOutputCoords();
+      setOutput(getA(${switched}));
     }
-    `}}function x9(n){let t=n.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let o=0;o<n.length;o++)r[n[o]]=e[o];return r.join()}class w9{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(t.length);for(let m=0;m<r.length;m++)r[m]=t[e[m]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Oe(this.rank),s=EI("rc",this.rank),c=new Array(this.rank);for(let m=0;m<e.length;m++)c[e[m]]=s[m];let l=`vec2(${c.slice(-2).join()})`,p=`++${s[this.rank-1]} < ${r[this.rank-1]}`,f=`getChannel(getA(${c.join()}), ${l})`;this.userCode=`
+    `}};function getSwitchedCoords(newDim){let rank=newDim.length;if(rank>6)throw Error(`Transpose for rank ${rank} is not yet supported`);let originalOrder=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],switchedCoords=new Array(rank);for(let i=0;i<newDim.length;i++)switchedCoords[newDim[i]]=originalOrder[i];return switchedCoords.join()}var TransposePackedProgram=class{constructor(aShape,newDim){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[newDim[i]];if(this.outputShape=outputShape,this.rank=outputShape.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let dtype=getCoordsDataType(this.rank),outputOrder=getVecChannels("rc",this.rank),switchedOrder=new Array(this.rank);for(let i=0;i<newDim.length;i++)switchedOrder[newDim[i]]=outputOrder[i];let innerDims=`vec2(${switchedOrder.slice(-2).join()})`,nextColumn=`++${outputOrder[this.rank-1]} < ${outputShape[this.rank-1]}`,getc=`getChannel(getA(${switchedOrder.join()}), ${innerDims})`;this.userCode=`
     void main() {
-      ${o} rc = getOutputCoords();
+      ${dtype} rc = getOutputCoords();
       vec4 result = vec4(0.);
-      result[0] = ${f};
-      if(${p}) {
-        result[1] = ${f};
+      result[0] = ${getc};
+      if(${nextColumn}) {
+        result[1] = ${getc};
       }
-      --${s[this.rank-1]};
-      if(++${s[this.rank-2]} < ${r[this.rank-2]}) {
-        result[2] = ${f};
-        if(${p}) {
-          result[3] = ${f};
+      --${outputOrder[this.rank-1]};
+      if(++${outputOrder[this.rank-2]} < ${outputShape[this.rank-2]}) {
+        result[2] = ${getc};
+        if(${nextColumn}) {
+          result[3] = ${getc};
         }
       }
       setOutput(result);
     }
-    `}}function W1(n,t,e){let r=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new w9(n.shape,t):new b9(n.shape,t);return e.runWebGLProgram(r,[n],n.dtype)}let v9={kernelName:Eu,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{reductionIndices:o,keepDims:s}=t,c=e,l=r.shape.length,p=Vt(o,r.shape),f=p,m=ar(f,l),y=m!=null,b=c.shouldExecuteOnCPU([r]),v=r;if(y){if(b){let I=c.texData.get(v.dataId),P=I.values,E=new Array(l);for(let q=0;q<E.length;q++)E[q]=r.shape[m[q]];let L=R1(P,r.shape,r.dtype,m,E);v=c.makeTensorInfo(E,r.dtype);let B=c.texData.get(v.dataId);B.values=L}else v=W1(r,m,c);f=xr(f.length,l)}ir("max",f,l);let[T,N]=Rn(v.shape,f),S=T;s&&(S=Pn(T,p));let D;if(b){let I=c.texData.get(v.dataId),P=I.values,E=WX(P,G(N),S,r.dtype);D=c.makeTensorInfo(S,r.dtype);let L=c.texData.get(D.dataId);L.values=E}else D=y9(v,N,S,c);return y&&c.disposeIntermediateTensorInfo(v),D}};function T9(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;ph(o,"maxPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Xn(o.shape,s,c,f,l,p);if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))return Is({inputs:{x:o},backend:e});let y=new fh(m,"max",!1);return e.runWebGLProgram(y,[o],o.dtype)}let k9={kernelName:Du,backendName:"webgl",kernelFunc:T9};function N9(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s,output:c}=t,l=s;ph([s,c],"maxPoolBackprop");let{filterSize:p,strides:f,pad:m,dimRoundingMode:y}=r,b=Xn(l.shape,p,f,1,m,y),v=!0,T=new fh(b,"max",v),N=e.runWebGLProgram(T,[l],l.dtype),S=new nJ(b),D=e.runWebGLProgram(S,[o,N],l.dtype);return e.disposeIntermediateTensorInfo(N),D}let _9={kernelName:zf,backendName:"webgl",kernelFunc:N9};function C9(n,t,e,r){let o=new fh(e,"max",!1),s=r.runWebGLProgram(o,[n],"float32");o=new fh(e,"max",!0,!0,t);let c=r.runWebGLProgram(o,[n],"float32");return[s,c]}let S9={kernelName:Wf,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{filterSize:o,strides:s,pad:c,includeBatchInIndex:l}=t,p=e;_(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let f=[1,1];_(fn(s,f),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${f}'`);let m=Xn(r.shape,o,s,f,c),[y,b]=C9(r,l,m,p);return[y,b]}};function $9(n,t,e,r){let o=G(t),s=G(n.shape),c=s/o,l=Es({inputs:{x:n},attrs:{shape:[c,o]},backend:r}),p=kE(l,"float32","mean",r),f=Es({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),f}let I9={kernelName:kx,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{keepDims:o,axis:s}=t,c=e,l=r.shape.length,p=Vt(s,r.shape),f=p,m=ar(f,l),y=m!=null,b=c.shouldExecuteOnCPU([r]),v=[],T=r;if(y){if(b){let P=c.texData.get(T.dataId),E=P.values,L=new Array(l);for(let H=0;H<L.length;H++)L[H]=r.shape[m[H]];let B=R1(E,r.shape,r.dtype,m,L);T=c.makeTensorInfo(L,r.dtype);let q=c.texData.get(T.dataId);q.values=B}else T=W1(r,m,c);v.push(T),f=xr(f.length,l)}ir("sum",f,l);let[N,S]=Rn(T.shape,f),D=N;o&&(D=Pn(N,p));let I=$9(T,S,D,c);for(let P of v)c.disposeIntermediateTensorInfo(P);return I}};class E9{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((m,y)=>m[0]+t[y]+m[1]);let o=t.length,s=Oe(o),c=e.map(m=>m[0]).join(","),l=e.map((m,y)=>m[0]+t[y]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),f=r==="reflect"?0:1;if(o===1){this.userCode=`
-        int start = ${c};
-        int end = ${l};
+    `}};function transposeImpl2(x,perm,backend3){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new TransposePackedProgram(x.shape,perm):new TransposeProgram(x.shape,perm);return backend3.runWebGLProgram(program,[x],x.dtype)}var maxConfig2={kernelName:Max,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{reductionIndices,keepDims}=attrs,webglBackend=backend3,xRank=x.shape.length,origAxes=util_exports.parseAxisParam(reductionIndices,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),maxInputIsTransposed=permutedAxes!=null,shouldExecuteOnCPU=webglBackend.shouldExecuteOnCPU([x]),maxInput=x;if(maxInputIsTransposed){if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(maxInput.dataId),values=xTexData.values,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[permutedAxes[i]];let maxInputValues=transposeImplCPU(values,x.shape,x.dtype,permutedAxes,newShape);maxInput=webglBackend.makeTensorInfo(newShape,x.dtype);let maxInputData=webglBackend.texData.get(maxInput.dataId);maxInputData.values=maxInputValues}else maxInput=transposeImpl2(x,permutedAxes,webglBackend);axes=backend_util_exports.getInnerMostAxes(axes.length,xRank)}backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(maxInput.shape,axes),outShape=maxOutShape;keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes));let out;if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(maxInput.dataId),values=xTexData.values,outValues=maxImplCPU(values,util_exports.sizeFromShape(reduceShape),outShape,x.dtype);out=webglBackend.makeTensorInfo(outShape,x.dtype);let outData=webglBackend.texData.get(out.dataId);outData.values=outValues}else out=maxImpl2(maxInput,reduceShape,outShape,webglBackend);return maxInputIsTransposed&&webglBackend.disposeIntermediateTensorInfo(maxInput),out}};function maxPool3(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex2(x,"maxPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))return identity3({inputs:{x},backend:backend3});let maxPoolProgram=new Pool2DProgram(convInfo,"max",!1);return backend3.runWebGLProgram(maxPoolProgram,[x],x.dtype)}var maxPoolConfig2={kernelName:MaxPool,backendName:"webgl",kernelFunc:maxPool3};function maxPoolBackprop3(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex2([input2,output],"maxPoolBackprop");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),getPositions=!0,maxPoolPositionsProgram=new Pool2DProgram(convInfo,"max",getPositions),maxPoolPositions2=backend3.runWebGLProgram(maxPoolPositionsProgram,[x],x.dtype),maxPoolBackPropProgram=new MaxPool2DBackpropProgram(convInfo),result=backend3.runWebGLProgram(maxPoolBackPropProgram,[dy,maxPoolPositions2],x.dtype);return backend3.disposeIntermediateTensorInfo(maxPoolPositions2),result}var maxPoolBackpropConfig2={kernelName:MaxPoolBackprop,backendName:"webgl",kernelFunc:maxPoolBackprop3};function maxPoolWithArgmaxImpl2(x,includeBatchInIndex,convInfo,backend3){let program=new Pool2DProgram(convInfo,"max",!1),poolOutput=backend3.runWebGLProgram(program,[x],"float32");program=new Pool2DProgram(convInfo,"max",!0,!0,includeBatchInIndex);let indexOutput=backend3.runWebGLProgram(program,[x],"float32");return[poolOutput,indexOutput]}var maxPoolWithArgmaxConfig2={kernelName:MaxPoolWithArgmax,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{filterSize,strides,pad:pad11,includeBatchInIndex}=attrs,webglBackend=backend3;util_exports.assert(x.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);let dilations=[1,1];util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11),[result,indexes]=maxPoolWithArgmaxImpl2(x,includeBatchInIndex,convInfo,webglBackend);return[result,indexes]}};function meanImpl(x,reduceShape,outShape,backend3){let inSize=util_exports.sizeFromShape(reduceShape),xSize=util_exports.sizeFromShape(x.shape),batchSize=xSize/inSize,reshapedInput=reshape90({inputs:{x},attrs:{shape:[batchSize,inSize]},backend:backend3}),reduced=reduce(reshapedInput,"float32","mean",backend3),reshapedOutput=reshape90({inputs:{x:reduced},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(reshapedInput),backend3.disposeIntermediateTensorInfo(reduced),reshapedOutput}var meanConfig={kernelName:Mean,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{keepDims,axis}=attrs,webglBackend=backend3,xRank=x.shape.length,origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),meanInputIsTransposed=permutedAxes!=null,shouldExecuteOnCPU=webglBackend.shouldExecuteOnCPU([x]),intermediates=[],meanInput=x;if(meanInputIsTransposed){if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(meanInput.dataId),values=xTexData.values,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[permutedAxes[i]];let meanInputValues=transposeImplCPU(values,x.shape,x.dtype,permutedAxes,newShape);meanInput=webglBackend.makeTensorInfo(newShape,x.dtype);let meanInputData=webglBackend.texData.get(meanInput.dataId);meanInputData.values=meanInputValues}else meanInput=transposeImpl2(x,permutedAxes,webglBackend);intermediates.push(meanInput),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank)}backend_util_exports.assertAxesAreInnerMostDims("sum",axes,xRank);let[meanOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(meanInput.shape,axes),outShape=meanOutShape;keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(meanOutShape,origAxes));let out=meanImpl(meanInput,reduceShape,outShape,webglBackend);for(let i of intermediates)webglBackend.disposeIntermediateTensorInfo(i);return out}},MirrorPadProgram=class{constructor(xShape,paddings,mode){this.variableNames=["x"],this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),unpackedCoords=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,rank),offset=mode==="reflect"?0:1;if(rank===1){this.userCode=`
+        int start = ${start};
+        int end = ${end};
 
         void main() {
           int outC = getOutputCoords();
           if (outC < start) {
-            outC = start * 2 - outC - ${f};
+            outC = start * 2 - outC - ${offset};
           } else if(outC >= end) {
-            outC = (end - 1) * 2 - outC + ${f};
+            outC = (end - 1) * 2 - outC + ${offset};
           }
           setOutput(getX(outC - start));
         }
       `;return}this.userCode=`
-      ${s} start = ${s}(${c});
-      ${s} end = ${s}(${l});
+      ${dtype} start = ${dtype}(${start});
+      ${dtype} end = ${dtype}(${end});
 
       void main() {
-        ${s} outC = getOutputCoords();
-        for (int i = 0; i < ${o}; i++) {
+        ${dtype} outC = getOutputCoords();
+        for (int i = 0; i < ${rank}; i++) {
           if (outC[i] < start[i]) {
-            outC[i] = start[i] * 2 - outC[i] - ${f};
+            outC[i] = start[i] * 2 - outC[i] - ${offset};
           } else if(outC[i] >= end[i]) {
-            outC[i] = (end[i] - 1) * 2 - outC[i] + ${f};
+            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
           }
         }
-        ${s} coords = outC - start;
-        setOutput(getX(${p}));
+        ${dtype} coords = outC - start;
+        setOutput(getX(${unpackedCoords}));
       }
-    `}}class D9{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((T,N)=>T[0]+t[N]+T[1]);let o=t.length,s=Oe(o),c=e.map(T=>T[0]).join(","),l=e.map((T,N)=>T[0]+t[N]).join(","),p=Jn("rc",o),f=Jn("source",o),m=`${p[o-1]} < ${this.outputShape[o-1]}`,y=o===1?"source":`vec2(${f.slice(-2).join()})`,b=r==="reflect"?0:1,v="";if(o===1){let T=`
-        ${s} source = rc;
+    `}},MirrorPadPackedProgram=class{constructor(xShape,paddings,mode){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),coords2=getChannels("rc",rank),source=getChannels("source",rank),cLimit=`${coords2[rank-1]} < ${this.outputShape[rank-1]}`,innerDims=rank===1?"source":`vec2(${source.slice(-2).join()})`,offset=mode==="reflect"?0:1,mainLoop="";if(rank===1){let padSetup=`
+        ${dtype} source = rc;
         if (source < start) {
-          source = start * 2 - source - ${b};
+          source = start * 2 - source - ${offset};
         } else if (source >= end) {
-          source = (end - 1) * 2 - source + ${b};
+          source = (end - 1) * 2 - source + ${offset};
         }
         source -= start;
-      `;v=`
-        ${s} rc = outputLoc;
-        ${T}
-        result[0] = getChannel(getX(${f.join()}), ${y});
-        ${p[o-1]} += 1;
-        if(${m}) {
-          ${T}
-          result[1] = getChannel(getX(${f.join()}), ${y});
+      `;mainLoop=`
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords2[rank-1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
         }
-      `}else{let T=`
-        ${s} source = rc;
-        ${s} lt = ${s}(lessThan(source, start));
-        ${s} gte = ${s}(greaterThanEqual(source, end));
-        ${s} orig = 1 - (lt + gte);
+      `}else{let padSetup=`
+        ${dtype} source = rc;
+        ${dtype} lt = ${dtype}(lessThan(source, start));
+        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
+        ${dtype} orig = 1 - (lt + gte);
         source = orig * source +
-                lt * (start * 2 - source - ${b}) +
-                gte * ((end - 1) * 2 - source + ${b});
+                lt * (start * 2 - source - ${offset}) +
+                gte * ((end - 1) * 2 - source + ${offset});
         source -= start;
-      `;v=`
-        ${s} rc = outputLoc;
-        ${T}
-        result[0] = getChannel(getX(${f.join()}), ${y});
-        ${p[o-1]} += 1;
-        if(${m}) {
-          ${T}
-          result[1] = getChannel(getX(${f.join()}), ${y});
+      `;mainLoop=`
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords2[rank-1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
         }
         rc = outputLoc;
-        ${p[o-2]} += 1;
-        if(${p[o-2]} < ${this.outputShape[o-2]}) {
-          ${T}
-          result[2] = getChannel(getX(${f.join()}), ${y});
-          ${p[o-1]} += 1;
-          if(${m}) {
-            ${T}
-            result[3] = getChannel(getX(${f.join()}), ${y});
+        ${coords2[rank-2]} += 1;
+        if(${coords2[rank-2]} < ${this.outputShape[rank-2]}) {
+          ${padSetup}
+          result[2] = getChannel(getX(${source.join()}), ${innerDims});
+          ${coords2[rank-1]} += 1;
+          if(${cLimit}) {
+            ${padSetup}
+            result[3] = getChannel(getX(${source.join()}), ${innerDims});
           }
         }
       `}this.userCode=`
-      const ${s} start = ${s}(${c});
-      const ${s} end = ${s}(${l});
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
 
       void main() {
-        ${s} outputLoc = getOutputCoords();
+        ${dtype} outputLoc = getOutputCoords();
         vec4 result = vec4(0.);
-        ${v}
+        ${mainLoop}
         setOutput(result);
       }
-    `}}let A9=({inputs:n,backend:t,attrs:e})=>{let{x:r}=n,{paddings:o,mode:s}=e,c=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new D9(r.shape,o,s):new E9(r.shape,o,s),l=t.runWebGLProgram(c,[r],r.dtype);return l},F9={kernelName:Au,backendName:"webgl",kernelFunc:A9};let NE={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class _E{constructor(t,e,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=le(e,r),this.userCode=`
+    `}},mirrorPadKernelFunc=({inputs,backend:backend3,attrs})=>{let{x}=inputs,{paddings,mode}=attrs,program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MirrorPadPackedProgram(x.shape,paddings,mode):new MirrorPadProgram(x.shape,paddings,mode),output=backend3.runWebGLProgram(program,[x],x.dtype);return output},mirrorPadConfig2={kernelName:MirrorPad,backendName:"webgl",kernelFunc:mirrorPadKernelFunc},COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},BinaryOpComplexProgram=class{constructor(op22,aShape,bShape){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
-        ${t}
+        ${op22}
       }
 
       void main() {
@@ -3935,36 +3940,36 @@ return a / b;`,r9=`
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}}let CE="return a * b;";function R9(n){let{inputs:t,backend:e}=n,{a:r,b:o}=t,s=Kn(r.dtype,o.dtype);if(r.dtype==="complex64"){let l=e.texData.get(r.dataId),p=e.texData.get(o.dataId),f=new _E(NE.REAL,r.shape,o.shape),m=new _E(NE.IMAG,r.shape,o.shape),y=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}],b=e.runWebGLProgram(f,y,"float32"),v=e.runWebGLProgram(m,y,"float32"),T=wl({inputs:{real:b,imag:v},backend:e});return e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v),T}if(e.shouldExecuteOnCPU([r,o])){let l=e.texData.get(r.dataId),p=e.texData.get(o.dataId),[f,m]=VX(r.shape,o.shape,l.values,p.values,s),y=e.makeTensorInfo(m,s),b=e.texData.get(y.dataId);return b.values=f,y}let c;return ct().getBool("WEBGL_PACK_BINARY_OPERATIONS")?c=new Ss(CE,r.shape,o.shape):c=new Hn(CE,r.shape,o.shape),e.runWebGLProgram(c,[r,o],s)}let P9={kernelName:Tc,backendName:"webgl",kernelFunc:R9};let O9={kernelName:$x,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Yc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l}=e,p=t,f=p.readSync(r.dataId),m=p.readSync(o.dataId),y=s,b=c,v=l;return tm(f,m,y,b,v)}};let L9=em,M9={kernelName:Vf,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Yc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:p}=e,f=t,m=f.readSync(r.dataId),y=f.readSync(o.dataId),{selectedIndices:b,validOutputs:v}=L9(m,y,s,c,l,p);return[b,v]}};let B9=nm,z9={kernelName:Gf,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Yc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l,softNmsSigma:p}=e,f=t,m=f.readSync(r.dataId),y=f.readSync(o.dataId),b=s,v=c,T=l,N=p,{selectedIndices:S,selectedScores:D}=B9(m,y,b,v,T,N);return[S,D]}};class W9{constructor(t,e,r,o){this.variableNames=["Image"],this.outputShape=[];let s=t[1],c=t[2],l=Math.sin(e).toFixed(3),p=Math.cos(e).toFixed(3);this.outputShape=t;let[f,m]=uv(o,s,c),y=f.toFixed(3),b=m.toFixed(3),v="";typeof r=="number"?v=`float outputValue = ${r.toFixed(2)};`:v=`
-        vec3 fill = vec3(${r.join(",")});
+    `}},MUL="return a * b;";function multiply3(args){let{inputs,backend:backend3}=args,{a,b}=inputs,dtype=backend_util_exports.upcastType(a.dtype,b.dtype);if(a.dtype==="complex64"){let aData=backend3.texData.get(a.dataId),bData=backend3.texData.get(b.dataId),realProgram=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,a.shape,b.shape),imagProgram=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,a.shape,b.shape),inputs2=[{dataId:aData.complexTensorInfos.real.dataId,dtype:aData.complexTensorInfos.real.dtype,shape:a.shape},{dataId:aData.complexTensorInfos.imag.dataId,dtype:aData.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:bData.complexTensorInfos.real.dataId,dtype:bData.complexTensorInfos.real.dtype,shape:b.shape},{dataId:bData.complexTensorInfos.imag.dataId,dtype:bData.complexTensorInfos.imag.dtype,shape:b.shape}],realPart=backend3.runWebGLProgram(realProgram,inputs2,"float32"),imagPart=backend3.runWebGLProgram(imagProgram,inputs2,"float32"),complexOutput=complex10({inputs:{real:realPart,imag:imagPart},backend:backend3});return backend3.disposeIntermediateTensorInfo(realPart),backend3.disposeIntermediateTensorInfo(imagPart),complexOutput}if(backend3.shouldExecuteOnCPU([a,b])){let aData=backend3.texData.get(a.dataId),bData=backend3.texData.get(b.dataId),[outValues,outShape]=multiplyImplCPU(a.shape,b.shape,aData.values,bData.values,dtype),out=backend3.makeTensorInfo(outShape,dtype),outData=backend3.texData.get(out.dataId);return outData.values=outValues,out}let program;return env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?program=new BinaryOpPackedProgram(MUL,a.shape,b.shape):program=new BinaryOpProgram(MUL,a.shape,b.shape),backend3.runWebGLProgram(program,[a,b],dtype)}var multiplyConfig2={kernelName:Multiply,backendName:"webgl",kernelFunc:multiply3},nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold;return kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal)}},nonMaxSuppressionV4Impl3=kernel_impls_exports.nonMaxSuppressionV4Impl,nonMaxSuppressionV4Config2={kernelName:NonMaxSuppressionV4,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl3(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[selectedIndices,validOutputs]}},nonMaxSuppressionV5Impl3=kernel_impls_exports.nonMaxSuppressionV5Impl,nonMaxSuppressionV5Config2={kernelName:NonMaxSuppressionV5,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl3(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[selectedIndices,selectedScores]}},RotateProgram=class{constructor(imageShape,radians,fillValue,center){this.variableNames=["Image"],this.outputShape=[];let imageHeight=imageShape[1],imageWidth=imageShape[2],sinFactor=Math.sin(radians).toFixed(3),cosFactor=Math.cos(radians).toFixed(3);this.outputShape=imageShape;let[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),centerXString=centerX.toFixed(3),centerYString=centerY.toFixed(3),fillSnippet="";typeof fillValue=="number"?fillSnippet=`float outputValue = ${fillValue.toFixed(2)};`:fillSnippet=`
+        vec3 fill = vec3(${fillValue.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) - ${y}) * ${p} - (float(y) - ${b}) * ${l};
-          float coordYFloat = (float(x) - ${y}) * ${l} + (float(y) - ${b}) * ${p};
-          int coordX = int(round(coordXFloat + ${y}));
-          int coordY = int(round(coordYFloat + ${b}));
-          ${v}
-          if(coordX >= 0 && coordX < ${c} && coordY >= 0 && coordY < ${s}) {
+          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};
+          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};
+          int coordX = int(round(coordXFloat + ${centerXString}));
+          int coordY = int(round(coordYFloat + ${centerYString}));
+          ${fillSnippet}
+          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
             outputValue = getImage(coords[0], coordY, coordX, coords[3]);
           }
           setOutput(outputValue);
         }
-    `}}let V9={kernelName:Zf,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{image:r}=n,{radians:o,fillValue:s,center:c}=t,l=e,p=new W9(r.shape,o,s,c),f=l.runWebGLProgram(p,[r],r.dtype);return f}};let G9=gE+`
+    `}},rotateWithOffsetConfig2={kernelName:RotateWithOffset,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,webglBackend=backend3,program=new RotateProgram(image3.shape,radians,fillValue,center),output=webglBackend.runWebGLProgram(program,[image3],image3.dtype);return output}},SIN=CHECK_NAN_SNIPPET_UNARY+`
   return sin(x);
-`,U9=ig(G9),q9={kernelName:kc,backendName:"webgl",kernelFunc:U9};let H9="return x * x;",j9=ig(H9),K9={kernelName:Xf,backendName:"webgl",kernelFunc:j9};let SE="return (a - b) * (a - b);",X9=vl({opSnippet:SE,packedOpSnippet:SE}),Y9={kernelName:Nc,backendName:"webgl",kernelFunc:X9};let $E="return a - b;",J9=vl({opSnippet:$E,packedOpSnippet:$E,supportsComplex:!0,cpuKernelImpl:qX}),Z9={kernelName:_c,backendName:"webgl",kernelFunc:J9};let Q9="return tan(x);",tQ=ig(Q9),eQ={kernelName:Cc,backendName:"webgl",kernelFunc:tQ};let nQ={kernelName:ju,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{perm:o}=t,s=e,c=r.shape.length,l=new Array(c);for(let f=0;f<l.length;f++)l[f]=r.shape[o[f]];let p;if(s.shouldExecuteOnCPU([r])){let f=s.texData.get(r.dataId),m=f.values,y=R1(m,r.shape,r.dtype,o,l);p=s.makeTensorInfo(l,r.dtype);let b=s.texData.get(p.dataId);b.values=y}else p=W1(r,o,s);return p}};function 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To get the original bytes, call tensor.bytes().")}return a}async bytes(){this.throwIfDisposed();let a=await es().read(this.dataId);return this.dtype==="string"?a:new Uint8Array(a.buffer)}dispose(){if(this.isDisposed)return;es().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(a=!1){return Ol.print(this,a)}clone(){return this.throwIfDisposed(),Ol.clone(this)}toString(a=!1){let u=this.dataSync();return J2(u,this.shape,this.dtype,a)}cast(a){return this.throwIfDisposed(),Ol.cast(this,a)}variable(a=!0,u,h){return this.throwIfDisposed(),es().makeVariable(this,a,u,h)}};Object.defineProperty(z,Symbol.hasInstance,{value:i=>!!i&&i.data!=null&&i.dataSync!=null&&i.throwIfDisposed!=null});var _h=class extends z{constructor(a,u,h,d){super(a.shape,a.dtype,a.dataId,d);this.trainable=u,this.name=h}assign(a){if(a.dtype!==this.dtype)throw new Error(`dtype of the new value (${a.dtype}) and previous value (${this.dtype}) must match`);if(!Os(a.shape,this.shape))throw new Error(`shape of the new value (${a.shape}) and previous value (${this.shape}) must match`);es().disposeTensor(this),this.dataId=a.dataId,es().incRef(this,null)}dispose(){es().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(_h,Symbol.hasInstance,{value:i=>i instanceof z&&i.assign!=null&&i.assign instanceof Function});var nD;(function(i){i.R0="R0",i.R1="R1",i.R2="R2",i.R3="R3",i.R4="R4",i.R5="R5",i.R6="R6"})(nD||(nD={}));var bT;(function(i){i.float32="float32",i.int32="int32",i.bool="int32",i.complex64="complex64"})(bT||(bT={}));var xT;(function(i){i.float32="float32",i.int32="int32",i.bool="bool",i.complex64="complex64"})(xT||(xT={}));var wT;(function(i){i.float32="float32",i.int32="float32",i.bool="float32",i.complex64="complex64"})(wT||(wT={}));var vT;(function(i){i.float32="complex64",i.int32="complex64",i.bool="complex64",i.complex64="complex64"})(vT||(vT={}));var zQ={float32:wT,int32:bT,bool:xT,complex64:vT};function rD(i,a){if(i==="string"||a==="string"){if(i==="string"&&a==="string")return"string";throw new Error(`Can not upcast ${i} with ${a}`)}return zQ[i][a]}function he(i,a){if(i.dtype===a.dtype)return[i,a];let u=rD(i.dtype,a.dtype);return[i.cast(u),a.cast(u)]}function pb(i){let a=[],u=new Set;return oD(i,a,u),a}function oD(i,a,u){if(i==null)return;if(i instanceof z){a.push(i);return}if(!WQ(i))return;let h=i;for(let d in h){let g=h[d];u.has(g)||(u.add(g),oD(g,a,u))}}function WQ(i){return Array.isArray(i)||typeof i=="object"}var TT=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(let a in this.registeredVariables)this.registeredVariables[a].dispose()}},La=class{constructor(a){this.ENV=a,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new TT}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let a=this.getSortedBackends();for(let u=0;u<a.length;u++){let h=a[u],d=await this.initializeBackend(h).success;if(d){await this.setBackend(h);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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){let{name:a,asyncInit:u}=this.initializeBackendsAndReturnBest();if(u)throw new Error(`The highest priority backend '${a}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(a)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(a){if(!(a in this.registry))if(a in this.registryFactory){let{asyncInit:u}=this.initializeBackend(a);if(u)return null}else return null;return this.registry[a]}findBackendFactory(a){return a in this.registryFactory?this.registryFactory[a].factory:null}registerBackend(a,u,h=1){return a in this.registryFactory?(console.warn(`${a} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[a]={factory:u,priority:h},!0)}async setBackend(a){if(this.registryFactory[a]==null)throw new Error(`Backend name '${a}' not found in registry`);if(this.backendName=a,this.registry[a]==null){this.backendInstance=null;let{success:u,asyncInit:h}=this.initializeBackend(a),d=h?await u:u;if(!d)return!1}return this.backendInstance=this.registry[a],this.setupRegisteredKernels(),this.profiler=new mT(this.backendInstance),!0}setupRegisteredKernels(){let a=hT(this.backendName);a.forEach(u=>{u.setupFunc!=null&&u.setupFunc(this.backendInstance)})}disposeRegisteredKernels(a){let u=hT(a);u.forEach(h=>{h.disposeFunc!=null&&h.disposeFunc(this.registry[a])})}initializeBackend(a){let u=this.registryFactory[a];if(u==null)throw new Error(`Cannot initialize backend ${a}, no registration found.`);try{let h=u.factory();if(h&&!(h instanceof nT)&&typeof h.then=="function"){let d=++this.pendingBackendInitId,g=h.then(x=>d<this.pendingBackendInitId?!1:(this.registry[a]=x,this.pendingBackendInit=null,!0)).catch(x=>(d<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${a} failed`),console.warn(x.stack||x.message)),!1));return this.pendingBackendInit=g,{success:g,asyncInit:!0}}else return this.registry[a]=h,{success:!0,asyncInit:!1}}catch(h){return console.warn(`Initialization of backend ${a} failed`),console.warn(h.stack||h.message),{success:!1,asyncInit:!1}}}removeBackend(a){if(!(a in this.registryFactory))throw new Error(`${a} backend not found in registry`);this.backendName===a&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,a in this.registry&&(this.disposeRegisteredKernels(a),this.registry[a].dispose(),delete this.registry[a]),delete this.registryFactory[a],this.backendName===a&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,u)=>this.registryFactory[u].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let a=this.getSortedBackends();for(let u=0;u<a.length;u++){let h=a[u],{success:d,asyncInit:g}=this.initializeBackend(h);if(g||d)return{name:h,asyncInit:g}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(a,u){let h=this.state.tensorInfo.get(u),d=h.backend,g=this.readSync(u);d.disposeData(u),h.backend=a,a.move(u,g,h.shape,h.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(a,u){let h=null;if(u==null){if(typeof a!="function")throw new Error("Please provide a function to tidy()");u=a}else{if(typeof a!="string"&&!(a instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof u!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");h=a}let d;return this.scopedRun(()=>this.startScope(h),()=>this.endScope(d),()=>(d=u(),d instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),d))}scopedRun(a,u,h){a();try{let d=h();return u(),d}catch(d){throw u(),d}}nextTensorId(){return La.nextTensorId++}nextVariableId(){return La.nextVariableId++}clone(a){let u=this.makeTensorFromDataId(a.dataId,a.shape,a.dtype),h={x:a},d=x=>({x:()=>{let w="float32",k={x},C={dtype:w};return V.runKernelFunc($=>$.cast(x,w),k,null,Pl,C)}}),g=[];return this.addTapeNode(this.state.activeScope.name,h,[u],d,g,{}),u}runKernel(a,u,h,d,g){let x=null,w=null;return this.runKernelFunc(x,u,w,a,h,d,g)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(a,u,h){let d=this.backend.numDataIds(),g=0;h.forEach(k=>{g+=k.dtype==="complex64"?3:1});let x=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],w=d-u-g-x;if(w>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${w} data ids) after running '${a}'`)}runKernelFunc(a,u,h,d,g,x,w){let k,C=[],$=this.isTapeOn();d==null&&(d=this.state.activeScope!=null?this.state.activeScope.name:"");let F=this.state.numBytes,_=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let W,et=cb(d,this.backendName),tt;if(et!=null)W=()=>{let mt=this.backend.numDataIds();tt=et.kernelFunc({inputs:u,attrs:g,backend:this.backend});let lt=Array.isArray(tt)?tt:[tt];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,mt,lt);let gt=lt.map(({dataId:_t,shape:Gt,dtype:se})=>this.makeTensorFromDataId(_t,Gt,se));if($){let _t=this.getTensorsForGradient(d,u,gt);if(_t==null){w==null&&(w=[]);let Gt=gt.filter((se,fe)=>w[fe]);_t=(x||[]).slice().concat(Gt)}C=this.saveTensorsForBackwardMode(_t)}return gt};else{let mt=lt=>{if(!$)return;C=lt.map(gt=>this.keep(this.clone(gt)))};W=()=>{let lt=this.backend.numDataIds();tt=this.tidy(()=>a(this.backend,mt));let gt=Array.isArray(tt)?tt:[tt];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,lt,gt),gt}}let G;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?k=W():(G=this.profiler.profileKernel(d,u,()=>W()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(G),k=G.outputs)}),$&&this.addTapeNode(d,u,k,h,C,g),this.state.profiling&&this.state.activeProfile.kernels.push({name:d,bytesAdded:this.state.numBytes-F,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-_,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(mt=>u[mt]!=null?u[mt].shape:null),outputShapes:k.map(mt=>mt.shape),kernelTimeMs:G.timeMs,extraInfo:G.extraInfo}),Array.isArray(tt)?k:k[0]}saveTensorsForBackwardMode(a){let u=a.map(h=>this.keep(this.clone(h)));return u}getTensorsForGradient(a,u,h){let d=pT(a);if(d!=null){let g=d.inputsToSave||[],x=d.outputsToSave||[],w;d.saveAllInputs?(U(Array.isArray(u),()=>"saveAllInputs is true, expected inputs to be an array."),w=Object.keys(u).map(C=>u[C])):w=g.map(C=>u[C]);let k=h.filter((C,$)=>x[$]);return w.concat(k)}return null}makeTensor(a,u,h,d){if(a==null)throw new Error("Values passed to engine.makeTensor() are null");h=h||"float32",d=d||this.backend;let g=a;h==="string"&&xh(a[0])&&(g=a.map(k=>H2(k)));let x=d.write(g,u,h),w=new z(u,h,x,this.nextTensorId());if(this.incRef(w,d),h==="string"){let k=this.state.tensorInfo.get(x),C=UE(g);this.state.numBytes+=C-k.bytes,k.bytes=C}return w}makeTensorFromDataId(a,u,h,d){h=h||"float32";let g=new z(u,h,a,this.nextTensorId());return this.incRef(g,d),g}makeVariable(a,u=!0,h,d){h=h||this.nextVariableId().toString(),d!=null&&d!==a.dtype&&(a=a.cast(d));let g=new _h(a,u,h,this.nextTensorId());if(this.state.registeredVariables[g.name]!=null)throw new Error(`Variable with name ${g.name} was already registered`);return this.state.registeredVariables[g.name]=g,this.incRef(g,this.backend),g}incRef(a,u){let h=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,h===0){this.state.numDataBuffers++;let d=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(d=a.size*GE(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:u||this.backend,dtype:a.dtype,shape:a.shape,bytes:d,refCount:0}),this.state.numBytes+=d}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof _h||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let u=this.state.tensorInfo.get(a.dataId),h=u.refCount;h<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=u.bytes),this.state.numDataBuffers--,u.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let a in this.state.registeredVariables){let u=this.state.registeredVariables[a];this.disposeVariable(u)}}disposeVariable(a){this.disposeTensor(a),this.state.registeredVariables[a.name]!=null&&delete this.state.registeredVariables[a.name]}memory(){let a=this.backend.memory();return a.numTensors=this.state.numTensors,a.numDataBuffers=this.state.numDataBuffers,a.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(a.unreliable=!0,a.reasons==null&&(a.reasons=[]),a.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),a}async profile(a){this.state.profiling=!0;let u=this.state.numBytes,h=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await a(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-u,this.state.activeProfile.newTensors=this.state.numTensors-h;for(let d of this.state.activeProfile.kernels)d.kernelTimeMs=await d.kernelTimeMs,d.extraInfo=await d.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(a,u,h,d,g,x){let w={id:this.state.nextTapeNodeId++,kernelName:a,inputs:u,outputs:h,saved:g},k=pT(a);k!=null&&(d=k.gradFunc),d!=null&&(w.gradient=C=>(C=C.map(($,F)=>{if($==null){let _=h[F],W=Oa(_.size,_.dtype);return this.makeTensor(W,_.shape,_.dtype)}return $}),d(C.length>1?C:C[0],g,x))),this.state.activeTape.push(w)}keep(a){return a.kept=!0,a}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(a){let u={track:[],name:"unnamed scope",id:this.state.nextScopeId++};a&&(u.name=a),this.state.scopeStack.push(u),this.state.activeScope=u}endScope(a){let u=pb(a),h=new Set(u.map(g=>g.id));for(let g=0;g<this.state.activeScope.track.length;g++){let x=this.state.activeScope.track[g];!x.kept&&!h.has(x.id)&&x.dispose()}let d=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],u.forEach(g=>{!g.kept&&g.scopeId===d.id&&this.track(g)})}gradients(a,u,h,d=!1){if(U(u.length>0,()=>"gradients() received an empty list of xs."),h!=null&&h.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${h.dtype}'`);let g=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",a));U(g instanceof z,()=>"The result y returned by f() must be a tensor.");let x=K2(this.state.activeTape,u,g);if(!d&&x.length===0&&u.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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h=Ui.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=oo(h,d).div(Ui.scalar(255)),x=Kh(g,u.dense0,!0);return x=Kh(x,u.dense1),x=Kh(x,u.dense2),x=Kh(x,u.dense3),x=Ui.avgPool(x,[7,7],[2,2],"valid"),x})}async forward(a){return this.forwardInput(await ze(a))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(a){return $R(a)}extractParams(a){return SR(a)}},DR=te(ee()),Yl=te(ee());function Jh(i,a){return Yl.tidy(()=>Yl.add(Yl.matMul(i,a.weights),a.bias))}function IR(i,a,u){let h=[],{extractWeights:d,getRemainingWeights:g}=nr(i),x=Lb(d,h),w=x(a,u,"fc");if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:h,params:{fc:w}}}function ER(i){let a=[],u=$r(i,a);function h(g){let x=u(`${g}/weights`,2),w=u(`${g}/bias`,1);return{weights:x,bias:w}}let d={fc:h("fc")};return er(i,a),{params:d,paramMappings:a}}function Vb(i){let a={},u={};return Object.keys(i).forEach(h=>{let d=h.startsWith("fc")?u:a;d[h]=i[h]}),{featureExtractorMap:a,classifierMap:u}}var Zh=class extends Fn{constructor(a,u){super(a);this._faceFeatureExtractor=u}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(a){let{params:u}=this;if(!u)throw new Error(`${this._name} - load model before inference`);return DR.tidy(()=>{let h=a instanceof as?this.faceFeatureExtractor.forwardInput(a):a;return Jh(h.as2D(h.shape[0],-1),u.fc)})}dispose(a=!0){this.faceFeatureExtractor.dispose(a),super.dispose(a)}loadClassifierParams(a){let{params:u,paramMappings:h}=this.extractClassifierParams(a);this._params=u,this._paramMappings=h}extractClassifierParams(a){return IR(a,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(a){let{featureExtractorMap:u,classifierMap:h}=Vb(a);return this.faceFeatureExtractor.loadFromWeightMap(u),ER(h)}extractParams(a){let u=this.getClassifierChannelsIn(),h=this.getClassifierChannelsOut(),d=h*u+h,g=a.slice(0,a.length-d),x=a.slice(a.length-d);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(x)}},Gb=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ks=class{constructor(a){if(a.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${a.length}`);Gb.forEach((u,h)=>{this[u]=a[h]})}asSortedArray(){return Gb.map(a=>({expression:a,probability:this[a]})).sort((a,u)=>u.probability-a.probability)}},Qh=class extends Zh{constructor(a=new Yh){super("FaceExpressionNet",a)}forwardInput(a){return Jl.tidy(()=>Jl.softmax(this.runNet(a)))}async forward(a){return this.forwardInput(await ze(a))}async predictExpressions(a){let u=await ze(a),h=await this.forwardInput(u),d=await Promise.all(Jl.unstack(h).map(async x=>{let w=await x.data();return x.dispose(),w}));h.dispose();let g=d.map(x=>new Ks(x));return u.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Ub(i){return i.expressions instanceof Ks}function tf(i,a){let u={expressions:a};return Object.assign({},i,u)}function Hrt(i,a,u=.1,h){let d=Array.isArray(a)?a:[a];d.forEach(g=>{let x=g instanceof Ks?g:Ub(g)?g.expressions:void 0;if(!x)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let w=x.asSortedArray(),k=w.filter(F=>F.probability>u),C=Eo(g)?g.detection.box.bottomLeft:h||new Yt(0,0),$=new Ri(k.map(F=>`${F.expression} (${Ia(F.probability)})`),C);$.draw(i)})}function qi(i){return Eo(i)&&i.landmarks instanceof Cr&&i.unshiftedLandmarks instanceof Cr&&i.alignedRect instanceof Ve}function sc(i,a){let{box:u}=i.detection,h=a.shiftBy(u.x,u.y),d=h.align(),{imageDims:g}=i.detection,x=new 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k=d(`${w}/separable_conv0`),C=d(`${w}/separable_conv1`),$=d(`${w}/separable_conv2`);return{separable_conv0:k,separable_conv1:C,separable_conv2:$}}return{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:g,extractMainBlockParams:x}}function FR(i,a){let u=[],{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:g,extractMainBlockParams:x}=Xrt(i,u),w=h("entry_flow/conv_in"),k=g("entry_flow/reduction_block_0"),C=g("entry_flow/reduction_block_1"),$={conv_in:w,reduction_block_0:k,reduction_block_1:C},F={};Jo(a,0,1).forEach(tt=>{F[`main_block_${tt}`]=x(`middle_flow/main_block_${tt}`)});let _=g("exit_flow/reduction_block"),W=d("exit_flow/separable_conv"),et={reduction_block:_,separable_conv:W};return er(i,u),{params:{entry_flow:$,middle_flow:F,exit_flow:et},paramMappings:u}}function RR(i,a,u){return pn.add(pn.conv2d(i,a.filters,u,"same"),a.bias)}function Bk(i,a,u=!0){let h=u?pn.relu(i):i;return h=yr(h,a.separable_conv0,[1,1]),h=yr(pn.relu(h),a.separable_conv1,[1,1]),h=pn.maxPool(h,[3,3],[2,2],"same"),h=pn.add(h,RR(i,a.expansion_conv,[2,2])),h}function Yrt(i,a){let u=yr(pn.relu(i),a.separable_conv0,[1,1]);return u=yr(pn.relu(u),a.separable_conv1,[1,1]),u=yr(pn.relu(u),a.separable_conv2,[1,1]),u=pn.add(u,i),u}var zk=class extends Fn{constructor(a){super("TinyXception");this._numMainBlocks=a}forwardInput(a){let{params:u}=this;if(!u)throw new Error("TinyXception - load model before inference");return pn.tidy(()=>{let h=pn.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=oo(h,d).div(pn.scalar(256)),x=pn.relu(RR(g,u.entry_flow.conv_in,[2,2]));return x=Bk(x,u.entry_flow.reduction_block_0,!1),x=Bk(x,u.entry_flow.reduction_block_1),Jo(this._numMainBlocks,0,1).forEach(w=>{x=Yrt(x,u.middle_flow[`main_block_${w}`])}),x=Bk(x,u.exit_flow.reduction_block),x=pn.relu(yr(x,u.exit_flow.separable_conv,[1,1])),x})}async forward(a){return this.forwardInput(await ze(a))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(a){return FR(a,this._numMainBlocks)}extractParams(a){return AR(a,this._numMainBlocks)}};function PR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=nr(i),d=Lb(u,a),g=d(512,1,"fc/age"),x=d(512,2,"fc/gender");if(h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{paramMappings:a,params:{fc:{age:g,gender:x}}}}function OR(i){let a=[],u=$r(i,a);function h(g){let x=u(`${g}/weights`,2),w=u(`${g}/bias`,1);return{weights:x,bias:w}}let d={fc:{age:h("fc/age"),gender:h("fc/gender")}};return er(i,a),{params:d,paramMappings:a}}var cs;(function(i){i.FEMALE="female",i.MALE="male"})(cs||(cs={}));var ef=class extends Fn{constructor(a=new zk(2)){super("AgeGenderNet");this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(a){let{params:u}=this;if(!u)throw new Error(`${this._name} - load model before inference`);return ls.tidy(()=>{let h=a instanceof as?this.faceFeatureExtractor.forwardInput(a):a,d=ls.avgPool(h,[7,7],[2,2],"valid").as2D(h.shape[0],-1),g=Jh(d,u.fc.age).as1D(),x=Jh(d,u.fc.gender);return{age:g,gender:x}})}forwardInput(a){return ls.tidy(()=>{let{age:u,gender:h}=this.runNet(a);return{age:u,gender:ls.softmax(h)}})}async forward(a){return this.forwardInput(await ze(a))}async predictAgeAndGender(a){let u=await ze(a),h=await this.forwardInput(u),d=ls.unstack(h.age),g=ls.unstack(h.gender),x=d.map((k,C)=>({ageTensor:k,genderTensor:g[C]})),w=await Promise.all(x.map(async({ageTensor:k,genderTensor:C})=>{let $=(await k.data())[0],F=(await C.data())[0],_=F>.5,W=_?cs.MALE:cs.FEMALE,et=_?F:1-F;return k.dispose(),C.dispose(),{age:$,gender:W,genderProbability:et}}));return h.age.dispose(),h.gender.dispose(),u.isBatchInput?w:w[0]}getDefaultModelName(){return"age_gender_model"}dispose(a=!0){this.faceFeatureExtractor.dispose(a),super.dispose(a)}loadClassifierParams(a){let{params:u,paramMappings:h}=this.extractClassifierParams(a);this._params=u,this._paramMappings=h}extractClassifierParams(a){return PR(a)}extractParamsFromWeigthMap(a){let{featureExtractorMap:u,classifierMap:h}=Vb(a);return this.faceFeatureExtractor.loadFromWeightMap(u),OR(h)}extractParams(a){let u=512*1+1+(512*2+2),h=a.slice(0,a.length-u),d=a.slice(a.length-u);return this.faceFeatureExtractor.extractWeights(h),this.extractClassifierParams(d)}};var Ir=te(ee()),nf=class extends Zh{postProcess(a,u,h){let d=h.map(({width:x,height:w})=>{let k=u/Math.max(w,x);return{width:x*k,height:w*k}}),g=d.length;return Ir.tidy(()=>{let x=(F,_)=>Ir.stack([Ir.fill([68],F,"float32"),Ir.fill([68],_,"float32")],1).as2D(1,136).as1D(),w=(F,_)=>{let{width:W,height:et}=d[F];return _(W,et)?Math.abs(W-et)/2:0},k=F=>w(F,(_,W)=>_<W),C=F=>w(F,(_,W)=>W<_),$=a.mul(Ir.fill([g,136],u,"float32")).sub(Ir.stack(Array.from(Array(g),(F,_)=>x(k(_),C(_))))).div(Ir.stack(Array.from(Array(g),(F,_)=>x(d[_].width,d[_].height))));return $})}forwardInput(a){return Ir.tidy(()=>{let u=this.runNet(a);return this.postProcess(u,a.inputSize,a.inputDimensions.map(([h,d])=>({height:h,width:d})))})}async forward(a){return this.forwardInput(await ze(a))}async detectLandmarks(a){let u=await ze(a),h=Ir.tidy(()=>Ir.unstack(this.forwardInput(u))),d=await Promise.all(h.map(async(g,x)=>{let w=Array.from(await g.data()),k=w.filter(($,F)=>fg(F)),C=w.filter(($,F)=>!fg(F));return new Fa(Array(68).fill(0).map(($,F)=>new Yt(k[F],C[F])),{height:u.getInputHeight(x),width:u.getInputWidth(x)})}));return h.forEach(g=>g.dispose()),u.isBatchInput?d:d[0]}getClassifierChannelsOut(){return 136}},ic=class extends nf{constructor(a=new Yh){super("FaceLandmark68Net",a)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Hi=te(ee());function LR(i){let a=[],{extractDenseBlock3Params:u}=Wb(i,a),h={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return er(i,a),{params:h,paramMappings:a}}function MR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=nr(i),{extractDenseBlock3Params:d}=Bb(u,a),g=d(3,32,"dense0",!0),x=d(32,64,"dense1"),w=d(64,128,"dense2");if(h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{paramMappings:a,params:{dense0:g,dense1:x,dense2:w}}}var Wk=class extends Fn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(a){let{params:u}=this;if(!u)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Hi.tidy(()=>{let h=Hi.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=oo(h,d).div(Hi.scalar(255)),x=Rb(g,u.dense0,!0);return x=Rb(x,u.dense1),x=Rb(x,u.dense2),x=Hi.avgPool(x,[14,14],[2,2],"valid"),x})}async forward(a){return this.forwardInput(await ze(a))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(a){return LR(a)}extractParams(a){return MR(a)}},rf=class extends nf{constructor(a=new Wk){super("FaceLandmark68TinyNet",a)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},Vk=class extends ic{};var jr=te(ee()),Zl=te(ee()),Hb=te(ee());function BR(i,a){return Hb.add(Hb.mul(i,a.weights),a.biases)}function Gk(i,a,u,h,d="same"){let{filters:g,bias:x}=a.conv,w=Zl.conv2d(i,g,u,d);return w=Zl.add(w,x),w=BR(w,a.scale),h?Zl.relu(w):w}function zR(i,a){return Gk(i,a,[1,1],!0)}function Uk(i,a){return Gk(i,a,[1,1],!1)}function jb(i,a){return Gk(i,a,[2,2],!0,"valid")}var Er=te(ee());function Jrt(i,a){function u(w,k,C){let $=i(w),F=$.length/(k*C*C);if(q1(F))throw new Error(`depth has to be an integer: ${F}, weights.length: ${$.length}, 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se={conv32_down:x,conv32_1:w,conv32_2:k,conv32_3:C,conv64_down:$,conv64_1:F,conv64_2:_,conv64_3:W,conv128_down:et,conv128_1:tt,conv128_2:G,conv256_down:mt,conv256_1:lt,conv256_2:gt,conv256_down_out:_t,fc:Gt};return{params:se,paramMappings:h}}function Zrt(i,a){let u=$r(i,a);function h(x){let w=u(`${x}/scale/weights`,1),k=u(`${x}/scale/biases`,1);return{weights:w,biases:k}}function d(x){let w=u(`${x}/conv/filters`,4),k=u(`${x}/conv/bias`,1),C=h(x);return{conv:{filters:w,bias:k},scale:C}}function g(x){return{conv1:d(`${x}/conv1`),conv2:d(`${x}/conv2`)}}return{extractConvLayerParams:d,extractResidualLayerParams:g}}function VR(i){let 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w=x.mean([1,2]),k=jr.matMul(w,u.fc);return k})}async forward(a){return this.forwardInput(await ze(a))}async computeFaceDescriptor(a){let u=await ze(a),h=jr.tidy(()=>jr.unstack(this.forwardInput(u))),d=await Promise.all(h.map(g=>g.data()));return h.forEach(g=>g.dispose()),u.isBatchInput?d:d[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(a){return VR(a)}extractParams(a){return WR(a)}};function GR(i){let a=new ac;return a.extractWeights(i),a}function sf(i,a){let u={descriptor:a};return Object.assign({},i,u)}function UR(i){return typeof i.age=="number"}function af(i,a){let u={age:a};return Object.assign({},i,u)}function qR(i){return(i.gender===cs.MALE||i.gender===cs.FEMALE)&&Cl(i.genderProbability)}function cf(i,a,u){let h={gender:a,genderProbability:u};return Object.assign({},i,h)}var Oo=te(ee()),Po=te(ee());function Qrt(i,a){function u(k,C){let $=Po.tensor4d(i(3*3*k),[3,3,k,1]),F=Po.tensor1d(i(k)),_=Po.tensor1d(i(k)),W=Po.tensor1d(i(k)),et=Po.tensor1d(i(k));return a.push({paramPath:`${C}/filters`},{paramPath:`${C}/batch_norm_scale`},{paramPath:`${C}/batch_norm_offset`},{paramPath:`${C}/batch_norm_mean`},{paramPath:`${C}/batch_norm_variance`}),{filters:$,batch_norm_scale:F,batch_norm_offset:_,batch_norm_mean:W,batch_norm_variance:et}}function h(k,C,$,F,_){let W=Po.tensor4d(i(k*C*$*$),[$,$,k,C]),et=Po.tensor1d(i(C));return a.push({paramPath:`${F}/filters`},{paramPath:`${F}/${_?"batch_norm_offset":"bias"}`}),{filters:W,bias:et}}function d(k,C,$,F){let{filters:_,bias:W}=h(k,C,$,F,!0);return{filters:_,batch_norm_offset:W}}function g(k,C,$){let F=u(k,`${$}/depthwise_conv`),_=d(k,C,1,`${$}/pointwise_conv`);return{depthwise_conv:F,pointwise_conv:_}}function x(){let k=d(3,32,3,"mobilenetv1/conv_0"),C=g(32,64,"mobilenetv1/conv_1"),$=g(64,128,"mobilenetv1/conv_2"),F=g(128,128,"mobilenetv1/conv_3"),_=g(128,256,"mobilenetv1/conv_4"),W=g(256,256,"mobilenetv1/conv_5"),et=g(256,512,"mobilenetv1/conv_6"),tt=g(512,512,"mobilenetv1/conv_7"),G=g(512,512,"mobilenetv1/conv_8"),mt=g(512,512,"mobilenetv1/conv_9"),lt=g(512,512,"mobilenetv1/conv_10"),gt=g(512,512,"mobilenetv1/conv_11"),_t=g(512,1024,"mobilenetv1/conv_12"),Gt=g(1024,1024,"mobilenetv1/conv_13");return{conv_0:k,conv_1:C,conv_2:$,conv_3:F,conv_4:_,conv_5:W,conv_6:et,conv_7:tt,conv_8:G,conv_9:mt,conv_10:lt,conv_11:gt,conv_12:_t,conv_13:Gt}}function w(){let k=d(1024,256,1,"prediction_layer/conv_0"),C=d(256,512,3,"prediction_layer/conv_1"),$=d(512,128,1,"prediction_layer/conv_2"),F=d(128,256,3,"prediction_layer/conv_3"),_=d(256,128,1,"prediction_layer/conv_4"),W=d(128,256,3,"prediction_layer/conv_5"),et=d(256,64,1,"prediction_layer/conv_6"),tt=d(64,128,3,"prediction_layer/conv_7"),G=h(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),mt=h(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),lt=h(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),gt=h(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),_t=h(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Gt=h(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),se=h(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),fe=h(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),_e=h(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Ue=h(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Vt=h(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),ln=h(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),Ce={box_encoding_predictor:G,class_predictor:mt},or={box_encoding_predictor:lt,class_predictor:gt},ti={box_encoding_predictor:_t,class_predictor:Gt},ei={box_encoding_predictor:se,class_predictor:fe},gc={box_encoding_predictor:_e,class_predictor:Ue},gn={box_encoding_predictor:Vt,class_predictor:ln};return{conv_0:k,conv_1:C,conv_2:$,conv_3:F,conv_4:_,conv_5:W,conv_6:et,conv_7:tt,box_predictor_0:Ce,box_predictor_1:or,box_predictor_2:ti,box_predictor_3:ei,box_predictor_4:gc,box_predictor_5:gn}}return{extractMobilenetV1Params:x,extractPredictionLayerParams:w}}function HR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=nr(i),{extractMobilenetV1Params:d,extractPredictionLayerParams:g}=Qrt(u,a),x=d(),w=g(),k=Po.tensor3d(u(5118*4),[1,5118,4]),C={extra_dim:k};if(a.push({paramPath:"output_layer/extra_dim"}),h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{params:{mobilenetv1:x,prediction_layer:w,output_layer:C},paramMappings:a}}function tot(i,a){let u=$r(i,a);function h(C,$,F){let _=u(`${C}/Conv2d_${$}_pointwise/weights`,4,`${F}/filters`),W=u(`${C}/Conv2d_${$}_pointwise/convolution_bn_offset`,1,`${F}/batch_norm_offset`);return{filters:_,batch_norm_offset:W}}function d(C){let $=`mobilenetv1/conv_${C}`,F=`MobilenetV1/Conv2d_${C}_depthwise`,_=`${$}/depthwise_conv`,W=`${$}/pointwise_conv`,et=u(`${F}/depthwise_weights`,4,`${_}/filters`),tt=u(`${F}/BatchNorm/gamma`,1,`${_}/batch_norm_scale`),G=u(`${F}/BatchNorm/beta`,1,`${_}/batch_norm_offset`),mt=u(`${F}/BatchNorm/moving_mean`,1,`${_}/batch_norm_mean`),lt=u(`${F}/BatchNorm/moving_variance`,1,`${_}/batch_norm_variance`);return{depthwise_conv:{filters:et,batch_norm_scale:tt,batch_norm_offset:G,batch_norm_mean:mt,batch_norm_variance:lt},pointwise_conv:h("MobilenetV1",C,W)}}function g(){return{conv_0:h("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:d(1),conv_2:d(2),conv_3:d(3),conv_4:d(4),conv_5:d(5),conv_6:d(6),conv_7:d(7),conv_8:d(8),conv_9:d(9),conv_10:d(10),conv_11:d(11),conv_12:d(12),conv_13:d(13)}}function x(C,$){let F=u(`${C}/weights`,4,`${$}/filters`),_=u(`${C}/biases`,1,`${$}/bias`);return{filters:F,bias:_}}function w(C){let $=x(`Prediction/BoxPredictor_${C}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${C}/box_encoding_predictor`),F=x(`Prediction/BoxPredictor_${C}/ClassPredictor`,`prediction_layer/box_predictor_${C}/class_predictor`);return{box_encoding_predictor:$,class_predictor:F}}function k(){return{conv_0:h("Prediction",0,"prediction_layer/conv_0"),conv_1:h("Prediction",1,"prediction_layer/conv_1"),conv_2:h("Prediction",2,"prediction_layer/conv_2"),conv_3:h("Prediction",3,"prediction_layer/conv_3"),conv_4:h("Prediction",4,"prediction_layer/conv_4"),conv_5:h("Prediction",5,"prediction_layer/conv_5"),conv_6:h("Prediction",6,"prediction_layer/conv_6"),conv_7:h("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:w(0),box_predictor_1:w(1),box_predictor_2:w(2),box_predictor_3:w(3),box_predictor_4:w(4),box_predictor_5:w(5)}}return{extractMobilenetV1Params:g,extractPredictionLayerParams:k}}function jR(i){let a=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:h}=tot(i,a),d=i["Output/extra_dim"];if(a.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Rs(d))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${d}`);let g={mobilenetv1:u(),prediction_layer:h(),output_layer:{extra_dim:d}};return er(i,a),{params:g,paramMappings:a}}var Xs=te(ee()),ji=te(ee());function lo(i,a,u){return ji.tidy(()=>{let h=ji.conv2d(i,a.filters,u,"same");return h=ji.add(h,a.batch_norm_offset),ji.clipByValue(h,0,6)})}var eot=.0010000000474974513;function not(i,a,u){return Xs.tidy(()=>{let h=Xs.depthwiseConv2d(i,a.filters,u,"same");return h=Xs.batchNorm(h,a.batch_norm_mean,a.batch_norm_variance,a.batch_norm_offset,a.batch_norm_scale,eot),Xs.clipByValue(h,0,6)})}function rot(i){return[2,4,6,12].some(a=>a===i)?[2,2]:[1,1]}function KR(i,a){return Xs.tidy(()=>{let u,h=lo(i,a.conv_0,[2,2]),d=[a.conv_1,a.conv_2,a.conv_3,a.conv_4,a.conv_5,a.conv_6,a.conv_7,a.conv_8,a.conv_9,a.conv_10,a.conv_11,a.conv_12,a.conv_13];if(d.forEach((g,x)=>{let w=x+1,k=rot(w);h=not(h,g.depthwise_conv,k),h=lo(h,g.pointwise_conv,[1,1]),w===11&&(u=h)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:h,conv11:u}})}function XR(i,a,u,h,d){let g=i.shape[0],x=Math.min(u,g),w=a.map(($,F)=>({score:$,boxIndex:F})).filter($=>$.score>d).sort(($,F)=>F.score-$.score),k=$=>$<=h?1:0,C=[];return w.forEach($=>{if(C.length>=x)return;let F=$.score;for(let _=C.length-1;_>=0;--_){let W=oot(i,$.boxIndex,C[_]);if(W===0)continue;if($.score*=k(W),$.score<=d)break}F===$.score&&C.push($.boxIndex)}),C}function oot(i,a,u){let h=i.arraySync(),d=Math.min(h[a][0],h[a][2]),g=Math.min(h[a][1],h[a][3]),x=Math.max(h[a][0],h[a][2]),w=Math.max(h[a][1],h[a][3]),k=Math.min(h[u][0],h[u][2]),C=Math.min(h[u][1],h[u][3]),$=Math.max(h[u][0],h[u][2]),F=Math.max(h[u][1],h[u][3]),_=(x-d)*(w-g),W=($-k)*(F-C);if(_<=0||W<=0)return 0;let et=Math.max(d,k),tt=Math.max(g,C),G=Math.min(x,$),mt=Math.min(w,F),lt=Math.max(G-et,0)*Math.max(mt-tt,0);return lt/(_+W-lt)}var Mt=te(ee());function sot(i){let a=Mt.unstack(Mt.transpose(i,[1,0])),u=[Mt.sub(a[2],a[0]),Mt.sub(a[3],a[1])],h=[Mt.add(a[0],Mt.div(u[0],Mt.scalar(2))),Mt.add(a[1],Mt.div(u[1],Mt.scalar(2)))];return{sizes:u,centers:h}}function iot(i,a){let{sizes:u,centers:h}=sot(i),d=Mt.unstack(Mt.transpose(a,[1,0])),g=Mt.div(Mt.mul(Mt.exp(Mt.div(d[2],Mt.scalar(5))),u[0]),Mt.scalar(2)),x=Mt.add(Mt.mul(Mt.div(d[0],Mt.scalar(10)),u[0]),h[0]),w=Mt.div(Mt.mul(Mt.exp(Mt.div(d[3],Mt.scalar(5))),u[1]),Mt.scalar(2)),k=Mt.add(Mt.mul(Mt.div(d[1],Mt.scalar(10)),u[1]),h[1]);return Mt.transpose(Mt.stack([Mt.sub(x,g),Mt.sub(k,w),Mt.add(x,g),Mt.add(k,w)]),[1,0])}function YR(i,a,u){return Mt.tidy(()=>{let h=i.shape[0],d=iot(Mt.reshape(Mt.tile(u.extra_dim,[h,1,1]),[-1,4]),Mt.reshape(i,[-1,4]));d=Mt.reshape(d,[h,d.shape[0]/h,4]);let g=Mt.sigmoid(Mt.slice(a,[0,0,1],[-1,-1,-1])),x=Mt.slice(g,[0,0,0],[-1,-1,1]);x=Mt.reshape(x,[h,x.shape[1]]);let w=Mt.unstack(d),k=Mt.unstack(x);return{boxes:w,scores:k}})}var uf=te(ee()),lf=te(ee());function cc(i,a){return lf.tidy(()=>{let u=i.shape[0],h=lf.reshape(oc(i,a.box_encoding_predictor),[u,-1,1,4]),d=lf.reshape(oc(i,a.class_predictor),[u,-1,3]);return{boxPredictionEncoding:h,classPrediction:d}})}function JR(i,a,u){return uf.tidy(()=>{let h=lo(i,u.conv_0,[1,1]),d=lo(h,u.conv_1,[2,2]),g=lo(d,u.conv_2,[1,1]),x=lo(g,u.conv_3,[2,2]),w=lo(x,u.conv_4,[1,1]),k=lo(w,u.conv_5,[2,2]),C=lo(k,u.conv_6,[1,1]),$=lo(C,u.conv_7,[2,2]),F=cc(a,u.box_predictor_0),_=cc(i,u.box_predictor_1),W=cc(d,u.box_predictor_2),et=cc(x,u.box_predictor_3),tt=cc(k,u.box_predictor_4),G=cc($,u.box_predictor_5),mt=uf.concat([F.boxPredictionEncoding,_.boxPredictionEncoding,W.boxPredictionEncoding,et.boxPredictionEncoding,tt.boxPredictionEncoding,G.boxPredictionEncoding],1),lt=uf.concat([F.classPrediction,_.classPrediction,W.classPrediction,et.classPrediction,tt.classPrediction,G.classPrediction],1);return{boxPredictions:mt,classPredictions:lt}})}var Kr=class{constructor({minConfidence:a,maxResults:u}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=a||.5,this._maxResults=u||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${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}},Ki=class extends Fn{constructor(){super("SsdMobilenetv1")}forwardInput(a){let{params:u}=this;if(!u)throw new Error("SsdMobilenetv1 - load model before inference");return Oo.tidy(()=>{let h=Oo.cast(a.toBatchTensor(512,!1),"float32"),d=Oo.sub(Oo.mul(h,Oo.scalar(.007843137718737125)),Oo.scalar(1)),g=KR(d,u.mobilenetv1),{boxPredictions:x,classPredictions:w}=JR(g.out,g.conv11,u.prediction_layer);return YR(x,w,u.output_layer)})}async forward(a){return this.forwardInput(await ze(a))}async locateFaces(a,u={}){let{maxResults:h,minConfidence:d}=new Kr(u),g=await ze(a),{boxes:x,scores:w}=this.forwardInput(g),k=x[0],C=w[0];for(let gt=1;gt<x.length;gt++)x[gt].dispose(),w[gt].dispose();let $=Array.from(await C.data()),F=.5,_=XR(k,$,h,F,d),W=g.getReshapedInputDimensions(0),et=g.inputSize,tt=et/W.width,G=et/W.height,mt=k.arraySync(),lt=_.map(gt=>{let[_t,Gt]=[Math.max(0,mt[gt][0]),Math.min(1,mt[gt][2])].map(_e=>_e*G),[se,fe]=[Math.max(0,mt[gt][1]),Math.min(1,mt[gt][3])].map(_e=>_e*tt);return new Ve($[gt],new Aa(se,_t,fe-se,Gt-_t),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return k.dispose(),C.dispose(),lt}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(a){return jR(a)}extractParams(a){return HR(a)}};function qk(i){let a=new Ki;return a.extractWeights(i),a}function ZR(i){return qk(i)}var Hk=class extends Ki{},QR=.4,tP=[new Yt(.738768,.874946),new Yt(2.42204,2.65704),new Yt(4.30971,7.04493),new Yt(10.246,4.59428),new Yt(12.6868,11.8741)],eP=[new Yt(1.603231,2.094468),new Yt(6.041143,7.080126),new Yt(2.882459,3.518061),new Yt(4.266906,5.178857),new Yt(9.041765,10.66308)],nP=[117.001,114.697,97.404],rP="tiny_yolov2_model",oP="tiny_yolov2_separable_conv_model",He=te(ee()),Kb=i=>typeof i=="number";function Xb(i){if(!i)throw new Error(`invalid config: ${i}`);if(typeof i.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${i.withSeparableConvs}`);if(!Kb(i.iouThreshold)||i.iouThreshold<0||i.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${i.iouThreshold}`);if(!Array.isArray(i.classes)||!i.classes.length||!i.classes.every(a=>typeof a=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(i.classes)}`);if(!Array.isArray(i.anchors)||!i.anchors.length||!i.anchors.map(a=>a||{}).every(a=>Kb(a.x)&&Kb(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(i.anchors)}`);if(i.meanRgb&&(!Array.isArray(i.meanRgb)||i.meanRgb.length!==3||!i.meanRgb.every(Kb)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(i.meanRgb)}`)}var po=te(ee()),uo=te(ee());function Ql(i){return uo.tidy(()=>{let a=uo.mul(i,uo.scalar(.10000000149011612));return uo.add(uo.relu(uo.sub(i,a)),a)})}function Ys(i,a){return po.tidy(()=>{let u=po.pad(i,[[0,0],[1,1],[1,1],[0,0]]);return u=po.conv2d(u,a.conv.filters,[1,1],"valid"),u=po.sub(u,a.bn.sub),u=po.mul(u,a.bn.truediv),u=po.add(u,a.conv.bias),Ql(u)})}var Xi=te(ee());function Js(i,a){return Xi.tidy(()=>{let u=Xi.pad(i,[[0,0],[1,1],[1,1],[0,0]]);return u=Xi.separableConv2d(u,a.depthwise_filter,a.pointwise_filter,[1,1],"valid"),u=Xi.add(u,a.bias),Ql(u)})}var jk=te(ee());function aot(i,a){let u=jl(i,a);function h(x,w){let k=jk.tensor1d(i(x)),C=jk.tensor1d(i(x));return 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0,Ce=w(gt||lt||mt,5*u,1,"conv8");$={conv0:_t,conv1:Gt,conv2:se,conv3:fe,conv4:_e,conv5:Ue,conv6:Vt,conv7:ln,conv8:Ce}}else{let[F,_,W,et,tt,G,mt,lt,gt]=h,_t=k(F,_,"conv0"),Gt=k(_,W,"conv1"),se=k(W,et,"conv2"),fe=k(et,tt,"conv3"),_e=k(tt,G,"conv4"),Ue=k(G,mt,"conv5"),Vt=k(mt,lt,"conv6"),ln=k(lt,gt,"conv7"),Ce=w(gt,5*u,1,"conv8");$={conv0:_t,conv1:Gt,conv2:se,conv3:fe,conv4:_e,conv5:Ue,conv6:Vt,conv7:ln,conv8:Ce}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:$,paramMappings:x}}function cot(i,a){let u=$r(i,a);function h(w){let k=u(`${w}/sub`,1),C=u(`${w}/truediv`,1);return{sub:k,truediv:C}}function d(w){let k=u(`${w}/filters`,4),C=u(`${w}/bias`,1);return{filters:k,bias:C}}function g(w){let k=d(`${w}/conv`),C=h(`${w}/bn`);return{conv:k,bn:C}}let x=Xl(u);return{extractConvParams:d,extractConvWithBatchNormParams:g,extractSeparableConvParams:x}}function iP(i,a){let u=[],{extractConvParams:h,extractConvWithBatchNormParams:d,extractSeparableConvParams:g}=cot(i,u),x;if(a.withSeparableConvs){let w=a.filterSizes&&a.filterSizes.length||9;x={conv0:a.isFirstLayerConv2d?h("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:w>7?g("conv6"):void 0,conv7:w>8?g("conv7"):void 0,conv8:h("conv8")}}else x={conv0:d("conv0"),conv1:d("conv1"),conv2:d("conv2"),conv3:d("conv3"),conv4:d("conv4"),conv5:d("conv5"),conv6:d("conv6"),conv7:d("conv7"),conv8:h("conv8")};return er(i,u),{params:x,paramMappings:u}}var Yb;(function(i){i[i.XS=224]="XS",i[i.SM=320]="SM",i[i.MD=416]="MD",i[i.LG=608]="LG"})(Yb||(Yb={}));var Lo=class{constructor({inputSize:a,scoreThreshold:u}={}){this._name="TinyYolov2Options";if(this._inputSize=a||416,this._scoreThreshold=u||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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}},Kk=class extends Fn{constructor(a){super("TinyYolov2");Xb(a),this._config=a}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(a,u){let h=Ys(a,u.conv0);return h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv1),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv2),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv3),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv4),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv5),h=He.maxPool(h,[2,2],[1,1],"same"),h=Ys(h,u.conv6),h=Ys(h,u.conv7),oc(h,u.conv8,"valid",!1)}runMobilenet(a,u){let h=this.config.isFirstLayerConv2d?Ql(oc(a,u.conv0,"valid",!1)):Js(a,u.conv0);return h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv1),h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv2),h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv3),h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv4),h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv5),h=He.maxPool(h,[2,2],[1,1],"same"),h=u.conv6?Js(h,u.conv6):h,h=u.conv7?Js(h,u.conv7):h,oc(h,u.conv8,"valid",!1)}forwardInput(a,u){let{params:h}=this;if(!h)throw new Error("TinyYolov2 - load model before inference");return He.tidy(()=>{let d=He.cast(a.toBatchTensor(u,!1),"float32");return d=this.config.meanRgb?oo(d,this.config.meanRgb):d,d=d.div(He.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(d,h):this.runTinyYolov2(d,h)})}async forward(a,u){return await this.forwardInput(await ze(a),u)}async detect(a,u={}){let{inputSize:h,scoreThreshold:d}=new Lo(u),g=await ze(a),x=await this.forwardInput(g,h),w=He.tidy(()=>He.unstack(x)[0].expandDims()),k={width:g.getInputWidth(0),height:g.getInputHeight(0)},C=await this.extractBoxes(w,g.getReshapedInputDimensions(0),d);x.dispose(),w.dispose();let $=C.map(G=>G.box),F=C.map(G=>G.score),_=C.map(G=>G.classScore),W=C.map(G=>this.config.classes[G.label]),et=yg($.map(G=>G.rescale(h)),F,this.config.iouThreshold,!0),tt=et.map(G=>new Ps(F[G],_[G],W[G],$[G],k));return tt}getDefaultModelName(){return""}extractParamsFromWeigthMap(a){return iP(a,this.config)}extractParams(a){let u=this.config.filterSizes||Kk.DEFAULT_FILTER_SIZES,h=u?u.length:void 0;if(h!==7&&h!==8&&h!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${h} filterSizes in config`);return sP(a,this.config,this.boxEncodingSize,u)}async extractBoxes(a,u,h){let{width:d,height:g}=u,x=Math.max(d,g),w=x/d,k=x/g,C=a.shape[1],$=this.config.anchors.length,[F,_,W]=He.tidy(()=>{let mt=a.reshape([C,C,$,this.boxEncodingSize]),lt=mt.slice([0,0,0,0],[C,C,$,4]),gt=mt.slice([0,0,0,4],[C,C,$,1]),_t=this.withClassScores?He.softmax(mt.slice([0,0,0,5],[C,C,$,this.config.classes.length]),3):He.scalar(0);return[lt,gt,_t]}),et=[],tt=await _.array(),G=await F.array();for(let mt=0;mt<C;mt++)for(let lt=0;lt<C;lt++)for(let gt=0;gt<$;gt++){let _t=Sl(tt[mt][lt][gt][0]);if(!h||_t>h){let Gt=(lt+Sl(G[mt][lt][gt][0]))/C*w,se=(mt+Sl(G[mt][lt][gt][1]))/C*k,fe=Math.exp(G[mt][lt][gt][2])*this.config.anchors[gt].x/C*w,_e=Math.exp(G[mt][lt][gt][3])*this.config.anchors[gt].y/C*k,Ue=Gt-fe/2,Vt=se-_e/2,ln={row:mt,col:lt,anchor:gt},{classScore:Ce,label:or}=this.withClassScores?await this.extractPredictedClass(W,ln):{classScore:1,label:0};et.push({box:new Da(Ue,Vt,Ue+fe,Vt+_e),score:_t,classScore:_t*Ce,label:or,...ln})}}return F.dispose(),_.dispose(),W.dispose(),et}async extractPredictedClass(a,u){let{row:h,col:d,anchor:g}=u,x=await a.array();return Array(this.config.classes.length).fill(0).map((w,k)=>x[h][d][g][k]).map((w,k)=>({classScore:w,label:k})).reduce((w,k)=>w.classScore>k.classScore?w:k)}},tu=Kk;tu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var lc=class extends tu{constructor(a=!0){let u=Object.assign({},{withSeparableConvs:a,iouThreshold:QR,classes:["face"]},a?{anchors:eP,meanRgb:nP}:{anchors:tP,withClassScores:!0});super(u)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(a,u){let h=await this.detect(a,u);return h.map(d=>new Ve(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?oP:rP}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}};function aP(i,a=!0){let u=new lc(a);return u.extractWeights(i),u}var pf=class extends Lo{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},Xr=class{async then(a){return a(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},ff=te(ee()),Xk=te(ee());async function uc(i,a,u,h,d=({alignedRect:g})=>g){let g=i.map(k=>qi(k)?d(k):k.detection),x=h||(a instanceof Xk.Tensor?await rc(a,g):await nc(a,g)),w=await u(x);return x.forEach(k=>k instanceof Xk.Tensor&&k.dispose()),w}async function eu(i,a,u,h,d){return uc([i],a,async g=>u(g[0]),h,d)}var cP=.4,lP=[new Yt(1.603231,2.094468),new Yt(6.041143,7.080126),new Yt(2.882459,3.518061),new Yt(4.266906,5.178857),new Yt(9.041765,10.66308)],uP=[117.001,114.697,97.404],pc=class extends tu{constructor(){let a={withSeparableConvs:!0,iouThreshold:cP,classes:["face"],anchors:lP,meanRgb:uP,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(a)}get anchors(){return this.config.anchors}async locateFaces(a,u){let h=await this.detect(a,u);return h.map(d=>new Ve(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}},Te={ssdMobilenetv1:new Ki,tinyFaceDetector:new pc,tinyYolov2:new lc,faceLandmark68Net:new ic,faceLandmark68TinyNet:new rf,faceRecognitionNet:new ac,faceExpressionNet:new Qh,ageGenderNet:new ef},Yk=(i,a)=>Te.ssdMobilenetv1.locateFaces(i,a),pP=(i,a)=>Te.tinyFaceDetector.locateFaces(i,a),hP=(i,a)=>Te.tinyYolov2.locateFaces(i,a),Jk=i=>Te.faceLandmark68Net.detectLandmarks(i),fP=i=>Te.faceLandmark68TinyNet.detectLandmarks(i),dP=i=>Te.faceRecognitionNet.computeFaceDescriptor(i),mP=i=>Te.faceExpressionNet.predictExpressions(i),gP=i=>Te.ageGenderNet.predictAgeAndGender(i),Zk=i=>Te.ssdMobilenetv1.load(i),yP=i=>Te.tinyFaceDetector.load(i),bP=i=>Te.tinyYolov2.load(i),xP=i=>Te.faceLandmark68Net.load(i),wP=i=>Te.faceLandmark68TinyNet.load(i),vP=i=>Te.faceRecognitionNet.load(i),TP=i=>Te.faceExpressionNet.load(i),kP=i=>Te.ageGenderNet.load(i),NP=Zk,_P=Yk,CP=Jk,Qk=class extends Xr{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.extractedFaces=h}},ou=class extends Qk{async run(){let a=await this.parentTask,u=await uc(a,this.input,async h=>await Promise.all(h.map(d=>Te.faceExpressionNet.predictExpressions(d))),this.extractedFaces);return a.map((h,d)=>tf(h,u[d]))}withAgeAndGender(){return new nu(this,this.input)}},su=class extends Qk{async run(){let a=await this.parentTask;if(!a)return;let u=await eu(a,this.input,h=>Te.faceExpressionNet.predictExpressions(h),this.extractedFaces);return tf(a,u)}withAgeAndGender(){return new ru(this,this.input)}},dc=class extends ou{withAgeAndGender(){return new hc(this,this.input)}withFaceDescriptors(){return new Zs(this,this.input)}},mc=class extends su{withAgeAndGender(){return new fc(this,this.input)}withFaceDescriptor(){return new Qs(this,this.input)}},tN=class extends Xr{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.extractedFaces=h}},nu=class extends tN{async run(){let a=await this.parentTask,u=await uc(a,this.input,async h=>await Promise.all(h.map(d=>Te.ageGenderNet.predictAgeAndGender(d))),this.extractedFaces);return a.map((h,d)=>{let{age:g,gender:x,genderProbability:w}=u[d];return af(cf(h,x,w),g)})}withFaceExpressions(){return new ou(this,this.input)}},ru=class extends tN{async run(){let a=await this.parentTask;if(!a)return;let{age:u,gender:h,genderProbability:d}=await eu(a,this.input,g=>Te.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return af(cf(a,h,d),u)}withFaceExpressions(){return new su(this,this.input)}},hc=class extends nu{withFaceExpressions(){return new dc(this,this.input)}withFaceDescriptors(){return new Zs(this,this.input)}},fc=class extends ru{withFaceExpressions(){return new mc(this,this.input)}withFaceDescriptor(){return new Qs(this,this.input)}},hf=class extends Xr{constructor(a,u){super();this.parentTask=a;this.input=u}},Zs=class extends hf{async run(){let a=await this.parentTask,u=await uc(a,this.input,h=>Promise.all(h.map(d=>Te.faceRecognitionNet.computeFaceDescriptor(d))),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return u.map((h,d)=>sf(a[d],h))}withFaceExpressions(){return new dc(this,this.input)}withAgeAndGender(){return new hc(this,this.input)}},Qs=class extends hf{async run(){let a=await this.parentTask;if(!a)return;let u=await eu(a,this.input,h=>Te.faceRecognitionNet.computeFaceDescriptor(h),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return sf(a,u)}withFaceExpressions(){return new mc(this,this.input)}withAgeAndGender(){return new fc(this,this.input)}},df=class extends Xr{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.useTinyLandmarkNet=h}get landmarkNet(){return this.useTinyLandmarkNet?Te.faceLandmark68TinyNet:Te.faceLandmark68Net}},mf=class extends df{async run(){let a=await this.parentTask,u=a.map(g=>g.detection),h=this.input instanceof ff.Tensor?await rc(this.input,u):await nc(this.input,u),d=await Promise.all(h.map(g=>this.landmarkNet.detectLandmarks(g)));return h.forEach(g=>g instanceof ff.Tensor&&g.dispose()),a.map((g,x)=>sc(g,d[x]))}withFaceExpressions(){return new dc(this,this.input)}withAgeAndGender(){return new hc(this,this.input)}withFaceDescriptors(){return new Zs(this,this.input)}},gf=class extends df{async run(){let a=await this.parentTask;if(!a)return;let{detection:u}=a,h=this.input instanceof ff.Tensor?await rc(this.input,[u]):await nc(this.input,[u]),d=await this.landmarkNet.detectLandmarks(h[0]);return h.forEach(g=>g instanceof ff.Tensor&&g.dispose()),sc(a,d)}withFaceExpressions(){return new mc(this,this.input)}withAgeAndGender(){return new fc(this,this.input)}withFaceDescriptor(){return new Qs(this,this.input)}},yf=class extends Xr{constructor(a,u=new Kr){super();this.input=a;this.options=u}},iu=class extends yf{async run(){let{input:a,options:u}=this,h=u instanceof pf?d=>Te.tinyFaceDetector.locateFaces(d,u):u instanceof Kr?d=>Te.ssdMobilenetv1.locateFaces(d,u):u instanceof Lo?d=>Te.tinyYolov2.locateFaces(d,u):null;if(!h)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return h(a)}runAndExtendWithFaceDetections(){return new Promise(async a=>{let u=await this.run();return a(u.map(h=>Ai({},h)))})}withFaceLandmarks(a=!1){return new mf(this.runAndExtendWithFaceDetections(),this.input,a)}withFaceExpressions(){return new ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new nu(this.runAndExtendWithFaceDetections(),this.input)}},bf=class extends yf{async run(){let a=await new iu(this.input,this.options),u=a[0];return a.forEach(h=>{h.score>u.score&&(u=h)}),u}runAndExtendWithFaceDetection(){return new Promise(async a=>{let u=await this.run();return a(u?Ai({},u):void 0)})}withFaceLandmarks(a=!1){return new gf(this.runAndExtendWithFaceDetection(),this.input,a)}withFaceExpressions(){return new su(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ru(this.runAndExtendWithFaceDetection(),this.input)}};function SP(i,a=new Kr){return new bf(i,a)}function xf(i,a=new Kr){return new iu(i,a)}async function eN(i,a){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await xf(i,new Kr(a?{minConfidence:a}:{})).withFaceLandmarks().withFaceDescriptors()}async function $P(i,a={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await xf(i,new Lo(a)).withFaceLandmarks().withFaceDescriptors()}var IP=eN;function Jb(i,a){if(i.length!==a.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let u=Array.from(i),h=Array.from(a);return Math.sqrt(u.map((d,g)=>d-h[g]).reduce((d,g)=>d+Math.pow(g,2),0))}var wf=class{constructor(a,u=.6){this._distanceThreshold=u;let h=Array.isArray(a)?a:[a];if(!h.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let d=1,g=()=>`person ${d++}`;this._labeledDescriptors=h.map(x=>{if(x instanceof Qo)return x;if(x instanceof Float32Array)return new Qo(g(),[x]);if(x.descriptor&&x.descriptor instanceof Float32Array)return new Qo(g(),[x.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(a,u){return u.map(h=>Jb(h,a)).reduce((h,d)=>h+d,0)/(u.length||1)}matchDescriptor(a){return this.labeledDescriptors.map(({descriptors:u,label:h})=>new $l(h,this.computeMeanDistance(a,u))).reduce((u,h)=>u.distance<h.distance?u:h)}findBestMatch(a){let u=this.matchDescriptor(a);return u.distance<this.distanceThreshold?u:new $l("unknown",u.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(a=>a.toJSON())}}static fromJSON(a){let u=a.labeledDescriptors.map(h=>Qo.fromJSON(h));return new wf(u,a.distanceThreshold)}};function EP(i){let a=new pc;return a.extractWeights(i),a}function nN(i,a){let{width:u,height:h}=new jn(a.width,a.height);if(u<=0||h<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:h})}`);if(Array.isArray(i))return i.map(d=>nN(d,{width:u,height:h}));if(qi(i)){let d=i.detection.forSize(u,h),g=i.unshiftedLandmarks.forSize(d.box.width,d.box.height);return sc(Ai(i,d),g)}return Eo(i)?Ai(i,i.detection.forSize(u,h)):i instanceof Cr||i instanceof Ve?i.forSize(u,h):i}var DP="0.8.8",uot=typeof process!="undefined",pot=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",FP={faceapi:DP,node:uot,browser:pot};return lot;})();
+`,sin6=unaryKernelFunc2(SIN),sinConfig2={kernelName:Sin,backendName:"webgl",kernelFunc:sin6},SQUARE="return x * x;",square25=unaryKernelFunc2(SQUARE),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square25},SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3},SUB="return a - b;",subKernelFunc=binaryKernelFunc2({opSnippet:SUB,packedOpSnippet:SUB,supportsComplex:!0,cpuKernelImpl:subImplCPU}),subConfig2={kernelName:Sub,backendName:"webgl",kernelFunc:subKernelFunc},TAN="return tan(x);",tan5=unaryKernelFunc2(TAN),tanConfig2={kernelName:Tan,backendName:"webgl",kernelFunc:tan5},transposeConfig2={kernelName:Transpose,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{perm}=attrs,webglBackend=backend3,xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let out;if(webglBackend.shouldExecuteOnCPU([x])){let xTexData=webglBackend.texData.get(x.dataId),values=xTexData.values,outValues=transposeImplCPU(values,x.shape,x.dtype,perm,newShape);out=webglBackend.makeTensorInfo(newShape,x.dtype);let outData=webglBackend.texData.get(out.dataId);outData.values=outValues}else out=transposeImpl2(x,perm,webglBackend);return out}};function unique7(args){let{inputs,attrs,backend:backend3}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend3.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend3.makeTensorInfo(outputShape,x.dtype,outputValues),backend3.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique7},kernelConfigs2=[addConfig2,atan2Config,avgPoolConfig2,avgPoolBackpropConfig2,batchNormConfig2,castConfig2,complexConfig2,concatConfig2,cosConfig2,divConfig2,fftConfig2,flipLeftRightConfig2,fromPixelsConfig,identityConfig2,ifftConfig2,imagConfig2,maxConfig2,maxPoolConfig2,maxPoolBackpropConfig2,maxPoolWithArgmaxConfig2,meanConfig,mirrorPadConfig2,multiplyConfig2,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,realConfig2,reshapeConfig2,rotateWithOffsetConfig2,sinConfig2,squareConfig2,subConfig2,squaredDifferenceConfig2,tanConfig2,transposeConfig2,uniqueConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version14="2.7.0",version16={"tfjs-core":version,"tfjs-backend-cpu":version10,"tfjs-backend-webgl":version12,"tfjs-data":version8,"tfjs-layers":version2,"tfjs-converter":version6,tfjs:version14},CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend3){wasmFusedMatMul=backend3.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2}=attrs,aId=backend3.dataIdMap.get(a.dataId).id,bId=backend3.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend3.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend3.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc8;function setupFunc2(backend3){wasmFunc8=backend3.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend3,inputs:{x}}=args,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:kernelFunc3}}var absConfig2=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc8;function setupFunc2(backend3){wasmFunc8=backend3.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend3,inputs}=args,{a,b}=inputs,aId=backend3.dataIdMap.get(a.dataId).id,bId=backend3.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend3.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend3.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc8(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend3){wasmFunc=backend3.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend3}=args,out=backend3.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend3.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),inVals=backend3.typedArrayFromHeap(x),outVals=backend3.typedArrayFromHeap(out);return outVals.set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4},wasmTranspose;function setup2(backend3){wasmTranspose=backend3.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose19(args){let{inputs,backend:backend3,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend3});return cloned.shape=outShape,cloned}let out=backend3.makeOutput(outShape,x.dtype),xId=backend3.dataIdMap.get(x.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose19,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend3){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose19({inputs:{x},attrs:{perm:permutedAxes},backend:backend3});let xId=backend3.dataIdMap.get(x.dataId).id,transposedId=backend3.dataIdMap.get(xTransposed.dataId).id;transposedId!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend3){wasmFunc2=backend3.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend3,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend3.makeOutput(outShape,"int32"),outId=backend3.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend3.disposeData(transposed.dataId),out}var argMaxConfig={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3},wasmAvgPool;function setup4(backend3){wasmAvgPool=backend3.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend3}=args,x=inputs.x,xId=backend3.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape91(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape91},wasmBatchMatMul;function setup5(backend3){wasmBatchMatMul=backend3.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul2(args){let{inputs,backend:backend3,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShapeOuterDims=batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2),outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape91({inputs:{x:a},backend:backend3,attrs:{shape:a3dShape}}),b3d=reshape91({inputs:{x:b},backend:backend3,attrs:{shape:b3dShape}}),a3dId=backend3.dataIdMap.get(a3d.dataId).id,b3dId=backend3.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend3.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend3.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig2={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul2};function cast51(args){let{inputs:{x},attrs:{dtype},backend:backend3}=args,out=backend3.makeOutput(x.shape,dtype),inVals=backend3.typedArrayFromHeap(x),outVals=backend3.typedArrayFromHeap(out);return outVals.set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast51},wasmClip;function setup6(backend3){wasmClip=backend3.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat19(args){let{inputs,backend:backend3}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),out=backend3.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);backend_util_exports.assertParamsConsistent(shapes,axis);let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend3.typedArrayFromHeap(input2)),outVals=backend3.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat19},wasmConv2d;function setup7(backend3){wasmConv2d=backend3.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d11(args){let{inputs,attrs,backend:backend3}=args,{x,filter}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad11,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig2={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d11},wasmConv2DBackpropInput;function setup8(backend3){wasmConv2DBackpropInput=backend3.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput3(args){let{backend:backend3,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad11,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend3.makeOutput(convInfo.inShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,dyId=backend3.dataIdMap.get(dy.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig2={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput3},cosConfig3=createUnaryKernelConfig(Cos),InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend3){wasmCropAndResize=backend3.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize2(args){let{backend:backend3,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend3.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast51({backend:backend3,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend3.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend3.dataIdMap.get(boxes.dataId).id,boxIndId=backend3.dataIdMap.get(boxInd.dataId).id,out=backend3.makeOutput(outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend3.disposeData(castedData.dataId),out}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize2},wasmCumsum;function setup10(backend3){wasmCumsum=backend3.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum6(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse12}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose19({inputs:{x},attrs:{perm:permutation},backend:backend3}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend3.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend3.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend3.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse12?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose19({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend3}),backend3.disposeData(permutedX.dataId),backend3.disposeData(permutedOut.dataId)}return out}var cumsumConfig={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum6},wasmDepthToSpace;function setup11(backend3){wasmDepthToSpace=backend3.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace2(args){let{backend:backend3,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend3.makeOutput(outputShape,"float32"),xData=backend3.dataIdMap.get(x.dataId),xId=xData.id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend3.dataIdMap.get(out.dataId).id,channelsLast=dataFormat==="NHWC"?1:0;return wasmDepthToSpace(xId,blockSize,channelsLast,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace2},wasmDepthwiseConv2d;function setup12(backend3){wasmDepthwiseConv2d=backend3.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend3}=args,{x,filter}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad11,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5},supportsFullBroadcast2=!0,divConfig3=createBinaryKernelConfig(Div,supportsFullBroadcast2),supportsFullBroadcast3=!1,equalConfig=createBinaryKernelConfig(Equal,supportsFullBroadcast3,"bool"),expConfig2=createUnaryKernelConfig(Exp);function fill6(args){let{attrs:{shape,value,dtype},backend:backend3}=args,out=backend3.makeOutput(shape,dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(value),out}var fillConfig2={kernelName:Fill,backendName:"wasm",kernelFunc:fill6},wasmFlipLeftRight;function setup13(backend3){wasmFlipLeftRight=backend3.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend3}=args,{image:image3}=inputs,out=backend3.makeOutput(image3.shape,image3.dtype),imageId=backend3.dataIdMap.get(image3.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},supportsFullBroadcast4=!1,floorDivConfig=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast4),wasmBatchNorm;function setup14(backend3){wasmBatchNorm=backend3.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend3,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean7,variance,offset,scale:scale22}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,meanId=backend3.dataIdMap.get(mean7.dataId).id,varianceId=backend3.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend3.dataIdMap.get(offset.dataId).id:0,scaleId=scale22!=null?backend3.dataIdMap.get(scale22.dataId).id:0,out=backend3.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend3.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend3){wasmFusedConv2d=backend3.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d(args){let{inputs,attrs,backend:backend3}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dilations,dataFormat,dimRoundingMode,activation:activation2}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,outId),out}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d},wasmFusedDepthwiseConv2d;function setup16(backend3){wasmFusedDepthwiseConv2d=backend3.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend3}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dilations,dataFormat,dimRoundingMode,activation:activation2}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,outId),out}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend3){wasmGatherNd=backend3.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd(args){let{backend:backend3,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend3.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xData=backend3.dataIdMap.get(params.dataId),xId=xData.id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd},wasmGather;function setup18(backend3){wasmGather=backend3.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV2(args){let{backend:backend3,inputs,attrs}=args,{x,indices}=inputs,{axis}=attrs,newShape=x.shape.slice();newShape[axis]=util_exports.sizeFromShape(indices.shape);let stridesSize=x.shape.length-1,out=backend3.makeOutput(newShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let xData=backend3.dataIdMap.get(x.dataId),xId=xData.id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,outId=backend3.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(newShape)).buffer);wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,axis,outStridesBytes,outId);let parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis);return out.shape=shapeInfo.outputShape,out}var gatherV2Config={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV2},supportsFullBroadcast5=!1,greaterConfig=createBinaryKernelConfig(Greater,supportsFullBroadcast5,"bool"),supportsFullBroadcast6=!1,greaterEqualConfig=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessConfig=createBinaryKernelConfig(Less,supportsFullBroadcast7,"bool"),supportsFullBroadcast8=!1,lessEqualConfig=createBinaryKernelConfig(LessEqual,supportsFullBroadcast8,"bool"),logConfig2=createUnaryKernelConfig(Log),supportsFullBroadcast9=!1,logicalAndConfig=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast9,"bool"),wasmMax;function setup19(backend3){wasmMax=backend3.wasm.cwrap(Max,null,["number, number, number"])}function max9(args){let{backend:backend3,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max9},supportsFullBroadcast10=!1,maximumConfig=createBinaryKernelConfig(Maximum,supportsFullBroadcast10),wasmMaxPool;function setup20(backend3){wasmMaxPool=backend3.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend3}=args,x=inputs.x,xId=backend3.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMin;function setup21(backend3){wasmMin=backend3.wasm.cwrap(Min,null,["number, number, number"])}function min7(args){let{backend:backend3,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig={kernelName:Min,backendName:"wasm",setupFunc:setup21,kernelFunc:min7},supportsFullBroadcast11=!1,minimumConfig=createBinaryKernelConfig(Minimum,supportsFullBroadcast11),supportsFullBroadcast12=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast12),negateConfig=createUnaryKernelConfig(Negate);function parseResultStruct(backend3,resOffset){let result=new Int32Array(backend3.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend3.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc3;function setup22(backend3){wasmFunc3=backend3.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc3(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pSelectedScores),backend3.wasm._free(pValidOutputs);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices);return selectedIndicesTensor}var nonMaxSuppressionV3Config2={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup22,kernelFunc},wasmFunc4;function setup23(backend3){wasmFunc4=backend3.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV4(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pSelectedScores);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend3.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup23,kernelFunc:nonMaxSuppressionV4},wasmFunc5;function setup24(backend3){wasmFunc5=backend3.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pValidOutputs);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend3.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup24,kernelFunc:kernelFunc2},supportsFullBroadcast13=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast13,"bool"),wasmOneHot;function setup25(backend3){wasmOneHot=backend3.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot2(args){let{inputs,backend:backend3,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend3.makeOutput([...indices.shape,depth],"int32"),outId=backend3.dataIdMap.get(out.dataId).id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig={kernelName:OneHot,backendName:"wasm",setupFunc:setup25,kernelFunc:oneHot2};function onesLike2(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(1),out}var onesLikeConfig={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike2},wasmPadV2;function setup26(backend3){wasmPadV2=backend3.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad10(args){let{inputs:{x},backend:backend3,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(outShape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config2={kernelName:PadV2,backendName:"wasm",kernelFunc:pad10,setupFunc:setup26},supportsFullBroadcast14=!1,powConfig=createBinaryKernelConfig(Pow,supportsFullBroadcast14),wasmPrelu;function setup27(backend3){wasmPrelu=backend3.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu8(args){let{inputs,backend:backend3}=args,{x,alpha}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,weightsId=backend3.dataIdMap.get(alpha.dataId).id,out=backend3.makeOutput(x.shape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig2={kernelName:Prelu,backendName:"wasm",setupFunc:setup27,kernelFunc:prelu8},reluConfig2=createUnaryKernelConfig(Relu),relu6Config2=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup28(backend3){wasmResizeBilinear=backend3.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number"])}function resizeBilinear2(args){let{backend:backend3,inputs,attrs}=args,{images}=inputs,{alignCorners,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend3.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast51({backend:backend3,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend3.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend3.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend3.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,outId),castedData!=null&&backend3.disposeData(castedData.dataId),out}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup28,kernelFunc:resizeBilinear2},wasmReverse;function setup29(backend3){wasmReverse=backend3.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse11(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend3});let out=backend3.makeOutput(x.shape,x.dtype),xId=backend3.dataIdMap.get(x.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape91({inputs:{x:out},attrs:{shape:x.shape},backend:backend3})}var reverseConfig={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse11,setupFunc:setup29},wasmRotate;function setup30(backend3){wasmRotate=backend3.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend3,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend3.makeOutput(image3.shape,image3.dtype),imageId=backend3.dataIdMap.get(image3.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup30},rsqrtConfig2=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup31(backend3){wasmScatterNd=backend3.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd(args){let{backend:backend3,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend3.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,updatesData=backend3.dataIdMap.get(updates.dataId),updatesId=updatesData.id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup31,kernelFunc:scatterNd},wasmSelect;function setup32(backend3){wasmSelect=backend3.wasm.cwrap(SelectV2,null,["number","number","number","number","number"])}function select(args){let{inputs,backend:backend3}=args,{condition,t,e}=inputs,conditionId=backend3.dataIdMap.get(condition.dataId).id,tId=backend3.dataIdMap.get(t.dataId).id,eId=backend3.dataIdMap.get(e.dataId).id,out=backend3.makeOutput(t.shape,t.dtype),outId=backend3.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectV2Config={kernelName:SelectV2,backendName:"wasm",kernelFunc:select,setupFunc:setup32},wasmFunc6;function setup33(backend3){wasmFunc6=backend3.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid8(args){let{backend:backend3,inputs:{x}}=args,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc6(xId,outId),out}var sigmoidConfig2={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup33,kernelFunc:sigmoid8},sinConfig3=createUnaryKernelConfig(Sin);function slice20(args){let{inputs:{x},attrs:{begin,size},backend:backend3}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend3.typedArrayFromHeap(x),out=backend3.makeOutput(size_,x.dtype),outVals=backend3.typedArrayFromHeap(out),xStrides=util_exports.computeStrides(x.shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return outVals.set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}let rank=x.shape.length;return rank===2?slice2d3(xVals,xStrides[0],outVals,begin_,size_):rank===3?slice3d3(xVals,xStrides[0],xStrides[1],outVals,begin_,size_):rank===4?slice4d3(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_):genericSliceSlow(xVals,x,outVals,begin_,size_),out}function slice2d3(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d3(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d3(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}function genericSliceSlow(xVals,xInfo,outVals,begin,size){let outBuf=buffer(size,xInfo.dtype,outVals),xBuf=buffer(xInfo.shape,xInfo.dtype,xVals);for(let i=0;i<outBuf.size;++i){let loc=outBuf.indexToLoc(i),xLoc=loc.map((idx,j)=>idx+begin[j]);outVals[i]=xBuf.get(...xLoc)}}var sliceConfig2={kernelName:Slice,backendName:"wasm",kernelFunc:slice20},wasmFunc7;function setup34(backend3){wasmFunc7=backend3.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend3,inputs:{logits},attrs:{dim}}=args,xId=backend3.dataIdMap.get(logits.dataId).id,out=backend3.makeOutput(logits.shape,logits.dtype),outId=backend3.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId,channels,batch),out}var softmaxConfig={kernelName:Softmax,backendName:"wasm",setupFunc:setup34,kernelFunc:softmax5};function split12(args){let{inputs,attrs,backend:backend3}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice20({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend3});return begin[$axis]+=s,xSlice})}var splitVConfig={kernelName:SplitV,backendName:"wasm",kernelFunc:split12},sqrtConfig2=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup35(backend3){wasmStridedSlice=backend3.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice2(args){let{backend:backend3,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape91({inputs:{x},attrs:{shape:newShape},backend:backend3}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided){let xSliced=slice20({inputs:{x},attrs:{begin,size},backend:backend3});return reshape91({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend3})}let out=backend3.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend3.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend3.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape91({inputs:{x:out},attrs:{shape:outShape},backend:backend3})}var stridedSliceConfig={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup35,kernelFunc:stridedSlice2},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup36(backend3){wasmSum=backend3.wasm.cwrap(Sum,null,["number, number, number"])}function sum28(args){let{backend:backend3,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig={kernelName:Sum,backendName:"wasm",setupFunc:setup36,kernelFunc:sum28},tanhConfig2=createUnaryKernelConfig(Tanh),wasmTile;function setup37(backend3){wasmTile=backend3.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile11(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend3.makeOutput(newShape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig={kernelName:Tile,backendName:"wasm",setupFunc:setup37,kernelFunc:tile11};function unpack(args){let{inputs,backend:backend3,attrs}=args,{value}=inputs,{axis}=attrs,numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice20({inputs:{x:value},attrs:{begin,size},backend:backend3});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack};function zerosLike2(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(0),out}var zerosLikeConfig={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike2},kernelConfigs3=[absConfig2,addConfig3,addNConfig,argMaxConfig,avgPoolConfig3,batchMatMulConfig2,castConfig3,clipByValueConfig,concatConfig3,conv2DConfig2,conv2DBackpropInputConfig2,cosConfig3,cropAndResizeConfig,cumsumConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig2,divConfig3,equalConfig,expConfig2,fillConfig2,flipLeftRightConfig3,floorDivConfig,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig3,lessConfig,lessEqualConfig,logConfig2,logicalAndConfig,maxConfig3,maximumConfig,maxPoolConfig3,minConfig,minimumConfig,multiplyConfig3,negateConfig,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig,onesLikeConfig,padV2Config2,powConfig,preluConfig2,reluConfig2,relu6Config2,reshapeConfig3,resizeBilinearConfig,reverseConfig,rotateWithOffsetConfig3,rsqrtConfig2,scatterNdConfig,selectV2Config,sigmoidConfig2,sinConfig3,sliceConfig2,softmaxConfig,splitVConfig,sqrtConfig2,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig,subConfig3,sumConfig,tanhConfig2,tileConfig,transposeConfig3,unpackConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),WASM_PRIORITY=2,BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine15())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();f();let kernelMs=util_exports.now()-start;return{kernelMs}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer11=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer11,memoryOffset,size);case"int32":return new Int32Array(buffer11,memoryOffset,size);case"bool":return new Uint8Array(buffer11,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var _scriptDir = undefined; var WasmBackendModuleThreadedSimd = "+tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{if(initialized)return;if(initAborted)return;initAborted=!0;let rejectMsg="Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers";reject({message:rejectMsg})}})}function typedArrayFromBuffer(buffer11,dtype){switch(dtype){case"float32":return new Float32Array(buffer11);case"int32":return new Int32Array(buffer11);case"bool":return new Uint8Array(buffer11);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version17="2.7.0";var 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});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var utils_exports={};__export(utils_exports,{computeReshapedDimensions:()=>computeReshapedDimensions,getCenterPoint:()=>getCenterPoint,isDimensions:()=>isDimensions,isEven:()=>isEven2,isFloat:()=>isFloat,isTensor:()=>isTensor,isTensor1D:()=>isTensor1D,isTensor2D:()=>isTensor2D,isTensor3D:()=>isTensor3D,isTensor4D:()=>isTensor4D,isValidNumber:()=>isValidNumber,isValidProbablitiy:()=>isValidProbablitiy,range:()=>range2,round:()=>round2});var Dimensions=class{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)}};function isTensor(tensor163,dim){return tensor163 instanceof Tensor&&tensor163.shape.length===dim}function isTensor1D(tensor163){return isTensor(tensor163,1)}function isTensor2D(tensor163){return isTensor(tensor163,2)}function isTensor3D(tensor163){return isTensor(tensor163,3)}function isTensor4D(tensor163){return isTensor(tensor163,4)}function isFloat(num){return num%1!==0}function isEven2(num){return num%2===0}function round2(num,prec=2){let 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){let scale3=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale3),Math.round(height*scale3))}function getCenterPoint(pts){return pts.reduce((sum29,pt)=>sum29.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range2(num,start,step9){return Array(num).fill(0).map((_,i)=>start+i*step9)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&0<=num&&num<=1}var Point=class{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))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){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`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),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)}`);let[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}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(){let[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(){let[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,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,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){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{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()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{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})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min9,x)=>x<min9?x:min9,Infinity),minY=ys.reduce((min9,y)=>y<min9?y:min9,Infinity),maxX=xs.reduce((max11,x)=>max11<x?x:max11,0),maxY=ys.reduce((max11,y)=>max11<y?y:max11,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression2(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}function normalize(x,meanRgb){return tidy(()=>{let[r,g,b]=meanRgb,avg_r=fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=concat([avg_r,avg_g,avg_b],3);return sub(x,avg_rgb)})}function padToSquare(imgTensor,isCenterImage=!1){return tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmount2=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmount2,fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],paddingTensorPrepend=isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,tensorsToStack=[paddingTensorPrepend,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>cast(t,"float32"));return concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array2=inputArray.slice();for(let i=array2.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array2[i];array2[i]=array2[j],array2[j]=x}return array2}function sigmoid2(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{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){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},options);return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),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){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class 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)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round2(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{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(json18){let descriptors=json18.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json18.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!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]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){let extension={detection};return Object.assign({},sourceObj,extension)}function createBrowserEnv(){let fetch3=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},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:fetch3,readFile}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}let readFile=fs?function(filePath){return new Promise((res,rej)=>{fs.readFile(filePath,function(err,buffer6){return err?rej(err):res(buffer6)})})}:function(){throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)};return{readFile}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=function(){if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=function(){if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch3=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},fileSystem=createFileSystem();return{Canvas:Canvas||class{},CanvasRenderingContext2D:global.CanvasRenderingContext2D||class{},Image:Image||class{},ImageData:global.ImageData||class{},Video:global.HTMLVideoElement||class{},createCanvasElement,createImageElement,fetch:fetch3,...fileSystem}}function isBrowser2(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var isNodejs=__toModule(require_isNodejs()),environment;function getEnv(){if(!environment)throw new Error("getEnv - environment is not defined, check isNodejs() and isBrowser()");return environment}function setEnv(env18){environment=env18}function initialize(){if(isBrowser2())return setEnv(createBrowserEnv());if(isNodejs.isNodejs())return setEnv(createNodejsEnv())}function monkeyPatch(env18){if(environment||initialize(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas=environment.Canvas,Image=environment.Image}=env18;environment.Canvas=Canvas,environment.Image=Image,environment.createCanvasElement=env18.createCanvasElement||(()=>new Canvas),environment.createImageElement=env18.createImageElement||(()=>new Image),environment.ImageData=env18.ImageData||environment.ImageData,environment.Video=env18.Video||environment.Video,environment.fetch=env18.fetch||environment.fetch,environment.readFile=env18.readFile||environment.readFile}var env2={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser:isBrowser2,isNodejs:isNodejs.isNodejs};initialize();function resolveInput(arg){return!env2.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env2.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}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={}));var DrawTextFieldOptions=class{constructor(options={}){let{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}},DrawTextField=class{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){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions(Object.assign({},defaultDrawLabelOptions,drawLabelOptions))}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){let detectionsArray=Array.isArray(detections)?detections:[detections];detectionsArray.forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round2(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}function isMediaLoaded(media){let{Image,Video}=env2.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}function awaitMediaLoaded(media){return new Promise((resolve,reject)=>{if(media instanceof env2.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)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{if(!(buf instanceof Blob))return reject("bufferToImage - expected buf to be of type: Blob");let reader=new FileReader;reader.onload=()=>{if(typeof reader.result!="string")return reject("bufferToImage - expected reader.result to be a string, in onload");let img=env2.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input2){let{Image,Video}=env2.getEnv();return input2 instanceof Image?new Dimensions(input2.naturalWidth,input2.naturalHeight):input2 instanceof Video?new Dimensions(input2.videoWidth,input2.videoHeight):new Dimensions(input2.width,input2.height)}function createCanvas2({width,height}){let{createCanvasElement}=env2.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env2.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas2({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env2.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await browser_exports.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input2){let{Image,Canvas,Video}=env2.getEnv();return input2 instanceof Image||input2 instanceof Canvas||input2 instanceof Video}var EPSILON_FLOAT323=1e-7,EPSILON_FLOAT163=1e-4,KernelBackend2=class{time(f){return notYetImplemented2("time")}read(dataId){return notYetImplemented2("read")}readSync(dataId){return notYetImplemented2("readSync")}numDataIds(){return notYetImplemented2("numDataIds")}disposeData(dataId){return notYetImplemented2("disposeData")}write(values,shape,dtype){return notYetImplemented2("write")}move(dataId,values,shape,dtype){return notYetImplemented2("move")}memory(){return notYetImplemented2("memory")}floatPrecision(){return notYetImplemented2("floatPrecision")}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT323:EPSILON_FLOAT163}batchMatMul(a,b,transposeA,transposeB){return notYetImplemented2("batchMatMul")}fusedBatchMatMul({a,b,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}){return notYetImplemented2("fusedBatchMatMul")}slice(x,begin,size){return notYetImplemented2("slice")}stridedSlice(x,begin,end,strides){return notYetImplemented2("stridedSlice")}unstack(x,axis){return notYetImplemented2("unstack")}reverse(a,axis){return notYetImplemented2("reverse")}concat(tensors,axis){return notYetImplemented2("concat")}neg(a){return notYetImplemented2("neg")}add(a,b){return notYetImplemented2("add")}addN(tensors){return notYetImplemented2("addN")}subtract(a,b){return notYetImplemented2("subtract")}multiply(a,b){return notYetImplemented2("multiply")}realDivide(a,b){return notYetImplemented2("realDivide")}floorDiv(a,b){return notYetImplemented2("floorDiv")}sum(x,axes){return notYetImplemented2("sum")}prod(x,axes){return notYetImplemented2("prod")}unsortedSegmentSum(x,segmentIds,numSegments){return notYetImplemented2("unsortedSegmentSum")}argMin(x,axis){return notYetImplemented2("argMin")}argMax(x,axis){return notYetImplemented2("argMax")}equal(a,b){return notYetImplemented2("equal")}notEqual(a,b){return notYetImplemented2("notEqual")}less(a,b){return notYetImplemented2("less")}lessEqual(a,b){return notYetImplemented2("lessEqual")}greater(a,b){return notYetImplemented2("greater")}greaterEqual(a,b){return notYetImplemented2("greaterEqual")}logicalNot(a){return notYetImplemented2("logicalNot")}logicalAnd(a,b){return notYetImplemented2("logicalAnd")}logicalOr(a,b){return notYetImplemented2("logicalOr")}where(condition){return notYetImplemented2("where")}select(condition,a,b){return notYetImplemented2("select")}topk(x,k,sorted){return notYetImplemented2("topk")}min(x,axes){return notYetImplemented2("min")}minimum(a,b){return notYetImplemented2("minimum")}mod(a,b){return notYetImplemented2("mod")}max(x,axes){return notYetImplemented2("max")}maximum(a,b){return notYetImplemented2("maximum")}all(x,axes){return notYetImplemented2("all")}any(x,axes){return notYetImplemented2("any")}squaredDifference(a,b){return notYetImplemented2("squaredDifference")}ceil(x){return notYetImplemented2("ceil")}floor(x){return notYetImplemented2("floor")}round(x){return notYetImplemented2("round")}sign(x){return notYetImplemented2("sign")}isNaN(x){return notYetImplemented2("isNaN")}isInf(x){return notYetImplemented2("isInf")}isFinite(x){return notYetImplemented2("isFinite")}pow(a,b){return notYetImplemented2("pow")}exp(x){return notYetImplemented2("exp")}expm1(x){return notYetImplemented2("expm1")}softmax(x,dim){return notYetImplemented2("softmax")}log(x){return notYetImplemented2("log")}log1p(x){return notYetImplemented2("log1p")}sqrt(x){return notYetImplemented2("sqrt")}rsqrt(x){return notYetImplemented2("rsqrt")}square(x){return notYetImplemented2("square")}reciprocal(x){return notYetImplemented2("reciprocal")}relu(x){return notYetImplemented2("relu")}relu6(x){return notYetImplemented2("relu6")}prelu(x,a){return notYetImplemented2("prelu")}elu(x){return notYetImplemented2("elu")}eluDer(dy,y){return notYetImplemented2("eluDer")}selu(x){return notYetImplemented2("selu")}int(x){return notYetImplemented2("int")}clip(x,min9,max11){return notYetImplemented2("clip")}abs(x){return notYetImplemented2("abs")}complexAbs(x){return notYetImplemented2("complexAbs")}sigmoid(x){return notYetImplemented2("sigmoid")}softplus(x){return notYetImplemented2("softplus")}sin(x){return notYetImplemented2("sin")}cos(x){return notYetImplemented2("cos")}tan(x){return notYetImplemented2("tan")}asin(x){return notYetImplemented2("asin")}acos(x){return notYetImplemented2("acos")}atan(x){return notYetImplemented2("atan")}atan2(a,b){return notYetImplemented2("atan2")}sinh(x){return notYetImplemented2("sinh")}cosh(x){return notYetImplemented2("cosh")}tanh(x){return notYetImplemented2("tanh")}asinh(x){return notYetImplemented2("asinh")}acosh(x){return notYetImplemented2("acosh")}atanh(x){return notYetImplemented2("atanh")}erf(x){return notYetImplemented2("erf")}step(x,alpha){return notYetImplemented2("step")}fusedConv2d({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){return notYetImplemented2("fusedConv2d")}conv2d(x,filter,convInfo){return notYetImplemented2("conv2d")}conv2dDerInput(dy,filter,convInfo){return notYetImplemented2("conv2dDerInput")}conv2dDerFilter(x,dY,convInfo){return notYetImplemented2("conv2dDerFilter")}fusedDepthwiseConv2D({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){return notYetImplemented2("fusedDepthwiseConv2D")}depthwiseConv2D(input2,filter,convInfo){return notYetImplemented2("depthwiseConv2D")}depthwiseConv2DDerInput(dy,filter,convInfo){return 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Abs2="Abs",Acos2="Acos",Acosh2="Acosh",Add3="Add",AddN2="AddN",All2="All",Any2="Any",ArgMax2="ArgMax",ArgMin2="ArgMin",Asin2="Asin",Asinh2="Asinh",Atan3="Atan",Atanh2="Atanh",Atan22="Atan2",AvgPool2="AvgPool",AvgPoolBackprop2="AvgPoolBackprop",AvgPool3D2="AvgPool3D",AvgPool3DBackprop2="AvgPool3DBackprop",BatchMatMul2="BatchMatMul",BatchToSpaceND2="BatchToSpaceND",BroadcastTo2="BroadcastTo",Cast2="Cast",Ceil2="Ceil",ClipByValue2="ClipByValue",Complex2="Complex",Concat2="Concat",Conv2D3="Conv2D",Conv2DBackpropFilter2="Conv2DBackpropFilter",Conv2DBackpropInput2="Conv2DBackpropInput",Conv3D3="Conv3D",Conv3DBackpropFilterV22="Conv3DBackpropFilterV2",Conv3DBackpropInputV22="Conv3DBackpropInputV2",Cos2="Cos",Cosh2="Cosh",Cumsum2="Cumsum",CropAndResize2="CropAndResize",DepthToSpace2="DepthToSpace",DepthwiseConv2dNative2="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter2="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput2="DepthwiseConv2dNativeBackpropInput",Dilation2D2="Dilation2D",Dilation2DBackpropInput2="Dilation2DBackpropInput",Dilation2DBackpropFilter2="Dilation2DBackpropFilter",Div2="Div",Elu3="Elu",EluGrad2="EluGrad",Erf2="Erf",Equal2="Equal",Exp2="Exp",Expm12="Expm1",FFT2="FFT",Fill2="Fill",FlipLeftRight2="FlipLeftRight",Floor2="Floor",FloorDiv2="FloorDiv",FusedBatchNorm2="FusedBatchNorm",GatherV22="GatherV2",Greater2="Greater",GreaterEqual2="GreaterEqual",Identity3="Identity",IFFT2="IFFT",Imag2="Imag",IsFinite2="IsFinite",IsInf2="IsInf",IsNan2="IsNan",Less2="Less",LessEqual2="LessEqual",Log2="Log",Log1p2="Log1p",LogicalAnd2="LogicalAnd",LogicalNot2="LogicalNot",LogicalOr2="LogicalOr",LogSoftmax3="LogSoftmax",LRN2="LRN",LRNBackprop2="LRNBackprop",Max2="Max",Maximum3="Maximum",MaxPool2="MaxPool",MaxPoolBackprop2="MaxPoolBackprop",MaxPool3D2="MaxPool3D",MaxPool3DBackprop2="MaxPool3DBackprop",Mean2="Mean",Min2="Min",Minimum3="Minimum",MirrorPad2="MirrorPad",Mod2="Mod",Multiply3="Multiply",Negate2="Negate",NotEqual2="NotEqual",NonMaxSuppressionV32="NonMaxSuppressionV3",NonMaxSuppressionV42="NonMaxSuppressionV4",NonMaxSuppressionV52="NonMaxSuppressionV5",OnesLike2="OnesLike",OneHot2="OneHot",PadV22="PadV2",Pow2="Pow",Prelu2="Prelu",Prod2="Prod",Range2="Range",Real2="Real",Reciprocal2="Reciprocal",Relu3="Relu",Reshape3="Reshape",ResizeNearestNeighbor2="ResizeNearestNeighbor",ResizeNearestNeighborGrad2="ResizeNearestNeighborGrad",ResizeBilinear2="ResizeBilinear",ResizeBilinearGrad2="ResizeBilinearGrad",Relu63="Relu6",Reverse2="Reverse",Round2="Round",Rsqrt2="Rsqrt",SelectV22="SelectV2",Selu3="Selu",Slice2="Slice",Sin2="Sin",Sinh2="Sinh",Sign2="Sign",Sigmoid3="Sigmoid",Softplus3="Softplus",Sqrt2="Sqrt",Sum2="Sum",SpaceToBatchND2="SpaceToBatchND",SplitV2="SplitV",Softmax4="Softmax",SquaredDifference2="SquaredDifference",Square2="Square",Sub2="Sub",StridedSlice2="StridedSlice",Tan2="Tan",Tanh3="Tanh",Tile2="Tile",TopK2="TopK",Transpose2="Transpose",Unique2="Unique",Unpack2="Unpack",UnsortedSegmentSum2="UnsortedSegmentSum",ZerosLike2="ZerosLike",Step2="Step",FromPixels2="FromPixels",RotateWithOffset2="RotateWithOffset";var kernelRegistry2=getGlobal2("kernelRegistry",()=>new Map),gradRegistry2=getGlobal2("gradRegistry",()=>new Map);function getKernel2(kernelName,backendName){let key=makeKey2(kernelName,backendName);return kernelRegistry2.get(key)}function getGradient2(kernelName){return gradRegistry2.get(kernelName)}function getKernelsForBackend2(backendName){let it=kernelRegistry2.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config2]=value,[backend3]=key.split("_");backend3===backendName&&result.push(config2)}return result}function registerGradient2(config2){let{kernelName}=config2;gradRegistry2.has(kernelName)&&(env12().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`)),gradRegistry2.set(kernelName,config2)}function makeKey2(kernelName,backendName){return`${backendName}_${kernelName}`}function noConversionNeeded2(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof 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Logger2)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors2(tensorVals,output.dtype,kernelName)})}let kernelProfile={kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")};return kernelProfile}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors2(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger2=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad2(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad2(name,25),rank=result.rank,size=result.size,shape=rightPad2(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY2(tape2,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape2.length;i++){let node2=tape2[i],nodeInputs=node2.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node2.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node2.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape2.length-1;i>=0;i--){let node2=tape2[i],nodeInputs=node2.inputs;for(let j=0;j<node2.outputs.length;j++)if(tensorsLeadToY[node2.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node2.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape2.length;i++){let node2=tape2[i];if(nodesFromX[node2.id]&&nodesToY[node2.id]){let prunedInputs={};for(let inputName in node2.inputs){let nodeInput=node2.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node2);prunedNode.inputs=prunedInputs,prunedNode.outputs=node2.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients2(tensorAccumulatedGradientMap,filteredTape,tidy3,add20){for(let i=filteredTape.length-1;i>=0;i--){let node2=filteredTape[i],dys=[];if(node2.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node2.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);let inputGradients=node2.gradient(dys);for(let inputName in node2.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy3(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node2.inputs[inputName];if(!arraysEqual2(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add20(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS2=20,FORMAT_NUM_FIRST_LAST_VALS2=3,FORMAT_NUM_SIG_DIGITS2=7;function tensorToString2(vals,shape,dtype,verbose){let strides=computeStrides2(shape),padPerCol=computeMaxSizePerColumn2(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString2(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
+`)),lines.join(`
+`)}function computeMaxSizePerColumn2(vals,shape,dtype,strides){let n=sizeFromShape2(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples2(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){let offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString2(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString2(val,pad7,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS2))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS2))}j`:isString2(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString2(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS2)).toString(),rightPad2(valStr,pad7)}function boolNumToString2(v){return v===0?"false":"true"}function subTensorToString2(vals,shape,dtype,strides,padPerCol,isLast=!0){let storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){let complexTuple=createComplexTuples2(vals);return[valToString2(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString2(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS2){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS2*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS2)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples2(firstVals),lastVals=createComplexTuples2(lastVals)),["["+firstVals.map((x,i)=>valToString2(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString2(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS2+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples2(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString2(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS2){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS2;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString2(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS2;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString2(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString2(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}let sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
+`;for(let i=2;i<rank;i++)newLineSep+=`
+`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples2(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer2=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape2(shape),values!=null){let n=values.length;assert3(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType2(dtype,this.size),this.strides=computeStrides2(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert3(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn2().makeTensor(this.values,this.shape,this.dtype)}},trackerFn2=null,opHandler3=null,deprecationWarningFn2=null;function setTensorTracker2(fn){trackerFn2=fn}function setOpHandler2(handler){opHandler3=handler}function setDeprecationWarningFn2(fn){deprecationWarningFn2=fn}var Tensor2=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape2(shape),this.strides=computeStrides2(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler3.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler3.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray2(this.shape,vals)}arraySync(){return toNestedArray2(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn2().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString2(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn2().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString2(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn2().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){if(this.isDisposed)return;trackerFn2().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler3.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler3.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString2(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler3.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn2().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor2,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable2=class extends Tensor2{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual2(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn2().disposeTensor(this),this.dataId=newValue.dataId,trackerFn2().incRef(this,null)}dispose(){trackerFn2().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable2,Symbol.hasInstance,{value:instance=>instance instanceof Tensor2&&instance.assign!=null&&instance.assign instanceof Function});var Rank2;(function(Rank3){Rank3.R0="R0",Rank3.R1="R1",Rank3.R2="R2",Rank3.R3="R3",Rank3.R4="R4",Rank3.R5="R5",Rank3.R6="R6"})(Rank2||(Rank2={}));var UpcastInt32AndMap2;(function(UpcastInt32AndMap3){UpcastInt32AndMap3.float32="float32",UpcastInt32AndMap3.int32="int32",UpcastInt32AndMap3.bool="int32",UpcastInt32AndMap3.complex64="complex64"})(UpcastInt32AndMap2||(UpcastInt32AndMap2={}));var UpcastBoolAndMap2;(function(UpcastBoolAndMap3){UpcastBoolAndMap3.float32="float32",UpcastBoolAndMap3.int32="int32",UpcastBoolAndMap3.bool="bool",UpcastBoolAndMap3.complex64="complex64"})(UpcastBoolAndMap2||(UpcastBoolAndMap2={}));var UpcastFloat32AndMap2;(function(UpcastFloat32AndMap3){UpcastFloat32AndMap3.float32="float32",UpcastFloat32AndMap3.int32="float32",UpcastFloat32AndMap3.bool="float32",UpcastFloat32AndMap3.complex64="complex64"})(UpcastFloat32AndMap2||(UpcastFloat32AndMap2={}));var UpcastComplex64AndMap2;(function(UpcastComplex64AndMap3){UpcastComplex64AndMap3.float32="complex64",UpcastComplex64AndMap3.int32="complex64",UpcastComplex64AndMap3.bool="complex64",UpcastComplex64AndMap3.complex64="complex64"})(UpcastComplex64AndMap2||(UpcastComplex64AndMap2={}));var upcastTypeMap2={float32:UpcastFloat32AndMap2,int32:UpcastInt32AndMap2,bool:UpcastBoolAndMap2,complex64:UpcastComplex64AndMap2};function upcastType2(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap2[typeA][typeB]}function makeTypesMatch2(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType2(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function getTensorsInContainer2(result){let list=[],seen=new Set;return walkTensorContainer2(result,list,seen),list}function walkTensorContainer2(container,list,seen){if(container==null)return;if(container instanceof Tensor2){list.push(container);return}if(!isIterable3(container))return;let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer2(val,list,seen))}}function isIterable3(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState2=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine2=class{constructor(ENV7){this.ENV=ENV7,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState2}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],success=await this.initializeBackend(backendName).success;if(success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName),result=asyncInit?await success:success;if(!result)return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler2(this.backendInstance),!0}setupRegisteredKernels(){let kernels=getKernelsForBackend2(this.backendName);kernels.forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){let kernels=getKernelsForBackend2(backendName);kernels.forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend3=registryFactoryEntry.factory();if(backend3&&!(backend3 instanceof KernelBackend2)&&typeof backend3.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend3.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend3,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend3,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend3,backend3.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine2.nextTensorId++}nextVariableId(){return Engine2.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE2.runKernelFunc(backend3=>backend3.cast(dy,dtype),gradInputs,null,Cast2,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3,kernel=getKernel2(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(({dataId,shape,dtype})=>this.makeTensorFromDataId(dataId,shape,dtype));if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{let saveFunc=tensors=>{if(!isTapeOn)return;saved=tensors.map(tensor163=>this.keep(this.clone(tensor163)))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){let saved=tensors.map(tensor163=>this.keep(this.clone(tensor163)));return saved}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient2(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert3(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend3){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend3=backend3||this.backend;let backendVals=values;dtype==="string"&&isString2(values[0])&&(backendVals=values.map(d=>encodeString2(d)));let dataId=backend3.write(backendVals,shape,dtype),t=new Tensor2(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend3),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray2(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend3){dtype=dtype||"float32";let t=new Tensor2(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend3),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable2(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend3){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement2(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend3||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable2||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId),refCount=info.refCount;refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient2(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray2(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer2(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor163=this.state.activeScope.track[i];!tensor163.kept&&!tensorsToTrackInParentSet.has(tensor163.id)&&tensor163.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor163=>{!tensor163.kept&&tensor163.scopeId===oldScope.id&&this.track(tensor163)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert3(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert3(y instanceof Tensor2,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY2(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones3(y.shape):dy,backpropagateGradients2(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add3);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node2=>{for(let tensor163 of node2.saved)tensor163.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert3(isFunction2(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert3(inputs.every(t=>t instanceof Tensor2),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert3(res.value instanceof Tensor2,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert3(isFunction2(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert3(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert3(grads2.every(t=>t instanceof Tensor2),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.readSync(dataId)}read(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.read(dataId)}async time(query){let start=now2(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now2()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState2;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine2.nextTensorId=0;Engine2.nextVariableId=0;function ones3(shape){let values=makeOnesTypedArray2(sizeFromShape2(shape),"float32");return ENGINE2.makeTensor(values,shape,"float32")}function getOrMakeEngine2(){let ns=getGlobalNamespace2();if(ns._tfengine==null){let environment12=new Environment2(ns);ns._tfengine=new Engine2(environment12)}return setEnvironmentGlobal2(ns._tfengine.ENV),setTensorTracker2(()=>ns._tfengine),ns._tfengine}var ENGINE2=getOrMakeEngine2();function add3(a,b){let inputs={a,b};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.add(a,b);return save([a,b]),res},inputs,null,Add3)}function isBrowser4(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var ENV6=env12();ENV6.registerFlag("DEBUG",()=>!1,debugValue=>{debugValue&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV6.registerFlag("IS_BROWSER",()=>isBrowser4());ENV6.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV6.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV6.registerFlag("PROD",()=>!1);ENV6.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV6.getBool("DEBUG"));ENV6.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV6.registerFlag("IS_TEST",()=>!1);function inferShape2(val,dtype){let firstElem=val;if(isTypedArray2(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray2(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env12().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency2(val,shape,[]),shape}function deepAssertShapeConsistency2(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray2(val)){assert3(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert3(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert3(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency2(val[i],subShape,indices.concat(i))}function assertDtype2(expectedDtype,actualDType,argName,functionName){if(expectedDtype==null)return;if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}function convertToTensor2(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor2)return assertDtype2(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype2(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype2(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray2(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape2(x,inferredDtype);!isTypedArray2(x)&&!Array.isArray(x)&&(x=[x]);let skipTypedArray=!0,values=inferredDtype!=="string"?toTypedArray2(x,inferredDtype):flatten2(x,[],skipTypedArray);return ENGINE2.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray2(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);let tensors=arg;return tensors.map((t,i)=>convertToTensor2(t,`${argName}[${i}]`,functionName),parseAsDtype)}var OP_SCOPE_SUFFIX2="__op";function op2(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX2;let f2=(...args)=>{ENGINE2.startScope(opName);try{let result=fn(...args);return isPromise2(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE2.endScope(result),result}catch(ex){throw ENGINE2.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_2(real9,imag9){let $real=convertToTensor2(real9,"real","complex"),$imag=convertToTensor2(imag9,"imag","complex");assertShapesMatch2($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let forward=backend3=>backend3.complex($real,$imag),inputs={real:$real,imag:$imag};return ENGINE2.runKernelFunc(forward,inputs,null,Complex2)}var complex2=op2({complex_:complex_2});function makeTensor2(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype2(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray2(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions2(shape);let providedSize=sizeFromShape2(shape),inferredSize=sizeFromShape2(inferredShape);assert3(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape2(shape.slice(i)):!0;assert3(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray2(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray2(values,dtype):flatten2(values,[],!0),ENGINE2.makeTensor(values,shape,dtype)}function tensor5(values,shape,dtype){let inferredShape=inferShape2(values,dtype);return makeTensor2(values,shape,inferredShape,dtype)}var useNodeBuffer2=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength2(str){return useNodeBuffer2?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String2(buffer6){if(useNodeBuffer2)return Buffer.from(buffer6).toString("base64");let buf=new Uint8Array(buffer6),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer2(str){if(useNodeBuffer2){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer6=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer6.set([s.charCodeAt(i)],i);return buffer6.buffer}function getModelArtifactsInfoForJSON2(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength2(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength2(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}var IORouterRegistry2=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry2.instance==null&&(IORouterRegistry2.instance=new IORouterRegistry2),IORouterRegistry2.instance}static registerSaveRouter(saveRouter){IORouterRegistry2.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry2.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry2.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry2.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[],routers=handlerType==="load"?IORouterRegistry2.getInstance().loadRouters:IORouterRegistry2.getInstance().saveRouters;return routers.forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}};var DATABASE_NAME2="tensorflowjs",DATABASE_VERSION2=1,MODEL_STORE_NAME2="models_store",INFO_STORE_NAME2="model_info_store";function getIndexedDBFactory2(){if(!env12().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase2(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME2,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME2,{keyPath:"modelPath"})}var BrowserIndexedDB2=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory2(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME2,DATABASE_VERSION2);openRequest.onupgradeneeded=()=>setUpDatabase2(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME2,"readonly"),modelStore=modelTx.objectStore(MODEL_STORE_NAME2),getRequest=modelStore.get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON2(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME2,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME2),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME2,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME2),putModelRequest=modelStore.put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME2);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB2.URL_SCHEME="indexeddb://";var indexedDBRouter2=url=>env12().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserIndexedDB2.URL_SCHEME))?browserIndexedDB2(url.slice(BrowserIndexedDB2.URL_SCHEME.length)):null;IORouterRegistry2.registerSaveRouter(indexedDBRouter2);IORouterRegistry2.registerLoadRouter(indexedDBRouter2);function browserIndexedDB2(modelPath){return new BrowserIndexedDB2(modelPath)}function maybeStripScheme3(key){return key.startsWith(BrowserIndexedDB2.URL_SCHEME)?key.slice(BrowserIndexedDB2.URL_SCHEME.length):key}var BrowserIndexedDBManager2=class{constructor(){this.indexedDB=getIndexedDBFactory2()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME2,DATABASE_VERSION2);openRequest.onupgradeneeded=()=>setUpDatabase2(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME2,"readonly"),store=tx.objectStore(INFO_STORE_NAME2),getAllInfoRequest=store.getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme3(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME2,DATABASE_VERSION2);openRequest.onupgradeneeded=()=>setUpDatabase2(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME2,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME2),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME2,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME2),deleteModelRequest=modelStore.delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}};var PATH_SEPARATOR2="/",PATH_PREFIX2="tensorflowjs_models",INFO_SUFFIX2="info",MODEL_TOPOLOGY_SUFFIX2="model_topology",WEIGHT_SPECS_SUFFIX2="weight_specs",WEIGHT_DATA_SUFFIX2="weight_data",MODEL_METADATA_SUFFIX2="model_metadata";function getModelKeys2(path){return{info:[PATH_PREFIX2,path,INFO_SUFFIX2].join(PATH_SEPARATOR2),topology:[PATH_PREFIX2,path,MODEL_TOPOLOGY_SUFFIX2].join(PATH_SEPARATOR2),weightSpecs:[PATH_PREFIX2,path,WEIGHT_SPECS_SUFFIX2].join(PATH_SEPARATOR2),weightData:[PATH_PREFIX2,path,WEIGHT_DATA_SUFFIX2].join(PATH_SEPARATOR2),modelMetadata:[PATH_PREFIX2,path,MODEL_METADATA_SUFFIX2].join(PATH_SEPARATOR2)}}function getModelPathFromKey2(key){let items=key.split(PATH_SEPARATOR2);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR2)}function maybeStripScheme4(key){return key.startsWith(BrowserLocalStorage2.URL_SCHEME)?key.slice(BrowserLocalStorage2.URL_SCHEME.length):key}var BrowserLocalStorage2=class{constructor(modelPath){if(!env12().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys2(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON2(modelArtifacts);try{return this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String2(modelArtifacts.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata})),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,out.userDefinedMetadata=metadata.userDefinedMetadata}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer2(weightDataBase64),out}};BrowserLocalStorage2.URL_SCHEME="localstorage://";var localStorageRouter2=url=>env12().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserLocalStorage2.URL_SCHEME))?browserLocalStorage2(url.slice(BrowserLocalStorage2.URL_SCHEME.length)):null;IORouterRegistry2.registerSaveRouter(localStorageRouter2);IORouterRegistry2.registerLoadRouter(localStorageRouter2);function browserLocalStorage2(modelPath){return new BrowserLocalStorage2(modelPath)}var BrowserLocalStorageManager2=class{constructor(){assert3(env12().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert3(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX2+PATH_SEPARATOR2,suffix=PATH_SEPARATOR2+INFO_SUFFIX2;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey2(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme4(path);let keys=getModelKeys2(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}};var URL_SCHEME_SUFFIX2="://",ModelStoreManagerRegistry2=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry2.instance==null&&(ModelStoreManagerRegistry2.instance=new ModelStoreManagerRegistry2),ModelStoreManagerRegistry2.instance}static registerManager(scheme,manager){assert3(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX2)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX2))),assert3(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry2.getInstance();assert3(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};var PlatformBrowser2=class{fetch(path,init2){return fetch(path,init2)}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}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env12().get("IS_BROWSER")){env12().setPlatform("browser",new PlatformBrowser2);try{ModelStoreManagerRegistry2.registerManager(BrowserLocalStorage2.URL_SCHEME,new BrowserLocalStorageManager2)}catch(err){}try{ModelStoreManagerRegistry2.registerManager(BrowserIndexedDB2.URL_SCHEME,new BrowserIndexedDBManager2)}catch(err){}}var getNodeFetch2={importFetch:()=>require_browser2()},systemFetch2,PlatformNode2=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env12().global.fetch!=null?env12().global.fetch(path,requestInits):(systemFetch2==null&&(systemFetch2=getNodeFetch2.importFetch()),systemFetch2(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env12().get("IS_NODE")&&env12().setPlatform("node",new PlatformNode2);function buffer2(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions2(shape),new TensorBuffer2(shape,dtype,values)}function cast_2(x,dtype){let $x=convertToTensor2(x,"x","cast");if(!isValidDtype2(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE2.runKernelFunc(backend3=>backend3.cast($x,dtype),inputs,null,Cast2,attrs)}var cast2=op2({cast_:cast_2});function clone_2(x){let $x=convertToTensor2(x,"x","clone",null),forward=()=>ENGINE2.makeTensorFromDataId($x.dataId,$x.shape,$x.dtype),inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Identity3)}var clone2=op2({clone_:clone_2});function print3(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine2();var opHandler4={buffer:buffer2,cast:cast2,clone:clone2,print:print3};setOpHandler2(opHandler4);function reshape_2(x,shape){let $x=convertToTensor2(x,"x","reshape",null),inputs={x:$x},attrs={shape},forward=(backend3,save)=>(shape=inferFromImplicitShape2(shape,$x.size),assert3($x.size===sizeFromShape2(shape),()=>"new shape and old shape must have the same number of elements."),save([$x]),backend3.reshape($x,shape));return ENGINE2.runKernelFunc(forward,inputs,null,Reshape3,attrs)}var reshape2=op2({reshape_:reshape_2});function matMul_2(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor2(a,"a","matMul"),$b=convertToTensor2(b,"b","matMul");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{save([$a,$b]);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape2(outerDimsA),batchDimB=sizeFromShape2(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;assert3($a.rank>=2&&$b.rank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`),assert3(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShapeOuterDims=batchDimA>batchDimB?outerDimsA:outerDimsB,outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape2($a,[batchDimA,innerShapeA,outerShapeA]):reshape2($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape2($b,[batchDimB,outerShapeB,innerShapeB]):reshape2($b,[batchDimB,innerShapeB,outerShapeB]),res3d=backend3.batchMatMul(a3D,b3D,transposeA,transposeB);return reshape2(res3d,outShape)},inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE2.runKernelFunc(forward,inputs,null,BatchMatMul2,attrs)}var matMul3=op2({matMul_:matMul_2});function oneHot_2(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let $indices=convertToTensor2(indices,"indices","oneHot","int32"),outShape=[...$indices.shape,depth],forward=(backend3,save)=>(save([$indices]),reshape2(backend3.oneHot(reshape2($indices,[$indices.size]),depth,onValue,offValue),outShape)),inputs={indices:$indices},attrs={depth,onValue,offValue};return ENGINE2.runKernelFunc(forward,inputs,null,OneHot2,attrs)}var oneHot3=op2({oneHot_:oneHot_2});function transpose_2(x,perm){let $x=convertToTensor2(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert3($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert3(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE2.runKernelFunc(backend3=>backend3.transpose($x,perm),inputs,null,Transpose2,attrs)}var transpose2=op2({transpose_:transpose_2}),browser_exports2={};__export(browser_exports2,{fromPixels:()=>fromPixels3,toPixels:()=>toPixels2});function tensor3d2(values,shape,dtype){if(assertNonNull2(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape2(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor2(values,shape,inferredShape,dtype)}var fromPixels2DContext3;function fromPixels_2(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}let kernel=getKernel2(FromPixels2,ENGINE2.backendName);if(kernel!=null){let inputs={pixels},attrs={numChannels};return ENGINE2.runKernel(FromPixels2,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext3==null&&(fromPixels2DContext3=document.createElement("canvas").getContext("2d")),fromPixels2DContext3.canvas.width=width,fromPixels2DContext3.canvas.height=height,fromPixels2DContext3.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext3.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}let outShape=[height,width,numChannels];return tensor3d2(values,outShape,"int32")}async function toPixels2(img,canvas){let $img=convertToTensor2(img,"img","toPixels");if(!(img instanceof Tensor2)){let originalImgTensor=$img;$img=cast2(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels3=op2({fromPixels_:fromPixels_2});function assertParamsValid2(input2,begin,size){let inputRank=input2.shape.length;assert3(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert3(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert3(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes2(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape5(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims2(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis2(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes2(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes2(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims2(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims2(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims2(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis2(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis2(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis2(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims2(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes2(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis2(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims2(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes2(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis2(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp2(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis2(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis2(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp2(0,start,axisSize-1),start}function stopForAxis2(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp2(0,stop,axisSize):stop=clamp2(-1,stop,axisSize-1),stop}function parseSliceParams2(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert3(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert3(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function deprecationWarn2(msg){env12().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn2(deprecationWarn2);function tidy2(nameOrFn,fn){return ENGINE2.tidy(nameOrFn,fn)}function dispose2(container){let tensors=getTensorsInContainer2(container);tensors.forEach(tensor163=>tensor163.dispose())}function add_2(a,b){let $a=convertToTensor2(a,"a","add"),$b=convertToTensor2(b,"b","add");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.add($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Add3)}var add4=op2({add_:add_2});function floorDiv_2(a,b){let $a=convertToTensor2(a,"a","floorDiv"),$b=convertToTensor2(b,"b","floorDiv");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.floorDiv($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,FloorDiv2)}var floorDiv2=op2({floorDiv_:floorDiv_2});function div_2(a,b){let $a=convertToTensor2(a,"a","div"),$b=convertToTensor2(b,"b","div");if([$a,$b]=makeTypesMatch2($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv2($a,$b);let forward=(backend3,save)=>{let res=backend3.realDivide($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE2.runKernelFunc(forward,inputs,null,Div2,attrs)}var div2=op2({div_:div_2});function mul_2(a,b){let $a=convertToTensor2(a,"a","mul"),$b=convertToTensor2(b,"b","mul");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.multiply($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Multiply3)}var mul2=op2({mul_:mul_2});function abs_2(x){let $x=convertToTensor2(x,"x","abs"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>(save([$x]),$x.dtype==="complex64"?backend3.complexAbs($x):backend3.abs($x)),inputs,null,Abs2)}var abs2=op2({abs_:abs_2});function acos_2(x){let $x=convertToTensor2(x,"x","acos"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.acos($x);return save([$x]),res},inputs,null,Acos2)}var acos2=op2({acos_:acos_2});function acosh_2(x){let $x=convertToTensor2(x,"x","acosh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.acosh($x);return save([$x]),res},inputs,null,Acosh2)}var acosh2=op2({acosh_:acosh_2});function axesAreInnerMostDims2(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations2(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes2(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim2(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations2(shape,reduceSubShape,axes)}function getAxesPermutation2(axes,rank){if(axesAreInnerMostDims2(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation2(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes2(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function all_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","all","bool"),forward=backend3=>{let origAxes=parseAxisParam2(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation2(axes,$x.rank);permutedAxes!=null&&($x=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank));let res=backend3.all($x,axes);if(keepDims){let newShape=expandShapeToKeepDim2(res.shape,origAxes);return reshape2(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,All2,attrs)}var all2=op2({all_:all_2});function any_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","any","bool"),forward=backend3=>{let origAxes=parseAxisParam2(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation2(axes,$x.rank);permutedAxes!=null&&($x=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank));let res=backend3.any($x,axes);if(keepDims){let newShape=expandShapeToKeepDim2(res.shape,origAxes);return reshape2(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Any2,attrs)}var any2=op2({any_:any_2});function argMax_2(x,axis=0){let $x=convertToTensor2(x,"x","argMax"),forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam2(axis,$x.shape),permutedAxes=getAxesPermutation2(axes,$x.rank);return permutedAxes!=null&&($x=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank)),backend3.argMax($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE2.runKernelFunc(forward,inputs,null,ArgMax2,attrs)}var argMax2=op2({argMax_:argMax_2});function argMin_2(x,axis=0){let $x=convertToTensor2(x,"x","argMin"),forward=(backend3,save)=>{save([$x]),axis==null&&(axis=0);let axes=parseAxisParam2(axis,$x.shape),permutedAxes=getAxesPermutation2(axes,$x.rank);return permutedAxes!=null&&($x=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank)),backend3.argMin($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE2.runKernelFunc(forward,inputs,null,ArgMin2,attrs)}var argMin2=op2({argMin_:argMin_2});function asin_2(x){let $x=convertToTensor2(x,"x","asin"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.asin($x);return save([$x]),res},inputs,null,Asin2)}var asin2=op2({asin_:asin_2});function asinh_2(x){let $x=convertToTensor2(x,"x","asinh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.asinh($x);return save([$x]),res},inputs,null,Asinh2)}var asinh2=op2({asinh_:asinh_2});function atan_2(x){let $x=convertToTensor2(x,"x","atan"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.atan($x);return save([$x]),res},inputs,null,Atan3)}var atan3=op2({atan_:atan_2});function atan2_2(a,b){let $a=convertToTensor2(a,"a","atan2"),$b=convertToTensor2(b,"b","atan2");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.atan2($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Atan22)}var atan22=op2({atan2_:atan2_2});function atanh_2(x){let $x=convertToTensor2(x,"x","atanh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.atanh($x);return save([$x]),res},inputs,null,Atanh2)}var atanh2=op2({atanh_:atanh_2});function computePool2DInfo2(inShape,filterSize,strides,dilations,pad7,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam2(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo2(inShape,filterShape,strides,dilations,pad7,roundingMode,!1,dataFormat)}function computePool3DInfo2(inShape,filterSize,strides,dilations,pad7,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam2(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo2(inShape,filterShape,strides,dilations,pad7,!1,$dataFormat,roundingMode)}function computeConv2DInfo2(inShape,filterShape,strides,dilations,pad7,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam2(strides),[dilationHeight,dilationWidth]=parseTupleParam2(dilations),effectiveFilterHeight=getEffectiveFilterSize2(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize2(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo2(pad7,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo2(inShape,filterShape,strides,dilations,pad7,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam2(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam2(dilations),effectiveFilterDepth=getEffectiveFilterSize2(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize2(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize2(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo2(pad7,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D2(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad2(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=conditionalRound2((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert3(isInt2(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound2((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert3(isInt2(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputRows,outputCols]}function computeOutputShape4D2(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad2(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=conditionalRound2((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode);assert3(isInt2(outputDepths),()=>`The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);let outputRows=conditionalRound2((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert3(isInt2(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound2((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert3(isInt2(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad2(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize2(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam2(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam2(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize2(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo2(pad7,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad7=="number"){let padType=pad7===0?"VALID":"NUMBER";padInfo={top:pad7,bottom:pad7,left:pad7,right:pad7,type:padType};let outShape=computeOutputShape2D2([inHeight,inWidth],filterHeight,strideHeight,pad7,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad7==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad7==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad7=="object"){let top=dataFormat==="channelsLast"?pad7[1][0]:pad7[2][0],bottom=dataFormat==="channelsLast"?pad7[1][1]:pad7[2][1],left=dataFormat==="channelsLast"?pad7[2][0]:pad7[3][0],right=dataFormat==="channelsLast"?pad7[2][1]:pad7[3][1],padType=top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT";padInfo={top,bottom,left,right,type:padType},outHeight=conditionalRound2((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=conditionalRound2((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad7}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo2(pad7,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad7=="number"){let padType=pad7===0?"VALID":"NUMBER";padInfo={top:pad7,bottom:pad7,left:pad7,right:pad7,front:pad7,back:pad7,type:padType};let outShape=computeOutputShape4D2([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad7,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad7==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad7==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad7}`);return{padInfo,outDepth,outHeight,outWidth}}function conditionalRound2(value,roundingMode){if(!roundingMode)return value;switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne2(param){let[dimA,dimB,dimC]=parseTupleParam2(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne2(strides,dilations){return tupleValuesAreOne2(strides)||tupleValuesAreOne2(dilations)}function convertConv2DDataFormat2(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function avgPool_2(x,filterSize,strides,pad7,dimRoundingMode){let $x=convertToTensor2(x,"x","avgPool","float32"),dilations=1;assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo2(x4D.shape,filterSize,strides,1,pad7,dimRoundingMode);return save([x4D]),convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual2(convInfo.inShape,convInfo.outShape)?x4D.clone():backend3.avgPool(x4D,convInfo)},inputs={x:x4D},attrs={filterSize,strides,pad:pad7,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,AvgPool2,attrs);return res=cast2(res,$x.dtype),reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool5=op2({avgPool_:avgPool_2});function assertParamsConsistent2(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert3(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert3(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert3(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape6(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}function concat_2(tensors,axis=0){assert3(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray2(tensors,"tensors","concat");$tensors[0].dtype==="complex64"&&$tensors.forEach(tensor163=>{if(tensor163.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor163.dtype}. `)});let forward=(backend3,save)=>{let $axis=parseAxisParam2(axis,$tensors[0].shape)[0],outShape=computeOutShape6($tensors.map(t=>t.shape),$axis);if(sizeFromShape2(outShape)===0)return tensor5([],outShape);if($tensors=$tensors.filter(t=>t.size>0),$tensors.length===1)return $tensors[0];let shapes=$tensors.map(t=>t.shape);assertParamsConsistent2(shapes,$axis);let res=backend3.concat($tensors,$axis);return save($tensors),res},inputs=$tensors,attr={axis};return ENGINE2.runKernelFunc(forward,inputs,null,Concat2,attr)}var concat2=op2({concat_:concat_2});function sigmoid_2(x){let $x=convertToTensor2(x,"x","sigmoid"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.sigmoid($x);return save([res]),res},inputs,null,Sigmoid3)}var sigmoid3=op2({sigmoid_:sigmoid_2});function slice_2(x,begin,size){let $x=convertToTensor2(x,"x","slice");if($x.rank===0)throw new Error("Slicing scalar is not possible");let forward=(backend3,save)=>{let[begin_,size_]=parseSliceParams2($x,begin,size);return assertParamsValid2($x,begin_,size_),save([$x]),backend3.slice($x,begin_,size_)},inputs={x:$x},attrs={begin,size};return ENGINE2.runKernelFunc(forward,inputs,null,Slice2,attrs)}var slice2=op2({slice_:slice_2});function tanh_2(x){let $x=convertToTensor2(x,"x","tanh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let y=backend3.tanh($x);return save([y]),y},inputs,null,Tanh3)}var tanh3=op2({tanh_:tanh_2});function batchToSpaceND_2(x,blockShape,crops){let $x=convertToTensor2(x,"x","batchToSpaceND"),prod6=blockShape.reduce((a,b)=>a*b);assert3($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert3(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert3($x.shape[0]%prod6===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod6}`);let forward=backend3=>backend3.batchToSpaceND($x,blockShape,crops),inputs={x:$x},attrs={blockShape,crops};return ENGINE2.runKernelFunc(forward,inputs,null,BatchToSpaceND2,attrs)}var batchToSpaceND2=op2({batchToSpaceND_:batchToSpaceND_2});function xAs4D2(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape2(x,[1,1,1,x.size]):x.rank===2?x4D=reshape2(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_2(x,mean7,variance,offset,scale3,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor2(x,"x","batchNorm"),$mean=convertToTensor2(mean7,"mean","batchNorm"),$variance=convertToTensor2(variance,"variance","batchNorm"),$scale;scale3!=null&&($scale=convertToTensor2(scale3,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor2(offset,"offset","batchNorm")),assert3($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert3($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert3($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let x4D=xAs4D2($x),forward=(backend3,save)=>(save([x4D,$mean,$variance,$scale]),backend3.batchNorm(x4D,as1DOr4D2($mean),as1DOr4D2($variance),as1DOr4D2($offset),as1DOr4D2($scale),varianceEpsilon)),inputs={x:x4D,scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE2.runKernelFunc(forward,inputs,null,FusedBatchNorm2,attrs);return reshape2(res,$x.shape)}function as1DOr4D2(x){return x==null?null:x.rank===0?reshape2(x,[x.size]):x.rank===1?x:x.rank===2?reshape2(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x}var batchNorm4=op2({batchNorm_:batchNorm_2});function broadcastTo_2(x,shape){let input2=convertToTensor2(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape2(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);let axes=reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0);if(axes.length===0)return clone2(input2);let forward=backend3=>backend3.tile(input2,reps),inputs={x:input2},attrs={shape,inputShape};return ENGINE2.runKernelFunc(forward,inputs,null,BroadcastTo2,attrs)}var broadcastTo2=op2({broadcastTo_:broadcastTo_2});function ceil_2(x){let $x=convertToTensor2(x,"x","ceil"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.ceil($x),inputs,null,Ceil2)}var ceil2=op2({ceil_:ceil_2});function clipByValue_2(x,clipValueMin,clipValueMax){let $x=convertToTensor2(x,"x","clipByValue");assert3(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.clip($x,clipValueMin,clipValueMax);return save([$x]),res},inputs,null,ClipByValue2,attrs)}var clipByValue2=op2({clipByValue_:clipByValue_2});function conv2d_2(x,filter,strides,pad7,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor2(x,"x","conv2d"),$filter=convertToTensor2(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert3($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert3(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let forward=(backend3,save)=>{let $dataFormat=convertConv2DDataFormat2(dataFormat),convInfo=computeConv2DInfo2(x4D.shape,$filter.shape,strides,dilations,pad7,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2d(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad7,dataFormat,dilations,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,Conv2D3,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d2=op2({conv2d_:conv2d_2});function conv1d_2(x,filter,stride,pad7,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor2(x,"x","conv1d"),$filter=convertToTensor2(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape2($x,[1,$x.shape[0],$x.shape[1]])),assert3(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert3($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`),assert3(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert3(eitherStridesOrDilationsAreOne2(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert3(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape2($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape2(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),strides=[1,stride],dilations=[1,dilation],conv2dDataFormat="NHWC",res=conv2d2(input4D,filter4D,strides,pad7,conv2dDataFormat,dilations,dimRoundingMode);return reshapedTo3D?reshape2(res,[res.shape[2],res.shape[3]]):reshape2(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d2=op2({conv1d_:conv1d_2});function conv2DBackpropInput_2(xShape,dy,filter,strides,pad7,dataFormat="NHWC",dimRoundingMode){assert3(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert3(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert3(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert3(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert3(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert3(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=(backend3,save)=>{let dilations=1,$dataFormat=convertConv2DDataFormat2(dataFormat),convInfo=computeConv2DInfo2(xShape4D,filter.shape,strides,dilations,pad7,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2dDerInput(dy4D,filter,convInfo);return save([dy4D,filter]),res2},inputs={dy:dy4D,filter},attrs={strides,pad:pad7,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE2.runKernelFunc(forward,inputs,null,Conv2DBackpropInput2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput4=op2({conv2DBackpropInput_:conv2DBackpropInput_2});function conv2dTranspose_2(x,filter,outputShape,strides,pad7,dimRoundingMode){let $x=convertToTensor2(x,"x","conv2dTranspose"),$filter=convertToTensor2(filter,"filter","conv2dTranspose");return conv2DBackpropInput4(outputShape,$x,$filter,strides,pad7,"NHWC",dimRoundingMode)}var conv2dTranspose3=op2({conv2dTranspose_:conv2dTranspose_2});function conv3DBackpropInput_2(xShape,dy,filter,strides,pad7){assert3(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert3(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert3(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert3(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert3(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert3(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo2(xShape5D,filter.shape,strides,dilations,pad7);return backend3.conv3dDerInput(dy5D,filter,convInfo)},inputs={dy:dy5D,filter},attrs={pad:pad7,strides,inputShape:xShape5D},res=ENGINE2.runKernelFunc(forward,inputs,null,Conv3DBackpropInputV22,attrs);return reshapedTo5D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput2=op2({conv3DBackpropInput_:conv3DBackpropInput_2});function cos_2(x){let $x=convertToTensor2(x,"x","cos"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.cos($x);return save([$x]),res},inputs,null,Cos2)}var cos2=op2({cos_:cos_2});function cosh_2(x){let $x=convertToTensor2(x,"x","cosh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.cosh($x);return save([$x]),res},inputs,null,Cosh2)}var cosh2=op2({cosh_:cosh_2});function cumsum_2(x,axis=0,exclusive=!1,reverse8=!1){let $x=convertToTensor2(x,"x","cumsum"),forward=(backend3,save)=>{let permutation=getAxesPermutation2([axis],$x.rank),permutedX=$x;permutation!=null&&(permutedX=transpose2($x,permutation));let permutedAxis=getInnerMostAxes2(1,$x.rank)[0],value=backend3.cumsum(permutedX,permutedAxis,exclusive,reverse8);if(save([$x]),permutation!=null){let reversePermutation=getUndoAxesPermutation2(permutation);value=transpose2(value,reversePermutation)}return value},inputs={x:$x},attrs={axis,exclusive,reverse:reverse8};return ENGINE2.runKernelFunc(forward,inputs,null,Cumsum2,attrs)}var cumsum2=op2({cumsum_:cumsum_2});function depthToSpace_2(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor2(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert3(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
+    ${$x.shape}`),assert3(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputWidth} and ${blockSize} for depthToSpace with input shape
+        ${$x.shape}`),assert3(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let forward=backend3=>backend3.depthToSpace($x,blockSize,dataFormat),inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE2.runKernelFunc(forward,inputs,null,DepthToSpace2,attrs)}var depthToSpace3=op2({depthToSpace_:depthToSpace_2});function depthwiseConv2d_2(x,filter,strides,pad7,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor2(x,"x","depthwiseConv2d"),$filter=convertToTensor2(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert3($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert3(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1]),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computeConv2DInfo2(x4D.shape,$filter.shape,strides,dilations,pad7,dimRoundingMode,!0),res2=backend3.depthwiseConv2D(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad7,dataFormat,dilations,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,DepthwiseConv2dNative2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d6=op2({depthwiseConv2d_:depthwiseConv2d_2});function dilation2d_2(x,filter,strides,pad7,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor2(x,"x","dilation2d"),$filter=convertToTensor2(filter,"filter","dilation2d");assert3($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert3($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert3(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad7,dilations},res=ENGINE2.runKernel(Dilation2D2,inputs,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d2=op2({dilation2d_:dilation2d_2});function getReductionAxes2(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape2(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_2(a,b){let $a=convertToTensor2(a,"a","equal"),$b=convertToTensor2(b,"b","equal");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=backend3=>backend3.equal($a,$b),inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Equal2)}var equal2=op2({equal_:equal_2});function where_2(condition,a,b){let $a=convertToTensor2(a,"a","where"),$b=convertToTensor2(b,"b","where"),$condition=convertToTensor2(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape2($a.shape,$b.shape),$broadcastedA=broadcastTo2($a,broadcastShape),$broadcastedB=broadcastTo2($b,broadcastShape);$condition.rank===1&&assert3($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch2($condition.shape,$broadcastedB.shape,"Error in where: ");let forward=(backend3,save)=>{let res=backend3.select($condition,$broadcastedA,$broadcastedB);return save([$condition]),res},inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE2.runKernelFunc(forward,inputs,null,SelectV22)}var where2=op2({where_:where_2});function zerosLike_2(x){let $x=convertToTensor2(x,"x","zerosLike"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.zerosLike($x),inputs,null,ZerosLike2)}var zerosLike3=op2({zerosLike_:zerosLike_2});function divNoNan_2(a,b){let $a=convertToTensor2(a,"a","div"),$b=convertToTensor2(b,"b","div");[$a,$b]=makeTypesMatch2($a,$b);let divResult=div2($a,$b),zeros10=zerosLike3(divResult),bEqualsZero=equal2($b,zeros10);return where2(bEqualsZero,zeros10,divResult)}var divNoNan2=op2({divNoNan_:divNoNan_2});function dot_2(t1,t2){let $t1=convertToTensor2(t1,"t1","dot"),$t2=convertToTensor2(t2,"t2","dot");assert3(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert3(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape2($t1,[1,-1]),t22D=reshape2($t2,[-1,1]),t1t2=matMul3(t12D,t22D);return reshape2(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape2($t1,[1,-1]),t22D=reshape2($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul3(t12D,t22D);return reshape2(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape2($t2,[-1,1]),t1t2=matMul3($t1,t22D);return reshape2(t1t2,[t1t2.size])}else{let t22D=reshape2($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul3($t1,t22D);return t1t2}}var dot2=op2({dot_:dot_2});function elu_2(x){let $x=convertToTensor2(x,"x","elu"),forward=(backend3,save)=>{let y=backend3.elu($x);return save([y]),y},inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Elu3)}var elu2=op2({elu_:elu_2});function erf_2(x){let $x=convertToTensor2(x,"x","erf");assert3($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast2($x,"float32"));let inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.erf($x);return save([$x]),res},inputs,null,Erf2)}var erf2=op2({erf_:erf_2});function exp_2(x){let $x=convertToTensor2(x,"x","exp"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.exp($x);return save([res]),res},inputs,null,Exp2)}var exp2=op2({exp_:exp_2});function expandDims_2(x,axis=0){let parseAs=null,$x=convertToTensor2(x,"x","expandDims",parseAs);assert3(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let newShape=$x.shape.slice();return axis<0&&(assert3(-($x.rank+1)<=axis,()=>`Axis must be in the interval [${-($x.rank+1)}, ${$x.rank}]`),axis=$x.rank+axis+1),newShape.splice(axis,0,1),reshape2($x,newShape)}var expandDims3=op2({expandDims_:expandDims_2});function expm1_2(x){let $x=convertToTensor2(x,"x","expm1"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.expm1($x);return save([$x]),res},inputs,null,Expm12)}var expm12=op2({expm1_:expm1_2});function tile_2(x,reps){let parseAs=null,$x=convertToTensor2(x,"x","tile",parseAs);assert3($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let forward=(backend3,save)=>{let res=backend3.tile($x,reps);return save([$x]),res},inputsToSave=[$x],inputs={x:$x},attrs={reps};return ENGINE2.runKernelFunc(forward,inputs,null,Tile2,attrs,inputsToSave)}var tile2=op2({tile_:tile_2});function eye_2(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer2([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape2(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile2(expandDims3(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile2(expandDims3(expandDims3(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile2(expandDims3(expandDims3(expandDims3(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye2=op2({eye_:eye_2});function fill2(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE2.runKernelFunc(backend3=>backend3.fill(shape,value,dtype),{},null,Fill2,attrs)}function floor_2(x){let $x=convertToTensor2(x,"x","floor"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.floor($x),inputs,null,Floor2)}var floor2=op2({floor_:floor_2});function collectGatherOpShapeInfo2(x,indices,axis){let dimSize=x.shape[axis],outputShape=[],batchSize=1,sliceSize=1;for(let i=0;i<axis;i++)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=0;i<indices.rank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<x.rank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,dimSize,outputShape}}function gather_2(x,indices,axis=0){let $x=convertToTensor2(x,"x","gather"),$indices=convertToTensor2(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis},forward=(backend3,save)=>{let parsedAxis=parseAxisParam2(axis,$x.shape)[0],shapeInfo=collectGatherOpShapeInfo2($x,$indices,parsedAxis),res=backend3.gather($x,reshape2($indices,[$indices.size]),parsedAxis);return save([$x,$indices]),reshape2(res,shapeInfo.outputShape)};return ENGINE2.runKernelFunc(forward,inputs,null,GatherV22,attrs)}var gather2=op2({gather_:gather_2});function greater_2(a,b){let $a=convertToTensor2(a,"a","greater"),$b=convertToTensor2(b,"b","greater");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=backend3=>backend3.greater($a,$b),inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Greater2)}var greater2=op2({greater_:greater_2});function greaterEqual_2(a,b){let $a=convertToTensor2(a,"a","greaterEqual"),$b=convertToTensor2(b,"b","greaterEqual");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.greaterEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,GreaterEqual2)}var greaterEqual2=op2({greaterEqual_:greaterEqual_2});function imag_2(input2){let $input=convertToTensor2(input2,"input","imag"),forward=backend3=>backend3.imag($input),inputs={input:$input};return ENGINE2.runKernelFunc(forward,inputs,null,Imag2)}var imag2=op2({imag_:imag_2});function isFinite_2(x){let $x=convertToTensor2(x,"x","isFinite"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.isFinite($x),inputs,null,IsFinite2)}var isFinite4=op2({isFinite_:isFinite_2});function isInf_2(x){let $x=convertToTensor2(x,"x","isInf"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.isInf($x),inputs,null,IsInf2)}var isInf3=op2({isInf_:isInf_2});function isNaN_2(x){let $x=convertToTensor2(x,"x","isNaN"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.isNaN($x),inputs,null,IsNan2)}var isNaN4=op2({isNaN_:isNaN_2});function maximum_2(a,b){let $a=convertToTensor2(a,"a","maximum"),$b=convertToTensor2(b,"b","maximum");[$a,$b]=makeTypesMatch2($a,$b),$a.dtype==="bool"&&($a=cast2($a,"int32"),$b=cast2($b,"int32")),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.maximum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Maximum3)}var maximum2=op2({maximum_:maximum_2});function scalar2(value,dtype){if((isTypedArray2(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray2(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");let shape=[],inferredShape=[];return makeTensor2(value,shape,inferredShape,dtype)}function leakyRelu_2(x,alpha=.2){let $x=convertToTensor2(x,"x","leakyRelu");return maximum2(mul2(scalar2(alpha),$x),$x)}var leakyRelu2=op2({leakyRelu_:leakyRelu_2});function less_2(a,b){let $a=convertToTensor2(a,"a","less"),$b=convertToTensor2(b,"b","less");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=backend3=>backend3.less($a,$b),inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Less2)}var less2=op2({less_:less_2});function lessEqual_2(a,b){let $a=convertToTensor2(a,"a","lessEqual"),$b=convertToTensor2(b,"b","lessEqual");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.lessEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,LessEqual2)}var lessEqual2=op2({lessEqual_:lessEqual_2});function localResponseNormalization_2(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor2(x,"x","localResponseNormalization");assert3($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
+               rank ${$x.rank}.`),assert3(isInt2(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let forward=(backend3,save)=>{let y=backend3.localResponseNormalization4D(x4D,depthRadius,bias,alpha,beta);return save([x4D,y]),y},inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE2.runKernelFunc(forward,inputs,null,LRN2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization2=op2({localResponseNormalization_:localResponseNormalization_2});function log_2(x){let $x=convertToTensor2(x,"x","log"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.log($x);return save([$x]),res},inputs,null,Log2)}var log2=op2({log_:log_2});function log1p_2(x){let $x=convertToTensor2(x,"x","log1p"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.log1p($x);return save([$x]),res},inputs,null,Log1p2)}var log1p2=op2({log1p_:log1p_2});function customGrad2(f){return ENGINE2.customGrad(f)}function neg_2(x){let $x=convertToTensor2(x,"x","neg"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.neg($x),inputs,null,Negate2)}var neg2=op2({neg_:neg_2});function softplus_2(x){let $x=convertToTensor2(x,"x","softplus"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.softplus($x);return save([$x]),res},inputs,null,Softplus3)}var softplus2=op2({softplus_:softplus_2});function logSigmoid_2(x){let $x=convertToTensor2(x,"x","logSigmoid"),customOp=customGrad2(x2=>{let value=neg2(softplus2(neg2(x2))),gradFunc=dy=>{let derX=mul2(dy,sigmoid3(neg2(x2)));return derX};return{value,gradFunc}});return customOp($x)}var logSigmoid2=op2({logSigmoid_:logSigmoid_2});function max_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","max"),forward=(backend3,save)=>{let origAxes=parseAxisParam2(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation2(axes,$x.rank),maxInput=$x;permutedAxes!=null&&(maxInput=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,maxInput.rank));let y=backend3.max(maxInput,axes);permutedAxes!=null&&maxInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim2(res.shape,parseAxisParam2(axis,$x.shape));res=reshape2(res,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={reductionIndices:axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Max2,attrs)}var max2=op2({max_:max_2});function sub_2(a,b){let $a=convertToTensor2(a,"a","sub"),$b=convertToTensor2(b,"b","sub");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.subtract($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Sub2)}var sub2=op2({sub_:sub_2});function sum_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","sum");$x.dtype==="bool"&&($x=cast2($x,"int32"));let forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam2(axis,$x.shape),permutation=getAxesPermutation2(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2($x,permutation),reductionAxes=getInnerMostAxes2(reductionAxes.length,$x.rank));let value=backend3.sum(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim2(value.shape,axes);value=reshape2(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Sum2,attrs)}var sum3=op2({sum_:sum_2});function logSoftmax_2(logits,axis=-1){let $logits=convertToTensor2(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);let forward=(backend3,save)=>{let keepDims=!0,xMax=max2(logits,axis,!0),shifted=sub2(logits,xMax),value=sub2(cast2(shifted,"float32"),log2(sum3(exp2(shifted),axis,keepDims)));return save([value]),value},inputs={logits:$logits},attrs={axis};return ENGINE2.runKernelFunc(forward,inputs,null,LogSoftmax3,attrs)}var logSoftmax2=op2({logSoftmax_:logSoftmax_2});function logSumExp_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","logSumExp"),axes=parseAxisParam2(axis,$x.shape),xMax=max2($x,axes,!0),a=sub2($x,xMax),b=exp2(a),c=sum3(b,axes),d=log2(c),res=add4(reshape2(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim2(res.shape,axes);return reshape2(res,newShape)}return res}var logSumExp2=op2({logSumExp_:logSumExp_2});function logicalAnd_2(a,b){let $a=convertToTensor2(a,"a","logicalAnd","bool"),$b=convertToTensor2(b,"b","logicalAnd","bool");assertAndGetBroadcastShape2($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(backend3=>backend3.logicalAnd($a,$b),inputs,null,LogicalAnd2)}var logicalAnd2=op2({logicalAnd_:logicalAnd_2});function logicalNot_2(x){let $x=convertToTensor2(x,"x","logicalNot","bool"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.logicalNot($x),inputs,null,LogicalNot2)}var logicalNot3=op2({logicalNot_:logicalNot_2});function logicalOr_2(a,b){let $a=convertToTensor2(a,"a","logicalOr","bool"),$b=convertToTensor2(b,"b","logicalOr","bool");assertAndGetBroadcastShape2($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(backend3=>backend3.logicalOr($a,$b),inputs,null,LogicalOr2)}var logicalOr2=op2({logicalOr_:logicalOr_2});function logicalXor_2(a,b){let $a=convertToTensor2(a,"a","logicalXor","bool"),$b=convertToTensor2(b,"b","logicalXor","bool");return assertAndGetBroadcastShape2($a.shape,$b.shape),logicalAnd2(logicalOr2(a,b),logicalNot3(logicalAnd2(a,b)))}var logicalXor2=op2({logicalXor_:logicalXor_2});function maxPool_2(x,filterSize,strides,pad7,dimRoundingMode){let $x=convertToTensor2(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo2(x4D.shape,filterSize,strides,1,pad7,dimRoundingMode),y;return convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual2(convInfo.inShape,convInfo.outShape)?y=x4D.clone():y=backend3.maxPool(x4D,convInfo),save([x4D,y]),y},inputs={x:x4D},attrs={filterSize,strides,pad:pad7,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,MaxPool2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool5=op2({maxPool_:maxPool_2});function zeros2(shape,dtype="float32"){if(dtype==="complex64"){let real9=zeros2(shape,"float32"),imag9=zeros2(shape,"float32");return complex2(real9,imag9)}let values=makeZerosTypedArray2(sizeFromShape2(shape),dtype);return ENGINE2.makeTensor(values,shape,dtype)}function ones4(shape,dtype="float32"){if(dtype==="complex64"){let real9=ones4(shape,"float32"),imag9=zeros2(shape,"float32");return complex2(real9,imag9)}let values=makeOnesTypedArray2(sizeFromShape2(shape),dtype);return ENGINE2.makeTensor(values,shape,dtype)}function mean_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","mean"),axes=parseAxisParam2(axis,$x.shape),shapes=computeOutAndReduceShapes2($x.shape,axes),reduceShape=shapes[1],reduceSize=sizeFromShape2(reduceShape),inputs={x:$x},attrs={axis,keepDims},forward=()=>{let reduceSizeScalar=scalar2(reduceSize),xReduce=reduceSizeScalar.dtype===$x.dtype?$x:cast2($x,reduceSizeScalar.dtype),res=div2(xReduce,reduceSizeScalar);return sum3(res,axis,keepDims)},customOp=customGrad2(x2=>{let value=ENGINE2.runKernelFunc(forward,inputs,null,Mean2,attrs),gradFunc=dy=>{let expandedDyShape=x2.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape2(dy,expandedDyShape),derX=div2(mul2(expandedDy,ones4(x2.shape,"float32")),reduceSize);return derX};return{value,gradFunc}});return customOp($x)}var mean2=op2({mean_:mean_2});function min_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","min"),forward=(backend3,save)=>{let origAxes=parseAxisParam2(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation2(axes,$x.rank),minInput=$x;permutedAxes!=null&&(minInput=transpose2($x,permutedAxes),axes=getInnerMostAxes2(axes.length,$x.rank));let y=backend3.min(minInput,axes);permutedAxes!=null&&minInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim2(res.shape,origAxes);res=reshape2(y,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Min2,attrs)}var min2=op2({min_:min_2});function minimum_2(a,b){let $a=convertToTensor2(a,"a","minimum"),$b=convertToTensor2(b,"b","minimum");[$a,$b]=makeTypesMatch2($a,$b),$a.dtype==="bool"&&($a=cast2($a,"int32"),$b=cast2($b,"int32")),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.minimum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Minimum3)}var minimum2=op2({minimum_:minimum_2});function mirrorPad_2(x,paddings,mode){assert3(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor2(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert3(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert3(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert3(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE2.runKernel(MirrorPad2,inputs,attrs)}var mirrorPad3=op2({mirrorPad_:mirrorPad_2});function mod_2(a,b){let $a=convertToTensor2(a,"a","mod"),$b=convertToTensor2(b,"b","mod");[$a,$b]=makeTypesMatch2($a,$b);let forward=(backend3,save)=>{let res=backend3.mod($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,Mod2)}var mod2=op2({mod_:mod_2});function square_2(x){let $x=convertToTensor2(x,"x","square"),attrs={},inputsToSave=[$x],outputsToSave=[];return ENGINE2.runKernelFunc((backend3,save)=>(save([$x]),backend3.square($x)),{x:$x},null,"Square",attrs,inputsToSave,outputsToSave)}var square2=op2({square_:square_2});function notEqual_2(a,b){let $a=convertToTensor2(a,"a","notEqual"),$b=convertToTensor2(b,"b","notEqual");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=backend3=>backend3.notEqual($a,$b),inputs={a:$a,b:$b};return ENGINE2.runKernelFunc(forward,inputs,null,NotEqual2)}var notEqual4=op2({notEqual_:notEqual_2});function real_2(input2){let $input=convertToTensor2(input2,"input","real"),forward=backend3=>backend3.real($input),inputs={input:$input};return ENGINE2.runKernelFunc(forward,inputs,null,Real2)}var real2=op2({real_:real_2});function onesLike_2(x){let $x=convertToTensor2(x,"x","onesLike"),forward=(backend3,save)=>{if($x.dtype==="complex64"){let r=onesLike3(real2($x)),i=zerosLike3(imag2($x));return complex2(r,i)}return backend3.onesLike($x)},inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,OnesLike2)}var onesLike3=op2({onesLike_:onesLike_2});function pad_2(x,paddings,constantValue=0){let $x=convertToTensor2(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let forward=(backend3,save)=>(save([$x]),backend3.pad($x,paddings,constantValue)),attrs={paddings,constantValue},inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,PadV22,attrs)}var pad2=op2({pad_:pad_2});function spaceToBatchND_2(x,blockShape,paddings){let $x=convertToTensor2(x,"x","spaceToBatchND");assert3($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert3(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert3($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let forward=backend3=>backend3.spaceToBatchND($x,blockShape,paddings),inputs={x:$x},attrs={blockShape,paddings};return ENGINE2.runKernelFunc(forward,inputs,null,SpaceToBatchND2,attrs)}var spaceToBatchND3=op2({spaceToBatchND_:spaceToBatchND_2});function pool_2(input2,windowShape,poolingType,pad7,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad7===0&&(pad7="valid");let $x=convertToTensor2(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo2(x4D.shape,windowShape,strides,dilations,pad7),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad7==="same"?basePadding=withSpaceToBatchBasePaddings2([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings2([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad7:"valid",convertedX=isDilationOne?x4D:spaceToBatchND3(x4D,dilation,adjustedPadding),forwardOp=poolingType==="avg"?()=>avgPool5(convertedX,windowShape,strides,convertedPad):()=>maxPool5(convertedX,windowShape,strides,convertedPad),y=forwardOp(),res=isDilationOne?y:batchToSpaceND2(y,dilation,adjustedCrops);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings2(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings2(filterShape,dilation){let dilatedFilterShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)),padExtraShape=dilatedFilterShape.map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool2=op2({pool_:pool_2});function pow_2(base2,exp14){let $base=convertToTensor2(base2,"base","pow"),$exp=convertToTensor2(exp14,"exp","pow");[$base,$exp]=makeTypesMatch2($base,$exp);let inputs={a:$base,b:$exp},forward=(backend3,save)=>{let y=backend3.pow($base,$exp);return save([$base,$exp,y]),y};return ENGINE2.runKernelFunc(forward,inputs,null,Pow2)}var pow2=op2({pow_:pow_2});function prelu_2(x,alpha){let $x=convertToTensor2(x,"x","prelu"),$alpha=convertToTensor2(alpha,"alpha","prelu"),forward=(backend3,save)=>{let res=backend3.prelu($x,$alpha);return save([$x,$alpha]),res},inputs={x:$x,alpha:$alpha};return ENGINE2.runKernelFunc(forward,inputs,null,Prelu2)}var prelu2=op2({prelu_:prelu_2});function prod_2(x,axis=null,keepDims=!1){let $x=convertToTensor2(x,"x","prod");$x.dtype==="bool"&&($x=cast2($x,"int32"));let forward=backend3=>{let axes=parseAxisParam2(axis,$x.shape),permutation=getAxesPermutation2(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose2($x,permutation),reductionAxes=getInnerMostAxes2(reductionAxes.length,$x.rank));let value=backend3.prod(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim2(value.shape,axes);value=reshape2(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE2.runKernelFunc(forward,inputs,null,Prod2,attrs)}var prod2=op2({prod_:prod_2});function tensor1d2(values,dtype){assertNonNull2(values);let inferredShape=inferShape2(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");let shape=null;return makeTensor2(values,shape,inferredShape,dtype)}function range3(start,stop,step9=1,dtype="float32"){if(step9===0)throw new Error("Cannot have a step of zero");let forward=()=>{let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step9<0,decreasingRangePositiveStep=stop<start&&step9>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return zeros2([0],dtype);let numElements=Math.abs(Math.ceil((stop-start)/step9)),values=makeZerosTypedArray2(numElements,dtype);stop<start&&step9===1&&(step9=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d2(values,dtype)},attrs={start,stop,step:step9,dtype};return ENGINE2.runKernelFunc(forward,{},null,Range2,attrs)}function reciprocal_2(x){let $x=convertToTensor2(x,"x","reciprocal"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.reciprocal($x);return save([$x]),res},inputs,null,Reciprocal2)}var reciprocal2=op2({reciprocal_:reciprocal_2});function relu_2(x){let $x=convertToTensor2(x,"x","relu"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast2($x,"int32"):backend3.relu($x)),inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Relu3)}var relu2=op2({relu_:relu_2});function relu6_2(x){let $x=convertToTensor2(x,"x","relu6"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast2($x,"int32"):backend3.relu6($x)),inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Relu63)}var relu62=op2({relu6_:relu6_2});function reverse_2(x,axis){let $x=convertToTensor2(x,"x","reverse"),forward=backend3=>{let axes=parseAxisParam2(axis,$x.shape);if($x.rank===0)return clone2($x);let res=backend3.reverse($x,axes);return reshape2(res,$x.shape)},inputs={x:$x},attrs={dims:axis};return ENGINE2.runKernelFunc(forward,inputs,null,Reverse2,attrs)}var reverse2=op2({reverse_:reverse_2});function round_2(x){let $x=convertToTensor2(x,"x","round"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.round($x),inputs,null,Round2)}var round3=op2({round_:round_2});function rsqrt_2(x){let $x=convertToTensor2(x,"x","rsqrt"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.rsqrt($x);return save([$x]),res},inputs,null,Rsqrt2)}var rsqrt2=op2({rsqrt_:rsqrt_2});function selu_2(x){let $x=convertToTensor2(x,"x","selu"),forward=(backend3,save)=>{let res=backend3.selu($x);return save([$x]),res},inputs={x:$x};return ENGINE2.runKernelFunc(forward,inputs,null,Selu3)}var selu2=op2({selu_:selu_2});function separableConv2d_2(x,depthwiseFilter,pointwiseFilter,strides,pad7,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor2(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor2(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor2(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape2($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert3(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert3($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert3($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert3($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert3($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert3($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d6(x4D,$depthwiseFilter,strides,pad7,dataFormat,dilation),pointwiseStride=1,res=conv2d2(depthwise,$pointwiseFilter,pointwiseStride,"valid",dataFormat);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d3=op2({separableConv2d_:separableConv2d_2});function sign_2(x){let $x=convertToTensor2(x,"x","sign"),inputs={x:$x};return ENGINE2.runKernelFunc(backend3=>backend3.sign($x),inputs,null,Sign2)}var sign2=op2({sign_:sign_2});function sin_2(x){let $x=convertToTensor2(x,"x","sin"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.sin($x);return save([$x]),res},inputs,null,Sin2)}var sin2=op2({sin_:sin_2});function sinh_2(x){let $x=convertToTensor2(x,"x","sinh"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.sinh($x);return save([$x]),res},inputs,null,Sinh2)}var sinh2=op2({sinh_:sinh_2});function softmax_2(logits,dim=-1){let $logits=convertToTensor2(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE2.runKernelFunc((backend3,save)=>{let y=backend3.softmax($logits,dim);return save([y]),y},inputs,null,Softmax4,attrs)}var softmax2=op2({softmax_:softmax_2});function fft_2(input2){assert3(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE2.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=input2.as2D(batch,innerDimensionSize),result=backend3.fft(input2D);return result.reshape(input2.shape)},inputs,null,FFT2)}var fft2=op2({fft_:fft_2});function ifft_2(input2){assert3(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE2.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=reshape2(input2,[batch,innerDimensionSize]),result=backend3.ifft(input2D);return reshape2(result,input2.shape)},inputs,null,IFFT2)}var ifft2=op2({ifft_:ifft_2});function irfft_2(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape2(input2,[batch,innerDimensionSize]);ret=ifft2(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape2(real2(input2),[batch,innerDimensionSize]),imagInput=reshape2(imag2(input2),[batch,innerDimensionSize]),realConjugate=reverse2(slice2(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul2(reverse2(slice2(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar2(-1)),r=concat2([realInput,realConjugate],1),i=concat2([imagInput,imagConjugate],1),complexInput=reshape2(complex2(r,i),[outputShape[0],outputShape[1]]);ret=ifft2(complexInput)}if(ret=real2(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape2(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft2=op2({irfft_:irfft_2});function prepareSplitSize2(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert3(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert3(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert3(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}function split_2(x,numOrSizeSplits,axis=0){let $x=convertToTensor2(x,"x","split"),forward=(backend3,_)=>{let $axis=parseAxisParam2(axis,$x.shape)[0],splitSizes=prepareSplitSize2($x,numOrSizeSplits,$axis);return backend3.split($x,splitSizes,$axis)},inputs={x:$x},attr={numOrSizeSplits,axis};return ENGINE2.runKernelFunc(forward,inputs,null,SplitV2,attr)}var split2=op2({split_:split_2});function rfft_2(input2,fftLength){assert3(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice2(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat2([input2,zeros2(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike3(adjustedInput),complexInput=reshape2(complex2(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft2(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real2(ret),imagValues=imag2(ret),realComplexConjugate=split2(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split2(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape2(complex2(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft2=op2({rfft_:rfft_2});function sqrt_2(x){let $x=convertToTensor2(x,"x","sqrt"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.sqrt($x);return save([$x]),res},inputs,null,Sqrt2)}var sqrt2=op2({sqrt_:sqrt_2});function squaredDifference_2(a,b){let $a=convertToTensor2(a,"a","squaredDifference"),$b=convertToTensor2(b,"b","squaredDifference");[$a,$b]=makeTypesMatch2($a,$b),assertAndGetBroadcastShape2($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.squaredDifference($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE2.runKernelFunc(forward,inputs,null,SquaredDifference2,attrs)}var squaredDifference4=op2({squaredDifference_:squaredDifference_2});function squeeze_2(x,axis){let $x=convertToTensor2(x,"x","squeeze");return reshape2($x,squeezeShape2($x.shape,axis).newShape)}var squeeze2=op2({squeeze_:squeeze_2});function stack_2(tensors,axis=0){let $tensors=convertToTensorArray2(tensors,"tensors","stack");if(assert3($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length===1)return expandDims3($tensors[0],axis);let rank=$tensors[0].rank,shape=$tensors[0].shape,dtype=$tensors[0].dtype;assert3(axis<=rank,()=>"Axis must be <= rank of the tensor"),$tensors.forEach(t=>{assertShapesMatch2(shape,t.shape,"All tensors passed to stack must have matching shapes"),assert3(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=$tensors.map(t=>expandDims3(t,axis));return concat2(expandedTensors,axis)}var stack2=op2({stack_:stack_2});function step_2(x,alpha=0){let $x=convertToTensor2(x,"x","step"),inputs={x:$x},attrs={alpha};return ENGINE2.runKernelFunc(backend3=>backend3.step($x,alpha),inputs,null,Step2,attrs)}var step2=op2({step_:step_2});function stridedSlice_2(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let $x=convertToTensor2(x,"x","stridedSlice"),forward=backend3=>{strides==null&&(strides=new Array(begin.length));let ellipsisAxes=maskToAxes2(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=$x.rank-begin.length,expandAxes=maskToAxes2(newAxisMask),newShape=$x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)}),$x=reshape2($x,newShape);let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes2($x.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=maskToAxes2(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=computeOutShape5(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided)return reshape2(slice2($x,begin,size),outShape);let res=backend3.stridedSlice($x,begin,end,strides);return reshape2(res,outShape)},inputs={x:$x},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE2.runKernelFunc(forward,inputs,null,StridedSlice2,attrs)}var stridedSlice3=op2({stridedSlice_:stridedSlice_2});function tan_2(x){let $x=convertToTensor2(x,"x","tan"),inputs={x:$x};return ENGINE2.runKernelFunc((backend3,save)=>{let res=backend3.tan($x);return save([$x]),res},inputs,null,Tan2)}var tan2=op2({tan_:tan_2});function tensor2d2(values,shape,dtype){if(assertNonNull2(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape2(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor2(values,shape,inferredShape,dtype)}function topk_2(x,k=1,sorted=!0){let $x=convertToTensor2(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE2.runKernelFunc(b=>b.topk($x,k,sorted),inputs,null,TopK2,attrs);return{values,indices}}var topk2=op2({topk_:topk_2});function unique_2(x,axis=0){let $x=convertToTensor2(x,"x","unique",null);assert3($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE2.runKernel(Unique2,inputs,attrs);return{values,indices}}var unique2=op2({unique_:unique_2});function unsortedSegmentSum_2(x,segmentIds,numSegments){let $x=convertToTensor2(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor2(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert3(isInt2(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments},forward=(backend3,save)=>{let res=backend3.unsortedSegmentSum($x,$segmentIds,numSegments);return save([$segmentIds]),res};return ENGINE2.runKernelFunc(forward,inputs,null,UnsortedSegmentSum2,attrs)}var unsortedSegmentSum2=op2({unsortedSegmentSum_:unsortedSegmentSum_2});function unstack_2(x,axis=0){let $x=convertToTensor2(x,"x","unstack");assert3(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`),axis<0&&(axis+=$x.shape.length);let inputs={value:$x},attrs={axis},forward=backend3=>backend3.unstack($x,axis);return ENGINE2.runKernelFunc(forward,inputs,null,Unpack2,attrs)}var unstack2=op2({unstack_:unstack_2});function notEqualStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","notEqualStrict"),$b=convertToTensor2(b,"b","notEqualStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual4($a,$b)}function lessStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","lessStrict"),$b=convertToTensor2(b,"b","lessStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in lessStrict: "),less2($a,$b)}function equalStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","equalStrict"),$b=convertToTensor2(b,"b","equalStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in equalStrict: "),equal2($a,$b)}function lessEqualStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","lessEqualStrict"),$b=convertToTensor2(b,"b","lessEqualStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual2($a,$b)}function greaterStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","greaterStrict"),$b=convertToTensor2(b,"b","greaterStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in greaterStrict: "),greater2($a,$b)}function greaterEqualStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","greaterEqualStrict"),$b=convertToTensor2(b,"b","greaterEqualStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual2($a,$b)}var equalStrict2=op2({equalStrict_:equalStrict_2}),greaterEqualStrict2=op2({greaterEqualStrict_:greaterEqualStrict_2}),greaterStrict2=op2({greaterStrict_:greaterStrict_2}),lessEqualStrict2=op2({lessEqualStrict_:lessEqualStrict_2}),lessStrict2=op2({lessStrict_:lessStrict_2}),notEqualStrict2=op2({notEqualStrict_:notEqualStrict_2});function addStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","addStrict"),$b=convertToTensor2(b,"b","addStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in addStrict: "),add4($a,$b)}function subStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","subStrict"),$b=convertToTensor2(b,"b","subStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in subStrict: "),sub2($a,$b)}function powStrict_2(base2,exp14){return deprecationWarn2("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch2(base2.shape,exp14.shape,"Error in powStrict: "),pow2(base2,exp14)}function mulStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","mul"),$b=convertToTensor2(b,"b","mul");return assertShapesMatch2($a.shape,$b.shape,"Error in multiplyStrict: "),mul2($a,$b)}function divStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","div"),$b=convertToTensor2(b,"b","div");return assertShapesMatch2($a.shape,$b.shape,"Error in divideStrict: "),div2($a,$b)}function modStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","modStrict"),$b=convertToTensor2(b,"b","modStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in modStrict: "),mod2($a,$b)}function minimumStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","minimumStrict"),$b=convertToTensor2(b,"b","minimumStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in minimumStrict: "),minimum2($a,$b)}function maximumStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","maximumStrict"),$b=convertToTensor2(b,"b","maximumStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in maximumStrict: "),maximum2($a,$b)}function squaredDifferenceStrict_2(a,b){deprecationWarn2("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor2(a,"a","squaredDifferenceStrict"),$b=convertToTensor2(b,"b","squaredDifferenceStrict");return assertShapesMatch2($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference4($a,$b)}var addStrict2=op2({addStrict_:addStrict_2}),divStrict2=op2({divStrict_:divStrict_2}),maximumStrict2=op2({maximumStrict_:maximumStrict_2}),minimumStrict2=op2({minimumStrict_:minimumStrict_2}),modStrict2=op2({modStrict_:modStrict_2}),mulStrict2=op2({mulStrict_:mulStrict_2}),powStrict2=op2({powStrict_:powStrict_2}),squaredDifferenceStrict2=op2({squaredDifferenceStrict_:squaredDifferenceStrict_2}),subStrict2=op2({subStrict_:subStrict_2});function norm_2(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor2(x,"x","norm");let norm6=normImpl2(x,ord,axis),keepDimsShape=norm6.shape;if(keepDims){let axes=parseAxisParam2(axis,x.shape);keepDimsShape=expandShapeToKeepDim2(norm6.shape,axes)}return reshape2(norm6,keepDimsShape)}function normImpl2(x,p2,axis=null){if(x.rank===0)return abs2(x);if(x.rank!==1&&axis===null)return normImpl2(reshape2(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum3(abs2(x),axis);if(p2===Infinity)return max2(abs2(x),axis);if(p2===-Infinity)return min2(abs2(x),axis);if(p2==="euclidean"||p2===2)return sqrt2(sum3(pow2(abs2(x),scalar2(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max2(sum3(abs2(x),axis[0]),axis[1]-1);if(p2===Infinity)return max2(sum3(abs2(x),axis[1]),axis[0]);if(p2===-Infinity)return min2(sum3(abs2(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt2(sum3(square2(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm2=op2({norm_:norm_2});function enclosingPowerOfTwo2(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow2(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d2(newValues,"float32")}function conv2DBackpropFilter_2(x,dy,filterShape,strides,pad7,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert3(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert3(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert3(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert3(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert3(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=backend3=>{let dilations=1,$dataFormat=convertConv2DDataFormat2(dataFormat),convInfo=computeConv2DInfo2(x4D.shape,filterShape,strides,dilations,pad7,dimRoundingMode,!1,$dataFormat);return backend3.conv2dDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad7,dataFormat,dimRoundingMode,filterShape};return ENGINE2.runKernelFunc(forward,inputs,null,Conv2DBackpropFilter2,attrs)}var conv2DBackpropFilter3=op2({conv2DBackpropFilter_:conv2DBackpropFilter_2});function depthwiseConv2dNativeBackpropFilter_2(x,dy,filterShape,strides,pad7,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo2(x.shape,filterShape,strides,dilations,pad7,dimRoundingMode,!0);return backend3.depthwiseConv2DDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad7,dimRoundingMode,dilations,filterShape};return ENGINE2.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropFilter2,attrs)}var depthwiseConv2dNativeBackpropFilter3=op2({depthwiseConv2dNativeBackpropFilter_:depthwiseConv2dNativeBackpropFilter_2});function depthwiseConv2dNativeBackpropInput_2(xShape,dy,filter,strides,pad7,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo2(xShape,filter.shape,strides,dilations,pad7,dimRoundingMode,!0);return backend3.depthwiseConv2DDerInput(dy4D,filter,convInfo)},inputs={dy:dy4D,filter},attrs={strides,pad:pad7,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE2.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropInput2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput3=op2({depthwiseConv2dNativeBackpropInput_:depthwiseConv2dNativeBackpropInput_2});function hammingWindow_2(windowLength){return cosineWindow2(windowLength,.54,.46)}var hammingWindow2=op2({hammingWindow_:hammingWindow_2});function hannWindow_2(windowLength){return cosineWindow2(windowLength,.5,.5)}var hannWindow2=op2({hannWindow_:hannWindow_2});function frame_2(signal3,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal3.size;)output.push(slice2(signal3,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal3.size;){let padLen=start+frameLength-signal3.size,pad7=concat2([slice2(signal3,start,frameLength-padLen),fill2([padLen],padValue)]);output.push(pad7),start+=frameStep}return output.length===0?tensor2d2([],[0,frameLength]):reshape2(concat2(output),[output.length,frameLength])}var frame2=op2({frame_:frame_2});function stft_2(signal3,frameLength,frameStep,fftLength,windowFn=hannWindow2){fftLength==null&&(fftLength=enclosingPowerOfTwo2(frameLength));let framedSignal=frame2(signal3,frameLength,frameStep),windowedSignal=mul2(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft2(slice2(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat2(output)}var stft2=op2({stft_:stft_2});function cropAndResize_2(image3,boxes,boxInd,cropSize,method,extrapolationValue){let $image=convertToTensor2(image3,"image","cropAndResize"),$boxes=convertToTensor2(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor2(boxInd,"boxInd","cropAndResize","int32");method=method||"bilinear",extrapolationValue=extrapolationValue||0;let numBoxes=$boxes.shape[0];assert3($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert3($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert3($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert3(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert3(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert3(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let forward=backend3=>backend3.cropAndResize($image,$boxes,$boxInd,cropSize,method,extrapolationValue),inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize},res=ENGINE2.runKernelFunc(forward,inputs,null,CropAndResize2,attrs);return res}var cropAndResize3=op2({cropAndResize_:cropAndResize_2});function flipLeftRight_2(image3){let $image=convertToTensor2(image3,"image","flipLeftRight","float32");assert3($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},res=ENGINE2.runKernel(FlipLeftRight2,inputs,{});return res}var flipLeftRight3=op2({flipLeftRight_:flipLeftRight_2});function rotateWithOffset_2(image3,radians,fillValue=0,center=.5){let $image=convertToTensor2(image3,"image","rotateWithOffset","float32");assert3($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center},res=ENGINE2.runKernel(RotateWithOffset2,inputs,attrs);return res}var rotateWithOffset3=op2({rotateWithOffset_:rotateWithOffset_2});function nonMaxSuppSanityCheck2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert3(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert3(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert3(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert3(scores.rank===1,()=>"scores must be a 1D tensor"),assert3(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert3(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor2(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE2.runKernelFunc(b=>b.nonMaxSuppression($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold),{boxes:$boxes,scores:$scores},null,NonMaxSuppressionV32,attrs)}var nonMaxSuppression3=op2({nonMaxSuppression_:nonMaxSuppression_2});function binaryInsert2(arr,element,comparator){let index=binarySearch2(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch2(arr,target,comparator){return binarySearch_2(arr,target,comparator||defaultComparator2)}function defaultComparator2(a,b){return a>b?1:a<b?-1:0}function binarySearch_2(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl3(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0).selectedIndices}function nonMaxSuppressionV4Impl4(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl4(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator2);let scale3=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou3=intersectionOverUnion2(boxes,boxIndex,selectedIndices[j]);if(iou3>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight2(iouThreshold,scale3,iou3),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert2(candidates,candidate,ascendingComparator2))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices:tensor1d2(selectedIndices,"int32")};return returnScoresTensor&&(result.selectedScores=tensor1d2(selectedScores,"float32")),returnValidOutputs&&(result.validOutputs=scalar2(validOutputs,"int32")),result}function intersectionOverUnion2(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight2(iouThreshold,scale3,iou3){let weight=Math.exp(scale3*iou3*iou3);return iou3<=iouThreshold?weight:0}function ascendingComparator2(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor2(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV3Impl3(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionAsync2=nonMaxSuppressionAsync_2;function nonMaxSuppressionWithScore_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor2(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE2.runKernel(NonMaxSuppressionV52,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore2=op2({nonMaxSuppressionWithScore_:nonMaxSuppressionWithScore_2});async function nonMaxSuppressionWithScoreAsync_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor2(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV5Impl4(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionWithScoreAsync2=nonMaxSuppressionWithScoreAsync_2;function nonMaxSuppressionPadded_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor2(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE2.runKernel(NonMaxSuppressionV42,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded2=op2({nonMaxSuppressionPadded_:nonMaxSuppressionPadded_2});async function nonMaxSuppressionPaddedAsync_2(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor2(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor2(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck2($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),res=nonMaxSuppressionV4Impl4(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionPaddedAsync2=nonMaxSuppressionPaddedAsync_2;function resizeBilinear_2(images,size,alignCorners=!1){let $images=convertToTensor2(images,"images","resizeBilinear");assert3($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert3(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape2($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,forward=(backend3,save)=>(save([batchImages]),backend3.resizeBilinear(batchImages,newHeight,newWidth,alignCorners)),inputs={images:batchImages},attrs={alignCorners,size},res=ENGINE2.runKernelFunc(forward,inputs,null,ResizeBilinear2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear3=op2({resizeBilinear_:resizeBilinear_2});function resizeNearestNeighbor_2(images,size,alignCorners=!1){let $images=convertToTensor2(images,"images","resizeNearestNeighbor");assert3($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert3(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert3($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape2($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,inputs={images:batchImages},attrs={alignCorners,size},forward=(backend3,save)=>(save([batchImages]),backend3.resizeNearestNeighbor(batchImages,newHeight,newWidth,alignCorners)),res=ENGINE2.runKernelFunc(forward,inputs,null,ResizeNearestNeighbor2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor2=op2({resizeNearestNeighbor_:resizeNearestNeighbor_2});function bandPart_2(a,numLower,numUpper){assert3(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert3(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor2(a,"a","bandPart");assert3($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape2(range3(0,M,1,"int32"),[-1,1]),j=range3(0,N,1,"int32"),ij=sub2(i,j),inBand=logicalAnd2(lessEqual2(ij,scalar2(+numLower,"int32")),greaterEqual2(ij,scalar2(-numUpper,"int32"))),zero=zeros2([M,N],$a.dtype);return reshape2(stack2(unstack2(reshape2($a,[-1,M,N])).map(mat=>where2(inBand,mat,zero))),shape)}var bandPart2=op2({bandPart_:bandPart_2});function gramSchmidt_2(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert3(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert3(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split2(xs,xs.shape[0],0).map(x=>squeeze2(x,[0]));assert3(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE2.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul2(sum3(mul2(ys[j],x)),ys[j]);x=sub2(x,proj)}return div2(x,norm2(x,"euclidean"))}));return inputIsTensor2D?stack2(ys,0):ys}var gramSchmidt2=op2({gramSchmidt_:gramSchmidt_2});function qr_2(x,fullMatrices=!1){if(assert3(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d2(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack2(reshape2(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d2(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape2(stack2(q2ds,0),x.shape),r=reshape2(stack2(r2ds,0),x.shape);return[q,r]}}function qr2d2(x,fullMatrices=!1){return ENGINE2.tidy(()=>{assert3(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye2(m),r=clone2(x),one2D=tensor2d2([[1]],[1,1]),w=clone2(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE2.tidy(()=>{let rjEnd1=slice2(r,[j,j],[m-j,1]),normX=norm2(rjEnd1),rjj=slice2(r,[j,j],[1,1]),s=where2(greater2(rjj,0),tensor2d2([[-1]]),tensor2d2([[1]])),u1=sub2(rjj,mul2(s,normX)),wPre=div2(rjEnd1,u1);wPre.shape[0]===1?w=clone2(one2D):w=concat2([one2D,slice2(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg2(div2(matMul3(s,u1),normX)),rjEndAll=slice2(r,[j,0],[m-j,n]),tauTimesW=mul2(tau,w),wT=transpose2(w);if(j===0)r=sub2(rjEndAll,matMul3(tauTimesW,matMul3(wT,rjEndAll)));else{let rTimesTau=sub2(rjEndAll,matMul3(tauTimesW,matMul3(wT,rjEndAll)));r=concat2([slice2(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose2(tauTimesW),qAllJEnd=slice2(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub2(qAllJEnd,matMul3(matMul3(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub2(qAllJEnd,matMul3(matMul3(qAllJEnd,w),tawTimesWT));q=concat2([slice2(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose2([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice2(q,[0,0],[m,n]),r=slice2(r,[0,0],[n,n])),[q,r]})}var qr2=op2({qr_:qr_2});var Reduction2;(function(Reduction3){Reduction3[Reduction3.NONE=0]="NONE",Reduction3[Reduction3.MEAN=1]="MEAN",Reduction3[Reduction3.SUM=2]="SUM",Reduction3[Reduction3.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction2||(Reduction2={}));function computeWeightedLoss_2(losses3,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor2(losses3,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul2($losses,$weights);if(reduction===Reduction2.NONE)return weightedLoss;if(reduction===Reduction2.SUM)return sum3(weightedLoss);if(reduction===Reduction2.MEAN){if($weights==null)return mean2(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div2(sum3(weightedLoss),sum3($weights));return broadcastFactor>1?div2(result,scalar2(broadcastFactor)):result}}if(reduction===Reduction2.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div2(sum3(weightedLoss),scalar2($losses.size));{let broadcastedWeights=mul2($weights,ones4($losses.shape)),numNonZeros=cast2(sum3(notEqual4(broadcastedWeights,scalar2(0))),"float32");return div2(sum3(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction}`)}var computeWeightedLoss3=op2({computeWeightedLoss_:computeWeightedLoss_2});function absoluteDifference_2(labels,predictions,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","absoluteDifference"),$predictions=convertToTensor2(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","absoluteDifference")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses3=abs2(sub2($labels,$predictions));return computeWeightedLoss3(losses3,$weights,reduction)}var absoluteDifference2=op2({absoluteDifference_:absoluteDifference_2});function cosineDistance_2(labels,predictions,axis,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","cosineDistance"),$predictions=convertToTensor2(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","cosineDistance")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar2(1),losses3=sub2(one,sum3(mul2($labels,$predictions),axis,!0));return computeWeightedLoss3(losses3,$weights,reduction)}var cosineDistance2=op2({cosineDistance_:cosineDistance_2});function hingeLoss_2(labels,predictions,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","hingeLoss"),$predictions=convertToTensor2(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","hingeLoss")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar2(1);$labels=sub2(mul2(scalar2(2),$labels),one);let losses3=relu2(sub2(one,mul2($labels,$predictions)));return computeWeightedLoss3(losses3,$weights,reduction)}var hingeLoss2=op2({hingeLoss_:hingeLoss_2});function huberLoss_2(labels,predictions,weights,delta=1,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","huberLoss"),$predictions=convertToTensor2(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","huberLoss")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar2(delta),error=abs2(sub2($predictions,$labels)),quadratic=minimum2(error,deltaScalar),linear=sub2(error,quadratic),losses3=add4(mul2(scalar2(.5),square2(quadratic)),mul2(deltaScalar,linear));return computeWeightedLoss3(losses3,$weights,reduction)}var huberLoss2=op2({huberLoss_:huberLoss_2});function logLoss_2(labels,predictions,weights,epsilon4=1e-7,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","logLoss"),$predictions=convertToTensor2(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","logLoss")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar2(1),epsilonScalar=scalar2(epsilon4),l13=neg2(mul2($labels,log2(add4($predictions,epsilonScalar)))),l23=mul2(sub2(one,$labels),log2(add4(sub2(one,$predictions),epsilonScalar))),losses3=sub2(l13,l23);return computeWeightedLoss3(losses3,$weights,reduction)}var logLoss2=op2({logLoss_:logLoss_2});function meanSquaredError_2(labels,predictions,weights,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor2(labels,"labels","meanSquaredError"),$predictions=convertToTensor2(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor2(weights,"weights","meanSquaredError")),assertShapesMatch2($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses3=squaredDifference4($labels,$predictions);return computeWeightedLoss3(losses3,$weights,reduction)}var meanSquaredError4=op2({meanSquaredError_:meanSquaredError_2});function sigmoidCrossEntropyWithLogits_2(labels,logits){let $labels=convertToTensor2(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor2(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch2($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu2($logits),outputXTarget=mul2($logits,$labels),sigmoidOutput=log1p2(exp2(neg2(abs2($logits))));return add4(sub2(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_2(multiClassLabels,logits,weights,labelSmoothing=0,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor2(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor2(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor2(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch2($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar2(labelSmoothing),one=scalar2(1),half=scalar2(.5);$multiClassLabels=add4(mul2($multiClassLabels,sub2(one,labelSmoothingScalar)),mul2(half,labelSmoothingScalar))}let losses3=sigmoidCrossEntropyWithLogits_2($multiClassLabels,$logits);return computeWeightedLoss3(losses3,$weights,reduction)}var sigmoidCrossEntropy2=op2({sigmoidCrossEntropy_:sigmoidCrossEntropy_2});function softmaxCrossEntropyWithLogits_2(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);let customOp=customGrad2((labels2,logits2,save)=>{let keepDims=!0,lse=logSumExp2(logits2,[dim],keepDims),logResult=sub2(cast2(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg2(mul2(logResult,labels2)),value=sum3(costVector,[dim]),gradFunc=(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim2(dy.shape,[dim]);return[mul2(reshape2(dy,dyShape),sub2(cast2(labels3,"float32"),exp2(logResult2))),mul2(reshape2(dy,dyShape),sub2(exp2(logResult2),cast2(labels3,"float32")))]};return{value,gradFunc}});return customOp(labels,logits)}function softmaxCrossEntropy_2(onehotLabels,logits,weights,labelSmoothing=0,reduction=Reduction2.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor2(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor2(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor2(weights,"weights","softmaxCrossEntropy")),assertShapesMatch2($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar2(labelSmoothing),one=scalar2(1),numClasses=scalar2($onehotLabels.shape[1]);$onehotLabels=add4(mul2($onehotLabels,sub2(one,labelSmoothingScalar)),div2(labelSmoothingScalar,numClasses))}let losses3=softmaxCrossEntropyWithLogits_2($onehotLabels,$logits);return computeWeightedLoss3(losses3,$weights,reduction)}var softmaxCrossEntropy2=op2({softmaxCrossEntropy_:softmaxCrossEntropy_2});var spectral2={fft:fft2,ifft:ifft2,rfft:rfft2,irfft:irfft2},signal2={hammingWindow:hammingWindow2,hannWindow:hannWindow2,frame:frame2,stft:stft2},image2={flipLeftRight:flipLeftRight3,resizeNearestNeighbor:resizeNearestNeighbor2,resizeBilinear:resizeBilinear3,rotateWithOffset:rotateWithOffset3,cropAndResize:cropAndResize3,nonMaxSuppression:nonMaxSuppression3,nonMaxSuppressionAsync:nonMaxSuppressionAsync2,nonMaxSuppressionWithScore:nonMaxSuppressionWithScore2,nonMaxSuppressionWithScoreAsync:nonMaxSuppressionWithScoreAsync2,nonMaxSuppressionPadded:nonMaxSuppressionPadded2,nonMaxSuppressionPaddedAsync:nonMaxSuppressionPaddedAsync2},linalg2={bandPart:bandPart2,gramSchmidt:gramSchmidt2,qr:qr2},losses2={absoluteDifference:absoluteDifference2,computeWeightedLoss:computeWeightedLoss3,cosineDistance:cosineDistance2,hingeLoss:hingeLoss2,huberLoss:huberLoss2,logLoss:logLoss2,meanSquaredError:meanSquaredError4,sigmoidCrossEntropy:sigmoidCrossEntropy2,softmaxCrossEntropy:softmaxCrossEntropy2};var SELU_SCALEALPHA2=1.7580993408473768,SELU_SCALE2=1.0507009873554805;var absGradConfig2={kernelName:Abs2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,step2(cast2(x,"float32"),-1))}}};var acosGradConfig2={kernelName:Acos2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square2(cast2(x,"float32")),b=sqrt2(sub2(scalar2(1),a));return neg2(div2(dy,b))}}}};var acoshGradConfig2={kernelName:Acosh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt2(sub2(square2(cast2(x,"float32")),1));return div2(dy,a)}}}};var addGradConfig2={kernelName:Add3,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,b.shape)};return{a:derA,b:derB}}};var addNGradConfig2={kernelName:AddN2,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}};var argMaxGradConfig2={kernelName:ArgMax2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike3(x)}}};var argMinGradConfig2={kernelName:ArgMin2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike3(x)}}};var asinGradConfig2={kernelName:Asin2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,sqrt2(sub2(scalar2(1),square2(cast2(x,"float32")))))}}};var asinhGradConfig2={kernelName:Asinh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt2(add4(scalar2(1),square2(cast2(x,"float32"))));return div2(dy,a)}}}};var atan2GradConfig2={kernelName:Atan22,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let d=add4(square2(a),square2(b)),res=mul2(dy,div2(b,d)),reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,a.shape)},derB=()=>{let d=add4(square2(a),square2(b)),res=neg2(mul2(dy,div2(a,d))),reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,b.shape)};return{a:derA,b:derB}}};var atanGradConfig2={kernelName:Atan3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,add4(square2(cast2(x,"float32")),1))}}};var atanhGradConfig2={kernelName:Atanh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,sub2(scalar2(1),square2(cast2(x,"float32"))))}}};function avgPool3dBackprop_2(dy,input2,filterSize,strides,dilations=[1,1,1],pad7,dimRoundingMode){let $dy=convertToTensor2(dy,"dy","avgPool3dBackprop"),$input=convertToTensor2(input2,"input","avgPool3dBackprop"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape2($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape2($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert3(dy5D.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert3(input5D.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=backend3=>{let convInfo=computePool3DInfo2(input5D.shape,filterSize,strides,dilations,pad7,dimRoundingMode);return backend3.avgPool3dBackprop(dy5D,input5D,convInfo)},inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad7,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,AvgPool3DBackprop2,attrs);return reshapedTo5D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dBackprop2=op2({avgPool3dBackprop_:avgPool3dBackprop_2});var avgPool3DGradConfig2={kernelName:AvgPool3D2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad7,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dBackprop2(dy,x,filterSize,strides,$dilations,pad7,dimRoundingMode)}}};function avgPoolBackprop_2(dy,input2,filterSize,strides,pad7){let $dy=convertToTensor2(dy,"dy","avgPoolBackprop"),$input=convertToTensor2(input2,"input","avgPoolBackprop");assert3($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape2($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape2($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert3(dy4D.rank===4,()=>`Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`),assert3(input4D.rank===4,()=>`Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);let forward=backend3=>{let convInfo=computePool2DInfo2(input4D.shape,filterSize,strides,1,pad7);return backend3.avgPoolBackprop(dy4D,input4D,convInfo)},inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad7},res=ENGINE2.runKernelFunc(forward,inputs,null,AvgPoolBackprop2,attrs);return reshapedTo4D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolBackprop4=op2({avgPoolBackprop_:avgPoolBackprop_2});var avgPoolGradConfig2={kernelName:AvgPool2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad7}=attrs;return{x:()=>avgPoolBackprop4(dy,x,filterSize,strides,pad7)}}};var batchMatMulGradConfig2={kernelName:BatchMatMul2,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul3(dy,b,!1,!0),b:()=>matMul3(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul3(dy,b,!1,!1),b:()=>matMul3(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul3(b,dy,!1,!0),b:()=>matMul3(a,dy,!1,!1)}:{a:()=>matMul3(b,dy,!0,!0),b:()=>matMul3(dy,a,!0,!0)}}};var batchToSpaceNDGradConfig2={kernelName:BatchToSpaceND2,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND3(dy,blockShape,crops)}}};var broadcastToGradConfig2={kernelName:BroadcastTo2,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum3(dy,axes,!0)}}};var castGradConfig2={kernelName:Cast2,gradFunc:dy=>({x:()=>dy.clone()})};var ceilGradConfig2={kernelName:Ceil2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var clipByValueGradConfig2={kernelName:ClipByValue2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where2(logicalAnd2(greaterEqual2(x,clipValueMin),lessEqual2(x,clipValueMax)),dy,zerosLike3(dy))}}};var concatGradConfig2={kernelName:Concat2,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam2(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]),derTensors=split2(dy,sizeSplits,$axis);return derTensors.map(t=>()=>t)}};var conv2DGradConfig2={kernelName:Conv2D3,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad7,dataFormat}=attrs;return assert3(tupleValuesAreOne2(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput4(x4D.shape,dy,$filter,strides,pad7,dataFormat),filter:()=>conv2DBackpropFilter3(x4D,dy,$filter.shape,strides,pad7,dataFormat)}}};var conv2DBackpropInputGradConfig2={kernelName:Conv2DBackpropInput2,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad7,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d2(ddx,filter,strides,pad7,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter3(ddx,dy,filter.shape,strides,pad7,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_2(x,dy,filterShape,strides,pad7){let x5D=x;x.rank===4&&(x5D=reshape2(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape2(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert3(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert3(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert3(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert3(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert3(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo2(x5D.shape,filterShape,strides,dilations,pad7);return backend3.conv3dDerFilter(x5D,dy5D,convInfo)},inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad7,filterShape};return ENGINE2.runKernelFunc(forward,inputs,null,Conv3DBackpropFilterV22,attrs)}var conv3DBackpropFilter2=op2({conv3DBackpropFilter_:conv3DBackpropFilter_2});var conv3DGradConfig2={kernelName:Conv3D3,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad7}=attrs;assert3(tupleValuesAreOne2(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput2(x5D.shape,dy,$filter,strides,pad7),filter:()=>conv3DBackpropFilter2(x5D,dy,$filter.shape,strides,pad7)}}};var cosGradConfig2={kernelName:Cos2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(neg2(sin2(cast2(x,"float32"))),dy)}}};var coshGradConfig2={kernelName:Cosh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(sinh2(cast2(x,"float32")),dy)}}};var cumsumGradConfig2={kernelName:Cumsum2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse8}=attrs;return{x:()=>{let permutation=getAxesPermutation2([axis],x.rank),out=cumsum2(dy,axis,exclusive,!reverse8);return permutation!=null&&(out=transpose2(out,permutation)),out}}}};var depthwiseConv2dNativeGradConfig2={kernelName:DepthwiseConv2dNative2,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad7,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert3(tupleValuesAreOne2($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert3(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert3(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert3(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert3(eitherStridesOrDilationsAreOne2(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`),{x:()=>depthwiseConv2dNativeBackpropInput3(x.shape,dy,filter,strides,pad7,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter3(x,dy,filter.shape,strides,pad7,dilations,dimRoundingMode)}}};var dilation2dGradConfig2={kernelName:Dilation2D2,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE2.runKernel(Dilation2DBackpropInput2,inputInputs,attrs),filter:()=>ENGINE2.runKernel(Dilation2DBackpropFilter2,filterInputs,attrs)}}};var divGradConfig2={kernelName:Div2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=div2(dy,cast2(b,"float32")),reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),a.shape):res},derB=()=>{let res=mul2(dy,cast2(a,"float32")),reduceAxes=getReductionAxes2(b.shape,outShape);reduceAxes.length>0&&(res=reshape2(sum3(res,reduceAxes),b.shape));let tmp=square2(b);return neg2(div2(res,cast2(tmp,"float32")))};return{a:derA,b:derB}}};var eluGradConfig2={kernelName:Elu3,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,backPropKernelFunc=backend3=>backend3.eluDer(dy,y),inputs={dy,y};return{x:()=>ENGINE2.runKernelFunc(backPropKernelFunc,inputs,null,EluGrad2)}}};var erfGradConfig2={kernelName:Erf2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul2(exp2(neg2(square2(x))),2/Math.sqrt(Math.PI));return{x:()=>mul2(dy,a)}}};var expGradConfig2={kernelName:Exp2,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul2(dy,y)}}};var expm1GradConfig2={kernelName:Expm12,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,exp2(x))}}};var floorGradConfig2={kernelName:Floor2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var floorDivGradConfig2={kernelName:FloorDiv2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=div2(dy,cast2(b,"float32")),reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),a.shape):res},derB=()=>{let res=mul2(dy,cast2(a,"float32")),reduceAxes=getReductionAxes2(b.shape,outShape);reduceAxes.length>0&&(res=reshape2(sum3(res,reduceAxes),b.shape));let tmp=square2(b);return neg2(div2(res,cast2(tmp,"float32")))};return{a:derA,b:derB}}};var fusedBatchNormGradConfig2={kernelName:FusedBatchNorm2,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean7,variance,scale3]=saved,scaleValue=scale3==null?scalar2(1):scale3,reductionAxes=getReductionAxes2(mean7.shape,x.shape),tileShape=[];if(mean7.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}let xMinusMean=sub2(x,mean7),dyTimesScaleValue=mul2(dy,scaleValue),oneOverSqrtVariance=rsqrt2(add4(variance,scalar2(varianceEpsilon))),minusHalfRCube=mul2(mul2(mul2(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar2(-.5)),derX=()=>mean7.rank===1?reshape2(mul2(mul2(dy,tile2(reshape2(oneOverSqrtVariance,[1,1,1,mean7.shape[0]]),tileShape)),scaleValue),x.shape):reshape2(mul2(mul2(dy,oneOverSqrtVariance),scaleValue),x.shape),derMean=()=>{let meanDer=mul2(mul2(oneOverSqrtVariance,scalar2(-1)),dyTimesScaleValue);return mean7.rank===1&&(meanDer=sum3(meanDer,reductionAxes)),reshape2(meanDer,mean7.shape)},derVariance=()=>{let varianceDer=mul2(mul2(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean7.rank===1&&(varianceDer=sum3(varianceDer,reductionAxes)),reshape2(varianceDer,mean7.shape)},derScale=()=>{let xMinusMean2TimesRsqrt=mul2(xMinusMean,oneOverSqrtVariance),scaleDer=mul2(dy,xMinusMean2TimesRsqrt);return mean7.rank===1&&(scaleDer=sum3(scaleDer,reductionAxes)),reshape2(scaleDer,mean7.shape)},derOffset=()=>{let offsetDer=dy;return mean7.rank===1&&(offsetDer=sum3(offsetDer,reductionAxes)),reshape2(offsetDer,mean7.shape)};return{x:derX,mean:derMean,variance:derVariance,scale:derScale,offset:derOffset}}};var gatherGradConfig2={kernelName:GatherV22,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam2(axis,x.shape)[0],derX=()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange2(0,outerDims),innerAxesIndices=arrayRange2(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat2([outerShape,[indicesSize],innerShape]),values=reshape2(dy,valuesShape),reshapedIndices=reshape2(indices,[indicesSize]),transposeDims=arrayConcat2([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose2(values,transposeDims),paramsGrad=unsortedSegmentSum2(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation2(transposeDims);return paramsGrad=transpose2(paramsGrad,invertTransposeDims),paramsGrad};return{x:derX,indices:()=>indices}}};function arrayRange2(start,stop){let result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat2(arrays){let result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}var greaterEqualGradConfig2={kernelName:GreaterEqual2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>zerosLike3(a),b:()=>zerosLike3(b)}}};var identityGradConfig2={kernelName:Identity3,gradFunc:dy=>({x:()=>cast2(dy,"float32")})};var isFiniteGradConfig2={kernelName:IsFinite2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var isInfGradConfig2={kernelName:IsInf2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var isNanGradConfig2={kernelName:IsNan2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var log1pGradConfig2={kernelName:Log1p2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,add4(x,1))}}};var logGradConfig2={kernelName:Log2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,cast2(x,"float32"))}}};var logSoftmaxGradConfig2={kernelName:LogSoftmax3,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax7=exp2(value);return sub2(dy,mul2(sum3(dy,axis,keepDims),softmax7))}}}};function localResponseNormalizationBackprop_2(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let forward=backend3=>backend3.LRNGrad(dy,x,y,depthRadius,bias,alpha,beta),inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE2.runKernelFunc(forward,inputs,null,LRNBackprop2,attrs)}var localResponseNormalizationBackprop2=op2({localResponseNormalizationBackprop_:localResponseNormalizationBackprop_2});var lrnGradConfig2={kernelName:LRN2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop2(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax2(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape2(y,expandShapeToKeepDim2(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape2(dy,expandShapeToKeepDim2(dy.shape,origAxes))),{x:()=>{let dx=mul2(dy,cast2(equal2(xOrig,y),dy.dtype));return dx}}}var maxGradConfig2={kernelName:Max2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam2(reductionIndices,x.shape),maxGrad=gradForMinAndMax2(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}};var maximumGradConfig2={kernelName:Maximum3,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul2(dy,cast2(greaterEqual2(a,b),"float32")),derB=()=>mul2(dy,cast2(less2(a,b),"float32"));return{a:derA,b:derB}}};function maxPool3dBackprop_2(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad7,dimRoundingMode){let $dy=convertToTensor2(dy,"dy","maxPool3dBackprop"),$input=convertToTensor2(input2,"input","maxPool3dBackprop"),$output=convertToTensor2(output,"output","maxPool3dBackprop"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape2($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape2($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape2($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert3(dy5D.rank===5,()=>`Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert3(input5D.rank===5,()=>`Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert3(output5D.rank===5,()=>`Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`),assert3(eitherStridesOrDilationsAreOne2(strides,dilations),()=>`Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=backend3=>{let convInfo=computePool3DInfo2(input5D.shape,filterSize,strides,dilations,pad7,dimRoundingMode);return backend3.maxPool3dBackprop(dy5D,input5D,output5D,convInfo)},inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad7,dimRoundingMode},res=ENGINE2.runKernelFunc(forward,inputs,null,MaxPool3DBackprop2,attrs);return reshapedTo5D?reshape2(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dBackprop2=op2({maxPool3dBackprop_:maxPool3dBackprop_2});var maxPool3DGradConfig2={kernelName:MaxPool3D2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad7,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dBackprop2(dy,x,y,filterSize,strides,$dilations,pad7,dimRoundingMode)}}};function maxPoolBackprop_2(dy,input2,output,filterSize,strides,pad7,dimRoundingMode){let $dy=convertToTensor2(dy,"dy","maxPoolBackprop"),$input=convertToTensor2(input2,"input","maxPoolBackprop"),$output=convertToTensor2(output,"output","maxPoolBackprop");assert3($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert3($dy.rank===4,()=>`Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`),assert3($input.rank===4,()=>`Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert3(isInt2(pad7),()=>`Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad7}.`);let forward=backend3=>{let convInfo=computePool2DInfo2($input.shape,filterSize,strides,1,pad7,dimRoundingMode);return backend3.maxPoolBackprop($dy,$input,$output,convInfo)},inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad7,dimRoundingMode};return ENGINE2.runKernelFunc(forward,inputs,null,MaxPoolBackprop2,attrs)}var maxPoolBackprop4=op2({maxPoolBackprop_:maxPoolBackprop_2});var maxPoolGradConfig2={kernelName:MaxPool2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad7}=attrs;return{x:()=>maxPoolBackprop4(dy,x,y,filterSize,strides,pad7)}}};var minGradConfig2={kernelName:Min2,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam2(axis,x.shape),minGrad=gradForMinAndMax2(dy,y,x,origAxes);return{x:()=>minGrad.x()}}};var minimumGradConfig2={kernelName:Minimum3,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul2(dy,cast2(lessEqual2(a,b),"float32")),derB=()=>mul2(dy,cast2(greater2(a,b),"float32"));return{a:derA,b:derB}}};var mirrorPadGradConfig2={kernelName:MirrorPad2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice2(dy,begin,x.shape)}}};var modGradConfig2={kernelName:Mod2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0?reshape2(sum3(dy,reduceAxes),a.shape):dy},derB=()=>{let res=mul2(dy,neg2(floor2(div2(a,b)))),reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}};var multiplyGradConfig2={kernelName:Multiply3,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=mul2(dy,cast2(b,"float32")),reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),a.shape):res},derB=()=>{let res=mul2(dy,cast2(a,"float32")),reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0?reshape2(sum3(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}};var negateGradConfig2={kernelName:Negate2,gradFunc:dy=>({x:()=>neg2(dy)})};var oneHotGradConfig2={kernelName:OneHot2,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros2(indices.shape,"float32")}}};var onesLikeGradConfig2={kernelName:OnesLike2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var padV2GradConfig2={kernelName:PadV22,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice2(dy,begin,x.shape)}}};var powGradConfig2={kernelName:Pow2,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp14=b,outShape=assertAndGetBroadcastShape2(base2.shape,exp14.shape),derBase=()=>{let expFloat=cast2(exp14,"float32"),res=mul2(dy,mul2(expFloat,pow2(base2,sub2(expFloat,scalar2(1))))),reduceAxes=getReductionAxes2(base2.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,base2.shape)},derExp=()=>{let condition=greater2(base2,0),logBase=where2(condition,log2(base2),zerosLike3(base2)),res=mul2(dy,mul2(y,logBase)),reduceAxes=getReductionAxes2(exp14.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,exp14.shape)};return{a:derBase,b:derExp}}};var preluGradConfig2={kernelName:Prelu2,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater2(x,0);return{x:()=>where2(mask,dy,mul2(dy,alpha)),alpha:()=>{let res=where2(mask,zerosLike3(dy),mul2(dy,x)),reduceAxes=getReductionAxes2(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,alpha.shape)}}}};var reciprocalGradConfig2={kernelName:Reciprocal2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,neg2(square2(x)))}}};var relu6GradConfig2={kernelName:Relu63,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul2(lessEqual2(x,6),step2(x));return{x:()=>mul2(dy,cast2(mask,"float32"))}}};var reluGradConfig2={kernelName:Relu3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,cast2(step2(x),"float32"))}}};var reshapeGradConfig2={kernelName:Reshape3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape2(dy,x.shape)}}};var resizeBilinearGradConfig2={kernelName:ResizeBilinear2,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeBilinearBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE2.runKernelFunc(backPropKernelFunc,inputs,null,ResizeBilinearGrad2,attrs);return{images:imagesDer}}};var resizeNearestNeighborGradConfig2={kernelName:ResizeNearestNeighbor2,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeNearestNeighborBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE2.runKernelFunc(backPropKernelFunc,inputs,null,ResizeNearestNeighborGrad2,attrs);return{images:imagesDer}}};var reverseGradConfig2={kernelName:Reverse2,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam2(dims,dy.shape);return{x:()=>reverse2(dy,axes)}}};var roundGradConfig2={kernelName:Round2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var rsqrtGradConfig2={kernelName:Rsqrt2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg2(div2(dy,mul2(pow2(x,1.5),2)))}}};var selectV2PoolGradConfig2={kernelName:SelectV22,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast2(zerosLike3(condition),"float32"),t:()=>mul2(dy,cast2(condition,dy.dtype)),e:()=>mul2(dy,cast2(logicalNot3(condition),dy.dtype))}}};var seluGradConfig2={kernelName:Selu3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater2(x,scalar2(0)),scaleAlpha2=scalar2(SELU_SCALEALPHA2),scale3=scalar2(SELU_SCALE2),greaterThanZeroDer=mul2(dy,scale3),lessEqualZeroDer=mul2(mul2(dy,scaleAlpha2),exp2(cast2(x,"float32")));return where2(mask,greaterThanZeroDer,lessEqualZeroDer)}}}};var sigmoidGradConfig2={kernelName:Sigmoid3,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul2(dy,mul2(y,sub2(scalar2(1),y)))}}};var signGradConfig2={kernelName:Sign2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var sinGradConfig2={kernelName:Sin2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(cos2(cast2(x,"float32")),dy)}}};var sinhGradConfig2={kernelName:Sinh2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(cosh2(cast2(x,"float32")),dy)}}};var sliceGradConfig2={kernelName:Slice2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams2(x,begin,size),paddings=[];for(let i=0;i<dy.rank;i++)paddings.push([begin_[i],inputShape[i]-begin_[i]-size_[i]]);return{x:()=>pad2(dy,paddings)}}};var softmaxGradConfig2={kernelName:Softmax4,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul2(dy,y);return{logits:()=>sub2(dyTimesY,mul2(sum3(dyTimesY,[dim],keepDims),y))}}};var softplusGradConfig2={kernelName:Softplus3,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,sigmoid3(x))}}};var spaceToBatchNDGradConfig2={kernelName:SpaceToBatchND2,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND2(dy,blockShape,paddings)}}};var splitVGradConfig2={kernelName:SplitV2,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat2(dy,axis)}}};var sqrtGradConfig2={kernelName:Sqrt2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,mul2(sqrt2(cast2(x,"float32")),2))}}};var squareGradConfig2={kernelName:Square2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul2(dy,mul2(cast2(x,"float32"),2))}}};var squaredDifferenceGradConfig2={kernelName:SquaredDifference2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar2(2),derA=()=>mul2(dy,mul2(two,sub2(a,b))),derB=()=>mul2(dy,mul2(two,sub2(b,a)));return{a:derA,b:derB}}};var stepGradConfig2={kernelName:Step2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var subGradConfig2={kernelName:Sub2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape2(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes2(a.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes2(b.shape,outShape);return reduceAxes.length>0&&(res=sum3(res,reduceAxes)),reshape2(neg2(res),b.shape)};return{a:derA,b:derB}}};var sumGradConfig2={kernelName:Sum2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs,axes=parseAxisParam2(axis,x.shape);axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape2(dy,expandedDyShape),derX=mul2(expandedDy,ones4(x.shape,"float32"));return{x:()=>derX}}};var tanGradConfig2={kernelName:Tan2,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div2(dy,square2(cos2(x)))}}};var tanhGradConfig2={kernelName:Tanh3,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul2(sub2(scalar2(1),square2(y)),dy)}}};var tileGradConfig2={kernelName:Tile2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs,derX=()=>{let xGrad=zerosLike3(x);if(x.rank===1)for(let i=0;i<reps[0];++i)xGrad=add4(xGrad,slice2(dy,[i*x.shape[0]],[x.shape[0]]));else if(x.rank===2)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)xGrad=add4(xGrad,slice2(dy,[i*x.shape[0],j*x.shape[1]],[x.shape[0],x.shape[1]]));else if(x.rank===3)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)xGrad=add4(xGrad,slice2(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2]],[x.shape[0],x.shape[1],x.shape[2]]));else if(x.rank===4)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)for(let l=0;l<reps[3];++l)xGrad=add4(xGrad,slice2(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2],l*x.shape[3]],[x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);return xGrad};return{x:derX}}};var transposeGradConfig2={kernelName:Transpose2,gradFunc:(dy,saved,attrs)=>{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation2(perm);return{x:()=>transpose2(dy,undoPerm)}}};var unpackGradConfig2={kernelName:Unpack2,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack2(dy,axis)}}};var unsortedSegmentSumGradConfig2={kernelName:UnsortedSegmentSum2,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved,derX=()=>gatherDropNegatives2(dy,segmentIds);return{x:derX}}};function gatherDropNegatives2(x,indices){let zeroClippedIndices=maximum2(indices,zerosLike3(indices)),gathered=gather2(x,zeroClippedIndices),isPositive=greaterEqual2(indices,scalar2(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims3(isPositive,i+1);isPositive=logicalAnd2(isPositive,ones4(gathered.shape,"bool"));let zeroSlice=zerosLike3(gathered);return where2(isPositive,gathered,zeroSlice)}var zerosLikeGradConfig2={kernelName:ZerosLike2,gradFunc:dy=>({x:()=>zerosLike3(dy)})};var gradConfigs2=[absGradConfig2,acosGradConfig2,acoshGradConfig2,addGradConfig2,addNGradConfig2,argMaxGradConfig2,argMinGradConfig2,asinGradConfig2,asinhGradConfig2,atan2GradConfig2,atanGradConfig2,atanhGradConfig2,avgPool3DGradConfig2,avgPoolGradConfig2,batchMatMulGradConfig2,batchToSpaceNDGradConfig2,broadcastToGradConfig2,castGradConfig2,ceilGradConfig2,clipByValueGradConfig2,concatGradConfig2,conv2DBackpropInputGradConfig2,conv2DGradConfig2,conv3DGradConfig2,cosGradConfig2,coshGradConfig2,cumsumGradConfig2,depthwiseConv2dNativeGradConfig2,dilation2dGradConfig2,divGradConfig2,eluGradConfig2,erfGradConfig2,expGradConfig2,expm1GradConfig2,floorDivGradConfig2,floorGradConfig2,fusedBatchNormGradConfig2,gatherGradConfig2,greaterEqualGradConfig2,identityGradConfig2,isFiniteGradConfig2,isInfGradConfig2,isNanGradConfig2,log1pGradConfig2,logGradConfig2,logSoftmaxGradConfig2,lrnGradConfig2,maxGradConfig2,maxGradConfig2,maximumGradConfig2,maxPool3DGradConfig2,maxPoolGradConfig2,minGradConfig2,minimumGradConfig2,mirrorPadGradConfig2,modGradConfig2,multiplyGradConfig2,negateGradConfig2,oneHotGradConfig2,onesLikeGradConfig2,padV2GradConfig2,padV2GradConfig2,powGradConfig2,preluGradConfig2,reciprocalGradConfig2,relu6GradConfig2,reluGradConfig2,reshapeGradConfig2,resizeBilinearGradConfig2,resizeNearestNeighborGradConfig2,reverseGradConfig2,roundGradConfig2,rsqrtGradConfig2,selectV2PoolGradConfig2,seluGradConfig2,sigmoidGradConfig2,signGradConfig2,sinGradConfig2,sinhGradConfig2,sliceGradConfig2,softmaxGradConfig2,softplusGradConfig2,spaceToBatchNDGradConfig2,spaceToBatchNDGradConfig2,splitVGradConfig2,splitVGradConfig2,sqrtGradConfig2,squaredDifferenceGradConfig2,squareGradConfig2,stepGradConfig2,subGradConfig2,sumGradConfig2,tanGradConfig2,tanhGradConfig2,tileGradConfig2,transposeGradConfig2,unpackGradConfig2,unsortedSegmentSumGradConfig2,zerosLikeGradConfig2];for(let gradientConfig of gradConfigs2)registerGradient2(gradientConfig);Tensor2.prototype.abs=function(){return this.throwIfDisposed(),abs2(this)};Tensor2.prototype.acos=function(){return this.throwIfDisposed(),acos2(this)};Tensor2.prototype.acosh=function(){return this.throwIfDisposed(),acosh2(this)};Tensor2.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict2(this,x)};Tensor2.prototype.add=function(b){return this.throwIfDisposed(),add4(this,b)};Tensor2.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all2(this,axis,keepDims)};Tensor2.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any2(this,axis,keepDims)};Tensor2.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax2(this,axis)};Tensor2.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin2(this,axis)};Tensor2.prototype.asScalar=function(){return this.throwIfDisposed(),assert3(this.size===1,()=>"The array must have only 1 element."),reshape2(this,[])};Tensor2.prototype.asType=function(dtype){return this.throwIfDisposed(),cast2(this,dtype)};Tensor2.prototype.as1D=function(){return this.throwIfDisposed(),reshape2(this,[this.size])};Tensor2.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape2(this,[rows,columns])};Tensor2.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape2(this,[rows,columns,depth])};Tensor2.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape2(this,[rows,columns,depth,depth2])};Tensor2.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape2(this,[rows,columns,depth,depth2,depth3])};Tensor2.prototype.asin=function(){return this.throwIfDisposed(),asin2(this)};Tensor2.prototype.asinh=function(){return this.throwIfDisposed(),asinh2(this)};Tensor2.prototype.atan=function(){return this.throwIfDisposed(),atan3(this)};Tensor2.prototype.atan2=function(b){return this.throwIfDisposed(),atan22(this,b)};Tensor2.prototype.atanh=function(){return this.throwIfDisposed(),atanh2(this)};Tensor2.prototype.avgPool=function(filterSize,strides,pad7,dimRoundingMode){return this.throwIfDisposed(),avgPool5(this,filterSize,strides,pad7,dimRoundingMode)};Tensor2.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND2(this,blockShape,crops)};Tensor2.prototype.batchNorm=function(mean7,variance,offset,scale3,varianceEpsilon){return this.throwIfDisposed(),batchNorm4(this,mean7,variance,offset,scale3,varianceEpsilon)};Tensor2.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo2(this,shape)};Tensor2.prototype.cast=function(dtype){return this.throwIfDisposed(),cast2(this,dtype)};Tensor2.prototype.ceil=function(){return this.throwIfDisposed(),ceil2(this)};Tensor2.prototype.clipByValue=function(min9,max11){return this.throwIfDisposed(),clipByValue2(this,min9,max11)};Tensor2.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor2&&(x=[x]),concat2([this,...x],axis)};Tensor2.prototype.conv1d=function(filter,stride,pad7,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d2(this,filter,stride,pad7,dataFormat,dilation,dimRoundingMode)};Tensor2.prototype.conv2dTranspose=function(filter,outputShape,strides,pad7,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose3(this,filter,outputShape,strides,pad7,dimRoundingMode)};Tensor2.prototype.conv2d=function(filter,strides,pad7,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d2(this,filter,strides,pad7,dataFormat,dilations,dimRoundingMode)};Tensor2.prototype.cos=function(){return this.throwIfDisposed(),cos2(this)};Tensor2.prototype.cosh=function(){return this.throwIfDisposed(),cosh2(this)};Tensor2.prototype.cumsum=function(axis,exclusive,reverse8){return this.throwIfDisposed(),cumsum2(this,axis,exclusive,reverse8)};Tensor2.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace3(this,blockSize,dataFormat)};Tensor2.prototype.depthwiseConv2D=function(filter,strides,pad7,dataFormat,dilations,dimRoundingMode){return deprecationWarn2("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d6(this,filter,strides,pad7,dataFormat,dilations,dimRoundingMode)};Tensor2.prototype.depthwiseConv2d=function(filter,strides,pad7,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d6(this,filter,strides,pad7,dataFormat,dilations,dimRoundingMode)};Tensor2.prototype.dilation2d=function(filter,strides,pad7,dilations,dataFormat){return this.throwIfDisposed(),dilation2d2(this,filter,strides,pad7,dilations,dataFormat)};Tensor2.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan2(this,b)};Tensor2.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict2(this,x)};Tensor2.prototype.div=function(b){return this.throwIfDisposed(),div2(this,b)};Tensor2.prototype.dot=function(b){return this.throwIfDisposed(),dot2(this,b)};Tensor2.prototype.elu=function(){return this.throwIfDisposed(),elu2(this)};Tensor2.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict2(this,x)};Tensor2.prototype.equal=function(b){return this.throwIfDisposed(),equal2(this,b)};Tensor2.prototype.erf=function(){return this.throwIfDisposed(),erf2(this)};Tensor2.prototype.exp=function(){return this.throwIfDisposed(),exp2(this)};Tensor2.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims3(this,axis)};Tensor2.prototype.expm1=function(){return this.throwIfDisposed(),expm12(this)};Tensor2.prototype.fft=function(){return this.throwIfDisposed(),fft2(this)};Tensor2.prototype.flatten=function(){return this.throwIfDisposed(),reshape2(this,[this.size])};Tensor2.prototype.floor=function(){return this.throwIfDisposed(),floor2(this)};Tensor2.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv2(this,b)};Tensor2.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather2(this,indices,axis)};Tensor2.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict2(this,x)};Tensor2.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual2(this,b)};Tensor2.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict2(this,x)};Tensor2.prototype.greater=function(b){return this.throwIfDisposed(),greater2(this,b)};Tensor2.prototype.ifft=function(){return this.throwIfDisposed(),ifft2(this)};Tensor2.prototype.irfft=function(){return this.throwIfDisposed(),irfft2(this)};Tensor2.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite4(this)};Tensor2.prototype.isInf=function(){return this.throwIfDisposed(),isInf3(this)};Tensor2.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN4(this)};Tensor2.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu2(this,alpha)};Tensor2.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict2(this,x)};Tensor2.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual2(this,b)};Tensor2.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict2(this,x)};Tensor2.prototype.less=function(b){return this.throwIfDisposed(),less2(this,b)};Tensor2.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization2(this,depthRadius,bias,alpha,beta)};Tensor2.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid2(this)};Tensor2.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax2(this,axis)};Tensor2.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp2(this,axis,keepDims)};Tensor2.prototype.log=function(){return this.throwIfDisposed(),log2(this)};Tensor2.prototype.log1p=function(){return this.throwIfDisposed(),log1p2(this)};Tensor2.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd2(this,b)};Tensor2.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot3(this)};Tensor2.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr2(this,b)};Tensor2.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor2(this,b)};Tensor2.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul3(this,b,transposeA,transposeB)};Tensor2.prototype.maxPool=function(filterSize,strides,pad7,dimRoundingMode){return this.throwIfDisposed(),maxPool5(this,filterSize,strides,pad7,dimRoundingMode)};Tensor2.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max2(this,axis,keepDims)};Tensor2.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict2(this,x)};Tensor2.prototype.maximum=function(b){return this.throwIfDisposed(),maximum2(this,b)};Tensor2.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean2(this,axis,keepDims)};Tensor2.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min2(this,axis,keepDims)};Tensor2.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict2(this,x)};Tensor2.prototype.minimum=function(b){return this.throwIfDisposed(),minimum2(this,b)};Tensor2.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad3(this,paddings,mode)};Tensor2.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict2(this,x)};Tensor2.prototype.mod=function(b){return this.throwIfDisposed(),mod2(this,b)};Tensor2.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict2(this,x)};Tensor2.prototype.mul=function(b){return this.throwIfDisposed(),mul2(this,b)};Tensor2.prototype.neg=function(){return this.throwIfDisposed(),neg2(this)};Tensor2.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm2(this,ord,axis,keepDims)};Tensor2.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict2(this,x)};Tensor2.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual4(this,b)};Tensor2.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot3(this,depth,onValue,offValue)};Tensor2.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike3(this)};Tensor2.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad2(this,paddings,constantValue)};Tensor2.prototype.pool=function(windowShape,poolingType,padding,dilationRate,strides){return this.throwIfDisposed(),pool2(this,windowShape,poolingType,padding,dilationRate,strides)};Tensor2.prototype.powStrict=function(exp14){return this.throwIfDisposed(),powStrict2(this,exp14)};Tensor2.prototype.pow=function(exp14){return this.throwIfDisposed(),pow2(this,exp14)};Tensor2.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu2(this,alpha)};Tensor2.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod2(this,axis,keepDims)};Tensor2.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal2(this)};Tensor2.prototype.relu=function(){return this.throwIfDisposed(),relu2(this)};Tensor2.prototype.relu6=function(){return this.throwIfDisposed(),relu62(this)};Tensor2.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape2(this,x.shape)};Tensor2.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape2(this,shape)};Tensor2.prototype.resizeBilinear=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeBilinear3(this,newShape2D,alignCorners)};Tensor2.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeNearestNeighbor2(this,newShape2D,alignCorners)};Tensor2.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse2(this,axis)};Tensor2.prototype.rfft=function(){return this.throwIfDisposed(),rfft2(this)};Tensor2.prototype.round=function(){return this.throwIfDisposed(),round3(this)};Tensor2.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt2(this)};Tensor2.prototype.selu=function(){return this.throwIfDisposed(),selu2(this)};Tensor2.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad7,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d3(this,depthwiseFilter,pointwiseFilter,strides,pad7,dilation,dataFormat)};Tensor2.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid3(this)};Tensor2.prototype.sign=function(){return this.throwIfDisposed(),sign2(this)};Tensor2.prototype.sin=function(){return this.throwIfDisposed(),sin2(this)};Tensor2.prototype.sinh=function(){return this.throwIfDisposed(),sinh2(this)};Tensor2.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice2(this,begin,size)};Tensor2.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax2(this,dim)};Tensor2.prototype.softplus=function(){return this.throwIfDisposed(),softplus2(this)};Tensor2.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND3(this,blockShape,paddings)};Tensor2.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split2(this,numOrSizeSplits,axis)};Tensor2.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt2(this)};Tensor2.prototype.square=function(){return this.throwIfDisposed(),square2(this)};Tensor2.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference4(this,b)};Tensor2.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict2(this,x)};Tensor2.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze2(this,axis)};Tensor2.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor2?[this,x]:[this,...x];return stack2(tensorsToBeStacked,axis)};Tensor2.prototype.step=function(alpha){return this.throwIfDisposed(),step2(this,alpha)};Tensor2.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice3(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor2.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict2(this,x)};Tensor2.prototype.sub=function(b){return this.throwIfDisposed(),sub2(this,b)};Tensor2.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum3(this,axis,keepDims)};Tensor2.prototype.tan=function(){return this.throwIfDisposed(),tan2(this)};Tensor2.prototype.tanh=function(){return this.throwIfDisposed(),tanh3(this)};Tensor2.prototype.tile=function(reps){return this.throwIfDisposed(),tile2(this,reps)};Tensor2.prototype.toBool=function(){return this.throwIfDisposed(),cast2(this,"bool")};Tensor2.prototype.toFloat=function(){return this.throwIfDisposed(),cast2(this,"float32")};Tensor2.prototype.toInt=function(){return this.throwIfDisposed(),cast2(this,"int32")};Tensor2.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk2(this,k,sorted)};Tensor2.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose2(this,perm)};Tensor2.prototype.unique=function(axis){return this.throwIfDisposed(),unique2(this,axis)};Tensor2.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum2(this,segmentIds,numSegments)};Tensor2.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack2(this,axis)};Tensor2.prototype.where=function(condition,x){return this.throwIfDisposed(),where2(condition,this,x)};Tensor2.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike3(this)};function imageToSquare(input2,inputSize,centerImage=!1){let{Image,Canvas}=env2.getEnv();if(!(input2 instanceof Image||input2 instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input2),scale3=inputSize/Math.max(dims.height,dims.width),width=scale3*dims.width,height=scale3*dims.height,targetCanvas=createCanvas2({width:inputSize,height:inputSize}),inputCanvas=input2 instanceof Canvas?input2:createCanvasFromMedia(input2),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;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((input2,idx)=>{if(isTensor3D(input2)){this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape;return}if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`);this._imageTensors[idx]=input2,this._inputDimensions[idx]=input2.shape.slice(1);return}let canvas=input2 instanceof env2.getEnv().Canvas?input2:createCanvasFromMedia(input2);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 range2(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");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tidy2(()=>{let inputTensors=range2(this.batchSize,0,1).map(batchIdx=>{let input2=this.getInput(batchIdx);if(input2 instanceof Tensor2){let imgTensor=isTensor4D(input2)?input2:input2.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=image2.resizeBilinear(imgTensor,[inputSize,inputSize])),imgTensor.as3D(inputSize,inputSize,3)}if(input2 instanceof env2.getEnv().Canvas)return browser_exports2.fromPixels(imageToSquare(input2,inputSize,isCenterInputs));throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input2}`)}),batchTensor=stack2(inputTensors.map(t=>cast2(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3);return batchTensor})}};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");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input2,i)=>{if(!isMediaElement(input2)&&!isTensor3D(input2)&&!isTensor4D(input2))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input2)){let batchSize=input2.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input2=>isMediaElement(input2)&&awaitMediaLoaded(input2))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input2,detections){let{Canvas}=env2.getEnv(),canvas=input2;if(!(input2 instanceof Canvas)){let netInput=await toNetInput(input2);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height));return boxes.map(({x,y,width,height})=>{let faceImg=createCanvas2({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)),faceTensors=boxes.map(({x,y,width,height})=>slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]));return faceTensors})}async function fetchOrThrow(url,init2){let fetch3=env2.getEnv().fetch,res=await fetch3(url,init2);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),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: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return io_exports.loadWeights(manifest,modelBaseUri)}function matchDimensions(input2,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input2.width=width,input2.height=height,{width,height}}var NeuralNetwork=class{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){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor163){let{obj,objProp}=this.traversePropertyPath(paramPath);obj[objProp].dispose(),obj[objProp]=tensor163}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:tensor163})=>{this.reassignParamFromPath(path,tensor163.variable())})}freeze(){this.getTrainableParams().forEach(({path,tensor:variable4})=>{let tensor163=tensor4(variable4.dataSync());variable4.dispose(),this.reassignParamFromPath(path,tensor163)})}dispose(throwOnRedispose=!0){this.getParamList().forEach(param=>{if(throwOnRedispose&&param.tensor.isDisposed)throw new Error(`param tensor has already been disposed for path ${param.path}`);param.tensor.dispose()}),this._params=void 0}serializeParams(){return new Float32Array(this.getParamList().map(({tensor:tensor163})=>Array.from(tensor163.dataSync())).reduce((flat,arr)=>flat.concat(arr)))}async load(weightsOrUrl){if(weightsOrUrl instanceof Float32Array){this.extractWeights(weightsOrUrl);return}await this.loadFromUri(weightsOrUrl)}async loadFromUri(uri){if(uri&&typeof uri!="string")throw new Error(`${this._name}.loadFromUri - expected model uri`);let 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`);let{readFile}=env2.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(filePath2=>readFile(filePath2).then(buf=>buf.buffer))),loadWeights2=io_exports.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights2(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{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");let 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}),{obj,objProp}=result;if(!obj||!objProp||!(obj[objProp]instanceof Tensor))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`);return{obj,objProp}}};function depthwiseSeparableConv(x,params,stride){return tidy(()=>{let out=separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=add2(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return relu(add2(out1,add2(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tidy(()=>{let out1=relu(isFirstLayer?add2(conv2d(x,denseBlockParams.conv0.filters,isScaleDown?[2,2]:[1,1],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,isScaleDown?[2,2]:[1,1])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=relu(add2(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=relu(add2(out1,add2(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return relu(add2(out1,add2(out2,add2(out3,out4))))})}function convLayer(x,params,padding="same",withRelu=!1){return tidy(()=>{let out=add2(conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}function extractConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,filterSize,mappedPrefix){let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}function extractFCParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let fc_weights=tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let depthwise_filter=tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return function(prefix){let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return function(originalPath,paramRank,mappedPath){let tensor163=weightMap[originalPath];if(!isTensor(tensor163,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor163}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor163}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return function(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=avgPool(out,[7,7],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap(weightMap)}extractParams(weights){return extractParams(weights)}};function fullyConnectedLayer(x,params){return tidy(()=>add2(matMul(x,params.weights),params.bias))}function extractParams3(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),fc=extractFCParams(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap3(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams3(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap3(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{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)}},FaceExpressionNet=class extends FaceProcessor{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceExpressionNet",faceFeatureExtractor)}forwardInput(input2){return tidy(()=>softmax(this.runNet(input2)))}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictExpressions(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){let extension={expressions};return Object.assign({},sourceObj,extension)}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){let faceExpressionsArray=Array.isArray(faceExpressions)?faceExpressions:[faceExpressions];faceExpressionsArray.forEach(e=>{let 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");let sorted=expr.asSortedArray(),resultsToDisplay=sorted.filter(expr2=>expr2.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0),drawTextField=new DrawTextField(resultsToDisplay.map(expr2=>`${expr2.expression} (${round2(expr2.probability)})`),anchor);drawTextField.draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims),extension={landmarks,unshiftedLandmarks,alignedRect};return Object.assign({},sourceObj,extension)}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,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)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{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(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let 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){let faceLandmarksArray=Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks];faceLandmarksArray.forEach(f=>{let 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)})}function extractorsFactory3(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams5(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory3(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range2(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),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: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory3(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap5(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory3(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range2(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return add2(conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=maxPool(out,[3,3],[2,2],"same"),out=add2(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(relu(out),params.separable_conv2,[1,1]),out=add2(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(scalar(256)),out=relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range2(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap5(weightMap,this._numMainBlocks)}extractParams(weights){return extractParams5(weights,this._numMainBlocks)}};function extractParams7(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap7(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input2){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tidy(()=>{let bottleneckFeatures=input2 instanceof NetInput?this.faceFeatureExtractor.forwardInput(input2):input2,pooled=avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input2){return tidy(()=>{let{age,gender}=this.runNet(input2);return{age,gender:softmax(gender)}})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async predictAgeAndGender(input2){let netInput=await toNetInput(input2),out=await this.forwardInput(netInput),ages=unstack(out.age),genders=unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams7(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap7(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale3=inputSize/Math.max(height,width);return{width:width*scale3,height:height*scale3}}),batchSize=inputDimensions.length;return tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>stack([fill([68],fillX,"float32"),fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding2=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding2(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding2(batchIdx,(w,h)=>h<w),landmarkTensors=output.mul(fill([batchSize,136],inputSize,"float32")).sub(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(getPaddingX(batchIdx),getPaddingY(batchIdx))))).div(stack(Array.from(Array(batchSize),(_,batchIdx)=>createInterleavedTensor(inputDimensions[batchIdx].width,inputDimensions[batchIdx].height))));return landmarkTensors})}forwardInput(input2){return tidy(()=>{let out=this.runNet(input2);return this.postProcess(out,input2.inputSize,input2.inputDimensions.map(([height,width])=>({height,width})))})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async detectLandmarks(input2){let netInput=await toNetInput(input2),landmarkTensors=tidy(()=>unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven2(i)),yCoords=landmarksArray.filter((_,i)=>!isEven2(i));return new FaceLandmarks68(Array(68).fill(0).map((_,i)=>new Point(xCoords[i],yCoords[i])),{height:netInput.getInputHeight(batchIdx),width:netInput.getInputWidth(batchIdx)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input2){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=avgPool(out,[14,14],[2,2],"valid"),out})}async forward(input2){return this.forwardInput(await toNetInput(input2))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMapTiny(weightMap)}extractParams(weights){return extractParamsTiny(weights)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};function scale2(x,params){return add2(mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=conv2d(x,filters,strides,padding);return out=add2(out,bias),out=scale2(out,params.scale),withRelu?relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}function extractorsFactory5(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),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: ${filterSize}`);return tidy(()=>transpose(tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tensor1d(extractWeights(numWeights)),biases=tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale3=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale3}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams9(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory5(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tidy(()=>transpose(tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return{params,paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale3=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale3}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap9(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory6(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),fc=weightMap.fc;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let 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 disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=add2(out,x),out=relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=avgPool(x,2,2,"valid"),zeros10=zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3],isAdjustShape=pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2];if(isAdjustShape){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=zeros(padShapeX);out=concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=zeros(padShapeY);out=concat([out,zerosH],2)}return pooled=isPad?concat([pooled,zeros10],3):pooled,out=add2(pooled,out),out=relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input2){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(150,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(scalar(256)),out=convDown(normalized,params.conv32_down);out=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);let globalAvg=out.mean([1,2]),fullyConnected=matMul(globalAvg,params.fc);return fullyConnected})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async computeFaceDescriptor(input2){let netInput=await toNetInput(input2),faceDescriptorTensors=tidy(()=>unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap9(weightMap)}extractParams(weights){return extractParams9(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){let extension={descriptor};return Object.assign({},sourceObj,extension)}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){let extension={age};return Object.assign({},sourceObj,extension)}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){let extension={gender,genderProbability};return Object.assign({},sourceObj,extension)}function extractorsFactory7(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tensor1d(extractWeights(numChannels)),batch_norm_offset=tensor1d(extractWeights(numChannels)),batch_norm_mean=tensor1d(extractWeights(numChannels)),batch_norm_variance=tensor1d(extractWeights(numChannels));return 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`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),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(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),box_predictor_0={box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1={box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2={box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3={box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4={box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},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){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory7(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),extra_dim=tensor3d(extractWeights(5118*4),[1,5118,4]),output_layer={extra_dim};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${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){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory8(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function pointwiseConvLayer(x,params,strides){return tidy(()=>{let out=conv2d(x,params.filters,strides,"same");return out=add2(out,params.batch_norm_offset),clipByValue(out,0,6)})}var epsilon3=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tidy(()=>{let out=depthwiseConv2d(x,params.filters,strides,"same");return out=batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon3),clipByValue(out,0,6)})}function getStridesForLayerIdx(layerIdx){return[2,4,6,12].some(idx=>idx===layerIdx)?[2,2]:[1,1]}function mobileNetV1(x,params){return tidy(()=>{let conv11,out=pointwiseConvLayer(x,params.conv_0,[2,2]),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];if(convPairParams.forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function nonMaxSuppression4(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou3=IOU(boxes,c.boxIndex,selected[j]);if(iou3===0)continue;if(c.score*=suppressFunc(iou3),c.score<=scoreThreshold)break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function getCenterCoordinatesAndSizesLayer(x){let vec=unstack(transpose(x,[1,0])),sizes=[sub(vec[2],vec[0]),sub(vec[3],vec[1])],centers=[add2(vec[0],div(sizes[0],scalar(2))),add2(vec[1],div(sizes[1],scalar(2)))];return{sizes,centers}}function decodeBoxesLayer(x0,x1){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=unstack(transpose(x1,[1,0])),div0_out=div(mul(exp(div(vec[2],scalar(5))),sizes[0]),scalar(2)),add0_out=add2(mul(div(vec[0],scalar(10)),sizes[0]),centers[0]),div1_out=div(mul(exp(div(vec[3],scalar(5))),sizes[1]),scalar(2)),add1_out=add2(mul(div(vec[1],scalar(10)),sizes[1]),centers[1]);return transpose(stack([sub(add0_out,div0_out),sub(add1_out,div1_out),add2(add0_out,div0_out),add2(add1_out,div1_out)]),[1,0])}function outputLayer(boxPredictions,classPredictions,params){return tidy(()=>{let batchSize=boxPredictions.shape[0],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]);let scoresAndClasses=sigmoid(slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=unstack(boxes),scoresByBatch=unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}function boxPredictionLayer(x,params){return tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||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}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input2){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(512,!1),"float32"),x=sub(mul(batchTensor,scalar(.007843137718737125)),scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{boxPredictions,classPredictions}=predictionLayer(features.out,features.conv11,params.prediction_layer);return outputLayer(boxPredictions,classPredictions,params.output_layer)})}async forward(input2){return this.forwardInput(await toNetInput(input2))}async locateFaces(input2,options={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input2),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),iouThreshold=.5,indices=nonMaxSuppression4(boxes,scoresData,maxResults,iouThreshold,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[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)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap11(weightMap)}extractParams(weights){return extractParams11(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.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)],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)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME2="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",isNumber3=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(!isNumber3(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=>isNumber3(a.x)&&isNumber3(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(isNumber3)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config2.meanRgb)}`)}function leaky(x){return tidy(()=>{let min9=mul(x,scalar(.10000000149011612));return add2(relu(sub(x,min9)),min9)})}function convWithBatchNorm(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=conv2d(out,params.conv.filters,[1,1],"valid"),out=sub(out,params.bn.sub),out=mul(out,params.bn.truediv),out=add2(out,params.conv.bias),leaky(out)})}function depthwiseSeparableConv3(x,params){return tidy(()=>{let out=pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=add2(out,params.bias),leaky(out)})}function extractorsFactory9(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub30=tensor1d(extractWeights(size)),truediv=tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub30,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams13(weights,config2,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory9(extractWeights,paramMappings),params;if(config2.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config2.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),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: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory10(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub30=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub30,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap13(weightMap,config2){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory10(weightMap,paramMappings),params;if(config2.withSeparableConvs){let 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")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}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={}));var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.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}},TinyYolov2Base2=class 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);return out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=convWithBatchNorm(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=convWithBatchNorm(out,params.conv6),out=convWithBatchNorm(out,params.conv7),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv3(x,params.conv0);return out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv1),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv2),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv3),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv4),out=maxPool(out,[2,2],[2,2],"same"),out=depthwiseSeparableConv3(out,params.conv5),out=maxPool(out,[2,2],[1,1],"same"),out=params.conv6?depthwiseSeparableConv3(out,params.conv6):out,out=params.conv7?depthwiseSeparableConv3(out,params.conv7):out,convLayer(out,params.conv8,"valid",!1)}forwardInput(input2,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tidy(()=>{let batchTensor=cast(input2.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(scalar(256)),this.config.withSeparableConvs?this.runMobilenet(batchTensor,params):this.runTinyYolov2(batchTensor,params)})}async forward(input2,inputSize){return await this.forwardInput(await toNetInput(input2),inputSize)}async detect(input2,forwardParams={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input2),out=await this.forwardInput(netInput,inputSize),out0=tidy(()=>unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]),indices=nonMaxSuppression2(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0),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){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,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){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),classScores=this.withClassScores?softmax(reshaped.slice([0,0,0,5],[numCells,numCells,numBoxes,this.config.classes.length]),3):scalar(0);return[boxes,scores,classScores]}),results=[],scoresData=await scoresTensor.array(),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++){let score=sigmoid2(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid2(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid2(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,width2=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,height2=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-width2/2,y=ctY-height2/2,pos={row,col,anchor},{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})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,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((max11,curr)=>max11.classScore>curr.classScore?max11:curr)}},TinyYolov2Base=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config2=Object.assign({},{withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"]},withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0});super(config2)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){let objectDetections=await this.detect(input2,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_NAME2}extractParamsFromWeigthMap(weightMap){return super.extractParamsFromWeigthMap(weightMap)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function extractAllFacesAndComputeResults(parentResults,input2,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input2 instanceof Tensor?await extractFaceTensors(input2,faceBoxes):await extractFaces(input2,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input2,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input2,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,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)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config2={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config2)}get anchors(){return this.config.anchors}async locateFaces(input2,forwardParams){let objectDetections=await this.detect(input2,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)}},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},ssdMobilenetv1=(input2,options)=>nets.ssdMobilenetv1.locateFaces(input2,options),tinyFaceDetector=(input2,options)=>nets.tinyFaceDetector.locateFaces(input2,options),tinyYolov23=(input2,options)=>nets.tinyYolov2.locateFaces(input2,options),detectFaceLandmarks=input2=>nets.faceLandmark68Net.detectLandmarks(input2),detectFaceLandmarksTiny=input2=>nets.faceLandmark68TinyNet.detectLandmarks(input2),computeFaceDescriptor=input2=>nets.faceRecognitionNet.computeFaceDescriptor(input2),recognizeFaceExpressions=input2=>nets.faceExpressionNet.predictExpressions(input2),predictAgeAndGender=input2=>nets.ageGenderNet.predictAgeAndGender(input2),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,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)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input2,extractedFaces){super();this.parentTask=parentTask;this.input=input2;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,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)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{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)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input2){super();this.parentTask=parentTask;this.input=input2}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask,descriptors=await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}));return descriptors.map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class extends ComposableTask{constructor(parentTask,input2,useTinyLandmarkNet){super();this.parentTask=parentTask;this.input=input2;this.useTinyLandmarkNet=useTinyLandmarkNet}get landmarkNet(){return this.useTinyLandmarkNet?nets.faceLandmark68TinyNet:nets.faceLandmark68Net}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof Tensor&&f.dispose()),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)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input2,options=new SsdMobilenetv1Options){super();this.input=input2;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input:input2,options}=this,faceDetectionFunction=options instanceof TinyFaceDetectorOptions?input3=>nets.tinyFaceDetector.locateFaces(input3,options):options instanceof SsdMobilenetv1Options?input3=>nets.ssdMobilenetv1.locateFaces(input3,options):options instanceof TinyYolov2Options?input3=>nets.tinyYolov2.locateFaces(input3,options):null;if(!faceDetectionFunction)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return faceDetectionFunction(input2)}runAndExtendWithFaceDetections(){return new Promise(async res=>{let detections=await this.run();return res(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async res=>{let detection=await this.run();return res(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input2,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input2,options)}function detectAllFaces(input2,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input2,options)}async function allFacesSsdMobilenetv1(input2,minConfidence){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input2,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input2,forwardParams={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input2,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+Math.pow(diff,2),0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count2=1,createUniqueLabel=()=>`person ${count2++}`;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){let 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(json18){let labeledDescriptors=json18.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json18.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{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)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var version3="0.8.9",node=typeof process!="undefined",browser=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version4={faceapi:version3,node,browser};return src_exports;})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/face-api.js.map b/dist/face-api.js.map
index ca6075c..31f7338 100644
--- a/dist/face-api.js.map
+++ b/dist/face-api.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["../node_modules/node-fetch/browser.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/util_base.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/environment.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/global_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/profiler.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tape.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/types.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/engine.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/device_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/flags.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/types.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js", 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"../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sign.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sin.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sinh.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/softmax.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/softplus.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/space_to_batch_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/split.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sqrt.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/square.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/squared_difference.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/squared_difference_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/squeeze.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/stack.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/step.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/strided_slice.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sub_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sub.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sum.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/tan.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/tile.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/to_bool.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/to_float.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/to_int.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/topk.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/unique.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/unsorted_segment_sum.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/unstack.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/where.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/zeros_like.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts", "../node_modules/@tensorflow/tfjs-core/src/index.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
-  "sourcesContent": ["\"use strict\";\n\n// ref: https://github.com/tc39/proposal-global\nvar getGlobal = function () {\n\t// the only reliable means to get the global object is\n\t// `Function('return this')()`\n\t// However, this causes CSP violations in Chrome apps.\n\tif (typeof self !== 'undefined') { return self; }\n\tif (typeof window !== 'undefined') { return window; }\n\tif (typeof global !== 'undefined') { return global; }\n\tthrow new Error('unable to locate global object');\n}\n\nvar global = getGlobal();\n\nmodule.exports = exports = global.fetch;\n\n// Needed for TypeScript and Webpack.\nif (global.fetch) {\n\texports.default = global.fetch.bind(global);\n}\n\nexports.Headers = global.Headers;\nexports.Request = global.Request;\nexports.Response = global.Response;", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n/**\n * This method asserts whether an object is a Promise instance.\n * @param object\n */\n// tslint:disable-next-line: no-any\nexport function isPromise(object) {\n    //  We chose to not use 'obj instanceOf Promise' for two reasons:\n    //  1. It only reliably works for es6 Promise, not other Promise\n    //  implementations.\n    //  2. It doesn't work with framework that uses zone.js. zone.js monkey patch\n    //  the async calls, so it is possible the obj (patched) is comparing to a\n    //  pre-patched Promise.\n    return object && object.then && typeof object.then === 'function';\n}\n//# sourceMappingURL=util_base.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isPromise } from './util_base';\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (isPromise(flagValue)) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const MirrorPad = 'MirrorPad';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport * as base from './util_base';\nexport * from './util_base';\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = base.flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        base.checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { isPromise } from '../util';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (isPromise(result)) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n                realTensor.dispose();\n                imageTensor.dispose();\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        save([$a, $b]);\n        const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n        const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n        const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n        const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n        const outerDimsA = $a.shape.slice(0, -2);\n        const outerDimsB = $b.shape.slice(0, -2);\n        const batchDimA = util.sizeFromShape(outerDimsA);\n        const batchDimB = util.sizeFromShape(outerDimsB);\n        const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n        util.assert($a.rank >= 2 && $b.rank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the ` +\n            `same or at least one input batch dimension must be 1. Got input ` +\n            `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n        util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n            `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n            `${$b.shape} and transposeA=${transposeA}` +\n            ` and transposeB=${transposeB} must match.`);\n        const outShapeOuterDims = batchDimA > batchDimB ? outerDimsA : outerDimsB;\n        const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n        const a3D = transposeA ?\n            reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n            reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n        const b3D = transposeB ?\n            reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n            reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n        const res3d = backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n        return reshape(res3d, outShape);\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = { transposeA, transposeB };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    const product = matMul(oneHotLabelsT, oneHotPredictions);\n    return cast(product, 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D, filter };\n    const attrs = { pad, strides, inputShape: xShape5D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { customGrad } from '../gradients';\nimport { Mean } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    const forward = () => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === $x.dtype ?\n            $x :\n            cast($x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        return sum(res, axis, keepDims);\n    };\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const value = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Mean, attrs);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MirrorPad } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` using mirror padding.\n *\n * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.\n *\n * ```js\n * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);\n * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * In \"reflect\" mode, the padded regions do not include the borders,\n * while in \"symmetric\" mode the padded regions do include the borders.\n * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,\n * then the output is `[1, 2, 3, 2, 1]` in \"reflect\" mode, and\n * `[1, 2, 3, 3, 2]` in \"symmetric\" mode.\n * If `mode` is \"reflect\" then both `paddings[D, 0]` and `paddings[D, 1]`\n * must be no greater than `x.shape[D] - 1`. If mode is \"symmetric\"\n * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than\n * `x.shape[D]`\n * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction mirrorPad_(x, paddings, mode) {\n    util.assert(mode === 'reflect' || mode === 'symmetric', () => `Invalid mode. Mode must be either reflect or symmetric. ` +\n        `Got ${mode}.`);\n    const $x = convertToTensor(x, 'x', 'mirrorPad');\n    if ($x.rank === 0) {\n        throw new Error('mirrorPad(scalar) is not defined. ' +\n            'Pass non-scalar to mirrorPad');\n    }\n    util.assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. ` +\n        `Got ${paddings.length}.`);\n    const shapeOffset = mode === 'reflect' ? 1 : 0;\n    for (let i = 0; i < $x.rank; i++) {\n        util.assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n        util.assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&\n            paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal ` +\n            `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` +\n            `shape ${$x.shape}`);\n    }\n    const attrs = { paddings, mode };\n    const inputs = { x: $x };\n    return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nexport const mirrorPad = op({ mirrorPad_ });\n//# sourceMappingURL=mirror_pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,\n * refer to `tf.mirrorPad`\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    if ($x.dtype === 'bool') {\n        // bool is not an allowed type for the underlying kernel.\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// 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", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations = [1, 1], dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        return backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dimRoundingMode, dilations, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter, attrs);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations = [1, 1], dimRoundingMode) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computeConv2DInfo(xShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        return backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dimRoundingMode, dilations, inputShape: xShape };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a single 3D image or a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mirrorPad } from './mirror_pad';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, dy: dy5D };\n    const attrs = { strides, pad, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad, dilations, dimRoundingMode),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad, dilations, dimRoundingMode),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return dx;\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const x = saved[0];\n        const y = saved[1];\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n        return {\n            x: () => {\n                return maxGrad['x']();\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n        return {\n            x: () => {\n                return minGrad['x']();\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MirrorPad } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const mirrorPadGradConfig = {\n    kernelName: MirrorPad,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=MirrorPad_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { mirrorPadGradConfig } from './gradients/MirrorPad_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    mirrorPadGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mirrorPad } from '../../ops/mirror_pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mirrorPad = function (paddings, mode) {\n    this.throwIfDisposed();\n    return mirrorPad(this, paddings, mode);\n};\n//# sourceMappingURL=mirror_pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mirror_pad';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context, resourceManager) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context, resourceManager));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor from tensorsMap based on input name.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function getTensor(name, tensorsMap, context, resourceManager) {\n    const [nodeName, index] = parseNodeName(name);\n    if (resourceManager != null) {\n        const tensor = resourceManager.getHashTableHandleByName(nodeName);\n        if (tensor != null) {\n            return tensor;\n        }\n    }\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "export const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "export const json = [\n    {\n        'tfOpName': 'HashTable',\n        'category': 'hash_table',\n        'inputs': [],\n        'attrs': [\n            { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' },\n            {\n                'tfName': 'use_node_name_sharing',\n                'name': 'useNodeNameSharing',\n                'type': 'bool'\n            },\n            { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' },\n            { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' },\n        ]\n    },\n    {\n        'tfOpName': 'HashTableV2',\n        'category': 'hash_table',\n        'inputs': [],\n        'attrs': [\n            { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' },\n            {\n                'tfName': 'use_node_name_sharing',\n                'name': 'useNodeNameSharing',\n                'type': 'bool'\n            },\n            { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' },\n            { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' },\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableImport',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'values', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableImportV2',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'values', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableFind',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableFindV2',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=hash_table.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'MirrorPad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'mode', 'name': 'mode', 'type': 'string' }]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as hashTable from './op_list/hash_table';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation, hashTable\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { keep, scalar, stack, tidy, unstack, util } from '@tensorflow/tfjs-core';\n/**\n * Hashtable contains a set of tensors, which can be accessed by key.\n */\nexport class HashTable {\n    /**\n     * Constructor of HashTable. Creates a hash table.\n     *\n     * @param keyDType `dtype` of the table keys.\n     * @param valueDType `dtype` of the table values.\n     */\n    constructor(keyDType, valueDType) {\n        this.keyDType = keyDType;\n        this.valueDType = valueDType;\n        this.handle = scalar(0);\n        // tslint:disable-next-line: no-any\n        this.tensorMap = new Map();\n        keep(this.handle);\n    }\n    get id() {\n        return this.handle.id;\n    }\n    /**\n     * Dispose the tensors and handle and clear the hashtable.\n     */\n    clearAndClose() {\n        this.tensorMap.forEach(value => value.dispose());\n        this.tensorMap.clear();\n        this.handle.dispose();\n    }\n    /**\n     * The number of items in the hash table.\n     */\n    size() {\n        return this.tensorMap.size;\n    }\n    /**\n     * Replaces the contents of the table with the specified keys and values.\n     * @param keys Keys to store in the hashtable.\n     * @param values Values to store in the hashtable.\n     */\n    async import(keys, values) {\n        this.checkKeyAndValueTensor(keys, values);\n        // We only store the primitive values of the keys, this allows lookup\n        // to be O(1).\n        const $keys = await keys.data();\n        // Clear the hashTable before inserting new values.\n        this.tensorMap.forEach(value => value.dispose());\n        this.tensorMap.clear();\n        return tidy(() => {\n            const $values = unstack(values);\n            const keysLength = $keys.length;\n            const valuesLength = $values.length;\n            util.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ` +\n                `${keysLength} elements, the values has ${valuesLength} ` +\n                `elements.`);\n            for (let i = 0; i < keysLength; i++) {\n                const key = $keys[i];\n                const value = $values[i];\n                keep(value);\n                this.tensorMap.set(key, value);\n            }\n            return this.handle;\n        });\n    }\n    /**\n     * Looks up keys in a hash table, outputs the corresponding values.\n     *\n     * Performs batch lookups, for every element in the key tensor, `find`\n     * stacks the corresponding value into the return tensor.\n     *\n     * If an element is not present in the table, the given `defaultValue` is\n     * used.\n     *\n     * @param keys Keys to look up. Must have the same type as the keys of the\n     *     table.\n     * @param defaultValue The scalar `defaultValue` is the value output for keys\n     *     not present in the table. It must also be of the same type as the\n     *     table values.\n     */\n    async find(keys, defaultValue) {\n        this.checkKeyAndValueTensor(keys, defaultValue);\n        const $keys = await keys.data();\n        return tidy(() => {\n            const result = [];\n            for (let i = 0; i < $keys.length; i++) {\n                const key = $keys[i];\n                const value = this.findWithDefault(key, defaultValue);\n                result.push(value);\n            }\n            return stack(result);\n        });\n    }\n    // tslint:disable-next-line: no-any\n    findWithDefault(key, defaultValue) {\n        const result = this.tensorMap.get(key);\n        return result != null ? result : defaultValue;\n    }\n    checkKeyAndValueTensor(key, value) {\n        if (key.dtype !== this.keyDType) {\n            throw new Error(`Expect key dtype ${this.keyDType}, but got ` +\n                `${key.dtype}`);\n        }\n        if (value.dtype !== this.valueDType) {\n            throw new Error(`Expect value dtype ${this.valueDType}, but got ` +\n                `${value.dtype}`);\n        }\n    }\n}\n//# sourceMappingURL=hash_table.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { HashTable } from '../../executor/hash_table';\nimport { getParamValue } from './utils';\nexport const executeOp = async (node, tensorMap, context, resourceManager) => {\n    switch (node.op) {\n        case 'HashTable':\n        case 'HashTableV2': {\n            const keyDType = getParamValue('keyDType', node, tensorMap, context);\n            const valueDType = getParamValue('valueDType', node, tensorMap, context);\n            const hashTable = new HashTable(keyDType, valueDType);\n            resourceManager.addHashTable(node.name, hashTable);\n            return [hashTable.handle];\n        }\n        case 'LookupTableImport':\n        case 'LookupTableImportV2': {\n            const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n            const keys = getParamValue('keys', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const hashTable = resourceManager.getHashTableById(handle.id);\n            return [await hashTable.import(keys, values)];\n        }\n        case 'LookupTableFind':\n        case 'LookupTableFindV2': {\n            const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n            const keys = getParamValue('keys', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            const hashTable = resourceManager.getHashTableById(handle.id);\n            return [await hashTable.find(keys, defaultValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'hash_table';\n//# sourceMappingURL=hash_table_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'MirrorPad': {\n            return [tfOps.mirrorPad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('mode', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as hashTable from './executors/hash_table_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function executeOp(node, tensorMap, context, resourceManager) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'hash_table':\n                return hashTable.executeOp(node, tensorMap, context, resourceManager);\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (tfc.util.isPromise(value)) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nconst HASH_TABLE_OPS = [\n    'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',\n    'LookupTableFind', 'LookupTableFindV2'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\nexport function isHashTable(node) {\n    return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    /**\n     * Set `ResourceManager` shared by executors of a model.\n     * @param resourceManager: `ResourceManager` of the `GraphModel`.\n     */\n    set resourceManager(resourceManager) {\n        this._resourceManager = resourceManager;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs Optional. output node name from the Tensorflow model, if\n     * no outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context, this._resourceManager);\n                    if (util.isPromise(tensors)) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs Optional. output node name from the Tensorflow model,\n     * if no outputs are specified, the default outputs of the model would be\n     * used. You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     * @param isFunctionExecution Optional. Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param outputNames Optional. output node name from the Tensorflow model,\n     * if no outputs are specified, the default outputs of the model would be\n     * used. You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);\n        // First nodes to execute include inputNodes, weights, and initNodes.\n        const stack = [\n            ...inputNodes, ...this.graph.weights, ...(this._initNodes || [])\n        ].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not in the tensorMap yet, this include\n            // inputNodes and internal initNodes.\n            if (tensorMap[item.node.name] == null) {\n                const tensors = executeOp(item.node, tensorMap, context, this._resourceManager);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (util.isPromise(tensors)) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * Contains global resources of a model.\n */\nexport class ResourceManager {\n    constructor(hashTableNameToHandle = {}, hashTableMap = {}) {\n        this.hashTableNameToHandle = hashTableNameToHandle;\n        this.hashTableMap = hashTableMap;\n    }\n    /**\n     * Register a `HashTable` in the resource manager.\n     *\n     * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,\n     * where id is the table handle tensor's id.\n     *\n     * @param name Op node name that creates the `HashTable`.\n     * @param hashTable The `HashTable` to be added to resource manager.\n     */\n    addHashTable(name, hashTable) {\n        this.hashTableNameToHandle[name] = hashTable.handle;\n        this.hashTableMap[hashTable.id] = hashTable;\n    }\n    /**\n     * Get the table handle by node name.\n     * @param name Op node name that creates the `HashTable`. This name is also\n     *     used in the inputs list of lookup and import `HashTable` ops.\n     */\n    getHashTableHandleByName(name) {\n        return this.hashTableNameToHandle[name];\n    }\n    /**\n     * Get the actual `HashTable` by its handle tensor's id.\n     * @param id The id of the handle tensor.\n     */\n    getHashTableById(id) {\n        return this.hashTableMap[id];\n    }\n    /**\n     * Dispose `ResourceManager`, including its hashTables and tensors in them.\n     */\n    dispose() {\n        for (const key in this.hashTableMap) {\n            this.hashTableMap[key].clearAndClose();\n            delete this.hashTableMap[key];\n        }\n        for (const name in this.hashTableNameToHandle) {\n            this.hashTableNameToHandle[name].dispose();\n            delete this.hashTableNameToHandle[name];\n        }\n    }\n}\n//# sourceMappingURL=resource_manager.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nimport { ResourceManager } from './resource_manager';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n        this.resourceManager = new ResourceManager();\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        // Attach a model-level resourceManager to each executor to share resources,\n        // such as `HashTable`.\n        this.executor.resourceManager = this.resourceManager;\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            // Attach a model-level resourceManager to the initializer, the\n            // hashTables created from when executing the initializer will be stored\n            // in the resourceManager.\n            this.initializer.resourceManager = this.resourceManager;\n            this.initializer.executeAsync({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors and resourceManager.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n        this.resourceManager.dispose();\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (tf.util.isPromise(value)) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        let outId;\n        if (dtype === 'string' && values != null && values.length > 0 &&\n            util.isString(values[0])) {\n            const encodedValues = values.map(d => util.encodeString(d));\n            outId = this.write(encodedValues, shape, dtype);\n        }\n        else {\n            outId = this.write(values, shape, dtype);\n        }\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            // TODO(lina128): Use fill kernel directly once this kernel is\n            // modularized.\n            return tf.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const abs = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: abs,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceil = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceil,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const exp = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: exp,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floor = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floor,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const log = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: log,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrt = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrt,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared functionality among backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { notEqualImpl } from './kernels/NotEqual';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { squaredDifferenceImpl } from './kernels/SquaredDifference';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const elu = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: elu,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nconst preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\nexport function prelu(args) {\n    const { inputs, backend } = args;\n    const { x, alpha } = inputs;\n    assertNotComplex([x, alpha], 'prelu');\n    const aVals = backend.data.get(x.dataId).values;\n    const bVals = backend.data.get(alpha.dataId).values;\n    const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, x.dtype);\n    return backend.makeTensorInfo(resultShape, x.dtype, resultData);\n}\nexport const preluConfig = {\n    kernelName: Prelu,\n    backendName: 'cpu',\n    kernelFunc: prelu,\n};\n//# sourceMappingURL=Prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const relu = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));\nexport const reluConfig = {\n    kernelName: Relu,\n    backendName: 'cpu',\n    kernelFunc: relu,\n};\n//# sourceMappingURL=Relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const relu6 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));\nexport const relu6Config = {\n    kernelName: Relu6,\n    backendName: 'cpu',\n    kernelFunc: relu6,\n};\n//# sourceMappingURL=Relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../kernels/Elu';\nimport { identity } from '../kernels/Identity';\nimport { prelu } from '../kernels/Prelu';\nimport { relu } from '../kernels/Relu';\nimport { relu6 } from '../kernels/Relu6';\nexport function applyActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return identity({ inputs: { x }, backend });\n    }\n    else if (activation === 'relu') {\n        return relu({ inputs: { x }, backend });\n    }\n    else if (activation === 'elu') {\n        return elu({ inputs: { x }, backend });\n    }\n    else if (activation === 'relu6') {\n        return relu6({ inputs: { x }, backend });\n    }\n    else if (activation === 'prelu') {\n        return prelu({ inputs: { x, alpha: preluActivationWeights }, backend });\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\n//# sourceMappingURL=fused_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul, buffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { reshape } from './Reshape';\nexport function batchMatMul(args) {\n    const { inputs, backend, attrs } = args;\n    const { a, b } = inputs;\n    const { transposeA, transposeB } = attrs;\n    assertNotComplex([a, b], 'matMul');\n    const aRank = a.shape.length;\n    const bRank = b.shape.length;\n    const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n    const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n    const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n    const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n    const outerDimsA = a.shape.slice(0, -2);\n    const outerDimsB = b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n    util.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the ` +\n        `same or at least one input batch dimension must be 1. Got input ` +\n        `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n    const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);\n    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +\n        `${b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] :\n        [batchDimA, outerShapeA, innerShapeA];\n    const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] :\n        [batchDimB, innerShapeB, outerShapeB];\n    // The rest of the implementation is designed to operate on rank-3 tensors\n    const a3d = reshape({ inputs: { x: a }, backend, attrs: { shape: a3dShape } });\n    const b3d = reshape({ inputs: { x: b }, backend, attrs: { shape: b3dShape } });\n    const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n    const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n    const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n    const batchDim = Math.max(batchDimA, batchDimB);\n    const a3dValues = backend.data.get(a3d.dataId).values;\n    const b3dValues = backend.data.get(b3d.dataId).values;\n    const a3dStrides = util.computeStrides(a3d.shape);\n    const b3dStrides = util.computeStrides(b3d.shape);\n    const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n        [a3dStrides[0], 1, a3dStrides[1]] :\n        [a3dStrides[0], a3dStrides[1], 1];\n    const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n        [1, b3dStrides[1], b3dStrides[0]] :\n        [b3dStrides[1], 1, b3dStrides[0]];\n    const size = leftDim * rightDim;\n    const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n    const resVals = result.values;\n    const blockSize = backend.blockSize;\n    for (let bi = 0; bi < batchDim; bi++) {\n        for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n            for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                    // for when blockSize doesn't evenly divide the input\n                    const iBlock = Math.min(i0 + blockSize, leftDim);\n                    const jBlock = Math.min(j0 + blockSize, rightDim);\n                    const kBlock = Math.min(k0 + blockSize, sharedDim);\n                    for (let i = i0; i < iBlock; i++) {\n                        for (let j = j0; j < jBlock; j++) {\n                            let sum = 0.0;\n                            for (let k = k0; k < kBlock; k++) {\n                                const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;\n                                const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;\n                                const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];\n                                const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];\n                                sum += aVal * bVal;\n                            }\n                            resVals[bi * size + (i * rightDim + j)] += sum;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    backend.disposeIntermediateTensorInfo(a3d);\n    backend.disposeIntermediateTensorInfo(b3d);\n    // set correct shape on output.\n    return backend.makeTensorInfo(outShape, result.dtype, result.values);\n}\nexport const batchMatMulConfig = {\n    kernelName: BatchMatMul,\n    backendName: 'cpu',\n    kernelFunc: batchMatMul,\n};\n//# sourceMappingURL=BatchMatMul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { _FusedMatMul } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { batchMatMul } from './BatchMatMul';\nexport function _fusedMatMul(args) {\n    const { inputs, backend, attrs } = args;\n    const { a, b, bias, preluActivationWeights } = inputs;\n    const { transposeA, transposeB, activation } = attrs;\n    let current;\n    let addRes;\n    let activationRes;\n    const intermediates = [];\n    const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend });\n    current = matMulRes;\n    if (bias) {\n        addRes = add({ inputs: { a: current, b: bias }, backend });\n        intermediates.push(current);\n        current = addRes;\n    }\n    if (activation) {\n        activationRes =\n            applyActivation(backend, current, activation, preluActivationWeights);\n        intermediates.push(current);\n        current = activationRes;\n    }\n    for (const i of intermediates) {\n        backend.disposeIntermediateTensorInfo(i);\n    }\n    return current;\n}\nexport const _fusedMatMulConfig = {\n    kernelName: _FusedMatMul,\n    backendName: 'cpu',\n    kernelFunc: _fusedMatMul,\n};\n//# sourceMappingURL=_FusedMatMul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acos = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acos,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosh = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acosh,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asin = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asin,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinh = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinh,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atan = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atan,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanh = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanh,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNorm(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNorm,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clip = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clip,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis: $axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis: $axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2D, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode } = attrs;\n    assertNotComplex([x, filter], 'conv2d');\n    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const xBatchStride = xStrides[0];\n    const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];\n    const xColStride = isChannelsLast ? xStrides[2] : 1;\n    const xChannelStride = isChannelsLast ? 1 : xStrides[1];\n    const yBatchStride = y.strides[0];\n    const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n    const yColStride = isChannelsLast ? y.strides[2] : 1;\n    const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xBatchStride;\n        const yOffset1 = b * yBatchStride;\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset2 = yOffset1 + yR * yRowStride;\n            const xRCorner = yR * convInfo.strideHeight - padTop;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const xR = xRCorner + wR * dilationHeight;\n                if (xR < 0 || xR >= convInfo.inHeight) {\n                    continue;\n                }\n                const wOffset1 = wR * filterStrides[0];\n                const xOffset2 = xOffset1 + xR * xRowStride;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const yOffset3 = yOffset2 + yC * yColStride;\n                    const xCCorner = yC * convInfo.strideWidth - padLeft;\n                    for (let wC = 0; wC < filterWidth; ++wC) {\n                        const xC = xCCorner + wC * dilationWidth;\n                        if (xC < 0 || xC >= convInfo.inWidth) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wC * filterStrides[1];\n                        const xOffset3 = xOffset2 + xC * xColStride;\n                        let wOffset3 = wOffset2;\n                        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                            const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                yVals[yOffset3 + d2 * yChannelStride] +=\n                                    xVal * wVals[wOffset3 + d2];\n                            }\n                            wOffset3 += convInfo.outChannels;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, yVals);\n}\nexport const conv2DConfig = {\n    kernelName: Conv2D,\n    backendName: 'cpu',\n    kernelFunc: conv2D\n};\n//# sourceMappingURL=Conv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2DBackpropFilter, TensorBuffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2DBackpropFilter(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, pad, dataFormat, dimRoundingMode, filterShape } = attrs;\n    assertNotComplex([x, dy], 'conv2dBackpropFilter');\n    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad, dimRoundingMode, false /* depthwise */, $dataFormat);\n    const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n    const dW = new TensorBuffer(convInfo.filterShape, 'float32');\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const xVals = backend.data.get(x.dataId).values;\n    const dyVals = backend.data.get(dy.dataId).values;\n    const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n    const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n    for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n        for (let wC = 0; wC < filterWidth; ++wC) {\n            const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n            const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                if (isChannelsLast) {\n                                    dotProd += xBuf.get(b, xR, xC, d1) *\n                                        dyBuf.get(b, yR, yC, d2);\n                                }\n                                else {\n                                    dotProd += xBuf.get(b, d1, xR, xC) *\n                                        dyBuf.get(b, d2, yR, yC);\n                                }\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, d2);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nexport const conv2DBackpropFilterConfig = {\n    kernelName: Conv2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: conv2DBackpropFilter\n};\n//# sourceMappingURL=Conv2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2DBackpropInput, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2DBackpropInput(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { inputShape, strides, pad, dataFormat, dimRoundingMode } = attrs;\n    assertNotComplex([dy, filter], 'conv2dBackpropInput');\n    const filterStrides = util.computeStrides(filter.shape);\n    const dyStrides = util.computeStrides(dy.shape);\n    let $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2] = filterStrides;\n    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n    $dataFormat = convInfo.dataFormat;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const isChannelsLast = $dataFormat === 'channelsLast';\n    const xBatchStride = dx.strides[0];\n    const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n    const xColStride = isChannelsLast ? dx.strides[2] : 1;\n    const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n    const yBatchStride = dyStrides[0];\n    const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n    const yColStride = isChannelsLast ? dyStrides[2] : 1;\n    const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            for (let xR = 0; xR < inHeight; ++xR) {\n                const xRCorner = xR - topPad;\n                const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                for (let xC = 0; xC < inWidth; ++xC) {\n                    const xCCorner = xC - leftPad;\n                    const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                    const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                    let dotProd = 0;\n                    for (let yR = xRMin; yR < yRMax; ++yR) {\n                        const wR = yR * strideHeight - xRCorner;\n                        for (let yC = xCMin; yC < yCMax; ++yC) {\n                            const wC = yC * strideWidth - xCCorner;\n                            const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                            const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                            for (let d2 = 0; d2 < outChannels; ++d2) {\n                                const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                const weight = fltValues[fltOffset + d2];\n                                dotProd += pixel * weight;\n                            }\n                        }\n                    }\n                    const dxOffset = xBatchStride * b + xRowStride * xR +\n                        xColStride * xC + xChannelStride * d1;\n                    dxValues[dxOffset] = dotProd;\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const conv2DBackpropInputConfig = {\n    kernelName: Conv2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: conv2DBackpropInput\n};\n//# sourceMappingURL=Conv2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3D, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dilations } = attrs;\n    assertNotComplex([x, filter], 'conv3d');\n    const convInfo = backend_util.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad);\n    const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;\n    const padFront = padInfo.front;\n    const padLeft = padInfo.left;\n    const padTop = padInfo.top;\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xStrides[0];\n        const yOffset1 = b * y.strides[0];\n        for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n            const yOffset2 = yOffset1 + yF * y.strides[1];\n            const xFCorner = yF * convInfo.strideDepth - padFront;\n            for (let wF = 0; wF < filterDepth; ++wF) {\n                const xF = xFCorner + wF * dilationDepth;\n                if (xF < 0 || xF >= convInfo.inDepth) {\n                    continue;\n                }\n                const wOffset1 = wF * filterStrides[0];\n                const xOffset2 = xOffset1 + xF * xStrides[1];\n                for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                    const yOffset3 = yOffset2 + yR * y.strides[2];\n                    const xRCorner = yR * convInfo.strideHeight - padTop;\n                    for (let wR = 0; wR < filterHeight; ++wR) {\n                        const xR = xRCorner + wR * dilationHeight;\n                        if (xR < 0 || xR >= convInfo.inHeight) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wR * filterStrides[1];\n                        const xOffset3 = xOffset2 + xR * xStrides[2];\n                        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                            const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                            const xCCorner = yC * convInfo.strideWidth - padLeft;\n                            for (let wC = 0; wC < filterWidth; ++wC) {\n                                const xC = xCCorner + wC * dilationWidth;\n                                if (xC < 0 || xC >= convInfo.inWidth) {\n                                    continue;\n                                }\n                                const wOffset3 = wOffset2 + wC * filterStrides[2];\n                                const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                let wOffset4 = wOffset3;\n                                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                    const xVal = xVals[xOffset4 + d1];\n                                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                        yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                    }\n                                    wOffset4 += convInfo.outChannels;\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nexport const conv3DConfig = {\n    kernelName: Conv3D,\n    backendName: 'cpu',\n    kernelFunc: conv3D\n};\n//# sourceMappingURL=Conv3D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3DBackpropFilterV2, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3DBackpropFilterV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, pad, filterShape } = attrs;\n    assertNotComplex([x, dy], 'conv3dBackpropFilterV2');\n    const xStrides = util.computeStrides(x.shape);\n    const dyStrides = util.computeStrides(dy.shape);\n    const convInfo = backend_util.computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad);\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dw = new TensorBuffer(convInfo.filterShape, 'float32');\n    const dwValues = dw.values;\n    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n    const xValues = backend.data.get(x.dataId).values;\n    const [xS0, xS1, xS2, xS3] = xStrides;\n    const frontPad = convInfo.padInfo.front;\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    for (let wF = 0; wF < filterDepth; ++wF) {\n        const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n        const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n        const wOffset1 = wF * dwS0;\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            const wOffset2 = wR * dwS1 + wOffset1;\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                const wOffset3 = wC * dwS2 + wOffset2;\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    const wOffset4 = d1 * dwS3 + wOffset3;\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            const xOffset1 = b * xS0;\n                            const yOffset1 = b * dyS0;\n                            for (let yF = yFMin; yF < yFMax; ++yF) {\n                                const xF = wF + yF * strideDepth - frontPad;\n                                const xOffset2 = xF * xS1 + xOffset1;\n                                const yOffset2 = yF * dyS1 + yOffset1;\n                                for (let yR = yRMin; yR < yRMax; ++yR) {\n                                    const xR = wR + yR * strideHeight - topPad;\n                                    const xOffset3 = xR * xS2 + xOffset2;\n                                    const yOffset3 = yR * dyS2 + yOffset2;\n                                    for (let yC = yCMin; yC < yCMax; ++yC) {\n                                        const xC = wC + yC * strideWidth - leftPad;\n                                        const xOffset4 = xC * xS3 + xOffset3;\n                                        const yOffset4 = yC * dyS3 + yOffset3;\n                                        dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                    }\n                                }\n                            }\n                        }\n                        dwValues[wOffset4 + d2] = dotProd;\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\nexport const conv3DBackpropFilterV2Config = {\n    kernelName: Conv3DBackpropFilterV2,\n    backendName: 'cpu',\n    kernelFunc: conv3DBackpropFilterV2\n};\n//# sourceMappingURL=Conv3DBackpropFilterV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3DBackpropInputV2, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3DBackpropInputV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { pad, strides, inputShape } = attrs;\n    assertNotComplex([dy], 'conv3dBackpropInputV2');\n    const dyStrides = util.computeStrides(dy.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const convInfo = backend_util.computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n    const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n    const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            // Frames of depth\n            for (let xF = 0; xF < inDepth; ++xF) {\n                const xFCorner = xF - frontPad;\n                const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                // Rows as per standard 2d matrix notation\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    // Columns as per standard 2d matrix notation\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yF = xFMin; yF < yFMax; ++yF) {\n                            const wF = yF * strideDepth - xFCorner;\n                            for (let yR = xRMin; yR < yRMax; ++yR) {\n                                const wR = yR * strideHeight - xRCorner;\n                                for (let yC = xCMin; yC < yCMax; ++yC) {\n                                    const wC = yC * strideWidth - xCCorner;\n                                    const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                    const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                        fltS1 * (filterHeight - 1 - wR) +\n                                        fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                    for (let d2 = 0; d2 < outChannels; ++d2) {\n                                        const pixel = dyValues[dyOffset + d2];\n                                        const weight = fltValues[fltOffset + d2];\n                                        dotProd += pixel * weight;\n                                    }\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                            dotProd;\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const conv3DBackpropInputV2Config = {\n    kernelName: Conv3DBackpropInputV2,\n    backendName: 'cpu',\n    kernelFunc: conv3DBackpropInputV2\n};\n//# sourceMappingURL=Conv3DBackpropInputV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cos = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cos,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosh = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: cosh,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNative, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNative(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dilations, dimRoundingMode } = attrs;\n    assertNotComplex([x, filter], 'depthwiseConv2DNative');\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    let $dilations = dilations;\n    if ($dilations == null) {\n        $dilations = [1, 1];\n    }\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n        `1. Got strides ${strides} and dilations '${$dilations}'`);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n    const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;\n    const padLeft = padInfo.left;\n    const padTop = padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xStrides[0];\n        const yOffset1 = b * y.strides[0];\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset2 = yOffset1 + yR * y.strides[1];\n            const xRCorner = yR * convInfo.strideHeight - padLeft;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const xR = xRCorner + wR * dilationHeight;\n                if (xR < 0 || xR >= convInfo.inHeight) {\n                    continue;\n                }\n                const wOffset1 = wR * filterStrides[0];\n                const xOffset2 = xOffset1 + xR * xStrides[1];\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const yOffset3 = yOffset2 + yC * y.strides[2];\n                    const xCCorner = yC * convInfo.strideWidth - padTop;\n                    for (let wC = 0; wC < filterWidth; ++wC) {\n                        const xC = xCCorner + wC * dilationWidth;\n                        if (xC < 0 || xC >= convInfo.inWidth) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wC * filterStrides[1];\n                        const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                        let yOffset4 = yOffset3;\n                        let wOffset3 = wOffset2;\n                        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                            const xVal = xVals[xOffset3 + d1];\n                            for (let q = 0; q < chMul; ++q) {\n                                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                            }\n                            yOffset4 += chMul;\n                            wOffset3 += chMul;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nexport const depthwiseConv2dNativeConfig = {\n    kernelName: DepthwiseConv2dNative,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNative\n};\n//# sourceMappingURL=DepthwiseConv2dNative.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNativeBackpropFilter, TensorBuffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNativeBackpropFilter(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, dilations, pad, dimRoundingMode, filterShape } = attrs;\n    assertNotComplex([x, dy], 'depthwiseConv2dNativeBackpropFilter');\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n    const dW = new TensorBuffer(convInfo.filterShape, 'float32');\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const xVals = backend.data.get(x.dataId).values;\n    const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n    const dyVals = backend.data.get(dy.dataId).values;\n    const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n    for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n        for (let wC = 0; wC < filterWidth; ++wC) {\n            const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n            const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                const d1 = Math.trunc(d2 / chMul);\n                const dm = d2 % chMul;\n                let dotProd = 0;\n                for (let b = 0; b < convInfo.batchSize; ++b) {\n                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                        const xR = wR + yR * strideHeight - topPad;\n                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                            const xC = wC + yC * strideWidth - leftPad;\n                            dotProd += xBuf.get(b, xR, xC, d1) *\n                                dyBuf.get(b, yR, yC, d2);\n                        }\n                    }\n                }\n                dW.set(dotProd, wR, wC, d1, dm);\n            }\n        }\n    }\n    return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nexport const depthwiseConv2dNativeBackpropFilterConfig = {\n    kernelName: DepthwiseConv2dNativeBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNativeBackpropFilter\n};\n//# sourceMappingURL=DepthwiseConv2dNativeBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNativeBackpropInput, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNativeBackpropInput(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { strides, dilations, pad, dimRoundingMode, inputShape } = attrs;\n    assertNotComplex([dy, filter], 'depthwiseConv2DNativeBackpropInput');\n    const dyStrides = util.computeStrides(dy.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const convInfo = backend_util.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2] = dyStrides;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2] = filterStrides;\n    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const chMul = outChannels / inChannels;\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            for (let xR = 0; xR < inHeight; ++xR) {\n                const xRCorner = xR - topPad;\n                const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                for (let xC = 0; xC < inWidth; ++xC) {\n                    const xCCorner = xC - leftPad;\n                    const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                    const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                    let dotProd = 0;\n                    for (let yR = xRMin; yR < yRMax; ++yR) {\n                        const wR = yR * strideHeight - xRCorner;\n                        for (let yC = xCMin; yC < yCMax; ++yC) {\n                            const wC = yC * strideWidth - xCCorner;\n                            const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                            const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                            for (let dm = 0; dm < chMul; ++dm) {\n                                const d2 = d1 * chMul + dm;\n                                const pixel = dyValues[dyOffset + d2];\n                                const weight = fltValues[fltOffset + dm];\n                                dotProd += pixel * weight;\n                            }\n                        }\n                    }\n                    dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const depthwiseConv2dNativeBackpropInputConfig = {\n    kernelName: DepthwiseConv2dNativeBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNativeBackpropInput\n};\n//# sourceMappingURL=DepthwiseConv2dNativeBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erf = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erf,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Fill, util } from '@tensorflow/tfjs-core';\nexport function fill(args) {\n    const { backend, attrs } = args;\n    const { shape, value, dtype } = attrs;\n    const $dtype = dtype || util.inferDtype(value);\n    const values = util.getArrayFromDType($dtype, util.sizeFromShape(shape));\n    fillValues(values, value, $dtype);\n    return backend.makeTensorInfo(shape, $dtype, values);\n}\nexport const fillConfig = {\n    kernelName: Fill,\n    backendName: 'cpu',\n    kernelFunc: fill\n};\nfunction fillValues(values, value, dtype) {\n    if (dtype === 'string') {\n        values.fill(value);\n    }\n    else {\n        values.fill(value);\n    }\n}\n//# sourceMappingURL=Fill.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedConv2D } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { conv2D } from './Conv2D';\nexport function fusedConv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter, bias, preluActivationWeights } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation } = attrs;\n    let result = conv2D({\n        inputs: { x, filter },\n        backend,\n        attrs: { strides, pad, dataFormat, dilations, dimRoundingMode }\n    });\n    if (bias) {\n        const resultOld = result;\n        result = add({ inputs: { a: result, b: bias }, backend });\n        backend.disposeIntermediateTensorInfo(resultOld);\n    }\n    if (activation) {\n        const resultOld = result;\n        result =\n            applyActivation(backend, result, activation, preluActivationWeights);\n        backend.disposeIntermediateTensorInfo(resultOld);\n    }\n    return result;\n}\nexport const fusedConv2DConfig = {\n    kernelName: FusedConv2D,\n    backendName: 'cpu',\n    kernelFunc: fusedConv2D\n};\n//# sourceMappingURL=FusedConv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedDepthwiseConv2D } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { depthwiseConv2dNative } from './DepthwiseConv2dNative';\nexport function fusedDepthwiseConv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter, bias, preluActivationWeights } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation } = attrs;\n    let result = depthwiseConv2dNative({\n        inputs: { x, filter },\n        backend,\n        attrs: { strides, pad, dataFormat, dilations, dimRoundingMode }\n    });\n    if (bias) {\n        const oldResult = result;\n        result = add({ inputs: { a: result, b: bias }, backend });\n        backend.disposeIntermediateTensorInfo(oldResult);\n    }\n    if (activation) {\n        const oldResult = result;\n        result =\n            applyActivation(backend, result, activation, preluActivationWeights);\n        backend.disposeIntermediateTensorInfo(oldResult);\n    }\n    return result;\n}\nexport const fusedDepthwiseConv2DConfig = {\n    kernelName: FusedDepthwiseConv2D,\n    backendName: 'cpu',\n    kernelFunc: fusedDepthwiseConv2D\n};\n//# sourceMappingURL=FusedDepthwiseConv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFinite = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFinite,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInf = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInf,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaN = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaN,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1p = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1p,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNot = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNot,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MirrorPad, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function mirrorPad(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    assertNotComplex(x, 'mirrorPad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const end = paddings.map((p, i) => p[0] + x.shape[i]);\n    const offset = mode === 'reflect' ? 0 : 1;\n    const xVals = backend.data.get(x.dataId).values;\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    for (let i = 0; i < resultSize; i++) {\n        let coords = util.indexToLoc(i, resultRank, resultStrides);\n        for (let i = 0; i < resultRank; i++) {\n            if (coords[i] < start[i]) {\n                coords[i] = start[i] * 2 - coords[i] - offset;\n            }\n            else if (coords[i] >= end[i]) {\n                coords[i] = (end[i] - 1) * 2 - coords[i] + offset;\n            }\n        }\n        coords = coords.map((c, i) => c - start[i]);\n        const inIndex = util.locToIndex(coords, xRank, xStrides);\n        resVals[i] = xVals[inIndex];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const mirrorPadConfig = {\n    kernelName: MirrorPad,\n    backendName: 'cpu',\n    kernelFunc: mirrorPad\n};\n//# sourceMappingURL=MirrorPad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocal = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocal,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const round = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: round,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const selu = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: selu,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoid = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoid,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sign = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: sign,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sin = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sin,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinh = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinh,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplus = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplus,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrt = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrt,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const step = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: step,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tan = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tan,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanh = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanh,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { _fusedMatMulConfig } from './kernels/_FusedMatMul';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchMatMulConfig } from './kernels/BatchMatMul';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { conv2DConfig } from './kernels/Conv2D';\nimport { conv2DBackpropFilterConfig } from './kernels/Conv2DBackpropFilter';\nimport { conv2DBackpropInputConfig } from './kernels/Conv2DBackpropInput';\nimport { conv3DConfig } from './kernels/Conv3D';\nimport { conv3DBackpropFilterV2Config } from './kernels/Conv3DBackpropFilterV2';\nimport { conv3DBackpropInputV2Config } from './kernels/Conv3DBackpropInputV2';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { depthwiseConv2dNativeConfig } from './kernels/DepthwiseConv2dNative';\nimport { depthwiseConv2dNativeBackpropFilterConfig } from './kernels/DepthwiseConv2dNativeBackpropFilter';\nimport { depthwiseConv2dNativeBackpropInputConfig } from './kernels/DepthwiseConv2dNativeBackpropInput';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { fillConfig } from './kernels/Fill';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { fusedConv2DConfig } from './kernels/FusedConv2D';\nimport { fusedDepthwiseConv2DConfig } from './kernels/FusedDepthwiseConv2D';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { mirrorPadConfig } from './kernels/MirrorPad';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { preluConfig } from './kernels/Prelu';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reluConfig } from './kernels/Relu';\nimport { relu6Config } from './kernels/Relu6';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    _fusedMatMulConfig,\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchMatMulConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    conv2DBackpropFilterConfig,\n    conv2DBackpropInputConfig,\n    conv2DConfig,\n    conv3DBackpropFilterV2Config,\n    conv3DBackpropInputV2Config,\n    conv3DConfig,\n    cosConfig,\n    coshConfig,\n    depthwiseConv2dNativeConfig,\n    depthwiseConv2dNativeBackpropFilterConfig,\n    depthwiseConv2dNativeBackpropInputConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    fillConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    fusedConv2DConfig,\n    fusedDepthwiseConv2DConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    mirrorPadConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    preluConfig,\n    realConfig,\n    reciprocalConfig,\n    reluConfig,\n    relu6Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function clearWebGLContext(webGLVersion) {\n    delete contexts[webGLVersion];\n}\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        let batchASnippet = 'rc.x';\n        let batchBSnippet = 'rc.x';\n        if (aShape[0] < bShape[0]) {\n            batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;\n        }\n        else if (bShape[0] < aShape[0]) {\n            batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          int batchA = ${batchASnippet};\n          int batchB = ${batchBSnippet};\n          vec4 a = getMatrixA(batchA, ${aSample});\n          vec4 b = getMatrixB(batchB, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { div, engine, env, max, range, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, {\n            shape,\n            dtype,\n            values,\n            usage: TextureUsage.UPLOAD,\n            refCount: 1,\n            complexParentRefCount: 0\n        });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, {\n            shape,\n            dtype,\n            values,\n            usage: TextureUsage.UPLOAD,\n            refCount: 1,\n            complexParentRefCount: 0\n        });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensorInfos, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensorInfos, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([\n                this.read(complexTensorInfos.real.dataId),\n                this.read(complexTensorInfos.imag.dataId)\n            ]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        // Trying to dispose a textureData that has a 'kept' refCount, e.g. trying\n        // to dispose a tensor whose data bucket is shared with a complex tensor. In\n        // this case we are removing a reference to the textureData, but we\n        // shouldn't actually dispose the texture.\n        if (this.texData.get(dataId).complexParentRefCount > 0) {\n            this.texData.get(dataId).refCount--;\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensorInfos } = this.texData.get(dataId);\n        if (complexTensorInfos != null) {\n            this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--;\n            this.disposeIntermediateTensorInfo(complexTensorInfos.real);\n            this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--;\n            this.disposeIntermediateTensorInfo(complexTensorInfos.imag);\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const batch = Math.max(a.shape[0], b.shape[0]);\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            // TODO(annxingyuan): Call multiply directly as part of batchMatMul\n            // modularization.\n            const product = tf.mul(a3D, b3D);\n            return product.sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, b.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const batch = Math.max(a.shape[0], b.shape[0]);\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, b.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(annxingyuan): Call sub directly as part of softmax kernel\n        // modularization.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, w2Row.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\n/**\n * In WebGL data is stored in GPU textures which can't be efficiently copied, so\n * complex tensors share data with their real and imaginary components. Complex\n * tensors increment the `complexParentRefCount` properties of the underlying\n * data buckets to prevent them from being disposed, as the engine's disposal\n * logic does not account for data sharing by complex tensors.\n *\n * When a complex tensor is disposed, it will explicitly decrease the\n * `complexParentRefCount` properties of its underlying components.\n */\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.texData.get(complexInfo.dataId);\n    const realTensorInfo = identity({ inputs: { x: real }, backend });\n    const realData = backend.texData.get(realTensorInfo.dataId);\n    realData.complexParentRefCount++;\n    const imagTensorInfo = identity({ inputs: { x: imag }, backend });\n    const imagData = backend.texData.get(imagTensorInfo.dataId);\n    imagData.complexParentRefCount++;\n    complex.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'webgl',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, upcastType } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { complex } from '../kernels/Complex';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        if (supportsComplex && a.dtype === 'complex64') {\n            const aData = webglBackend.texData.get(a.dataId);\n            const bData = webglBackend.texData.get(b.dataId);\n            const [real, imag] = [\n                [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n                [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n            ].map(complexParts => {\n                const [aPart, bPart] = complexParts;\n                const aHandle = {\n                    dataId: aPart.dataId,\n                    dtype: aPart.dtype,\n                    shape: a.shape\n                };\n                const bHandle = {\n                    dataId: bPart.dataId,\n                    dtype: bPart.dtype,\n                    shape: b.shape\n                };\n                const program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n                return webglBackend.runWebGLProgram(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n            });\n            const complexOutput = complex({ inputs: { real, imag }, backend: webglBackend });\n            webglBackend.disposeIntermediateTensorInfo(real);\n            webglBackend.disposeIntermediateTensorInfo(imag);\n            // TODO(annxingyuan): Implement CPU forwarding for complex inputs.\n            return complexOutput;\n        }\n        const $dtype = dtype || upcastType(a.dtype, b.dtype);\n        if (webglBackend.shouldExecuteOnCPU([a, b]) && cpuKernelImpl != null) {\n            const aData = webglBackend.texData.get(a.dataId);\n            const bData = webglBackend.texData.get(b.dataId);\n            const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, aData.values, bData.values, $dtype);\n            const out = webglBackend.makeTensorInfo(outShape, $dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n            return out;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') &&\n            packedOpSnippet != null;\n        let program;\n        if (shouldUsePackedProgram) {\n            program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);\n        }\n        else {\n            program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        }\n        return webglBackend.runWebGLProgram(program, [a, b], $dtype);\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nimport { addImplCPU as cpuAdd } from '../kernel_utils/shared';\nconst ADD = 'return a + b;';\nexport const addKernelFunc = binaryKernelFunc({\n    opSnippet: ADD,\n    packedOpSnippet: ADD,\n    supportsComplex: true,\n    cpuKernelImpl: cpuAdd\n});\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'webgl',\n    kernelFunc: addKernelFunc\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2 = binaryKernelFunc({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNorm = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNorm,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst NOT_EQUAL = `return float(a != b);`;\nexport const notEqual = binaryKernelFunc({ opSnippet: NOT_EQUAL, dtype: 'bool' });\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'webgl',\n    kernelFunc: notEqual,\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputData = backend.texData.get(input.dataId);\n    return identity({ inputs: { x: inputData.complexTensorInfos.real }, backend });\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'webgl',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnaryOpProgram } from '../unaryop_gpu';\nconst TO_INT = `return float(int(x));`;\nexport function int(input, backend) {\n    const program = new UnaryOpProgram(input.shape, TO_INT);\n    const output = backend.runWebGLProgram(program, [input], 'int32');\n    return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n//# sourceMappingURL=int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { notEqual } from './NotEqual';\nimport { real } from './Real';\nimport { int } from '../kernel_utils/int';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(annxingyuan): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        return int(x, backend);\n    }\n    if (dtype === 'bool') {\n        const zerosTensorInfo = backend.makeTensorInfo([], 'bool', util.getTypedArrayFromDType('bool', 1));\n        const binaryInputs = { a: x, b: zerosTensorInfo };\n        const result = notEqual({ inputs: binaryInputs, backend });\n        backend.disposeIntermediateTensorInfo(zerosTensorInfo);\n        return result;\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'webgl',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputData = backend.texData.get(input.dataId);\n    return identity({ inputs: { x: inputData.complexTensorInfos.imag }, backend });\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'webgl',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, env, util } from '@tensorflow/tfjs-core';\nimport { ConcatProgram } from '../concat_gpu';\nimport { ConcatPackedProgram } from '../concat_packed_gpu';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concatImpl(inputs, axis, backend) {\n    const dtype = inputs[0].dtype;\n    if (dtype === 'complex64') {\n        const reals = inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concatImpl(reals, axis, backend);\n        const imagConcated = concatImpl(imags, axis, backend);\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    if (inputs.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n        const midIndex = Math.floor(inputs.length / 2);\n        const leftSide = concatImpl(inputs.slice(0, midIndex), axis, backend);\n        const rightSide = concatImpl(inputs.slice(midIndex), axis, backend);\n        const result = concatImpl([leftSide, rightSide], axis, backend);\n        backend.disposeIntermediateTensorInfo(leftSide);\n        backend.disposeIntermediateTensorInfo(rightSide);\n        return result;\n    }\n    if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') &&\n        inputs[0].shape.length > 1) {\n        const program = new ConcatPackedProgram(inputs.map(t => t.shape), axis);\n        return backend.runWebGLProgram(program, inputs, dtype);\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), axis);\n    const tensors2D = inputs.map(x => reshape({\n        inputs: { x },\n        attrs: { shape: [-1, util.sizeFromShape(x.shape.slice(axis))] },\n        backend\n    }));\n    const program = new ConcatProgram(tensors2D.map(t => t.shape));\n    const result = backend.runWebGLProgram(program, tensors2D, dtype);\n    tensors2D.forEach(r => backend.disposeIntermediateTensorInfo(r));\n    const reshapedResult = reshape({ inputs: { x: result }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n}\n//# sourceMappingURL=Concat_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { concatImpl } from './Concat_impl';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    return concatImpl($inputs, $axis, backend);\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'webgl',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cos = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cos,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const div = binaryKernelFunc({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: div,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FFTProgram {\n    constructor(component, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        let opString;\n        if (component === 'real') {\n            opString = 'return real * expR - imag * expI;';\n        }\n        else if (component === 'imag') {\n            opString = 'return real * expI + imag * expR;';\n        }\n        else {\n            throw new Error(`FFT component must be either \"real\" or \"imag\", got ${component}.`);\n        }\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${opString}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FFTProgram } from '../fft_gpu';\nimport { complex } from './Complex';\nimport { reshape } from './Reshape';\nexport function fftImpl(x, inverse, backend) {\n    const xData = backend.texData.get(x.dataId);\n    const inputSize = util.sizeFromShape(x.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = x.shape[x.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({ inputs: { x }, backend, attrs: { shape: [batch, innerDimensionSize] } });\n    const xShape = input2D.shape;\n    const realProgram = new FFTProgram('real', xShape, inverse);\n    const imagProgram = new FFTProgram('imag', xShape, inverse);\n    const inputs = [\n        {\n            dataId: xData.complexTensorInfos.real.dataId,\n            dtype: xData.complexTensorInfos.real.dtype,\n            shape: xShape\n        },\n        {\n            dataId: xData.complexTensorInfos.imag.dataId,\n            dtype: xData.complexTensorInfos.imag.dtype,\n            shape: xShape\n        }\n    ];\n    const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');\n    const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');\n    const complexOutput = complex({ inputs: { real: realPart, imag: imagPart }, backend });\n    backend.disposeIntermediateTensorInfo(realPart);\n    backend.disposeIntermediateTensorInfo(imagPart);\n    const complexOutputReshaped = reshape({ inputs: { x: complexOutput }, backend, attrs: { shape: x.shape } });\n    backend.disposeIntermediateTensorInfo(complexOutputReshaped);\n    return complexOutputReshaped;\n}\n//# sourceMappingURL=FFT_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT } from '@tensorflow/tfjs-core';\nimport { fftImpl } from './FFT_impl';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    return fftImpl(input, false /* inverse */, backend);\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'webgl',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT } from '@tensorflow/tfjs-core';\nimport { fftImpl } from './FFT_impl';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    return fftImpl(input, true /* inverse */, backend);\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'webgl',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class MeanProgram {\n    constructor(reduceInfo, divisor) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `sumValue += dot(values, ones);`;\n        if (divisor != null) {\n            const denominator = 1 / divisor;\n            updateSnippet = `sumValue += dot(values * ${util.isInt(denominator) ? denominator.toPrecision(2) :\n                denominator}, ones);`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return 0.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${updateSnippet}\n        }\n        setOutput(sumValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mean_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { MeanProgram } from '../mean_gpu';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        let program;\n        let previousResult;\n        if (reductionType === 'mean') {\n            program = i === 0 ?\n                new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) :\n                new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });\n        }\n        else {\n            program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        }\n        previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeIntermediateTensorInfo(previousResult);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function meanImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, 'float32', 'mean', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Mean_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Mean, util } from '@tensorflow/tfjs-core';\nimport { meanImpl } from './Mean_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const meanConfig = {\n    kernelName: Mean,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { keepDims, axis } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const meanInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        const intermediates = [];\n        let meanInput = x;\n        if (meanInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(meanInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const meanInputData = webglBackend.texData.get(meanInput.dataId);\n                meanInputData.values = meanInputValues;\n            }\n            else {\n                meanInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            intermediates.push(meanInput);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);\n        const [meanOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(meanInput.shape, axes);\n        let outShape = meanOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(meanOutShape, origAxes);\n        }\n        const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);\n        for (const i of intermediates) {\n            webglBackend.disposeIntermediateTensorInfo(i);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class MirrorPadProgram {\n    constructor(xShape, paddings, mode) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        const offset = mode === 'reflect' ? 0 : 1;\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${offset};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${offset};\n          }\n          setOutput(getX(outC - start));\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${dtype} start = ${dtype}(${start});\n      ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outC = getOutputCoords();\n        for (int i = 0; i < ${rank}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${offset};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};\n          }\n        }\n        ${dtype} coords = outC - start;\n        setOutput(getX(${unpackedCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mirror_pad_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\n/**\n * Example shader code for\n * `mirrorPad(tf.tensor1d([1, 2, 3], 'int32'), [[2, 2]], 'reflect')`\n * ```\n *    const int start = int(2);\n *    const int end = int(5);\n *\n *    void main() {\n *       int outputLoc = getOutputCoords();\n *       vec4 result = vec4(0.);\n *\n *       int rc = outputLoc;\n *\n *       int source = rc;\n *       if (source < start) {\n *         source = start * 2 - source - 0;\n *       } else if (source >= end) {\n *         source = (end - 1) * 2 - source + 0;\n *       }\n *       source -= start;\n *\n *       result[0] = getChannel(getX(source), source);\n *       rc += 1;\n *       if(rc < 6) {\n *          int source = rc;\n *          if (source < start) {\n *            source = start * 2 - source - 0;\n *          } else if (source >= end) {\n *            source = (end - 1) * 2 - source + 0;\n *          }\n *          source -= start;\n *\n *         result[1] = getChannel(getX(source), source);\n *       }\n *\n *       setOutput(result);\n *     }\n * ```\n */\nexport class MirrorPadPackedProgram {\n    constructor(xShape, paddings, mode) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const offset = mode === 'reflect' ? 0 : 1;\n        let mainLoop = '';\n        if (rank === 1) {\n            const padSetup = `\n        ${dtype} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${offset};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${offset};\n        }\n        source -= start;\n      `;\n            mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        else {\n            const padSetup = `\n        ${dtype} source = rc;\n        ${dtype} lt = ${dtype}(lessThan(source, start));\n        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));\n        ${dtype} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${offset}) +\n                gte * ((end - 1) * 2 - source + ${offset});\n        source -= start;\n      `;\n            mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n        rc = outputLoc;\n        ${coords[rank - 2]} += 1;\n        if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {\n          ${padSetup}\n          result[2] = getChannel(getX(${source.join()}), ${innerDims});\n          ${coords[rank - 1]} += 1;\n          if(${cLimit}) {\n            ${padSetup}\n            result[3] = getChannel(getX(${source.join()}), ${innerDims});\n          }\n        }\n      `;\n        }\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mirror_pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, MirrorPad } from '@tensorflow/tfjs-core';\nimport { MirrorPadProgram } from '../mirror_pad_gpu';\nimport { MirrorPadPackedProgram } from '../mirror_pad_packed_gpu';\nexport const mirrorPadKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new MirrorPadPackedProgram(x.shape, paddings, mode) :\n        new MirrorPadProgram(x.shape, paddings, mode);\n    const output = backend.runWebGLProgram(program, [x], x.dtype);\n    return output;\n};\nexport const mirrorPadConfig = {\n    kernelName: MirrorPad,\n    backendName: 'webgl',\n    kernelFunc: mirrorPadKernelFunc,\n};\n//# sourceMappingURL=MirrorPad.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, env, Multiply } from '@tensorflow/tfjs-core';\nimport * as binaryop_complex_gpu from '../binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from '../binaryop_complex_gpu';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { multiplyImplCPU as cpuMultiply } from '../kernel_utils/shared';\nimport { complex } from './Complex';\nconst MUL = 'return a * b;';\nexport function multiply(args) {\n    const { inputs, backend } = args;\n    const { a, b } = inputs;\n    const dtype = backend_util.upcastType(a.dtype, b.dtype);\n    if (a.dtype === 'complex64') {\n        const aData = backend.texData.get(a.dataId);\n        const bData = backend.texData.get(b.dataId);\n        const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n        const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n        const inputs = [\n            {\n                dataId: aData.complexTensorInfos.real.dataId,\n                dtype: aData.complexTensorInfos.real.dtype,\n                shape: a.shape\n            },\n            {\n                dataId: aData.complexTensorInfos.imag.dataId,\n                dtype: aData.complexTensorInfos.imag.dtype,\n                shape: a.shape\n            },\n            {\n                dataId: bData.complexTensorInfos.real.dataId,\n                dtype: bData.complexTensorInfos.real.dtype,\n                shape: b.shape\n            },\n            {\n                dataId: bData.complexTensorInfos.imag.dataId,\n                dtype: bData.complexTensorInfos.imag.dtype,\n                shape: b.shape\n            }\n        ];\n        const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');\n        const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');\n        const complexOutput = complex({ inputs: { real: realPart, imag: imagPart }, backend });\n        backend.disposeIntermediateTensorInfo(realPart);\n        backend.disposeIntermediateTensorInfo(imagPart);\n        // TODO(annxingyuan): CPU forwarding for complex inputs.\n        return complexOutput;\n    }\n    if (backend.shouldExecuteOnCPU([a, b])) {\n        const aData = backend.texData.get(a.dataId);\n        const bData = backend.texData.get(b.dataId);\n        const [outValues, outShape] = cpuMultiply(a.shape, b.shape, aData.values, bData.values, dtype);\n        const out = backend.makeTensorInfo(outShape, dtype);\n        const outData = backend.texData.get(out.dataId);\n        outData.values = outValues;\n        return out;\n    }\n    let program;\n    if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n        program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);\n    }\n    else {\n        program = new BinaryOpProgram(MUL, a.shape, b.shape);\n    }\n    return backend.runWebGLProgram(program, [a, b], dtype);\n}\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'webgl',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sin = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sin,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const square = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: square,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifference = binaryKernelFunc({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifference,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nimport { subImplCPU as cpuSub } from '../kernel_utils/shared';\nconst SUB = 'return a - b;';\nexport const subKernelFunc = binaryKernelFunc({\n    opSnippet: SUB,\n    packedOpSnippet: SUB,\n    supportsComplex: true,\n    cpuKernelImpl: cpuSub\n});\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'webgl',\n    kernelFunc: subKernelFunc\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tan = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tan,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { addConfig } from './kernels/Add';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { meanConfig } from './kernels/Mean';\nimport { mirrorPadConfig } from './kernels/MirrorPad';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { realConfig } from './kernels/Real';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    addConfig,\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    divConfig,\n    fftConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    meanConfig,\n    mirrorPadConfig,\n    multiplyConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    realConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    subConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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"../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/equal.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/erf.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/exp.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/expand_dims.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/expm1.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/fft.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/flatten.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/floor.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/floorDiv.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/gather.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/greater_equal_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/greater_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/greater_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/greater.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/ifft.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/irfft.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/is_finite.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/is_inf.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/is_nan.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/leaky_relu.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/less_equal_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/less_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/less_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/less.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/local_response_normalization.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/log_sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/log_softmax.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/log_sum_exp.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/log.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/log1p.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/logical_and.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/logical_not.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/logical_or.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/logical_xor.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/mat_mul.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/max_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/max.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/maximum_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/maximum.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/mean.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/min.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/minimum_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/minimum.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/mirror_pad.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/mod_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/mod.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/mul_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/mul.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/neg.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/norm.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/not_equal_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/not_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/one_hot.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/ones_like.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/pad.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/pool.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/pow_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/pow.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/prelu.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/prod.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/reciprocal.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/relu.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/relu6.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/reshape_as.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/reshape.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/resize_bilinear.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/resize_nearest_neighbor.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/reverse.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/rfft.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/round.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/rsqrt.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/selu.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/separable_conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sign.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sin.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sinh.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/softmax.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/softplus.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/space_to_batch_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/split.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/sqrt.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/square.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/squared_difference.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/squared_difference_strict.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/squeeze.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/stack.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/step.ts", 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"../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/unsorted_segment_sum.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/unstack.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/where.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/zeros_like.ts", "../node_modules/@tensorflow/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts", "../node_modules/@tensorflow/tfjs-core/src/index.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/NeuralNetwork.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
+  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "", "// 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", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, "", "// 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", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, "", "// 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", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, "", "// 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", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: 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shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '../dist/tfjs.esm.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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diff --git a/dist/face-api.json b/dist/face-api.json
new file mode 100644
index 0000000..7043a0d
--- /dev/null
+++ b/dist/face-api.json
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diff --git a/dist/face-api.node-gpu.js b/dist/face-api.node-gpu.js
new file mode 100644
index 0000000..4fbf9b6
--- /dev/null
+++ b/dist/face-api.node-gpu.js
@@ -0,0 +1,9 @@
+
+  /*
+  Face-API
+  homepage: <https://github.com/vladmandic/face-api>
+  author: <https://github.com/vladmandic>'
+  */
+
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2),require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node-gpu")))}),require_isNodejs=__commonJS((exports2,module2)=>{__export(exports2,{isNodejs:()=>isNodejs3});function isNodejs3(){return typeof global=="object"&&!0&&typeof module2!="undefined"&&typeof process!="undefined"&&!!process.version}});__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});var tf42=__toModule(require_tfjs_esm()),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});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var 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});var tf=__toModule(require_tfjs_esm()),Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven(num){return num%2===0}function round(num,prec=2){let f=Math.pow(10,prec);return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&0<=num&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(Math.pow(this.x,2)+Math.pow(this.y,2))}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${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`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),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)}`);let[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}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(){let[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(){let[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,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,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){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{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()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{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})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmount2=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmount2,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],paddingTensorPrepend=isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,tensorsToStack=[paddingTensorPrepend,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{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){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},options);return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),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){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class 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)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{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){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!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]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){let extension={detection};return Object.assign({},sourceObj,extension)}function createBrowserEnv(){let fetch=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},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}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}let 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}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=function(){if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=function(){if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},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}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var isNodejs=__toModule(require_isNodejs()),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(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{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}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}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={}));var DrawTextFieldOptions=class{constructor(options={}){let{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}},DrawTextField=class{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){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions(Object.assign({},defaultDrawLabelOptions,drawLabelOptions))}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){let detectionsArray=Array.isArray(detections)?detections:[detections];detectionsArray.forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}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)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{if(!(buf instanceof Blob))return reject("bufferToImage - expected buf to be of type: Blob");let reader=new FileReader;reader.onload=()=>{if(typeof reader.result!="string")return reject("bufferToImage - expected reader.result to be a string, in onload");let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require("@tensorflow/tfjs-core"));function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;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)){let 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}let 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");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),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}`)}),batchTensor=tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3);return batchTensor})}};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");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height));return boxes.map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)),faceTensors=boxes.map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]));return faceTensors})}async function fetchOrThrow(url,init){let fetch=env.getEnv().fetch,res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),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: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm()),tf8=__toModule(require_tfjs_esm()),NeuralNetwork=class{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){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{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})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){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`);let 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`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(filePath2=>readFile(filePath2).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{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");let 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}),{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}}},tf10=__toModule(require_tfjs_esm()),tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let 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])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,filterSize,mappedPrefix){let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm()),SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return function(prefix){let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return function(originalPath,paramRank,mappedPath){let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return function(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),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)}},tf17=__toModule(require_tfjs_esm()),tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams3(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),fc=extractFCParams(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap3(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams3(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap3(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{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)}},FaceExpressionNet=class 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){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){let extension={expressions};return Object.assign({},sourceObj,extension)}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){let faceExpressionsArray=Array.isArray(faceExpressions)?faceExpressions:[faceExpressions];faceExpressionsArray.forEach(e=>{let 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");let sorted=expr.asSortedArray(),resultsToDisplay=sorted.filter(expr2=>expr2.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0),drawTextField=new DrawTextField(resultsToDisplay.map(expr2=>`${expr2.expression} (${round(expr2.probability)})`),anchor);drawTextField.draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims),extension={landmarks,unshiftedLandmarks,alignedRect};return Object.assign({},sourceObj,extension)}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,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)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{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(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let 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){let faceLandmarksArray=Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks];faceLandmarksArray.forEach(f=>{let 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)})}var tf20=__toModule(require_tfjs_esm()),tf19=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams5(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory3(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),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: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory3(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap5(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory3(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),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)}};function extractParams7(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap7(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams7(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap7(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm()),FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w),landmarkTensors=output.mul(tf21.fill([batchSize,136],inputSize,"float32")).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(()=>{let 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){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),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)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),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)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm()),tf24=__toModule(require_tfjs_esm()),tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),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: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams9(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory5(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return{params,paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap9(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory6(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),fc=weightMap.fc;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let 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 disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3],isAdjustShape=pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2];if(isAdjustShape){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf27.scalar(256)),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);let globalAvg=out.mean([1,2]),fullyConnected=tf27.matMul(globalAvg,params.fc);return fullyConnected})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap9(weightMap)}extractParams(weights){return extractParams9(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){let extension={descriptor};return Object.assign({},sourceObj,extension)}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){let extension={age};return Object.assign({},sourceObj,extension)}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){let extension={gender,genderProbability};return Object.assign({},sourceObj,extension)}var tf34=__toModule(require_tfjs_esm()),tf28=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return 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`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),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(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),box_predictor_0={box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1={box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2={box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3={box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4={box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},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){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory7(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),extra_dim=tf28.tensor3d(extractWeights(5118*4),[1,5118,4]),output_layer={extra_dim};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${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){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory8(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm()),tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),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,out=pointwiseConvLayer(x,params.conv_0,[2,2]),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];if(convPairParams.forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou3=IOU(boxes,c.boxIndex,selected[j]);if(iou3===0)continue;if(c.score*=suppressFunc(iou3),c.score<=scoreThreshold)break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],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){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),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(()=>{let batchSize=boxPredictions.shape[0],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]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm()),tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||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}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{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={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),iouThreshold=.5,indices=nonMaxSuppression2(boxes,scoresData,maxResults,iouThreshold,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[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)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap11(weightMap)}extractParams(weights){return extractParams11(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.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)],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)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",tf39=__toModule(require_tfjs_esm()),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: ${JSON.stringify(config2.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm()),tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv3(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory9(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams13(weights,config2,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory9(extractWeights,paramMappings),params;if(config2.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config2.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),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: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory10(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap13(weightMap,config2){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory10(weightMap,paramMappings),params;if(config2.withSeparableConvs){let 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")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}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={}));var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.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}},TinyYolov2Base2=class 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);return 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),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv3(x,params.conv0);return 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,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),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={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]),indices=nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0),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){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,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){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),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]}),results=[],scoresData=await scoresTensor.array(),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++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,width2=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,height2=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-width2/2,y=ctY-height2/2,pos={row,col,anchor},{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})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,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=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config2=Object.assign({},{withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"]},withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0});super(config2)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){let 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)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf41=__toModule(require_tfjs_esm()),tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,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)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config2={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config2)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){let 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)}},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},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov23=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,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)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,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)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{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)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask,descriptors=await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}));return descriptors.map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class 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}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),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)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,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=>{let detections=await this.run();return res(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async res=>{let detection=await this.run();return res(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+Math.pow(diff,2),0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,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){let 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){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{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)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var version="0.8.9",node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};
+//# sourceMappingURL=face-api.node-gpu.js.map
diff --git a/dist/face-api.node-gpu.js.map b/dist/face-api.node-gpu.js.map
new file mode 100644
index 0000000..0dc51b6
--- /dev/null
+++ b/dist/face-api.node-gpu.js.map
@@ -0,0 +1,7 @@
+{
+  "version": 3,
+  "sources": ["../src/tfjs/tf-node-gpu.js", "../src/env/isNodejs.ts", "../src/index.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/utils/index.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
+  "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../dist/tfjs.esm.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '../dist/tfjs.esm.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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+  "names": []
+}
diff --git a/dist/face-api.node-gpu.json b/dist/face-api.node-gpu.json
new file mode 100644
index 0000000..207bba7
--- /dev/null
+++ b/dist/face-api.node-gpu.json
@@ -0,0 +1,2614 @@
+{
+  "inputs": {
+    "dist/tfjs.esm.js": {
+      "bytes": 1046,
+      "imports": []
+    },
+    "src/draw/drawContour.ts": {
+      "bytes": 520,
+      "imports": []
+    },
+    "src/classes/Dimensions.ts": {
+      "bytes": 731,
+      "imports": [
+        {
+          "path": "src/utils/index.ts"
+        }
+      ]
+    },
+    "src/utils/index.ts": {
+      "bytes": 1770,
+      "imports": [
+        {
+          "path": "dist/tfjs.esm.js"
+        },
+        {
+          "path": "src/classes/index.ts"
+        },
+        {
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+}
diff --git a/dist/face-api.node.js b/dist/face-api.node.js
index 51990b1..4b4af47 100644
--- a/dist/face-api.node.js
+++ b/dist/face-api.node.js
@@ -1,4162 +1,9 @@
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KR=Object.freeze({__proto__:null,createScalarValue:K2,toTypedArray:na,now:cr,fetch:X2,encodeString:qp,decodeString:rh,shuffle:g,clamp:v,nearestLargerEven:w,sum:T,randUniform:N,distSquared:E,assert:k,assertShapesMatch:$,assertNonNull:M,flatten:G,sizeFromShape:O,isScalarShape:H,arraysEqual:K,isInt:nt,tanh:ct,sizeToSquarishShape:dt,createShuffledIndices:Ct,rightPad:St,repeatedTry:It,inferFromImplicitShape:Gt,parseAxisParam:Et,squeezeShape:Wt,getTypedArrayFromDType:_t,getArrayFromDType:Kt,checkConversionForErrors:Ne,isValidDtype:ce,hasEncodingLoss:me,isTypedArray:Re,bytesPerElement:gn,bytesFromStringArray:en,isString:dn,isBoolean:Zn,isNumber:Or,inferDtype:Us,isFunction:Ln,nearestDivisor:ms,computeStrides:Jt,toNestedArray:Mr,makeOnesTypedArray:Nv,makeZerosTypedArray:fu,makeZerosNestedTypedArray:Iv,assertNonNegativeIntegerDimensions:Ev,locToIndex:Qr,indexToLoc:ei,isPromise:ni});class XR{constructor(t,e){this.backendTimer=t,this.logger=e,e==null&&(this.logger=new JR)}profileKernel(t,e,r){let 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Reusing existing backend factory.`),!1):(this.registryFactory[t]={factory:e,priority:r},!0)}async setBackend(t){if(this.registryFactory[t]==null)throw new Error(`Backend name '${t}' not found in registry`);if(this.backendName=t,this.registry[t]==null){this.backendInstance=null;let{success:e,asyncInit:r}=this.initializeBackend(t),s=r?await e:e;if(!s)return!1}return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new XR(this.backendInstance),!0}setupRegisteredKernels(){let t=Gp(this.backendName);t.forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){let e=Gp(t);e.forEach(r=>{r.disposeFunc!=null&&r.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let r=e.factory();if(r&&!(r instanceof f)&&typeof r.then=="function"){let s=++this.pendingBackendInitId,u=r.then(l=>s<this.pendingBackendInitId?!1:(this.registry[t]=l,this.pendingBackendInit=null,!0)).catch(l=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${t} failed`),console.warn(l.stack||l.message)),!1));return this.pendingBackendInit=u,{success:u,asyncInit:!0}}else return this.registry[t]=r,{success:!0,asyncInit:!1}}catch(r){return console.warn(`Initialization of backend ${t} failed`),console.warn(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(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((t,e)=>this.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let r=t[e],{success:s,asyncInit:u}=this.initializeBackend(r);if(u||s)return{name:r,asyncInit:u}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(t,e){let r=this.state.tensorInfo.get(e),s=r.backend,u=this.readSync(e);s.disposeData(e),r.backend=t,t.move(e,u,r.shape,r.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(t,e){let r=null;if(e==null){if(typeof t!="function")throw new Error("Please provide a function to tidy()");e=t}else{if(typeof t!="string"&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof e!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=t}let s;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(s),()=>(s=e(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(t,e,r){t();try{let s=r();return e(),s}catch(s){throw e(),s}}nextTensorId(){return ku.nextTensorId++}nextVariableId(){return ku.nextVariableId++}clone(t){let e=this.makeTensorFromDataId(t.dataId,t.shape,t.dtype),r={x:t},s=l=>({x:()=>{let h="float32",p={x:l},m={dtype:h};return J.runKernelFunc(y=>y.cast(l,h),p,null,pu,m)}}),u=[];return this.addTapeNode(this.state.activeScope.name,r,[e],s,u,{}),e}runKernel(t,e,r,s,u){let l=null,h=null;return this.runKernelFunc(l,e,h,t,r,s,u)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,r){let s=this.backend.numDataIds(),u=0;r.forEach(p=>{u+=p.dtype==="complex64"?3:1});let l=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],h=s-e-u-l;if(h>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${h} data ids) after running '${t}'`)}runKernelFunc(t,e,r,s,u,l,h){let p,m=[],y=this.isTapeOn();s==null&&(s=this.state.activeScope!=null?this.state.activeScope.name:"");let b=this.state.numBytes,x=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let S,C=gy(s,this.backendName),I;if(C!=null)S=()=>{let R=this.backend.numDataIds();I=C.kernelFunc({inputs:e,attrs:u,backend:this.backend});let A=Array.isArray(I)?I:[I];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,R,A);let L=A.map(({dataId:_,shape:B,dtype:V})=>this.makeTensorFromDataId(_,B,V));if(y){let _=this.getTensorsForGradient(s,e,L);if(_==null){h==null&&(h=[]);let B=L.filter((V,q)=>h[q]);_=(l||[]).slice().concat(B)}m=this.saveTensorsForBackwardMode(_)}return L};else{let R=A=>{if(!y)return;m=A.map(L=>this.keep(this.clone(L)))};S=()=>{let A=this.backend.numDataIds();I=this.tidy(()=>t(this.backend,R));let L=Array.isArray(I)?I:[I];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(s,A,L),L}}let D;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?p=S():(D=this.profiler.profileKernel(s,e,()=>S()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(D),p=D.outputs)}),y&&this.addTapeNode(s,e,p,r,m,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:s,bytesAdded:this.state.numBytes-b,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-x,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(e).map(R=>e[R]!=null?e[R].shape:null),outputShapes:p.map(R=>R.shape),kernelTimeMs:D.timeMs,extraInfo:D.extraInfo}),Array.isArray(I)?p:p[0]}saveTensorsForBackwardMode(t){let e=t.map(r=>this.keep(this.clone(r)));return e}getTensorsForGradient(t,e,r){let s=vy(t);if(s!=null){let u=s.inputsToSave||[],l=s.outputsToSave||[],h;s.saveAllInputs?(k(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),h=Object.keys(e).map(m=>e[m])):h=u.map(m=>e[m]);let p=r.filter((m,y)=>l[y]);return h.concat(p)}return null}makeTensor(t,e,r,s){if(t==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",s=s||this.backend;let u=t;r==="string"&&dn(t[0])&&(u=t.map(p=>qp(p)));let l=s.write(u,e,r),h=new at(e,r,l,this.nextTensorId());if(this.incRef(h,s),r==="string"){let p=this.state.tensorInfo.get(l),m=en(u);this.state.numBytes+=m-p.bytes,p.bytes=m}return h}makeTensorFromDataId(t,e,r,s){r=r||"float32";let u=new at(e,r,t,this.nextTensorId());return this.incRef(u,s),u}makeVariable(t,e=!0,r,s){r=r||this.nextVariableId().toString(),s!=null&&s!==t.dtype&&(t=t.cast(s));let u=new ih(t,e,r,this.nextTensorId());if(this.state.registeredVariables[u.name]!=null)throw new Error(`Variable with name ${u.name} was already registered`);return this.state.registeredVariables[u.name]=u,this.incRef(u,this.backend),u}incRef(t,e){let r=this.state.tensorInfo.has(t.dataId)?this.state.tensorInfo.get(t.dataId).refCount:0;if(this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++,r===0){this.state.numDataBuffers++;let s=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(s=t.size*gn(t.dtype)),this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:s,refCount:0}),this.state.numBytes+=s}this.state.tensorInfo.get(t.dataId).refCount++,t instanceof ih||this.track(t)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;this.state.numTensors--,t.dtype==="string"&&this.state.numStringTensors--;let e=this.state.tensorInfo.get(t.dataId),r=e.refCount;r<=1?(t.dtype!=="complex64"&&(this.state.numBytes-=e.bytes),this.state.numDataBuffers--,e.backend.disposeData(t.dataId),this.state.tensorInfo.delete(t.dataId)):this.state.tensorInfo.get(t.dataId).refCount--}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,r,s,u,l){let h={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:r,saved:u},p=vy(t);p!=null&&(s=p.gradFunc),s!=null&&(h.gradient=m=>(m=m.map((y,b)=>{if(y==null){let x=r[b],S=fu(x.size,x.dtype);return this.makeTensor(S,x.shape,x.dtype)}return y}),s(m.length>1?m:m[0],u,l))),this.state.activeTape.push(h)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=mo(t),r=new Set(e.map(u=>u.id));for(let u=0;u<this.state.activeScope.track.length;u++){let l=this.state.activeScope.track[u];!l.kept&&!r.has(l.id)&&l.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],e.forEach(u=>{!u.kept&&u.scopeId===s.id&&this.track(u)})}gradients(t,e,r,s=!1){if(k(e.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let u=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));k(u instanceof at,()=>"The result y returned by f() must be a tensor.");let l=ZR(this.state.activeTape,e,u);if(!s&&l.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let h={};h[u.id]=r==null?uP(u.shape):r,QR(h,l,m=>this.tidy(m),cP);let p=e.map(m=>h[m.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(m=>{for(let y of m.saved)y.dispose()}),this.state.activeTape=null),{value:u,grads:p}})}customGrad(t){return k(Ln(t),()=>"The f passed in customGrad(f) must be a function."),(...e)=>{k(e.every(u=>u instanceof at),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let r,s={};return e.forEach((u,l)=>{s[l]=u}),this.runKernelFunc((u,l)=>(r=t(...e,l),k(r.value instanceof at,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),k(Ln(r.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),r.value),s,(u,l)=>{let h=r.gradFunc(u,l),p=Array.isArray(h)?h:[h];k(p.length===e.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),k(p.every(y=>y instanceof at),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let m={};return p.forEach((y,b)=>{m[b]=()=>y}),m})}}readSync(t){let e=this.state.tensorInfo.get(t);return e.backend.readSync(t)}read(t){let e=this.state.tensorInfo.get(t);return e.backend.read(t)}async time(t){let e=cr(),r=await this.backend.time(t);return r.wallMs=cr()-e,r}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new eS;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}ku.nextTensorId=0,ku.nextVariableId=0;function uP(n){let t=Nv(O(n),"float32");return J.makeTensor(t,n,"float32")}function nS(){let n=w2();if(n._tfengine==null){let t=new b2(n);n._tfengine=new ku(t)}return WR(n._tfengine.ENV),nP(()=>n._tfengine),n._tfengine}let J=nS();function cP(n,t){let e={a:n,b:t};return J.runKernelFunc((r,s)=>{let u=r.add(n,t);return s([n,t]),u},e,null,ri)}function lP(){return typeof navigator!="undefined"&&navigator!=null}function rS(){if(lP()){let n=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(n)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(n.substr(0,4))}return!1}function Sy(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var hP=Object.freeze({__proto__:null,isMobile:rS,isBrowser:Sy});let go=ft();go.registerFlag("DEBUG",()=>!1,n=>{n&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")}),go.registerFlag("IS_BROWSER",()=>Sy()),go.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined"),go.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)),go.registerFlag("PROD",()=>!1),go.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>go.getBool("DEBUG")),go.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0),go.registerFlag("IS_TEST",()=>!1);function Hs(n,t){let e=n;if(Re(n))return t==="string"?[]:[n.length];if(!Array.isArray(n))return[];let r=[];for(;Array.isArray(e)||Re(e)&&t!=="string";)r.push(e.length),e=e[0];return Array.isArray(n)&&ft().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&sS(n,r,[]),r}function sS(n,t,e){if(e=e||[],!Array.isArray(n)&&!Re(n)){k(t.length===0,()=>`Element arr[${e.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}k(t.length>0,()=>`Element arr[${e.join("][")}] should be a primitive, but is an array of ${n.length} elements`),k(n.length===t[0],()=>`Element arr[${e.join("][")}] should have ${t[0]} elements, but has ${n.length} elements`);let r=t.slice(1);for(let s=0;s<n.length;++s)sS(n[s],r,e.concat(s))}function oS(n,t,e,r){if(n==null)return;if(n!=="numeric"&&n!==t||n==="numeric"&&t==="string")throw new Error(`Argument '${e}' passed to '${r}' must be ${n} tensor, but got ${t} tensor`)}function z(n,t,e,r="numeric"){if(n instanceof at)return oS(r,n.dtype,t,e),n;let s=Us(n);if(s!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(s=r),oS(r,s,t,e),n==null||!Re(n)&&!Array.isArray(n)&&typeof n!="number"&&typeof n!="boolean"&&typeof n!="string"){let p=n==null?"null":n.constructor.name;throw new Error(`Argument '${t}' passed to '${e}' must be a Tensor or TensorLike, but got '${p}'`)}let u=Hs(n,s);!Re(n)&&!Array.isArray(n)&&(n=[n]);let l=!0,h=s!=="string"?na(n,s):G(n,[],l);return J.makeTensor(h,u,s)}function uh(n,t,e,r="numeric"){if(!Array.isArray(n))throw new Error(`Argument ${t} passed to ${e} must be a \`Tensor[]\` or \`TensorLike[]\``);let s=n;return s.map((u,l)=>z(u,`${t}[${l}]`,e),r)}let aS="__op";function X(n){let t=Object.keys(n);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. 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b=Cy[h],x=n.slice(s,s+m*b);if(h==="float32")y=new Float32Array(x);else if(h==="int32")y=new Int32Array(x);else if(h==="bool")y=new Uint8Array(x);else if(h==="complex64"){y=new Float32Array(x);let S=new Float32Array(y.length/2),C=new Float32Array(y.length/2);for(let R=0;R<S.length;R++)S[R]=y[R*2],C[R]=y[R*2+1];let I=vn(S,p,"float32"),D=vn(C,p,"float32");e[l]=vo(I,D),I.dispose(),D.dispose()}else throw new Error(`Unsupported dtype in weight '${l}': ${h}`);s+=m*b}h!=="complex64"&&(e[l]=vn(y,p,h))}return e}function pP(n){if(n===null)throw new Error(`Invalid input value: ${JSON.stringify(n)}`);let t=0,e=[];n.forEach(u=>{if(t+=u.byteLength,e.push(u.byteLength===u.buffer.byteLength?u:new u.constructor(u)),!(u instanceof Float32Array||u instanceof Int32Array||u instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${u.constructor.name}`)});let r=new Uint8Array(t),s=0;return e.forEach(u=>{r.set(new Uint8Array(u.buffer),s),s+=u.byteLength}),r.buffer}let Iy=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function iS(n){return Iy?Buffer.byteLength(n):new Blob([n]).size}function dP(n){if(Iy)return Buffer.from(n).toString("base64");let t=new Uint8Array(n),e="";for(let r=0,s=t.length;r<s;r++)e+=String.fromCharCode(t[r]);return btoa(e)}function mP(n){if(Iy){let r=Buffer.from(n,"base64");return r.buffer.slice(r.byteOffset,r.byteOffset+r.byteLength)}let t=atob(n),e=new Uint8Array(t.length);for(let r=0;r<t.length;++r)e.set([t.charCodeAt(r)],r);return e.buffer}function Zp(n){if(n.length===1)return n[0];let t=0;n.forEach(s=>{t+=s.byteLength});let e=new Uint8Array(t),r=0;return n.forEach(s=>{e.set(new Uint8Array(s),r),r+=s.byteLength}),e.buffer}function uS(n){let t="/";for(n=n.trim();n.endsWith(t);)n=n.slice(0,n.length-1);let e=n.split(t);return e[e.length-1]}function ch(n){if(n.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received 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Float32Array(s)}}class rn{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return rn.instance==null&&(rn.instance=new rn),rn.instance}static registerSaveRouter(t){rn.getInstance().saveRouters.push(t)}static registerLoadRouter(t){rn.getInstance().loadRouters.push(t)}static getSaveHandlers(t){return rn.getHandlers(t,"save")}static getLoadHandlers(t,e){return rn.getHandlers(t,"load",e)}static getHandlers(t,e,r){let s=[],u=e==="load"?rn.getInstance().loadRouters:rn.getInstance().saveRouters;return u.forEach(l=>{let h=l(t,r);h!==null&&s.push(h)}),s}}let wP=n=>rn.registerSaveRouter(n),xP=n=>rn.registerLoadRouter(n),Ey=n=>rn.getSaveHandlers(n),Dy=(n,t)=>rn.getLoadHandlers(n,t);let Qp="tensorflowjs",$y=1,si="models_store",sa="model_info_store";async function rht(){let n=Ay();return new Promise((t,e)=>{let r=n.deleteDatabase(Qp);r.onsuccess=()=>t(),r.onerror=s=>e(s)})}function Ay(){if(!ft().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let n=typeof window=="undefined"?self:window,t=n.indexedDB||n.mozIndexedDB||n.webkitIndexedDB||n.msIndexedDB||n.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function _y(n){let t=n.result;t.createObjectStore(si,{keyPath:"modelPath"}),t.createObjectStore(sa,{keyPath:"modelPath"})}class oi{constructor(t){if(this.indexedDB=Ay(),t==null||!t)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=t}async save(t){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,t)}async load(){return this.databaseAction(this.modelPath)}databaseAction(t,e){return new Promise((r,s)=>{let u=this.indexedDB.open(Qp,$y);u.onupgradeneeded=()=>_y(u),u.onsuccess=()=>{let l=u.result;if(e==null){let h=l.transaction(si,"readonly"),p=h.objectStore(si),m=p.get(this.modelPath);m.onsuccess=()=>{if(m.result==null)return l.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));r(m.result.modelArtifacts)},m.onerror=y=>(l.close(),s(m.error)),h.oncomplete=()=>l.close()}else{let h=ch(e),p=l.transaction(sa,"readwrite"),m=p.objectStore(sa),y=m.put({modelPath:this.modelPath,modelArtifactsInfo:h}),b;y.onsuccess=()=>{b=l.transaction(si,"readwrite");let x=b.objectStore(si),S=x.put({modelPath:this.modelPath,modelArtifacts:e,modelArtifactsInfo:h});S.onsuccess=()=>r({modelArtifactsInfo:h}),S.onerror=C=>{m=p.objectStore(sa);let I=m.delete(this.modelPath);I.onsuccess=()=>(l.close(),s(S.error)),I.onerror=D=>(l.close(),s(S.error))}},y.onerror=x=>(l.close(),s(y.error)),p.oncomplete=()=>{b==null?l.close():b.oncomplete=()=>l.close()}}},u.onerror=l=>s(u.error)})}}oi.URL_SCHEME="indexeddb://";let cS=n=>ft().getBool("IS_BROWSER")&&(!Array.isArray(n)&&n.startsWith(oi.URL_SCHEME))?TP(n.slice(oi.URL_SCHEME.length)):null;rn.registerSaveRouter(cS),rn.registerLoadRouter(cS);function TP(n){return new oi(n)}function kP(n){return n.startsWith(oi.URL_SCHEME)?n.slice(oi.URL_SCHEME.length):n}class SP{constructor(){this.indexedDB=Ay()}async listModels(){return new Promise((t,e)=>{let r=this.indexedDB.open(Qp,$y);r.onupgradeneeded=()=>_y(r),r.onsuccess=()=>{let s=r.result,u=s.transaction(sa,"readonly"),l=u.objectStore(sa),h=l.getAll();h.onsuccess=()=>{let p={};for(let m of h.result)p[m.modelPath]=m.modelArtifactsInfo;t(p)},h.onerror=p=>(s.close(),e(h.error)),u.oncomplete=()=>s.close()},r.onerror=s=>e(r.error)})}async removeModel(t){return t=kP(t),new Promise((e,r)=>{let s=this.indexedDB.open(Qp,$y);s.onupgradeneeded=()=>_y(s),s.onsuccess=()=>{let u=s.result,l=u.transaction(sa,"readwrite"),h=l.objectStore(sa),p=h.get(t),m;p.onsuccess=()=>{if(p.result==null)return u.close(),r(new Error(`Cannot find model with path '${t}' in IndexedDB.`));{let y=h.delete(t),b=()=>{m=u.transaction(si,"readwrite");let x=m.objectStore(si),S=x.delete(t);S.onsuccess=()=>e(p.result.modelArtifactsInfo),S.onerror=C=>r(p.error)};y.onsuccess=b,y.onerror=x=>(b(),u.close(),r(p.error))}},p.onerror=y=>(u.close(),r(p.error)),l.oncomplete=()=>{m==null?u.close():m.oncomplete=()=>u.close()}},s.onerror=u=>r(s.error)})}}let qs="/",ai="tensorflowjs_models",lS="info",CP="model_topology",NP="weight_specs",IP="weight_data",EP="model_metadata";function sht(){if(!ft().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");let n=window.localStorage,t=[];for(let e=0;e<n.length;++e){let r=n.key(e),s=ai+qs;if(r.startsWith(s)&&r.length>s.length){n.removeItem(r);let u=fS(r);t.indexOf(u)===-1&&t.push(u)}}return t}function hS(n){return{info:[ai,n,lS].join(qs),topology:[ai,n,CP].join(qs),weightSpecs:[ai,n,NP].join(qs),weightData:[ai,n,IP].join(qs),modelMetadata:[ai,n,EP].join(qs)}}function fS(n){let t=n.split(qs);if(t.length<3)throw new Error(`Invalid key format: ${n}`);return t.slice(1,t.length-1).join(qs)}function DP(n){return n.startsWith(ii.URL_SCHEME)?n.slice(ii.URL_SCHEME.length):n}class ii{constructor(t){if(!ft().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,t==null||!t)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=t,this.keys=hS(this.modelPath)}async save(t){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let e=JSON.stringify(t.modelTopology),r=JSON.stringify(t.weightSpecs),s=ch(t);try{return this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,e),this.LS.setItem(this.keys.weightSpecs,r),this.LS.setItem(this.keys.weightData,dP(t.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:t.format,generatedBy:t.generatedBy,convertedBy:t.convertedBy,userDefinedMetadata:t.userDefinedMetadata})),{modelArtifactsInfo:s}}catch(u){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){let t=JSON.parse(this.LS.getItem(this.keys.info));if(t==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(t.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let e={},r=JSON.parse(this.LS.getItem(this.keys.topology));if(r==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);e.modelTopology=r;let s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);e.weightSpecs=s;let u=this.LS.getItem(this.keys.modelMetadata);if(u!=null){let h=JSON.parse(u);e.format=h.format,e.generatedBy=h.generatedBy,e.convertedBy=h.convertedBy,e.userDefinedMetadata=h.userDefinedMetadata}let l=this.LS.getItem(this.keys.weightData);if(l==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return e.weightData=mP(l),e}}ii.URL_SCHEME="localstorage://";let pS=n=>ft().getBool("IS_BROWSER")&&(!Array.isArray(n)&&n.startsWith(ii.URL_SCHEME))?$P(n.slice(ii.URL_SCHEME.length)):null;rn.registerSaveRouter(pS),rn.registerLoadRouter(pS);function $P(n){return new ii(n)}class AP{constructor(){k(ft().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),k(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let t={},e=ai+qs,r=qs+lS;for(let s=0;s<this.LS.length;++s){let u=this.LS.key(s);if(u.startsWith(e)&&u.endsWith(r)){let l=fS(u);t[l]=JSON.parse(this.LS.getItem(u))}}return t}async removeModel(t){t=DP(t);let e=hS(t);if(this.LS.getItem(e.info)==null)throw new Error(`Cannot find model at path '${t}'`);let r=JSON.parse(this.LS.getItem(e.info));return this.LS.removeItem(e.info),this.LS.removeItem(e.topology),this.LS.removeItem(e.weightSpecs),this.LS.removeItem(e.weightData),r}}let Su="://";class Sr{constructor(){this.managers={}}static getInstance(){return Sr.instance==null&&(Sr.instance=new Sr),Sr.instance}static registerManager(t,e){k(t!=null,()=>"scheme must not be undefined or null."),t.endsWith(Su)&&(t=t.slice(0,t.indexOf(Su))),k(t.length>0,()=>"scheme must not be an empty string.");let r=Sr.getInstance();k(r.managers[t]==null,()=>`A model store manager is already registered for scheme '${t}'.`),r.managers[t]=e}static getManager(t){let e=this.getInstance().managers[t];if(e==null)throw new Error(`Cannot find model manager for scheme '${t}'`);return e}static getSchemes(){return Object.keys(this.getInstance().managers)}}function td(n){if(n.indexOf(Su)===-1)throw new Error(`The url string provided does not contain a scheme. 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u=e+Su+s;t[u]=r[s]}}return t}async function FP(n){let t=td(n),e=Sr.getManager(t.scheme);return e.removeModel(t.path)}async function RP(n,t){let e=!1;return dS(n,t,e)}async function PP(n,t){let e=!0;return dS(n,t,e)}class OP{fetch(t,e){return fetch(t,e)}now(){return performance.now()}encode(t,e){if(e!=="utf-8"&&e!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${e}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(t)}decode(t,e){return new TextDecoder(e).decode(t)}}if(ft().get("IS_BROWSER")){ft().setPlatform("browser",new OP);try{Sr.registerManager(ii.URL_SCHEME,new AP)}catch(n){}try{Sr.registerManager(oi.URL_SCHEME,new SP)}catch(n){}}let MP={importFetch:()=>Nx()},Cu;function oht(){Cu=null}function aht(n){Cu=n}function iht(){return Cu}class LP{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(t,e){return 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columns (${l}).`);t<0&&(t=u),e<0&&(e=l);let h=rt(Ch(0,u,1,"int32"),[-1,1]),p=Ch(0,l,1,"int32"),m=Mt(h,p),y=es(la(m,Ot(+t,"int32")),xo(m,Ot(-e,"int32"))),b=Se([u,l],r.dtype);return rt(mr(bs(rt(r,[-1,u,l])).map(x=>er(y,x,b))),s)}let Fz=X({bandPart_:_z});function Rz(n){let t;if(Array.isArray(n)){t=!1,k(n!=null&&n.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let s=n[0].shape[0];for(let u=1;u<n.length;++u)k(n[u].shape[0]===s,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${n[u].shape[0]} vs. ${s})`)}else t=!0,n=Er(n,n.shape[0],0).map(s=>fa(s,[0]));k(n.length<=n[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${n.length}) exceeds number of dimensions (${n[0].shape[0]}).`);let e=[],r=n;for(let s=0;s<n.length;++s)e.push(J.tidy(()=>{let u=r[s];if(s>0)for(let l=0;l<s;++l){let h=st(Xt(st(e[l],u)),e[l]);u=Mt(u,h)}return Ht(u,Od(u,"euclidean"))}));return t?mr(e,0):e}let Pz=X({gramSchmidt_:Rz});function Oz(n,t=!1){if(k(n.rank>=2,()=>`qr() 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q=re(B),j=ge(s,[0,m],[e,s.shape[1]-m]);if(m===0)s=Mt(j,xe(xe(j,h),q));else{let et=Mt(j,xe(xe(j,h),q));s=sn([ge(s,[0,0],[e,m]),et],1)}return[h,u,s]}),oe([y,b,x])}return!t&&e>r&&(s=ge(s,[0,0],[e,r]),u=ge(u,[0,0],[r,r])),[s,u]})}let Mz=X({qr_:Oz});(function(n){n[n.NONE=0]="NONE",n[n.MEAN=1]="MEAN",n[n.SUM=2]="SUM",n[n.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(o.Reduction||(o.Reduction={}));function Lz(n,t,e=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let r=z(n,"losses","computeWeightedLoss"),s=null;t!=null&&(s=z(t,"weights","computeWeightedLoss"));let u=s==null?r:st(r,s);if(e===o.Reduction.NONE)return u;if(e===o.Reduction.SUM)return Xt(u);if(e===o.Reduction.MEAN){if(s==null)return an(u);{let l=r.size/s.size,h=Ht(Xt(u),Xt(s));return l>1?Ht(h,Ot(l)):h}}if(e===o.Reduction.SUM_BY_NONZERO_WEIGHTS){if(s==null)return Ht(Xt(u),Ot(r.size));{let l=st(s,vs(r.shape)),h=Rt(Xt(ha(l,Ot(0))),"float32");return Ht(Xt(u),h)}}throw Error(`Unknown reduction: ${e}`)}let To=X({computeWeightedLoss_:Lz});function Bz(n,t,e,r=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=z(n,"labels","absoluteDifference"),u=z(t,"predictions","absoluteDifference"),l=null;e!=null&&(l=z(e,"weights","absoluteDifference")),$(s.shape,u.shape,"Error in absoluteDifference: ");let h=kn(Mt(s,u));return To(h,l,r)}let zz=X({absoluteDifference_:Bz});function Wz(n,t,e,r,s=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let u=z(n,"labels","cosineDistance"),l=z(t,"predictions","cosineDistance"),h=null;r!=null&&(h=z(r,"weights","cosineDistance")),$(u.shape,l.shape,"Error in cosineDistance: ");let p=Ot(1),m=Mt(p,Xt(st(u,l),e,!0));return To(m,h,s)}let Vz=X({cosineDistance_:Wz});function Uz(n,t,e,r=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=z(n,"labels","hingeLoss"),u=z(t,"predictions","hingeLoss"),l=null;e!=null&&(l=z(e,"weights","hingeLoss")),$(s.shape,u.shape,"Error in hingeLoss: ");let h=Ot(1);s=Mt(st(Ot(2),s),h);let p=Ys(Mt(h,st(s,u)));return To(p,l,r)}let Gz=X({hingeLoss_:Uz});function Hz(n,t,e,r=1,s=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let u=z(n,"labels","huberLoss"),l=z(t,"predictions","huberLoss"),h=null;e!=null&&(h=z(e,"weights","huberLoss")),$(u.shape,l.shape,"Error in huberLoss: ");let p=Ot(r),m=kn(Mt(l,u)),y=mi(m,p),b=Mt(m,y),x=Nt(st(Ot(.5),Oe(y)),st(p,b));return To(x,h,s)}let qz=X({huberLoss_:Hz});function jz(n,t,e,r=1e-7,s=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let u=z(n,"labels","logLoss"),l=z(t,"predictions","logLoss"),h=null;e!=null&&(h=z(e,"weights","logLoss")),$(u.shape,l.shape,"Error in logLoss: ");let p=Ot(1),m=Ot(r),y=on(st(u,Nr(Nt(l,m)))),b=st(Mt(p,u),Nr(Nt(Mt(p,l),m))),x=Mt(y,b);return To(x,h,s)}let Kz=X({logLoss_:jz});function Xz(n,t,e,r=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let s=z(n,"labels","meanSquaredError"),u=z(t,"predictions","meanSquaredError"),l=null;e!=null&&(l=z(e,"weights","meanSquaredError")),$(s.shape,u.shape,"Error in meanSquaredError: ");let h=Dh(s,u);return To(h,l,r)}let Yz=X({meanSquaredError_:Xz});function Jz(n,t){let e=z(n,"labels","sigmoidCrossEntropyWithLogits"),r=z(t,"logits","sigmoidCrossEntropyWithLogits");$(e.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let s=Ys(r),u=st(r,e),l=wd(Br(on(kn(r))));return Nt(Mt(s,u),l)}function Zz(n,t,e,r=0,s=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let u=z(n,"multiClassLabels","sigmoidCrossEntropy"),l=z(t,"logits","sigmoidCrossEntropy"),h=null;if(e!=null&&(h=z(e,"weights","sigmoidCrossEntropy")),$(u.shape,l.shape,"Error in sigmoidCrossEntropy: "),r>0){let m=Ot(r),y=Ot(1),b=Ot(.5);u=Nt(st(u,Mt(y,m)),st(b,m))}let p=Jz(u,l);return To(p,h,s)}let Qz=X({sigmoidCrossEntropy_:Zz});function t4(n,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${e}`);let r=Ks((s,u,l)=>{let h=!0,p=mb(u,[e],h),m=Mt(Rt(u,"float32"),p);l([s,m]);let y=on(st(m,s)),b=Xt(y,[e]),x=(S,C)=>{let[I,D]=C,R=zn(S.shape,[e]);return[st(rt(S,R),Mt(Rt(I,"float32"),Br(D))),st(rt(S,R),Mt(Br(D),Rt(I,"float32")))]};return{value:b,gradFunc:x}});return r(n,t)}function e4(n,t,e,r=0,s=o.Reduction.SUM_BY_NONZERO_WEIGHTS){let u=z(n,"onehotLabels","softmaxCrossEntropy"),l=z(t,"logits","softmaxCrossEntropy"),h=null;if(e!=null&&(h=z(e,"weights","softmaxCrossEntropy")),$(u.shape,l.shape,"Error in softmaxCrossEntropy: "),r>0){let m=Ot(r),y=Ot(1),b=Ot(u.shape[1]);u=Nt(st(u,Mt(y,m)),Ht(m,b))}let p=t4(u,l);return To(p,h,s)}let n4=X({softmaxCrossEntropy_:e4});let r4={fft:Ih,ifft:Bu,rfft:Eh,irfft:Fd},s4={hammingWindow:ez,hannWindow:AC,frame:_C,stft:oz},da={flipLeftRight:cz,resizeNearestNeighbor:PC,resizeBilinear:RC,rotateWithOffset:hz,cropAndResize:iz,nonMaxSuppression:pz,nonMaxSuppressionAsync:xz,nonMaxSuppressionWithScore:kz,nonMaxSuppressionWithScoreAsync:Cz,nonMaxSuppressionPadded:Iz,nonMaxSuppressionPaddedAsync:Dz},MC={bandPart:Fz,gramSchmidt:Pz,qr:Mz},o4={absoluteDifference:zz,computeWeightedLoss:To,cosineDistance:Vz,hingeLoss:Gz,huberLoss:qz,logLoss:Kz,meanSquaredError:Yz,sigmoidCrossEntropy:Qz,softmaxCrossEntropy:n4};class ko extends li{minimize(t,e=!1,r){let{value:s,grads:u}=this.computeGradients(t,r);if(r!=null){let l=r.map(h=>({name:h.name,tensor:u[h.name]}));this.applyGradients(l)}else this.applyGradients(u);return oe(u),e?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return db(t,e)}dispose(){this.iterations_!=null&&oe(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ot(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}}Object.defineProperty(ko,Symbol.hasInstance,{value:n=>n.minimize!=null&&n.computeGradients!=null&&n.applyGradients!=null});class Ah extends ko{constructor(t,e,r=null){super();this.learningRate=t,this.rho=e,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=J.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,s)=>{let u=J.registeredVariables[r],l=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${r}/accum_grad`,variable:ot(()=>fe(u).variable(l))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${r}/accum_var`,variable:ot(()=>fe(u).variable(l))});let h=Array.isArray(t)?t[s].tensor:t[r];if(h==null)return;let p=this.accumulatedGrads[s].variable,m=this.accumulatedUpdates[s].variable;ot(()=>{let y=Nt(st(p,this.rho),st(Oe(h),1-this.rho)),b=st(Ht(Wn(Nt(m,this.epsilon)),Wn(Nt(p,this.epsilon))),h),x=Nt(st(m,this.rho),st(Oe(b),1-this.rho));p.assign(y),m.assign(x);let S=Nt(st(b,-this.learningRate),u);u.assign(S)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(oe(this.accumulatedGrads.map(t=>t.variable)),oe(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,r=!1;this.accumulatedGrads=t.slice(0,e).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})),this.accumulatedUpdates=t.slice(e,e*2).map(s=>({originalName:s.name,variable:s.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}}Ah.className="Adadelta",kt(Ah);class _h extends ko{constructor(t,e=.1){super();this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,s)=>{let u=J.registeredVariables[r];if(this.accumulatedGrads[s]==null){let p=!1;this.accumulatedGrads[s]={originalName:`${r}/accumulator`,variable:ot(()=>Au(u.shape,this.initialAccumulatorValue).variable(p))}}let l=Array.isArray(t)?t[s].tensor:t[r];if(l==null)return;let h=this.accumulatedGrads[s].variable;ot(()=>{let p=Nt(h,Oe(l));h.assign(p);let m=Nt(st(Ht(l,Wn(Nt(p,J.backend.epsilon()))),-this.learningRate),u);u.assign(m)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&oe(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}}_h.className="Adagrad",kt(_h);class Fh extends ko{constructor(t,e,r,s=null){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],ot(()=>{this.accBeta1=Ot(e).variable(),this.accBeta2=Ot(r).variable()}),s==null&&(this.epsilon=J.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);ot(()=>{let r=Mt(1,this.accBeta1),s=Mt(1,this.accBeta2);e.forEach((u,l)=>{let h=J.registeredVariables[u],p=!1;this.accumulatedFirstMoment[l]==null&&(this.accumulatedFirstMoment[l]={originalName:`${u}/m`,variable:ot(()=>fe(h).variable(p))}),this.accumulatedSecondMoment[l]==null&&(this.accumulatedSecondMoment[l]={originalName:`${u}/v`,variable:ot(()=>fe(h).variable(p))});let m=Array.isArray(t)?t[l].tensor:t[u];if(m==null)return;let y=this.accumulatedFirstMoment[l].variable,b=this.accumulatedSecondMoment[l].variable,x=Nt(st(y,this.beta1),st(m,1-this.beta1)),S=Nt(st(b,this.beta2),st(Oe(m),1-this.beta2)),C=Ht(x,r),I=Ht(S,s);y.assign(x),b.assign(S);let D=Nt(st(Ht(C,Nt(Wn(I),this.epsilon)),-this.learningRate),h);h.assign(D)}),this.accBeta1.assign(st(this.accBeta1,this.beta1)),this.accBeta2.assign(st(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&oe(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&oe(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),ot(()=>{this.accBeta1.assign(ys(this.beta1,this.iterations_+1)),this.accBeta2.assign(ys(this.beta2,this.iterations_+1))});let e=t.length/2,r=!1;this.accumulatedFirstMoment=t.slice(0,e).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(s=>({originalName:s.name,variable:s.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}}Fh.className="Adam",kt(Fh);class Rh extends ko{constructor(t,e,r,s=null,u=0){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=s,this.decay=u,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],ot(()=>{this.iteration=Ot(0).variable(),this.accBeta1=Ot(e).variable()}),s==null&&(this.epsilon=J.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);ot(()=>{let r=Mt(1,this.accBeta1),s=Ht(-this.learningRate,Nt(st(this.iteration,this.decay),1));e.forEach((u,l)=>{let h=J.registeredVariables[u],p=!1;this.accumulatedFirstMoment[l]==null&&(this.accumulatedFirstMoment[l]={originalName:`${u}/m`,variable:fe(h).variable(p)}),this.accumulatedWeightedInfNorm[l]==null&&(this.accumulatedWeightedInfNorm[l]={originalName:`${u}/v`,variable:fe(h).variable(p)});let m=Array.isArray(t)?t[l].tensor:t[u];if(m==null)return;let y=this.accumulatedFirstMoment[l].variable,b=this.accumulatedWeightedInfNorm[l].variable,x=Nt(st(y,this.beta1),st(m,1-this.beta1)),S=st(b,this.beta2),C=kn(m),I=ts(S,C);y.assign(x),b.assign(I);let D=Nt(st(Ht(s,r),Ht(x,Nt(I,this.epsilon))),h);h.assign(D)}),this.iteration.assign(Nt(this.iteration,1)),this.accBeta1.assign(st(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&oe(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&oe(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){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(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}}Rh.className="Adamax",kt(Rh);class Uu extends ko{constructor(t){super();this.learningRate=t,this.setLearningRate(t)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,s)=>{let u=Array.isArray(t)?t[s].tensor:t[r];if(u==null)return;let l=J.registeredVariables[r];ot(()=>{let h=Nt(st(this.c,u),l);l.assign(h)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=An(Ot(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}}Uu.className="SGD",kt(Uu);class Ph extends Uu{constructor(t,e,r=!1){super(t);this.learningRate=t,this.momentum=e,this.useNesterov=r,this.accumulations=[],this.m=Ot(this.momentum)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,s)=>{let u=J.registeredVariables[r];if(this.accumulations[s]==null){let p=!1;this.accumulations[s]={originalName:`${r}/momentum`,variable:ot(()=>fe(u).variable(p))}}let l=this.accumulations[s].variable,h=Array.isArray(t)?t[s].tensor:t[r];if(h==null)return;ot(()=>{let p,m=Nt(st(this.m,l),h);this.useNesterov?p=Nt(st(this.c,Nt(h,st(m,this.m))),u):p=Nt(st(this.c,m),u),l.assign(m),u.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&oe(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}}Ph.className="Momentum",kt(Ph);class Oh extends ko{constructor(t,e=.9,r=0,s=null,u=!1){super();if(this.learningRate=t,this.decay=e,this.momentum=r,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=u,s==null&&(this.epsilon=J.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,s)=>{let u=J.registeredVariables[r],l=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${r}/rms`,variable:ot(()=>fe(u).variable(l))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${r}/momentum`,variable:ot(()=>fe(u).variable(l))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${r}/mg`,variable:ot(()=>fe(u).variable(l))});let h=Array.isArray(t)?t[s].tensor:t[r];if(h==null)return;let p=this.accumulatedMeanSquares[s].variable,m=this.accumulatedMoments[s].variable;ot(()=>{let y=Nt(st(p,this.decay),st(Oe(h),1-this.decay));if(this.centered){let b=this.accumulatedMeanGrads[s].variable,x=Nt(st(b,this.decay),st(h,1-this.decay)),S=Ht(st(h,this.learningRate),Wn(Mt(y,Nt(Oe(x),this.epsilon)))),C=Nt(st(m,this.momentum),S);p.assign(y),b.assign(x),m.assign(C);let I=Mt(u,C);u.assign(I)}else{let b=Nt(st(p,this.decay),st(Oe(h),1-this.decay)),x=Nt(st(m,this.momentum),Ht(st(h,this.learningRate),Wn(Nt(b,this.epsilon))));p.assign(b),m.assign(x);let S=Mt(u,x);u.assign(S)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&oe(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&oe(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&oe(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,r=!1;this.accumulatedMeanSquares=t.slice(0,e).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})),this.accumulatedMoments=t.slice(e,e*2).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(s=>({originalName:s.name,variable:s.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}}Oh.className="RMSProp",kt(Oh);class wi{static sgd(t){return new Uu(t)}static momentum(t,e,r=!1){return new Ph(t,e,r)}static rmsprop(t,e=.9,r=0,s=null,u=!1){return new Oh(t,e,r,s,u)}static 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Got: ${t.scale}`);this.scale=t.scale==null?1:t.scale,this.mode=t.mode==null?"fanIn":t.mode,_V(this.mode),this.distribution=t.distribution==null?"normal":t.distribution,FV(this.distribution),this.seed=t.seed}apply(t,e){let r=RV(t),s=r[0],u=r[1],l=this.scale;if(this.mode==="fanIn"?l/=Math.max(1,s):this.mode==="fanOut"?l/=Math.max(1,u):l/=Math.max(1,(s+u)/2),this.distribution==="normal"){let h=Math.sqrt(l);if(e=e||"float32",e!=="float32"&&e!=="int32")throw new Zt(`${this.getClassName()} does not support dType ${e}.`);return $h(t,0,h,e,this.seed)}else{let h=Math.sqrt(3*l);return yi(t,-h,h,e)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}vr.className="VarianceScaling",kt(vr);class tm extends vr{constructor(t){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return vr.className}}tm.className="GlorotUniform",kt(tm);class em extends vr{constructor(t){super({scale:1,mode:"fanAvg",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return vr.className}}em.className="GlorotNormal",kt(em);class nm extends vr{constructor(t){super({scale:2,mode:"fanIn",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return vr.className}}nm.className="HeNormal",kt(nm);class rm extends vr{constructor(t){super({scale:2,mode:"fanIn",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return vr.className}}rm.className="HeUniform",kt(rm);class sm extends vr{constructor(t){super({scale:1,mode:"fanIn",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return vr.className}}sm.className="LeCunNormal",kt(sm);class om extends vr{constructor(t){super({scale:1,mode:"fanIn",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return vr.className}}om.className="LeCunNormal",kt(om);class vw extends ss{constructor(t){super();if(this.DEFAULT_GAIN=1,this.gain=t.gain==null?this.DEFAULT_GAIN:t.gain,this.seed=t.seed,this.seed!=null)throw new Zt("Random seed is not implemented for Orthogonal Initializer yet.")}apply(t,e){return ot(()=>{if(t.length<2)throw new Zt("Shape must be at least 2D.");t[0]*t[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${t[0]*t[1]}) elements: Slowness may result.`);let r=t[0]>t[1]?[t[1],t[0]]:t,s=Zd(r,0,1,"float32"),u=MC.gramSchmidt(s);return t[0]>t[1]&&(u=u.transpose()),st(this.gain,u)})}getConfig(){return{gain:this.gain,seed:this.seed}}}vw.className="Orthogonal",kt(vw);let pN={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 dN(n,t={}){return zh(n,Lr.getMap().classNameMap,t,"initializer")}function cn(n){return Qb(n)}function Xe(n){if(typeof n=="string"){let t=n in pN?pN[n]:n;if(t==="GlorotNormal")return new em;if(t==="GlorotUniform")return new tm;if(t==="HeNormal")return new nm;if(t==="HeUniform")return new rm;if(t==="LeCunNormal")return new sm;if(t==="LeCunUniform")return new om;{let e={};return e.className=t,e.config={},dN(e)}}else return n instanceof ss?n:dN(n)}function PV(){return new hw}function OV(){return new Qd}function MV(n){return new fw(n)}function LV(n){return new pw(n)}function BV(n){return new dw(n)}function zV(n){return new mw(n)}function WV(n){return new gw(n)}function VV(n){return new vr(n)}function UV(n){return new tm(n)}function GV(n){return new em(n)}function HV(n){return new nm(n)}function qV(n){return new rm(n)}function jV(n){return new sm(n)}function KV(n){return new om(n)}function XV(n){return new vw(n)}var YV=Object.freeze({__proto__:null,zeros:PV,ones:OV,constant:MV,randomUniform:LV,randomNormal:BV,truncatedNormal:zV,identity:WV,varianceScaling:VV,glorotUniform:UV,glorotNormal:GV,heNormal:HV,heUniform:qV,leCunNormal:jV,leCunUniform:KV,orthogonal:XV});let JV=0;function mN(){return JV++}let am={};function im(n=""){return n in am||(am[n]=0),am[n]+=1,n+am[n].toString()}function yw(n){return Array.isArray(n)&&Array.isArray(n[0])}function um(n){return n.length===0?[]:Array.isArray(n[0])?n:[n]}function le(n){let t;if(Array.isArray(n)){if(n.length!==1)throw new Q(`Expected Tensor length to be 1; got ${n.length}`);t=n[0]}else t=n;return t}function Be(n){if(Array.isArray(n)&&Array.isArray(n[0])){if(n.length===1)return n=n,n[0];throw new Q(`Expected exactly 1 Shape; got ${n.length}`)}else return n}function cm(n){let t=0;for(let e of n)e.shape.length===0?t+=1:t+=e.shape.reduce((r,s)=>r*s);return t}let gN="Variable";class Ts{constructor(t,e="float32",r=gN,s=!0,u=null){this.dtype=e==null?"float32":e,this.shape=t.shape,this.id=mN(),r=r==null?gN:r,this.originalName=aN(r),this.name=iN(this.originalName),this.trainable_=s,this.constraint=u,this.val=iC(t,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(t){return this.assertNotDisposed(),ZV(this.val,t),this.val.id!==t.id&&(this.val.assign(t),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(t){this.trainable_=t,this.val.trainable=t}}function ZV(n,t){if(n.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(n.shape)+" vs. "+JSON.stringify(t.shape))}function Dht(n,t,e,r){return new Ts(n,t,e,!0,r)}function $ht(n,t,e){return new Ts(Se(n),t,e)}function Aht(n,t,e){return new Ts(fe(n),t,e)}function _ht(n,t,e){let r=vs(n);return new Ts(r,t,e)}function Fht(n,t,e){let r=Xn(n);return new Ts(r,t,e)}function Rht(n,t,e){return new Ts(yd(n),t,e)}function Pht(n,t,e,r,s,u="randomUniform"){return new Ts(yi(n,t,e,r),r,u)}function Oht(n,t=0,e=1,r,s,u="truncatedNormal"){if(r=r||"float32",r!=="float32"&&r!=="int32")throw new Zt(`randomNormal does not support dType ${r}.`);return new Ts($h(n,t,e,r,s),r,u)}function Mht(n,t=0,e=1,r,s,u="randomNormal"){if(r=r||"float32",r!=="float32"&&r!=="int32")throw new Zt(`randomNormalVariable does not support dType ${r}.`);return new Ts(wb(n,t,e,r,s),r,u)}function Lht(n,t){return n.write(t)}function Bht(n,t){return n.write(Nt(n.read(),t))}function zht(n,t){return n.write(Mt(n.read(),t))}function bw(n){return n.map(t=>t.read())}function ww(n){n.forEach(t=>{let e=t[0];e.write(t[1])})}function Wht(n,t){let e=t.map(s=>s.read()),r=db(n,e);return t.map(s=>r.grads[s.name])}class Fn{constructor(t){this.dtype=t.dtype,this.shape=t.shape,t.shape!=null?this.ndim=t.shape.length:this.ndim=t.ndim,this.maxNDim=t.maxNDim,this.minNDim=t.minNDim,this.axes=t.axes||{}}}class ks{constructor(t,e,r,s,u,l,h){this.dtype=t,this.shape=e,this.sourceLayer=r,this.inputs=s,this.callArgs=u,this.outputTensorIndex=h,this.id=mN(),l!=null&&(this.originalName=aN(l),this.name=iN(this.originalName)),this.rank=e.length}}let QV=0;class lm{constructor(t,e){this.callArgs=e,this.id=QV++,this.outboundLayer=t.outboundLayer,this.inboundLayers=t.inboundLayers,this.nodeIndices=t.nodeIndices,this.tensorIndices=t.tensorIndices,this.inputTensors=t.inputTensors,this.outputTensors=t.outputTensors,this.inputMasks=t.inputMasks,this.outputMasks=t.outputMasks,this.inputShapes=t.inputShapes,this.outputShapes=t.outputShapes;for(let r of t.inboundLayers)r!=null&&r.outboundNodes.push(this);t.outboundLayer.inboundNodes.push(this)}getConfig(){let t=[];for(let e of this.inboundLayers)e!=null?t.push(e.name):t.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:t,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let tU=0;class Te extends li{constructor(t={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=tU++,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 e=t.name;if(!e){let r=this.getClassName();e=Co(r)+"_"+im(r)}if(this.name=e,this.trainable_=t.trainable==null?!0:t.trainable,t.inputShape!=null||t.batchInputShape!=null){let r;if(t.batchInputShape!=null)r=t.batchInputShape;else if(t.inputShape!=null){let u=null;t.batchSize!=null&&(u=t.batchSize),r=[u].concat(t.inputShape)}this.batchInputShape=r;let s=t.dtype;s==null&&(s=t.inputDType),s==null&&(s="float32"),this.dtype=s}t.weights!=null?this.initialWeights=t.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(t,e){return t.name+"_ib-"+e.toString()}getNodeAtIndex(t,e){if(this.inboundNodes.length===0)throw new ns(`The layer has never been called and thus has no defined ${e}.`);if(this.inboundNodes.length<=t)throw new Q(`Asked to get ${e} at node ${t}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[t]}getInputAt(t){return gr(this.getNodeAtIndex(t,"input").inputTensors)}getOutputAt(t){return gr(this.getNodeAtIndex(t,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Js(`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 Js(`Layer ${this.name} is not connected, no input to return.`);return gr(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Js(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Js(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return gr(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(t=>t())}get updates(){return this._updates}get built(){return this._built}set built(t){this._built=t}get trainable(){return this.trainable_}set trainable(t){this._trainableWeights.forEach(e=>e.trainable=t),this.trainable_=t}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(t=>t.trainable):[]}set trainableWeights(t){this._trainableWeights=t}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(t=>!t.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(t){this._nonTrainableWeights=t}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(t){if(t=Ge(t),this.inputSpec==null||this.inputSpec.length===0)return;let e=Ge(this.inputSpec);if(t.length!==e.length)throw new Q(`Layer ${this.name} expects ${e.length} inputs, but it received ${t.length} input tensors. Input received: ${t}`);for(let r=0;r<t.length;r++){let s=t[r],u=e[r];if(u==null)continue;let l=s.rank;if(u.ndim!=null&&l!==u.ndim)throw new Q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${u.ndim}, found ndim=${l}`);if(u.maxNDim!=null&&l>u.maxNDim)throw new Q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${u.maxNDim}, found ndim=${l}`);if(u.minNDim!=null&&l<u.minNDim)throw new Q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${u.minNDim}, found ndim=${l}.`);if(u.dtype!=null&&s.dtype!==u.dtype)throw new Q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${u.dtype}, found dtype=${s.dtype}.`);if(u.axes){let h=s.shape;for(let p in u.axes){let m=Number(p),y=u.axes[p],b=m>=0?h[m]:h[h.length+m];if(y!=null&&[y,null].indexOf(b)===-1)throw new Q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${m} of input shape to have value ${y} but got shape ${h}.`)}}if(u.shape!=null)for(let h=0;h<u.shape.length;++h){let p=u.shape[h],m=s.shape[h];if(p!=null&&m!=null&&p!==m)throw new Q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${u.shape}, found shape=${s.shape}.`)}}}call(t,e){return t}invokeCallHook(t,e){this._callHook!=null&&this._callHook(t,e)}setCallHook(t){this._callHook=t}clearCallHook(){this._callHook=null}apply(t,e){e=e||{},this.assertNotDisposed();let r=Ge(t),s=!0;for(let l of r)if(!(l instanceof ks)){s=!1;break}let u=!0;for(let l of r)if(l instanceof ks){u=!1;break}if(s===u)throw new Q("Arguments to apply() must be all SymbolicTensors or all Tensors");return Si(this.name,()=>{if(!this.built){this.assertInputCompatibility(t);let l=[];for(let h of Ge(t))l.push(h.shape);this.build(gr(l)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&u&&(this._refCount=1)}if(this.assertInputCompatibility(t),u){let l=this.call(t,e),h=Ge(l),p=[];for(let m of h)r.indexOf(m)!==-1&&(m=m.clone()),p.push(m);if(l=gr(p),this.activityRegularizer!=null)throw new Zt("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return l}else{let l=eU(t),h=this.computeOutputShape(l),p,m=nU(t);if(this.warnOnIncompatibleInputShape(Array.isArray(t)?l[0]:l),h!=null&&h.length>0&&Array.isArray(h[0])?p=h.map((y,b)=>new ks(m,y,this,Ge(t),e,this.name,b)):p=new ks(m,h,this,Ge(t),e,this.name),this.addInboundNode(t,p,null,null,l,h,e),this._refCount++,this.activityRegularizer!=null)throw new Zt("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return p}})}warnOnIncompatibleInputShape(t){if(this.batchInputShape==null)return;if(t.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(t)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let e=!1;this.batchInputShape.forEach((r,s)=>{r!=null&&t[s]!=null&&t[s]!==r&&(e=!0)}),e&&console.warn(`The shape of the input tensor (${JSON.stringify(t)}) 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 Js(`The layer ${this.name} has never been called and thus has no defined output shape.`);let t=[];for(let e of this.inboundNodes){let r=JSON.stringify(e.outputShapes);t.indexOf(r)===-1&&t.push(r)}if(t.length===1){let e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&e.length===1?e[0]:e}else throw new Js(`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 ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return cm(this.weights)}build(t){this.built=!0}getWeights(t=!1){return bw(t?this.trainableWeights:this.weights)}setWeights(t){ot(()=>{let e=this.weights;if(e.length!==t.length)throw new Q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${t.length}, but the layer was expecting ${e.length} weights. Provided weights: ${t}...`);if(e.length===0)return;let r=[],s=bw(e);for(let u=0;u<s.length;++u){let l=s[u],h=e[u],p=t[u];if(!K(l.shape,p.shape))throw new Q(`Layer weight shape ${l.shape} not compatible with provided weight shape ${p.shape}`);r.push([h,p])}ww(r)})}addWeight(t,e,r,s,u,l,h){if(this._addedWeightNames.indexOf(t)!==-1)throw new Q(`Duplicate weight name ${t} for layer ${this.name}`);this._addedWeightNames.push(t),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(s=Xe("zeros"));let p=s.apply(e,r),m=new Ts(p,r,t,l,h);return p.dispose(),u!=null&&this.addLoss(()=>u.apply(m.read())),l==null&&(l=!0),l?this._trainableWeights.push(m):this._nonTrainableWeights.push(m),m}setFastWeightInitDuringBuild(t){this.fastWeightInitDuringBuild=t}addLoss(t){if(t==null||Array.isArray(t)&&t.length===0)return;t=Ge(t),this._losses!==void 0&&this._losses!==null&&this.losses.push(...t)}computeOutputShape(t){return t}computeMask(t,e){if(!this.supportsMasking){if(e!=null)if(Array.isArray(e))e.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return e}addInboundNode(t,e,r,s,u,l,h=null){let p=Ge(t);e=Ge(e),r=Ge(r),s=Ge(s),u=um(u),l=um(l);let m=[],y=[],b=[];for(let x of p)m.push(x.sourceLayer),y.push(x.nodeIndex),b.push(x.tensorIndex);new lm({outboundLayer:this,inboundLayers:m,nodeIndices:y,tensorIndices:b,inputTensors:p,outputTensors:e,inputMasks:r,outputMasks:s,inputShapes:u,outputShapes:l},h);for(let x=0;x<e.length;x++)e[x].sourceLayer=this,e[x].nodeIndex=this.inboundNodes.length-1,e[x].tensorIndex=x}getConfig(){let t={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(t.batchInputShape=this.batchInputShape),this.dtype!=null&&(t.dtype=this.dtype),t}disposeWeights(){return this.weights.forEach(t=>t.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let t=0;return--this._refCount===0&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}}}function eU(n){n=Ge(n);let t=[];for(let e of n)t.push(e.shape);return gr(t)}function nU(n){return"float32"}function vN(n,t,e){if((t==null||e!=null&&e>0)&&(t=n.sourceLayer,e=n.nodeIndex),t.inboundNodes.length===0)return[n];{let r=t.inboundNodes[e];if(r.inboundLayers.length===0)return r.inputTensors;{let s=[];for(let u=0;u<r.inboundLayers.length;u++){let l=r.inputTensors[u],h=r.inboundLayers[u],p=r.nodeIndices[u],m=vN(l,h,p);for(let y of m)s.indexOf(y)===-1&&s.push(y)}return s}}}class Ku extends Te{constructor(t){super({dtype:t.dtype,name:t.name!=null?t.name:im("input").toString()});if(t.batchSize==null&&(t.batchSize=null),t.sparse==null&&(t.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=t.sparse,t.inputShape!=null&&t.batchInputShape!=null)throw new Q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let e=t.batchInputShape;if(e==null){if(t.inputShape==null)throw new Q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");e=[t.batchSize].concat(t.inputShape)}else if(t.batchSize!=null)throw new Q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=t.dtype||"float32";this.batchInputShape=e,this.dtype=r,this.inputSpec=[{shape:e}];let s=new ks(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new lm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[e],outputShapes:[e]})}apply(t,e){throw new Q(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}Ku.className="InputLayer",kt(Ku);function yN(n){if(n.batchShape==null&&n.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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${p}`),r(`Non-trainable params: ${m}`),r("_".repeat(t))}function SU(n){let t;return n.collectedTrainableWeights!=null?t=cm(n.collectedTrainableWeights):t=cm(n.trainableWeights),t}function CU(n){let t=!0,e=[],r=[];for(let s in n.nodesByDepth)e.push(n.nodesByDepth[s]);for(let s of e){if(s.length>1||s.length===1&&s[0].inboundLayers.length>1){t=!1;break}r.push(...s)}if(t)for(let s of n.layers){let u=!1;for(let l of s.inboundNodes)if(r.indexOf(l)!==-1)if(u){t=!1;break}else u=!0;if(!t)break}return t}function ym(n,t,e=console.log){let r="";for(let s=0;s<n.length;++s)s>0&&(r=r.slice(0,r.length-1)+" "),r+=n[s],r=r.slice(0,t[s]),r+=" ".repeat(t[s]-r.length);e(r)}function NU(n,t,e){let r;try{r=JSON.stringify(n.outputShape)}catch(h){r="multiple"}let s=n.name,u=n.getClassName(),l=[`${s} (${u})`,r,n.countParams().toString()];ym(l,t,e)}function IU(n,t,e,r){let s;try{s=JSON.stringify(n.outputShape)}catch(y){s="multiple"}let u=[];for(let y of n.inboundNodes){if(e!=null&&e.length>0&&e.indexOf(y)===-1)continue;for(let b=0;b<y.inboundLayers.length;++b){let x=y.inboundLayers[b].name,S=y.nodeIndices[b],C=y.tensorIndices[b];u.push(`${x}[${S}][${C}]`)}}let l=n.name,h=n.getClassName(),p=u.length===0?"":u[0],m=[`${l} (${h})`,s,n.countParams().toString(),p];ym(m,t,r);for(let y=1;y<u.length;++y)ym(["","","",u[y]],t,r)}function FN(n,t,e){return(n==="inboundNodes"||n==="outputLayers"||n==="inputLayers")&&t===0&&typeof e=="string"}function Kh(n,t){if(n===null)return null;if(typeof n=="string")return ki(n);if(typeof n=="number"||typeof n=="boolean")return n;if(n instanceof Array){let e=[],r=n.length;for(let s=0;s<r;++s){let u=n[s];FN(t,s,u)?e.push(u):e.push(Kh(u,t))}return e}else{let e={};for(let r of Object.keys(n)){let s=n[r];if(r==="name"&&typeof s=="string")e[r]=s;else{let u=ki(r);e[u]=Kh(s,u)}}return e}}function Ew(n,t){if(n==null)return null;if(typeof n=="string")return Co(n);if(typeof n=="number"||typeof n=="boolean")return n;if(n instanceof Array){let e=[],r=n.length;for(let s=0;s<r;++s){let u=n[s];FN(t,s,u)?e.push(u):e.push(Ew(u,t))}return e}else{let e={};for(let r of Object.keys(n)){let s=n[r],u=Co(r);(r==="name"||r==="className")&&typeof s=="string"?e[u]=s:e[u]=Ew(s,r)}return e}}let bm="2.7.0";function EU(n,t){if(n.dtype==null||n.dtype===t.dtype)return t;try{return Rt(t,n.dtype)}catch(e){throw new Q(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${n.name}' (${n.dtype}).`)}}class wa{constructor(t){if(this.id2Value={},this.id2Mask={},this.name2Id={},t instanceof wa)for(let e in t.id2Value)this.id2Value[e]=t.id2Value[e],e in t.id2Mask&&(this.id2Mask[e]=t.id2Mask[e]);else{if(t==null)return;for(let e of t)this.add(e.key,e.value)}}add(t,e,r){if(this.id2Value[t.id]==null)this.id2Value[t.id]=EU(t,e),this.name2Id[t.name]=t.id,r!=null&&(this.id2Mask[t.id]=r);else throw new Q(`Duplicate key: name=${t.name}, id=${t.id}`);return this}addFeed(t){this.add(t.key,t.value)}hasKey(t){return this.id2Value[t.id]!=null}names(){return Object.keys(this.name2Id)}getValue(t){if(t instanceof ks){if(this.id2Value[t.id]==null)throw new Q(`Nonexistent key: ${t.name}`);return this.id2Value[t.id]}else{let e=this.name2Id[t];if(e==null)throw new Q(`Feed dict has no SymbolicTensor name: ${t}`);return this.id2Value[e]}}getMask(t){if(t instanceof ks){if(this.id2Value[t.id]==null)throw new Q(`Nonexistent key: ${t.name}`);return this.id2Mask[t.id]}else{let e=this.name2Id[t];if(e==null)throw new Q(`Feed dict has no SymbolicTensor name: ${t}`);return this.id2Mask[e]}}disposeMasks(){this.id2Mask!=null&&oe(this.id2Mask)}}let Dw={},RN={};function Xh(n,t,e,r){let s=e==null?!1:e.training,u=Array.isArray(n),l=u?n:[n],h=l.map(C=>C.name),p=[],m=t.names();for(let C of h)m.indexOf(C)!==-1?p.push(t.getValue(C)):p.push(null);r!=null&&(r.maxNumTensors=-Infinity,r.minNumTensors=Infinity);let y=h.join(",")+"|"+t.names().join(","),b,x;if(Dw[y]==null){let C=DU(l,t);b=C.sorted,x=C.recipientCounts,Dw[y]=b,RN[y]=x}b=Dw[y],x={},s||Object.assign(x,RN[y]);let S=new wa(t);for(let C=0;C<b.length;++C){if(r!=null){let et=id().numTensors;et>r.maxNumTensors&&(r.maxNumTensors=et),et<r.minNumTensors&&(r.minNumTensors=et)}let I=b[C],D=I.sourceLayer;if(D instanceof Ku)continue;let R=[],A=[],L=[],_=!1;for(let et of I.inputs){let tt=S.getValue(et),ht=S.getMask(et);R.push(tt),A.push(ht),ht!=null&&(_=!0),s||(x[et.name]--,x[et.name]===0&&!t.hasKey(et)&&h.indexOf(et.name)===-1&&!tt.isDisposed&&et.sourceLayer.stateful!==!0&&L.push(tt))}_&&(e=e||{},e.mask=A[0]);let B=Ge(D.apply(R,e)),V=null;D.supportsMasking&&(V=D.computeMask(R,A));let q=AU(I),j=Array.isArray(q)?q:[q];for(let et=0;et<j.length;++et){S.hasKey(j[et])||S.add(j[et],B[et],Array.isArray(V)?V[0]:V);let tt=h.indexOf(j[et].name);tt!==-1&&(p[tt]=B[et])}s||oe(L)}return S.disposeMasks(),u?p:p[0]}function DU(n,t){k(n!=null&&n.length>0,()=>"Expected at least one fetch, got none");let e=[],r={};if(n.length===1){let s=PN(n[0],t);e=s.sorted,r=s.recipientMap}else{let s=new Set;for(let u of n){let{sorted:l,recipientMap:h}=PN(u,t);for(let p of l)s.has(p.name)||(e.push(p),s.add(p.name));for(let p in h)r[p]==null&&(r[p]=new Set),h[p].forEach(m=>r[p].add(m))}}return{sorted:e,recipientCounts:$U(r)}}function $U(n){let t={};for(let e in n)t[e]=n[e].size;return t}function PN(n,t){let e=new Set,r=[],s={};for(let h of t.names())e.add(h);let u=[],l=[];for(u.push(n);u.length>0;){let h=u[u.length-1];if(e.has(h.name)){u.pop();continue}let p=l[l.length-1]===u.length-1;if(h.inputs.length===0||p)u.pop(),r.push(h),e.add(h.name),p&&l.pop();else{l.push(u.length-1);for(let m of h.inputs){if(s[m.name]==null&&(s[m.name]=new Set),s[m.name].add(h.name),e.has(m.name))continue;u.push(m)}}}return{sorted:r,recipientMap:s}}function AU(n){let t;if(n.sourceLayer.inboundNodes.length===1)t=n.sourceLayer.output;else{let e=null;for(let r=0;r<n.sourceLayer.inboundNodes.length;++r)for(let s of n.sourceLayer.inboundNodes[r].outputTensors)if(s.id===n.id){e=r;break}t=n.sourceLayer.getOutputAt(e)}return t}class Cs extends Te{constructor(t){super({});if(this.containerNodes=new Set,this.name=t.name,this.name==null){let A=this.getClassName().toLowerCase();this.name=im(A)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(t.inputs)?this.inputs=t.inputs.slice():this.inputs=[t.inputs],Array.isArray(t.outputs)?this.outputs=t.outputs.slice():this.outputs=[t.outputs],ma(this.inputs).length!==this.inputs.length)throw new Q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(A=>A.name)}`);ma(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let L=A.sourceLayer,_=A.nodeIndex,B=A.tensorIndex;this.outputLayers.push(L),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(B)}for(let A of this.inputs){let L=A.sourceLayer,_=A.nodeIndex,B=A.tensorIndex;Vr(_===0,"input layer has >1 nodes"),Vr(B===0,"input layer has >1 tensors"),this.inputLayers.push(L),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(B)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let L=this.inputLayers[A];if(!(L instanceof Ku))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${t.inputs}. Input ${A} (0-based) originates from layer type ${L.getClassName()}.`);this.inputNames.push(L.name),this.feedInputShapes.push(L.batchInputShape),this.feedInputNames.push(L.name)}for(let A of this.outputLayers)this.outputNames.push(A.name);this.internalInputShapes=this.inputs.map(A=>A.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let e={},r={},s={},u={},l={},h=[],p=(A,L,_,B,V,q)=>{(B==null||V==null||q==null)&&(B=A.sourceLayer,V=A.nodeIndex,q=A.tensorIndex);let j=B.inboundNodes[V];if(_.indexOf(j)!==-1)throw new ns(`The tensor ${A.name} at layer "${B.name}" is part of a cycle.`);if(L.indexOf(j)!==-1)return;this.containerNodes.add(Cs.nodeKey(B,V)),B.id in l||(l[B.id]=Object.keys(l).length),_.indexOf(j)===-1&&_.push(j);let et=j.inboundLayers.length;for(let tt=0;tt<et;tt++){let ht=j.inputTensors[tt],gt=j.inboundLayers[tt],vt=j.nodeIndices[tt],bt=j.tensorIndices[tt];p(ht,L,_,gt,vt,bt)}for(L.push(j);_.indexOf(j)>=0;)_.splice(_.indexOf(j),1);h.push(j)},m=[],y=[];for(let A of this.outputs)p(A,m,y);let b=h.slice().reverse();for(let A of b){r[A.id]=A,A.id in e||(e[A.id]=0);let L=e[A.id],_=s[A.outboundLayer.id]==null?0:s[A.outboundLayer.id];L=Math.max(L,_),s[A.outboundLayer.id]=L,u[A.outboundLayer.id]=A.outboundLayer,e[A.id]=L;for(let B=0;B<A.inboundLayers.length;B++){let V=A.inboundLayers[B],q=A.nodeIndices[B],j=V.inboundNodes[q],et=e[j.id]==null?0:e[j.id];e[j.id]=Math.max(L+1,et),r[j.id]=j}}let x={};for(let A in e){let L=e[A];L in x||(x[L]=[]),x[L].push(r[A])}let S={};for(let A in s){let L=s[A];L in S||(S[L]=[]),S[L].push(u[A])}let C=Object.keys(S).map(A=>parseInt(A,10)).sort(Xd);this.layers=[];for(let A of C){let L=S[A];L.sort((_,B)=>{let V=l[_.id],q=l[B.id];return V<q?-1:V>q?1:0});for(let _ of L)_ instanceof Cs&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=S,C=Object.keys(x).map(A=>parseInt(A,10)).sort(Xd);let I=this.inputs.slice(),D=[];for(let A of C)for(let L of x[A]){let _=L.outboundLayer;if(_!=null){for(let B of L.inputTensors)if(I.indexOf(B)===-1)throw new ns(`Graph disconnected: cannot obtain value for tensor ${B} at layer "${_.name}". The following previous layers were accessed without issue: ${D}`);for(let B of L.outputTensors)I.push(B);D.push(_.name)}}this.nodesByDepth=x;let R=this.layers.map(A=>A.name);for(let A of R){let L=R.filter(_=>_===A).length;if(L!==1)throw new ns(`The name "${A}" is used ${L} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(R))}this.outboundNodes=[],this.inboundNodes=[],new lm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let t={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let e of this.layers)t.numDisposedVariables+=e.dispose().numDisposedVariables;for(let e of this.internalContainerRefs)t.numDisposedVariables+=e.dispose().numDisposedVariables}return t.refCountAfterDispose=this._refCount,t}get trainable(){return this.trainable_}set trainable(t){this.layers.forEach(e=>{e._trainableWeights.forEach(r=>r.trainable=t)}),this.trainable_=t}get trainableWeights(){if(this._trainableWeights.length>0)throw new Q("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 t=[];for(let e of this.layers)t=t.concat(e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.layers)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let r of this.layers)e.push(...r.trainableWeights);return e.concat(t)}return t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(t,e=!0){let r={},s=0;for(let l of this.layers)for(let h of l.weights){if(r[h.originalName]!=null)throw new Q(`Duplicate weight name: ${h.originalName}`);r[h.originalName]=h,s++}let u=[];for(let l in t){let h=l;if(r[l]==null){let p=l.split("/"),m=p.slice(0,-2).concat([p[p.length-1]]);h=m.join("/")}if(r[h]!=null)u.push([r[h],t[l]]);else if(e)throw new Q(`Provided weight data has no target variable: ${l}`);delete r[h]}if(e){let l=[];for(let h in r)l.push(h);if(l.length>0)throw new Q(`${l.length} of ${s} weights are not set: ${l}`)}ww(u)}updatedConfig(){let t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion=`tfjs-layers ${bm}`,e.backend="TensorFlow.js",e}toJSON(t,e=!0){let r=Ew(this.updatedConfig());return e?JSON.stringify(r):r}call(t,e){return ot(()=>{t=Ge(t);let r=new wa;for(let s=0;s<this.inputs.length;++s)r.add(this.inputs[s],t[s]);return Xh(this.outputs,r,e)})}computeMask(t,e){return ot(()=>{t=Ge(t);let r;return e==null?r=Ti(null,t.length):r=Ge(e),this.runInternalGraph(t,r)[1]})}computeOutputShape(t){let e=um(t);if(e.length!==this.inputLayers.length)throw new Q(`Invalid inputShape argument ${t}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let h=0;h<e.length;h++){let p=this.inputLayers[h],m=e[h],y=p.name+"_0_0";r[y]=m}let s=Object.keys(this.nodesByDepth).map(h=>parseInt(h,10)).sort(Xd);if(s.length>1)for(let h of s){let p=this.nodesByDepth[h];for(let m of p){let y=m.outboundLayer;if(this.inputLayers.map(I=>I.id).indexOf(y.id)!==-1)continue;let b=[];for(let I=0;I<m.inboundLayers.length;I++){let D=m.inboundLayers[I],R=m.nodeIndices[I],A=m.tensorIndices[I],L=`${D.name}_${R}_${A}`,_=r[L];b.push(_)}let x=y.computeOutputShape(gr(b)),S=um(x),C=y.inboundNodes.indexOf(m);for(let I=0;I<S.length;I++){let D=`${y.name}_${C}_${I}`;r[D]=S[I]}}}let u=[],l=[];for(let h=0;h<this.outputLayers.length;h++){let p=this.outputLayers[h],m=this.outputLayersNodeIndices[h],y=this.outputLayersTensorIndices[h],b=`${p.name}_${m}_${y}`;l.push(b)}for(let h=0;h<l.length;h++){let p=l[h];Vr(p in r),u.push(r[p])}return gr(u)}runInternalGraph(t,e){e==null&&(e=Ti(null,t.length));let r={};for(let p=0;p<this.inputs.length;++p){let m=this.inputs[p],y=t[p],b=e[p];r[m.id]=[y,b]}let s=Object.keys(this.nodesByDepth).map(p=>parseInt(p,10)).sort(Xd);for(let p of s){let m=this.nodesByDepth[p];for(let y of m){let b=y.outboundLayer,x=y.inputTensors,S=y.outputTensors,C=new Array;for(let I of x)I.id in r&&C.push(r[I.id]);if(C.length===x.length){let I={},D,R,A,L;if(y.callArgs!=null&&(I=y.callArgs),C.length===1){let[_,B]=C[0];I.mask==null&&(I.mask=B),A=Ge(b.call(_,I)),L=Ge(b.computeMask(_,B)),D=[_],R=[B]}else D=C.map(_=>_[0]),R=C.map(_=>_[1]),I.mask==null&&(I.mask=R),A=Ge(b.call(D,I)),L=Ge(b.computeMask(D,R));if(b.activityRegularizer)throw new Zt("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<S.length;++_){let B=S[_],V=A[_],q=L[_];r[B.id]=[V,q]}}}}let u=[],l=[],h=[];for(let p of this.outputs){Vr(p.id in r,`Could not compute output ${p.name} : ${p.id}`);let[m,y]=r[p.id];h.push(m.shape),u.push(m),l.push(y)}return[u,l,h]}buildNodeConversionMap(t){let e={},r;for(let s of this.layers){r=s instanceof Cs?1:0;for(let u=0;u<s.inboundNodes.length;u++){let l=Cs.nodeKey(s,u);this.containerNodes.has(l)&&(e[l]=r,r+=1)}}return e}getLayer(t,e){if(e!=null){if(this.layers.length<=e)throw new Q(`Was asked to retrieve layer at index ${e}, but model only has ${this.layers.length} layer(s).`);return this.layers[e]}else if(t==null)throw new Q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===t)return r;throw new Q(`No such layer: ${t}`)}calculateLosses(){return ot(()=>{let t=[];for(let e of this.layers)for(let r=0;r<e.inboundNodes.length;++r){let s=Cs.nodeKey(e,r);this.containerNodes.has(s)&&t.push(...e.calculateLosses())}return t})}getConfig(){let t={name:this.name},e=this.buildNodeConversionMap(this.layers),r=[];for(let l of this.layers){let h=l.getClassName(),p=l.getConfig(),m=[];for(let b=0;b<l.inboundNodes.length;b++){let x=l.inboundNodes[b],S=Cs.nodeKey(l,b),C={};if(this.containerNodes.has(S)){if(x.callArgs)try{JSON.stringify(x.callArgs),C=x.callArgs}catch(I){console.warn(`Layer ${l.name} was passed non-serializable keyword arguments: ${x.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),C={}}if(x.inboundLayers.length>0){let I=[];for(let D=0;D<x.inboundLayers.length;D++){let R=x.inboundLayers[D],A=x.nodeIndices[D],L=x.tensorIndices[D],_=Cs.nodeKey(R,A),B=e[_];B==null&&(B=0),I.push([R.name,B,L,C])}m.push(I)}}}let y={};y.name=l.name,y.className=h,y.config=p,y.inboundNodes=m,r.push(y)}t.layers=r;let s=[];for(let l=0;l<this.inputLayers.length;l++){let h=this.inputLayers[l],p=this.inputLayersNodeIndices[l],m=Cs.nodeKey(h,p);if(!this.containerNodes.has(m))continue;let y=e[m];y==null&&(y=0);let b=this.inputLayersTensorIndices[l];s.push([h.name,y,b])}t.inputLayers=s;let u=[];for(let l=0;l<this.outputLayers.length;l++){let h=this.outputLayers[l],p=this.outputLayersNodeIndices[l],m=Cs.nodeKey(h,p);if(!this.containerNodes.has(m))continue;let y=e[m];y==null&&(y=0);let b=this.outputLayersTensorIndices[l];u.push([h.name,y,b])}return t.outputLayers=u,t}static fromConfig(t,e,r={},s=!1){let u={},l={};function h(D,R){D.name in l?l[D.name].push(R):l[D.name]=[R]}function p(D,R){let A=[],L;for(let _ of R){let B=_[0],V=_[1],q=_[2];if(L=_[3]==null?{}:_[3],!(B in u)){h(D,R);return}let j=u[B];if(j.inboundNodes.length<=V){h(D,R);return}let et=j.inboundNodes[V];A.push(et.outputTensors[q])}A.length>0&&D.apply(gr(A),L)}function m(D){let R=D.name,A=Ss(D,e.customObjects!=null?e.customObjects:{});A.setFastWeightInitDuringBuild(s),u[R]=A;let L=D.inboundNodes;L.forEach(_=>{if(!(_ instanceof Array))throw new Q(`Corrupted configuration, expected array for nodeData: ${_}`);h(A,_)})}let y=e.name,b=e.layers;for(let D of b)m(D);for(;!uV(l);)for(let D of b){let R=u[D.name];if(R.name in l){let A=l[R.name];delete l[R.name];for(let L of A)p(R,L)}}let x=[],S=[],C=e.inputLayers;for(let D of C){let R=D[0],A=D[1],L=D[2];Vr(R in u);let _=u[R],B=_.inboundNodes[A].outputTensors;x.push(B[L])}let I=e.outputLayers;for(let D of I){let R=D[0],A=D[1],L=D[2];Vr(R in u);let _=u[R],B=_.inboundNodes[A].outputTensors;S.push(B[L])}return new t({inputs:x,outputs:S,name:y})}get stateful(){if(this._stateful)throw new Q("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(let t of this.layers)if(t.stateful)return!0;return!1}resetStates(){ot(()=>{this.layers.forEach(t=>{t.stateful&&t.resetStates()})})}}function ON(n,t,e){let r=t.length;if(n==null||Array.isArray(n)&&n.length===0)return t.map(s=>null);if(r===1)return Array.isArray(n)&&n.length===1?n:typeof n=="object"&&t[0]in n?[n[t[0]]]:[n];if(Array.isArray(n)){if(n.length!==r)throw new Error(`Provided ${e} is an array of ${n.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return n}else if(typeof n=="object"&&Object.keys(n).length>0&&typeof n[Object.keys(n)[0]]=="object"){let s=[];return t.forEach(u=>{u in n?s.push(n[u]):s.push(null)}),s}else throw new Error(`The model has multiple (${r}) outputs, so ${e} must be either an array with ${r} elements or an object with ${t} keys. Provided ${e} not understood: ${JSON.stringify(n)}`)}function MN(n,t){return ON(n,t,"classWeight")}function eft(n,t){return ON(n,t,"sampleWeight")}async function LN(n,t,e,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(e!=null){let s=ot(()=>{if(n.shape.length===1)return n.clone();if(n.shape.length===2)if(n.shape[1]>1){let h=1;return n.argMax(h)}else{if(n.shape[1]===1)return n.reshape([n.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${n.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${n.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),u=Array.from(await s.data());oe(s);let l=[];return u.forEach(h=>{if(e[h]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${h} exists in the data but not in classWeight`);l.push(e[h])}),Ir(l,"float32")}else return null}function _U(n,t){return st(n,t)}let FU=32;function BN(n,t){let e,r,s=t;e=s.xs,r=s.ys,k(e!=null&&r!=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}`);let u=zN("input",n.inputNames,e),l=zN("output",n.outputNames,r),h=u[0].shape[0];k(u.length===n.inputs.length,()=>`LayersModel has ${n.inputs.length} inputs, but the dataset provides ${u.length} inputs.  (Expected input keys: ${JSON.stringify(n.inputNames)})`),k(l.length===n.outputs.length,()=>`LayersModel has ${n.outputs.length} outputs, but the dataset provides ${l.length} outputs.  (Expected output keys: ${JSON.stringify(n.outputNames)})`);for(let p=0;p<u.length;p++)k(u[p].shape[0]===h,()=>`Batch size mismatch: input ${n.inputNames[p]} has ${u[p].shape[0]}; expected  ${h} based on input ${n.inputNames[0]}.`);for(let p=0;p<l.length;p++)k(l[p].shape[0]===h,()=>`Batch size mismatch: output ${n.outputNames[p]} has ${l[p].shape[0]}; expected  ${h} based on input ${n.inputNames[0]}.`);return{xs:u,ys:l}}function zN(n,t,e){if(e instanceof at)return[e];if(Array.isArray(e))return k(e.length===t.length,()=>`Received an array of ${e.length} Tensors, but expected ${t.length} to match the ${n} keys ${t}.`),e;{let r=[];for(let s of t){if(e[s]==null)throw new Q(`The feature data generated by the dataset lacks the required ${n} key '${s}'.`);r.push(e[s])}return r}}function RU(n){if(n.length===3)throw new Zt("Validation with sample weights is not implemented yet.");return{xs:n[0],ys:n[1]}}async function PU(n,t,e){let r=e.batchesPerEpoch!=null;if(k(n.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k(e!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k(e.epochs!=null&&e.epochs>0&&Number.isInteger(e.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${e.epochs}`),k(!r||e.batchesPerEpoch>0&&Number.isInteger(e.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${e.batchesPerEpoch}`),k(e.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),n.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");n.isTraining=!0;try{let s=e.validationData!=null,u,l;if(s)if(WN(e.validationData))k(e.validationBatches==null||e.validationBatches>0&&Number.isInteger(e.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${e.validationBatches}`);else{let D=RU(e.validationData);u=D.xs,l=D.ys}let h=n.makeTrainFunction(),p=n.getDedupedMetricsNames(),m;s?m=p.slice().concat(p.map(D=>"val_"+D)):m=p.slice();let y=SN(e.callbacks,e.yieldEvery),b=e.verbose==null?1:e.verbose,{callbackList:x,history:S}=CN(y,b,e.epochs,null,null,OU(t,e),null,s,m);x.setModel(n),n.history=S,await x.onTrainBegin(),n.stopTraining_=!1;let C=e.initialEpoch==null?0:e.initialEpoch,I=await t.iterator();for(;C<e.epochs;){let D={};await x.onEpochBegin(C);let R=0,A=0;for(r||(I=await t.iterator());r?R<e.batchesPerEpoch:!0;){let L=await I.next();if(r&&L.done){console.warn(`You provided \`batchesPerEpoch\` as ${e.batchesPerEpoch}, but your dataset iterator ran out of data after ${R} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${e.batchesPerEpoch*e.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(L.value!=null){let{xs:_,ys:B}=BN(n,L.value),V={};V.batch=A,V.size=_[0].shape[0],await x.onBatchBegin(A,V);let q=[];if(e.classWeight!=null){let tt=MN(e.classWeight,n.outputNames);for(let ht=0;ht<tt.length;++ht)q.push(await LN(B[ht],null,tt[ht]))}let j=_.concat(B).concat(q),et=h(j);oe(j);for(let tt=0;tt<p.length;++tt){let ht=p[tt],gt=et[tt];V[ht]=gt,An(gt)}await x.onBatchEnd(A,V),bN(V),A++,R++}if(r?R>=e.batchesPerEpoch:L.done){if(s){let _;WN(e.validationData)?_=Ge(await n.evaluateDataset(e.validationData,{batches:e.validationBatches})):_=Ge(n.evaluate(u,l,{batchSize:e.validationBatchSize==null?FU:e.validationBatchSize,verbose:0}));for(let B=0;B<n.metricsNames.length;++B)D[`val_${n.metricsNames[B]}`]=_[B]}break}if(n.stopTraining_)break}if(await x.onEpochEnd(C,D),C++,n.stopTraining_)break}return await x.onTrainEnd(),await n.history.syncData(),n.history}finally{n.isTraining=!1}}function OU(n,t){let e=null;return t.batchesPerEpoch!=null?e=t.batchesPerEpoch:Number.isFinite(n.size)&&(e=n.size),e}function WN(n){return typeof n.iterator=="function"}function MU(n){return typeof n.next=="function"}async function LU(n,t,e){e=e||{};let r=e.batches!=null,s=n.testFunction,u=[];if(e.verbose>0)throw new Zt("Verbose mode is not implemented yet.");k(!r||e.batches>0&&Number.isInteger(e.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(e.batches)}`);let l=MU(t)?t:await t.iterator(),h=0,p=0;for(;r?p<e.batches:!0;){let m=await l.next();if(u=ot(()=>{if(m.value){let{xs:y,ys:b}=BN(n,m.value),x=y.concat(b),S=ot(()=>s(x));if(oe(x),p===0)for(let I=0;I<S.length;++I)u.push(Ot(0));let C=x[0].shape[0];for(let I=0;I<S.length;++I){let D=S[I],R=u[I];u[I]=ot(()=>Nt(u[I],st(C,D))),p>0&&oe(R)}oe(S),h+=C,++p}return u}),m.done){r&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${e.batches} batches). 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Found: ${t[0].shape[0]} sample(s).`);return[t,e]}async standardizeUserData(t,e,r,s,u=!0,l){let[h,p]=this.standardizeUserDataXY(t,e,u,l);if(r!=null)throw new Error("sample weight is not supported yet.");let m=null;if(s!=null){let y=MN(s,this.outputNames);m=[];for(let b=0;b<y.length;++b)m.push(await LN(p[b],null,y[b]))}return[h,p,m]}testLoop(t,e,r,s=0,u){return ot(()=>{let l=this.checkNumSamples(e,r,u,"steps"),h=[];if(s>0)throw new Zt("Verbose mode is not implemented yet.");if(u!=null)throw new Zt("steps mode in testLoop() is not implemented yet");{let p=_w(l,r),m=Ir(xs(0,l));for(let y=0;y<p.length;++y){let b=p[y][0],x=p[y][1],S=Ci(m,b,x-b),C=Aw(e,S),I=t(C);if(y===0)for(let D=0;D<I.length;++D)h.push(Ot(0));for(let D=0;D<I.length;++D){let R=I[D];h[D]=Nt(h[D],st(x-b,R))}}for(let y=0;y<h.length;++y)h[y]=Ht(h[y],l)}return h})}getDedupedMetricsNames(){let t=this.metricsNames,e=[];for(let r=0;r<t.length;++r){let s=t[r],u=s;if(QC(t,s)>1){let l=QC(t.slice(0,r),s);u+=`_${l}`}e.push(u)}return e}makeTrainFunction(){return t=>{let e=[],r=t.slice(0,this.inputs.length),s=t.slice(this.inputs.length,this.inputs.length+this.outputs.length),u=t.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),l=[],h=()=>{let b=[];for(let I=0;I<this.inputs.length;++I)b.push({key:this.inputs[I],value:r[I]});let x=new wa(b),S=Xh(this.outputs,x,{training:!0}),C;for(let I=0;I<this.lossFunctions.length;++I){let D=this.lossFunctions[I],R=D(s[I],S[I]);u[I]!=null&&(R=_U(R,u[I]));let A=an(R);e.push(A),I===0?C=R:C=Nt(C,R)}for(let I=0;I<this.metricsTensors.length;++I){let D;if(this.outputs.length>1&&I<this.outputs.length)D=e[I];else{let R=this.metricsTensors[I][0],A=this.metricsTensors[I][1];D=an(R(s[A],S[A]))}An(D),l.push(D)}return C=an(C),this.calculateLosses().forEach(I=>{C=Nt(C,I)}),C},p=this.collectedTrainableWeights.map(b=>b.read()),m=!0,y=this.optimizer_.minimize(h,m,p);return[y].concat(l)}}makeTestFunction(){this.testFunction=t=>ot(()=>{let e=[],r,s=t.slice(0,this.inputs.length),u=t.slice(this.inputs.length,this.inputs.length+this.outputs.length),l=[];for(let m=0;m<this.inputs.length;++m)l.push({key:this.inputs[m],value:s[m]});let h=new wa(l),p=Xh(this.outputs,h);for(let m=0;m<this.lossFunctions.length;++m){let y=this.lossFunctions[m],b=an(y(u[m],p[m]));m===0?r=b:r=Nt(r,b),e.push(r)}for(let m=0;m<this.metricsTensors.length;++m){let y=this.metricsTensors[m][0],b=this.metricsTensors[m][1],x=an(y(u[b],p[b]));e.push(x)}return e})}async fit(t,e,r={}){return zU(this,t,e,r)}async fitDataset(t,e){return PU(this,t,e)}async trainOnBatch(t,e){let r=await this.standardizeUserData(t,e),s=r[0],u=r[1],l=this.makeTrainFunction(),h=l(s.concat(u)),p=[];for(let m of h){let y=await m.data();p.push(y[0])}return oe(h),gr(p)}getNamedWeights(t){let e=[],r=t!=null&&t.trainableOnly,s=r?this.trainableWeights:this.weights,u=this.getWeights(r);for(let l=0;l<s.length;++l){if(r&&!s[l].trainable)continue;e.push({name:s[l].originalName,tensor:u[l]})}return e}set stopTraining(t){this.stopTraining_=t}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(t){this.optimizer_!==t&&(this.optimizer_=t,this.isOptimizerOwned=!1)}dispose(){let t=super.dispose();if(t.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let e=id().numTensors;this.optimizer_.dispose(),t.numDisposedVariables+=e-id().numTensors}return t}getLossIdentifiers(){let t;if(typeof this.loss=="string")t=Co(this.loss);else if(Array.isArray(this.loss)){for(let e of this.loss)if(typeof e!="string")throw new Error("Serialization of non-string loss is not supported.");t=this.loss.map(e=>Co(e))}else{let e=Object.keys(this.loss);t={};let r=this.loss;for(let s of e)if(typeof r[s]=="string")t[s]=Co(r[s]);else throw new Error("Serialization of non-string loss is not supported.")}return t}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Co(vm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(t=>Co(vm(t)));{let t={};for(let e in this.metrics)t[e]=Co(vm(this.metrics[e]));return t}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(t){if(t.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(t.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(t.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let e=Kh(t.optimizer_config),r=Ss(e),s;if(typeof t.loss=="string")s=ki(t.loss);else if(Array.isArray(t.loss))s=t.loss.map(l=>ki(l));else if(t.loss!=null){s={};for(let l in t.loss)s[l]=ki(t.loss[l])}let u;if(Array.isArray(t.metrics))u=t.metrics.map(l=>ki(l));else if(t.metrics!=null){u={};for(let l in t.metrics)u[l]=ki(t.metrics[l])}this.compile({loss:s,metrics:u,optimizer:r})}async save(t,e){if(typeof t=="string"){let m=Ey(t);if(m.length===0)throw new Q(`Cannot find any save handlers for URL '${t}'`);if(m.length>1)throw new Q(`Found more than one (${m.length}) save handlers for URL '${t}'`);t=m[0]}if(t.save==null)throw new Q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Ny(this.getNamedWeights(e)),s=!1,u=null,l=this.toJSON(u,s),h={modelTopology:l,format:HU,generatedBy:`TensorFlow.js tfjs-layers v${bm}`,convertedBy:null},p=e==null?!1:e.includeOptimizer;if(p&&this.optimizer!=null){h.trainingConfig=this.getTrainingConfig();let m="optimizer",{data:y,specs:b}=await Ny(await this.optimizer.getWeights(),m);r.specs.push(...b),r.data=Zp([r.data,y])}if(this.userDefinedMetadata!=null){let m=!0;_N(this.userDefinedMetadata,this.name,m),h.userDefinedMetadata=this.userDefinedMetadata}return h.weightData=r.data,h.weightSpecs=r.specs,t.save(h)}setUserDefinedMetadata(t){_N(t,this.name),this.userDefinedMetadata=t}getUserDefinedMetadata(){return this.userDefinedMetadata}}Io.className="Model",kt(Io);class qN extends Io{}qN.className="Functional",kt(qN);async function qU(n,t){"modelTopology"in n||(n={modelTopology:n}),n=n;let e=n.modelTopology;e.model_config!=null&&(e=e.model_config);let r=Kh(e),s=Ss(r,t);if(n.weightsManifest!=null){let u=await bS(n.weightsManifest,n.pathPrefix,s.weights.map(h=>h.originalName)),l={};for(let h of s.weights)l[h.originalName]=u[h.originalName];s.loadWeights(l),oe(u)}return s}async function jU(n,t){if(t==null&&(t={}),typeof n=="string"){let e=Dy(n,t);if(e.length===0)e.push(ed(n,t));else if(e.length>1)throw new Q(`Found more than one (${e.length}) load handlers for URL '${n}'`);n=e[0]}return KU(n,void 0,t)}async function KU(n,t,e){if(e==null&&(e={}),n.load==null)throw new Q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await n.load(),s=r.modelTopology;s.model_config!=null&&(s=s.model_config);let u=e.strict==null?!0:e.strict,l=r.weightData!=null&&r.weightSpecs!=null&&u,h=Ss(Kh(s),t,l),p=r.trainingConfig;if(p!=null&&h.loadTrainingConfig(p),r.userDefinedMetadata!=null&&h.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new Q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:m,optimizerWeights:y}=XU(r.weightData,r.weightSpecs);h.loadWeights(m,u),h.optimizer!=null&&y.length>0&&await h.optimizer.setWeights(y),oe(m),oe(y.map(b=>b.tensor))}return h}function XU(n,t){let e=Jp(n,t),r={},s=[];return t.forEach(u=>{u.group==="optimizer"?s.push({name:u.name,tensor:e[u.name]}):r[u.name]=e[u.name]}),{modelWeights:r,optimizerWeights:s}}class Ii extends Io{constructor(t){super({inputs:[],outputs:[]});if(t=t||{},this.trainable=!0,this.built=!1,this.name=t.name!=null?t.name:im("sequential_"),t.layers!=null)for(let e of t.layers)this.add(e)}checkShape(t){let e=t.inboundNodes[0].outputTensors[0].shape;if(e.some(r=>r<0))throw new Q(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let e=t instanceof Ii||t instanceof Io,r;if(e){if(r=t,r.outputs.length!==1)throw new Q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(r.inputs.length!==1)throw new Q("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(t.inboundNodes.length===0){if(t.batchInputShape==null)throw new Q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=yN({batchShape:t.batchInputShape,dtype:t.dtype,name:t.name+"_input"});t.apply(s)}if(e)this.outputs=r.outputs,this.inputs=r.inputs;else{if(t.inboundNodes.length!==1)throw new Q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${t.name} which has ${t.inboundNodes.length} pre-existing inbound connections.`);if(t.inboundNodes[0].outputTensors.length!==1)throw new Q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[t.inboundNodes[0].outputTensors[0]],this.inputs=vN(this.outputs[0])}this.inboundNodes=[],new lm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Ti(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=t.apply(this.outputs[0]);if(Array.isArray(s))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(t),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(t),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{let t=this.layers.length-1;this.layers[t].outboundNodes=[],this.outputs=[this.layers[t].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(t,e){return this.model==null&&this.build(),this.model.call(t,e)}build(t){if(Be(t),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 Io({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(t,e,r=console.log){this.built||this.build(),super.summary(t,e,r)}setWeights(t){this.model==null&&this.build(),this.model.setWeights(t)}evaluate(t,e,r={}){if(!this.built)throw new ns("The model needs to be compiled before being used.");return this.model.evaluate(t,e,r)}async evaluateDataset(t,e){if(!this.built)throw new ns("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),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(t){this.model.optimizer=t}async fit(t,e,r={}){if(!this.built)throw new ns("The model needs to be compiled before being used.");return this.model.fit(t,e,r)}async fitDataset(t,e){if(!this.built)throw new ns("The model needs to be compiled before being used.");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,r={},s=!1){let u,l={};if(e instanceof Array){if(!(e[0].className!=null)||e[0].className==="Merge")throw new Q("Legacy serialization format not supported yet.");u=e}else k(e.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),u=e.layers,delete e.layers,l=e;let h=new t(l);if(!(h instanceof Ii))throw new Zt(`Sequential.fromConfig called on non-Sequential input: ${h}`);for(let p of u){let m=void 0,y=Ss(p,m,s);s&&y.setFastWeightInitDuringBuild(!0),h.add(y)}return h}set stopTraining(t){if(this.model==null)throw new Q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new Q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let r={};r.className=e.getClassName(),r.config=e.getConfig(),t.push(r)}return{name:this.name,layers:t}}}Ii.className="Sequential",kt(Ii);function YU(n){return new Io(n)}function JU(n){return new Ii(n)}function ZU(n,t){return t==null&&(t={}),jU(n,t)}function jN(n){return yN(n)}function QU(n,t){Gr.registerCallbackConstructor(n,t)}class Dr extends li{getConfig(){return{}}}class KN extends Dr{apply(t,e=1){return IV(t,e)}}KN.className="elu",kt(KN);class XN extends Dr{apply(t){return Ed(t)}}XN.className="selu",kt(XN);class YN extends Dr{apply(t){return Ys(t)}}YN.className="relu",kt(YN);class JN extends Dr{apply(t){return ot(()=>mi(6,Ys(t)))}}JN.className="relu6",kt(JN);class ZN extends Dr{apply(t){return t}}ZN.className="linear",kt(ZN);class QN extends Dr{apply(t){return js(t)}}QN.className="sigmoid",kt(QN);class tI extends Dr{apply(t){return DV(t)}}tI.className="hardSigmoid",kt(tI);class eI extends Dr{apply(t){return Ru(t)}}eI.className="softplus",kt(eI);class nI extends Dr{apply(t){return EV(t)}}nI.className="softsign",kt(nI);class rI extends Dr{apply(t){return Du(t)}}rI.className="tanh",kt(rI);class Rw extends Dr{apply(t,e=-1){return bi(t,e)}}Rw.className="softmax",kt(Rw);class sI extends Dr{apply(t,e=-1){return Td(t,e)}}sI.className="logSoftmax",kt(sI);class oI extends Dr{apply(t,e=1){return ot(()=>js(t.mul(e)).mul(t))}}oI.className="swish",kt(oI);function xa(n){return n.getClassName()}function Pw(n,t={}){return zh(n,Lr.getMap().classNameMap,t,"activation")}function Ta(n){if(n==null){let t={};return t.className="linear",t.config={},Pw(t)}if(typeof n=="string"){let t={};return t.className=n,t.config={},Pw(t)}else return n instanceof Dr?n:Pw(n)}function Ow(n){if(n!=null&&typeof n!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${n}`)}class aI extends li{}class Jh extends aI{constructor(t){super();Ow(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return ot(()=>{let e=Se([1]);return this.hasL1&&(e=Nt(e,Xt(st(this.l1,kn(t))))),this.hasL2&&(e=Nt(e,Xt(st(this.l2,Hh(t))))),e.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}}Jh.className="L1L2",kt(Jh);function tG(n){return Ow(n),new Jh({l1:n!=null?n.l1:null,l2:0})}function eG(n){return Ow(n),new Jh({l2:n!=null?n.l2:null,l1:0})}let iI={l1l2:"L1L2"};function ze(n){return Qb(n)}function uI(n,t={}){return zh(n,Lr.getMap().classNameMap,t,"regularizer")}function Ye(n){if(n==null)return null;if(typeof n=="string"){let t=n in iI?iI[n]:n,e={className:t,config:{}};return uI(e)}else return n instanceof aI?n:uI(n)}class Mw extends Te{constructor(t){super(t==null?{}:t);this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=le(t);let r=Ys(t);return this.maxValue!=null&&(r=fr(r,0,this.maxValue)),r}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}}Mw.className="ReLU",kt(Mw);class Lw extends Te{constructor(t){super(t==null?{}:t);this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let r=le(t);return bd(r,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}}Lw.className="LeakyReLU",kt(Lw);class Bw extends Te{constructor(t){super(t==null?{}:t);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=Xe(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ye(t.alphaRegularizer),this.alphaConstraint=In(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new Q(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Be(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)e[s-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let s=1;s<t.length;++s)r[s]=t[s];this.inputSpec=[new Fn({ndim:t.length,axes:r})],this.built=!0}call(t,e){return t=le(t),Sh(t,this.alpha.read())}getConfig(){let t={alphaInitializer:cn(this.alphaInitializer),alphaRegularizer:ze(this.alphaRegularizer),alphaConstraint:Nn(this.alphaConstraint),sharedAxes:this.sharedAxes},e=super.getConfig();return Object.assign(t,e),t}}Bw.className="PReLU",kt(Bw);class zw extends Te{constructor(t){super(t==null?{}:t);if(this.DEFAULT_ALPHA=1,t==null&&(t={}),t.alpha!=null&&t.alpha!==this.DEFAULT_ALPHA)throw new Zt(`Non-default alpha value (${t.alpha}) is not supported by the ELU layer yet.`);this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let r=le(t);return $u(r)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}}zw.className="ELU",kt(zw);class Ww extends Te{constructor(t){super(t==null?{}:t);this.DEFAULT_THETA=1,t==null&&(t={}),this.theta=t.theta==null?this.DEFAULT_THETA:t.theta}call(t,e){let r=le(t);return r.mul(Uh(r.greater(this.theta),"float32"))}computeOutputShape(t){return t}getConfig(){let t={theta:this.theta},e=super.getConfig();return Object.assign(t,e),t}}Ww.className="ThresholdedReLU",kt(Ww);class Vw extends Te{constructor(t){super(t==null?{}:t);this.DEFAULT_AXIS=1,t==null&&(t={}),this.softmax=new Rw().apply,this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis}call(t,e){let r=le(t);return this.softmax(r,this.axis)}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}}Vw.className="Softmax",kt(Vw);function Ju(n,t,e){if(typeof n=="number")return Ti(n,t);if(n.length!==t)throw new Q(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${n.length} elements.`);for(let r=0;r<t;++r){let s=n[r];if(!xV(s))throw new Q(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(n)} including a non-integer number ${s}`)}return n}function Ns(n,t,e,r,s=1){if(n==null)return n;let u=t+(t-1)*(s-1),l;return e==="same"?l=n:l=n-u+1,Math.floor((l+r-1)/r)}function wm(n,t,e,r){if(n==null)return null;if(r==="valid")n=n*t+va([e-t,0]);else if(r==="same")n=n*t;else throw new Q(`Unsupport padding mode: ${r}.`);return n}function Uw(n,t){return ot(()=>(un(t),t==="channelsFirst"?re(n,[0,2,3,1]):n))}function cI(n,t){return ot(()=>(un(t),t==="channelsFirst"?re(n,[0,2,3,4,1]):n))}function lI(n,t,e,r=1,s="valid",u,l=1){return ot(()=>{if(u==null&&(u=ws()),un(u),n.shape.length!==3)throw new Q(`The input of a conv1dWithBias operation should be 3, but is ${n.shape.length} instead.`);if(t.shape.length!==3)throw new Q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new Q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(u==="channelsFirst"&&(n=re(n,[0,2,1])),s==="causal")throw new Zt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let h=dd(n,t,r,s==="same"?"same":"valid","NWC",l);return e!=null&&(h=Qs(h,e)),h})}function nft(n,t,e=1,r="valid",s,u=1){return ot(()=>(un(s),lI(n,t,null,e,r,s,u)))}function rft(n,t,e=[1,1],r="valid",s,u){return ot(()=>(un(s),Gw(n,t,null,e,r,s,u)))}function Gw(n,t,e,r=[1,1],s="valid",u,l,h=null){return ot(()=>{if(u==null&&(u=ws()),un(u),n.rank!==3&&n.rank!==4)throw new Q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${n.rank}.`);if(t.rank!==3&&t.rank!==4)throw new Q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${n.rank}.`);let p=Uw(n,u);if(s==="causal")throw new Zt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return p=Pb({x:p,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:l,dataFormat:"NHWC",bias:e,activation:h}),u==="channelsFirst"&&(p=re(p,[0,3,1,2])),p})}function sft(n,t,e=[1,1,1],r="valid",s,u){return ot(()=>(un(s),hI(n,t,null,e,r,s,u)))}function hI(n,t,e,r=[1,1,1],s="valid",u,l){return ot(()=>{if(u==null&&(u=ws()),un(u),n.rank!==4&&n.rank!==5)throw new Q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${n.rank}.`);if(t.rank!==4&&t.rank!==5)throw new Q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${n.rank}.`);let h=cI(n,u);if(s==="causal")throw new Zt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return h=ab(h,t,r,s==="same"?"same":"valid","NDHWC",l),e!=null&&(h=Qs(h,e)),u==="channelsFirst"&&(h=re(h,[0,4,1,2,3])),h})}class xm extends Te{constructor(t,e){super(e);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",xm.verifyArgs(e),this.rank=t,_n(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Zt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ju(e.kernelSize,t,"kernelSize"),this.strides=Ju(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,Ur(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,un(this.dataFormat),this.activation=Ta(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=Xe(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=In(e.biasConstraint),this.biasRegularizer=Ye(e.biasRegularizer),this.activityRegularizer=Ye(e.activityRegularizer),this.dilationRate=Ju(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new Q(`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 Q(`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 Q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(Vr("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!ew(t.kernelSize,"number",1,3))throw new Q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:xa(this.activation),useBias:this.useBias,biasInitializer:cn(this.biasInitializer),biasRegularizer:ze(this.biasRegularizer),activityRegularizer:ze(this.activityRegularizer),biasConstraint:Nn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}class Zu extends xm{constructor(t,e){super(t,e);this.kernel=null,Zu.verifyArgs(e),this.filters=e.filters,_n(this.filters,"filters"),this.kernelInitializer=Xe(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=In(e.kernelConstraint),this.kernelRegularizer=Ye(e.kernelRegularizer)}build(t){t=Be(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Q(`The channel dimension of the input should be defined. Found ${t[e]}`);let r=t[e],s=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",s,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:{[e]:r}}],this.built=!0}call(t,e){return ot(()=>{t=le(t);let r,s=this.bias==null?null:this.bias.read(),u=eN(this.activation.getClassName());if(u!=null&&this.rank===2)r=Gw(t,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,u);else{if(this.rank===1)r=lI(t,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=Gw(t,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=hI(t,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Zt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(t){t=Be(t);let e=[],r=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let u=0;u<r.length;++u){let l=Ns(r[u],this.kernelSize[u],this.padding,this.strides[u],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[u]);e.push(l)}let s=[t[0]];return this.dataFormat==="channelsLast"?(s=s.concat(e),s.push(this.filters)):(s.push(this.filters),s=s.concat(e)),s}getConfig(){let t={filters:this.filters,kernelInitializer:cn(this.kernelInitializer),kernelRegularizer:ze(this.kernelRegularizer),kernelConstraint:Nn(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new Q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}}class Qu extends Zu{constructor(t){super(2,t);Qu.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!ew(t.kernelSize,"number",1,2))throw new Q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}}Qu.className="Conv2D",kt(Qu);class Zh extends Zu{constructor(t){super(3,t);Zh.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new Q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}}Zh.className="Conv3D",kt(Zh);class Hw extends Qu{constructor(t){super(t);if(this.inputSpec=[new Fn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new Q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Be(t),t.length!==4)throw new Q("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Q("The channel dimension of the inputs should be defined. Found `None`.");let r=t[e],s=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Fn({ndim:4,axes:{[e]:r}})],this.built=!0}call(t,e){return ot(()=>{let r=le(t);if(r.shape.length!==4)throw new Q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let s=r.shape,u=s[0],l,h;this.dataFormat==="channelsFirst"?(l=2,h=3):(l=1,h=2);let p=s[l],m=s[h],y=this.kernelSize[0],b=this.kernelSize[1],x=this.strides[0],S=this.strides[1],C=wm(p,x,y,this.padding),I=wm(m,S,b,this.padding),D=[u,C,I,this.filters];this.dataFormat!=="channelsLast"&&(r=re(r,[0,2,3,1]));let R=md(r,this.kernel.read(),D,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(R=re(R,[0,3,1,2])),this.bias!=null&&(R=Qs(R,this.bias.read(),this.dataFormat)),this.activation!=null&&(R=this.activation.apply(R)),R})}computeOutputShape(t){t=Be(t);let e=t.slice(),r,s,u;this.dataFormat==="channelsFirst"?(r=1,s=2,u=3):(r=3,s=1,u=2);let l=this.kernelSize[0],h=this.kernelSize[1],p=this.strides[0],m=this.strides[1];return e[r]=this.filters,e[s]=wm(e[s],p,l,this.padding),e[u]=wm(e[u],m,h,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}}Hw.className="Conv2DTranspose",kt(Hw);class fI extends Zu{constructor(t,e){super(t,e);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new Q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new Q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new Q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Xe(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ye(e.depthwiseRegularizer),this.depthwiseConstraint=In(e.depthwiseConstraint),this.pointwiseInitializer=Xe(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ye(e.pointwiseRegularizer),this.pointwiseConstraint=In(e.pointwiseConstraint)}build(t){if(t=Be(t),t.length<this.rank+2)throw new Q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new Q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let r=t[e],s=this.kernelSize.concat([r,this.depthMultiplier]),u=[];for(let h=0;h<this.rank;++h)u.push(1);u.push(r*this.depthMultiplier,this.filters);let l=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,l,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",u,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,l,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,l,this.biasConstraint):this.bias=null,this.inputSpec=[new Fn({ndim:this.rank+2,axes:{[e]:r}})],this.built=!0}call(t,e){return ot(()=>{t=le(t);let r;if(this.rank===1)throw new Zt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=re(t,[0,2,3,1])),r=Sb(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=Qs(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=re(r,[0,3,1,2])),r})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=cn(this.depthwiseInitializer),t.pointwiseInitializer=cn(this.pointwiseInitializer),t.depthwiseRegularizer=ze(this.depthwiseRegularizer),t.pointwiseRegularizer=ze(this.pointwiseRegularizer),t.depthwiseConstraint=Nn(this.depthwiseConstraint),t.pointwiseConstraint=Nn(this.pointwiseConstraint),t}}fI.className="SeparableConv";class qw extends fI{constructor(t){super(2,t)}}qw.className="SeparableConv2D",kt(qw);class Qh extends Zu{constructor(t){super(1,t);Qh.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!ew(t.kernelSize,"number",1,1))throw new Q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}}Qh.className="Conv1D",kt(Qh);class jw extends Te{constructor(t){super(t);typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return ot(()=>{if(t=le(t),this.dataFormat==="channelsLast"){let r=Jd(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Jd(r,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Jd(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Jd(r,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}jw.className="Cropping2D",kt(jw);class Kw extends Te{constructor(t){super(t);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],r=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,r]}else{let e=t[1]==null?null:this.size[0]*t[1],r=t[2]==null?null:this.size[1]*t[2];return[t[0],e,r,t[3]]}}call(t,e){return ot(()=>{let r=le(t),s=r.shape;if(this.dataFormat==="channelsFirst"){r=re(r,[0,2,3,1]);let u=this.size[0]*s[2],l=this.size[1]*s[3],h=r.resizeNearestNeighbor([u,l]);return re(h,[0,3,1,2])}else{let u=this.size[0]*s[1],l=this.size[1]*s[2];return r.resizeNearestNeighbor([u,l])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}Kw.className="UpSampling2D",kt(Kw);function nG(n,t,e=[1,1],r="valid",s,u){return ot(()=>{s==null&&(s=ws()),un(s);let l=Uw(n,s);if(n.rank!==4)throw new Q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${n.rank}-D`);if(t.rank!==4)throw new Q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return l=pi(l,t,e,r==="same"?"same":"valid","NHWC",u),s==="channelsFirst"&&(l=re(l,[0,3,1,2])),l})}class Xw extends xm{constructor(t){super(2,t);this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Xe(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=In(t.depthwiseConstraint),this.depthwiseRegularizer=Ye(t.depthwiseRegularizer)}build(t){if(t=Be(t),t.length<4)throw new Q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new Q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let r=t[e],s=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return ot(()=>{t=le(t);let r=nG(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=Qs(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(t){t=Be(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],s=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,u=Ns(e,this.kernelSize[0],this.padding,this.strides[0]),l=Ns(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],s,u,l]:[t[0],u,l,s]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=cn(this.depthwiseInitializer),t.depthwiseRegularizer=ze(this.depthwiseRegularizer),t.depthwiseConstraint=Nn(this.depthwiseRegularizer),t}}Xw.className="DepthwiseConv2D",kt(Xw);function pI(n,t,e,r){if(Array.isArray(n)){if(t!=null||e!=null)throw new Q("When inputs is an array, neither initialState or constants should be provided");r!=null&&(e=n.slice(n.length-r,n.length),n=n.slice(0,n.length-r)),n.length>1&&(t=n.slice(1,n.length)),n=n[0]}function s(u){return u==null||Array.isArray(u)?u:[u]}return t=s(t),e=s(e),{inputs:n,initialState:t,constants:e}}function dI(n,t,e,r=!1,s,u,l=!1,h=!1){return ot(()=>{let p=t.shape.length;if(p<3)throw new Q(`Input should be at least 3D, but is ${p}D.`);let m=[1,0].concat(xs(2,p));if(t=re(t,m),u!=null)throw new Zt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");l&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=s.asType("bool").asType("float32"),s.rank===p-1&&(s=pr(s,-1)),s=re(s,m)),r&&(t=Wr(t,0),s!=null&&(s=Wr(s,0)));let y=[],b,x=e,S=t.shape[0],C=bs(t),I;s!=null&&(I=bs(s));for(let R=0;R<S;++R){let A=C[R],L=ot(()=>n(A,x));if(s==null)b=L[0],x=L[1];else{let _=ot(()=>{let B=I[R],V=Xn(B).sub(B),q=L[0].mul(B).add(x[0].mul(V)),j=x.map((et,tt)=>L[1][tt].mul(B).add(et.mul(V)));return{output:q,newStates:j}});b=_.output,x=_.newStates}h&&y.push(b)}let D;if(h){let R=1;D=mr(y,R)}return[b,D,x]})}class Is extends Te{constructor(t){super(t);let e;if(t.cell==null)throw new Q("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Sm({cells:t.cell}):e=t.cell,e.stateSize==null)throw new Q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Fn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return xs(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){yw(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let r=e[0],s;if(this.returnSequences?s=[t[0],t[1],r]:s=[t[0],r],this.returnState){let u=[];for(let l of e)u.push([t[0],l]);return[s].concat(u)}else return s}computeMask(t,e){return ot(()=>{Array.isArray(e)&&(e=e[0]);let r=this.returnSequences?e:null;if(this.returnState){let s=this.states.map(u=>null);return[r].concat(s)}else return r})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let r=0;r<t;++r)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){let e=null;if(this.numConstants!=null)throw new Zt("Constants support is not implemented in RNN yet.");yw(t)&&(t=t[0]),t=t;let r=this.stateful?t[0]:null,s=t.slice(2);this.inputSpec[0]=new Fn({shape:[r,null,...s]});let u=[t[0]].concat(t.slice(2));if(e!=null)throw new Zt("Constants support is not implemented in RNN yet.");this.cell.build(u);let l;if(Array.isArray(this.cell.stateSize)?l=this.cell.stateSize:l=[this.cell.stateSize],this.stateSpec!=null){if(!K(this.stateSpec.map(h=>h.shape[h.shape.length-1]),l))throw new Q(`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=l.map(h=>new Fn({shape:[null,h]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){ot(()=>{if(!this.stateful)throw new Js("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new Q("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(s=>Se([r,s])):this.states_=[Se([r,this.cell.stateSize])];else if(t==null)oe(this.states_),this.keptStates!=null&&(oe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Se([r,s])):this.states_[0]=Se([r,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):oe(this.states_);for(let s=0;s<this.states_.length;++s){let u=t[s],l=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,h=[r,l];if(!K(u.shape,h))throw new Q(`State ${s} is incompatible with layer ${this.name}: expected shape=${h}, received shape=${u.shape}`);this.states_[s]=u}}this.states_=this.states_.map(s=>An(s.clone()))})}apply(t,e){let r=e==null?null:e.initialState,s=e==null?null:e.constants;e==null&&(e={});let u=pI(t,r,s,this.numConstants);t=u.inputs,r=u.initialState,s=u.constants;let l=[],h=[];if(r!=null){e.initialState=r,l=l.concat(r),this.stateSpec=[];for(let m of r)this.stateSpec.push(new Fn({shape:m.shape}));h=h.concat(this.stateSpec)}s!=null&&(e.constants=s,l=l.concat(s),this.numConstants=s.length);let p=l[0]instanceof ks;if(p){let m=[t].concat(l),y=this.inputSpec.concat(h),b=this.inputSpec;this.inputSpec=y;let x=super.apply(m,e);return this.inputSpec=b,x}else return super.apply(t,e)}call(t,e){return ot(()=>{let r=e==null?null:e.mask,s=e==null?null:e.training,u=e==null?null:e.initialState;t=le(t),u==null&&(this.stateful?u=this.states_:u=this.getInitialState(t));let l=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(u.length!==l)throw new Q(`RNN Layer has ${l} state(s) but was passed ${u.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let h={training:s},p=(C,I)=>{let D=this.cell.call([C].concat(I),h);return[D[0],D.slice(1)]},m=dI(p,t,u,this.goBackwards,r,null,this.unroll,this.returnSequences),y=m[0],b=m[1],x=m[2];this.stateful&&this.resetStates(x,s);let S=this.returnSequences?b:y;return this.returnState?[S].concat(x):S})}getInitialState(t){return ot(()=>{let e=Se(t.shape);return e=Xt(e,[1,2]),e=Gh(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?cw(e,[1,r]):e):this.cell.stateSize>1?[cw(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===Is.className&&(e.cell={className:this.cell.getClassName(),config:r}),Object.assign({},r,t,e)}static fromConfig(t,e,r={}){let s=e.cell,u=Ss(s,r);return new t(Object.assign(e,{cell:u}))}}Is.className="RNN",kt(Is);class tc extends Te{}class Tm extends tc{constructor(t){super(t);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,_n(this.units,"units"),this.activation=Ta(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=Xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ye(t.kernelRegularizer),this.recurrentRegularizer=Ye(t.recurrentRegularizer),this.biasRegularizer=Ye(t.biasRegularizer),this.kernelConstraint=In(t.kernelConstraint),this.recurrentConstraint=In(t.recurrentConstraint),this.biasConstraint=In(t.biasConstraint),this.dropout=ju([1,va([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=ju([1,va([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Be(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return ot(()=>{if(t=t,t.length!==2)throw new Q(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let r=t[1];t=t[0];let s=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ka({ones:()=>Xn(t),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ka({ones:()=>Xn(r),rate:this.recurrentDropout,training:s}));let u,l=this.dropoutMask,h=this.recurrentDropoutMask;l!=null?u=Zs(st(t,l),this.kernel.read()):u=Zs(t,this.kernel.read()),this.bias!=null&&(u=Qs(u,this.bias.read())),h!=null&&(r=st(r,h));let p=Nt(u,Zs(r,this.recurrentKernel.read()));return this.activation!=null&&(p=this.activation.apply(p)),[p,p]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:xa(this.activation),useBias:this.useBias,kernelInitializer:cn(this.kernelInitializer),recurrentInitializer:cn(this.recurrentInitializer),biasInitializer:cn(this.biasInitializer),kernelRegularizer:ze(this.kernelRegularizer),recurrentRegularizer:ze(this.recurrentRegularizer),biasRegularizer:ze(this.biasRegularizer),activityRegularizer:ze(this.activityRegularizer),kernelConstraint:Nn(this.kernelConstraint),recurrentConstraint:Nn(this.recurrentConstraint),biasConstraint:Nn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},t,e)}}Tm.className="SimpleRNNCell",kt(Tm);class Yw extends Is{constructor(t){t.cell=new Tm(t),super(t)}call(t,e){return ot(()=>{this.cell.dropoutMask!=null&&(oe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(oe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,s=e==null?null:e.training,u=e==null?null:e.initialState;return super.call(t,{mask:r,training:s,initialState:u})})}static fromConfig(t,e){return new t(e)}}Yw.className="SimpleRNN",kt(Yw);class km extends tc{constructor(t){super(t);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",t.resetAfter)throw new Q("GRUCell does not support reset_after parameter set to true.");this.units=t.units,_n(this.units,"units"),this.activation=Ta(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Ta(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=Xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ye(t.kernelRegularizer),this.recurrentRegularizer=Ye(t.recurrentRegularizer),this.biasRegularizer=Ye(t.biasRegularizer),this.kernelConstraint=In(t.kernelConstraint),this.recurrentConstraint=In(t.recurrentConstraint),this.biasConstraint=In(t.biasConstraint),this.dropout=ju([1,va([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=ju([1,va([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Be(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return ot(()=>{if(t=t,t.length!==2)throw new Q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training==null?!1:e.training,s=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ka({ones:()=>Xn(t),rate:this.dropout,training:r,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ka({ones:()=>Xn(s),rate:this.recurrentDropout,training:r,count:3}));let u=this.dropoutMask,l=this.recurrentDropoutMask,h,p,m;0<this.dropout&&this.dropout<1&&(t=st(t,u[0]));let y=Zs(t,this.kernel.read());this.useBias&&(y=Qs(y,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=st(s,l[0]));let b=this.recurrentKernel.read(),[x,S]=Er(b,[2*this.units,this.units],b.rank-1),C=Zs(s,x),[I,D,R]=Er(y,3,y.rank-1),[A,L]=Er(C,2,C.rank-1);h=this.recurrentActivation.apply(Nt(I,A)),p=this.recurrentActivation.apply(Nt(D,L));let _=Zs(st(p,s),S);m=this.activation.apply(Nt(R,_));let B=Nt(st(h,s),st(Nt(1,on(h)),m));return[B,B]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:xa(this.activation),recurrentActivation:xa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:cn(this.kernelInitializer),recurrentInitializer:cn(this.recurrentInitializer),biasInitializer:cn(this.biasInitializer),kernelRegularizer:ze(this.kernelRegularizer),recurrentRegularizer:ze(this.recurrentRegularizer),biasRegularizer:ze(this.biasRegularizer),activityRegularizer:ze(this.activityRegularizer),kernelConstraint:Nn(this.kernelConstraint),recurrentConstraint:Nn(this.recurrentConstraint),biasConstraint:Nn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},t,e)}}km.className="GRUCell",kt(km);class Jw extends Is{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new km(t),super(t)}call(t,e){return ot(()=>{this.cell.dropoutMask!=null&&(oe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(oe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,s=e==null?null:e.training,u=e==null?null:e.initialState;return super.call(t,{mask:r,training:s,initialState:u})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}Jw.className="GRU",kt(Jw);class tf extends tc{constructor(t){super(t);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=t.units,_n(this.units,"units"),this.activation=Ta(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=Ta(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=Xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ye(t.kernelRegularizer),this.recurrentRegularizer=Ye(t.recurrentRegularizer),this.biasRegularizer=Ye(t.biasRegularizer),this.kernelConstraint=In(t.kernelConstraint),this.recurrentConstraint=In(t.recurrentConstraint),this.biasConstraint=In(t.biasConstraint),this.dropout=ju([1,va([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=ju([1,va([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Be(t);let r=t[t.length-1];this.kernel=this.addWeight("kernel",[r,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 s;if(this.useBias){if(this.unitForgetBias){let u=this.biasInitializer,l=this.units;s=new(e=class extends ss{apply(p,m){let y=u.apply([l]),b=new Qd().apply([l]),x=u.apply([l*2]);return lN(lN(y,b),x)}},e.className="CustomInit",e)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return ot(()=>{let r=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new Q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let s=t[1],u=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ka({ones:()=>Xn(t),rate:this.dropout,training:r,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ka({ones:()=>Xn(s),rate:this.recurrentDropout,training:r,count:4}));let l=this.dropoutMask,h=this.recurrentDropoutMask,p,m,y,b;0<this.dropout&&this.dropout<1&&(t=st(t,l[0]));let x=Zs(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=st(s,h[0])),x=Nt(x,Zs(s,this.recurrentKernel.read())),this.useBias&&(x=Qs(x,this.bias.read()));let[S,C,I,D]=Er(x,4,x.rank-1);p=this.recurrentActivation.apply(S),m=this.recurrentActivation.apply(C),y=Nt(st(m,u),st(p,this.activation.apply(I))),b=this.recurrentActivation.apply(D);let R=st(b,this.activation.apply(y));return[R,R,y]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:xa(this.activation),recurrentActivation:xa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:cn(this.kernelInitializer),recurrentInitializer:cn(this.recurrentInitializer),biasInitializer:cn(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ze(this.kernelRegularizer),recurrentRegularizer:ze(this.recurrentRegularizer),biasRegularizer:ze(this.biasRegularizer),activityRegularizer:ze(this.activityRegularizer),kernelConstraint:Nn(this.kernelConstraint),recurrentConstraint:Nn(this.recurrentConstraint),biasConstraint:Nn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},t,e)}}tf.className="LSTMCell",kt(tf);class Zw extends Is{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new tf(t),super(t)}call(t,e){return ot(()=>{this.cell.dropoutMask!=null&&(oe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(oe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,s=e==null?null:e.training,u=e==null?null:e.initialState;return super.call(t,{mask:r,training:s,initialState:u})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}Zw.className="LSTM",kt(Zw);class Sm extends tc{constructor(t){super(t);this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return ot(()=>{t=t;let r=t.slice(1),s=[];for(let h of this.cells.slice().reverse())Array.isArray(h.stateSize)?s.push(r.splice(0,h.stateSize.length)):s.push(r.splice(0,1));s.reverse();let u=[],l;for(let h=0;h<this.cells.length;++h){let p=this.cells[h];r=s[h],h===0?l=[t[0]].concat(r):l=[l[0]].concat(r),l=p.call(l,e),u.push(l.slice(1))}r=[];for(let h of u.slice().reverse())r.push(...h);return[l[0]].concat(r)})}build(t){yw(t)&&(t=t[0]),t=t;let e;this.cells.forEach((r,s)=>{Si(`RNNCell_${s}`,()=>{r.build(t),Array.isArray(r.stateSize)?e=r.stateSize[0]:e=r.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=u=>({className:u.getClassName(),config:u.getConfig()}),r=this.cells.map(e),s={cells:r};return Object.assign({},t,s)}static fromConfig(t,e,r={}){let s=[];for(let u of e.cells)s.push(Ss(u,r));return new t({cells:s})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let r of this.cells)e.push(...r.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return bw(t)}setWeights(t){let e=[];for(let r of this.cells){let s=r.weights.length,u=t.splice(s);for(let l=0;l<r.weights.length;++l)e.push([r.weights[l],u[l]])}ww(e)}}Sm.className="StackedRNNCells",kt(Sm);function ka(n){let{ones:t,rate:e,training:r=!1,count:s=1}=n,u=()=>fN(t(),e),l=()=>qh(u,t,r);if(!s||s<=1)return An(l().clone());let h=Array(s).fill(void 0).map(l);return h.map(p=>An(p.clone()))}var rG=function(n,t){var e={};for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&t.indexOf(r)<0&&(e[r]=n[r]);if(n!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,r=Object.getOwnPropertySymbols(n);s<r.length;s++)t.indexOf(r[s])<0&&Object.prototype.propertyIsEnumerable.call(n,r[s])&&(e[r[s]]=n[r[s]]);return e};class oft extends tc{}class mI extends Is{constructor(t){if(t.unroll)throw new Zt("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new Zt("It is not possible at the moment to stack convolutional cells.");super(t);this.inputSpec=[new Fn({ndim:5})]}call(t,e){return ot(()=>{if(this.cell.dropoutMask!=null&&(oe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(oe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new Q("ConvRNN2D cell does not support constants");let r=e==null?null:e.mask,s=e==null?null:e.training,u=e==null?null:e.initialState;return super.call(t,{mask:r,training:s,initialState:u})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return ot(()=>{let{stateSize:e}=this.cell,r=t.shape,s=this.computeSingleOutputShape(r),u=[s[0],...s.slice(2)],l=Se(u);return Array.isArray(e)?Array(e.length).fill(l):[l]})}resetStates(t,e=!1){ot(()=>{if(!this.stateful)throw new Js("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,s=this.computeSingleOutputShape(r),u=[s[0],...s.slice(2)],l=r[0];if(l==null)throw new Q("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(()=>Se(u)):this.states_=[Se(u)];else if(t==null)oe(this.states_),this.keptStates!=null&&(oe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Se(u)):this.states_[0]=Se(u);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):oe(this.states_);for(let h=0;h<this.states_.length;++h){let p=t[h],m=u;if(!K(p.shape,m))throw new Q(`State ${h} is incompatible with layer ${this.name}: expected shape=${m}, received shape=${p.shape}`);this.states_[h]=p}}this.states_=this.states_.map(h=>An(h.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:r,kernelSize:s,padding:u,strides:l,dilationRate:h}=this.cell,p=e==="channelsFirst",m=t[p?3:2],y=t[p?4:3],b=Ns(m,s[0],u,l[0],h[0]),x=Ns(y,s[1],u,l[1],h[1]),S=[...t.slice(0,2),...p?[r,b,x]:[b,x,r]];return S}}mI.className="ConvRNN2D";class Cm extends tf{constructor(t){let{filters:e,kernelSize:r,strides:s,padding:u,dataFormat:l,dilationRate:h}=t;super(Object.assign({},t,{units:e}));this.filters=e,_n(this.filters,"filters"),this.kernelSize=Ju(r,2,"kernelSize"),this.kernelSize.forEach(p=>_n(p,"kernelSize")),this.strides=Ju(s||1,2,"strides"),this.strides.forEach(p=>_n(p,"strides")),this.padding=u||"valid",Ur(this.padding),this.dataFormat=l||"channelsLast",un(this.dataFormat),this.dilationRate=Ju(h||1,2,"dilationRate"),this.dilationRate.forEach(p=>_n(p,"dilationRate"))}build(t){var e;t=Be(t);let r=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[r]==null)throw new Q(`The channel dimension of the input should be defined. Found ${t[r]}`);let s=t[r],u=4,l=this.kernelSize.concat([s,this.filters*u]);this.kernel=this.addWeight("kernel",l,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let h=this.kernelSize.concat([this.filters,this.filters*u]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",h,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let p;if(this.unitForgetBias){let m=this.biasInitializer,y=this.filters;p=new(e=class extends ss{apply(x,S){let C=m.apply([y]),I=vs([y]),D=m.apply([y*2]);return uw([C,I,D])}},e.className="CustomInit",e)}else p=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*u],null,p,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return ot(()=>{if(t.length!==3)throw new Q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training||!1,s=t[0],u=t[1],l=t[2],h=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ka({ones:()=>Xn(s),rate:this.dropout,training:r,count:h}));let p=this.dropoutMask,m=(Dt,$t,Lt)=>!$t||!$t[Lt]?Dt:st($t[Lt],Dt),y=m(s,p,0),b=m(s,p,1),x=m(s,p,2),S=m(s,p,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ka({ones:()=>Xn(u),rate:this.recurrentDropout,training:r,count:h}));let C=this.recurrentDropoutMask,I=m(u,C,0),D=m(u,C,1),R=m(u,C,2),A=m(u,C,3),L=3,[_,B,V,q]=Er(this.kernel.read(),h,L),[j,et,tt,ht]=this.useBias?Er(this.bias.read(),h):[null,null,null,null];y=this.inputConv(y,_,j,this.padding),b=this.inputConv(b,B,et,this.padding),x=this.inputConv(x,V,tt,this.padding),S=this.inputConv(S,q,ht,this.padding);let[gt,vt,bt,yt]=Er(this.recurrentKernel.read(),h,L);I=this.recurrentConv(I,gt),D=this.recurrentConv(D,vt),R=this.recurrentConv(R,bt),A=this.recurrentConv(A,yt);let mt=this.recurrentActivation.apply(Nt(y,I)),xt=this.recurrentActivation.apply(Nt(b,D)),wt=Nt(st(xt,l),st(mt,this.activation.apply(Nt(x,R)))),Tt=st(this.recurrentActivation.apply(Nt(S,A)),this.activation.apply(wt));return[Tt,Tt,wt]})}getConfig(){let t=super.getConfig(),{units:e}=t,r=rG(t,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},r,s)}inputConv(t,e,r,s){let u=wo(t,e,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?Qs(u,r,this.dataFormat):u}recurrentConv(t,e){let r=1;return wo(t,e,r,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Cm.className="ConvLSTM2DCell",kt(Cm);class Qw extends mI{constructor(t){let e=new Cm(t);super(Object.assign({},t,{cell:e}))}static fromConfig(t,e){return new t(e)}}Qw.className="ConvLSTM2D",kt(Qw);class Nm extends Te{constructor(t){super(t);this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,r=[];for(let s=0;s<this.noiseShape.length;++s)r.push(this.noiseShape[s]==null?e[s]:this.noiseShape[s]);return r}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t);if(0<this.rate&&this.rate<1){let s=e.training==null?!1:e.training,u=this.getNoiseShape(r),l=qh(()=>fN(r,this.rate,u,this.seed),()=>r,s);return l}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}}Nm.className="Dropout",kt(Nm);class t0 extends Nm{constructor(t){super(t);this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}}t0.className="SpatialDropout1D",kt(t0);class e0 extends Te{constructor(t){super(t);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,_n(this.units,"units"),this.activation=Ta(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=Xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=In(t.kernelConstraint),this.biasConstraint=In(t.biasConstraint),this.kernelRegularizer=Ye(t.kernelRegularizer),this.biasRegularizer=Ye(t.biasRegularizer),this.activityRegularizer=Ye(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Be(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Be(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t),s=eN(this.activation.getClassName()),u;return s!=null?u=Zs(r,this.kernel.read(),s,this.bias?this.bias.read():null):(u=Zs(r,this.kernel.read()),this.bias!=null&&(u=Qs(u,this.bias.read())),this.activation!=null&&(u=this.activation.apply(u))),u})}getConfig(){let t={units:this.units,activation:xa(this.activation),useBias:this.useBias,kernelInitializer:cn(this.kernelInitializer),biasInitializer:cn(this.biasInitializer),kernelRegularizer:ze(this.kernelRegularizer),biasRegularizer:ze(this.biasRegularizer),activityRegularizer:ze(this.activityRegularizer),kernelConstraint:Nn(this.kernelConstraint),biasConstraint:Nn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}e0.className="Dense",kt(e0);class n0 extends Te{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Be(t);for(let e of t.slice(1))if(e==null)throw new Q(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],ga(t,1)]}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t);if(this.dataFormat==="channelsFirst"&&r.rank>1){let s=[0];for(let u=2;u<r.rank;++u)s.push(u);s.push(1),r=r.transpose(s)}return NV(r)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}}n0.className="Flatten",kt(n0);class r0 extends Te{constructor(t){super(t);this.supportsMasking=!0,this.activation=Ta(t.activation)}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t);return this.activation.apply(r)})}getConfig(){let t={activation:xa(this.activation)},e=super.getConfig();return Object.assign(t,e),t}}r0.className="Activation",kt(r0);class s0 extends Te{constructor(t){super(t);this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return ot(()=>(t=le(t),SV(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}}s0.className="RepeatVector",kt(s0);class o0 extends Te{constructor(t){super(t);this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let r="Total size of new array must be unchanged.",s=e.slice(),u=1,l=null;for(let p=0;p<s.length;++p){let m=s[p];if(this.isUnknown(m))if(l===null)l=p;else throw new Q("Can only specifiy one unknown dimension.");else u*=m}let h=ga(t);if(l!==null){if(u===0||h%u!==0)throw new Q(r);s[l]=h/u}else if(h!==u)throw new Q(r);return s}computeOutputShape(t){let e=!1;for(let r=0;r<t.length;++r)if(this.isUnknown(t[r])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t),s=r.shape,u=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return r.reshape(u)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}}o0.className="Reshape",kt(o0);class a0 extends Te{constructor(t){super(t);if(t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=xs(1,t.dims.length+1);if(!K(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Fn({ndim:this.dims.length+1})]}computeOutputShape(t){t=Be(t);let e=t.slice();return this.dims.forEach((r,s)=>{e[s+1]=t[r]}),e}call(t,e){return re(le(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}}a0.className="Permute",kt(a0);class i0 extends Te{constructor(t){super(t==null?{}:t);this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let r=le(t),s=-1;return hh(ha(r,this.maskValue),s)}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t),s=-1,u=!0,l=hh(ha(r,this.maskValue),s,u),h=r.mul(l.asType(r.dtype));return h})}}i0.className="Masking",kt(i0);class u0 extends Te{constructor(t){super(t);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(Ge(t.inputLength))}this.inputDim=t.inputDim,_n(this.inputDim,"inputDim"),this.outputDim=t.outputDim,_n(this.outputDim,"outputDim"),this.embeddingsInitializer=Xe(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ye(t.embeddingsRegularizer),this.activityRegularizer=Ye(t.activityRegularizer),this.embeddingsConstraint=In(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return ot(()=>this.maskZero?(t=le(t),ha(t,fe(t))):null)}computeOutputShape(t){if(t=Be(t),this.inputLength==null)return[...t,this.outputDim];let e=Ge(this.inputLength);if(e.length!==t.length-1)throw new Q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let r=0;for(let s=0;s<e.length;++s){let u=e[s],l=t[s+1];if(u!=null&&l!=null&&u!==l)throw new Q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);u==null&&(e[r]=l),r++}}return[t[0],...e,this.outputDim]}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t);r.dtype!=="int32"&&(r=Uh(r,"int32"));let s=hN(this.embeddings.read(),r.as1D());return s.reshape(Be(this.computeOutputShape(r.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:cn(this.embeddingsInitializer),embeddingsRegularizer:ze(this.embeddingsRegularizer),activityRegularizer:ze(this.activityRegularizer),embeddingsConstraint:Nn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}}u0.className="Embedding",kt(u0);class Ei extends Te{constructor(t){super(t||{});this.supportsMasking=!0}mergeFunction(t){throw new Zt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let r=t.slice(0,t.length-e.length);for(let s=0;s<e.length;++s){let u=t[t.length-e.length+s],l=e[s];if(u==null||l==null||u<0||l<0)r.push(null);else if(u===1)r.push(l);else if(l===1)r.push(u);else{if(u!==l)throw new Q("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));r.push(u)}}return r}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Be(t)]),t=t,t.length<2)throw new Q(`A merge layer should be called on an Array of at least 2 inputs. Got ${t.length} input(s).`);let e=[];for(let u of t)u!=null&&u[0]!==null&&e.push(u[0]);if(e=ma(e),e.length>1)throw new Q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let r=t[0]==null?null:t[0].slice(1);for(let u=1;u<t.length;++u){let l=t[u]==null?null:t[u].slice(1);r=this.computeElementwiseOpOutputShape(r,l)}let s=t.map(u=>u.length);t.indexOf(null)===-1&&ma(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return ot(()=>{if(t=t,this.reshapeRequired){let r=[],s=t.map(u=>u.rank);if(s.indexOf(null)===-1){let u=va(s);for(let l of t){let h=l.rank;for(let p=0;p<u-h;++p)l=Gh(l,1);r.push(l)}return this.mergeFunction(r)}else{let u=!1;for(let p of t){let m=p.rank;if(m==null){let y=p.shape,b=y[0],x=y.slice(1).concat([b]),S=p.reshape([b].concat(ga(y.slice(1))));S=re(S,[1,0]),S=S.reshape(x),r.push(S),u=!0}else if(m>1){let y=xs(1,m).concat([0]);r.push(re(p,y)),u=!0}else r.push(p)}let l=this.mergeFunction(r),h=l.rank;if(u){if(h==null){let p=l.shape,m=p.length,y=p[m-1],b=[y].concat(p.slice(0,p.length-1));l=re(l.reshape([-1,y]),[1,0]).reshape(b)}else if(h>1){let p=[h-1].concat(xs(0,h-1));l=re(l,p)}}return l}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let s=1;s<t.length;++s){let u=t[s]==null?null:t[s].slice(1);e=this.computeElementwiseOpOutputShape(e,u)}let r=[];for(let s of t)s!=null&&s[0]!==null&&r.push(s[0]);return r=ma(r),r.length===1?e=r.concat(e):e=[null].concat(e),e}computeMask(t,e){return ot(()=>{if(e==null)return null;if(!Array.isArray(e))throw new Q("`mask` should be an Array");if(!Array.isArray(t))throw new Q("`inputs` should be an Array");if(e.length!==t.length)throw new Q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(s=>s==null))return null;e=e.map(s=>s==null?s:pr(s,0));let r=e[0];for(let s=1;s<e.length-1;++s)r=es(r,e[s]);return r})}}class ef extends Ei{constructor(t){super(t)}mergeFunction(t){return ot(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Nt(e,t[r]);return e})}}ef.className="Add",kt(ef);function aft(n){if(Array.isArray(n)){let t=new ef({});return t.apply(n)}else return new ef(n)}class nf extends Ei{constructor(t){super(t)}mergeFunction(t){return ot(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=st(e,t[r]);return e})}}nf.className="Multiply",kt(nf);function ift(n){if(Array.isArray(n)){let t=new nf({});return t.apply(n)}else return new nf(n)}class rf extends Ei{constructor(t){super(t)}mergeFunction(t){return ot(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Nt(e,t[r]);return st(1/t.length,e)})}}rf.className="Average",kt(rf);function uft(n){if(Array.isArray(n)){let t=new rf({});return t.apply(n)}else return new rf(n)}class sf extends Ei{constructor(t){super(t)}mergeFunction(t){return ot(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=ts(e,t[r]);return e})}}sf.className="Maximum",kt(sf);function cft(n){if(Array.isArray(n)){let t=new sf({});return t.apply(n)}else return new sf(n)}class of extends Ei{constructor(t){super(t)}mergeFunction(t){return ot(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=mi(e,t[r]);return e})}}of.className="Minimum",kt(of);function lft(n){if(Array.isArray(n)){let t=new of({});return t.apply(n)}else return new of(n)}class af extends Ei{constructor(t){super(t);this.DEFAULT_AXIS=-1,t==null&&(t={}),this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){if(!(Array.isArray(t)&&Array.isArray(t[0]))||t.length===1)throw new Q("A `Concatenate` layer should be called on a list of at least 2 inputs");t=t;let e=!0;for(let s of t)if(s!=null){e=!1;break}if(e)return;let r=[];for(let s=0;s<t.length;++s){let u=t[s].slice();u.splice(this.axis,1);let l=!1;for(let h of r)if(K(h,u)){l=!0;break}l||r.push(u)}if(r.length>1)throw new Q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return ot(()=>uw(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new Q("A `Concatenate` layer should be called on a list of inputs.");let e=t,r=e[0].slice(),s=this.axis<0?r.length+this.axis:this.axis;for(let u of e.slice(1)){if(r[s]==null||u[s]==null){r[s]=null;break}r[s]+=u[s]}return r}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new Q("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new Q("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new Q(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return ot(()=>{let r=!0;if(e.forEach(l=>{if(l!=null){r=!1;return}}),r)return null;let s=[];for(let l=0;l<t.length;++l)e[l]==null?s.push(Xn(t[l]).asType("bool")):e[l].rank<t[l].rank?s.push(pr(e[l],-1)):s.push(e[l]);let u=sn(s,this.axis);return cd(u,-1,!1)})}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}}af.className="Concatenate",kt(af);function hft(n){if(Array.isArray(n)){let t=new af({});return t.apply(n)}else return new af(n)}function uf(n,t){for(;n<0;)n+=t;return n}function sG(n,t,e){if(n.shape.length>3||t.shape.length>3)throw new Zt("batchDot is not implemented for tensors of 4D or higher rank yet");if(k(n.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${n.shape.length}`),k(n.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),n.dtype==="complex64"||t.dtype==="complex64")throw new Zt("batchDot is not implemented for complex64-type Tensors yet.");let r=n.shape.length,s=t.shape.length;e==null&&(e=[r-1,s-2]);let u=e;return ot(()=>{let l;if(r>s){l=r-s;let p=[];for(let m=0;m<l;++m)p.push(1);t=t.reshape(t.shape.concat(p))}else if(s>r){l=s-r;let p=[];for(let m=0;m<l;++m)p.push(1);n=n.reshape(n.shape.concat(p))}else l=0;let h;if(n.shape.length===2&&t.shape.length===2)u[0]===u[1]?h=n.mul(t).sum(u[0]):h=n.transpose([1,0]).mul(t).sum(u[1]);else{let p=u[0]!==n.shape.length-1,m=u[1]===t.shape.length-1;h=n.matMul(t,p,m)}if(l>0){let p;r>s?p=r+s-3:p=r-1;let m=[];for(let y=p;y<p+l;++y)m.push(y);h=h.squeeze(m)}return h.shape.length===1&&(h=h.expandDims(1)),h})}class c0 extends Ei{constructor(t){super(t);this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){k(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],r=t[1];if(e.length>3||r.length>3)throw new Zt("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(e,r);if(e[s[0]]!==r[s[1]])throw new Q(`Dimension incompatibility: ${e[s[0]]} !== ${r[s[1]]}`)}mergeFunction(t){if(t.length!==2)throw new Q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],r=t[1],s;return Array.isArray(this.axes)?s=this.axes.map((u,l)=>uf(u,t[l].shape.length)):s=[uf(this.axes,e.shape.length),uf(this.axes,r.shape.length)],this.normalize&&(e=hm(e,s[0]),r=hm(r,s[1])),sG(e,r,s)}interpretAxes(t,e){let r;return Array.isArray(this.axes)?r=this.axes:r=[uf(this.axes,t.length),uf(this.axes,e.length)],r}computeOutputShape(t){k(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),r=t[1].slice();if(e.length>3||r.length>3)throw new Zt("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(e,r);e.splice(s[0],1),r.splice(s[1],1),r.splice(0,1);let u=e.concat(r);return u.length===1&&u.push(1),u}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}}c0.className="Dot",kt(c0);class l0 extends Te{constructor(t){super(t);this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t),s=()=>Zd(r.shape,0,this.stddev).add(r),u=qh(s,()=>r,e.training||!1);return u})}}l0.className="GaussianNoise",kt(l0);class h0 extends Te{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return ot(()=>{this.invokeCallHook(t,e);let r=le(t);if(this.rate>0&&this.rate<1){let s=()=>{let u=Math.sqrt(this.rate/(1-this.rate));return r.mul(Zd(r.shape,1,u))};return qh(s,()=>r,e.training||!1)}return r})}}h0.className="GaussianDropout",kt(h0);class f0 extends Te{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||le(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return ot(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(t),s=()=>{let u=le(t),l=1.6732632423543772,h=1.0507009873554805,p=-l*h,m=xo(yi(r),this.rate);m=Uh(m,"float32");let y=((1-this.rate)*(1+this.rate*p**2))**-.5,b=-y*p*this.rate,x=u.mul(m).add(m.add(-1).mul(p));return x.mul(y).add(b)};return qh(s,()=>le(t),e.training||!1)}return t})}}f0.className="AlphaDropout",kt(f0);function cf(n,t,e,r,s,u=.001){let l;if(n.rank===2)l=zS(n,t,e,r,s,u);else if(n.rank===3)l=WS(n,t,e,r,s,u);else if(n.rank===4)l=VS(n,t,e,r,s,u);else throw new Zt(`batchNormalization is not implemented for array of rank ${n.rank} yet`);return l}function oG(n,t,e,r,s=.001){return ot(()=>{let u=Cd(n,r),l=u.mean,h=u.variance,p=cf(n,l,h,e,t,s);return[p,l,h]})}function aG(n,t,e,r,s=.001){return ot(()=>{let u=Cd(n,r),l=u.mean,h=u.variance,p=[];for(let C of xs(0,n.rank))r.indexOf(C)!==-1?p.push(1):p.push(n.shape[C]);let m=l.reshape(p),y=h.reshape(p),b=t==null?null:t.reshape(p),x=e==null?null:e.reshape(p),S=cf(n,m,y,x,b,s);return[S,l,h]})}function iG(n,t,e,r,s=.001){return K(r.slice().sort(),xs(0,n.rank-1))?oG(n,t,e,r,s):aG(n,t,e,r,s)}class p0 extends Te{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=Xe(t.betaInitializer||"zeros"),this.gammaInitializer=Xe(t.gammaInitializer||"ones"),this.movingMeanInitializer=Xe(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Xe(t.movingVarianceInitializer||"ones"),this.betaConstraint=In(t.betaConstraint),this.gammaConstraint=In(t.gammaConstraint),this.betaRegularizer=Ye(t.betaRegularizer),this.gammaRegularizer=Ye(t.gammaRegularizer)}build(t){t=Be(t);let e=this.axis>=0?this.axis:this.axis+t.length,r=t[e];if(r==null)throw new Q(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Fn({ndim:t.length,axes:{[e]:r}})];let s=[r];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return ot(()=>{let r=e.training==null?!1:e.training,s=le(t),u=s.shape,l=u.length,h=xs(0,l),p=this.axis>=0?this.axis:this.axis+l;h.splice(p,1);let m=Ti(1,l);m[p]=u[p];let y=h.slice();y.sort();let b=!K(y,xs(0,l).slice(0,l-1)),x=()=>{if(b){let A=this.movingMean.read().reshape(m),L=this.movingVariance.read().reshape(m),_=this.center?this.beta.read().reshape(m):null,B=this.scale?this.gamma.read().reshape(m):null;return cf(s,A,L,_,B,this.epsilon)}else return cf(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return x();let[S,C,I]=iG(s,this.gamma.read(),this.beta.read(),h,this.epsilon),D=(A,L,_)=>{ot(()=>{let B=1-_,V=A.read(),q=V.sub(L).mul(B);A.write(V.sub(q))})},R=()=>{D(this.movingMean,C,this.momentum),D(this.movingVariance,I,this.momentum)};return R(),S})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:cn(this.betaInitializer),gammaInitializer:cn(this.gammaInitializer),movingMeanInitializer:cn(this.movingMeanInitializer),movingVarianceInitializer:cn(this.movingVarianceInitializer),betaRegularizer:ze(this.betaRegularizer),gammaRegularizer:ze(this.gammaRegularizer),betaConstraint:Nn(this.betaConstraint),gammaConstraint:Nn(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}}p0.className="BatchNormalization",kt(p0);class d0 extends Te{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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(let e of this.axis)if(!Number.isInteger(e))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=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=Xe(t.betaInitializer||"zeros"),this.gammaInitializer=Xe(t.gammaInitializer||"ones"),this.betaRegularizer=Ye(t.betaRegularizer),this.gammaRegularizer=Ye(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Be(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let u=0;u<this.axis.length;++u)this.axis[u]<0&&(this.axis[u]+=e);for(let u of this.axis)if(u<0||u>=e)throw new Error(`Invalid axis: ${u}`);if(this.axis.length!==ma(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(u=>t[u]),s=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(t,e){let r=le(t),s=r.shape,u=s.length;return ot(()=>{let l=!0,{mean:h,variance:p}=Cd(r,this.axis,l),m=Ti(1,u);for(let I of this.axis)m[I]=s[I];let y=I=>I!=null&&I.shape.length!==u&&this.axis!==[u-1]?I.reshape(m):I,b=y(this.gamma.read()),x=y(this.beta.read()),S=[],C=[];for(let I=0;I<u;++I)this.axis.indexOf(I)!==-1?(S.push(s[I]),C.push(1)):(S.push(1),C.push(s[I]));return h=h.tile(S),p=p.tile(S),b=b.tile(C),x=x.tile(C),cf(r,h,p,x,b,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:cn(this.betaInitializer),gammaInitializer:cn(this.gammaInitializer),betaRegularizer:ze(this.betaRegularizer),gammaRegularizer:ze(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}}d0.className="LayerNormalization",kt(d0);function fft(n,t){return ot(()=>{if(n.rank!==3)throw new Q(`temporalPadding expects input tensor to be 3-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new Q(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);let e=[[0,0],t,[0,0]];return Xs(n,e)})}function uG(n,t,e){return ot(()=>{if(n.rank!==4)throw new Q(`temporalPadding expects input tensor to be 4-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new Q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=ws()),e!=="channelsLast"&&e!=="channelsFirst")throw new Q(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return e==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Xs(n,r)})}class m0 extends Te{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?ws():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding=="number")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new Q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,r;if(typeof t.padding[0]=="number")e=[t.padding[0],t.padding[0]],r=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new Q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new Q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);r=t.padding[1]}this.padding=[e,r]}this.inputSpec=[new Fn({ndim:4})]}computeOutputShape(t){t=Be(t);let e,r;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?r=t[3]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],t[1],e,r]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?r=t[2]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],e,r,t[3]])}call(t,e){return ot(()=>uG(le(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}m0.className="ZeroPadding2D",kt(m0);function Im(n,t,e,r,s,u){return ot(()=>{un(s),sN(u),Ur(r),e==null&&(e=[1,1]),r==null&&(r="valid"),s==null&&(s=ws()),u==null&&(u="max"),n=Uw(n,s);let l,h=r==="same"?"same":"valid";return u==="max"?l=Th(n,t,e,h):l=dh(n,t,e,h),s==="channelsFirst"&&(l=re(l,[0,3,1,2])),l})}function gI(n,t,e,r,s,u){return ot(()=>{un(s),sN(u),Ur(r),e==null&&(e=[1,1,1]),r==null&&(r="valid"),s==null&&(s=ws()),u==null&&(u="max"),n=cI(n,s);let l,h=r==="same"?"same":"valid";return u==="max"?l=gb(n,t,e,h):l=rb(n,t,e,h),s==="channelsFirst"&&(l=re(l,[0,4,1,2,3])),l})}class vI extends Te{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new Q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(_n(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new Q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);_n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,Ur(this.padding),this.inputSpec=[new Fn({ndim:3})]}computeOutputShape(t){t=Be(t);let e=Ns(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return ot(()=>{this.invokeCallHook(t,e),t=Gh(le(t),2);let r=this.poolingFunction(le(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return fa(r,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}}class g0 extends vI{constructor(t){super(t)}poolingFunction(t,e,r,s,u){return un(u),Ur(s),Im(t,e,r,s,u,"max")}}g0.className="MaxPooling1D",kt(g0);class v0 extends vI{constructor(t){super(t)}poolingFunction(t,e,r,s,u){return un(u),Ur(s),Im(t,e,r,s,u,"avg")}}v0.className="AveragePooling1D",kt(v0);class yI extends Te{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new Q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];_n(this.poolSize,"poolSize"),_n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,un(this.dataFormat),Ur(this.padding),this.inputSpec=[new Fn({ndim:4})]}computeOutputShape(t){t=Be(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=Ns(e,this.poolSize[0],this.padding,this.strides[0]),r=Ns(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r]:[t[0],e,r,t[3]]}call(t,e){return ot(()=>(this.invokeCallHook(t,e),this.poolingFunction(le(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class y0 extends yI{constructor(t){super(t)}poolingFunction(t,e,r,s,u){return un(u),Ur(s),Im(t,e,r,s,u,"max")}}y0.className="MaxPooling2D",kt(y0);class b0 extends yI{constructor(t){super(t)}poolingFunction(t,e,r,s,u){return un(u),Ur(s),Im(t,e,r,s,u,"avg")}}b0.className="AveragePooling2D",kt(b0);class bI extends Te{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new Q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];_n(this.poolSize,"poolSize"),_n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,un(this.dataFormat),Ur(this.padding),this.inputSpec=[new Fn({ndim:5})]}computeOutputShape(t){t=Be(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],s=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=Ns(e,this.poolSize[0],this.padding,this.strides[0]),r=Ns(r,this.poolSize[1],this.padding,this.strides[1]),s=Ns(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r,s]:[t[0],e,r,s,t[4]]}call(t,e){return ot(()=>(this.invokeCallHook(t,e),this.poolingFunction(le(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class w0 extends bI{constructor(t){super(t)}poolingFunction(t,e,r,s,u){return un(u),Ur(s),gI(t,e,r,s,u,"max")}}w0.className="MaxPooling3D",kt(w0);class x0 extends bI{constructor(t){super(t)}poolingFunction(t,e,r,s,u){return un(u),Ur(s),gI(t,e,r,s,u,"avg")}}x0.className="AveragePooling3D",kt(x0);class wI extends Te{constructor(t){super(t);this.inputSpec=[new Fn({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Zt}}class T0 extends wI{constructor(t){super(t||{})}call(t,e){return ot(()=>{let r=le(t);return an(r,1)})}}T0.className="GlobalAveragePooling1D",kt(T0);class k0 extends wI{constructor(t){super(t||{})}call(t,e){return ot(()=>{let r=le(t);return dr(r,1)})}}k0.className="GlobalMaxPooling1D",kt(k0);class xI extends Te{constructor(t){super(t);this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,un(this.dataFormat),this.inputSpec=[new Fn({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Zt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class S0 extends xI{call(t,e){return ot(()=>{let r=le(t);return this.dataFormat==="channelsLast"?an(r,[1,2]):an(r,[2,3])})}}S0.className="GlobalAveragePooling2D",kt(S0);class C0 extends xI{call(t,e){return ot(()=>{let r=le(t);return this.dataFormat==="channelsLast"?dr(r,[1,2]):dr(r,[2,3])})}}C0.className="GlobalMaxPooling2D",kt(C0);class TI extends Te{constructor(t){super(t);this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,r={}){let s=e.layer,u=Ss(s,r);delete e.layer;let l={layer:u};return Object.assign(l,e),new t(l)}}class N0 extends TI{constructor(t){super(t);this.supportsMasking=!0}build(t){if(t=Be(t),t.length<3)throw new Q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Be(t);let e=[t[0]].concat(t.slice(2)),r=this.layer.computeOutputShape(e),s=t[1];return[r[0],s].concat(r.slice(1))}call(t,e){return ot(()=>{t=le(t);let r=(l,h)=>{let p=le(this.layer.call(l,e));return[p,[]]},s=dI(r,t,[],!1,null,null,!1,!0),u=s[1];return u})}}N0.className="TimeDistributed",kt(N0);function cG(n){Hu(yV,"BidirectionalMergeMode",n)}let lG="concat";class I0 extends TI{constructor(t){super(t);let e=t.layer.getConfig(),r={};r.className=t.layer.getClassName(),r.config=e,this.forwardLayer=Ss(r),e.goBackwards=!(e.goBackwards===!0);let s={};if(s.className=t.layer.getClassName(),s.config=e,this.backwardLayer=Ss(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?lG:t.mergeMode,cG(this.mergeMode),t.weights)throw new Zt("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,r=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,r)),this.backwardLayer.setWeights(t.slice(r))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let r,s,u;return this.returnState&&(u=e.slice(1)),r=e[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,s=[r]):this.mergeMode==null?s=[r,r.slice()]:s=[r],this.returnState?this.mergeMode==null?s.concat(u).concat(u.slice()):[r].concat(u).concat(u.slice()):gr(s)}apply(t,e){let r=e==null?null:e.initialState,s=e==null?null:e.constants;e==null&&(e={});let u=pI(t,r,s,this.numConstants);if(t=u.inputs,r=u.initialState,s=u.constants,Array.isArray(t)&&(r=t.slice(1),t=t[0]),(r==null||r.length===0)&&s==null)return super.apply(t,e);let l=[],h=[];if(r!=null){let m=r.length;if(m%2>0)throw new Q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=r,l.push(...r);let y=r.map(b=>new Fn({shape:b.shape}));this.forwardLayer.stateSpec=y.slice(0,m/2),this.backwardLayer.stateSpec=y.slice(m/2),h.push(...y)}if(s!=null)throw new Zt("Support for constants in Bidirectional layers is not implemented yet.");let p=l[0]instanceof ks;for(let m of l)if(m instanceof ks!==p)throw new Q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(p){let m=[t].concat(l),y=this.inputSpec.concat(h),b=this.inputSpec;this.inputSpec=y;let x=super.apply(m,e);return this.inputSpec=b,x}else return super.apply(t,e)}call(t,e){return ot(()=>{let r=e.initialState,s,u;if(r==null)s=this.forwardLayer.call(t,e),u=this.backwardLayer.call(t,e);else{let p=r.slice(0,r.length/2),m=r.slice(r.length/2);s=this.forwardLayer.call(t,Object.assign(e,{initialState:p})),u=this.backwardLayer.call(t,Object.assign(e,{initialState:m}))}let l;this.returnState&&(Array.isArray(s)&&(l=s.slice(1).concat(u.slice(1))),s=s[0],u=u[0]),this.returnSequences&&(u=Wr(u,1));let h;return this.mergeMode==="concat"?h=uw([s,u]):this.mergeMode==="sum"?h=Nt(s,u):this.mergeMode==="ave"?h=st(.5,Nt(s,u)):this.mergeMode==="mul"?h=st(s,u):this.mergeMode==null&&(h=[s,u]),this.returnState?this.mergeMode==null?h.concat(l):[h].concat(l):h})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){Si(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),Si(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[e,e]:r=e:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let s=this.forwardLayer.states,u=s.map(l=>null);return Array.isArray(r)?r.concat(u).concat(u):[r].concat(u).concat(u)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let r=Ss(e.layer);if(delete e.layer,e.numConstants!=null)throw new Zt("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=e;return s.layer=r,new t(s)}}I0.className="Bidirectional",kt(I0);function hG(n){return new Ku(n)}function fG(n){return new zw(n)}function pG(n){return new Mw(n)}function dG(n){return new Lw(n)}function mG(n){return new Bw(n)}function gG(n){return new Vw(n)}function vG(n){return new Ww(n)}function yG(n){return new Qh(n)}function bG(n){return new Qu(n)}function wG(n){return new Hw(n)}function xG(n){return 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uq=Object.freeze({__proto__:null,json:iq});let 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lq=Object.freeze({__proto__:null,json:cq});let hq=[{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"}]}];var fq=Object.freeze({__proto__:null,json:hq});let pq=[{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}]}];var dq=Object.freeze({__proto__:null,json:pq});let mq=[{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}]}];var gq=Object.freeze({__proto__:null,json:mq});let vq=[{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}]}];var yq=Object.freeze({__proto__:null,json:vq});let bq=[{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"}]}];var wq=Object.freeze({__proto__:null,json:bq});let xq=[{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}]}];var Tq=Object.freeze({__proto__:null,json:xq});let kq=[{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}]}];var Sq=Object.freeze({__proto__:null,json:kq});let Cq=[{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:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{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:[]}];var Nq=Object.freeze({__proto__:null,json:Cq});class _I{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let t=[KH,YH,ZH,tq,nq,sq,aq,dq,fq,uq,gq,yq,wq,Tq,Sq,Nq,lq],e=[].concat(...t.map(r=>r.json));this.opMappers=e.reduce((r,s)=>(r[s.tfOpName]=s,r),{})}transformGraph(t,e={}){let r=t.node,s=[],u=[],l=[],h=r.reduce((I,D)=>(I[D.name]=this.mapNode(D),D.op.startsWith("Placeholder")?s.push(I[D.name]):D.op==="Const"?u.push(I[D.name]):(D.input==null||D.input.length===0)&&l.push(I[D.name]),I),{}),p=[],m=[],y={},b={};e!=null&&(y=this.mapSignatureEntries(e.inputs),b=this.mapSignatureEntries(e.outputs));let x=Object.keys(h);x.forEach(I=>{let D=h[I];D.inputNames.forEach(R=>{let[A]=Eo(R);D.inputs.push(h[A]),h[A].children.push(D)})}),Object.keys(b).length===0?x.forEach(I=>{let D=h[I];D.children.length===0&&m.push(D)}):Object.keys(b).forEach(I=>{let[D]=Eo(I),R=h[D];R!=null&&(R.signatureKey=b[I],m.push(R))}),Object.keys(y).length>0?Object.keys(y).forEach(I=>{let[D]=Eo(I),R=h[D];R&&(R.signatureKey=y[I],p.push(R))}):p=s;let S={};t.library!=null&&t.library.function!=null&&(S=t.library.function.reduce((I,D)=>(I[D.signature.name]=this.mapFunction(D),I),{}));let C={nodes:h,inputs:p,outputs:m,weights:u,placeholders:s,signature:e,functions:S};return l.length>0&&(C.initNodes=l),C}mapSignatureEntries(t){return Object.keys(t||{}).reduce((e,r)=>(e[t[r].name]=r,e),{})}mapNode(t){let e=AI(t.op)||this.opMappers[t.op]||{};t.attr==null&&(t.attr={});let r={name:t.name,op:t.op,category:e.category,inputNames:(t.input||[]).map(s=>s.startsWith("^")?s.substr(1):s),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:t.attr};return e.inputs!=null&&(r.inputParams=e.inputs.reduce((s,u)=>(s[u.name]={type:u.type,inputIndexStart:u.start,inputIndexEnd:u.end},s),{})),e.attrs!=null&&(r.attrParams=e.attrs.reduce((s,u)=>{let l=u.type,h;switch(u.type){case"string":h=_0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=_0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"string[]":h=z0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=z0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"number":h=R0(t.attr,u.tfName,u.defaultValue||0),h===void 0&&!!u.tfDeprecatedName&&(h=R0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"number[]":h=B0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=B0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"bool":h=F0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=F0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"bool[]":h=V0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=V0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"shape":h=L0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=L0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"shape[]":h=W0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=W0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"dtype":h=O0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=O0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"dtype[]":h=M0(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=M0(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"func":h=RI(t.attr,u.tfName,u.defaultValue),h===void 0&&!!u.tfDeprecatedName&&(h=RI(t.attr,u.tfDeprecatedName,u.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${u.type} for op: ${t.op}`)}return s[u.name]={value:h,type:l},s},{})),r}mapFunction(t){let e=t.nodeDef,r=[],s=[],u={};e!=null&&(u=e.reduce((b,x)=>(b[x.name]=this.mapNode(x),x.op==="Const"&&s.push(b[x.name]),b),{}));let l=[],h=[];t.signature.inputArg.forEach(b=>{let[x]=Eo(b.name),S={name:x,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:P0(b.type),type:"dtype"}},children:[]};S.signatureKey=b.name,l.push(S),u[x]=S});let p=Object.keys(u);p.forEach(b=>{let x=u[b];x.inputNames.forEach(S=>{let[C]=Eo(S);x.inputs.push(u[C]),u[C].children.push(x)})});let m=t.ret;t.signature.outputArg.forEach(b=>{let[x,S]=Eo(m[b.name]),C=u[x];C!=null&&(C.defaultOutput=S,h.push(C))});let y=this.mapArgsToSignature(t);return{nodes:u,inputs:l,outputs:h,weights:s,placeholders:r,signature:y}}mapArgsToSignature(t){return{methodName:t.signature.name,inputs:t.signature.inputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r),e),{}),outputs:t.signature.outputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r,t.ret),e),{})}}mapArgToTensorInfo(t,e){let r=t.name;return e!=null&&(r=e[r]),{name:r,dtype:t.type}}}function Iq(n){let t=ft().global;if(typeof t.atob!="undefined")return t.atob(n);if(typeof Buffer!="undefined")return new Buffer(n,"base64").toString();throw new Error("Unable to decode base64 in this environment. 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Dq=(n,t,e)=>{switch(n.op){case"BiasAdd":case"AddV2":case"Add":return[Nt(P("a",n,t,e),P("b",n,t,e))];case"AddN":return[LS(P("tensors",n,t,e))];case"FloorMod":case"Mod":return[Sd(P("a",n,t,e),P("b",n,t,e))];case"Mul":return[st(P("a",n,t,e),P("b",n,t,e))];case"RealDiv":case"Div":return[Ht(P("a",n,t,e),P("b",n,t,e))];case"DivNoNan":return[cb(P("a",n,t,e),P("b",n,t,e))];case"FloorDiv":return[ud(P("a",n,t,e),P("b",n,t,e))];case"Sub":return[Mt(P("a",n,t,e),P("b",n,t,e))];case"Minimum":return[mi(P("a",n,t,e),P("b",n,t,e))];case"Maximum":return[ts(P("a",n,t,e),P("b",n,t,e))];case"Pow":return[ys(P("a",n,t,e),P("b",n,t,e))];case"SquaredDifference":return[Dh(P("a",n,t,e),P("b",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},dft="arithmetic";let 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r=P("tensorArrayId",n,t,e),s=P("tensor",n,t,e),u=P("lengths",n,t,e),l=e.getTensorArray(r.id);return l.split(u,s),[l.idTensor]}case"TensorArraySizeV3":{let r=P("tensorArrayId",n,t,e),s=e.getTensorArray(r.id);return[Ot(s.size(),"int32")]}case"TensorArrayCloseV3":{let r=P("tensorArrayId",n,t,e),s=e.getTensorArray(r.id);return s.clearAndClose(),[s.idTensor]}case"TensorListSetItem":{let r=P("tensorListId",n,t,e),s=P("index",n,t,e),u=P("tensor",n,t,e),l=e.getTensorList(r.id);return l.setItem(s,u),[l.idTensor]}case"TensorListGetItem":{let r=P("tensorListId",n,t,e),s=P("index",n,t,e),u=P("elementShape",n,t,e),l=P("elementDType",n,t,e),h=e.getTensorList(r.id);return[h.getItem(s,u,l)]}case"TensorListScatterV2":case"TensorListScatter":{let r=P("indices",n,t,e),s=P("tensor",n,t,e),u=P("elementShape",n,t,e),l=P("numElements",n,t,e),h=Pq(s,r,u,l);return e.addTensorList(h),[h.idTensor]}case"TensorListReserve":{let r=P("elementShape",n,t,e),s=P("elementDType",n,t,e),u=P("numElements",n,t,e),l=Rq(r,s,u);return e.addTensorList(l),[l.idTensor]}case"TensorListGather":{let r=P("tensorListId",n,t,e),s=P("indices",n,t,e),u=P("elementShape",n,t,e),l=P("elementDType",n,t,e),h=e.getTensorList(r.id);return[h.gather(s,l,u)]}case"TensorListStack":{let r=P("tensorListId",n,t,e),s=P("elementShape",n,t,e),u=P("elementDType",n,t,e),l=P("numElements",n,t,e),h=e.getTensorList(r.id);return[h.stack(s,u,l)]}case"TensorListFromTensor":{let r=P("tensor",n,t,e),s=P("elementShape",n,t,e),u=P("elementDType",n,t,e),l=Fq(r,s,u);return e.addTensorList(l),[l.idTensor]}case"TensorListConcat":{let r=P("tensorListId",n,t,e),s=e.getTensorList(r.id),u=P("dtype",n,t,e),l=P("elementShape",n,t,e);return[s.concat(u,l)]}case"TensorListPushBack":{let r=P("tensorListId",n,t,e),s=P("tensor",n,t,e),u=e.getTensorList(r.id);return u.pushBack(s),[u.idTensor]}case"TensorListPopBack":{let 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m=P("strides",n,t,e),y=$m(n,t,e),b=P("dataFormat",n,t,e).toUpperCase(),x=P("dilations",n,t,e),[S,C]=P("args",n,t,e);return{stride:m,pad:y,dataFormat:b,dilations:x,biasArg:S,preluArg:C,activationFunc:s}}let Lq=(n,t,e)=>{switch(n.op){case"Conv1D":{let r=P("stride",n,t,e),s=P("pad",n,t,e),u=P("dataFormat",n,t,e).toUpperCase(),l=P("dilation",n,t,e);return[dd(P("x",n,t,e),P("filter",n,t,e),r,s,u,l)]}case"Conv2D":{let 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r=P("strides",n,t,e),s=P("pad",n,t,e),u=P("kernelSize",n,t,e);return[gb(P("x",n,t,e),[u[1],u[2],u[3]],[r[1],r[2],r[3]],s)]}case"Dilation2D":{let r=P("strides",n,t,e),s=P("pad",n,t,e),u=P("dilations",n,t,e),l=r[1],h=r[2],p=u[1],m=u[2];return[ub(P("x",n,t,e),P("filter",n,t,e),[l,h],s,[p,m],"NHWC")]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},vft="convolution";let Bq=(n,t,e)=>{switch(n.op){case"Fill":{let r=P("shape",n,t,e),s=P("dtype",n,t,e),u=P("value",n,t,e);return[Au(r,u,s)]}case"LinSpace":{let r=P("start",n,t,e),s=P("stop",n,t,e),u=P("num",n,t,e);return[QS(r,s,u)]}case"Multinomial":{let r=P("logits",n,t,e),s=P("numSamples",n,t,e),u=P("seed",n,t,e);return[rC(r,s,u)]}case"OneHot":{let 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zq=async(n,t,e)=>{switch(n.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:s,maxOutputSize:u,iouThreshold:l,scoreThreshold:h,softNmsSigma:p}=U0(n,t,e),m=await da.nonMaxSuppressionWithScoreAsync(r,s,u,l,h,p);return[m.selectedIndices,m.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:s,maxOutputSize:u,iouThreshold:l,scoreThreshold:h}=U0(n,t,e),p=P("padToMaxOutputSize",n,t,e),m=await da.nonMaxSuppressionPaddedAsync(r,s,u,l,h,p);return[m.selectedIndices,m.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:s,maxOutputSize:u,iouThreshold:l,scoreThreshold:h}=U0(n,t,e);return[await da.nonMaxSuppressionAsync(r,s,u,l,h)]}case"Where":{let r=Rt(P("condition",n,t,e),"bool"),s=[await Ab(r)];return r.dispose(),s}case"ListDiff":return oC(P("x",n,t,e),P("y",n,t,e));default:throw TypeError(`Node type ${n.op} is not implemented`)}},bft="dynamic";let Wq=(n,t,e)=>{switch(n.op){case"TopKV2":{let r=P("x",n,t,e),s=P("k",n,t,e),u=P("sorted",n,t,e),l=Db(r,s,u);return[l.values,l.indices]}case"Unique":{let r=P("x",n,t,e),s=Rd(r);return[s.values,s.indices]}case"UniqueV2":{let r=P("x",n,t,e),s=P("axis",n,t,e),u=Rd(r,s);return[u.values,u.indices]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},wft="evaluation";let Vq=(n,t,e)=>{switch(n.op){case"Const":return t[n.name];case"PlaceholderWithDefault":let r=P("default",n,t,e);return[yr(n.name,t,e)||r];case"Placeholder":return[yr(n.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let m=P("x",n,t,e);return[Do(m)]}case"IdentityN":return P("x",n,t,e).map(m=>Do(m));case"Snapshot":let s=P("x",n,t,e);return[Do(s)];case"Shape":return[Ir(P("x",n,t,e).shape,"int32")];case"ShapeN":return P("x",n,t,e).map(m=>Ir(m.shape));case"Size":return[Ot(P("x",n,t,e).size,"int32")];case"Rank":return[Ot(P("x",n,t,e).rank,"int32")];case"NoOp":return[Ot(1)];case"Print":let u=P("x",n,t,e),l=P("data",n,t,e),h=P("message",n,t,e),p=P("summarize",n,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(h);for(let m=0;m<l.length;m++)console.log(Array.prototype.slice.call(l[m].dataSync()).slice(0,p));return[u];default:throw TypeError(`Node type ${n.op} is not implemented`)}},xft="graph";class Uq{constructor(t,e){this.keyDType=t,this.valueDType=e,this.handle=Ot(0),this.tensorMap=new Map,An(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(t=>t.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return this.tensorMap.forEach(s=>s.dispose()),this.tensorMap.clear(),ot(()=>{let s=bs(e),u=r.length,l=s.length;k(u===l,()=>`The number of elements doesn't match, keys has ${u} elements, the values has ${l} elements.`);for(let h=0;h<u;h++){let p=r[h],m=s[h];An(m),this.tensorMap.set(p,m)}return this.handle})}async find(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return ot(()=>{let s=[];for(let u=0;u<r.length;u++){let l=r[u],h=this.findWithDefault(l,e);s.push(h)}return mr(s)})}findWithDefault(t,e){let r=this.tensorMap.get(t);return r!=null?r:e}checkKeyAndValueTensor(t,e){if(t.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${t.dtype}`);if(e.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`)}}let Gq=async(n,t,e,r)=>{switch(n.op){case"HashTable":case"HashTableV2":{let s=P("keyDType",n,t,e),u=P("valueDType",n,t,e),l=new Uq(s,u);return r.addHashTable(n.name,l),[l.handle]}case"LookupTableImport":case"LookupTableImportV2":{let s=P("tableHandle",n,t,e,r),u=P("keys",n,t,e),l=P("values",n,t,e),h=r.getHashTableById(s.id);return[await h.import(u,l)]}case"LookupTableFind":case"LookupTableFindV2":{let s=P("tableHandle",n,t,e,r),u=P("keys",n,t,e),l=P("defaultValue",n,t,e),h=r.getHashTableById(s.id);return[await h.find(u,l)]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Tft="hash_table";let Hq=(n,t,e)=>{switch(n.op){case"ResizeBilinear":{let r=P("images",n,t,e),s=P("size",n,t,e),u=P("alignCorners",n,t,e);return[da.resizeBilinear(r,[s[0],s[1]],u)]}case"ResizeNearestNeighbor":{let r=P("images",n,t,e),s=P("size",n,t,e),u=P("alignCorners",n,t,e);return[da.resizeNearestNeighbor(r,[s[0],s[1]],u)]}case"CropAndResize":{let r=P("image",n,t,e),s=P("boxes",n,t,e),u=P("boxInd",n,t,e),l=P("cropSize",n,t,e),h=P("method",n,t,e),p=P("extrapolationValue",n,t,e);return[da.cropAndResize(r,s,u,l,h,p)]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},kft="image";let qq=(n,t,e)=>{switch(n.op){case"Equal":return[gs(P("a",n,t,e),P("b",n,t,e))];case"NotEqual":return[ha(P("a",n,t,e),P("b",n,t,e))];case"Greater":return[zr(P("a",n,t,e),P("b",n,t,e))];case"GreaterEqual":return[xo(P("a",n,t,e),P("b",n,t,e))];case"Less":return[wh(P("a",n,t,e),P("b",n,t,e))];case"LessEqual":return[la(P("a",n,t,e),P("b",n,t,e))];case"LogicalAnd":return[es(P("a",n,t,e),P("b",n,t,e))];case"LogicalNot":return[xh(P("a",n,t,e))];case"LogicalOr":return[kd(P("a",n,t,e),P("b",n,t,e))];case"Select":case"SelectV2":return[er(P("condition",n,t,e),P("a",n,t,e),P("b",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Sft="logical";let jq=(n,t,e)=>{switch(n.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[xe(P("a",n,t,e),P("b",n,t,e),P("transposeA",n,t,e),P("transposeB",n,t,e))];case"Transpose":return[re(P("x",n,t,e),P("perm",n,t,e))];case"_FusedMatMul":let[r,s]=P("fusedOps",n,t,e),u=r==="biasadd",l=s==="prelu",h=P("numArgs",n,t,e);if(u){if(l&&h!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!l&&h!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[p,m]=P("args",n,t,e);return[Wd({a:P("a",n,t,e),b:P("b",n,t,e),transposeA:P("transposeA",n,t,e),transposeB:P("transposeB",n,t,e),bias:p,activation:s,preluActivationWeights:m})];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Cft="matrices";let Kq=(n,t,e)=>{switch(n.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[fi(P("x",n,t,e),P("mean",n,t,e),P("variance",n,t,e),P("offset",n,t,e),P("scale",n,t,e),P("epsilon",n,t,e))];case"FusedBatchNormV3":return[fi(P("x",n,t,e),P("mean",n,t,e),P("variance",n,t,e),P("offset",n,t,e),P("scale",n,t,e),P("epsilon",n,t,e))];case"LRN":return[pb(P("x",n,t,e),P("radius",n,t,e),P("bias",n,t,e),P("alpha",n,t,e),P("beta",n,t,e))];case"Softmax":return[bi(P("x",n,t,e))];case"LogSoftmax":return[Td(P("x",n,t,e))];case"SparseToDense":return[_b(P("sparseIndices",n,t,e),P("outputShape",n,t,e),P("sparseValues",n,t,e),P("defaultValue",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Nft="normalization";let Xq=(n,t,e)=>{switch(n.op){case"Max":{let r=P("axis",n,t,e),s=P("keepDims",n,t,e);return[dr(P("x",n,t,e),r,s)]}case"Mean":{let r=P("axis",n,t,e),s=P("keepDims",n,t,e);return[an(P("x",n,t,e),r,s)]}case"Min":{let r=P("axis",n,t,e),s=P("keepDims",n,t,e);return[Pu(P("x",n,t,e),r,s)]}case"Sum":{let r=P("axis",n,t,e),s=P("keepDims",n,t,e);return[Xt(P("x",n,t,e),r,s)]}case"All":{let r=P("axis",n,t,e),s=P("keepDims",n,t,e);return[cd(P("x",n,t,e),r,s)]}case"Any":{let r=P("axis",n,t,e),s=P("keepDims",n,t,e);return[hh(P("x",n,t,e),r,s)]}case"ArgMax":{let r=P("axis",n,t,e);return[fh(P("x",n,t,e),r)]}case"ArgMin":{let r=P("axis",n,t,e);return[Xy(P("x",n,t,e),r)]}case"Prod":{let r=P("axis",n,t,e),s=P("keepDims",n,t,e);return[Nd(P("x",n,t,e),r,s)]}case"Cumsum":{let r=P("axis",n,t,e),s=P("exclusive",n,t,e),u=P("reverse",n,t,e);return[vd(P("x",n,t,e),r,s,u)]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ift="reduction";let Yq=(n,t,e)=>{switch(n.op){case"ConcatV2":case"Concat":{let r=P("n",n,t,e),s=P("axis",n,t,e),u=P("tensors",n,t,e);return u=u.slice(0,r),[sn(u,s)]}case"GatherV2":case"Gather":{let r=P("axis",n,t,e),s=P("x",n,t,e),u=P("indices",n,t,e);return[Fu(s,Rt(u,"int32"),r)]}case"ReverseV2":case"Reverse":{let r=P("axis",n,t,e),s=P("x",n,t,e);return[Wr(s,r)]}case"Slice":{let r=P("begin",n,t,e),s=P("size",n,t,e);return[ge(P("x",n,t,e),r,s)]}case"StridedSlice":{let r=P("begin",n,t,e),s=P("end",n,t,e),u=P("strides",n,t,e),l=P("beginMask",n,t,e),h=P("endMask",n,t,e),p=P("ellipsisMask",n,t,e),m=P("newAxisMask",n,t,e),y=P("shrinkAxisMask",n,t,e),b=P("x",n,t,e);return[Ib(b,r,s,u,l,h,p,m,y)]}case"Pack":return ot(()=>{let r=P("axis",n,t,e),s=P("tensors",n,t,e),u=s[0].shape,l=fa(s[0]).shape,h=s.map(p=>{let m=K(p.shape,u);if(!m&&!K(fa(p).shape,l))throw new Error("the input tensors shape does not match");return m?p:rt(p,u)});return[mr(h,r)]});case"Unpack":{let r=P("axis",n,t,e),s=P("tensor",n,t,e);return bs(s,r)}case"Tile":{let r=P("reps",n,t,e);return[ca(P("x",n,t,e),r)]}case"Split":case"SplitV":{let r=P("axis",n,t,e),s=P("numOrSizeSplits",n,t,e),u=P("x",n,t,e);return Er(u,s,r)}case"ScatterNd":{let r=P("indices",n,t,e),s=P("values",n,t,e),u=P("shape",n,t,e);return[SC(r,s,u)]}case"GatherNd":{let r=P("x",n,t,e),s=P("indices",n,t,e);return[CC(r,s)]}case"SparseToDense":{let r=P("sparseIndices",n,t,e),s=P("outputShape",n,t,e),u=P("sparseValues",n,t,e),l=P("defaultValue",n,t,e);return[_b(r,u,s,u.dtype===l.dtype?l:Rt(l,u.dtype))]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Eft="slice_join";let Jq=(n,t,e)=>{switch(n.op){case"FFT":return[Ih(P("x",n,t,e))];case"IFFT":return[Bu(P("x",n,t,e))];case"RFFT":return[Eh(P("x",n,t,e))];case"IRFFT":return[Fd(P("x",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Dft="spectral";let Zq=(n,t,e)=>{switch(n.op){case"Cast":return[Rt(P("x",n,t,e),P("dtype",n,t,e))];case"ExpandDims":{let r=P("axis",n,t,e);return[pr(P("x",n,t,e),r)]}case"Squeeze":{let r=P("axis",n,t,e);return[fa(P("x",n,t,e),r)]}case"Reshape":return[rt(P("x",n,t,e),P("shape",n,t,e))];case"MirrorPad":return[vb(P("x",n,t,e),P("padding",n,t,e),P("mode",n,t,e))];case"PadV2":case"Pad":return[Xs(P("x",n,t,e),P("padding",n,t,e),P("constantValue",n,t,e))];case"SpaceToBatchND":{let r=P("blockShape",n,t,e),s=P("paddings",n,t,e);return[kh(P("x",n,t,e),r,s)]}case"BatchToSpaceND":{let r=P("blockShape",n,t,e),s=P("crops",n,t,e);return[mh(P("x",n,t,e),r,s)]}case"DepthToSpace":{let r=P("blockSize",n,t,e),s=P("dataFormat",n,t,e).toUpperCase();return[ib(P("x",n,t,e),r,s)]}case"BroadcastTo":return[gh(P("x",n,t,e),P("shape",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},$ft="transformation";function MI(n,t,e,r){let s=((u,l,h)=>{switch(u.category){case"arithmetic":return ot(()=>Dq(u,l,h));case"basic_math":return ot(()=>$q(u,l,h));case"control":return Mq(u,l,h);case"convolution":return ot(()=>Lq(u,l,h));case"creation":return ot(()=>Bq(u,l,h));case"dynamic":return zq(u,l,h);case"evaluation":return ot(()=>Wq(u,l,h));case"image":return ot(()=>Hq(u,l,h));case"graph":return ot(()=>Vq(u,l,h));case"logical":return ot(()=>qq(u,l,h));case"matrices":return ot(()=>jq(u,l,h));case"normalization":return ot(()=>Kq(u,l,h));case"reduction":return ot(()=>Xq(u,l,h));case"slice_join":return ot(()=>Yq(u,l,h));case"spectral":return ot(()=>Jq(u,l,h));case"transformation":return ot(()=>Zq(u,l,h));case"hash_table":return Gq(u,l,h,r);case"custom":let p=AI(u.op);if(p&&p.customExecutor)return p.customExecutor(new Eq(u,l,h));throw TypeError(`Custom op ${u.op} is not registered.`);default:throw TypeError(`Unknown op '${u.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(n,t,e);return ni(s)?s.then(u=>[].concat(u)):[].concat(s)}class LI{constructor(t={},e={},r={},s={}){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=r,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let r=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(r))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}}function BI(n,t,e,r){let s=new Set,u=[],l=null,h=null,p=new Set,m=Object.keys(n).map(x=>$r(x)[0]),y=[];r!=null&&(y=r.map(x=>$r(x.name)[0]));let b=[...t];for(;b.length>0;){let x=b.pop();if((zI(x)||rj(x)||sj(x))&&(l==null&&(l=x,h=l.children.map(S=>S.name).filter(S=>s.has(S)))),s.add(x.name),e[x.name]!=null)continue;if(m.indexOf(x.name)!==-1)continue;if(y.indexOf(x.name)!==-1)continue;if(x.inputs.length===0){u.push(x.name);continue}x.inputs.forEach(S=>{if(p.has(S.name))return;p.add(S.name),b.push(S)})}return{inputs:n,outputs:t,usedNodes:s,missingInputs:u,dynamicNode:l,syncInputs:h}}function Qq(n,t,e){let{usedNodes:r,inputs:s}=e,u=[],l=Object.keys(s).map(y=>$r(y)[0]).map(y=>n.nodes[y]),h=n.initNodes;l.forEach(y=>{r.has(y.name)&&u.push(y)}),n.weights.forEach(y=>{r.has(y.name)&&u.push(y)}),h!=null&&h.forEach(y=>{r.has(y.name)&&u.push(y)});let p=new Set,m=[];for(;u.length>0;){let y=u.pop();p.add(y.name),t[y.name]||m.push(y),y.children.forEach(b=>{!p.has(b.name)&&r.has(b.name)&&b.inputs.every(x=>p.has(x.name))&&u.push(b)})}return m}let tj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],ej=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],nj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function zI(n){return tj.indexOf(n.op)>=0}function rj(n){return ej.indexOf(n.op)>=0}function sj(n){return nj.indexOf(n.op)>=0}class Am{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(r=>{this._functionExecutorMap[r]=new Am(t.functions[r],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(t){let e=Object.keys(t).map(r=>t[r].map(s=>s.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let r=t.map(u=>u.name).sort(),s=e.map(u=>u.name).sort();return r.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(t,e){let r=BI(t,e,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:u,syncInputs:l}=r;if(u!=null)throw new Error(`This execution contains the node '${u.name}', which has the dynamic op '${u.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${l}]`);if(s.length>0){let h=e.map(m=>m.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${h}] from the provided inputs [${p}]. Missing the following inputs: [${s}]`)}return Qq(this.graph,this.weightMap,r)}execute(t,e){t=this.mapInputs(t);let r=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let s=r.map(b=>this.graph.nodes[$r(b)[0]]),u=e.map(b=>$r(b)[0]),l=u.map(b=>this.graph.nodes[b]);l.length===0&&(l=this._outputs);let h=this.getCompilationKey(s,l),p=this.compiledMap.get(h);p==null&&(p=this.compile(t,l),this.compiledMap.set(h,p));let m={},y={};return ot(()=>{let b=new LI(this.weightMap,m,y,this.functionExecutorMap),x=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[D,R]=$r(I),A=[];A[R]=t[I],x[D]=A});let S=this.getFrozenTensorIds(x),C={};for(let I=0;I<p.length;I++){let D=p[I];if(!x[D.name]){let R=MI(D,x,b,this._resourceManager);if(ni(R))throw new Error(`The execution of the op '${D.op}' returned a promise. Please use model.executeAsync() instead.`);x[D.name]=R,this.checkTensorForDisposal(D.name,D,x,b,S,u,C)}}return this.parent==null&&b.dispose(S),e.map(I=>yr(I,x,b))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(r=>t[r]).map(r=>r.map(s=>s.id)));return new Set(e)}checkTensorForDisposal(t,e,r,s,u,l,h){if(e.category==="control"||l.indexOf(t)!==-1)return;r[t].forEach(p=>{p!=null&&(h[p.id]=(h[p.id]||0)+e.children.length)}),e.inputs.forEach(p=>{if(p.category!=="control"){let m=qH(p.name,r,s);m!=null&&m.forEach(y=>{if(y&&!u.has(y.id)){let b=h[y.id];b===1?(y.dispose(),delete h[y.id]):b!=null&&h[y.id]--}})}})}async executeAsync(t,e){return this._executeAsync(t,e)}async _executeAsync(t,e,r=!1,s={},u={}){r||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));let l=new LI(this.weightMap,s,u,this.functionExecutorMap),h=await this.executeWithControlFlow(t,l,e,r),p=e.map(x=>yr(x,h,l)),m=p.map(x=>x.id),y=Object.keys(t).map(x=>t[x].id),b=new Set([...m,...y,...this.weightIds]);return Object.keys(h).forEach(x=>{let S=h[x];S.forEach(C=>{C&&!C.isDisposed&&!b.has(C.id)&&C.dispose()})}),this.parent==null&&l.dispose(b),p}async executeFunctionAsync(t,e,r){let s=t.reduce((u,l,h)=>(u[this.inputs[h].name]=l,u),{});return this._executeAsync(s,this.outputNodes,!0,e,r)}async executeWithControlFlow(t,e,r,s){let u=Object.keys(t),l=u.map(L=>this.graph.nodes[$r(L)[0]]),h=r.map(L=>$r(L)[0]),p=h.map(L=>this.graph.nodes[L]);p.length===0&&(p=this._outputs);let{usedNodes:m,missingInputs:y,dynamicNode:b,syncInputs:x}=BI(t,p,this.weightMap,this._initNodes),S=[...l,...this.graph.weights,...this._initNodes||[]].map(L=>({node:L,contexts:e.currentContext})),C=Object.assign({},this.weightMap);Object.keys(t).forEach(L=>{let[_,B]=$r(L),V=[];V[B]=t[L],C[_]=V});let I={},D=this.getFrozenTensorIds(C),R={};for(;S.length>0;){let L=this.processStack(l,S,e,C,R,D,h,I,m);await Promise.all(L)}b==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let A=p.filter(L=>!zI(L)&&!yr(L.name,C,e)).map(L=>L.name);if(A.length>0){let L="";throw b!=null&&(L=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${x}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${u}]. Consider providing the following inputs: [${y}]. ${L}`)}return C}processStack(t,e,r,s,u,l,h,p,m){let y=[];for(;e.length>0;){let b=e.pop();r.currentContext=b.contexts;let x="";if(b.node.op==="Enter"&&P("isConstant",b.node,s,r)&&([x]=Eo(b.node.name,r)),s[b.node.name]==null){let S=MI(b.node,s,r,this._resourceManager);x||([x]=Eo(b.node.name,r));let C=r.currentContext;ni(S)?y.push(S.then(I=>(s[x]=I,r.currentContext=C,this.checkTensorForDisposal(x,b.node,s,r,l,h,p),this.processChildNodes(b.node,e,r,s,u,m),I))):(s[x]=S,this.checkTensorForDisposal(x,b.node,s,r,l,h,p),this.processChildNodes(b.node,e,r,s,u,m))}else this.processChildNodes(b.node,e,r,s,u,m)}return y}processChildNodes(t,e,r,s,u,l){t.children.forEach(h=>{let[p]=Eo(h.name,r);if(u[p]||!l.has(h.name))return;h.op==="Merge"?h.inputNames.some(m=>!!yr(m,s,r))&&(u[p]=!0,e.push({contexts:r.currentContext,node:h})):h.inputNames.every(m=>!!yr(m,s,r))&&(u[p]=!0,e.push({contexts:r.currentContext,node:h}))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let r=t[e],[s]=$r(e),u=this.graph.nodes[s];if(u.attrParams.shape&&u.attrParams.shape.value){let l=u.attrParams.shape.value,h=l.length===r.shape.length&&r.shape.every((p,m)=>l[m]===-1||l[m]===p);k(h,()=>`The shape of dict['${u.name}'] provided in model.execute(dict) must be [${l}], but was [${r.shape}]`)}u.attrParams.dtype&&u.attrParams.dtype.value&&k(r.dtype===u.attrParams.dtype.value,()=>`The dtype of dict['${u.name}'] provided in model.execute(dict) must be ${u.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(t){let e={};for(let r in t)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let s=this._signature.inputs[r];e[s.name]=t[r]}else e[r]=t[r];return e}checkInputs(t){let e=Object.keys(t).filter(r=>{let[s]=$r(r);return this.graph.nodes[s]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[e]!=null){let r=this._signature.outputs[e];return r.name}return e},{})}checkOutputs(t){t.forEach(e=>{let[r]=$r(e);if(!this.graph.nodes[r])throw new Error(`The output '${e}' is not found in the graph`)})}}class oj{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}}let aj="?tfjs-format=file",ij="model.json";class WI{constructor(t,e={}){this.modelUrl=t,this.loadOptions=e,this.version="n/a",e==null&&(this.loadOptions={}),this.resourceManager=new oj}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=ed(t,this.loadOptions);else{let e=Dy(t,this.loadOptions);if(e.length===0)e.push(ed(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=await this.handler.load();return this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,r={};this.artifacts.userDefinedMetadata!=null&&(r=this.artifacts.userDefinedMetadata.signature),this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let s=Jp(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Am(_I.Instance.transformGraph(e,r)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null){let u=_I.Instance.transformGraph(t.modelInitializer);this.initializer=new Am(u),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(t,e){if(typeof t=="string"){let r=Ey(t);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${t}'`);t=r[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}predict(t,e){return this.execute(t,this.outputNodes)}normalizeInputs(t){if(!(t instanceof at)&&!Array.isArray(t))return t;if(t=Array.isArray(t)?t:[t],t.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${t.length} input tensors.`);return this.inputNodes.reduce((e,r,s)=>(e[r]=t[s],e),{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}execute(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=this.executor.execute(t,e);return r.length>1?r:r[0]}async executeAsync(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=await this.executor.executeAsync(t,e);return r.length>1?r:r[0]}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,r)=>(e[r]=[t[r]],e),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function uj(n,t={}){if(n==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(n.load==null&&(n.endsWith("/")||(n=n+"/"),n=`${n}${ij}${aj}`));let e=new WI(n,t);return await e.load(),e}let VI="2.7.0";function cj(n,t){return _m(n,t)}function _m(n,t,e=new Map,r=new Set){if(n==null)return null;if(r.has(n))throw new Error("Circular references are not supported.");if(e.has(n))return e.get(n);let s=t(n);if(s.recurse&&s.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(s.recurse)if(nc(n)){let u=Array.isArray(n)?[]:{};r.add(n);for(let l in n){let h=n[l],p=_m(h,t,e,r);u[l]=p}return r.delete(n),u}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return e.set(n,s.value),s.value}function lj(n,t=GI){return UI(n,t)}function UI(n,t,e=new Set){let r=n[0];if(e.has(r))throw new Error("Circular references are not supported.");let s=t(n);if(s.recurse&&s.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(s.recurse)if(nc(r)){let u=Array.isArray(r)?[]:{};e.add(r);for(let l in r){let h=n.map(m=>m[l]),p=UI(h,t,e);u[l]=p}return e.delete(r),u}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return s.value}function GI(n){return n===null?null:nc(n[0])?{value:null,recurse:!0}:{value:n,recurse:!1}}async function HI(n,t){let e=new Map;_m(n,t,e);for(let s of Array.from(e.keys())){let u=e.get(s);if(ni(u)){let l=await u;e.set(s,l)}}let r=_m(n,t,e);return r}function nc(n){return n!=null&&!ArrayBuffer.isView(n)&&(Array.isArray(n)||typeof n=="object"&&!(n instanceof at))}function hj(n){return n==null||fj(n)||Array.isArray(n)||typeof n=="object"&&n instanceof at||Re(n)}function fj(n){return n===null||typeof n!="object"&&typeof n!="function"}function pj(n){return cj(n,dj)}function dj(n){return n instanceof at?{value:n.clone(),recurse:!1}:nc(n)?{value:null,recurse:!0}:{value:n,recurse:!1}}class qI{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),r=this.get(e);return this.set(e,this.pop()),r}}class Fm extends qI{constructor(){super(Fm.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),r=this.length();for(let s=0;s<r;s++)e[s]=this.get(this.wrap(this.begin+s));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}}Fm.INITIAL_CAPACITY=32;function jI(n){return new gj(n)}function Aft(n){let t=n;return lf(()=>({value:t++,done:!1}))}function lf(n){return new vj(n)}function KI(n,t){return new YI(n,t)}function _ft(n,t,e){return KI(lf(n).take(t),e)}function mj(n,t=Sa.FAIL){return new Nj(n,t)}class Rn{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],r=await t.next();for(;!r.done;)e.push(r.value),r=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),r=t(e.value);for(;!e.done&&r;)e=await this.next(),r=t(e.value)}handleErrors(t){return new Sj(this,t)}filter(t){return new Tj(this,t)}map(t){return new kj(this,t)}mapAsync(t){return new XI(this,t)}serialMapAsync(t){return new XI(this,t).serial()}flatmap(t){return new Cj(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new xj(this,t,e)}columnMajorBatch(t,e=!0,r=GI){let s=this.rowMajorBatch(t,e);return s.map(u=>lj(u,r))}concatenate(t,e){return new YI(jI([this,t]),e)}take(t){return t<0||t==null?this:new wj(this,t)}skip(t){return t<0||t==null?this:new bj(this,t)}prefetch(t){return new JI(this,t)}shuffle(t,e){return new Ij(this,t,e)}serial(){return new yj(this)}}class gj extends Rn{constructor(t){super();this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:pj(t),done:!1}}}class vj extends Rn{constructor(t){super();this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}}class yj extends Rn{constructor(t){super();this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class bj extends Rn{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;oe(t.value)}return this.upstream.next()}}class wj extends Rn{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class xj extends Rn{constructor(t,e,r=!0){super();this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}}class Tj extends Rn{constructor(t,e){super();this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;oe(t.value)}}}class kj extends Rn{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=mo(t.value),r=this.transform(t.value),s=mo(r);for(let u of e)Xp(u,s)||u.dispose();return{value:r,done:!1}}}class Sj extends Rn{constructor(t,e){super();this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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(t){if(!this.handler(t))return{value:null,done:!0}}}}class XI extends Rn{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=mo(t.value),r=await this.transform(t.value),s=mo(r);for(let u of e)Xp(u,s)||u.dispose();return{value:r,done:!1}}}class G0 extends Rn{constructor(){super();this.outputQueue=new Fm,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class Cj extends G0{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=mo(t.value),r=this.transform(t.value),s=mo(r);this.outputQueue.pushAll(r);for(let u of e)Xp(u,s)||u.dispose();return!0}}class YI extends Rn{constructor(t,e){super();this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){let t="TODO: fill in upstream of chained summaries";return`${t} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}}var Sa;(function(n){n[n.FAIL=0]="FAIL",n[n.SHORTEST=1]="SHORTEST",n[n.LONGEST=2]="LONGEST"})(Sa||(Sa={}));class Nj extends Rn{constructor(t,e=Sa.FAIL){super();this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){let t="TODO: fill in upstream of zip summaries";return`{${t}} -> Zip`}async nextState(t){await t;let e=0,r=0;function s(l){if(l instanceof Rn){let h=l.next();return{value:h.then(p=>(e++,p.done&&r++,p.value)),recurse:!1}}else return{value:null,recurse:!0}}let u=await HI(this.iterators,s);if(e===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case Sa.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Sa.SHORTEST:return{value:null,done:!0};case Sa.LONGEST:default:}return this.count++,{value:u,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class JI extends Rn{constructor(t,e){super();this.upstream=t,this.bufferSize=e,this.buffer=new qI(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}}class Ij extends JI{constructor(t,e,r){super(t,e);this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=Lu(r||cr().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}}class rc{constructor(){this.size=null}batch(t,e=!0){let r=this;k(t>0,()=>`batchSize needs to be positive, but it is
-      ${t}`);let s;return this.size===Infinity||this.size==null?s=this.size:e?s=Math.ceil(this.size/t):s=Math.floor(this.size/t),Ar(async()=>(await r.iterator()).columnMajorBatch(t,e,$j),s)}concatenate(t){let e=this,r;return this.size===Infinity||t.size===Infinity?r=Infinity:this.size!=null&&t.size!=null?r=this.size+t.size:r=null,Ar(async()=>(await e.iterator()).concatenate(await t.iterator()),r)}filter(t){let e=this,r;return this.size===Infinity?r=Infinity:r=null,Ar(async()=>(await e.iterator()).filter(s=>ot(()=>t(s))),r)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return Ar(async()=>(await e.iterator()).map(r=>ot(()=>t(r))),this.size)}mapAsync(t){let e=this;return Ar(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return Ar(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,r;return this.size!=null&&t>0?r=this.size*t:t===0?r=0:this.size!=null&&(t===void 0||t<0)?r=Infinity:r=null,Ar(async()=>{let s=lf(async()=>({value:await e.iterator(),done:!1}));return KI(s.take(t))},r)}skip(t){let e=this,r;return this.size!=null&&t>=0&&this.size>=t?r=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?r=0:r=null,Ar(async()=>(await e.iterator()).skip(t),r)}shuffle(t,e,r=!0){if(t==null||t<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)`);let s=this,u=Lu(e||cr().toString());return Ar(async()=>{let l=u.int32();return r&&(l+=u.int32()),(await s.iterator()).shuffle(t,l.toString())},this.size)}take(t){let e=this,r;return this.size!=null&&this.size>t?r=t:this.size!=null&&this.size<=t?r=this.size:r=null,Ar(async()=>(await e.iterator()).take(t),r)}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()}}rc.MAX_BUFFER_SIZE=1e4;function Ar(n,t=null){return new class extends rc{constructor(){super(...arguments);this.size=t}async iterator(){return n()}}}function Ej(n){return Ar(async()=>jI(n),n.length)}function Dj(n){if(!nc(n))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(n))for(let e=0;e<n.length;e++)t=t==null?n[e].size:Math.min(t,n[e].size);else if(n instanceof Object)for(let e in n)t=t==null?n[e].size:Math.min(t,n[e].size);return Ar(async()=>{let e=await HI(n,r=>{if(r instanceof rc)return{value:r.iterator(),recurse:!1};if(nc(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return mj(e,Sa.SHORTEST)},t)}function $j(n){if(n===null)return null;let t=n[0];if(hj(t)){let e=Aj(n);return{value:e,recurse:!1}}return{value:null,recurse:!0}}function Aj(n){if(n.length===0)throw new Error("Can't make a batch of zero elements.");return n[0]instanceof at?mr(n):vn(n)}class ZI extends rc{constructor(t){super();this.input=t}async iterator(){let t=await this.input.iterator(),e=t.decodeUTF8(),r=e.split(`
-`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s));return r}}let Rm='"',hf=Symbol("out"),QI=Symbol("field"),Pm=Symbol("quote"),H0=Symbol("quoteafterquote"),tE=Symbol("quoteinquote");class eE extends rc{constructor(t,e){super();this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new ZI(t),e||(e={}),this.hasHeader=!(e.hasHeader===!1),this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(k(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&k(t.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 ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((s,u)=>(s[u]=s[u]+1||1,s),{}),r=Object.keys(e).filter(s=>e[s]>1);if(k(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs)for(let s of Object.keys(this.columnConfigs)){let u=this.fullColumnNames.indexOf(s);if(u===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await this.base.iterator(),e=await t.next();if(e.done)throw new Error("No data was found for CSV parsing.");let r=e.value,s=this.parseRow(r,!1);return s}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),r={},s={};for(let u=0;u<this.fullColumnNames.length;u++){let l=this.fullColumnNames[u],h=this.columnConfigs?this.columnConfigs[l]:null;if(this.configuredColumnsOnly&&!h)continue;{let p=e[u],m=null;if(p==="")if(h&&h.default!==void 0)m=h.default;else{if(h&&(h.required||h.isLabel))throw new Error(`Required column ${l} is empty in this line: ${t}`);m=void 0}else{let y=Number(p);if(isNaN(y))h&&h.dtype==="bool"?m=this.getBoolean(p):m=p;else if(!h||!h.dtype)m=y;else switch(h.dtype){case"float32":m=y;break;case"int32":m=Math.floor(y);break;case"bool":m=this.getBoolean(p);break;default:m=y}}h&&h.isLabel?s[l]=m:r[l]=m}}return Object.keys(s).length===0?r:{xs:r,ys:s}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let r=[],s=0,u=t.length,l=hf;for(let h=0;h<u;h++)switch(l){case hf:switch(t.charAt(h)){case Rm:s=h+1,l=Pm;break;case this.delimiter:if(s=h+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),l=hf;break;default:l=QI,s=h;break}break;case QI:switch(t.charAt(h)){case this.delimiter:r.push(t.substring(s,h)),l=hf,s=h+1;break;default:}break;case Pm:switch(t.charAt(h)){case Rm:l=H0;break;default:}break;case H0:switch(t.charAt(h)){case this.delimiter:r.push(t.substring(s,h-1)),l=hf,s=h+1;break;case Rm:l=Pm;break;default:l=tE;break}break;case tE:switch(t.charAt(h)){case Rm:l=Pm;break;default:}break;default:}if(l===H0?r.push(t.substring(s,u-1)):r.push(t.substring(s)),e&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}}class q0 extends Rn{constructor(t){super();this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=!(t.includeSpectrogram===!1),this.includeWaveform=t.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(t={}){if(ft().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let e=new q0(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.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 t,e,r=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(r.freqDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(r.timeDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],r=0;return new Promise(s=>{let u=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&s({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(u),s({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,r=new Float32Array(t.length*e);return t.forEach((s,u)=>r.set(s,u*e)),r}getTensorFromAudioDataArray(t,e){let r=new Float32Array(O(e));return r.set(t,r.length-t.length),vn(r,e)}}class j0 extends Rn{constructor(t,e){super();if(this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ir([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,u=(1-r)/2,l=(1-s)/2,h=u+r,p=s+l;this.cropBox=pa([l,u,p,h],[1,4])}else this.cropBox=pa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(ft().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let r=new j0(t,e);return await r.start(),r}async start(){this.webcamConfig.facingMode&&k(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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=kS(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return ot(()=>{let e=t.toFloat().expandDims(0),r;r=da.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=r.shape;return r.reshape(s.slice(1))})}async capture(){return(await this.next()).value}stop(){let t=this.stream.getTracks();t.forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class nE{}class rE extends Rn{split(t){return new _j(this,t)}}class _j extends rE{constructor(t,e){super();this.upstream=t,this.impl=new Fj(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Fj extends G0{constructor(t,e){super();this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let r of e.slice(0,-1))this.outputQueue.push(r);return this.carryover=e[e.length-1],!0}}class Rj extends Rn{decodeUTF8(){return new Pj(this)}}class Pj extends rE{constructor(t){super();this.upstream=t,this.impl=new Oj(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Oj extends G0{constructor(t){super();if(this.upstream=t,ft().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=require("string_decoder");this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let r;return ft().get("IS_BROWSER")?r=this.decoder.decode(e,{stream:!0}):r=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(r),!0}}class sE extends Rj{constructor(t,e={}){super();this.file=t,this.options=e,k(t instanceof Uint8Array||(ft().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.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};let t=new Promise((e,r)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,s)));else{let u=new FileReader;u.onload=h=>{let p=u.result;if(p instanceof ArrayBuffer&&(p=new Uint8Array(p)),!(p instanceof Uint8Array))return r(new TypeError("FileReader returned unknown type."));e(p)},u.onabort=h=>r(new Error("Aborted")),u.onerror=h=>r(new Error(h.type));let l=this.file.slice(this.offset,s);u.readAsArrayBuffer(l)}this.offset=s});return{value:await t,done:!1}}}async function Mj(n,t={}){let e,r;typeof n=="string"?e=n:(e=n.url,r=Lj(n));let s=await X2(e,r);if(s.ok){let u=new Uint8Array(await s.arrayBuffer());return new sE(u,t)}else throw new Error(s.statusText)}let Lj=n=>{let t={method:n.method,headers:n.headers,body:n.body,mode:n.mode,credentials:n.credentials,cache:n.cache,redirect:n.redirect,referrer:n.referrer,integrity:n.integrity};return t};function oE(n){return typeof n=="string"&&n.substr(0,7)==="file://"}class aE extends nE{constructor(t,e={}){super();this.input=t,this.options=e}async iterator(){if(oE(this.input)&&ft().get("IS_NODE")){let t=require("fs");this.input=t.readFileSync(this.input.substr(7))}return new sE(this.input,this.options)}}class iE extends nE{constructor(t,e={}){super();this.url=t,this.fileOptions=e}async iterator(){return oE(this.url)?new aE(this.url,this.fileOptions).iterator():Mj(this.url,this.fileOptions)}}function Bj(n,t={}){return new eE(new iE(n),t)}function zj(n){let t=lf(n);return Ar(async()=>t)}function Wj(n){return Ar(async()=>{let t=await n();return lf(()=>t.next())})}async function Vj(n,t){return j0.create(n,t)}async function Uj(n){return q0.create(n)}let uE="2.7.0";var Gj=Object.freeze({__proto__:null,array:Ej,Dataset:rc,zip:Dj,CSVDataset:eE,TextLineDataset:ZI,csv:Bj,func:zj,generator:Wj,microphone:Uj,webcam:Vj,FileDataSource:aE,URLDataSource:iE,version_data:uE});function At(n,t){Array.isArray(n)||(n=[n]),n.forEach(e=>{e!=null&&k(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}let Hj=Vd,qj=Xb,jj=Yb,Kj=Jb,Xj=Pd;class Yj extends f{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new c(this,yo())}write(t,e,r){this.firstUse&&(this.firstUse=!1,ft().get("IS_NODE")&&Gu(`
-============================
-Hi there \u{1F44B}. 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.
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xt=0;xt<p;xt++){let wt=m[vt+xt],Tt=m[bt+xt],Dt=m[yt+xt],$t=m[mt+xt],Lt=wt+(Dt-wt)*ht,Pt=Tt+($t-Tt)*ht,Ft=Lt+(Pt-Lt)*_;y[S++]=Ft}}}return vn(y,[u,e,r,p])}resizeBilinearBackprop(t,e,r){At([t,e],"resizeBilinearBackprop");let[s,u,l,h]=e.shape,[,p,m]=t.shape,y=new Float32Array(s*u*l*h),b=[r&&p>1?u-1:u,r&&m>1?l-1:l],x=[r&&p>1?p-1:p,r&&m>1?m-1:m],S=b[0]/x[0],C=b[1]/x[1],I=this.readSync(t.dataId),D=0;for(let R=0;R<s;R++){let A=R*e.strides[0];for(let L=0;L<p;L++){let _=L*S,B=Math.floor(_),V=Math.min(Math.ceil(_),u-1),q=A+B*e.strides[1],j=A+V*e.strides[1],et=_-B,tt=1-et;for(let ht=0;ht<m;ht++){let gt=ht*C,vt=Math.floor(gt),bt=Math.min(Math.ceil(gt),l-1),yt=gt-vt,mt=1-yt,xt=q+vt*e.strides[2],wt=q+bt*e.strides[2],Tt=j+vt*e.strides[2],Dt=j+bt*e.strides[2],$t=tt*mt,Lt=tt*yt,Pt=et*mt,Ft=et*yt;for(let Vt=0;Vt<h;Vt++){let ee=I[D++];y[xt+Vt]+=ee*$t,y[wt+Vt]+=ee*Lt,y[Tt+Vt]+=ee*Pt,y[Dt+Vt]+=ee*Ft}}}}return Wu(y,[s,l,u,h],e.dtype)}resizeNearestNeighbor(t,e,r,s){At(t,"resizeNearestNeighbor");let[u,l,h,p]=t.shape,m=this.readSync(t.dataId),y=new Float32Array(u*e*r*p),b=[s&&e>1?l-1:l,s&&r>1?h-1:h],x=[s&&e>1?e-1:e,s&&r>1?r-1:r],S=b[0]/x[0],C=b[1]/x[1],I=0;for(let D=0;D<u;D++){let R=D*t.strides[0];for(let A=0;A<e;A++){let L=S*A,_=Math.min(l-1,s?Math.round(L):Math.floor(L)),B=R+_*t.strides[1];for(let V=0;V<r;V++){let q=C*V,j=Math.min(h-1,s?Math.round(q):Math.floor(q)),et=B+j*t.strides[2];for(let tt=0;tt<p;tt++){let ht=m[et+tt];y[I++]=ht}}}}return vn(y,[u,e,r,p],t.dtype)}resizeNearestNeighborBackprop(t,e,r){At([t,e],"resizeNearestNeighborBackprop");let[s,u,l,h]=e.shape,[,p,m]=t.shape,y=new Float32Array(s*u*l*h),b=this.readSync(t.dataId),x=[r&&p>1?u-1:u,r&&m>1?l-1:l],S=[r&&p>1?p-1:p,r&&m>1?m-1:m],C=x[0]/S[0],I=x[1]/S[1],D=1/C,R=1/I,A=Math.ceil(D)*2+2,L=Math.ceil(R)*2+2;for(let _=0;_<s;_++){let B=_*e.strides[0];for(let V=0;V<u;V++){let q=B+V*e.strides[1],j=Math.floor(V*D),et=Math.floor(j-A/2);for(let tt=0;tt<l;tt++){let ht=q+tt*e.strides[2],gt=Math.floor(tt*R),vt=Math.floor(gt-L/2);for(let bt=0;bt<h;bt++){let yt=0;for(let mt=0;mt<A;mt++){let xt=mt+et;if(xt<0||xt>=p)continue;let wt=B+xt*t.strides[1],Tt=xt*C,Dt=Math.min(u-1,r?Math.round(Tt):Math.floor(Tt));if(V!==Dt)continue;for(let $t=0;$t<L;$t++){let Lt=$t+vt;if(Lt<0||Lt>=m)continue;let Pt=wt+Lt*t.strides[2],Ft=Lt*I,Vt=Math.min(l-1,r?Math.round(Ft):Math.floor(Ft));tt===Vt&&(yt+=b[Pt+bt])}}y[ht+bt]=yt}}}}return Wu(y,e.shape,e.dtype)}localResponseNormalization4D(t,e,r,s,u){At(t,"localResponseNormalization4D");let l=t.shape[3],h=l-1,p=this.readSync(t.dataId),m=t.size,y=new Float32Array(m);function b(x){let S=x%l,C=x-S+Math.max(0,S-e),I=x-S+Math.min(S+e,h),D=0;for(;C<=I;C++){let R=p[C];D+=R*R}return D}for(let x=0;x<m;x++){let S=b(x),C=p[x]*Math.pow(r+s*S,-u);y[x]=C}return Wu(y,t.shape)}LRNGrad(t,e,r,s,u,l,h){At(t,"LRNGrad");let p=t.shape[3],m=this.readSync(t.dataId),y=this.readSync(e.dataId),b=this.readSync(r.dataId),x=new Float32Array(t.size),S=t.size;for(let C=0;C<S;C++){let I=C%p,D=C-I+Math.max(0,I-s),R=C-I+Math.min(p,I+s+1),A=0;for(let L=D;L<R;L++)A+=Math.pow(y[L],2);A=l*A+u;for(let L=D;L<R;L++){let _=-2*l*h*y[L]*b[C]/A;C===L&&(_+=Math.pow(A,-h)),_*=m[C],x[L]+=_}}return Wu(x,t.shape)}multinomial(t,e,r,s){At(t,"multinomial");let u=e?t:bi(t),l=u.shape[0],h=u.shape[1],p=Se([l,r],"int32"),m=this.readSync(p.dataId),y=this.readSync(u.dataId);for(let b=0;b<l;++b){let x=b*h,S=new Float32Array(h-1);S[0]=y[x];for(let D=1;D<S.length;++D)S[D]=S[D-1]+y[x+D];let C=Lu(s.toString()),I=b*r;for(let D=0;D<r;++D){let R=C();m[I+D]=S.length;for(let A=0;A<S.length;A++)if(R<S[A]){m[I+D]=A;break}}}return p}oneHot(t,e,r,s){At(t,"oneHot");let u=new Float32Array(t.size*e);u.fill(s);let l=this.readSync(t.dataId);for(let h=0;h<t.size;++h)l[h]>=0&&l[h]<e&&(u[h*e+l[h]]=r);return pa(u,[t.size,e],"int32")}nonMaxSuppression(t,e,r,s,u){At(t,"nonMaxSuppression");let l=this.readSync(t.dataId),h=this.readSync(e.dataId);return Hj(l,h,r,s,u)}depthToSpace(t,e,r){k(r==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${r}`),k(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`);let s=t.shape[0],u=t.shape[1],l=t.shape[2],h=t.shape[3],p=u*e,m=l*e,y=h/(e*e),b=this.readSync(t.dataId),x=new Float32Array(s*p*m*y),S=0;for(let C=0;C<s;++C)for(let I=0;I<p;++I){let D=Math.floor(I/e),R=I%e;for(let A=0;A<m;++A){let L=Math.floor(A/e),_=A%e,B=(R*e+_)*y;for(let V=0;V<y;++V){let q=V+B,j=q+h*(L+l*(D+u*C));x[S++]=b[j]}}}return Wu(x,[s,p,m,y])}broadcastedBinaryOp(t,e,r,s){let u=ve(t.shape,e.shape),l=Ae(u,r),h=this.readSync(t.dataId),p=this.readSync(e.dataId),m=di(t.shape,u),y=di(e.shape,u),b=l.values;if(m.length+y.length===0)for(let x=0;x<b.length;++x)b[x]=s(h[x%h.length],p[x%p.length]);else{let x=this.bufferSync(t),S=this.bufferSync(e);for(let C=0;C<b.length;++C){let I=l.indexToLoc(C),D=I.slice(-t.rank);m.forEach(_=>D[_]=0);let R=x.locToIndex(D),A=I.slice(-e.rank);y.forEach(_=>A[_]=0);let L=S.locToIndex(A);b[C]=s(h[R],p[L])}}return l.toTensor()}split(t,e,r){return qj(t,e,r)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(t,e,r,s,u,l){let[h,p,m,y]=t.shape,b=e.shape[0],[x,S]=s,C=Ae([b,x,S,y],"float32"),I=this.readSync(e.dataId),D=this.readSync(r.dataId),R=this.readSync(t.dataId),A=t.strides,L=C.strides;for(let _=0;_<b;_++){let B=_*4,V=I[B],q=I[B+1],j=I[B+2],et=I[B+3],tt=D[_];if(tt>=h)continue;let ht=x>1?(j-V)*(p-1)/(x-1):0,gt=S>1?(et-q)*(m-1)/(S-1):0;for(let vt=0;vt<x;vt++){let bt=x>1?V*(p-1)+vt*ht:.5*(V+j)*(p-1);if(bt<0||bt>p-1){for(let yt=0;yt<S;yt++)for(let mt=0;mt<y;mt++){let xt=mt+yt*L[2]+vt*L[1]+_*L[0];C.values[xt]=l}continue}if(u==="bilinear"){let yt=Math.floor(bt),mt=Math.ceil(bt),xt=bt-yt;for(let wt=0;wt<S;wt++){let Tt=S>1?q*(m-1)+wt*gt:.5*(q+et)*(m-1);if(Tt<0||Tt>m-1){for(let Pt=0;Pt<y;Pt++){let Ft=Pt+wt*L[2]+vt*L[1]+_*L[0];C.values[Ft]=l}continue}let Dt=Math.floor(Tt),$t=Math.ceil(Tt),Lt=Tt-Dt;for(let Pt=0;Pt<y;Pt++){let Ft=Pt+Dt*A[2]+yt*A[1]+tt*A[0],Vt=R[Ft];Ft=Pt+$t*A[2]+yt*A[1]+tt*A[0];let ee=R[Ft];Ft=Pt+Dt*A[2]+mt*A[1]+tt*A[0];let pe=R[Ft];Ft=Pt+$t*A[2]+mt*A[1]+tt*A[0];let we=R[Ft],de=Vt+(ee-Vt)*Lt,Ie=pe+(we-pe)*Lt;Ft=Pt+wt*L[2]+vt*L[1]+_*L[0],C.values[Ft]=de+(Ie-de)*xt}}}else for(let yt=0;yt<S;++yt){let mt=S>1?q*(m-1)+yt*gt:.5*(q+et)*(m-1);if(mt<0||mt>m-1){for(let Tt=0;Tt<y;Tt++){let Dt=Tt+yt*L[2]+vt*L[1]+_*L[0];C.values[Dt]=l}continue}let xt=Math.round(mt),wt=Math.round(bt);for(let Tt=0;Tt<y;Tt++){let Dt=Tt+xt*A[2]+wt*A[1]+tt*A[0],$t=Tt+yt*L[2]+vt*L[1]+_*L[0];C.values[$t]=R[Dt]}}}}return C.toTensor()}sparseToDense(t,e,r,s){let{sliceRank:u,numUpdates:l,sliceSize:h,strides:p,outputSize:m}=Iu(e,t,r),y=!1;return this.scatter(t,e,r,m,h,l,u,p,s,y)}gatherND(t,e){let r=e.shape,s=r[r.length-1],[u,l,h,p]=nd(t,e);if(l===0)return vn([],u,t.dtype);let m=new bn([l,h],t.dtype),y=this.readSync(e.dataId),b=this.readSync(t.dataId);for(let x=0;x<l;x++){let S=[],C=0;for(let I=0;I<s;I++){let D=y[x*s+I];C+=D*p[I],S.push(D)}if(C<0||C>=t.size/h)throw new Error(`Invalid indices: ${S} does not 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de=Lt+we*vt,Ie=we*x.strideWidth-R;for(let be=0;be<C;++be){let Je=Ie+be*D;if(Je<0||Je>=x.inWidth)continue;let ln=ee+be*V[1],hn=pe+Je*et,Me=ln;for(let Ze=0;Ze<x.inChannels;++Ze){let Ve=yt[hn+Ze*tt];for(let Un=0;Un<x.outChannels;++Un)xt[de+Un*bt]+=Ve*mt[Me+Un];Me+=x.outChannels}}}}}}return e.makeTensorInfo(_.shape,_.dtype,xt)}let oK={kernelName:gp,backendName:"cpu",kernelFunc:_E};function aK(n){let{inputs:t,backend:e,attrs:r}=n,{x:s,dy:u}=t,{strides:l,pad:h,dataFormat:p,dimRoundingMode:m,filterShape:y}=r;At([s,u],"conv2dBackpropFilter");let b=ua(p),x=Kn(s.shape,y,l,1,h,m,!1,b),{strideHeight:S,strideWidth:C,filterHeight:I,filterWidth:D}=x,R=x.dataFormat==="channelsLast",A=new bn(x.filterShape,"float32"),L=x.padInfo.left,_=x.padInfo.top,B=e.data.get(s.dataId).values,V=e.data.get(u.dataId).values,q=new bn(s.shape,s.dtype,B),j=new bn(u.shape,u.dtype,V);for(let et=0;et<I;++et){let tt=Math.max(0,Math.ceil((_-et)/S)),ht=Math.min(x.outHeight,(x.inHeight+_-et)/S);for(let gt=0;gt<D;++gt){let vt=Math.max(0,Math.ceil((L-gt)/C)),bt=Math.min(x.outWidth,(x.inWidth+L-gt)/C);for(let yt=0;yt<x.inChannels;++yt)for(let mt=0;mt<x.outChannels;++mt){let xt=0;for(let wt=0;wt<x.batchSize;++wt)for(let Tt=tt;Tt<ht;++Tt){let Dt=et+Tt*S-_;for(let $t=vt;$t<bt;++$t){let Lt=gt+$t*C-L;R?xt+=q.get(wt,Dt,Lt,yt)*j.get(wt,Tt,$t,mt):xt+=q.get(wt,yt,Dt,Lt)*j.get(wt,mt,Tt,$t)}}A.set(xt,et,gt,yt,mt)}}}return e.makeTensorInfo(A.shape,A.dtype,A.values)}let iK={kernelName:Ov,backendName:"cpu",kernelFunc:aK};function uK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:s,filter:u}=t,{inputShape:l,strides:h,pad:p,dataFormat:m,dimRoundingMode:y}=r;At([s,u],"conv2dBackpropInput");let b=Jt(u.shape),x=Jt(s.shape),S=ua(m),C=Kn(l,u.shape,h,1,p,y,!1,S),I=new bn(C.inShape,"float32"),D=I.values,R=e.data.get(s.dataId).values,A=e.data.get(u.dataId).values,[L,_,B]=b,{batchSize:V,filterHeight:q,filterWidth:j,inChannels:et,inHeight:tt,inWidth:ht,outChannels:gt,outHeight:vt,outWidth:bt,strideHeight:yt,strideWidth:mt}=C;S=C.dataFormat;let xt=q-1-C.padInfo.top,wt=j-1-C.padInfo.left,Tt=S==="channelsLast",Dt=I.strides[0],$t=Tt?I.strides[1]:I.strides[2],Lt=Tt?I.strides[2]:1,Pt=Tt?1:I.strides[1],Ft=x[0],Vt=Tt?x[1]:x[2],ee=Tt?x[2]:1,pe=Tt?1:x[1];for(let we=0;we<V;++we)for(let de=0;de<et;++de)for(let Ie=0;Ie<tt;++Ie){let be=Ie-xt,Je=Math.max(0,Math.ceil(be/yt)),ln=Math.min(vt,(q+be)/yt);for(let hn=0;hn<ht;++hn){let Me=hn-wt,Ze=Math.max(0,Math.ceil(Me/mt)),Ve=Math.min(bt,(j+Me)/mt),Un=0;for(let Gn=Je;Gn<ln;++Gn){let $s=Gn*yt-be;for(let qr=Ze;qr<Ve;++qr){let _i=qr*mt-Me,As=Ft*we+Vt*Gn+ee*qr,no=L*(q-1-$s)+_*(j-1-_i)+B*de;for(let Ia=0;Ia<gt;++Ia){let Ea=R[As+pe*Ia],Da=A[no+Ia];Un+=Ea*Da}}}let Po=Dt*we+$t*Ie+Lt*hn+Pt*de;D[Po]=Un}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let cK={kernelName:vp,backendName:"cpu",kernelFunc:uK};function lK(n){let{inputs:t,backend:e,attrs:r}=n,{x:s,filter:u}=t,{strides:l,pad:h,dilations:p}=r;At([s,u],"conv3d");let m=aa(s.shape,u.shape,l,p,h),{filterDepth:y,filterHeight:b,filterWidth:x,dilationDepth:S,dilationHeight:C,dilationWidth:I,padInfo:D}=m,R=D.front,A=D.left,L=D.top,_=new bn(m.outShape,s.dtype),B=e.data.get(s.dataId).values,V=e.data.get(u.dataId).values,q=_.values,j=Jt(s.shape),et=Jt(u.shape);for(let tt=0;tt<m.batchSize;++tt){let ht=tt*j[0],gt=tt*_.strides[0];for(let vt=0;vt<m.outDepth;++vt){let bt=gt+vt*_.strides[1],yt=vt*m.strideDepth-R;for(let mt=0;mt<y;++mt){let xt=yt+mt*S;if(xt<0||xt>=m.inDepth)continue;let wt=mt*et[0],Tt=ht+xt*j[1];for(let Dt=0;Dt<m.outHeight;++Dt){let $t=bt+Dt*_.strides[2],Lt=Dt*m.strideHeight-L;for(let Pt=0;Pt<b;++Pt){let Ft=Lt+Pt*C;if(Ft<0||Ft>=m.inHeight)continue;let Vt=wt+Pt*et[1],ee=Tt+Ft*j[2];for(let pe=0;pe<m.outWidth;++pe){let we=$t+pe*m.outChannels,de=pe*m.strideWidth-A;for(let Ie=0;Ie<x;++Ie){let be=de+Ie*I;if(be<0||be>=m.inWidth)continue;let Je=Vt+Ie*et[2],ln=ee+be*m.inChannels,hn=Je;for(let Me=0;Me<m.inChannels;++Me){let Ze=B[ln+Me];for(let Ve=0;Ve<m.outChannels;++Ve)q[we+Ve]+=Ze*V[hn+Ve];hn+=m.outChannels}}}}}}}}return e.makeTensorInfo(_.shape,_.dtype,_.values)}let hK={kernelName:yp,backendName:"cpu",kernelFunc:lK};function fK(n){let{inputs:t,backend:e,attrs:r}=n,{x:s,dy:u}=t,{strides:l,pad:h,filterShape:p}=r;At([s,u],"conv3dBackpropFilterV2");let m=Jt(s.shape),y=Jt(u.shape),b=aa(s.shape,p,l,1,h),x=b.strideDepth,S=b.strideHeight,C=b.strideWidth,I=b.filterDepth,D=b.filterHeight,R=b.filterWidth,A=new bn(b.filterShape,"float32"),L=A.values,[_,B,V,q]=A.strides,j=e.data.get(u.dataId).values,[et,tt,ht,gt]=y,vt=e.data.get(s.dataId).values,[bt,yt,mt,xt]=m,wt=b.padInfo.front,Tt=b.padInfo.left,Dt=b.padInfo.top;for(let $t=0;$t<I;++$t){let Lt=Math.max(0,Math.ceil((wt-$t)/x)),Pt=Math.min(b.outDepth,(b.inDepth+wt-$t)/x),Ft=$t*_;for(let Vt=0;Vt<D;++Vt){let ee=Math.max(0,Math.ceil((Dt-Vt)/S)),pe=Math.min(b.outHeight,(b.inHeight+Dt-Vt)/S),we=Vt*B+Ft;for(let de=0;de<R;++de){let Ie=Math.max(0,Math.ceil((Tt-de)/C)),be=Math.min(b.outWidth,(b.inWidth+Tt-de)/C),Je=de*V+we;for(let ln=0;ln<b.inChannels;++ln){let hn=ln*q+Je;for(let Me=0;Me<b.outChannels;++Me){let Ze=0;for(let Ve=0;Ve<b.batchSize;++Ve){let Un=Ve*bt,Po=Ve*et;for(let Gn=Lt;Gn<Pt;++Gn){let $s=$t+Gn*x-wt,qr=$s*yt+Un,_i=Gn*tt+Po;for(let As=ee;As<pe;++As){let no=Vt+As*S-Dt,Ia=no*mt+qr,Ea=As*ht+_i;for(let Da=Ie;Da<be;++Da){let yc=de+Da*C-Tt,mx=yc*xt+Ia,gx=Da*gt+Ea;Ze+=vt[mx+ln]*j[gx+Me]}}}}L[hn+Me]=Ze}}}}}return e.makeTensorInfo(A.shape,A.dtype,A.values)}let pK={kernelName:Mv,backendName:"cpu",kernelFunc:fK};function dK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:s,filter:u}=t,{pad:l,strides:h,inputShape:p}=r;At([s],"conv3dBackpropInputV2");let m=Jt(s.shape),y=Jt(u.shape),b=aa(p,u.shape,h,1,l),x=new bn(b.inShape,"float32"),S=x.values,[C,I,D,R]=x.strides,A=e.data.get(s.dataId).values,[L,_,B,V]=m,q=e.data.get(u.dataId).values,[j,et,tt,ht]=y,{batchSize:gt,filterDepth:vt,filterHeight:bt,filterWidth:yt,inChannels:mt,inDepth:xt,inHeight:wt,inWidth:Tt,outChannels:Dt,outDepth:$t,outHeight:Lt,outWidth:Pt,strideDepth:Ft,strideHeight:Vt,strideWidth:ee}=b,pe=vt-1-b.padInfo.front,we=bt-1-b.padInfo.top,de=yt-1-b.padInfo.left;for(let Ie=0;Ie<gt;++Ie)for(let be=0;be<mt;++be)for(let Je=0;Je<xt;++Je){let ln=Je-pe,hn=Math.max(0,Math.ceil(ln/Ft)),Me=Math.min($t,(vt+ln)/Ft);for(let Ze=0;Ze<wt;++Ze){let Ve=Ze-we,Un=Math.max(0,Math.ceil(Ve/Vt)),Po=Math.min(Lt,(bt+Ve)/Vt);for(let Gn=0;Gn<Tt;++Gn){let $s=Gn-de,qr=Math.max(0,Math.ceil($s/ee)),_i=Math.min(Pt,(yt+$s)/ee),As=0;for(let no=hn;no<Me;++no){let Ia=no*Ft-ln;for(let Ea=Un;Ea<Po;++Ea){let Da=Ea*Vt-Ve;for(let yc=qr;yc<_i;++yc){let mx=yc*ee-$s,gx=L*Ie+_*no+B*Ea+V*yc,fZ=j*(vt-1-Ia)+et*(bt-1-Da)+tt*(yt-1-mx)+ht*be;for(let Ym=0;Ym<Dt;++Ym){let pZ=A[gx+Ym],dZ=q[fZ+Ym];As+=pZ*dZ}}}}S[C*Ie+I*Je+D*Ze+R*Gn+be]=As}}}return e.makeTensorInfo(x.shape,x.dtype,x.values)}let mK={kernelName:Lv,backendName:"cpu",kernelFunc:dK};let gK=Pe(du,n=>Math.cos(n)),vK={kernelName:du,backendName:"cpu",kernelFunc:gK};let yK=Pe(Sl,n=>Math.cosh(n)),bK={kernelName:Sl,backendName:"cpu",kernelFunc:yK};function FE(n){let{inputs:t,backend:e,attrs:r}=n,{x:s,filter:u}=t,{strides:l,pad:h,dilations:p,dimRoundingMode:m}=r;At([s,u],"depthwiseConv2DNative");let y=Jt(s.shape),b=Jt(u.shape),x=p;x==null&&(x=[1,1]),k(wn(l,x),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${x}'`);let S=Kn(s.shape,u.shape,l,x,h,m,!0),{filterHeight:C,filterWidth:I,dilationHeight:D,dilationWidth:R,padInfo:A}=S,L=A.left,_=A.top,B=S.outChannels/S.inChannels,V=new bn(S.outShape,s.dtype),q=e.data.get(s.dataId).values,j=e.data.get(u.dataId).values,et=V.values;for(let tt=0;tt<S.batchSize;++tt){let ht=tt*y[0],gt=tt*V.strides[0];for(let vt=0;vt<S.outHeight;++vt){let bt=gt+vt*V.strides[1],yt=vt*S.strideHeight-L;for(let mt=0;mt<C;++mt){let xt=yt+mt*D;if(xt<0||xt>=S.inHeight)continue;let wt=mt*b[0],Tt=ht+xt*y[1];for(let Dt=0;Dt<S.outWidth;++Dt){let $t=bt+Dt*V.strides[2],Lt=Dt*S.strideWidth-_;for(let Pt=0;Pt<I;++Pt){let Ft=Lt+Pt*R;if(Ft<0||Ft>=S.inWidth)continue;let Vt=wt+Pt*b[1],ee=Tt+Ft*S.inChannels,pe=$t,we=Vt;for(let de=0;de<S.inChannels;++de){let Ie=q[ee+de];for(let be=0;be<B;++be)et[pe+be]+=Ie*j[we+be];pe+=B,we+=B}}}}}}return e.makeTensorInfo(V.shape,V.dtype,V.values)}let wK={kernelName:bp,backendName:"cpu",kernelFunc:FE};function xK(n){let{inputs:t,backend:e,attrs:r}=n,{x:s,dy:u}=t,{strides:l,dilations:h,pad:p,dimRoundingMode:m,filterShape:y}=r;At([s,u],"depthwiseConv2dNativeBackpropFilter");let b=Kn(s.shape,y,l,h,p,m,!0),{strideHeight:x,strideWidth:S,filterHeight:C,filterWidth:I}=b,D=new bn(b.filterShape,"float32"),R=b.padInfo.left,A=b.padInfo.top,L=b.outChannels/b.inChannels,_=e.data.get(s.dataId).values,B=new bn(s.shape,s.dtype,_),V=e.data.get(u.dataId).values,q=new bn(u.shape,u.dtype,V);for(let j=0;j<C;++j){let et=Math.max(0,Math.ceil((A-j)/x)),tt=Math.min(b.outHeight,(b.inHeight+A-j)/x);for(let ht=0;ht<I;++ht){let gt=Math.max(0,Math.ceil((R-ht)/S)),vt=Math.min(b.outWidth,(b.inWidth+R-ht)/S);for(let bt=0;bt<b.outChannels;++bt){let yt=Math.trunc(bt/L),mt=bt%L,xt=0;for(let wt=0;wt<b.batchSize;++wt)for(let Tt=et;Tt<tt;++Tt){let Dt=j+Tt*x-A;for(let $t=gt;$t<vt;++$t){let Lt=ht+$t*S-R;xt+=B.get(wt,Dt,Lt,yt)*q.get(wt,Tt,$t,bt)}}D.set(xt,j,ht,yt,mt)}}}return e.makeTensorInfo(D.shape,D.dtype,D.values)}let TK={kernelName:zv,backendName:"cpu",kernelFunc:xK};function kK(n){let{inputs:t,backend:e,attrs:r}=n,{dy:s,filter:u}=t,{strides:l,dilations:h,pad:p,dimRoundingMode:m,inputShape:y}=r;At([s,u],"depthwiseConv2DNativeBackpropInput");let b=Jt(s.shape),x=Jt(u.shape),S=Kn(y,u.shape,l,h,p,m,!0),C=new bn(S.inShape,"float32"),I=C.values,[D,R,A]=C.strides,L=e.data.get(s.dataId).values,[_,B,V]=b,q=e.data.get(u.dataId).values,[j,et,tt]=x,{batchSize:ht,filterHeight:gt,filterWidth:vt,inChannels:bt,inHeight:yt,inWidth:mt,outChannels:xt,outHeight:wt,outWidth:Tt,strideHeight:Dt,strideWidth:$t}=S,Lt=gt-1-S.padInfo.top,Pt=vt-1-S.padInfo.left,Ft=xt/bt;for(let Vt=0;Vt<ht;++Vt)for(let ee=0;ee<bt;++ee)for(let pe=0;pe<yt;++pe){let we=pe-Lt,de=Math.max(0,Math.ceil(we/Dt)),Ie=Math.min(wt,(gt+we)/Dt);for(let be=0;be<mt;++be){let Je=be-Pt,ln=Math.max(0,Math.ceil(Je/$t)),hn=Math.min(Tt,(vt+Je)/$t),Me=0;for(let Ze=de;Ze<Ie;++Ze){let Ve=Ze*Dt-we;for(let Un=ln;Un<hn;++Un){let Po=Un*$t-Je,Gn=_*Vt+B*Ze+V*Un,$s=j*(gt-1-Ve)+et*(vt-1-Po)+tt*ee;for(let qr=0;qr<Ft;++qr){let _i=ee*Ft+qr,As=L[Gn+_i],no=q[$s+qr];Me+=As*no}}}I[D*Vt+R*pe+A*be+ee]=Me}}return e.makeTensorInfo(C.shape,C.dtype,C.values)}let SK={kernelName:Wv,backendName:"cpu",kernelFunc:kK};let CK={kernelName:wp,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:s}=n,{strides:u,pad:l,dilations:h}=e,p=t,m=p.data.get(r.dataId).values,y=r.shape.length,b=p.data.get(s.dataId).values,x=s.shape.length,{batchSize:S,inHeight:C,inWidth:I,inChannels:D,outHeight:R,outWidth:A,padInfo:L,strideHeight:_,strideWidth:B,filterHeight:V,filterWidth:q,dilationHeight:j,dilationWidth:et,outShape:tt}=ld(r.shape,s.shape,u,l,"NHWC",h),ht=O(tt),gt=tt.length,vt=Kt(r.dtype,ht);for(let yt=0;yt<S;++yt)for(let mt=0;mt<R;++mt){let xt=mt*_-L.top;for(let wt=0;wt<A;++wt){let Tt=wt*B-L.left;for(let Dt=0;Dt<D;++Dt){let $t=Number.MIN_SAFE_INTEGER;for(let Pt=0;Pt<V;++Pt){let Ft=xt+Pt*j;if(Ft>=0&&Ft<C)for(let Vt=0;Vt<q;++Vt){let ee=Tt+Vt*et;if(ee>=0&&ee<I){let pe=Qr([yt,Ft,ee,Dt],y,Jt(r.shape)),we=Qr([Pt,Vt,Dt],x,Jt(s.shape)),de=m[pe]+b[we];de>$t&&($t=de)}}}let Lt=Qr([yt,mt,wt,Dt],gt,Jt(tt));vt[Lt]=$t}}}let bt=p.write(na(vt,r.dtype),tt,r.dtype);return{dataId:bt,shape:tt,dtype:r.dtype}}};let NK={kernelName:Tp,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:s,dy:u}=n,{strides:l,pad:h,dilations:p}=e,m=t,y=Mr(r.shape,m.data.get(r.dataId).values),b=Mr(s.shape,m.data.get(s.dataId).values),{batchSize:x,inHeight:S,inWidth:C,inChannels:I,outHeight:D,outWidth:R,padInfo:A,strideHeight:L,strideWidth:_,filterHeight:B,filterWidth:V,dilationHeight:q,dilationWidth:j,outShape:et}=ld(r.shape,s.shape,l,h,"NHWC",p);k(u.rank===et.length,()=>`Error in ${Tp}, dy must have the same rank as output ${et.length}, but got ${u.rank}`);let tt=Mr(et,m.data.get(u.dataId).values),ht=Iv(s.shape,s.dtype);for(let vt=0;vt<x;++vt)for(let bt=0;bt<D;++bt){let yt=bt*L-A.top;for(let mt=0;mt<R;++mt){let xt=mt*_-A.left;for(let wt=0;wt<I;++wt){let Tt=Number.MIN_SAFE_INTEGER,Dt=0,$t=0;for(let Lt=0;Lt<B;++Lt){let Pt=yt+Lt*q;if(Pt>=0&&Pt<S)for(let Ft=0;Ft<V;++Ft){let Vt=xt+Ft*j;if(Vt>=0&&Vt<C){let ee=y[vt][Pt][Vt][wt]+b[Lt][Ft][wt];ee>Tt&&(Tt=ee,Dt=Lt,$t=Ft)}}}ht[Dt][$t][wt]+=tt[vt][bt][mt][wt]}}}let gt=m.write(na(ht,r.dtype),s.shape,s.dtype);return{dataId:gt,shape:s.shape,dtype:s.dtype}}};let IK={kernelName:xp,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:s,dy:u}=n,{strides:l,pad:h,dilations:p}=e,m=t,y=Mr(r.shape,m.data.get(r.dataId).values),b=Mr(s.shape,m.data.get(s.dataId).values),{batchSize:x,inHeight:S,inWidth:C,inChannels:I,outHeight:D,outWidth:R,padInfo:A,strideHeight:L,strideWidth:_,filterHeight:B,filterWidth:V,dilationHeight:q,dilationWidth:j,outShape:et}=ld(r.shape,s.shape,l,h,"NHWC",p);k(u.rank===et.length,()=>`Error in ${xp}, dy must have the same rank as output ${et.length}, but got ${u.rank}`);let tt=Mr(et,m.data.get(u.dataId).values),ht=Iv(r.shape,r.dtype);for(let vt=0;vt<x;++vt)for(let bt=0;bt<D;++bt){let yt=bt*L-A.top;for(let mt=0;mt<R;++mt){let xt=mt*_-A.left;for(let wt=0;wt<I;++wt){let Tt=Number.MIN_SAFE_INTEGER,Dt=yt<0?0:yt,$t=xt<0?0:xt;for(let Lt=0;Lt<B;++Lt){let Pt=yt+Lt*q;if(Pt>=0&&Pt<S)for(let Ft=0;Ft<V;++Ft){let Vt=xt+Ft*j;if(Vt>=0&&Vt<C){let ee=y[vt][Pt][Vt][wt]+b[Lt][Ft][wt];ee>Tt&&(Tt=ee,Dt=Pt,$t=Vt)}}}ht[vt][Dt][$t][wt]+=tt[vt][bt][mt][wt]}}}let gt=m.write(na(ht,r.dtype),r.shape,r.dtype);return{dataId:gt,shape:r.shape,dtype:r.dtype}}};let EK=Ca((n,t)=>n/t),DK=sc(mu,EK),Q0={kernelName:mu,backendName:"cpu",kernelFunc:DK};let $K=zb,AK=Wb,_K=Vb,FK=Ub,RK=Gb,PK=Hb,OK=Pe(Nl,n=>{let t=Math.sign(n),e=Math.abs(n),r=1/(1+$K*e);return t*(1-((((PK*r+RK)*r+FK)*r+_K)*r+AK)*r*Math.exp(-e*e))}),MK={kernelName:Nl,backendName:"cpu",kernelFunc:OK};function RE(n,t,e){let r=n.shape,s=r[0],u=r[1],l=e.data.get(n.dataId),h=l.complexTensorInfos.real,p=l.complexTensorInfos.imag,m=[s,u],y=O(m),b=_t("float32",y),x=_t("float32",y);for(let D=0;D<s;D++){let R=X0({inputs:{x:h},backend:e,attrs:{begin:[D,0],size:[1,u]}}),A=X0({inputs:{x:p},backend:e,attrs:{begin:[D,0],size:[1,u]}}),L=Ds({inputs:{real:R,imag:A},backend:e}),{real:_,imag:B}=LK(L,t,e),V=So(_,B);for(let q=0;q<u;q++){let j=qb(V,q);b[D*u+q]=j.real,x[D*u+q]=j.imag}e.disposeIntermediateTensorInfo(R),e.disposeIntermediateTensorInfo(A),e.disposeIntermediateTensorInfo(L)}let S=e.makeTensorInfo(m,"float32",b),C=e.makeTensorInfo(m,"float32",x),I=Ds({inputs:{real:S,imag:C},backend:e});return e.disposeIntermediateTensorInfo(S),e.disposeIntermediateTensorInfo(C),I}function LK(n,t,e){let r=O(n.shape),s=e.data.get(n.dataId),u=e.data.get(s.complexTensorInfos.real.dataId).values,l=e.data.get(s.complexTensorInfos.imag.dataId).values;if(BK(r)){let h=tx(u,l,r,t,e),p=[n.shape[0],n.shape[1]];if(t){let 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+  author: <https://github.com/vladmandic>'
+  */
 
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-          if (candidate ${h} bestValue) {
-            bestValue = candidate;
-            bestIndex = inIdx;
-          }
-        }
-        setOutput(float(bestIndex));
-      }
-    `}}function VE(n,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${n}.${e}`)}function nr(n,t){return t===1?[n]:VE(n,t)}function tX(n,t){if(n===1)return"rc";let e="";for(let r=0;r<n;r++)e+=t[r],r<n-1&&(e+=",");return e}function rr(){let n,t,e,r,s,u,l,h,p,m;return ft().getNumber("WEBGL_VERSION")===2?(n="#version 300 es",t="in",e="out",r="in",s="texture",u="outputColor",l="out vec4 outputColor;",h=`
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-
-    highp float ebias = e + 127.0;
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-    c[2] += floor(ebias) * 128.0;
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-    return c / 255.0;
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-      int x;
-      int y;
-      int z;
-      int w;
-      int u;
-    };
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-    struct ivec6
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-      int x;
-      int y;
-      int z;
-      int w;
-      int u;
-      int v;
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-    uniform float NAN;
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-    ${n.defineSpecialInf}
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-    int imod(int x, int y) {
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-    int idiv(int a, int b, float sign) {
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-        res -= 1;
-      }
-      return res;
-    }
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-vec2 uvFromFlat(int texNumR, int texNumC, int index) {
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-}
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-  int texelIndex = index / 2;
-  int texR = texelIndex / texNumC;
-  int texC = texelIndex - texR * texNumC;
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-}
-`,lX=`
-vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
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-  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
-  int texR = texelIndex / texNumC;
-  int texC = texelIndex - texR * texNumC;
-  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
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-  int texR = index / texNumC;
-  int texC = index - texR * texNumC;
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-}
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-  float getChannel(vec4 frag, vec2 innerDims) {
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-      (modCoord.y == 0. ? frag.b : frag.a);
-  }
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-    float modCoord = mod(float(dim), 2.);
-    return modCoord == 0. ? frag.r : frag.g;
-  }
-`;function qE(){return`
-    int getOutputCoords() {
-      return 0;
-    }
-  `}function pX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return e[0]===1?`
-      int getOutputCoords() {
-        return 2 * int(resultUV.x * ${e[1]}.0);
-      }
-    `:e[1]===1?`
-      int getOutputCoords() {
-        return 2 * int(resultUV.y * ${e[0]}.0);
-      }
-    `:`
-    int getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
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-      return 2 * (resTexRC.x * ${e[1]} + resTexRC.y);
-    }
-  `}function dX(n,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 *
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-      return resTexRC.x * ${t[1]} + resTexRC.y;
-    }
-  `}function mX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[2]/2),s=r*Math.ceil(n[1]/2);return`
-    ivec3 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
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-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
-
-      int b = index / ${s};
-      index -= b * ${s};
-
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
-
-      return ivec3(b, r, c);
-    }
-  `}function gX(n,t){let e=$i(["r","c","d"],n);return`
-    ivec3 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      ${e}
-      return ivec3(r, c, d);
-    }
-  `}function vX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[n.length-1]/2),s=r*Math.ceil(n[n.length-2]/2),u=s,l="",h="b, r, c";for(let p=2;p<n.length-1;p++)u*=n[n.length-p-1],l=`
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-    `+l,h=`b${p}, `+h;return`
-    ivec${n.length} getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
-
-      ${l}
-
-      int b = index / ${s};
-      index -= b * ${s};
-
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
-
-      return ivec${n.length}(${h});
-    }
-  `}function yX(n,t){let e=$i(["r","c","d","d2"],n);return`
-    ivec4 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      ${e}
-      return ivec4(r, c, d, d2);
-    }
-  `}function bX(n,t){let e=$i(["r","c","d","d2","d3"],n);return`
-    ivec5 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
-                             ${t[1]}));
-
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-
-      ${e}
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-      ivec5 outShape = ivec5(r, c, d, d2, d3);
-      return outShape;
-    }
-  `}function wX(n,t){let e=$i(["r","c","d","d2","d3","d4"],n);return`
-    ivec6 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-        vec2(${t[0]}, ${t[1]}));
-      int index = resTexRC.x * ${t[1]} + resTexRC.y;
-
-      ${e}
-
-      ivec6 result = ivec6(r, c, d, d2, d3, d4);
-      return result;
-    }
-  `}function xX(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(K(n,t))return`
-      ivec2 getOutputCoords() {
-        return 2 * ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
-      }
-    `;let r=Math.ceil(n[1]/2);return`
-    ivec2 getOutputCoords() {
-      ivec2 resTexRC = ivec2(resultUV.yx *
-                             vec2(${e[0]}, ${e[1]}));
-
-      int index = resTexRC.x * ${e[1]} + resTexRC.y;
-      int r = 2 * (index / ${r});
-      int c = imod(index, ${r}) * 2;
-
-      return ivec2(r, c);
-    }
-  `}function TX(n,t){return K(n,t)?`
-      ivec2 getOutputCoords() {
-        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
-      }
-    `:n[1]===1?`
-      ivec2 getOutputCoords() {
-        ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(${t[0]}, ${t[1]}));
-        int index = resTexRC.x * ${t[1]} + resTexRC.y;
-        return ivec2(index, 0);
-      }
-    `:n[0]===1?`
-      ivec2 getOutputCoords() {
-        ivec2 resTexRC = ivec2(resultUV.yx *
-                               vec2(${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 / ${n[1]};
-      int c = index - r * ${n[1]};
-      return ivec2(r, c);
-    }
-  `}function Ai(n){return`offset${n}`}function kX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=rr();return`
-    vec4 ${e}() {
-      return ${r.texture2D}(${t}, halfCR);
-    }
-  `}function SX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`float ${e}() {return ${t};}`;let[r,s]=n.shapeInfo.texShape;if(r===1&&s===1)return`
-      float ${e}() {
-        return sampleTexture(${t}, halfCR);
-      }
-    `;let[u,l]=n.shapeInfo.texShape,h=Ai(t);return`
-    float ${e}() {
-      vec2 uv = uvFromFlat(${u}, ${l}, ${h});
-      return sampleTexture(${t}, uv);
-    }
-  `}function CX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=n.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],u=rr();return`
-    vec4 ${e}(int index) {
-      vec2 uv = packedUVfrom1D(
-        ${s[0]}, ${s[1]}, index);
-      return ${u.texture2D}(${t}, uv);
-    }
-  `}function NX(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`
-      float ${e}(int index) {
-        ${hc(n)}
-      }
-    `;let r=n.shapeInfo.texShape,s=r[0],u=r[1];if(u===1&&s===1)return`
-      float ${e}(int index) {
-        return sampleTexture(${t}, halfCR);
-      }
-    `;let l=Ai(t);return u===1?`
-      float ${e}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${l}) + 0.5) / ${s}.0);
-        return sampleTexture(${t}, uv);
-      }
-    `:s===1?`
-      float ${e}(int index) {
-        vec2 uv = vec2((float(index + ${l}) + 0.5) / ${u}.0, 0.5);
-        return sampleTexture(${t}, uv);
-      }
-    `:`
-    float ${e}(int index) {
-      vec2 uv = uvFromFlat(${s}, ${u}, index + ${l});
-      return sampleTexture(${t}, uv);
-    }
-  `}function IX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),s=n.shapeInfo.texShape,u=s[0],l=s[1],h=rr();if(s!=null&&K(t,s))return`
-      vec4 ${r}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${l}.0, ${u}.0);
-
-        return ${h.texture2D}(${e}, uv);
-      }
-    `;let p=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],m=Math.ceil(t[1]/2);return`
-    vec4 ${r}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${m}, ${p[0]}, ${p[1]}, row, col);
-      return ${h.texture2D}(${e}, uv);
-    }
-  `}function EX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),s=n.shapeInfo.texShape;if(s!=null&&K(t,s)){let b=s[0],x=s[1];return`
-    float ${r}(int row, int col) {
-      vec2 uv = (vec2(col, row) + halfCR) / vec2(${x}.0, ${b}.0);
-      return sampleTexture(${e}, uv);
-    }
-  `}let{newShape:u,keptDims:l}=Wt(t),h=u;if(h.length<t.length){let b=fc(n,h),x=["row","col"];return`
-      ${lc(b)}
-      float ${r}(int row, int col) {
-        return ${r}(${pc(x,l)});
-      }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col) {
-        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
-        ${hc(n)}
-      }
-    `;let p=s[0],m=s[1],y=Ai(e);return m===1?`
-    float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${y}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2(0.5, (index + 0.5) / ${p}.0);
-      return sampleTexture(${e}, uv);
-    }
-  `:p===1?`
-    float ${r}(int row, int col) {
-      float index = dot(vec3(row, col, ${y}), vec3(${t[1]}, 1, 1));
-      vec2 uv = vec2((index + 0.5) / ${m}.0, 0.5);
-      return sampleTexture(${e}, uv);
-    }
-  `:`
-  float ${r}(int row, int col) {
-    // Explicitly use integer operations as dot() only works on floats.
-    int index = row * ${t[1]} + col + ${y};
-    vec2 uv = uvFromFlat(${p}, ${m}, index);
-    return sampleTexture(${e}, uv);
-  }
-`}function DX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),s=n.shapeInfo.texShape,u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(t[0]===1){let b=t.slice(1),x=[1,2],S=fc(n,b),C=["b","row","col"];return`
-        ${HE(S)}
-        vec4 ${r}(int b, int row, int col) {
-          return ${r}(${pc(C,x)});
-        }
-      `}let l=u[0],h=u[1],p=Math.ceil(t[2]/2),m=p*Math.ceil(t[1]/2),y=rr();return`
-    vec4 ${r}(int b, int row, int col) {
-      vec2 uv = packedUVfrom3D(
-        ${l}, ${h}, ${m}, ${p}, b, row, col);
-      return ${y.texture2D}(${e}, uv);
-    }
-  `}function $X(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),s=t[1]*t[2],u=t[2],{newShape:l,keptDims:h}=Wt(t),p=l;if(p.length<t.length){let C=fc(n,p),I=["row","col","depth"];return`
-        ${lc(C)}
-        float ${r}(int row, int col, int depth) {
-          return ${r}(${pc(I,h)});
-        }
-      `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth) {
-        int index = round(dot(vec3(row, col, depth),
-                          vec3(${s}, ${u}, 1)));
-        ${hc(n)}
-      }
-    `;let m=n.shapeInfo.texShape,y=m[0],b=m[1],x=n.shapeInfo.flatOffset;if(b===s&&x==null)return`
-        float ${r}(int row, int col, int depth) {
-          float texR = float(row);
-          float texC = dot(vec2(col, depth), vec2(${u}, 1));
-          vec2 uv = (vec2(texC, texR) + halfCR) /
-                     vec2(${b}.0, ${y}.0);
-          return sampleTexture(${e}, uv);
-        }
-      `;if(b===u&&x==null)return`
-    float ${r}(int row, int col, int depth) {
-      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
-      float texC = float(depth);
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${b}.0, ${y}.0);
-      return sampleTexture(${e}, uv);
-    }
-  `;let S=Ai(e);return`
-      float ${r}(int row, int col, int depth) {
-        // Explicitly use integer operations as dot() only works on floats.
-        int index = row * ${s} + col * ${u} + depth + ${S};
-        vec2 uv = uvFromFlat(${y}, ${b}, index);
-        return sampleTexture(${e}, uv);
-      }
-  `}function AX(n){let t=n.shapeInfo.logicalShape,e=t.length,r=n.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),u=n.shapeInfo.texShape,l=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)],h=l[0],p=l[1],m=Math.ceil(t[e-1]/2),y=m*Math.ceil(t[e-2]/2),b="int b, int row, int col",x=`b * ${y} + (row / 2) * ${m} + (col / 2)`;for(let C=2;C<e-1;C++)b=`int b${C}, `+b,y*=t[e-C-1],x=`b${C} * ${y} + `+x;let S=rr();return`
-    vec4 ${s}(${b}) {
-      int index = ${x};
-      int texR = index / ${p};
-      int texC = index - texR * ${p};
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${h});
-      return ${S.texture2D}(${r}, uv);
-    }
-  `}function _X(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),s=t[3],u=t[2]*s,l=t[1]*u,{newShape:h,keptDims:p}=Wt(t);if(h.length<t.length){let C=fc(n,h),I=["row","col","depth","depth2"];return`
-      ${lc(C)}
-      float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${pc(I,p)});
-      }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth, int depth2) {
-        int index = round(dot(vec4(row, col, depth, depth2),
-                          vec4(${l}, ${u}, ${s}, 1)));
-        ${hc(n)}
-      }
-    `;let m=n.shapeInfo.flatOffset,y=n.shapeInfo.texShape,b=y[0],x=y[1];if(x===l&&m==null)return`
-      float ${r}(int row, int col, int depth, int depth2) {
-        float texR = float(row);
-        float texC =
-            dot(vec3(col, depth, depth2),
-                vec3(${u}, ${s}, 1));
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${x}.0, ${b}.0);
-        return sampleTexture(${e}, uv);
-      }
-    `;if(x===s&&m==null)return`
-      float ${r}(int row, int col, int depth, int depth2) {
-        float texR = dot(vec3(row, col, depth),
-                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
-        float texC = float(depth2);
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${x}.0, ${b}.0);
-        return sampleTexture(${e}, uv);
-      }
-    `;let S=Ai(e);return`
-    float ${r}(int row, int col, int depth, int depth2) {
-      // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${l} + col * ${u} +
-          depth * ${s} + depth2;
-      vec2 uv = uvFromFlat(${b}, ${x}, index + ${S});
-      return sampleTexture(${e}, uv);
-    }
-  `}function FX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),s=t[4],u=t[3]*s,l=t[2]*u,h=t[1]*l,{newShape:p,keptDims:m}=Wt(t);if(p.length<t.length){let I=fc(n,p),D=["row","col","depth","depth2","depth3"];return`
-      ${lc(I)}
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        return ${r}(${pc(D,m)});
-      }
-    `}if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        float index = dot(
-          vec4(row, col, depth, depth2),
-          vec4(${h}, ${l}, ${u}, ${s})) +
-          depth3;
-        ${hc(n)}
-      }
-    `;let y=n.shapeInfo.flatOffset,b=n.shapeInfo.texShape,x=b[0],S=b[1];if(S===h&&y==null)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        int texR = row;
-        float texC = dot(vec4(col, depth, depth2, depth3),
-                         vec4(${l}, ${u}, ${s}, 1));
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${S}.0, ${x}.0);
-        return sampleTexture(${e}, uv);
-      }
-    `;if(S===s&&y==null)return`
-      float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        float texR = dot(
-          vec4(row, col, depth, depth2),
-          vec4(${t[1]*t[2]*t[3]},
-               ${t[2]*t[3]}, ${t[3]}, 1));
-        int texC = depth3;
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${S}.0, ${x}.0);
-        return sampleTexture(${e}, uv);
-      }
-    `;let C=Ai(e);return`
-    float ${r}(int row, int col, int depth, int depth2, int depth3) {
-      // Explicitly use integer operations as dot() only works on floats.
-      int index = row * ${h} + col * ${l} + depth * ${u} +
-          depth2 * ${s} + depth3 + ${C};
-      vec2 uv = uvFromFlat(${x}, ${S}, index);
-      return sampleTexture(${e}, uv);
-    }
-  `}function RX(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:s,keptDims:u}=Wt(t);if(s.length<t.length){let D=fc(n,s),R=["row","col","depth","depth2","depth3","depth4"];return`
-      ${lc(D)}
-      float ${r}(int row, int col, int depth,
-                    int depth2, int depth3, int depth4) {
-        return ${r}(${pc(R,u)});
-      }
-    `}let l=t[5],h=t[4]*l,p=t[3]*h,m=t[2]*p,y=t[1]*m;if(n.shapeInfo.isUniform)return`
-      float ${r}(int row, int col, int depth,
-                  int depth2, int depth3, int depth4) {
-        int index = round(dot(
-          vec4(row, col, depth, depth2),
-          vec4(${y}, ${m}, ${p}, ${h})) +
-          dot(
-            vec2(depth3, depth4),
-            vec2(${l}, 1)));
-        ${hc(n)}
-      }
-    `;let b=n.shapeInfo.flatOffset,x=n.shapeInfo.texShape,S=x[0],C=x[1];if(C===y&&b==null)return`
-      float ${r}(int row, int col, int depth,
-                    int depth2, int depth3, int depth4) {
-        int texR = row;
-        float texC = dot(vec4(col, depth, depth2, depth3),
-          vec4(${m}, ${p}, ${h}, ${l})) +
-               float(depth4);
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                   vec2(${C}.0, ${S}.0);
-        return sampleTexture(${e}, uv);
-      }
-    `;if(C===l&&b==null)return`
-      float ${r}(int row, int col, int depth,
-                    int depth2, int depth3, int depth4) {
-        float texR = dot(vec4(row, col, depth, depth2),
-          vec4(${t[1]*t[2]*t[3]*t[4]},
-               ${t[2]*t[3]*t[4]},
-               ${t[3]*t[4]},
-               ${t[4]})) + float(depth3);
-        int texC = depth4;
-        vec2 uv = (vec2(texC, texR) + halfCR) /
-                  vec2(${C}.0, ${S}.0);
-        return sampleTexture(${e}, uv);
-      }
-    `;let I=Ai(e);return`
-    float ${r}(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 * ${y} + col * ${m} + depth * ${p} +
-          depth2 * ${h} + depth3 * ${l} + depth4 + ${I};
-      vec2 uv = uvFromFlat(${S}, ${C}, index);
-      return sampleTexture(${e}, uv);
-    }
-  `}function hc(n){let t=n.name,e=O(n.shapeInfo.logicalShape);return e<2?`return ${t};`:`
-    for (int i = 0; i < ${e}; i++) {
-      if (i == index) {
-        return ${t}[i];
-      }
-    }
-  `}function PX(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),s="get"+r+"AtOutCoords",u=n.shapeInfo.logicalShape.length,l=t.logicalShape.length,h=GE(n.shapeInfo.logicalShape,t.logicalShape),p=We(l),m=l-u,y,b=["x","y","z","w","u","v"];u===0?y="":l<2&&h.length>=1?y="coords = 0;":y=h.map(A=>`coords.${b[A+m]} = 0;`).join(`
-`);let x="";l<2&&u>0?x="coords":x=n.shapeInfo.logicalShape.map((A,L)=>`coords.${b[L+m]}`).join(", ");let S="return outputValue;",C=O(n.shapeInfo.logicalShape),I=C===1,D=O(t.logicalShape),R=D===1;if(u===1&&!I&&!R)S=`
-      return vec4(outputValue.xy, outputValue.xy);
-    `;else if(I&&!R)l===1?S=`
-        return vec4(outputValue.x, outputValue.x, 0., 0.);
-      `:S=`
-        return vec4(outputValue.x);
-      `;else if(h.length){let A=u-2,L=u-1;h.indexOf(A)>-1&&h.indexOf(L)>-1?S="return vec4(outputValue.x);":h.indexOf(A)>-1?S="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":h.indexOf(L)>-1&&(S="return vec4(outputValue.xx, outputValue.zz);")}return`
-    vec4 ${s}() {
-      ${p} coords = getOutputCoords();
-      ${y}
-      vec4 outputValue = get${r}(${x});
-      ${S}
-    }
-  `}function OX(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),s="get"+r+"AtOutCoords",u=t.texShape,l=n.shapeInfo.texShape,h=n.shapeInfo.logicalShape.length,p=t.logicalShape.length;if(!n.shapeInfo.isUniform&&h===p&&n.shapeInfo.flatOffset==null&&K(l,u))return`
-      float ${s}() {
-        return sampleTexture(${e}, resultUV);
-      }
-    `;let m=We(p),y=GE(n.shapeInfo.logicalShape,t.logicalShape),b=p-h,x,S=["x","y","z","w","u","v"];h===0?x="":p<2&&y.length>=1?x="coords = 0;":x=y.map(I=>`coords.${S[I+b]} = 0;`).join(`
-`);let C="";return p<2&&h>0?C="coords":C=n.shapeInfo.logicalShape.map((I,D)=>`coords.${S[D+b]}`).join(", "),`
-    float ${s}() {
-      ${m} coords = getOutputCoords();
-      ${x}
-      return get${r}(${C});
-    }
-  `}function We(n){if(n<=1)return"int";if(n===2)return"ivec2";if(n===3)return"ivec3";if(n===4)return"ivec4";if(n===5)return"ivec5";if(n===6)return"ivec6";throw Error(`GPU for rank ${n} is not yet supported`)}function fc(n,t){let e=JSON.parse(JSON.stringify(n));return e.shapeInfo.logicalShape=t,e}function pc(n,t){return t.map(e=>n[e]).join(", ")}class MX{constructor(t,e,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k(t.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let u=t[t.length-1],l=Math.ceil(u/e);this.outputShape=t.slice(0,-1),l>1&&this.outputShape.push(l),s||this.variableNames.push("bestIndicesA");let h=this.outputShape,p=h.length,m=We(p),y=nr("coords",p),b,x;if(l===1){x=p+1;let j=We(x);b=`
-        ${j} sourceLocR = ${j}(${y.join()}, 0);
-        ++${y[p-1]};
-        ${j} sourceLocG = ${j}(${y.join()}, 0);
-        ++${y[p-2]};
-        ${j} sourceLocA = ${j}(${y.join()}, 0);
-        --${y[p-1]};
-        ${j} sourceLocB = ${j}(${y.join()}, 0);
-        --${y[p-2]};`}else x=p,b=`
-        ${m} sourceLocR = coords;
-        ++${y[p-1]};
-        ${m} sourceLocG = coords;
-        ++${y[p-2]};
-        ${m} sourceLocA = coords;
-        --${y[p-1]};
-        ${m} sourceLocB = coords;
-        --${y[p-2]};`;let S=["x","y","z","w","u","v"].slice(0,x),C="."+S[x-1],I=S.map(j=>"int "+j),D=nr("sourceLocR",x-1).concat("inIdx.r"),R=nr("sourceLocG",x-1).concat("inIdx.g"),A=nr("sourceLocB",x-1).concat("inIdx.b"),L=nr("sourceLocA",x-1).concat("inIdx.a"),_=r==="max"?"greaterThan":"lessThan",B=s?"":`
-          inIdx = round(vec4(getBestIndicesAChannel(${D.join()}),
-                             getBestIndicesAChannel(${R.join()}),
-                             getBestIndicesAChannel(${A.join()}),
-                             getBestIndicesAChannel(${L.join()})));`,V=`vec4(
-            getAChannel(${D.join()}),
-            hasNextCol ? getAChannel(${R.join()}) : 0.,
-            hasNextRow ? getAChannel(${A.join()}) : 0.,
-            hasNextRow && hasNextCol ? getAChannel(${L.join()}) : 0.)`,q=s?"":`
-      float getBestIndicesAChannel(${I.join()}) {
-        return getChannel(getBestIndicesA(${S.join()}),
-                                          vec2(${S.slice(-2).join()}));
-      }`;this.userCode=`
-      float getAChannel(${I.join()}) {
-        return getChannel(getA(${S.join()}),
-                               vec2(${S.slice(-2).join()}));
-      }
-      ${q}
-      void main() {
-        ${m} coords = getOutputCoords();
-        bool hasNextCol = ${y[p-1]} < ${h[p-1]-1};
-        bool hasNextRow = ${y[p-2]} < ${h[p-2]-1};
-        ${b}
-        ivec4 srcIdx = ivec4(sourceLocR${C}, sourceLocG${C},
-          sourceLocB${C}, sourceLocA${C}) * ${e};
-        ivec4 inIdx = srcIdx;
-        vec4 bestIndex = vec4(inIdx);
-        vec4 bestValue = ${V};
-
-        for (int i = 0; i < ${e}; i++) {
-          inIdx = srcIdx;
-          ${B}
-          vec4 candidate = ${V};
-          bvec4 nan = isnan(candidate);
-          bvec4 replace = bvec4(
-            vec4(${_}(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 LX{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,s=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,h=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,y=p-1-t.padInfo.top,b=m-1-t.padInfo.left,x=1/(e*r);this.userCode=`
-      const ivec2 pads = ivec2(${y}, ${b});
-      const float avgMultiplier = float(${x});
-
-      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 < ${p};
-            wR += ${l}) {
-          float dyR = float(dyRCorner + wR) / ${s}.0;
-
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-
-          for (int wC = 0; wC < ${m};
-            wC+= ${h}) {
-            float dyC = float(dyCCorner + wC) / ${u}.0;
-
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
-                fract(dyC) > 0.0) {
-              continue;
-            }
-            int idyC = int(dyC);
-
-            float dyValue = getDy(b, idyR, idyC, d);
-
-            dotProd += dyValue * avgMultiplier;
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}class BX{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,s=t.filterWidth,u=t.strideDepth,l=t.strideHeight,h=t.strideWidth,p=t.dilationDepth,m=t.dilationHeight,y=t.dilationWidth,b=t.effectiveFilterDepth,x=t.effectiveFilterHeight,S=t.effectiveFilterWidth,C=b-1-t.padInfo.front,I=x-1-t.padInfo.top,D=S-1-t.padInfo.left,R=1/(e*r*s);this.userCode=`
-      const ivec3 pads = ivec3(${C}, ${I}, ${D});
-      const float avgMultiplier = float(${R});
-
-      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 < ${b};
-            wD += ${p}) {
-          float dyD = float(dyDCorner + wD) / ${u}.0;
-
-          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
-            continue;
-          }
-          int idyD = int(dyD);
-
-          for (int wR = 0; wR < ${x};
-              wR += ${m}) {
-            float dyR = float(dyRCorner + wR) / ${l}.0;
-
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
-                fract(dyR) > 0.0) {
-              continue;
-            }
-            int idyR = int(dyR);
-
-            for (int wC = 0; wC < ${S};
-                wC += ${y}) {
-              float dyC = float(dyCCorner + wC) / ${h}.0;
-
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
-                  fract(dyC) > 0.0) {
-                continue;
-              }
-              int idyC = int(dyC);
-
-              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
-
-              dotProd += dyValue * avgMultiplier;
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}let jE=`
-  if (isnan(a)) return a;
-  if (isnan(b)) return b;
-`,zX=`
-  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;
-  }
-`,WX=`
-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);
-`,Uft="return (a - b) * (a - b);",VX="return float(a == b);",UX="return float(a < b);",GX="return float(a <= b);",HX="return float(a > b);",qX="return float(a >= b);",jX="return float(a >= 1.0 && b >= 1.0);",KX="return float(a >= 1.0 || b >= 1.0);",XX=jE+`
-  return max(a, b);
-`,YX=jE+`
-  return min(a, b);
-`,JX=`if (b == 0.0) return NAN;
-  return mod(a, b);`,ZX="return (b >= 1.0) ? a : a * (b + 1.0);",KE="return (a < 0.) ? b * a : a;";class Yn{constructor(t,e,r){this.variableNames=["A","B"],this.outputShape=ve(e,r),this.userCode=`
-      float binaryOperation(float a, float b) {
-        ${t}
-      }
-
-      void main() {
-        float a = getAAtOutCoords();
-        float b = getBAtOutCoords();
-        setOutput(binaryOperation(a, b));
-      }
-    `}}let Gm=`
-  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;
-`,QX=`
-  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);
-`,tY=`
-  // 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));
-  `+Gm+`
-  return result;
-`,XE=`
-  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
-  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`,eY=`
-  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
-  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,nY=`
-  return vec4(equal(a, b));
-`,Gft=`
-  return vec4(notEqual(a, b));
-`,rY=`
-  return vec4(lessThan(a, b));
-`,sY=`
-  return vec4(lessThanEqual(a, b));
-`,oY=`
-  return vec4(greaterThan(a, b));
-`,aY=`
-  return vec4(greaterThanEqual(a, b));
-`,iY=`
-  return vec4(
-    vec4(greaterThanEqual(a, vec4(1.0))) *
-    vec4(greaterThanEqual(b, vec4(1.0))));
-`,uY=`
-  return min(
-    vec4(greaterThanEqual(a, vec4(1.0))) +
-    vec4(greaterThanEqual(b, vec4(1.0))),
-    vec4(1.0));
-`,cY=`
-  vec4 result = vec4(max(a, b));
-  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+Gm+`
-  return result;
-`,lY=`
-  vec4 result = vec4(min(a, b));
-  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+Gm+`
-  return result;
-`,hY=`
-  vec4 result = mod(a, b);
-  vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+Gm+`
-  return result;
-`;class Ao{constructor(t,e,r,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=ve(e,r);let u=this.outputShape.length,l="";if(s)if(u===0||O(this.outputShape)===1)l=`
-          result.y = 0.;
-          result.z = 0.;
-          result.w = 0.;
-        `;else{let h=We(u);if(l=`
-          ${h} coords = getOutputCoords();
-        `,u===1)l+=`
-            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
-            result.z = 0.;
-            result.w = 0.;
-          `;else{let p=nr("coords",u);l+=`
-            bool nextRowOutOfBounds =
-              (${p[u-2]} + 1) >= ${this.outputShape[u-2]};
-            bool nextColOutOfBounds =
-              (${p[u-1]} + 1) >= ${this.outputShape[u-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) {
-        ${t}
-      }
-
-      void main() {
-        vec4 a = getAAtOutCoords();
-        vec4 b = getBAtOutCoords();
-
-        vec4 result = binaryOperation(a, b);
-        ${l}
-
-        setOutput(result);
-      }
-    `}}class fY{constructor(t){this.variableNames=["A"],this.outputShape=t,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(t,e){return(r,s)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(s,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class pY{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,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(t,e){return(r,s)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(s,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(s,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class dY{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,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 mY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,s=t.padInfo.top,u=t.padInfo.left,l=t.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 < ${t.batchSize}; b++) {
-          for (int yR = 0; yR < ${t.outHeight}; yR++) {
-            int xR = wR + yR * ${e} - ${s};
-
-            if (xR < 0 || xR >= ${t.inHeight}) {
-              continue;
-            }
-
-            for (int yC = 0; yC < ${t.outWidth}; yC++) {
-              int xC = wC + yC * ${r} - ${u};
-
-              if (xC < 0 || xC >= ${t.inWidth}) {
-                continue;
-              }
-
-              if (${l}) {
-                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 gY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,s=t.strideHeight,u=t.strideWidth,l=t.dataFormat==="channelsLast",h=e-1-t.padInfo.top,p=r-1-t.padInfo.left,m=l?1:2,y=l?2:3,b=l?3:1;this.userCode=`
-      const ivec2 pads = ivec2(${h}, ${p});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords[0];
-        int d1 = coords[${b}];
-
-        ivec2 dyCorner = ivec2(coords[${m}], coords[${y}]) - 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 < ${e}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${s}.0;
-
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-
-          int wRPerm = ${e} - 1 - wR;
-
-          for (int wC = 0; wC < ${r}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${u}.0;
-
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
-                fract(dyC) > 0.0) {
-              continue;
-            }
-            int idyC = int(dyC);
-
-            int wCPerm = ${r} - 1 - wC;
-
-            for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
-
-              if (${l}) {
-                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 vY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,r=t.strideHeight,s=t.strideWidth,u=t.padInfo.front,l=t.padInfo.top,h=t.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 < ${t.batchSize}; b++) {
-          for (int yF = 0; yF < ${t.outDepth}; yF++) {
-            int xF = wF + yF * ${e} - ${u};
-
-            if (xF < 0 || xF >= ${t.inDepth}) {
-              continue;
-            }
-
-            for (int yR = 0; yR < ${t.outHeight}; yR++) {
-              int xR = wR + yR * ${r} - ${l};
-
-              if (xR < 0 || xR >= ${t.inHeight}) {
-                continue;
-              }
-
-              for (int yC = 0; yC < ${t.outWidth}; yC++) {
-                int xC = wC + yC * ${s} - ${h};
-
-                if (xC < 0 || xC >= ${t.inWidth}) {
-                  continue;
-                }
-
-                float dyValue = getDy(b, yF, yR, yC, d2);
-                float xValue = getX(b, xF, xR, xC, d1);
-                dotProd += (xValue * dyValue);
-              }
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}class yY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,s=t.filterWidth,u=t.strideDepth,l=t.strideHeight,h=t.strideWidth,p=e-1-t.padInfo.front,m=r-1-t.padInfo.top,y=s-1-t.padInfo.left;this.userCode=`
-      const ivec3 pads = ivec3(${p}, ${m}, ${y});
-
-      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 < ${e}; wF++) {
-          float dyF = float(dyFCorner + wF) / ${u}.0;
-
-          if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
-            continue;
-          }
-          int idyF = int(dyF);
-
-          int wFPerm = ${e} - 1 - wF;
-
-          for (int wR = 0; wR < ${r}; wR++) {
-            float dyR = float(dyRCorner + wR) / ${l}.0;
-
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
-              fract(dyR) > 0.0) {
-              continue;
-            }
-            int idyR = int(dyR);
-
-            int wRPerm = ${r} - 1 - wR;
-
-            for (int wC = 0; wC < ${s}; wC++) {
-              float dyC = float(dyCCorner + wC) / ${h}.0;
-
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
-                  fract(dyC) > 0.0) {
-                continue;
-              }
-              int idyC = int(dyC);
-
-              int wCPerm = ${s} - 1 - wC;
-
-              for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
-                float xValue = getDy(batch, idyF, idyR, idyC, d2);
-                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
-                dotProd += xValue * wValue;
-              }
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}class bY{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,s=t.padInfo.top,u=t.padInfo.left,l=t.outChannels/t.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 * ${l} + dm;
-
-        float dotProd = 0.0;
-
-        // TO DO: Vec4 over the batch size
-        for (int b = 0; b < ${t.batchSize}; b++) {
-          for (int yR = 0; yR < ${t.outHeight}; yR++) {
-            int xR = wR + yR * ${e} - ${s};
-
-            if (xR < 0 || xR >= ${t.inHeight}) {
-              continue;
-            }
-
-            for (int yC = 0; yC < ${t.outWidth}; yC++) {
-              int xC = wC + yC * ${r} - ${u};
-
-              if (xC < 0 || xC >= ${t.inWidth}) {
-                continue;
-              }
-
-              float dyValue = getDy(b, yR, yC, d2);
-              float xValue = getX(b, xR, xC, d1);
-              dotProd += (xValue * dyValue);
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}class wY{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,s=t.strideHeight,u=t.strideWidth,l=e-1-t.padInfo.top,h=r-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=`
-      const ivec2 pads = ivec2(${l}, ${h});
-
-      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 < ${e}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${s}.0;
-
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-
-          int wRPerm = ${e} - 1 - wR;
-
-          for (int wC = 0; wC < ${r}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${u}.0;
-
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
-                fract(dyC) > 0.0) {
-              continue;
-            }
-            int idyC = int(dyC);
-
-            int wCPerm = ${r} - 1 - wC;
-
-            // TO DO: Vec4 over the channelMul
-            for (int dm = 0; dm < ${p}; dm++) {
-              int d2 = d1 * ${p} + dm;
-              float xValue = getDy(batch, idyR, idyC, d2);
-              float wValue = getW(wRPerm, wCPerm, d1, dm);
-              dotProd += xValue * wValue;
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}class YE{constructor(t,e=!1,r=null,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let u=t.padInfo.top,l=t.padInfo.left,h=t.strideHeight,p=t.strideWidth,m=t.dilationHeight,y=t.dilationWidth,b=t.filterHeight,x=t.filterWidth,S=Math.floor(t.inChannels/4)*4,C=t.inChannels%4,I=t.dataFormat==="channelsLast",D=I?1:2,R=I?2:3,A=I?3:1,L="",_="";r&&(s?L=`float activation(float a) {
-          float b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:L=`
-          float activation(float x) {
-            ${r}
-          }
-        `,_="result = activation(result);");let B=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${L}
-
-      const ivec2 strides = ivec2(${h}, ${p});
-      const ivec2 pads = ivec2(${u}, ${l});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords[0];
-        int d2 = coords[${A}];
-
-        ivec2 xRCCorner =
-            ivec2(coords[${D}], coords[${R}]) * 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 < ${b}; wR++) {
-          int xR = xRCorner + wR * ${m};
-
-          if (xR < 0 || xR >= ${t.inHeight}) {
-            continue;
-          }
-
-          for (int wC = 0; wC < ${x}; wC++) {
-            int xC = xCCorner + wC * ${y};
-
-            if (xC < 0 || xC >= ${t.inWidth}) {
-              continue;
-            }
-
-            for (int d1 = 0; d1 < ${S}; d1 += 4) {
-              vec4 wValues = vec4(
-                getW(wR, wC, d1, d2),
-                getW(wR, wC, d1 + 1, d2),
-                getW(wR, wC, d1 + 2, d2),
-                getW(wR, wC, d1 + 3, d2)
-              );
-
-              if (${I}) {
-                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 (${C===1}) {
-
-              if (${I}) {
-                dotProd +=
-                    getX(batch, xR, xC, ${S}) *
-                    getW(wR, wC, ${S}, d2);
-              } else {
-                dotProd +=
-                    getX(batch, ${S}, xR, xC) *
-                    getW(wR, wC, ${S}, d2);
-              }
-
-            } else if (${C===2}) {
-              vec2 wValues = vec2(
-                getW(wR, wC, ${S}, d2),
-                getW(wR, wC, ${S} + 1, d2)
-              );
-
-              if (${I}) {
-                vec2 xValues = vec2(
-                  getX(batch, xR, xC, ${S}),
-                  getX(batch, xR, xC, ${S} + 1)
-                );
-                dotProd += dot(xValues, wValues);
-              } else {
-                vec2 xValues = vec2(
-                  getX(batch, ${S}, xR, xC),
-                  getX(batch, ${S} + 1, xR, xC)
-                );
-                dotProd += dot(xValues, wValues);
-              }
-
-            } else if (${C===3}) {
-              vec3 wValues = vec3(
-                getW(wR, wC, ${S}, d2),
-                getW(wR, wC, ${S} + 1, d2),
-                getW(wR, wC, ${S} + 2, d2)
-              );
-
-              if (${I}) {
-                vec3 xValues = vec3(
-                  getX(batch, xR, xC, ${S}),
-                  getX(batch, xR, xC, ${S} + 1),
-                  getX(batch, xR, xC, ${S} + 2)
-                );
-                dotProd += dot(xValues, wValues);
-              } else {
-                vec3 xValues = vec3(
-                  getX(batch, ${S}, xR, xC),
-                  getX(batch, ${S} + 1, xR, xC),
-                  getX(batch, ${S} + 2, xR, xC)
-                );
-                dotProd += dot(xValues, wValues);
-              }
-
-            }
-          }
-        }
-
-        float result = dotProd;
-        ${B}
-        ${_}
-        setOutput(result);
-      }
-    `}}class xY{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,r=t.padInfo.top,s=t.padInfo.left,u=t.strideDepth,l=t.strideHeight,h=t.strideWidth,p=t.dilationDepth,m=t.dilationHeight,y=t.dilationWidth,b=t.filterDepth,x=t.filterHeight,S=t.filterWidth,C=Math.floor(t.inChannels/4)*4,I=t.inChannels%4;this.userCode=`
-      const ivec3 strides = ivec3(${u}, ${l}, ${h});
-      const ivec3 pads = ivec3(${e}, ${r}, ${s});
-
-      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 < ${b}; wF++) {
-          int xF = xFCorner + wF * ${p};
-
-          if (xF < 0 || xF >= ${t.inDepth}) {
-            continue;
-          }
-
-          for (int wR = 0; wR < ${x}; wR++) {
-            int xR = xRCorner + wR * ${m};
-
-            if (xR < 0 || xR >= ${t.inHeight}) {
-              continue;
-            }
-
-            for (int wC = 0; wC < ${S}; wC++) {
-              int xC = xCCorner + wC * ${y};
-
-              if (xC < 0 || xC >= ${t.inWidth}) {
-                continue;
-              }
-
-              for (int d1 = 0; d1 < ${C}; d1 += 4) {
-                vec4 xValues = vec4(
-                  getX(batch, xF, xR, xC, d1),
-                  getX(batch, xF, xR, xC, d1 + 1),
-                  getX(batch, xF, xR, xC, d1 + 2),
-                  getX(batch, xF, xR, xC, d1 + 3)
-                );
-                vec4 wValues = vec4(
-                  getW(wF, wR, wC, d1, d2),
-                  getW(wF, wR, wC, d1 + 1, d2),
-                  getW(wF, wR, wC, d1 + 2, d2),
-                  getW(wF, wR, wC, d1 + 3, d2)
-                );
-
-                dotProd += dot(xValues, wValues);
-              }
-
-              if (${I===1}) {
-                dotProd +=
-                  getX(batch, xF, xR, xC, ${C}) *
-                  getW(wF, wR, wC, ${C}, d2);
-              } else if (${I===2}) {
-                vec2 xValues = vec2(
-                  getX(batch, xF, xR, xC, ${C}),
-                  getX(batch, xF, xR, xC, ${C} + 1)
-                );
-                vec2 wValues = vec2(
-                  getW(wF, wR, wC, ${C}, d2),
-                  getW(wF, wR, wC, ${C} + 1, d2)
-                );
-                dotProd += dot(xValues, wValues);
-              } else if (${I===3}) {
-                vec3 xValues = vec3(
-                  getX(batch, xF, xR, xC, ${C}),
-                  getX(batch, xF, xR, xC, ${C} + 1),
-                  getX(batch, xF, xR, xC, ${C} + 2)
-                );
-                vec3 wValues = vec3(
-                  getW(wF, wR, wC, ${C}, d2),
-                  getW(wF, wR, wC, ${C} + 1, d2),
-                  getW(wF, wR, wC, ${C} + 2, d2)
-                );
-                dotProd += dot(xValues, wValues);
-              }
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}class JE{constructor(t,e=!1,r=null,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let u=t.inHeight,l=t.inWidth,h=t.padInfo.top,p=t.padInfo.left,m=t.strideHeight,y=t.strideWidth,b=t.dilationHeight,x=t.dilationWidth,S=t.filterHeight,C=t.filterWidth,I=t.outChannels/t.inChannels,D="",R="";r&&(s?D=`float activation(float a) {
-          float b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:D=`
-          float activation(float x) {
-            ${r}
-          }
-        `,R="result = activation(result);");let A=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${D}
-
-      const ivec2 strides = ivec2(${m}, ${y});
-      const ivec2 pads = ivec2(${h}, ${p});
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords.x;
-        ivec2 xRCCorner = coords.yz * strides - pads;
-        int d2 = coords.w;
-        int d1 = d2 / ${I};
-        int q = d2 - d1 * ${I};
-
-        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 < ${S}; wR++) {
-          int xR = xRCorner + wR * ${b};
-
-          if (xR < 0 || xR >= ${u}) {
-            continue;
-          }
-
-          for (int wC = 0; wC < ${C}; wC++) {
-            int xC = xCCorner + wC * ${x};
-
-            if (xC < 0 || xC >= ${l}) {
-              continue;
-            }
-
-            float xVal = getX(batch, xR, xC, d1);
-            float wVal = getW(wR, wC, d1, q);
-            dotProd += xVal * wVal;
-          }
-        }
-
-        float result = dotProd;
-        ${A}
-        ${R}
-        setOutput(result);
-      }
-    `}}class ZE{constructor(t,e=!1,r=null,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;let u=t.inHeight,l=t.inWidth,h=t.padInfo.top,p=t.padInfo.left,m=t.strideHeight,y=t.strideWidth,b=t.dilationHeight,x=t.dilationWidth,S=t.filterHeight,C=t.filterWidth,I=C,D="int xR; int xC; int xCOffset;";for(let _=0;_<S;_++)for(let B=0;B<C;B++)D+=`
-          vec4 xTexelR${_}C${B*2} = vec4(0.);
-          vec4 wR${_}C${B} = vec4(0.);
-          vec4 xR${_}C${B} = vec4(0.);`;for(let _=0;_<S;_++)for(let B=0;B<I;B++){let V=B*2;if(D+=`
-          xR = xRCorner + ${_*b};
-          xC = xCCorner + ${V*x};
-        `,y===1){if(V<C&&(p%2===1?D+=`
-                xCOffset = xC + 1;
-                if(xR >= 0 && xR < ${u} && xCOffset >= 0 && xCOffset < ${l}) {
-                  xTexelR${_}C${V} = getX(batch, xR, xCOffset, d1);
-
-                  // Need to manually clear unused channels in case
-                  // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${l}) {
-                    xTexelR${_}C${V}.zw = vec2(0.);
-                  }
-                } else {
-                  xTexelR${_}C${V} = vec4(0.);
-                }
-
-                xCOffset = xC + 1 - 2;
-                if(xR >= 0 && xR < ${u} && xCOffset >= 0 && xCOffset < ${l}) {
-                  vec4 previous = getX(batch, xR, xCOffset, d1);
-
-                  // Need to manually clear unused channels in case
-                  // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${l}) {
-                    previous.zw = vec2(0.);
-                  }
-
-                  xR${_}C${V} = vec4(previous.zw, xTexelR${_}C${V}.xy);
-                } else {
-                  xR${_}C${V} = vec4(0, 0, xTexelR${_}C${V}.xy);
-                }
-              `:D+=`
-                if(xR >= 0 && xR < ${u} && xC >= 0 && xC < ${l}) {
-                  xTexelR${_}C${V} = getX(batch, xR, xC, d1);
-                } else {
-                  xTexelR${_}C${V} = vec4(0.);
-                }
-
-                xR${_}C${V} = xTexelR${_}C${V};
-              `,V+1<C)){let q=p%2===0?w(x):x;x%2===0&&p%2===1||x%2!==0&&p%2!==1?(D+=`
-                  xCOffset = xC + ${p%2} + ${q};
-
-                  if(xR >= 0 && xR < ${u} &&
-                    xCOffset >= 0 && xCOffset < ${l}) {
-                    xTexelR${_}C${V+2} = getX(batch, xR, xCOffset, d1);
-                  }
-                `,x>1&&(D+=`
-                    xCOffset -= 2;
-                    if(xR >= 0 && xR < ${u} &&
-                      xCOffset >= 0 && xCOffset < ${l}) {
-                      xTexelR${_}C${V} = getX(batch, xR, xCOffset, d1);
-                    } else {
-                      xTexelR${_}C${V} = vec4(0.);
-                    }
-                  `),D+=`
-                  xR${_}C${V+1} = vec4(
-                    xTexelR${_}C${V}.zw, xTexelR${_}C${V+2}.xy);
-                `):D+=`
-                  xCOffset = xC + ${q};
-
-                  if(xR >= 0 && xR < ${u} &&
-                    xCOffset >= 0 && xCOffset < ${l}) {
-                    xTexelR${_}C${V+2} = getX(batch, xR, xCOffset, d1);
-                  }
-
-                  xR${_}C${V+1} = xTexelR${_}C${V+2};
-                `}}else V<C&&(D+=`
-              if(xR >= 0 && xR < ${u}) {
-            `,p%2===1?(D+=`
-                xCOffset = xC + 1 - ${y};
-                if(xCOffset >= 0 && xCOffset < ${l}) {
-                  xTexelR${_}C${V} = getX(batch, xR, xCOffset, d1);
-                } else {
-                  xTexelR${_}C${V} = vec4(0.);
-                }
-
-                if(xC + 1 >= 0 && xC + 1 < ${l}) {
-                  xTexelR${_}C${V+2} = getX(batch, xR, xC + 1, d1);
-                } else {
-                  xTexelR${_}C${V+2} = vec4(0.);
-                }
-
-                xR${_}C${V} = vec4(
-                  xTexelR${_}C${V}.zw, xTexelR${_}C${V+2}.zw);
-              `,V+1<C&&(D+=`
-                  vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${y};
-                  if(xCOffset >= 0 && xCOffset < ${l}) {
-                    final = getX(batch, xR, xCOffset, d1);
-                  }
-                  xR${_}C${V+1} = vec4(xTexelR${_}C${V+2}.xy, final.xy);
-                `)):(D+=`
-                if(xC >= 0 && xC < ${l}) {
-                  xTexelR${_}C${V} = getX(batch, xR, xC, d1);
-                } else {
-                  xTexelR${_}C${V} = vec4(0.);
-                }
-
-                xCOffset = xC + ${y};
-                if(xCOffset >= 0 && xCOffset < ${l}) {
-                  xTexelR${_}C${V+2} = getX(batch, xR, xCOffset, d1);
-                } else {
-                  xTexelR${_}C${V+2} = vec4(0.);
-                }
-
-                xR${_}C${V} = vec4(
-                  xTexelR${_}C${V}.xy, xTexelR${_}C${V+2}.xy);
-              `,V+1<C&&(D+=`
-                  xR${_}C${V+1} = vec4(
-                    xTexelR${_}C${V}.zw, xTexelR${_}C${V+2}.zw);
-                `)),D+="}");V<C&&(D+=`
-            vec4 wTexelR${_}C${V} = getW(${_}, ${V}, d1, q);
-            wR${_}C${V} = vec4(wTexelR${_}C${V}.xz, wTexelR${_}C${V}.xz);
-          `,V+1<C&&(D+=`
-              vec4 wTexelR${_}C${V+1} = getW(${_}, ${V+1}, d1, q);
-              wR${_}C${V+1} =
-                vec4(wTexelR${_}C${V+1}.xz, wTexelR${_}C${V+1}.xz);`))}for(let _=0;_<S;_++)for(let B=0;B<C;B++)D+=`dotProd += xR${_}C${B} * wR${_}C${B};`;let R="",A="";r&&(s?R=`vec4 activation(vec4 a) {
-          vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${r}
-        }`:R=`vec4 activation(vec4 x) {
-          ${r}
-        }`,A="result = activation(result);");let L=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.userCode=`
-      ${R}
-
-      const ivec2 strides = ivec2(${m}, ${y});
-      const ivec2 pads = ivec2(${h}, ${p});
-
-      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.);
-
-        ${D}
-
-        vec4 result = dotProd;
-        ${L}
-        ${A}
-        setOutput(result);
-      }
-    `}}class TY{constructor(t,e,r,s,u){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[l,h,p,m]=t,[y]=e,[b,x]=r;this.outputShape=[y,b,x,m];let S=s==="bilinear"?1:0,[C,I]=[`${h-1}.0`,`${p-1}.0`],[D,R,A]=b>1?[`${(h-1)/(b-1)}`,"(y2-y1) * height_ratio",`y1*${C} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${C}`],[L,_,B]=x>1?[`${(p-1)/(x-1)}`,"(x2-x1) * width_ratio",`x1*${I} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${I}`];this.userCode=`
-      const float height_ratio = float(${D});
-      const float width_ratio = float(${L});
-      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 >= ${l}) {
-          return;
-        }
-
-        float height_scale = ${R};
-        float width_scale = ${_};
-
-        float in_y = ${A};
-        if( in_y < 0.0 || in_y > ${C} ) {
-          setOutput(float(${u}));
-          return;
-        }
-        float in_x = ${B};
-        if( in_x < 0.0 || in_x > ${I} ) {
-          setOutput(float(${u}));
-          return;
-        }
-
-        vec2 sourceFracIndexCR = vec2(in_x,in_y);
-        if(${S} == 1) {
-          // Compute the four integer indices.
-          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
-          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
-
-          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
-          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
-          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
-          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
-
-          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
-
-          float top = topLeft + (topRight - topLeft) * fracCR.x;
-          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
-          float newValue = top + (bottom - top) * fracCR.y;
-          setOutput(newValue);
-        } else {
-          // Compute the coordinators of nearest neighbor point.
-          ivec2 sourceNearestCR = ivec2(floor(
-            sourceFracIndexCR + vec2(0.5,0.5)));
-          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
-          setOutput(newValue);
-        }
-      }
-    `}}class QE{constructor(t,e,r){this.variableNames=["x"],this.outputShape=t;let s=t.length,u=e?"0.0":`getX(${tD(s,"coords")})`,l=t[t.length-1],h="",p="";e?(h=r?`end != ${l-1}`:"end != 0",p=r?"end + 1":"end - 1"):(h=r?`end + pow2 < ${l}`:"end >= pow2",p=r?"end + pow2":"end - pow2"),this.userCode=`
-      uniform float index;
-      void main() {
-        ${We(s)} coords = getOutputCoords();
-        int end = ${eD(s,"coords")};
-        float val = ${u};
-        int pow2 = int(pow(2.0, index));
-        if (${h}) {
-          int idx = ${p};
-          ${eD(s,"coords")} = idx;
-          val += getX(${tD(s,"coords")});
-        }
-        setOutput(val);
-      }
-    `}getCustomSetupFunc(t){return(e,r)=>{this.index==null&&(this.index=e.getUniformLocation(r,"index")),e.gl.uniform1f(this.index,t)}}}function tD(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.x, ${t}.y`;if(n===3)return`${t}.x, ${t}.y, ${t}.z`;if(n===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}function eD(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.y`;if(n===3)return`${t}.z`;if(n===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}class kY{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gf.DENSE;let e=yf(t),r=rr();this.outputShape=t,this.userCode=`
-      ivec3 outCoordsFromFlatIndex(int index) {
-        ${$i(["r","c","d"],t)}
-        return ivec3(r, c, d);
-      }
-
-      void main() {
-        ivec2 resTexRC = ivec2(resultUV.yx *
-          vec2(${e[0]}, ${e[1]}));
-        int index = 4 * (resTexRC.x * ${e[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);
-        }
-
-        ${r.output} = result;
-      }
-    `}}class SY{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=gf.DENSE;let e=yf(t),r=rr();this.outputShape=t,this.userCode=`
-      ivec3 outCoordsFromFlatIndex(int index) {
-        ${$i(["r","c","d"],t)}
-        return ivec3(r, c, d);
-      }
-
-      void main() {
-        ivec2 resTexRC = ivec2(resultUV.yx *
-          vec2(${e[0]}, ${e[1]}));
-        int index = 4 * (resTexRC.x * ${e[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));
-        }
-
-        ${r.output} = result;
-      }
-    `}}class CY{constructor(t,e,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=r,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 / ${e};
-      int offset_h = imod(h, ${e});
-      int in_w = w / ${e};
-      int offset_w = imod(w, ${e});
-      int offset_d = (offset_h * ${e} + offset_w) *
-        ${this.getOutputDepthSize()};
-      int in_d = d + offset_d;
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the current args. Shapes ${s} and ${l} must match`);if(e.isUniform&&u.isUniform)return;let h=e.texShape,p=u.isUniform?null:u.texData.texShape;if(!K(h,p))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${h} and ${p} must match`)})}function t7(n,t,e,r,s){iD(t.inShapeInfos,e),iD([t.outShapeInfo],[r]);let u=r.texData.texture,l=r.texData.texShape;r.texData.isPacked?n.setOutputPackedMatrixTexture(u,l[0],l[1]):n.setOutputMatrixTexture(u,l[0],l[1]),n.setProgram(t.webGLProgram),ft().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&n.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&n.gl.uniform1f(t.nanLoc,NaN),e.forEach((h,p)=>{let m=t.program.variableNames[p],y=t.uniformLocations[m],b=t.uniformLocations[`offset${m}`];if(y==null)return;if(h.isUniform){if(O(h.shape)<2)n.gl.uniform1f(y,h.uniformValues[0]);else{let x=h.uniformValues;x instanceof Float32Array||(x=new Float32Array(x)),n.gl.uniform1fv(y,x)}return}h.texData.slice!=null&&b!=null&&n.gl.uniform1i(b,h.texData.slice.flatOffset),n.setInputMatrixTexture(h.texData.texture,y,p)}),s!=null&&s(n,t.webGLProgram),n.executeProgram()}function e7(n,t,e){let r="";t.concat(e).forEach(l=>{let h=l.texData!=null&&l.texData.slice!=null&&l.texData.slice.flatOffset>0,p=l.isUniform?"uniform":l.texData.texShape;r+=`${l.shape}_${p}_${h}`});let s=n.userCode,u=n.constructor.name;return u+="_"+r+"_"+s,u}class n7{constructor(t,e,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let{filterWidth:s,inChannels:u,strideWidth:l,strideHeight:h,padInfo:p,outWidth:m,dilationWidth:y,dilationHeight:b,dataFormat:x}=r,{left:S,top:C}=p,I=u*s,D=rr(),R=x==="channelsLast",A=R?0:1,L=R?1:2,_="";for(let B=0;B<=1;B++)for(let V=0;V<=1;V++)_+=`
-          blockIndex = rc.y + ${V};
-          pos = rc.x + ${B};
-
-          if(blockIndex < ${t[1]} && pos < ${t[0]}) {
-            offsetY = int(blockIndex / (${m})) * ${h} - ${C};
-            d0 = offsetY + ${b} * (pos / ${I});
-
-            if(d0 < ${e[A]} && d0 >= 0) {
-
-              offsetX = int(mod(float(blockIndex), ${m}.) * ${l}. - ${S}.);
-              d1 = offsetX + ${y} * (int(mod(float(pos), ${I}.) / ${u}.));
-
-              if(d1 < ${e[L]} && d1 >= 0) {
-
-                ch = int(mod(float(pos), ${u}.));
-
-                if (${R}) {
-                  innerDims = vec2(d1, ch);
-                  result[${B*2+V}] = getChannel(
-                    getA(d0, int(innerDims.x),
-                    int(innerDims.y)), innerDims);
-                } else {
-                  innerDims = vec2(d0, d1);
-                  result[${B*2+V}] = 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;
-
-        ${_}
-
-        ${D.output} = result;
-      }
-    `}}class r7{constructor(t,e,r,s,u){this.variableNames=["x"],this.outputShape=[];let l=e,h=t[3]-1;this.outputShape=t;let p,m=`float(${r}) + float(${s}) * sum`;u===.5?p=`inversesqrt(${m})`:u===1?p=`1.0/(${m})`:p=`exp(log(${m}) * float(-${u}));`,this.userCode=`
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int r = coords[1];
-        int c = coords[2];
-        int d = coords[3];
-        float x = getX(b, r, c, d);
-        float sum = 0.0;
-        for (int j = -${l}; j <= ${l}; j++) {
-          int idx = d + j;
-          if (idx >= 0 && idx <=  ${h}) {
-            float z = getX(b, r, c, idx);
-            sum += z * z;
-          }
-        }
-        float val = x * ${p};
-        setOutput(val);
-      }
-    `}}class s7{constructor(t,e,r,s,u){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=r,this.alpha=s,this.beta=u,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 - ${e})));
-          int depthEnd = int(min(float(${this.depth}),
-              float(d + ${e} + 1)));
-
-          const int MIN_DEPTH_BEGIN = 0;
-          const int MAX_DEPTH_END = ${this.depth};
-
-          float norm = 0.0;
-          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
-            if (k < depthBegin){
-              continue;
-            }
-            else if (k >= depthBegin && k < depthEnd) {
-              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
-            }
-            else {
-              break;
-            }
-          }
-
-          norm = float(${s}) * norm + float(${r});
-
-          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
-            if (k < depthBegin){
-              continue;
-            }
-            else if (k >= depthBegin && k < depthEnd){
-              float dyi = -2.0 * float(${s})
-                * float(${u})
-                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
-                / norm;
-              if (k == d) {
-                dyi += pow(norm, -1.0 * ${u});
-              }
-              if (k == coords[3]) {
-                dyi *= getDy(b, r, c, d);
-                result += dyi;
-              }
-            }
-            else {
-              break;
-            }
-          }
-      }
-      setOutput(result);
-      }
-    `}}class o7{constructor(t,e,r,s,u){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let l=e,h=t[3]-1;this.outputShape=t;let p,m=`float(${r}) + float(${s}) * sum`;u===.5?p=`inversesqrt(${m})`:u===1?p=`1.0/(${m})`:p=`exp(log(${m}) * float(-${u}));`,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 - ${l};
-        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 = - ${l}; j <= ${l}; j++) {
-          ivec2 idx = depth + j;
-          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
-          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${h}));
-
-          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 * ${p};
-        setOutput(result);
-      }
-    `}}class a7{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,r=t.strideWidth,s=t.dilationHeight,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,h=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=u*l-1;this.userCode=`
-      const ivec2 pads = ivec2(${h}, ${p});
-
-      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 < ${u};
-          wR += ${s}) {
-          float dyR = float(dyRCorner + wR) / ${e}.0;
-
-          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
-            continue;
-          }
-          int idyR = int(dyR);
-
-          for (int wC = 0; wC < ${l}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${r}.0;
-
-            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
-                fract(dyC) > 0.0) {
-              continue;
-            }
-            int idyC = int(dyC);
-
-            float dyValue = getDy(b, idyR, idyC, d);
-            int maxPosValue = ${m} - int(getMaxPos(b, idyR, idyC, d));
-
-            // Get the current value, check it against the value from the
-            // position matrix.
-            int curPosValue = wR * ${l} + wC;
-            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
-
-            dotProd += dyValue * mask;
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}class i7{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,r=t.strideHeight,s=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,h=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,y=t.effectiveFilterWidth,b=p-1-t.padInfo.front,x=m-1-t.padInfo.top,S=y-1-t.padInfo.left,C=p*m*y-1;this.userCode=`
-      const ivec3 pads = ivec3(${b}, ${x}, ${S});
-
-      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 < ${p};
-           wD += ${u}) {
-          float dyD = float(dyDCorner + wD) / ${e}.0;
-
-          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
-            continue;
-          }
-          int idyD = int(dyD);
-
-          for (int wR = 0; wR < ${m};
-              wR += ${l}) {
-            float dyR = float(dyRCorner + wR) / ${r}.0;
-
-            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
-                fract(dyR) > 0.0) {
-              continue;
-            }
-            int idyR = int(dyR);
-
-            for (int wC = 0; wC < ${y};
-                wC += ${h}) {
-              float dyC = float(dyCCorner + wC) / ${s}.0;
-
-              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
-                  fract(dyC) > 0.0) {
-                continue;
-              }
-              int idyC = int(dyC);
-
-              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
-              int maxPosValue = ${C} -
-                  int(getMaxPos(batch, idyD, idyR, idyC, ch));
-
-              // Get the current value, check it against the value from the
-              // position matrix.
-              int curPosValue =
-                  wD * ${m} * ${y} +
-                  wR * ${y} + wC;
-              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
-
-              dotProd += dyValue * mask;
-            }
-          }
-        }
-        setOutput(dotProd);
-      }
-    `}}class cx{constructor(t,e,r,s=!1,u=!1,l=!1,h=null,p=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r;let m=s?t[1]:t[2],y=Math.ceil(m/2),b=s?"i * 2, rc.y":"rc.y, i * 2",x=u?"rc.z, i * 2":"i * 2, rc.z",S=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],C=u?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],I="",D="";h&&(p?I=`vec4 activation(vec4 a) {
-          vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${h}
-        }`:I=`vec4 activation(vec4 x) {
-          ${h}
-        }`,D="result = activation(result);");let R=l?"result += getBiasAtOutCoords();":"";l&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights");let A="rc.x",L="rc.x";t[0]<e[0]?A=`int(min(float(rc.x), ${t[0]-1}.))`:e[0]<t[0]&&(L=`int(min(float(rc.x), ${e[0]-1}.))`),this.userCode=`
-      ${I}
-
-      const float sharedDimension = ${y}.0;
-
-      vec4 dot2x2ARowBCol(ivec3 rc) {
-        vec4 result = vec4(0);
-        for (int i = 0; i < ${y}; i++) {
-          int batchA = ${A};
-          int batchB = ${L};
-          vec4 a = getMatrixA(batchA, ${b});
-          vec4 b = getMatrixB(batchB, ${x});
-
-          // These swizzled products need to be separately added.
-          // See: https://github.com/tensorflow/tfjs/issues/1735
-          result += (${S[0]} * ${C[0]});
-          result += (${S[1]} * ${C[1]});
-        }
-        return result;
-      }
-
-      void main() {
-        ivec3 rc = getOutputCoords();
-        vec4 result = dot2x2ARowBCol(rc);
-
-        ${R}
-
-        ${D}
-
-        setOutput(result);
-      }
-    `}}class u7{constructor(t,e,r){this.variableNames=["probs"],this.outputShape=[t,r],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 < ${e-1}; i++) {
-          cdf += getProbs(batch, i);
-
-          if (r < cdf) {
-            setOutput(float(i));
-            return;
-          }
-        }
-
-        // If no other event happened, last event happened.
-        setOutput(float(${e-1}));
-      }
-    `}getCustomSetupFunc(t){return(e,r)=>{this.seedLoc==null&&(this.seedLoc=e.getUniformLocation(r,"seed")),e.gl.uniform1f(this.seedLoc,t)}}}class c7{constructor(t,e,r,s){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int index = round(getIndices(coords.x));
-        setOutput(mix(float(${s}), float(${r}),
-                      float(index == coords.y)));
-      }
-    `}}class l7{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;let e=t.length;if(e===0)this.userCode=`
-        void main() {
-          setOutput(vec4(getA(), 0., 0., 0.));
-        }
-      `;else{let r=nr("rc",e),s=We(e),u=f7(e,t,r),l=p7(e,t[t.length-1],t[t.length-2],r),h=d7(t,r);this.userCode=`
-        void main() {
-          ${s} rc = getOutputCoords();
-
-          if(${u}) {
-            setOutput(vec4(0));
-          } else {
-            ${l}
-
-            setOutput(vec4(${h}));
-          }
-        }
-      `}}}function h7(n,t){let e=[];for(let r=0;r<=1;r++)for(let s=0;s<=1;s++){let u=`${r===0?"r":"rp1"}, ${s===0?"c":"cp1"}`;for(let l=2;l<n;l++)u=`${t[t.length-1-l]},`+u;e.push(u)}return e}function f7(n,t,e){if(n===1)return`rc > ${t[0]}`;let r="";for(let s=n-2;s<n;s++)r+=`${e[s]} >= ${t[s]}`,s<n-1&&(r+="||");return r}function p7(n,t,e,r){if(n===1)return"";let s=r.slice(-2);return`
-    int r = ${s[0]};
-    int c = ${s[1]};
-    int rp1 = r + 1;
-    int cp1 = c + 1;
-
-    bool cEdge = cp1 >= ${t};
-    bool rEdge = rp1 >= ${e};
-  `}function d7(n,t){let e=n.length,r=h7(e,t);return e===1?`getA(rc),
-            rc + 1 >= ${n[0]} ? 0. : getA(rc + 1),
-            0, 0`:`getA(${r[0]}),
-          cEdge ? 0. : getA(${r[1]}),
-          rEdge ? 0. : getA(${r[2]}),
-          rEdge || cEdge ? 0. : getA(${r[3]})`}class m7{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((m,y)=>m[0]+t[y]+m[1]);let s=t.length,u=We(s),l=e.map(m=>m[0]).join(","),h=e.map((m,y)=>m[0]+t[y]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
-        int start = ${l};
-        int end = ${h};
-
-        void main() {
-          int outC = getOutputCoords();
-          if (outC < start || outC >= end) {
-            setOutput(float(${r}));
-          } else {
-            setOutput(getX(outC - start));
-          }
-        }
-      `;return}this.userCode=`
-      ${u} start = ${u}(${l});
-      ${u} end = ${u}(${h});
-
-      void main() {
-        ${u} outC = getOutputCoords();
-        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
-          setOutput(float(${r}));
-        } else {
-          ${u} coords = outC - start;
-          setOutput(getX(${p}));
-        }
-      }
-    `}}class g7{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((I,D)=>I[0]+t[D]+I[1]);let s=t.length,u=We(s),l=e.map(I=>I[0]).join(","),h=e.map((I,D)=>I[0]+t[D]).join(","),p=nr("rc",s),m=nr("source",s),y=`${p[s-1]} < ${this.outputShape[s-1]}`,b=s===1?"source":`vec2(${m.slice(-2).join()})`,x=[`${u} rc = outputLoc;`,`${p[s-1]} += 1;
-       if(${y}) {
-      `,s===1?"":`}
-       rc = outputLoc;
-       ${p[s-2]} += 1;
-       if(${p[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":`  ${p[s-1]} += 1;
-         if(${y}) {`],S=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",C="";for(let I=0,D=s===1?2:4;I<D;I++)C+=`
-        ${x[I]}
-        if (${S}) {
-          result[${I}] = float(${r});
-        } else {
-          ${u} source = rc - start;
-          result[${I}] = getChannel(getX(${m.join()}), ${b});
-        }
-      `;C+=s===1?"} ":"}}",this.userCode=`
-      const ${u} start = ${u}(${l});
-      const ${u} end = ${u}(${h});
-
-      void main() {
-        ${u} outputLoc = getOutputCoords();
-        vec4 result = vec4(0.);
-        ${C}
-        setOutput(result);
-      }
-    `}}class xf{constructor(t,e,r,s=!1,u=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let l=t.filterWidth,h=t.strideHeight,p=t.strideWidth,m=t.dilationHeight,y=t.dilationWidth,b=t.effectiveFilterHeight,x=t.effectiveFilterWidth,S=t.padInfo.top,C=t.padInfo.left;this.outputShape=t.outShape;let I=e==="avg",D=`((batch  * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,R=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,A="0.0";if(I||(A="-1.0 / 1e-20"),r){let j=">=";this.userCode=`
-        const ivec2 strides = ivec2(${h}, ${p});
-        const ivec2 pads = ivec2(${S}, ${C});
-
-        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 < ${b};
-              wR += ${m}) {
-            int xR = xRCorner + wR;
-
-            if (xR < 0 || xR >= ${t.inHeight}) {
-              continue;
-            }
-
-            for (int wC = 0; wC < ${x};
-                wC += ${y}) {
-              int xC = xCCorner + wC;
-
-              if (xC < 0 || xC >= ${t.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 ${j} currMinMaxValue) {
-                minMaxValue = value;
-                minMaxValueFound = 1.0;
-                minMaxPosition = ${s?u?D:R:`wR * ${x} + wC`};
-              }
-            }
-          }
-          setOutput(float(minMaxPosition));
-        }
-      `;return}let L="max",_=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(_="avgValue / count");let B=Math.floor(l/4)*4,V=l%4,q=`
-      if (${I}) {
-        avgValue += dot(values, ones);
-      } else {
-        minMaxValue = ${L}(values, minMaxValue);
-      }
-    `;this.userCode=`
-      const ivec2 strides = ivec2(${h}, ${p});
-      const ivec2 pads = ivec2(${S}, ${C});
-      const float initializationValue = ${A};
-      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 >= ${t.inWidth}) {
-          return initializationValue;
-        }
-        count += 1.0;
-        return getX(batch, xR, xC, d);
-      }
-
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int batch = coords[0];
-        int d = coords[3];
-
-        ivec2 xRCCorner = coords.yz * strides - pads;
-        int xRCorner = xRCCorner.x;
-        int xCCorner = xRCCorner.y;
-
-        // max/min x(?, ?, d) to get y(yR, yC, d).
-        // ? = to be determined
-        vec4 minMaxValue = vec4(${A});
-        float avgValue = 0.0;
-        count = 0.0;
-
-        for (int wR = 0; wR < ${b};
-            wR += ${m}) {
-          int xR = xRCorner + wR;
-
-          if (xR < 0 || xR >= ${t.inHeight}) {
-            continue;
-          }
-
-          for (int wC = 0; wC < ${B}; wC += 4) {
-            int xC = xCCorner + wC * ${y};
-
-            vec4 values = vec4(
-              getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${y}, d),
-              getValue(batch, xR, xC + 2 * ${y}, d),
-              getValue(batch, xR, xC + 3 * ${y}, d)
-            );
-
-            ${q}
-          }
-
-          int xC = xCCorner + ${B};
-          if (${V===1}) {
-            vec4 values = vec4(
-              getValue(batch, xR, xC, d),
-              initializationValue,
-              initializationValue,
-              initializationValue
-            );
-
-            ${q}
-          } else if (${V===2}) {
-            vec4 values = vec4(
-              getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${y}, d),
-              initializationValue,
-              initializationValue
-            );
-
-            ${q}
-          } else if (${V===3}) {
-            vec4 values = vec4(
-              getValue(batch, xR, xC, d),
-              getValue(batch, xR, xC + ${y}, d),
-              getValue(batch, xR, xC + 2 * ${y}, d),
-              initializationValue
-            );
-
-            ${q}
-          }
-        }
-        setOutput(${_});
-      }
-    `}}class lx{constructor(t,e,r,s=!1,u=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let l=t.filterWidth,h=t.strideDepth,p=t.strideHeight,m=t.strideWidth,y=t.dilationDepth,b=t.dilationHeight,x=t.dilationWidth,S=t.effectiveFilterDepth,C=t.effectiveFilterHeight,I=t.effectiveFilterWidth,D=t.padInfo.front,R=t.padInfo.top,A=t.padInfo.left;this.outputShape=t.outShape;let L=e==="avg",_="0.0";if(L||(_="-1.0 / 1e-20"),r){let tt=">=";this.userCode=`
-        const ivec3 strides =
-            ivec3(${h}, ${p}, ${m});
-        const ivec3 pads = ivec3(${D}, ${R}, ${A});
-
-        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 < ${S};
-              wD += ${y}) {
-            int xD = xDCorner + wD;
-
-            if (xD < 0 || xD >= ${t.inDepth}) {
-              continue;
-            }
-
-            for (int wR = 0; wR < ${C};
-                wR += ${b}) {
-              int xR = xRCorner + wR;
-
-              if (xR < 0 || xR >= ${t.inHeight}) {
-                continue;
-              }
-
-              for (int wC = 0; wC < ${I};
-                  wC += ${x}) {
-                int xC = xCCorner + wC;
-
-                if (xC < 0 || xC >= ${t.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 ${tt} currMinMaxValue) {
-                  minMaxValue = value;
-                  minMaxValueFound = 1.0;
-                  minMaxPosition = ${s?u?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${C} * ${I} +
-                      wR * ${I} + wC`};
-                }
-              }
-            }
-          }
-          setOutput(float(minMaxPosition));
-        }
-      `;return}let B="max",V=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(V="avgValue / count");let q=Math.floor(l/4)*4,j=l%4,et=`
-      if (${L}) {
-        avgValue += dot(values, ones);
-      } else {
-        minMaxValue = ${B}(values, minMaxValue);
-      }
-    `;this.userCode=`
-      const ivec3 strides =
-        ivec3(${h}, ${p}, ${m});
-      const ivec3 pads = ivec3(${D}, ${R}, ${A});
-      const float initializationValue = ${_};
-      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 >= ${t.inWidth}) {
-          return initializationValue;
-        }
-        count += 1.0;
-        return getX(batch, xD, xR, xC, ch);
-      }
-
-      void main() {
-        ivec5 coords = getOutputCoords();
-        int batch = coords.x;
-        int ch = coords.u;
-
-        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
-        int xDCorner = xCorner.x;
-        int xRCorner = xCorner.y;
-        int xCCorner = xCorner.z;
-
-        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
-        // ? = to be determined
-        vec4 minMaxValue = vec4(${_});
-        float avgValue = 0.0;
-        count = 0.0;
-
-        for (int wD = 0; wD < ${S};
-            wD += ${y}) {
-          int xD = xDCorner + wD;
-
-          if (xD < 0 || xD >= ${t.inDepth}) {
-            continue;
-          }
-
-          for (int wR = 0; wR < ${C};
-            wR += ${b}) {
-            int xR = xRCorner + wR;
-
-            if (xR < 0 || xR >= ${t.inHeight}) {
-              continue;
-            }
-
-            for (int wC = 0; wC < ${q}; wC += 4) {
-              int xC = xCCorner + wC * ${x};
-
-              vec4 values = vec4(
-                getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${x}, ch),
-                getValue(batch, xD, xR, xC + 2 * ${x}, ch),
-                getValue(batch, xD, xR, xC + 3 * ${x}, ch)
-              );
-
-              ${et}
-            }
-
-            int xC = xCCorner + ${q};
-            if (${j===1}) {
-              vec4 values = vec4(
-                getValue(batch, xD, xR, xC, ch),
-                initializationValue,
-                initializationValue,
-                initializationValue
-              );
-
-              ${et}
-            } else if (${j===2}) {
-              vec4 values = vec4(
-                getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${x}, ch),
-                initializationValue,
-                initializationValue
-              );
-
-              ${et}
-            } else if (${j===3}) {
-              vec4 values = vec4(
-                getValue(batch, xD, xR, xC, ch),
-                getValue(batch, xD, xR, xC + ${x}, ch),
-                getValue(batch, xD, xR, xC + 2 * ${x}, ch),
-                initializationValue
-              );
-
-              ${et}
-            }
-          }
-          setOutput(${V});
-        }
-      }
-    `}}class uD{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:s,inSize:u,outSize:l}=t;this.outputShape=[s,l];let h="0.0",p="";e==="prod"?h="1.0":e==="min"?(h="1.0 / 1e-20",p="min"):e==="max"&&(h="-1.0 / 1e-20",p="max");let m=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?m="sumValue":e==="prod"?m="prodValue":e==="all"?m="allValue":e==="any"&&(m="anyValue");let y=Math.floor(r/4)*4,b=r%4,x=`
-      if (${e==="sum"}) {
-        sumValue += dot(values, ones);
-      } else if (${e==="prod"}) {
-        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
-        prodValue *= tmp[0] * tmp[1];
-      } else {
-        minMaxValue = ${p}(values, minMaxValue);
-      }
-    `,S="vec4";e==="all"?(h="1.0",x=`
-        bool reducedAllValue = all(values);
-        float floatedReducedAllValue = float(reducedAllValue);
-        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
-      `,S="bvec4"):e==="any"&&(h="0.0",x=`
-        bool reducedAnyValue = any(values);
-        float floatedReducedAnyValue = float(reducedAnyValue);
-        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
-      `,S="bvec4");let C="";u%r>0&&(C=`
-        if (inIdx < 0 || inIdx >= ${u}) {
-          return initializationValue;
-        }
-      `),this.userCode=`
-      const float initializationValue = ${h};
-      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
-
-      float getValue(int batch, int inIdx) {
-        ${C}
-        return getX(batch, inIdx);
-      }
-
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int batch = coords[0];
-        int outIdx = coords[1];
-        int inOffset = outIdx * ${r};
-
-        vec4 minMaxValue = vec4(${h});
-        float prodValue = 1.0;
-        float sumValue = 0.0;
-        float allValue = 1.0;
-        float anyValue = 0.0;
-
-        for (int i = 0; i < ${y}; i += 4) {
-          int inIdx = inOffset + i;
-          ${S} values = ${S}(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2),
-            getValue(batch, inIdx + 3)
-          );
-
-          ${x}
-        }
-
-        int inIdx = inOffset + ${y};
-        if (${b===1}) {
-          ${S} values = ${S}(
-            getValue(batch, inIdx),
-            initializationValue,
-            initializationValue,
-            initializationValue
-          );
-
-          ${x}
-        } else if (${b===2}) {
-          ${S} values = ${S}(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            initializationValue,
-            initializationValue
-          );
-
-          ${x}
-        } else if (${b===3}) {
-          ${S} values = ${S}(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2),
-            initializationValue
-          );
-
-          ${x}
-        }
-        setOutput(${m});
-      }
-    `}}class cD{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let r="";for(let s=0;s<4;s++){let u="thisRC = rc;";s%2===1&&(u+="thisRC.z += 1;"),s>1&&(u+="thisRC.y += 1;"),r+=`
-        ${u}
-        ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
-          int flatIndex = getFlatIndex(thisRC);
-
-          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
-          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
-
-          result[${s}] =
-            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
-        ${s>0?"}":""}
-      `}this.userCode=`
-      ${v7(e)}
-      ${ux(t)}
-
-      void main() {
-        ivec3 rc = getOutputCoords();
-
-        vec4 result = vec4(0.);
-
-        ivec3 thisRC;
-        int rows = ${t[1]};
-        int cols = ${t[2]};
-
-        ${r}
-
-        setOutput(result);
-      }
-    `}}function v7(n){let t=$i(["r","c","d"],n);return`
-    ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t}
-      return ivec3(r, c, d);
-    }
-  `}class y7{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,s,u]=e.shape,[,l,h]=t.shape,p=[r&&l>1?s-1:s,r&&h>1?u-1:u],m=[r&&l>1?l-1:l,r&&h>1?h-1:h],y=p[0]/m[0],b=p[1]/m[1],x=1/y,S=1/b,C=Math.ceil(x)*2+2,I=Math.ceil(S)*2+2;this.userCode=`
-      void main() {
-        ivec4 coords = getOutputCoords();
-        int b = coords[0];
-        int d = coords[3];
-        int r = coords[1];
-        int c = coords[2];
-
-        float accumulator = 0.0;
-
-        const float heightScale = float(${y});
-        const float widthScale = float(${b});
-
-        const float invHeightScale = float(${x});
-        const float invWidthScale = float(${S});
-
-        const int winHeight = int(${C});
-        const int winWidth = int(${I});
-
-        // 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 >= ${l}) {
-            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 >= ${h}) {
-              continue;
-            }
-
-            float dxR = float(dyR) * heightScale;
-            int topDxRIndex = int(floor(dxR));
-            int bottomDxRIndex = int(min(ceil(dxR), ${s-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), ${u-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 b7{constructor(t,e,r,s){this.variableNames=["A"],this.outputShape=[];let[u,l,h,p]=t;this.outputShape=[u,e,r,p];let m=[s&&e>1?l-1:l,s&&r>1?h-1:h],y=[s&&e>1?e-1:e,s&&r>1?r-1:r];this.userCode=`
-      const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${m[0]/y[0]},
-          ${m[1]/y[1]});
-      const vec2 inputShapeRC = vec2(${l}.0, ${h}.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 w7{constructor(t,e,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[u,l,h,p]=t;this.outputShape=[u,e,r,p];let m=[s&&e>1?l-1:l,s&&r>1?h-1:h],y=[s&&e>1?e-1:e,s&&r>1?r-1:r];this.userCode=`
-      const vec3 effectiveInputOverOutputRatioRC = vec3(
-          ${m[0]/y[0]},
-          ${m[1]/y[1]},
-          ${m[1]/y[1]});
-      const vec3 inputShapeRC = vec3(${l}.0, ${h}.0,
-                                     ${h}.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 < ${p-1};
-        bool hasNextRow = coords.z < ${r-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 x7{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,s,u]=e.shape,[,l,h]=t.shape,p=[r&&l>1?s-1:s,r&&h>1?u-1:u],m=[r&&l>1?l-1:l,r&&h>1?h-1:h],y=p[0]/m[0],b=p[1]/m[1],x=1/y,S=1/b,C=Math.ceil(x)*2+2,I=Math.ceil(S)*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(${y});
-        const float widthScale = float(${b});
-
-        const float invHeightScale = float(${x});
-        const float invWidthScale = float(${S});
-
-        const int winHeight = int(${C});
-        const int winWidth = int(${I});
-
-        // 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 >= ${l}) {
-            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 >= ${h}) {
-              continue;
-            }
-
-            float sourceFracRow =
-              float(${p[0]}) *
-                (float(dyR) / float(${m[0]}));
-
-            float sourceFracCol =
-                float(${p[1]}) *
-                  (float(dyC) / float(${m[1]}));
-
-            int sourceNearestRow = int(min(
-                float(int(${s}) - 1),
-                ${r} ? float(round(sourceFracRow)) :
-                                  float(floor(sourceFracRow))));
-
-            int sourceNearestCol = int(min(
-                float(int(${u}) - 1),
-                ${r} ? float(round(sourceFracCol)) :
-                                  float(floor(sourceFracCol))));
-
-            if (r == sourceNearestRow && c == sourceNearestCol) {
-              accumulator += getDy(b, dyR, dyC, d);
-            }
-          }
-        }
-        // End loop over dy
-
-        setOutput(accumulator);
-      }
-    `}}class T7{constructor(t,e,r,s){this.variableNames=["A"],this.outputShape=[];let[u,l,h,p]=t;this.outputShape=[u,e,r,p];let m=[s&&e>1?l-1:l,s&&r>1?h-1:h],y=[s&&e>1?e-1:e,s&&r>1?r-1:r],b=s?"0.5":"0.0";this.userCode=`
-      const vec2 effectiveInputOverOutputRatioRC = vec2(
-          ${m[0]/y[0]},
-          ${m[1]/y[1]});
-      const vec2 inputShapeRC = vec2(${l}.0, ${h}.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 + ${b})));
-
-        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
-
-        setOutput(newValue);
-      }
-    `}}class k7{constructor(t,e){this.variableNames=["x"];let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=t,r===1){this.userCode=`
-        void main() {
-          int coord = getOutputCoords();
-          setOutput(getX(${t[0]} - coord - 1));
-        }
-      `;return}let s=h=>e.indexOf(h)!==-1&&t[h]!==1?`${t[h]} - coords[${h}] - 1`:`coords[${h}]`,u=t.map((h,p)=>s(p)).join(","),l=We(r);this.userCode=`
-      void main() {
-        ${l} coords = getOutputCoords();
-        setOutput(getX(${u}));
-      }
-    `}}class S7{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=t;let s=nr("rc",r),u=`${s[r-1]} + 1 < ${this.outputShape[r-1]}`,l=`${s[r-2]} + 1 < ${this.outputShape[r-2]}`,h=We(r);r===1?this.userCode=`
-        void main(){
-          int rc = getOutputCoords();
-          vec4 result = vec4(0.);
-          result.r = getChannel(getX(${t[0]} - rc - 1),
-            ${t[0]} - rc - 1);
-          if(${u}){
-              result.g = getChannel(getX(${t[0]} - (rc  + 1) - 1),
-                ${t[0]} - (rc  + 1) - 1);
-          }
-          setOutput(result);
-        }
-      `:this.userCode=`
-        void main() {
-          ${h} rc = getOutputCoords();
-          vec4 result = vec4(0.);
-          result.r = ${p(s.slice())};
-          if(${u}){
-            result.g = ${m(s.slice())};
-          }
-          if(${l}) {
-            result.b = ${y(s.slice())};
-            if(${u}) {
-              result.a = ${b(s.slice())};
-            }
-          }
-          setOutput(result);
-        }
-    `;function p(C){return x(C)}function m(C){return C[r-1]="("+C[r-1]+" + 1)",x(C)}function y(C){return C[r-2]="("+C[r-2]+" + 1)",x(C)}function b(C){return C[r-1]="("+C[r-1]+" + 1)",C[r-2]="("+C[r-2]+" + 1)",x(C)}function x(C){let I=t.map((A,L)=>S(L,C)),D=I.join(","),R=I.slice(-2).join(",");return`getChannel(getX(${D}), vec2(${R}))`}function S(C,I){return e.indexOf(C)!==-1&&t[C]!==1?`${t[C]} - ${I[C]} - 1`:`${I[C]}`}}}class lD{constructor(t,e,r,s,u,l,h=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=l;let p=We(u.length),m=We(l.length),y="";r===1?y="i":r===2&&(y="i, j");let b=`getIndices(${y})`,x="";s===1?x="i":s===2&&(x="i, coords[1]");let S=`getUpdates(${x})`,C=e>1?"strides[j]":"strides";this.userCode=`
-        ${p} strides = ${p}(${u});
-
-        void main() {
-          ${m} coords = getOutputCoords();
-          float sum = 0.0;
-          bool found = false;
-          for (int i = 0; i < ${t}; i++) {
-            int flattenedIndex = 0;
-            for (int j = 0; j < ${e}; j++) {
-              int index = round(${b});
-              flattenedIndex += index * ${C};
-            }
-            if (flattenedIndex == coords[0]) {
-              sum += ${S};
-              found = true;
-            }
-          }
-          setOutput(mix(getDefaultValue(), sum, float(found)));
-        }
-      `}}class C7{constructor(t,e){this.variableNames=["x","segmentIds"];let r=t.windowSize,s=t.batchSize,u=t.inSize,l=t.numSegments,h=l*Math.ceil(u/r);this.outputShape=[s,h];let p="0.0",m="sumValue",y=Math.floor(r/4)*4,b=r%4,x=`
-        sumValue += dot(values, segFilter);
-    `,S="";u%r>0&&(S=`
-        if (inIdx < 0 || inIdx >= ${u}) {
-          return initializationValue;
-        }
-      `);let C="";u%r>0&&(C=`
-        if (inIdx < 0 || inIdx >= ${u}) {
-          return -1.0;
-        }
-      `),this.userCode=`
-      const float initializationValue = ${p};
-
-      float getValue(int batch, int inIdx) {
-        ${S}
-        return getX(batch, inIdx);
-      }
-
-      float getSegmentIdAtIndex(int inIdx) {
-        ${C}
-        return getSegmentIds(inIdx);
-      }
-
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int batch = coords[0];
-        int outIdx = coords[1];
-        int inOffset = int(floor(float(outIdx) / float(
-          ${l})) * float(${r}));
-        int currentSeg = int(mod(float(outIdx), float(${l})));
-
-        float sumValue = 0.0;
-
-        for (int i = 0; i < ${y}; 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
-          );
-
-          ${x}
-        }
-
-        int inIdx = inOffset + ${y};
-        if (${b===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
-          );
-
-          ${x}
-        } else if (${b===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
-          );
-
-          ${x}
-        } else if (${b===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
-          );
-
-          ${x}
-        }
-        setOutput(${m});
-      }
-    `}}class N7{constructor(t,e,r){this.variableNames=["c","a","b"],this.outputShape=e;let s,u;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)u="resRC",s="resRC";else{let h=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],m=[];for(let y=0;y<e.length;y++)m.push(`${h[y]}`),y<t&&p.push(`${h[y]}`);s=p.join(),u=m.join()}let l=We(r);this.userCode=`
-      void main() {
-        ${l} resRC = getOutputCoords();
-        float cVal = getC(${s});
-        if (cVal >= 1.0) {
-          setOutput(getA(${u}));
-        } else {
-          setOutput(getB(${u}));
-        }
-      }
-    `}}class I7{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=We(this.rank),r=`uniform int start[${this.rank}];`,s=E7(this.rank),u,l=t.map((h,p)=>`sourceLoc.${hx[p]} = start[${p}] + coords.${hx[p]};`);u=`
-        ${e} sourceLoc;
-        ${e} coords = getOutputCoords();
-        ${l.join(`
-`)}
-      `,this.userCode=`
-      ${r}
-      void main() {
-        ${u}
-        setOutput(getSource(${s}));
-      }
-    `}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}let hx=["x","y","z","w","u","v"];function E7(n){if(n===1)return"sourceLoc";if(n<=6)return hx.slice(0,n).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${n} is not yet supported`)}class D7{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;let e=We(this.rank),r=nr("coords",this.rank),s=nr("sourceLoc",this.rank),u=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,l=`getChannel(getSource(${s.join()}), ${u})`,h=`
-      result.x = ${l};
-      if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
-        ++${s[this.rank-1]};
-        result.y = ${l};
-        --${s[this.rank-1]};
-      }
-    `,p=this.rank===1?"":`
-      --${r[this.rank-1]};
-      if (++${r[this.rank-2]} < ${t[this.rank-2]}) {
-        ++${s[this.rank-2]};
-        result.z = ${l};
-        if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
-          ++${s[this.rank-1]};
-          result.w = ${l};
-        }
-      }
-    `,m=this.rank<=4?`sourceLoc = coords +
-            ${e}(${t.map((y,b)=>`start[${b}]`).join()});`:t.map((y,b)=>`${s[b]} = ${r[b]} + start[${b}];`).join(`
-`);this.userCode=`
-      uniform int start[${this.rank}];
-      void main() {
-        ${e} coords = getOutputCoords();
-        ${e} sourceLoc;
-        ${m}
-        vec4 result = vec4(0.);
-        ${h}
-        ${p}
-        setOutput(result);
-      }
-    `}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}class $7{constructor(t,e,r){this.variableNames=["x"],this.outputShape=r;let s=r.length,u=We(r.length),l=We(r.length),h="";if(s===1)h="coords * strides + begin";else{let p=0;h=r.map((m,y)=>(p++,r.length===1?`coords * strides[${y}] + begin[${y}]`:`coords[${p-1}] * strides[${y}] + begin[${y}]`)).join(",")}this.userCode=`
-      ${u} begin = ${u}(${t});
-      ${u} strides = ${u}(${e});
-
-      void main() {
-        ${l} coords = getOutputCoords();
-        setOutput(getX(${h}));
-      }
-    `}}class A7{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,r){let s=fD(e,r),u=pD(t,s,r);u in this.freeTextures||(this.freeTextures[u]=[]),u in this.usedTextures||(this.usedTextures[u]=[]);let l=hD(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[u].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=l,this.log();let p=this.freeTextures[u].shift();return this.usedTextures[u].push(p),p}let h;return s===Vn.PACKED_2X2_FLOAT32?h=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):s===Vn.PACKED_2X2_FLOAT16?h=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):s===Vn.UNPACKED_FLOAT32?h=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):s===Vn.UNPACKED_FLOAT16?h=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):s===Vn.PACKED_4X1_UNSIGNED_BYTE&&(h=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[u].push(h),this.numUsedTextures++,this._numBytesAllocated+=l,this.log(),h}releaseTexture(t,e,r,s){if(this.freeTextures==null)return;let u=fD(r,s),l=pD(e,u,s);l in this.freeTextures||(this.freeTextures[l]=[]);let h=hD(e,u,this.gpgpu.gl,this.gpgpu.textureConfig,s),p=ft().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t),this._numBytesAllocated-=h):(this.freeTextures[l].push(t),this.numFreeTextures++,this._numBytesFree+=h),this.numUsedTextures--;let m=this.usedTextures[l],y=m.indexOf(t);if(y<0)throw new Error("Cannot release a texture that was never provided by this texture manager");m.splice(y,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function _7(n,t){let e=n;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===n.RGBA)return 16;if(t===e.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function hD(n,t,e,r,s){let u=F7(t,r),l;if(s){let[p,m]=ic(n[0],n[1]);l=p*m}else{let[p,m]=vf(n[0],n[1]);l=p*m}let h=_7(e,u);return l*h}function F7(n,t){switch(n){case Vn.PACKED_2X2_FLOAT32:return oD(t);case Vn.PACKED_2X2_FLOAT16:return aD(t);case Vn.UNPACKED_FLOAT32:return nD(t);case Vn.UNPACKED_FLOAT16:return rD(t);case Vn.PACKED_4X1_UNSIGNED_BYTE:return sD(t);default:throw new Error(`Unknown physical texture type ${n}`)}}function R7(n){return ft().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?n?Vn.PACKED_2X2_FLOAT32:Vn.UNPACKED_FLOAT32:n?Vn.PACKED_2X2_FLOAT16:Vn.UNPACKED_FLOAT16}function fD(n,t){if(n===Hr.UPLOAD)return Vn.PACKED_2X2_FLOAT32;if(n===Hr.RENDER||n==null)return R7(t);if(n===Hr.DOWNLOAD||n===Hr.PIXELS)return Vn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${n}`)}function pD(n,t,e){return`${n[0]}_${n[1]}_${t}_${e}`}class P7{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let l=0;l<r.length;l++)r[l]=t[l]*e[l];this.outputShape=r,this.rank=r.length;let s=We(this.rank),u=O7(t);this.userCode=`
-      void main() {
-        ${s} resRC = getOutputCoords();
-        setOutput(getA(${u}));
-      }
-    `}}function O7(n){let t=n.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${n[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<n.length;s++)r.push(`imod(${e[s]}, ${n[s]})`);return r.join()}class ye{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.userCode=`
-      float unaryOperation(float x) {
-        ${e}
-      }
-
-      void main() {
-        float x = getAAtOutCoords();
-        float y = unaryOperation(x);
-
-        setOutput(y);
-      }
-    `}}let _o="if (isnan(x)) return x;",M7="return x;",dD="return abs(x);",mD=_o+`
-  return (x < 0.0) ? 0.0 : x;
-`,gD=_o+`
-  return (x < 0.0) ? 0.0 : min(6.0, x);
-`,vD="return (x >= 0.0) ? x : (exp(x) - 1.0);",L7=`
-  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
-  // see: https://arxiv.org/abs/1706.02515
-  float scaleAlpha = ${qd};
-  float scale = ${jd};
-  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`;function B7(n=0){return _o+`
-    return x > 0.0 ? 1.0 : float(${n});
-  `}let yD="return -x;",bD="return ceil(x);",wD="return floor(x);",z7=`
-  if (isnan(x)) { return 0.0; }
-  return sign(x);
-`,W7="return float(isnan(x));",V7="return float(isinf(x));",U7="return float(!isnan(x) && !isinf(x));",G7=`
-  // 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;
-    }
-  }
-`,xD="return exp(x);",TD="return exp(x) - 1.0;",H7=`if (x < 0.0) return NAN;
-  return log(x);`,q7="return log(1.0 + x);",j7="return sqrt(x);",K7="return inversesqrt(x);",X7="return 1.0 / (1.0 + exp(-1.0 * x));",Y7=`
-  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;
-`,J7=_o+`
-  if (abs(x) > 1.) {
-    return NAN;
-  }
-  return asin(x);
-`,Z7=_o+`
-  if (abs(x) > 1.) {
-    return NAN;
-  }
-  return acos(x);
-`,Q7=_o+`
-  return atan(x);
-`,tJ=`
-  float e2x = exp(x);
-  return (e2x - 1.0 / e2x) / 2.0;
-`,eJ=`
-  float e2x = exp(-x);
-  return (e2x + 1.0 / e2x) / 2.0;
-`,nJ=`
-  float e2x = exp(-2.0 * abs(x));
-  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,rJ=_o+"return log(x + sqrt(x * x + 1.0));",sJ=_o+`
-  if (x < 1.0) return NAN;
-  return log(x + sqrt(x * x - 1.0));`,oJ=_o+`
-  if ((x < -1.0) || (x > 1.0)) return NAN;
-  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,aJ=`
-  // Error function is calculated approximately with elementary function.
-  // See "Handbook of Mathematical Functions with Formulas,
-  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
-  float p = ${zb};
-  float a1 = ${Wb};
-  float a2 = ${Vb};
-  float a3 = ${Ub};
-  float a4 = ${Gb};
-  float a5 = ${Hb};
-
-  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));
-`,iJ="return 1.0 / x;",uJ="return float(!(x >= 1.0));",Hm="return x;";let cJ="return x;",lJ=`
-  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;
-`,kD=`
-  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;
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-  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;
-`,CD=`
-  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 Tf{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=`
-      vec4 unaryOperation(vec4 x) {
-        ${e}
-      }
-
-      void main() {
-        vec4 x = getAAtOutCoords();
-        vec4 y = unaryOperation(x);
-
-        setOutput(y);
-      }
-    `}}class hJ{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;let e=t.length,r=nr("rc",e),s=We(e),u=tX(e,r),l=r.slice(-2),h=e<=1?"rc":`vec2(${l.join(",")})`;this.userCode=`
-      void main() {
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-
-        setOutput(getChannel(packedInput, ${h}));
-      }
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Please use tf.complex(real, imag).");let s={};return this.texData.set(s,{shape:e,dtype:r,values:t,usage:Hr.UPLOAD,refCount:1,complexParentRefCount:0}),s}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,r,s){if(ft().getBool("DEBUG")&&this.checkNumericalProblems(e),s==="complex64")throw new Error("Cannot write to a complex64 dtype. 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p=this.dataRefCount.get(l.slice.origDataId)||1;return this.dataRefCount.set(l.slice.origDataId,p+1),u}stridedSlice(t,e,r,s){let u=this.tryRunOnCpuOrThrow([t],()=>this.cpuBackend.stridedSlice(t,e,r,s));if(u)return u;let l=sd(e,r,s);if(l.some(p=>p===0))return vn([],l);let h=new $7(e,s,l);return this.compileAndRun(h,[t])}reverse(t,e){let r=ft().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new S7(t.shape,e):new k7(t.shape,e);return this.compileAndRun(r,[t])}neg(t){let e=this.tryRunOnCpuOrThrow([t],()=>this.cpuBackend.neg(t));if(e)return e;if(ft().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,yD,t.dtype);let r=new ye(t.shape,yD);return this.compileAndRun(r,[t])}batchMatMul(t,e,r,s){let u=r?t.shape[2]:t.shape[1],l=s?e.shape[1]:e.shape[2],h=r?t.shape[1]:t.shape[2],p=Math.max(t.shape[0],e.shape[0]);if((u===1||l===1)&&h>ID){r&&(t=re(t,[0,2,1])),s&&(e=re(e,[0,2,1]));let b=l===1?t:t.as3D(p,h,1),x=l===1?2:1,S=l===1?e.as3D(p,1,h):e,C=st(b,S);return C.sum(x,!0)}let m=Qn(t.dtype,e.dtype),y=new cx(t.shape,e.shape,[p,u,l],r,s);return this.compileAndRun(y,[t,e],m)}fusedBatchMatMul({a:t,b:e,transposeA:r,transposeB:s,bias:u,activation:l,preluActivationWeights:h}){let p=r?t.shape[2]:t.shape[1],m=s?e.shape[1]:e.shape[2],y=Math.max(t.shape[0],e.shape[0]),b=Qn(t.dtype,e.dtype),x=u!=null,S=h!=null,C=l?jm(l,!0):null,I=new cx(t.shape,e.shape,[y,p,m],r,s,x,C,S),D=[t,e];return u&&D.push(u),h&&D.push(h),this.compileAndRun(I,D,b)}localResponseNormalization4D(t,e,r,s,u){let l=ft().getBool("WEBGL_PACK_NORMALIZATION")?new o7(t.shape,e,r,s,u):new r7(t.shape,e,r,s,u);return this.compileAndRun(l,[t])}LRNGrad(t,e,r,s,u,l,h){let p=new s7(e.shape,s,u,l,h);return this.compileAndRun(p,[e,r,t])}tile(t,e){if(t.dtype==="string"){let s=this.readSync(t.dataId),u=s.map(h=>rh(h)),l=Ae(t.shape,t.dtype,u);return pJ(l,e)}let r=new P7(t.shape,e);return this.compileAndRun(r,[t])}pad(t,e,r){let s=ft().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new g7(t.shape,e,r):new m7(t.shape,e,r);return 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this.compileAndRun(u,[t])}diag(t){let e=new NY(t.size);return this.compileAndRun(e,[t])}cropAndResize(t,e,r,s,u,l){let h=new TY(t.shape,e.shape,s,u,l);return this.compileAndRun(h,[t,e,r],"float32")}depthToSpace(t,e,r){k(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`);let s=t.shape[0],u=r==="NHWC"?t.shape[1]:t.shape[2],l=r==="NHWC"?t.shape[2]:t.shape[3],h=r==="NHWC"?t.shape[3]:t.shape[1],p=u*e,m=l*e,y=h/(e*e),b=r==="NHWC"?[s,p,m,y]:[s,y,p,m],x=new CY(b,e,r);return this.compileAndRun(x,[t])}split(t,e,r){return fJ(t,e,r)}scatterND(t,e,r){let{sliceRank:s,numUpdates:u,sliceSize:l,strides:h,outputSize:p}=Iu(e,t,r),m=[p/l,l],y=t.reshape([u,s]),b=e.reshape([u,l]);if(p===0)return GC(vn([]),r);let x=Ot(0),S=new lD(u,s,y.rank,b.rank,h,m),C=this.compileAndRun(S,[b,y,x]);return C.reshape(r)}sparseToDense(t,e,r,s){let{sliceRank:u,numUpdates:l,strides:h,outputSize:p}=Iu(e,t,r),m=!1,y=new lD(l,u,t.rank,e.rank,h,[p,1],m),b=this.compileAndRun(y,[e,t,s]);return b.reshape(r)}gatherND(t,e){let r=e.shape,s=r[r.length-1],[u,l,h,p]=nd(t,e),m=e.reshape([l,s]),y=t.reshape([t.size/h,h]),b=new RY(s,p,[l,h]),x=this.compileAndRun(b,[y,m]);return x.reshape(u)}fill(t,e,r){if(r=r||Us(e),r==="string"){let s=Kt(r,O(t));return s.fill(e),yo().makeTensor(s,t,r,this)}else{let s=new AY(t,e),u=s.getCustomSetupFunc(e);return this.compileAndRun(s,[],r,u)}}onesLike(t){if(t.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(t.shape,1,t.dtype)}zerosLike(t){return this.fill(t.shape,t.dtype==="string"?"":0,t.dtype)}linspace(t,e,r){return jb(t,e,r)}makeTensorInfo(t,e,r){let s=this.write(r,t,e);return this.texData.get(s).usage=null,{dataId:s,shape:t,dtype:e}}makeOutput(t,e,r){let{dataId:s}=this.makeTensorInfo(t,e,r);return yo().makeTensorFromDataId(s,t,e,this)}unpackTensor(t){let e=new hJ(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new l7(t.shape),r=!0;return 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For complex64 dtypes, please separate the program into real and imaginary parts.");let D=this.texData.get(I.dataId);if(D.texture==null){if(!t.packedInputs&&O(I.shape)<=ft().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:I.shape,texData:null,isUniform:!0,uniformValues:D.values};t.packedInputs&&(D.isPacked=!0,D.shape=I.shape)}else if(!!D.isPacked!==!!t.packedInputs)I=D.isPacked?this.unpackTensor(I):this.packTensor(I),p.push(I),D=this.texData.get(I.dataId);else if(D.isPacked&&!zm(D.shape,I.shape)){let R=I,A=I.shape;I.shape=D.shape,I=this.packedReshape(I,A),p.push(I),D=this.texData.get(I.dataId),R.shape=A}return this.uploadToGPU(I.dataId),{shape:I.shape,texData:D,isUniform:!1}});this.uploadToGPU(l.dataId);let y={shape:l.shape,texData:h,isUniform:!1},b=e7(t,m,y),x=this.getAndSaveBinary(b,()=>QY(this.gpgpu,t,m,y)),S=this.activeTimers!=null,C;if(S&&(C=this.startTimer()),t7(this.gpgpu,x,m,y,s),p.forEach(I=>this.disposeIntermediateTensorInfo(I)),S&&(C=this.endTimer(C),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(C)})),!ft().getBool("WEBGL_LAZILY_UNPACK")&&h.isPacked&&u===!1){let I=this.unpackTensor(l);return this.disposeIntermediateTensorInfo(l),I}return l}compileAndRun(t,e,r,s,u=!1){r=r||e[0].dtype;let l=this.runWebGLProgram(t,e,r,s,u);return yo().makeTensorFromDataId(l.dataId,l.shape,l.dtype)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!ft().getBool("IS_TEST")){let t=Object.keys(this.binaryCache);t.forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})}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=ot(()=>{if(!ft().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=ft().getBool("DEBUG");ft().set("DEBUG",!1);let e=this.abs(Ot(1e-8)).dataSync()[0];if(ft().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?gJ:vJ}uploadToGPU(t){let e=this.texData.get(t),{shape:r,dtype:s,values:u,texture:l,usage:h,isPacked:p}=e;if(l!=null)return;let m=this.activeTimers!=null,y;m&&(y=cr());let b=e.texShape;if(b==null&&(b=$8(r,p),e.texShape=b),u!=null){let x=ox(r),S,C=b[1],I=b[0],D=u instanceof Uint8Array;p?([C,I]=ic(b[0],b[1]),S=new $Y(x,[I,C],D)):S=new DY(x,[I,C],D);let R=this.makeTensorInfo([I,C],s);D?this.texData.get(R.dataId).usage=Hr.PIXELS:this.texData.get(R.dataId).usage=Hr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(R.dataId),C,I,u);let A=!0,L=this.runWebGLProgram(S,[R],s,null,A),_=this.texData.get(L.dataId);e.texture=_.texture,e.texShape=_.texShape,e.isPacked=_.isPacked,e.usage=_.usage,this.disposeIntermediateTensorInfo(R),this.texData.delete(L.dataId),e.values=null,m&&(this.uploadWaitMs+=cr()-y)}else{let x=this.acquireTexture(b,h,s,p);e.texture=x}}convertAndCacheOnCPU(t,e){let r=this.texData.get(t),{dtype:s}=r;return this.releaseGPUData(t),e!=null&&(r.values=kJ(e,s)),r.values}acquireTexture(t,e,r,s){if(this.numBytesInGPU+=this.computeBytes(t,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let u=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${u} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,s)}computeBytes(t,e){return t[0]*t[1]*gn(e)}tryRunOnCpuOrThrow(t,e){if(this.shouldExecuteOnCPU(t))try{return e()}catch(r){if(ft().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function kJ(n,t){if(t==="float32"||t==="complex64")return n;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(n.length):new Uint8Array(n.length);for(let r=0;r<e.length;++r)e[r]=Math.round(n[r]);return e}else throw new Error(`Unknown dtype ${t}`)}let SJ="2.7.0";function CJ(){ft().set("WEBGL_FORCE_F16_TEXTURES",!0)}Sy()&&Hy("webgl",()=>new TJ,2);let Hft={forceHalfFloat:CJ};function Fo(n){let{inputs:t,backend:e}=n,{x:r}=t;return e.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}let NJ={kernelName:Al,backendName:"webgl",kernelFunc:Fo};function dc(n){let{inputs:t,backend:e}=n,{real:r,imag:s}=t,u=e.makeTensorInfo(r.shape,"complex64"),l=e.texData.get(u.dataId),h=Fo({inputs:{x:r},backend:e}),p=e.texData.get(h.dataId);p.complexParentRefCount++;let m=Fo({inputs:{x:s},backend:e}),y=e.texData.get(m.dataId);return y.complexParentRefCount++,l.complexTensorInfos={real:h,imag:m},u}let IJ={kernelName:mp,backendName:"webgl",kernelFunc:dc};let ED="if (isnan(x)) return x;",EJ=`
-  if (isnan(a)) return a;
-  if (isnan(b)) return b;
-`,DJ=`
-  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 Km(n){return({inputs:t,backend:e})=>{let{x:r}=t,s=e,u=new ye(r.shape,n);return s.runWebGLProgram(u,[r],r.dtype)}}function mc({opSnippet:n,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:u}){return({inputs:l,backend:h})=>{let{a:p,b:m}=l,y=h;if(r&&p.dtype==="complex64"){let C=y.texData.get(p.dataId),I=y.texData.get(m.dataId),[D,R]=[[C.complexTensorInfos.real,I.complexTensorInfos.real],[C.complexTensorInfos.imag,I.complexTensorInfos.imag]].map(L=>{let[_,B]=L,V={dataId:_.dataId,dtype:_.dtype,shape:p.shape},q={dataId:B.dataId,dtype:B.dtype,shape:m.shape},j=new Yn(n,p.shape,m.shape);return y.runWebGLProgram(j,[V,q],Qn(_.dtype,B.dtype))}),A=dc({inputs:{real:D,imag:R},backend:y});return y.disposeIntermediateTensorInfo(D),y.disposeIntermediateTensorInfo(R),A}let b=u||Qn(p.dtype,m.dtype);if(y.shouldExecuteOnCPU([p,m])&&s!=null){let C=y.texData.get(p.dataId),I=y.texData.get(m.dataId),[D,R]=s(p.shape,m.shape,C.values,I.values,b),A=y.makeTensorInfo(R,b),L=y.texData.get(A.dataId);return L.values=D,A}let x=ft().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,S;return x?S=new Ao(t,p.shape,m.shape,e):S=new Yn(n,p.shape,m.shape),y.runWebGLProgram(S,[p,m],b)}}let DD="return a + b;",$J=mc({opSnippet:DD,packedOpSnippet:DD,supportsComplex:!0,cpuKernelImpl:B8}),AJ={kernelName:ri,backendName:"webgl",kernelFunc:$J};let _J=EJ+`
-  return atan(a, b);
-`,FJ=`
-  vec4 result = atan(a, b);
-  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+DJ+`
-  return result;
-`,RJ=mc({opSnippet:_J,packedOpSnippet:FJ}),PJ={kernelName:fp,backendName:"webgl",kernelFunc:RJ};function OJ(n){let{inputs:t,backend:e,attrs:r}=n,{x:s}=t;bf(s,"avgPool");let{filterSize:u,strides:l,pad:h,dimRoundingMode:p}=r,m=1;k(wn(l,m),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let y=tr(s.shape,u,l,m,h,p);if(y.filterWidth===1&&y.filterHeight===1&&K(y.inShape,y.outShape))return Fo({inputs:{x:s},backend:e});let b=new xf(y,"avg",!1);return e.runWebGLProgram(b,[s],"float32")}let MJ={kernelName:wl,backendName:"webgl",kernelFunc:OJ};function LJ(n){let{inputs:t,backend:e,attrs:r}=n,{dy:s,input:u}=t,l=u;bf([s,u],"avgPoolBackprop");let{filterSize:h,strides:p,pad:m}=r,y=tr(l.shape,h,p,1,m),b=new LX(y);return e.runWebGLProgram(b,[s],l.dtype)}let BJ={kernelName:pp,backendName:"webgl",kernelFunc:LJ};class zJ{constructor(t,e,r,s,u,l){this.outputShape=[],this.variableNames=["x","mean","variance"],ve(t,e),ve(t,r);let h="0.0";s!=null&&(ve(t,s),this.variableNames.push("offset"),h="getOffsetAtOutCoords()");let p="1.0";u!=null&&(ve(t,u),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
-      void main() {
-        float x = getXAtOutCoords();
-        float mean = getMeanAtOutCoords();
-        float variance = getVarianceAtOutCoords();
-        float offset = ${h};
-        float scale = ${p};
-        float inv = scale * inversesqrt(variance + float(${l}));
-        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
-      }
-    `}}class WJ{constructor(t,e,r,s,u,l){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],ve(t,e),ve(t,r);let h="vec4(0.0)";s!=null&&(ve(t,s),this.variableNames.push("offset"),h="getOffsetAtOutCoords()");let p="vec4(1.0)";u!=null&&(ve(t,u),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
-      void main() {
-        vec4 offset = ${h};
-        vec4 scale = ${p};
-
-        vec4 x = getXAtOutCoords();
-        vec4 mean = getMeanAtOutCoords();
-        vec4 variance = getVarianceAtOutCoords();
-
-        vec4 inv = scale * inversesqrt(variance + vec4(${l}));
-
-        setOutput((x - mean) * inv + offset);
-      }
-    `}}let VJ=({inputs:n,backend:t,attrs:e})=>{let{x:r,mean:s,variance:u,offset:l,scale:h}=n;k(s.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k(l==null||s.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k(h==null||s.shape.length===h.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=e;p==null&&(p=.001);let m=[r,s,u],y=null;l!=null&&(y=l.shape,m.push(l));let b=null;h!=null&&(b=h.shape,m.push(h));let x=ft().getBool("WEBGL_PACK_NORMALIZATION")?new WJ(r.shape,s.shape,u.shape,y,b,p):new zJ(r.shape,s.shape,u.shape,y,b,p),S=t.runWebGLProgram(x,m,m[0].dtype);return S},UJ={kernelName:$l,backendName:"webgl",kernelFunc:VJ};let GJ="return float(a != b);",$D=mc({opSnippet:GJ,dtype:"bool"}),HJ={kernelName:zl,backendName:"webgl",kernelFunc:$D};function fx(n){let{inputs:t,backend:e}=n,{input:r}=t,s=e.texData.get(r.dataId);return Fo({inputs:{x:s.complexTensorInfos.real},backend:e})}let qJ={kernelName:Rp,backendName:"webgl",kernelFunc:fx};let jJ="return float(int(x));";function KJ(n,t){let e=new ye(n.shape,jJ),r=t.runWebGLProgram(e,[n],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function px(n){let{inputs:t,backend:e,attrs:r}=n,{x:s}=t,{dtype:u}=r;if(u==="complex64"){if(s.dtype==="complex64")return Fo({inputs:{x:s},backend:e});let l=Se(s.shape),h=px({inputs:{x:s},backend:e,attrs:{dtype:"float32"}}),p=dc({inputs:{real:h,imag:l},backend:e});return l.dispose(),e.disposeIntermediateTensorInfo(h),p}if(s.dtype==="complex64"){let l=fx({inputs:{input:s},backend:e}),h=px({inputs:{x:l},backend:e,attrs:{dtype:u}});return e.disposeIntermediateTensorInfo(l),h}if(!me(s.dtype,u)){let l=Fo({inputs:{x:s},backend:e});return{dataId:l.dataId,shape:l.shape,dtype:u}}if(u==="int32")return KJ(s,e);if(u==="bool"){let l=e.makeTensorInfo([],"bool",_t("bool",1)),h={a:s,b:l},p=$D({inputs:h,backend:e});return e.disposeIntermediateTensorInfo(l),p}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${u}`)}let XJ={kernelName:pu,backendName:"webgl",kernelFunc:px};class YJ{constructor(t){this.outputShape=[],this.outputShape=bo(t,1),this.variableNames=t.map((l,h)=>`T${h}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let l=1;l<e.length;l++)e[l]=e[l-1]+t[l][1];let r=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let l=1;l<e.length;l++){let h=e[l-1];r.push(`else if (yC < ${e[l]}) setOutput(getT${l}(yR, yC-${h}));`)}let s=e.length,u=e[e.length-1];r.push(`else setOutput(getT${s}(yR, yC-${u}));`),this.userCode=`
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int yR = coords.x;
-        int yC = coords.y;
-
-        ${r.join(`
-        `)}
-      }
-    `}}class JJ{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=bo(t,e);let r=this.outputShape,s=r.length,u=We(s),l=nr("coords",s),h=["x","y","z","w","u","v"].slice(0,s);this.variableNames=t.map((I,D)=>`T${D}`);let p=new Array(t.length-1);p[0]=t[0][e];for(let I=1;I<p.length;I++)p[I]=p[I-1]+t[I][e];let m=h[e],y=h.slice(-2),b=h.join(),x=`if (${m} < ${p[0]}) {
-        return getChannel(
-            getT0(${b}), vec2(${y.join()}));
-        }`;for(let I=1;I<p.length;I++){let D=p[I-1];x+=`
-        if (${m} < ${p[I]}  && ${m} >= ${p[I-1]}) {
-          return getChannel(
-            getT${I}(${Xm(h,m,D)}),
-            vec2(${Xm(y,m,D)}));
-        }`}let S=p.length,C=p[p.length-1];x+=`
-        return getChannel(
-          getT${S}(${Xm(h,m,C)}),
-          vec2(${Xm(y,m,C)}));`,this.userCode=`
-      float getValue(${h.map(I=>"int "+I)}) {
-        ${x}
-      }
-
-      void main() {
-        ${u} coords = getOutputCoords();
-        vec4 result = vec4(getValue(${l}), 0., 0., 0.);
-
-        ${l[s-1]} = ${l[s-1]} + 1;
-        if (${l[s-1]} < ${r[s-1]}) {
-          result.g = getValue(${l});
-        }
-
-        ${l[s-2]} = ${l[s-2]} + 1;
-        if (${l[s-2]} < ${r[s-2]}) {
-          result.a = getValue(${l});
-        }
-
-        ${l[s-1]} = ${l[s-1]} - 1;
-        if (${l[s-2]} < ${r[s-2]} &&
-            ${l[s-1]} < ${r[s-1]}) {
-          result.b = getValue(${l});
-        }
-        setOutput(result);
-      }
-    `}}function Xm(n,t,e){let r=n.indexOf(t),s=n.map((u,l)=>l===r?`${u} - ${e}`:u);return s.join()}function AD(n){let{inputs:t,backend:e}=n,{input:r}=t,s=e.texData.get(r.dataId);return Fo({inputs:{x:s.complexTensorInfos.imag},backend:e})}let ZJ={kernelName:Np,backendName:"webgl",kernelFunc:AD};function QJ(n,t,e){let r=[uc(n.shape),...cc(n.shape)],s={dtype:n.dtype,shape:r,dataId:n.dataId},u=[uc(t),...cc(t)],l=new cD(u,r),h=!0,p=e.runWebGLProgram(l,[s],n.dtype,null,h);return{dataId:p.dataId,shape:t,dtype:p.dtype}}function Ro(n){let{inputs:t,backend:e,attrs:r}=n,{x:s}=t,{shape:u}=r,l=e,h=O(s.shape),p=Gt(u,h),m=O(p);k(h===m,()=>`The new shape (${p}) has ${m} elements and the old shape (${s.shape}) has ${h} elements. The new shape and old shape must have the same number of elements.`);let y=l.texData.get(s.dataId);return y.isPacked&&!zm(s.shape,p)&&!(y.texture!==null&&zm(y.shape,p))?QJ(s,p,l):(l.incRef(s.dataId),{dataId:s.dataId,shape:p,dtype:s.dtype})}let t9={kernelName:Ul,backendName:"webgl",kernelFunc:Ro};function gc(n,t,e){let r=n[0].dtype;if(r==="complex64"){let m=n.map(C=>fx({inputs:{input:C},backend:e})),y=n.map(C=>AD({inputs:{input:C},backend:e})),b=gc(m,t,e),x=gc(y,t,e),S=dc({inputs:{real:b,imag:x},backend:e});return m.forEach(C=>e.disposeIntermediateTensorInfo(C)),y.forEach(C=>e.disposeIntermediateTensorInfo(C)),e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(x),S}if(n.length>ft().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let m=Math.floor(n.length/2),y=gc(n.slice(0,m),t,e),b=gc(n.slice(m),t,e),x=gc([y,b],t,e);return e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),x}if(ft().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&n[0].shape.length>1){let m=new JJ(n.map(y=>y.shape),t);return e.runWebGLProgram(m,n,r)}let s=bo(n.map(m=>m.shape),t),u=n.map(m=>Ro({inputs:{x:m},attrs:{shape:[-1,O(m.shape.slice(t))]},backend:e})),l=new YJ(u.map(m=>m.shape)),h=e.runWebGLProgram(l,u,r);u.forEach(m=>e.disposeIntermediateTensorInfo(m));let p=Ro({inputs:{x:h},attrs:{shape:s},backend:e});return e.disposeIntermediateTensorInfo(h),p}function e9(n){let{inputs:t,backend:e,attrs:r}=n,{axis:s}=r,u=Et(s,t[0].shape)[0],l=bo(t.map(m=>m.shape),u);if(O(l)===0)return e.makeTensorInfo(l,t[0].dtype,[]);let h=t.filter(m=>O(m.shape)>0);if(h.length===1)return h[0];let p=h.map(m=>m.shape);return fd(p,u),gc(h,u,e)}let n9={kernelName:kl,backendName:"webgl",kernelFunc:e9};let r9=ED+`
-  return cos(x);
-`,s9=Km(r9),o9={kernelName:du,backendName:"webgl",kernelFunc:s9};let a9=`
-if (a == b) {
-  return 1.0;
-};
-return a / b;`,i9=`
-  // 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;
-`,u9=mc({opSnippet:a9,packedOpSnippet:i9,checkOutOfBounds:!0}),c9={kernelName:mu,backendName:"webgl",kernelFunc:u9};class _D{constructor(t,e,r){this.variableNames=["real","imag"];let s=e[1];this.outputShape=e;let u=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,l=r?`${s}.0`:"1.0",h;if(t==="real")h="return real * expR - imag * expI;";else if(t==="imag")h="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
-      const float exponentMultiplier = ${u};
-
-      float unaryOpComplex(float real, float expR, float imag, float expI) {
-        ${h}
-      }
-
-      float mulMatDFT(int batch, int index) {
-        float indexRatio = float(index) / float(${s});
-        float exponentMultiplierTimesIndexRatio =
-            exponentMultiplier * indexRatio;
-
-        float result = 0.0;
-
-        for (int i = 0; i < ${s}; 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) / ${l};
-        }
-
-        return result;
-      }
-
-      void main() {
-        ivec2 coords = getOutputCoords();
-        setOutput(mulMatDFT(coords[0], coords[1]));
-      }
-    `}}function FD(n,t,e){let r=e.texData.get(n.dataId),s=O(n.shape),u=n.shape[n.shape.length-1],l=s/u,h=Ro({inputs:{x:n},backend:e,attrs:{shape:[l,u]}}),p=h.shape,m=new _D("real",p,t),y=new _D("imag",p,t),b=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:p},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:p}],x=e.runWebGLProgram(m,b,"float32"),S=e.runWebGLProgram(y,b,"float32"),C=dc({inputs:{real:x,imag:S},backend:e});e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(S);let I=Ro({inputs:{x:C},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(I),I}function l9(n){let{inputs:t,backend:e}=n,{input:r}=t;return FD(r,!1,e)}let h9={kernelName:kp,backendName:"webgl",kernelFunc:l9};class f9{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
-        void main() {
-          ivec4 coords = getOutputCoords();
-          int x = coords[2];
-
-          int coordX = ${e} - x;
-          float outputValue;
-          if(coordX >= 0 && coordX < ${e}) {
-            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
-          } else {
-            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
-          }
-          setOutput(outputValue);
-        }
-    `}}let p9={kernelName:Sp,backendName:"webgl",kernelFunc:({inputs:n,backend:t})=>{let{image:e}=n,r=t,s=new f9(e.shape),u=r.runWebGLProgram(s,[e],e.dtype);return u}};class d9{constructor(t){this.variableNames=["A"];let e=rr(),[r,s]=t;this.outputShape=t,this.userCode=`
-      void main() {
-        ivec3 coords = getOutputCoords();
-        int texR = coords[0];
-        int texC = coords[1];
-        int depth = coords[2];
-        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${r}.0);
-
-        vec4 values = ${e.texture2D}(A, uv);
-        float value;
-        if (depth == 0) {
-          value = values.r;
-        } else if (depth == 1) {
-          value = values.g;
-        } else if (depth == 2) {
-          value = values.b;
-        } else if (depth == 3) {
-          value = values.a;
-        }
-
-        setOutput(floor(value * 255.0 + 0.5));
-      }
-    `}}class m9{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=rr(),[r,s]=t;this.outputShape=t,this.userCode=`
-      void main() {
-        ivec3 coords = getOutputCoords();
-        int texR = coords[0];
-        int texC = coords[1];
-        int depth = coords[2];
-
-        vec4 result = vec4(0.);
-
-        for(int row=0; row<=1; row++) {
-          for(int col=0; col<=1; col++) {
-            texC = coords[1] + row;
-            depth = coords[2] + col;
-
-            vec2 uv = (vec2(texC, texR) + halfCR) /
-                       vec2(${s}.0, ${r}.0);
-            vec4 values = ${e.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);
-          }
-        }
-
-        ${e.output} = result;
-      }
-    `}}let g9={kernelName:Bp,backendName:"webgl",kernelFunc:v9},vc;function v9(n){let{inputs:t,backend:e,attrs:r}=n,{pixels:s}=t,{numChannels:u}=r,l=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,h=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[p,m]=l?[s.videoWidth,s.videoHeight]:[s.width,s.height],y=[m,p],b=[m,p,u];(h||l)&&(vc==null&&(vc=document.createElement("canvas").getContext("2d")),vc.canvas.width=p,vc.canvas.height=m,vc.drawImage(s,0,0,p,m),s=vc.canvas);let x=e.makeTensorInfo(y,"int32");e.texData.get(x.dataId).usage=Hr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(x.dataId),s);let S=ft().getBool("WEBGL_PACK")?new m9(b):new d9(b),C=e.runWebGLProgram(S,[x],"int32");return e.disposeData(x.dataId),C}function y9(n){let{inputs:t,backend:e}=n,{input:r}=t;return FD(r,!0,e)}let b9={kernelName:Cp,backendName:"webgl",kernelFunc:y9};class RD{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:s,inSize:u,outSize:l}=t;this.outputShape=[s,l];let h=Math.floor(r/4)*4,p=r%4,m="sumValue += dot(values, ones);";if(e!=null){let b=1/e;m=`sumValue += dot(values * ${nt(b)?b.toPrecision(2):b}, ones);`}let y="";u%r>0&&(y=`
-        if (inIdx < 0 || inIdx >= ${u}) {
-          return 0.0;
-        }
-      `),this.userCode=`
-      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
-
-      float getValue(int batch, int inIdx) {
-        ${y}
-        return getX(batch, inIdx);
-      }
-
-      void main() {
-        ivec2 coords = getOutputCoords();
-        int batch = coords[0];
-        int outIdx = coords[1];
-        int inOffset = outIdx * ${r};
-
-        float sumValue = 0.0;
-
-        for (int i = 0; i < ${h}; i += 4) {
-          int inIdx = inOffset + i;
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2),
-            getValue(batch, inIdx + 3)
-          );
-
-          ${m}
-        }
-
-        int inIdx = inOffset + ${h};
-        if (${p===1}) {
-          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
-
-          ${m}
-        } else if (${p===2}) {
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1), 0.0, 0.0);
-
-          ${m}
-        } else if (${p===3}) {
-          vec4 values = vec4(
-            getValue(batch, inIdx),
-            getValue(batch, inIdx + 1),
-            getValue(batch, inIdx + 2), 0.0);
-
-          ${m}
-        }
-        setOutput(sumValue);
-      }
-    `}}function w9(n){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:n[1],r=yh(e);t.push({inSize:e,windowSize:r,outSize:Math.ceil(e/r)})}return t}function PD(n,t,e,r){let s=w9(n.shape),u=n;for(let l=0;l<s.length;l++){let{inSize:h,windowSize:p,outSize:m}=s[l],y,b;e==="mean"?y=l===0?new RD({windowSize:p,inSize:h,batchSize:n.shape[0],outSize:m},h):new RD({windowSize:p,inSize:h,batchSize:n.shape[0],outSize:m}):y=new uD({windowSize:p,inSize:h,batchSize:n.shape[0],outSize:m},e),b=u,u=r.runWebGLProgram(y,[u],t),b.dataId!==n.dataId&&r.disposeIntermediateTensorInfo(b)}return u}function x9(n,t,e,r){let s=O(t),u=O(n.shape),l=u/s,h=Ro({inputs:{x:n},attrs:{shape:[l,s]},backend:r}),p=PD(h,n.dtype,"max",r),m=Ro({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),m}class T9{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let l=0;l<r.length;l++)r[l]=t[e[l]];this.outputShape=r,this.rank=r.length;let s=We(this.rank),u=k9(e);this.userCode=`
-    void main() {
-      ${s} resRC = getOutputCoords();
-      setOutput(getA(${u}));
-    }
-    `}}function k9(n){let t=n.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let s=0;s<n.length;s++)r[n[s]]=e[s];return r.join()}class S9{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(t.length);for(let y=0;y<r.length;y++)r[y]=t[e[y]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=We(this.rank),u=VE("rc",this.rank),l=new Array(this.rank);for(let y=0;y<e.length;y++)l[e[y]]=u[y];let h=`vec2(${l.slice(-2).join()})`,p=`++${u[this.rank-1]} < ${r[this.rank-1]}`,m=`getChannel(getA(${l.join()}), ${h})`;this.userCode=`
-    void main() {
-      ${s} rc = getOutputCoords();
-      vec4 result = vec4(0.);
-      result[0] = ${m};
-      if(${p}) {
-        result[1] = ${m};
-      }
-      --${u[this.rank-1]};
-      if(++${u[this.rank-2]} < ${r[this.rank-2]}) {
-        result[2] = ${m};
-        if(${p}) {
-          result[3] = ${m};
-        }
-      }
-      setOutput(result);
-    }
-    `}}function dx(n,t,e){let r=ft().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new S9(n.shape,t):new T9(n.shape,t);return e.runWebGLProgram(r,[n],n.dtype)}let C9={kernelName:Ml,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{reductionIndices:s,keepDims:u}=t,l=e,h=r.shape.length,p=Et(s,r.shape),m=p,y=hr(m,h),b=y!=null,x=l.shouldExecuteOnCPU([r]),S=r;if(b){if(x){let A=l.texData.get(S.dataId),L=A.values,_=new Array(h);for(let q=0;q<_.length;q++)_[q]=r.shape[y[q]];let B=ix(L,r.shape,r.dtype,y,_);S=l.makeTensorInfo(_,r.dtype);let V=l.texData.get(S.dataId);V.values=B}else S=dx(r,y,l);m=Cr(m.length,h)}lr("max",m,h);let[C,I]=Bn(S.shape,m),D=C;u&&(D=zn(C,p));let R;if(x){let A=l.texData.get(S.dataId),L=A.values,_=H8(L,O(I),D,r.dtype);R=l.makeTensorInfo(D,r.dtype);let B=l.texData.get(R.dataId);B.values=_}else R=x9(S,I,D,l);return b&&l.disposeIntermediateTensorInfo(S),R}};function N9(n){let{inputs:t,backend:e,attrs:r}=n,{x:s}=t;bf(s,"maxPool");let{filterSize:u,strides:l,pad:h,dimRoundingMode:p}=r,m=1;k(wn(l,m),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let y=tr(s.shape,u,l,m,h,p);if(y.filterWidth===1&&y.filterHeight===1&&K(y.inShape,y.outShape))return Fo({inputs:{x:s},backend:e});let b=new xf(y,"max",!1);return e.runWebGLProgram(b,[s],s.dtype)}let I9={kernelName:Ll,backendName:"webgl",kernelFunc:N9};function E9(n){let{inputs:t,backend:e,attrs:r}=n,{dy:s,input:u,output:l}=t,h=u;bf([u,l],"maxPoolBackprop");let{filterSize:p,strides:m,pad:y,dimRoundingMode:b}=r,x=tr(h.shape,p,m,1,y,b),S=!0,C=new xf(x,"max",S),I=e.runWebGLProgram(C,[h],h.dtype),D=new a7(x),R=e.runWebGLProgram(D,[s,I],h.dtype);return e.disposeIntermediateTensorInfo(I),R}let D9={kernelName:Ep,backendName:"webgl",kernelFunc:E9};function $9(n,t,e,r){let s=new xf(e,"max",!1),u=r.runWebGLProgram(s,[n],"float32");s=new xf(e,"max",!0,!0,t);let l=r.runWebGLProgram(s,[n],"float32");return[u,l]}let A9={kernelName:Dp,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{filterSize:s,strides:u,pad:l,includeBatchInIndex:h}=t,p=e;k(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let m=[1,1];k(wn(u,m),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let y=tr(r.shape,s,u,m,l),[b,x]=$9(r,h,y,p);return[b,x]}};function _9(n,t,e,r){let s=O(t),u=O(n.shape),l=u/s,h=Ro({inputs:{x:n},attrs:{shape:[l,s]},backend:r}),p=PD(h,"float32","mean",r),m=Ro({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),m}let F9={kernelName:Yv,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{keepDims:s,axis:u}=t,l=e,h=r.shape.length,p=Et(u,r.shape),m=p,y=hr(m,h),b=y!=null,x=l.shouldExecuteOnCPU([r]),S=[],C=r;if(b){if(x){let L=l.texData.get(C.dataId),_=L.values,B=new Array(h);for(let j=0;j<B.length;j++)B[j]=r.shape[y[j]];let V=ix(_,r.shape,r.dtype,y,B);C=l.makeTensorInfo(B,r.dtype);let q=l.texData.get(C.dataId);q.values=V}else C=dx(r,y,l);S.push(C),m=Cr(m.length,h)}lr("sum",m,h);let[I,D]=Bn(C.shape,m),R=I;s&&(R=zn(I,p));let A=_9(C,D,R,l);for(let L of S)l.disposeIntermediateTensorInfo(L);return A}};class R9{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((y,b)=>y[0]+t[b]+y[1]);let s=t.length,u=We(s),l=e.map(y=>y[0]).join(","),h=e.map((y,b)=>y[0]+t[b]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),m=r==="reflect"?0:1;if(s===1){this.userCode=`
-        int start = ${l};
-        int end = ${h};
-
-        void main() {
-          int outC = getOutputCoords();
-          if (outC < start) {
-            outC = start * 2 - outC - ${m};
-          } else if(outC >= end) {
-            outC = (end - 1) * 2 - outC + ${m};
-          }
-          setOutput(getX(outC - start));
-        }
-      `;return}this.userCode=`
-      ${u} start = ${u}(${l});
-      ${u} end = ${u}(${h});
-
-      void main() {
-        ${u} outC = getOutputCoords();
-        for (int i = 0; i < ${s}; i++) {
-          if (outC[i] < start[i]) {
-            outC[i] = start[i] * 2 - outC[i] - ${m};
-          } else if(outC[i] >= end[i]) {
-            outC[i] = (end[i] - 1) * 2 - outC[i] + ${m};
-          }
-        }
-        ${u} coords = outC - start;
-        setOutput(getX(${p}));
-      }
-    `}}class P9{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((C,I)=>C[0]+t[I]+C[1]);let s=t.length,u=We(s),l=e.map(C=>C[0]).join(","),h=e.map((C,I)=>C[0]+t[I]).join(","),p=nr("rc",s),m=nr("source",s),y=`${p[s-1]} < ${this.outputShape[s-1]}`,b=s===1?"source":`vec2(${m.slice(-2).join()})`,x=r==="reflect"?0:1,S="";if(s===1){let C=`
-        ${u} source = rc;
-        if (source < start) {
-          source = start * 2 - source - ${x};
-        } else if (source >= end) {
-          source = (end - 1) * 2 - source + ${x};
-        }
-        source -= start;
-      `;S=`
-        ${u} rc = outputLoc;
-        ${C}
-        result[0] = getChannel(getX(${m.join()}), ${b});
-        ${p[s-1]} += 1;
-        if(${y}) {
-          ${C}
-          result[1] = getChannel(getX(${m.join()}), ${b});
-        }
-      `}else{let C=`
-        ${u} source = rc;
-        ${u} lt = ${u}(lessThan(source, start));
-        ${u} gte = ${u}(greaterThanEqual(source, end));
-        ${u} orig = 1 - (lt + gte);
-        source = orig * source +
-                lt * (start * 2 - source - ${x}) +
-                gte * ((end - 1) * 2 - source + ${x});
-        source -= start;
-      `;S=`
-        ${u} rc = outputLoc;
-        ${C}
-        result[0] = getChannel(getX(${m.join()}), ${b});
-        ${p[s-1]} += 1;
-        if(${y}) {
-          ${C}
-          result[1] = getChannel(getX(${m.join()}), ${b});
-        }
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"+("  Buffer shape="+this.shape);throw new Error(v)}c++}for(var w=a[a.length-1],T=0;T<a.length-1;++T)w+=this.strides[T]*a[T];return this.values[w]},o.prototype.locToIndex=function(a){if(this.rank===0)return 0;if(this.rank===1)return a[0];for(var i=a[a.length-1],c=0;c<a.length-1;++c)i+=this.strides[c]*a[c];return i},o.prototype.indexToLoc=function(a){if(this.rank===0)return[];if(this.rank===1)return[a];for(var i=new Array(this.shape.length),c=0;c<i.length-1;++c)i[c]=Math.floor(a/this.strides[c]),a-=i[c]*this.strides[c];return i[i.length-1]=a,i},Object.defineProperty(o.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),o.prototype.toTensor=function(){return io().makeTensor(this.values,this.shape,this.dtype)},o}(),io=null,$c=null;function NQ(o){io=o}function IQ(o){$c=o}var ut=function(){function o(a,i,c,f){this.kept=!1,this.isDisposedInternal=!1,this.shape=a.slice(),this.dtype=i||"float32",this.size=mn(a),this.strides=Nc(a),this.dataId=c,this.id=f,this.rankType=this.rank<5?this.rank.toString():"higher"}return Object.defineProperty(o.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),o.prototype.buffer=function(){return qt(this,void 0,void 0,function(){var a;return jt(this,function(i){switch(i.label){case 0:return[4,this.data()];case 1:return a=i.sent(),[2,$c.buffer(this.shape,this.dtype,a)]}})})},o.prototype.bufferSync=function(){return $c.buffer(this.shape,this.dtype,this.dataSync())},o.prototype.array=function(){return qt(this,void 0,void 0,function(){var a;return jt(this,function(i){switch(i.label){case 0:return[4,this.data()];case 1:return a=i.sent(),[2,Ic(this.shape,a)]}})})},o.prototype.arraySync=function(){return Ic(this.shape,this.dataSync())},o.prototype.data=function(){return qt(this,void 0,void 0,function(){var a,i;return jt(this,function(c){switch(c.label){case 0:return this.throwIfDisposed(),a=io().read(this.dataId),this.dtype==="string"?[4,a]:[3,2];case 1:i=c.sent();try{return[2,i.map(function(f){return UT(f)})]}catch(f){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}c.label=2;case 2:return[2,a]}})})},o.prototype.dataSync=function(){this.throwIfDisposed();var a=io().readSync(this.dataId);if(this.dtype==="string")try{return a.map(function(i){return UT(i)})}catch(i){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return a},o.prototype.bytes=function(){return qt(this,void 0,void 0,function(){var a;return jt(this,function(i){switch(i.label){case 0:return this.throwIfDisposed(),[4,io().read(this.dataId)];case 1:return a=i.sent(),this.dtype==="string"?[2,a]:[2,new Uint8Array(a.buffer)]}})})},o.prototype.dispose=function(){if(this.isDisposed)return;io().disposeTensor(this),this.isDisposedInternal=!0},Object.defineProperty(o.prototype,"isDisposed",{get:function(){return this.isDisposedInternal},enumerable:!0,configurable:!0}),o.prototype.throwIfDisposed=function(){if(this.isDisposed)throw new Error("Tensor is disposed.")},o.prototype.print=function(a){return a===void 0&&(a=!1),$c.print(this,a)},o.prototype.clone=function(){return this.throwIfDisposed(),$c.clone(this)},o.prototype.toString=function(a){a===void 0&&(a=!1);var i=this.dataSync();return CQ(i,this.shape,this.dtype,a)},o.prototype.cast=function(a){return this.throwIfDisposed(),$c.cast(this,a)},o.prototype.variable=function(a,i,c){return a===void 0&&(a=!0),this.throwIfDisposed(),io().makeVariable(this,a,i,c)},o}();Object.defineProperty(ut,Symbol.hasInstance,{value:function(o){return!!o&&o.data!=null&&o.dataSync!=null&&o.throwIfDisposed!=null}});var Bf=function(o){Uo(a,o);function a(i,c,f,d){var g=o.call(this,i.shape,i.dtype,i.dataId,d)||this;return g.trainable=c,g.name=f,g}return a.prototype.assign=function(i){if(i.dtype!==this.dtype)throw new Error("dtype of the new value ("+i.dtype+") and "+("previous value ("+this.dtype+") must match"));if(!Rs(i.shape,this.shape))throw new Error("shape of the new value ("+i.shape+") and "+("previous value ("+this.shape+") must match"));io().disposeTensor(this),this.dataId=i.dataId,io().incRef(this,null)},a.prototype.dispose=function(){io().disposeVariable(this),this.isDisposedInternal=!0},a}(ut);Object.defineProperty(Bf,Symbol.hasInstance,{value:function(o){return o instanceof ut&&o.assign!=null&&o.assign instanceof Function}});(function(o){o.R0="R0",o.R1="R1",o.R2="R2",o.R3="R3",o.R4="R4",o.R5="R5",o.R6="R6"})(F.Rank||(F.Rank={}));var HT;(function(o){o.float32="float32",o.int32="int32",o.bool="int32",o.complex64="complex64"})(HT||(HT={}));var qT;(function(o){o.float32="float32",o.int32="int32",o.bool="bool",o.complex64="complex64"})(qT||(qT={}));var jT;(function(o){o.float32="float32",o.int32="float32",o.bool="float32",o.complex64="complex64"})(jT||(jT={}));var KT;(function(o){o.float32="complex64",o.int32="complex64",o.bool="complex64",o.complex64="complex64"})(KT||(KT={}));var EQ={float32:jT,int32:HT,bool:qT,complex64:KT};function vg(o,a){if(o==="string"||a==="string"){if(o==="string"&&a==="string")return"string";throw new Error("Can not upcast "+o+" with "+a)}return EQ[o][a]}function DQ(o){return vg(o,"int32")}function pn(o,a){if(o.dtype===a.dtype)return[o,a];var i=vg(o.dtype,a.dtype);return[o.cast(i),a.cast(i)]}function xA(o,a){U(o.dtype===a.dtype,function(){return"The dtypes of the first("+o.dtype+") and"+(" second("+a.dtype+") input must match")})}function $Q(o,a){return a.some(function(i){return i.id===o.id})}function XT(o){var a=[],i=new Set;return TA(o,a,i),a}function TA(o,a,i){if(o==null)return;if(o instanceof ut){a.push(o);return}if(!AQ(o))return;var c=o;for(var f in c){var d=c[f];i.has(d)||(i.add(d),TA(d,a,i))}}function AQ(o){return Array.isArray(o)||typeof o=="object"}var _Q={__proto__:null,makeTypesMatch:pn,assertTypesMatch:xA,isTensorInList:$Q,getTensorsInContainer:XT};var kA=function(){function o(){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}}return o.prototype.dispose=function(){for(var a in this.registeredVariables)this.registeredVariables[a].dispose()},o}(),PQ=function(){function o(a){this.ENV=a,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new kA}return o.prototype.ready=function(){return qt(this,void 0,void 0,function(){var a,i,c,f;return jt(this,function(d){switch(d.label){case 0:if(this.pendingBackendInit!=null)return[2,this.pendingBackendInit.then(function(){})];if(this.backendInstance!=null)return[2];a=this.getSortedBackends(),i=0,d.label=1;case 1:return i<a.length?(c=a[i],[4,this.initializeBackend(c).success]):[3,5];case 2:return f=d.sent(),f?[4,this.setBackend(c)]:[3,4];case 3:return d.sent(),[2];case 4:return i++,[3,1];case 5:throw new Error("Could not initialize any backends, all backend initializations failed.")}})})},Object.defineProperty(o.prototype,"backend",{get:function(){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){var a=this.initializeBackendsAndReturnBest(),i=a.name,c=a.asyncInit;if(c)throw new Error("The highest priority backend '"+i+"' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods");this.setBackend(i)}return this.backendInstance},enumerable:!0,configurable:!0}),o.prototype.backendNames=function(){return Object.keys(this.registryFactory)},o.prototype.findBackend=function(a){if(!(a in this.registry))if(a in this.registryFactory){var i=this.initializeBackend(a).asyncInit;if(i)return null}else return null;return this.registry[a]},o.prototype.findBackendFactory=function(a){return a in this.registryFactory?this.registryFactory[a].factory:null},o.prototype.registerBackend=function(a,i,c){return c===void 0&&(c=1),a in this.registryFactory?(console.warn(a+" backend was already registered. Reusing existing backend factory."),!1):(this.registryFactory[a]={factory:i,priority:c},!0)},o.prototype.setBackend=function(a){return qt(this,void 0,void 0,function(){var i,c,f,d,g;return jt(this,function(v){switch(v.label){case 0:if(this.registryFactory[a]==null)throw new Error("Backend name '"+a+"' not found in registry");return this.backendName=a,this.registry[a]==null?(this.backendInstance=null,i=this.initializeBackend(a),c=i.success,f=i.asyncInit,f?[4,c]:[3,2]):[3,4];case 1:return g=v.sent(),[3,3];case 2:g=c,v.label=3;case 3:if(d=g,!d)return[2,!1];v.label=4;case 4:return this.backendInstance=this.registry[a],this.setupRegisteredKernels(),this.profiler=new xQ(this.backendInstance),[2,!0]}})})},o.prototype.setupRegisteredKernels=function(){var a=this,i=pg(this.backendName);i.forEach(function(c){c.setupFunc!=null&&c.setupFunc(a.backendInstance)})},o.prototype.disposeRegisteredKernels=function(a){var i=this,c=pg(a);c.forEach(function(f){f.disposeFunc!=null&&f.disposeFunc(i.registry[a])})},o.prototype.initializeBackend=function(a){var i=this,c=this.registryFactory[a];if(c==null)throw new Error("Cannot initialize backend "+a+", no registration found.");try{var f=c.factory();if(f&&!(f instanceof r$)&&typeof f.then=="function"){var d=++this.pendingBackendInitId,g=f.then(function(v){return d<i.pendingBackendInitId?!1:(i.registry[a]=v,i.pendingBackendInit=null,!0)}).catch(function(v){return d<i.pendingBackendInitId||(i.pendingBackendInit=null,console.warn("Initialization of backend "+a+" failed"),console.warn(v.stack||v.message)),!1});return this.pendingBackendInit=g,{success:g,asyncInit:!0}}else return this.registry[a]=f,{success:!0,asyncInit:!1}}catch(v){return console.warn("Initialization of backend "+a+" failed"),console.warn(v.stack||v.message),{success:!1,asyncInit:!1}}},o.prototype.removeBackend=function(a){if(!(a in this.registryFactory))throw new Error(a+" backend not found in registry");this.backendName===a&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,a in this.registry&&(this.disposeRegisteredKernels(a),this.registry[a].dispose(),delete this.registry[a]),delete this.registryFactory[a],this.backendName===a&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)},o.prototype.getSortedBackends=function(){var a=this;if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort(function(i,c){return a.registryFactory[c].priority-a.registryFactory[i].priority})},o.prototype.initializeBackendsAndReturnBest=function(){for(var a=this.getSortedBackends(),i=0;i<a.length;i++){var c=a[i],f=this.initializeBackend(c),d=f.success,g=f.asyncInit;if(g||d)return{name:c,asyncInit:g}}throw new Error("Could not initialize any backends, all backend initializations failed.")},o.prototype.moveData=function(a,i){var c=this.state.tensorInfo.get(i),f=c.backend,d=this.readSync(i);f.disposeData(i),c.backend=a,a.move(i,d,c.shape,c.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++},o.prototype.tidy=function(a,i){var c=this,f=null;if(i==null){if(typeof a!="function")throw new Error("Please provide a function to tidy()");i=a}else{if(typeof a!="string"&&!(a instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof i!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");f=a}var d;return this.scopedRun(function(){return c.startScope(f)},function(){return c.endScope(d)},function(){return d=i(),d instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),d})},o.prototype.scopedRun=function(a,i,c){a();try{var f=c();return i(),f}catch(d){throw i(),d}},o.prototype.nextTensorId=function(){return o.nextTensorId++},o.prototype.nextVariableId=function(){return o.nextVariableId++},o.prototype.clone=function(a){var i=this.makeTensorFromDataId(a.dataId,a.shape,a.dtype),c={x:a},f=function(g){return{x:function(){var v="float32",w={x:g},T={dtype:v};return Z.runKernelFunc(function(N){return N.cast(g,v)},w,null,fg,T)}}},d=[];return this.addTapeNode(this.state.activeScope.name,c,[i],f,d,{}),i},o.prototype.runKernel=function(a,i,c,f,d){var g=null,v=null;return this.runKernelFunc(g,i,v,a,c,f,d)},o.prototype.shouldCheckForMemLeaks=function(){return this.ENV.getBool("IS_TEST")},o.prototype.checkKernelForMemLeak=function(a,i,c){var f=this.backend.numDataIds(),d=0;c.forEach(function(w){d+=w.dtype==="complex64"?3:1});var g=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],v=f-i-d-g;if(v>0)throw new Error("Backend '"+this.backendName+"' has an internal memory leak "+("("+v+" data ids) after running '"+a+"'"))},o.prototype.runKernelFunc=function(a,i,c,f,d,g,v){var w=this,T,N=[],E=this.isTapeOn();f==null&&(f=this.state.activeScope!=null?this.state.activeScope.name:"");var k=this.state.numBytes,$=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);var M,G=BT(f,this.backendName),O;if(G!=null)M=function(){var nt=w.backend.numDataIds();O=G.kernelFunc({inputs:i,attrs:d,backend:w.backend});var ct=Array.isArray(O)?O:[O];w.shouldCheckForMemLeaks()&&w.checkKernelForMemLeak(f,nt,ct);var dt=ct.map(function(It){var Gt=It.dataId,Et=It.shape,Wt=It.dtype;return w.makeTensorFromDataId(Gt,Et,Wt)});if(E){var Ct=w.getTensorsForGradient(f,i,dt);if(Ct==null){v==null&&(v=[]);var St=dt.filter(function(It,Gt){return v[Gt]});Ct=(g||[]).slice().concat(St)}N=w.saveTensorsForBackwardMode(Ct)}return dt};else{var H=function(nt){if(!E)return;N=nt.map(function(ct){return w.keep(w.clone(ct))})};M=function(){var nt=w.backend.numDataIds();O=w.tidy(function(){return a(w.backend,H)});var ct=Array.isArray(O)?O:[O];return w.shouldCheckForMemLeaks()&&w.checkKernelForMemLeak(f,nt,ct),ct}}var K;return this.scopedRun(function(){return w.state.kernelDepth++},function(){return w.state.kernelDepth--},function(){!w.ENV.getBool("DEBUG")&&!w.state.profiling?T=M():(K=w.profiler.profileKernel(f,i,function(){return M()}),w.ENV.getBool("DEBUG")&&w.profiler.logKernelProfile(K),T=K.outputs)}),E&&this.addTapeNode(f,i,T,c,N,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:f,bytesAdded:this.state.numBytes-k,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-$,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(i).map(function(nt){return i[nt]!=null?i[nt].shape:null}),outputShapes:T.map(function(nt){return nt.shape}),kernelTimeMs:K.timeMs,extraInfo:K.extraInfo}),Array.isArray(O)?T:T[0]},o.prototype.saveTensorsForBackwardMode=function(a){var i=this,c=a.map(function(f){return i.keep(i.clone(f))});return c},o.prototype.getTensorsForGradient=function(a,i,c){var f=zT(a);if(f!=null){var d=f.inputsToSave||[],g=f.outputsToSave||[],v=void 0;f.saveAllInputs?(U(Array.isArray(i),function(){return"saveAllInputs is true, expected inputs to be an array."}),v=Object.keys(i).map(function(T){return i[T]})):v=d.map(function(T){return i[T]});var w=c.filter(function(T,N){return g[N]});return v.concat(w)}return null},o.prototype.makeTensor=function(a,i,c,f){if(a==null)throw new Error("Values passed to engine.makeTensor() are null");c=c||"float32",f=f||this.backend;var d=a;c==="string"&&Oa(a[0])&&(d=a.map(function(N){return WT(N)}));var g=f.write(d,i,c),v=new ut(i,c,g,this.nextTensorId());if(this.incRef(v,f),c==="string"){var w=this.state.tensorInfo.get(g),T=f$(d);this.state.numBytes+=T-w.bytes,w.bytes=T}return v},o.prototype.makeTensorFromDataId=function(a,i,c,f){c=c||"float32";var d=new ut(i,c,a,this.nextTensorId());return this.incRef(d,f),d},o.prototype.makeVariable=function(a,i,c,f){i===void 0&&(i=!0),c=c||this.nextVariableId().toString(),f!=null&&f!==a.dtype&&(a=a.cast(f));var d=new Bf(a,i,c,this.nextTensorId());if(this.state.registeredVariables[d.name]!=null)throw new Error("Variable with name "+d.name+" was already registered");return this.state.registeredVariables[d.name]=d,this.incRef(d,this.backend),d},o.prototype.incRef=function(a,i){var c=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,c===0){this.state.numDataBuffers++;var f=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(f=a.size*h$(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:i||this.backend,dtype:a.dtype,shape:a.shape,bytes:f,refCount:0}),this.state.numBytes+=f}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Bf||this.track(a)},o.prototype.disposeTensor=function(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;var i=this.state.tensorInfo.get(a.dataId),c=i.refCount;c<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=i.bytes),this.state.numDataBuffers--,i.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--},o.prototype.disposeVariables=function(){for(var a in this.state.registeredVariables){var i=this.state.registeredVariables[a];this.disposeVariable(i)}},o.prototype.disposeVariable=function(a){this.disposeTensor(a),this.state.registeredVariables[a.name]!=null&&delete this.state.registeredVariables[a.name]},o.prototype.memory=function(){var a=this.backend.memory();return a.numTensors=this.state.numTensors,a.numDataBuffers=this.state.numDataBuffers,a.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(a.unreliable=!0,a.reasons==null&&(a.reasons=[]),a.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),a},o.prototype.profile=function(a){return qt(this,void 0,void 0,function(){var i,c,f,d,g,v,w,T;return jt(this,function(N){switch(N.label){case 0:return this.state.profiling=!0,i=this.state.numBytes,c=this.state.numTensors,this.state.activeProfile.kernels=[],f=this.state.activeProfile,[4,a()];case 1:f.result=N.sent(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max.apply(Math,this.state.activeProfile.kernels.map(function(E){return E.totalBytesSnapshot})),this.state.activeProfile.newBytes=this.state.numBytes-i,this.state.activeProfile.newTensors=this.state.numTensors-c,d=0,g=this.state.activeProfile.kernels,N.label=2;case 2:return d<g.length?(v=g[d],w=v,[4,v.kernelTimeMs]):[3,6];case 3:return w.kernelTimeMs=N.sent(),T=v,[4,v.extraInfo];case 4:T.extraInfo=N.sent(),N.label=5;case 5:return d++,[3,2];case 6:return[2,this.state.activeProfile]}})})},o.prototype.isTapeOn=function(){return this.state.gradientDepth>0&&this.state.kernelDepth===0},o.prototype.addTapeNode=function(a,i,c,f,d,g){var v=this,w={id:this.state.nextTapeNodeId++,kernelName:a,inputs:i,outputs:c,saved:d},T=zT(a);T!=null&&(f=T.gradFunc),f!=null&&(w.gradient=function(N){return N=N.map(function(E,k){if(E==null){var $=c[k],M=Ec($.size,$.dtype);return v.makeTensor(M,$.shape,$.dtype)}return E}),f(N.length>1?N:N[0],d,g)}),this.state.activeTape.push(w)},o.prototype.keep=function(a){return a.kept=!0,a},o.prototype.startTape=function(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++},o.prototype.endTape=function(){this.state.gradientDepth--},o.prototype.startScope=function(a){var i={track:[],name:"unnamed scope",id:this.state.nextScopeId++};a&&(i.name=a),this.state.scopeStack.push(i),this.state.activeScope=i},o.prototype.endScope=function(a){for(var i=this,c=XT(a),f=new Set(c.map(function(w){return w.id})),d=0;d<this.state.activeScope.track.length;d++){var g=this.state.activeScope.track[d];!g.kept&&!f.has(g.id)&&g.dispose()}var v=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],c.forEach(function(w){!w.kept&&w.scopeId===v.id&&i.track(w)})},o.prototype.gradients=function(a,i,c,f){var d=this;if(f===void 0&&(f=!1),U(i.length>0,function(){return"gradients() received an empty list of xs."}),c!=null&&c.dtype!=="float32")throw new Error("dy must have 'float32' dtype, but has '"+c.dtype+"'");var g=this.scopedRun(function(){return d.startTape()},function(){return d.endTape()},function(){return d.tidy("forward",a)});U(g instanceof ut,function(){return"The result y returned by f() must be a tensor."});var v=TQ(this.state.activeTape,i,g);if(!f&&v.length===0&&i.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",function(){var w={};w[g.id]=c==null?FQ(g.shape):c,kQ(w,v,function(N){return d.tidy(N)},RQ);var T=i.map(function(N){return w[N.id]});return d.state.gradientDepth===0&&(d.state.activeTape.forEach(function(N){for(var E=0,k=N.saved;E<k.length;E++){var $=k[E];$.dispose()}}),d.state.activeTape=null),{value:g,grads:T}})},o.prototype.customGrad=function(a){var i=this;return U(Ma(a),function(){return"The f passed in customGrad(f) must be a function."}),function(){for(var c=[],f=0;f<arguments.length;f++)c[f]=arguments[f];U(c.every(function(v){return v instanceof ut}),function(){return"The args passed in customGrad(f)(x1, x2,...) must all be tensors"});var d,g={};return c.forEach(function(v,w){g[w]=v}),i.runKernelFunc(function(v,w){return d=a.apply(void 0,c.concat([w])),U(d.value instanceof ut,function(){return"The function f passed in customGrad(f) must return an object where `obj.value` 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w=f(`${v}/separable_conv0`),T=f(`${v}/separable_conv1`),N=f(`${v}/separable_conv2`);return{separable_conv0:w,separable_conv1:T,separable_conv2:N}}return{extractConvParams:c,extractSeparableConvParams:f,extractReductionBlockParams:d,extractMainBlockParams:g}}function nR(o,a){let i=[],{extractConvParams:c,extractSeparableConvParams:f,extractReductionBlockParams:d,extractMainBlockParams:g}=dlt(o,i),v=c("entry_flow/conv_in"),w=d("entry_flow/reduction_block_0"),T=d("entry_flow/reduction_block_1"),N={conv_in:v,reduction_block_0:w,reduction_block_1:T},E={};ro(a,0,1).forEach(G=>{E[`main_block_${G}`]=g(`middle_flow/main_block_${G}`)});let k=d("exit_flow/reduction_block"),$=f("exit_flow/separable_conv"),M={reduction_block:k,separable_conv:$};return ar(o,i),{params:{entry_flow:N,middle_flow:E,exit_flow:M},paramMappings:i}}function rR(o,a,i){return yn.add(yn.conv2d(o,a.filters,i,"same"),a.bias)}function o2(o,a,i=!0){let c=i?yn.relu(o):o;return 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He(a))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(a){return nR(a,this._numMainBlocks)}extractParams(a){return eR(a,this._numMainBlocks)}};function sR(o){let a=[],{extractWeights:i,getRemainingWeights:c}=ir(o),f=ev(i,a),d=f(512,1,"fc/age"),g=f(512,2,"fc/gender");if(c().length!==0)throw new Error(`weights remaing after extract: ${c().length}`);return{paramMappings:a,params:{fc:{age:d,gender:g}}}}function oR(o){let a=[],i=Fr(o,a);function c(d){let g=i(`${d}/weights`,2),v=i(`${d}/bias`,1);return{weights:g,bias:v}}let f={fc:{age:c("fc/age"),gender:c("fc/gender")}};return ar(o,a),{params:f,paramMappings:a}}var Zo;(function(o){o.FEMALE="female",o.MALE="male"})(Zo||(Zo={}));var cv=class extends jn{constructor(a=new a2(2)){super("AgeGenderNet");this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(a){let{params:i}=this;if(!i)throw new Error(`${this._name} - load model before inference`);return fo.tidy(()=>{let c=a instanceof Yo?this.faceFeatureExtractor.forwardInput(a):a,f=fo.avgPool(c,[7,7],[2,2],"valid").as2D(c.shape[0],-1),d=np(f,i.fc.age).as1D(),g=np(f,i.fc.gender);return{age:d,gender:g}})}forwardInput(a){return fo.tidy(()=>{let{age:i,gender:c}=this.runNet(a);return{age:i,gender:fo.softmax(c)}})}async forward(a){return this.forwardInput(await He(a))}async predictAgeAndGender(a){let i=await He(a),c=await this.forwardInput(i),f=fo.unstack(c.age),d=fo.unstack(c.gender),g=f.map((w,T)=>({ageTensor:w,genderTensor:d[T]})),v=await Promise.all(g.map(async({ageTensor:w,genderTensor:T})=>{let N=(await w.data())[0],E=(await T.data())[0],k=E>.5,$=k?Zo.MALE:Zo.FEMALE,M=k?E:1-E;return w.dispose(),T.dispose(),{age:N,gender:$,genderProbability:M}}));return 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Ct={conv32_down:g,conv32_1:v,conv32_2:w,conv32_3:T,conv64_down:N,conv64_1:E,conv64_2:k,conv64_3:$,conv128_down:M,conv128_1:G,conv128_2:O,conv256_down:H,conv256_1:K,conv256_2:nt,conv256_down_out:ct,fc:dt};return{params:Ct,paramMappings:c}}function vlt(o,a){let i=Fr(o,a);function c(g){let v=i(`${g}/scale/weights`,1),w=i(`${g}/scale/biases`,1);return{weights:v,biases:w}}function f(g){let v=i(`${g}/conv/filters`,4),w=i(`${g}/conv/bias`,1),T=c(g);return{conv:{filters:v,bias:w},scale:T}}function d(g){return{conv1:f(`${g}/conv1`),conv2:f(`${g}/conv2`)}}return{extractConvLayerParams:f,extractResidualLayerParams:d}}function fR(o){let 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N=`mobilenetv1/conv_${T}`,E=`MobilenetV1/Conv2d_${T}_depthwise`,k=`${N}/depthwise_conv`,$=`${N}/pointwise_conv`,M=i(`${E}/depthwise_weights`,4,`${k}/filters`),G=i(`${E}/BatchNorm/gamma`,1,`${k}/batch_norm_scale`),O=i(`${E}/BatchNorm/beta`,1,`${k}/batch_norm_offset`),H=i(`${E}/BatchNorm/moving_mean`,1,`${k}/batch_norm_mean`),K=i(`${E}/BatchNorm/moving_variance`,1,`${k}/batch_norm_variance`);return{depthwise_conv:{filters:M,batch_norm_scale:G,batch_norm_offset:O,batch_norm_mean:H,batch_norm_variance:K},pointwise_conv:c("MobilenetV1",T,$)}}function d(){return{conv_0:c("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:f(1),conv_2:f(2),conv_3:f(3),conv_4:f(4),conv_5:f(5),conv_6:f(6),conv_7:f(7),conv_8:f(8),conv_9:f(9),conv_10:f(10),conv_11:f(11),conv_12:f(12),conv_13:f(13)}}function g(T,N){let E=i(`${T}/weights`,4,`${N}/filters`),k=i(`${T}/biases`,1,`${N}/bias`);return{filters:E,bias:k}}function v(T){let N=g(`Prediction/BoxPredictor_${T}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${T}/box_encoding_predictor`),E=g(`Prediction/BoxPredictor_${T}/ClassPredictor`,`prediction_layer/box_predictor_${T}/class_predictor`);return{box_encoding_predictor:N,class_predictor:E}}function w(){return{conv_0:c("Prediction",0,"prediction_layer/conv_0"),conv_1:c("Prediction",1,"prediction_layer/conv_1"),conv_2:c("Prediction",2,"prediction_layer/conv_2"),conv_3:c("Prediction",3,"prediction_layer/conv_3"),conv_4:c("Prediction",4,"prediction_layer/conv_4"),conv_5:c("Prediction",5,"prediction_layer/conv_5"),conv_6:c("Prediction",6,"prediction_layer/conv_6"),conv_7:c("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:v(0),box_predictor_1:v(1),box_predictor_2:v(2),box_predictor_3:v(3),box_predictor_4:v(4),box_predictor_5:v(5)}}return{extractMobilenetV1Params:d,extractPredictionLayerParams:w}}function dR(o){let a=[],{extractMobilenetV1Params:i,extractPredictionLayerParams:c}=Tlt(o,a),f=o["Output/extra_dim"];if(a.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Wo(f))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${f}`);let d={mobilenetv1:i(),prediction_layer:c(),output_layer:{extra_dim:f}};return ar(o,a),{params:d,paramMappings:a}}var Qo=se(he()),Ja=se(he());function ls(o,a,i){return Ja.tidy(()=>{let c=Ja.conv2d(o,a.filters,i,"same");return c=Ja.add(c,a.batch_norm_offset),Ja.clipByValue(c,0,6)})}var klt=.0010000000474974513;function Slt(o,a,i){return Qo.tidy(()=>{let c=Qo.depthwiseConv2d(o,a.filters,i,"same");return c=Qo.batchNorm(c,a.batch_norm_mean,a.batch_norm_variance,a.batch_norm_offset,a.batch_norm_scale,klt),Qo.clipByValue(c,0,6)})}function Clt(o){return[2,4,6,12].some(a=>a===o)?[2,2]:[1,1]}function mR(o,a){return Qo.tidy(()=>{let i,c=ls(o,a.conv_0,[2,2]),f=[a.conv_1,a.conv_2,a.conv_3,a.conv_4,a.conv_5,a.conv_6,a.conv_7,a.conv_8,a.conv_9,a.conv_10,a.conv_11,a.conv_12,a.conv_13];if(f.forEach((d,g)=>{let v=g+1,w=Clt(v);c=Slt(c,d.depthwise_conv,w),c=ls(c,d.pointwise_conv,[1,1]),v===11&&(i=c)}),i===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:c,conv11:i}})}function gR(o,a,i,c,f){let d=o.shape[0],g=Math.min(i,d),v=a.map((N,E)=>({score:N,boxIndex:E})).filter(N=>N.score>f).sort((N,E)=>E.score-N.score),w=N=>N<=c?1:0,T=[];return v.forEach(N=>{if(T.length>=g)return;let E=N.score;for(let k=T.length-1;k>=0;--k){let $=Nlt(o,N.boxIndex,T[k]);if($===0)continue;if(N.score*=w($),N.score<=f)break}E===N.score&&T.push(N.boxIndex)}),T}function Nlt(o,a,i){let c=o.arraySync(),f=Math.min(c[a][0],c[a][2]),d=Math.min(c[a][1],c[a][3]),g=Math.max(c[a][0],c[a][2]),v=Math.max(c[a][1],c[a][3]),w=Math.min(c[i][0],c[i][2]),T=Math.min(c[i][1],c[i][3]),N=Math.max(c[i][0],c[i][2]),E=Math.max(c[i][1],c[i][3]),k=(g-f)*(v-d),$=(N-w)*(E-T);if(k<=0||$<=0)return 0;let M=Math.max(f,w),G=Math.max(d,T),O=Math.min(g,N),H=Math.min(v,E),K=Math.max(O-M,0)*Math.max(H-G,0);return K/(k+$-K)}var Ut=se(he());function Ilt(o){let a=Ut.unstack(Ut.transpose(o,[1,0])),i=[Ut.sub(a[2],a[0]),Ut.sub(a[3],a[1])],c=[Ut.add(a[0],Ut.div(i[0],Ut.scalar(2))),Ut.add(a[1],Ut.div(i[1],Ut.scalar(2)))];return{sizes:i,centers:c}}function Elt(o,a){let{sizes:i,centers:c}=Ilt(o),f=Ut.unstack(Ut.transpose(a,[1,0])),d=Ut.div(Ut.mul(Ut.exp(Ut.div(f[2],Ut.scalar(5))),i[0]),Ut.scalar(2)),g=Ut.add(Ut.mul(Ut.div(f[0],Ut.scalar(10)),i[0]),c[0]),v=Ut.div(Ut.mul(Ut.exp(Ut.div(f[3],Ut.scalar(5))),i[1]),Ut.scalar(2)),w=Ut.add(Ut.mul(Ut.div(f[1],Ut.scalar(10)),i[1]),c[1]);return Ut.transpose(Ut.stack([Ut.sub(g,d),Ut.sub(w,v),Ut.add(g,d),Ut.add(w,v)]),[1,0])}function vR(o,a,i){return Ut.tidy(()=>{let c=o.shape[0],f=Elt(Ut.reshape(Ut.tile(i.extra_dim,[c,1,1]),[-1,4]),Ut.reshape(o,[-1,4]));f=Ut.reshape(f,[c,f.shape[0]/c,4]);let d=Ut.sigmoid(Ut.slice(a,[0,0,1],[-1,-1,-1])),g=Ut.slice(d,[0,0,0],[-1,-1,1]);g=Ut.reshape(g,[c,g.shape[1]]);let v=Ut.unstack(f),w=Ut.unstack(g);return{boxes:v,scores:w}})}var ip=se(he()),ap=se(he());function ou(o,a){return ap.tidy(()=>{let i=o.shape[0],c=ap.reshape(ru(o,a.box_encoding_predictor),[i,-1,1,4]),f=ap.reshape(ru(o,a.class_predictor),[i,-1,3]);return{boxPredictionEncoding:c,classPrediction:f}})}function yR(o,a,i){return ip.tidy(()=>{let c=ls(o,i.conv_0,[1,1]),f=ls(c,i.conv_1,[2,2]),d=ls(f,i.conv_2,[1,1]),g=ls(d,i.conv_3,[2,2]),v=ls(g,i.conv_4,[1,1]),w=ls(v,i.conv_5,[2,2]),T=ls(w,i.conv_6,[1,1]),N=ls(T,i.conv_7,[2,2]),E=ou(a,i.box_predictor_0),k=ou(o,i.box_predictor_1),$=ou(f,i.box_predictor_2),M=ou(g,i.box_predictor_3),G=ou(w,i.box_predictor_4),O=ou(N,i.box_predictor_5),H=ip.concat([E.boxPredictionEncoding,k.boxPredictionEncoding,$.boxPredictionEncoding,M.boxPredictionEncoding,G.boxPredictionEncoding,O.boxPredictionEncoding],1),K=ip.concat([E.classPrediction,k.classPrediction,$.classPrediction,M.classPrediction,G.classPrediction,O.classPrediction],1);return{boxPredictions:H,classPredictions:K}})}var hs=class{constructor({minConfidence:a,maxResults:i}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=a||.5,this._maxResults=i||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},au=class extends jn{constructor(){super("SsdMobilenetv1")}forwardInput(a){let{params:i}=this;if(!i)throw new Error("SsdMobilenetv1 - load model before inference");return Vs.tidy(()=>{let c=Vs.cast(a.toBatchTensor(512,!1),"float32"),f=Vs.sub(Vs.mul(c,Vs.scalar(.007843137718737125)),Vs.scalar(1)),d=mR(f,i.mobilenetv1),{boxPredictions:g,classPredictions:v}=yR(d.out,d.conv11,i.prediction_layer);return vR(g,v,i.output_layer)})}async forward(a){return this.forwardInput(await He(a))}async locateFaces(a,i={}){let{maxResults:c,minConfidence:f}=new hs(i),d=await He(a),{boxes:g,scores:v}=this.forwardInput(d),w=g[0],T=v[0];for(let nt=1;nt<g.length;nt++)g[nt].dispose(),v[nt].dispose();let N=Array.from(await T.data()),E=.5,k=gR(w,N,c,E,f),$=d.getReshapedInputDimensions(0),M=d.inputSize,G=M/$.width,O=M/$.height,H=w.arraySync(),K=k.map(nt=>{let[ct,dt]=[Math.max(0,H[nt][0]),Math.min(1,H[nt][2])].map(It=>It*O),[Ct,St]=[Math.max(0,H[nt][1]),Math.min(1,H[nt][3])].map(It=>It*G);return new qe(N[nt],new kc(Ct,ct,St-Ct,dt-ct),{height:d.getInputHeight(0),width:d.getInputWidth(0)})});return w.dispose(),T.dispose(),K}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(a){return dR(a)}extractParams(a){return pR(a)}};function bR(o){let a=new au;return a.extractWeights(o),a}function Dlt(o){return bR(o)}var wR=class extends au{},xR=.4,TR=[new ue(.738768,.874946),new ue(2.42204,2.65704),new ue(4.30971,7.04493),new ue(10.246,4.59428),new ue(12.6868,11.8741)],kR=[new ue(1.603231,2.094468),new ue(6.041143,7.080126),new ue(2.882459,3.518061),new ue(4.266906,5.178857),new ue(9.041765,10.66308)],SR=[117.001,114.697,97.404],CR="tiny_yolov2_model",NR="tiny_yolov2_separable_conv_model",Ke=se(he()),gv=o=>typeof o=="number";function l2(o){if(!o)throw new Error(`invalid config: ${o}`);if(typeof o.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${o.withSeparableConvs}`);if(!gv(o.iouThreshold)||o.iouThreshold<0||o.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${o.iouThreshold}`);if(!Array.isArray(o.classes)||!o.classes.length||!o.classes.every(a=>typeof a=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(o.classes)}`);if(!Array.isArray(o.anchors)||!o.anchors.length||!o.anchors.map(a=>a||{}).every(a=>gv(a.x)&&gv(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(o.anchors)}`);if(o.meanRgb&&(!Array.isArray(o.meanRgb)||o.meanRgb.length!==3||!o.meanRgb.every(gv)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(o.meanRgb)}`)}var ps=se(he()),fs=se(he());function ol(o){return fs.tidy(()=>{let a=fs.mul(o,fs.scalar(.10000000149011612));return fs.add(fs.relu(fs.sub(o,a)),a)})}function ta(o,a){return ps.tidy(()=>{let i=ps.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return i=ps.conv2d(i,a.conv.filters,[1,1],"valid"),i=ps.sub(i,a.bn.sub),i=ps.mul(i,a.bn.truediv),i=ps.add(i,a.conv.bias),ol(i)})}var Za=se(he());function ea(o,a){return Za.tidy(()=>{let i=Za.pad(o,[[0,0],[1,1],[1,1],[0,0]]);return i=Za.separableConv2d(i,a.depthwise_filter,a.pointwise_filter,[1,1],"valid"),i=Za.add(i,a.bias),ol(i)})}var h2=se(he());function $lt(o,a){let i=Yc(o,a);function c(g,v){let w=h2.tensor1d(o(g)),T=h2.tensor1d(o(g));return a.push({paramPath:`${v}/sub`},{paramPath:`${v}/truediv`}),{sub:w,truediv:T}}function f(g,v,w){let T=i(g,v,3,`${w}/conv`),N=c(v,`${w}/bn`);return{conv:T,bn:N}}let d=Jc(o,a);return{extractConvParams:i,extractConvWithBatchNormParams:f,extractSeparableConvParams:d}}function IR(o,a,i,c){let{extractWeights:f,getRemainingWeights:d}=ir(o),g=[],{extractConvParams:v,extractConvWithBatchNormParams:w,extractSeparableConvParams:T}=$lt(f,g),N;if(a.withSeparableConvs){let[E,k,$,M,G,O,H,K,nt]=c,ct=a.isFirstLayerConv2d?v(E,k,3,"conv0"):T(E,k,"conv0"),dt=T(k,$,"conv1"),Ct=T($,M,"conv2"),St=T(M,G,"conv3"),It=T(G,O,"conv4"),Gt=T(O,H,"conv5"),Et=K?T(H,K,"conv6"):void 0,Wt=nt?T(K,nt,"conv7"):void 0,_t=v(nt||K||H,5*i,1,"conv8");N={conv0:ct,conv1:dt,conv2:Ct,conv3:St,conv4:It,conv5:Gt,conv6:Et,conv7:Wt,conv8:_t}}else{let[E,k,$,M,G,O,H,K,nt]=c,ct=w(E,k,"conv0"),dt=w(k,$,"conv1"),Ct=w($,M,"conv2"),St=w(M,G,"conv3"),It=w(G,O,"conv4"),Gt=w(O,H,"conv5"),Et=w(H,K,"conv6"),Wt=w(K,nt,"conv7"),_t=v(nt,5*i,1,"conv8");N={conv0:ct,conv1:dt,conv2:Ct,conv3:St,conv4:It,conv5:Gt,conv6:Et,conv7:Wt,conv8:_t}}if(d().length!==0)throw new Error(`weights remaing after extract: ${d().length}`);return{params:N,paramMappings:g}}function Alt(o,a){let i=Fr(o,a);function c(v){let w=i(`${v}/sub`,1),T=i(`${v}/truediv`,1);return{sub:w,truediv:T}}function f(v){let w=i(`${v}/filters`,4),T=i(`${v}/bias`,1);return{filters:w,bias:T}}function d(v){let w=f(`${v}/conv`),T=c(`${v}/bn`);return{conv:w,bn:T}}let g=Zc(i);return{extractConvParams:f,extractConvWithBatchNormParams:d,extractSeparableConvParams:g}}function ER(o,a){let i=[],{extractConvParams:c,extractConvWithBatchNormParams:f,extractSeparableConvParams:d}=Alt(o,i),g;if(a.withSeparableConvs){let v=a.filterSizes&&a.filterSizes.length||9;g={conv0:a.isFirstLayerConv2d?c("conv0"):d("conv0"),conv1:d("conv1"),conv2:d("conv2"),conv3:d("conv3"),conv4:d("conv4"),conv5:d("conv5"),conv6:v>7?d("conv6"):void 0,conv7:v>8?d("conv7"):void 0,conv8:c("conv8")}}else g={conv0:f("conv0"),conv1:f("conv1"),conv2:f("conv2"),conv3:f("conv3"),conv4:f("conv4"),conv5:f("conv5"),conv6:f("conv6"),conv7:f("conv7"),conv8:c("conv8")};return ar(o,i),{params:g,paramMappings:i}}var f2;(function(o){o[o.XS=224]="XS",o[o.SM=320]="SM",o[o.MD=416]="MD",o[o.LG=608]="LG"})(f2||(f2={}));var po=class{constructor({inputSize:a,scoreThreshold:i}={}){this._name="TinyYolov2Options";if(this._inputSize=a||416,this._scoreThreshold=i||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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}},p2=class extends jn{constructor(a){super("TinyYolov2");l2(a),this._config=a}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(a,i){let c=ta(a,i.conv0);return c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ta(c,i.conv1),c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ta(c,i.conv2),c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ta(c,i.conv3),c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ta(c,i.conv4),c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ta(c,i.conv5),c=Ke.maxPool(c,[2,2],[1,1],"same"),c=ta(c,i.conv6),c=ta(c,i.conv7),ru(c,i.conv8,"valid",!1)}runMobilenet(a,i){let c=this.config.isFirstLayerConv2d?ol(ru(a,i.conv0,"valid",!1)):ea(a,i.conv0);return c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ea(c,i.conv1),c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ea(c,i.conv2),c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ea(c,i.conv3),c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ea(c,i.conv4),c=Ke.maxPool(c,[2,2],[2,2],"same"),c=ea(c,i.conv5),c=Ke.maxPool(c,[2,2],[1,1],"same"),c=i.conv6?ea(c,i.conv6):c,c=i.conv7?ea(c,i.conv7):c,ru(c,i.conv8,"valid",!1)}forwardInput(a,i){let{params:c}=this;if(!c)throw new Error("TinyYolov2 - load model before inference");return Ke.tidy(()=>{let f=Ke.cast(a.toBatchTensor(i,!1),"float32");return f=this.config.meanRgb?Fs(f,this.config.meanRgb):f,f=f.div(Ke.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(f,c):this.runTinyYolov2(f,c)})}async forward(a,i){return await this.forwardInput(await He(a),i)}async detect(a,i={}){let{inputSize:c,scoreThreshold:f}=new po(i),d=await He(a),g=await this.forwardInput(d,c),v=Ke.tidy(()=>Ke.unstack(g)[0].expandDims()),w={width:d.getInputWidth(0),height:d.getInputHeight(0)},T=await this.extractBoxes(v,d.getReshapedInputDimensions(0),f);g.dispose(),v.dispose();let N=T.map(O=>O.box),E=T.map(O=>O.score),k=T.map(O=>O.classScore),$=T.map(O=>this.config.classes[O.label]),M=Fx(N.map(O=>O.rescale(c)),E,this.config.iouThreshold,!0),G=M.map(O=>new Fa(E[O],k[O],$[O],N[O],w));return G}getDefaultModelName(){return""}extractParamsFromWeigthMap(a){return ER(a,this.config)}extractParams(a){let i=this.config.filterSizes||p2.DEFAULT_FILTER_SIZES,c=i?i.length:void 0;if(c!==7&&c!==8&&c!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${c} filterSizes in config`);return IR(a,this.config,this.boxEncodingSize,i)}async extractBoxes(a,i,c){let{width:f,height:d}=i,g=Math.max(f,d),v=g/f,w=g/d,T=a.shape[1],N=this.config.anchors.length,[E,k,$]=Ke.tidy(()=>{let H=a.reshape([T,T,N,this.boxEncodingSize]),K=H.slice([0,0,0,0],[T,T,N,4]),nt=H.slice([0,0,0,4],[T,T,N,1]),ct=this.withClassScores?Ke.softmax(H.slice([0,0,0,5],[T,T,N,this.config.classes.length]),3):Ke.scalar(0);return[K,nt,ct]}),M=[],G=await k.array(),O=await E.array();for(let H=0;H<T;H++)for(let K=0;K<T;K++)for(let nt=0;nt<N;nt++){let ct=If(G[H][K][nt][0]);if(!c||ct>c){let dt=(K+If(O[H][K][nt][0]))/T*v,Ct=(H+If(O[H][K][nt][1]))/T*w,St=Math.exp(O[H][K][nt][2])*this.config.anchors[nt].x/T*v,It=Math.exp(O[H][K][nt][3])*this.config.anchors[nt].y/T*w,Gt=dt-St/2,Et=Ct-It/2,Wt={row:H,col:K,anchor:nt},{classScore:_t,label:Kt}=this.withClassScores?await this.extractPredictedClass($,Wt):{classScore:1,label:0};M.push({box:new Tc(Gt,Et,Gt+St,Et+It),score:ct,classScore:ct*_t,label:Kt,...Wt})}}return E.dispose(),k.dispose(),$.dispose(),M}async extractPredictedClass(a,i){let{row:c,col:f,anchor:d}=i,g=await a.array();return Array(this.config.classes.length).fill(0).map((v,w)=>g[c][f][d][w]).map((v,w)=>({classScore:v,label:w})).reduce((v,w)=>v.classScore>w.classScore?v:w)}},al=p2;al.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var il=class extends al{constructor(a=!0){let i=Object.assign({},{withSeparableConvs:a,iouThreshold:xR,classes:["face"]},a?{anchors:kR,meanRgb:SR}:{anchors:TR,withClassScores:!0});super(i)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(a,i){let c=await this.detect(a,i);return c.map(f=>new qe(f.score,f.relativeBox,{width:f.imageWidth,height:f.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?NR:CR}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}};function _lt(o,a=!0){let i=new il(a);return i.extractWeights(o),i}var vv=class extends po{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ds=class{async then(a){return a(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},up=se(he()),d2=se(he());async function iu(o,a,i,c,f=({alignedRect:d})=>d){let d=o.map(w=>su(w)?f(w):w.detection),g=c||(a instanceof d2.Tensor?await Kc(a,d):await jc(a,d)),v=await i(g);return g.forEach(w=>w instanceof d2.Tensor&&w.dispose()),v}async function ul(o,a,i,c,f){return iu([o],a,async d=>i(d[0]),c,f)}var DR=.4,$R=[new ue(1.603231,2.094468),new ue(6.041143,7.080126),new ue(2.882459,3.518061),new ue(4.266906,5.178857),new ue(9.041765,10.66308)],AR=[117.001,114.697,97.404],cl=class extends al{constructor(){let a={withSeparableConvs:!0,iouThreshold:DR,classes:["face"],anchors:$R,meanRgb:AR,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(a)}get anchors(){return this.config.anchors}async locateFaces(a,i){let c=await this.detect(a,i);return c.map(f=>new qe(f.score,f.relativeBox,{width:f.imageWidth,height:f.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}},$e={ssdMobilenetv1:new au,tinyFaceDetector:new cl,tinyYolov2:new il,faceLandmark68Net:new nl,faceLandmark68TinyNet:new lv,faceRecognitionNet:new sl,faceExpressionNet:new iv,ageGenderNet:new cv},_R=(o,a)=>$e.ssdMobilenetv1.locateFaces(o,a),Flt=(o,a)=>$e.tinyFaceDetector.locateFaces(o,a),Rlt=(o,a)=>$e.tinyYolov2.locateFaces(o,a),FR=o=>$e.faceLandmark68Net.detectLandmarks(o),Plt=o=>$e.faceLandmark68TinyNet.detectLandmarks(o),Olt=o=>$e.faceRecognitionNet.computeFaceDescriptor(o),Mlt=o=>$e.faceExpressionNet.predictExpressions(o),Llt=o=>$e.ageGenderNet.predictAgeAndGender(o),RR=o=>$e.ssdMobilenetv1.load(o),Blt=o=>$e.tinyFaceDetector.load(o),zlt=o=>$e.tinyYolov2.load(o),Wlt=o=>$e.faceLandmark68Net.load(o),Vlt=o=>$e.faceLandmark68TinyNet.load(o),Ult=o=>$e.faceRecognitionNet.load(o),Glt=o=>$e.faceExpressionNet.load(o),Hlt=o=>$e.ageGenderNet.load(o),qlt=RR,jlt=_R,Klt=FR,m2=class extends ds{constructor(a,i,c){super();this.parentTask=a;this.input=i;this.extractedFaces=c}},fl=class extends m2{async run(){let a=await this.parentTask,i=await iu(a,this.input,async c=>await Promise.all(c.map(f=>$e.faceExpressionNet.predictExpressions(f))),this.extractedFaces);return a.map((c,f)=>uv(c,i[f]))}withAgeAndGender(){return new ll(this,this.input)}},pl=class extends m2{async run(){let a=await this.parentTask;if(!a)return;let i=await ul(a,this.input,c=>$e.faceExpressionNet.predictExpressions(c),this.extractedFaces);return uv(a,i)}withAgeAndGender(){return new hl(this,this.input)}},lu=class extends fl{withAgeAndGender(){return new uu(this,this.input)}withFaceDescriptors(){return new Qa(this,this.input)}},hu=class extends pl{withAgeAndGender(){return new cu(this,this.input)}withFaceDescriptor(){return new ti(this,this.input)}},g2=class extends ds{constructor(a,i,c){super();this.parentTask=a;this.input=i;this.extractedFaces=c}},ll=class extends g2{async run(){let a=await this.parentTask,i=await iu(a,this.input,async c=>await Promise.all(c.map(f=>$e.ageGenderNet.predictAgeAndGender(f))),this.extractedFaces);return a.map((c,f)=>{let{age:d,gender:g,genderProbability:v}=i[f];return dv(mv(c,g,v),d)})}withFaceExpressions(){return new fl(this,this.input)}},hl=class extends g2{async run(){let a=await this.parentTask;if(!a)return;let{age:i,gender:c,genderProbability:f}=await ul(a,this.input,d=>$e.ageGenderNet.predictAgeAndGender(d),this.extractedFaces);return dv(mv(a,c,f),i)}withFaceExpressions(){return new pl(this,this.input)}},uu=class extends ll{withFaceExpressions(){return new lu(this,this.input)}withFaceDescriptors(){return new Qa(this,this.input)}},cu=class extends hl{withFaceExpressions(){return new hu(this,this.input)}withFaceDescriptor(){return new ti(this,this.input)}},yv=class extends ds{constructor(a,i){super();this.parentTask=a;this.input=i}},Qa=class extends yv{async run(){let a=await this.parentTask,i=await iu(a,this.input,c=>Promise.all(c.map(f=>$e.faceRecognitionNet.computeFaceDescriptor(f))),null,c=>c.landmarks.align(null,{useDlibAlignment:!0}));return i.map((c,f)=>pv(a[f],c))}withFaceExpressions(){return new lu(this,this.input)}withAgeAndGender(){return new uu(this,this.input)}},ti=class extends yv{async run(){let a=await this.parentTask;if(!a)return;let i=await ul(a,this.input,c=>$e.faceRecognitionNet.computeFaceDescriptor(c),null,c=>c.landmarks.align(null,{useDlibAlignment:!0}));return pv(a,i)}withFaceExpressions(){return new hu(this,this.input)}withAgeAndGender(){return new cu(this,this.input)}},bv=class extends ds{constructor(a,i,c){super();this.parentTask=a;this.input=i;this.useTinyLandmarkNet=c}get landmarkNet(){return this.useTinyLandmarkNet?$e.faceLandmark68TinyNet:$e.faceLandmark68Net}},wv=class extends bv{async run(){let a=await this.parentTask,i=a.map(d=>d.detection),c=this.input instanceof up.Tensor?await Kc(this.input,i):await jc(this.input,i),f=await Promise.all(c.map(d=>this.landmarkNet.detectLandmarks(d)));return c.forEach(d=>d instanceof up.Tensor&&d.dispose()),a.map((d,g)=>el(d,f[g]))}withFaceExpressions(){return new lu(this,this.input)}withAgeAndGender(){return new uu(this,this.input)}withFaceDescriptors(){return new Qa(this,this.input)}},xv=class extends bv{async run(){let a=await this.parentTask;if(!a)return;let{detection:i}=a,c=this.input instanceof up.Tensor?await Kc(this.input,[i]):await jc(this.input,[i]),f=await this.landmarkNet.detectLandmarks(c[0]);return c.forEach(d=>d instanceof up.Tensor&&d.dispose()),el(a,f)}withFaceExpressions(){return new hu(this,this.input)}withAgeAndGender(){return new cu(this,this.input)}withFaceDescriptor(){return new ti(this,this.input)}},Tv=class extends ds{constructor(a,i=new hs){super();this.input=a;this.options=i}},cp=class extends Tv{async run(){let{input:a,options:i}=this,c=i instanceof vv?f=>$e.tinyFaceDetector.locateFaces(f,i):i instanceof hs?f=>$e.ssdMobilenetv1.locateFaces(f,i):i instanceof po?f=>$e.tinyYolov2.locateFaces(f,i):null;if(!c)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return c(a)}runAndExtendWithFaceDetections(){return new Promise(async a=>{let i=await this.run();return a(i.map(c=>Mi({},c)))})}withFaceLandmarks(a=!1){return new wv(this.runAndExtendWithFaceDetections(),this.input,a)}withFaceExpressions(){return new fl(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new ll(this.runAndExtendWithFaceDetections(),this.input)}},kv=class extends Tv{async run(){let a=await new cp(this.input,this.options),i=a[0];return a.forEach(c=>{c.score>i.score&&(i=c)}),i}runAndExtendWithFaceDetection(){return new Promise(async a=>{let i=await this.run();return a(i?Mi({},i):void 0)})}withFaceLandmarks(a=!1){return new xv(this.runAndExtendWithFaceDetection(),this.input,a)}withFaceExpressions(){return new pl(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new hl(this.runAndExtendWithFaceDetection(),this.input)}};function Xlt(o,a=new hs){return new kv(o,a)}function Sv(o,a=new hs){return new cp(o,a)}async function PR(o,a){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await Sv(o,new hs(a?{minConfidence:a}:{})).withFaceLandmarks().withFaceDescriptors()}async function Ylt(o,a={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await Sv(o,new po(a)).withFaceLandmarks().withFaceDescriptors()}var Jlt=PR;function v2(o,a){if(o.length!==a.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let i=Array.from(o),c=Array.from(a);return Math.sqrt(i.map((f,d)=>f-c[d]).reduce((f,d)=>f+Math.pow(d,2),0))}var Cv=class{constructor(a,i=.6){this._distanceThreshold=i;let c=Array.isArray(a)?a:[a];if(!c.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let f=1,d=()=>`person ${f++}`;this._labeledDescriptors=c.map(g=>{if(g instanceof Vo)return g;if(g instanceof Float32Array)return new Vo(d(),[g]);if(g.descriptor&&g.descriptor instanceof Float32Array)return new Vo(d(),[g.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(a,i){return i.map(c=>v2(c,a)).reduce((c,f)=>c+f,0)/(i.length||1)}matchDescriptor(a){return this.labeledDescriptors.map(({descriptors:i,label:c})=>new Ef(c,this.computeMeanDistance(a,i))).reduce((i,c)=>i.distance<c.distance?i:c)}findBestMatch(a){let i=this.matchDescriptor(a);return i.distance<this.distanceThreshold?i:new Ef("unknown",i.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(a=>a.toJSON())}}static fromJSON(a){let i=a.labeledDescriptors.map(c=>Vo.fromJSON(c));return new Cv(i,a.distanceThreshold)}};function Zlt(o){let a=new cl;return a.extractWeights(o),a}function OR(o,a){let{width:i,height:c}=new or(a.width,a.height);if(i<=0||c<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:i,height:c})}`);if(Array.isArray(o))return o.map(f=>OR(f,{width:i,height:c}));if(su(o)){let f=o.detection.forSize(i,c),d=o.unshiftedLandmarks.forSize(f.box.width,f.box.height);return el(Mi(o,f),d)}return oo(o)?Mi(o,o.detection.forSize(i,c)):o instanceof Xr||o instanceof qe?o.forSize(i,c):o}var MR="0.8.8",tht=typeof process!="undefined",eht=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",nht={faceapi:MR,node:tht,browser:eht};
-/*! *****************************************************************************
-Copyright (c) Microsoft Corporation. All rights reserved.
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use
-this file except in compliance with the License. You may obtain a copy of the
-License at http://www.apache.org/licenses/LICENSE-2.0
-
-THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
-WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
-MERCHANTABLITY OR NON-INFRINGEMENT.
-
-See the Apache Version 2.0 License for specific language governing permissions
-and limitations under the License.
-***************************************************************************** */
-/**
- * @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
-* Copyright 2018 Google LLC. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 (the "License");
-* you may not use this file except in compliance with the License.
-* You may obtain a copy of the License at
-*
-* http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing, software
-* distributed under the License is distributed on an "AS IS" BASIS,
-* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-* See the License for the specific language governing permissions and
-* limitations under the License.
-* =============================================================================
-*/
-/** @license See the LICENSE file. */
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2),require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node")))}),require_isNodejs=__commonJS((exports2,module2)=>{__export(exports2,{isNodejs:()=>isNodejs3});function isNodejs3(){return typeof global=="object"&&!0&&typeof module2!="undefined"&&typeof process!="undefined"&&!!process.version}});__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});var tf42=__toModule(require_tfjs_esm()),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});function drawContour(ctx,points,isClosed=!1){if(ctx.beginPath(),points.slice(1).forEach(({x,y},prevIdx)=>{let from=points[prevIdx];ctx.moveTo(from.x,from.y),ctx.lineTo(x,y)}),isClosed){let from=points[points.length-1],to=points[0];if(!from||!to)return;ctx.moveTo(from.x,from.y),ctx.lineTo(to.x,to.y)}ctx.stroke()}var 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});var tf=__toModule(require_tfjs_esm()),Dimensions=class{constructor(width,height){if(!isValidNumber(width)||!isValidNumber(height))throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({width,height})}`);this._width=width,this._height=height}get width(){return this._width}get height(){return this._height}reverse(){return new Dimensions(1/this.width,1/this.height)}};function isTensor(tensor2,dim){return tensor2 instanceof tf.Tensor&&tensor2.shape.length===dim}function isTensor1D(tensor2){return isTensor(tensor2,1)}function isTensor2D(tensor2){return isTensor(tensor2,2)}function isTensor3D(tensor2){return isTensor(tensor2,3)}function isTensor4D(tensor2){return isTensor(tensor2,4)}function isFloat(num){return num%1!==0}function isEven(num){return num%2===0}function round(num,prec=2){let f=Math.pow(10,prec);return Math.floor(num*f)/f}function isDimensions(obj){return obj&&obj.width&&obj.height}function computeReshapedDimensions({width,height},inputSize){let scale2=inputSize/Math.max(height,width);return new Dimensions(Math.round(width*scale2),Math.round(height*scale2))}function getCenterPoint(pts){return pts.reduce((sum,pt)=>sum.add(pt),new Point(0,0)).div(new Point(pts.length,pts.length))}function range(num,start,step){return Array(num).fill(0).map((_,i)=>start+i*step)}function isValidNumber(num){return!!num&&num!==Infinity&&num!==-Infinity&&!isNaN(num)||num===0}function isValidProbablitiy(num){return isValidNumber(num)&&0<=num&&num<=1}var Point=class{constructor(x,y){this._x=x,this._y=y}get x(){return this._x}get y(){return this._y}add(pt){return new Point(this.x+pt.x,this.y+pt.y)}sub(pt){return new Point(this.x-pt.x,this.y-pt.y)}mul(pt){return new Point(this.x*pt.x,this.y*pt.y)}div(pt){return new Point(this.x/pt.x,this.y/pt.y)}abs(){return new Point(Math.abs(this.x),Math.abs(this.y))}magnitude(){return Math.sqrt(Math.pow(this.x,2)+Math.pow(this.y,2))}floor(){return new Point(Math.floor(this.x),Math.floor(this.y))}},Box=class{static isRect(rect){return!!rect&&[rect.x,rect.y,rect.width,rect.height].every(isValidNumber)}static assertIsValidBox(box,callee,allowNegativeDimensions=!1){if(!Box.isRect(box))throw new Error(`${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`)}constructor(_box,allowNegativeDimensions=!0){let box=_box||{},isBbox=[box.left,box.top,box.right,box.bottom].every(isValidNumber),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)}`);let[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}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(){let[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(){let[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,diff=Math.abs(width-height);return width<height&&(x-=diff/2,width+=diff),height<width&&(y-=diff/2,height+=diff),new Box({x,y,width,height})}rescale(s){let scaleX=isDimensions(s)?s.width:s,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){let{x,y,right,bottom}=this,clippedX=Math.max(x,0),clippedY=Math.max(y,0),newWidth=right-clippedX,newHeight=bottom-clippedY,clippedWidth=Math.min(newWidth,imgWidth-clippedX),clippedHeight=Math.min(newHeight,imgHeight-clippedY);return new Box({x:clippedX,y:clippedY,width:clippedWidth,height:clippedHeight}).floor()}shift(sx,sy){let{width,height}=this,x=this.x+sx,y=this.y+sy;return new Box({x,y,width,height})}padAtBorders(imageHeight,imageWidth){let w=this.width+1,h=this.height+1,dx=1,dy=1,edx=w,edy=h,x=this.left,y=this.top,ex=this.right,ey=this.bottom;return ex>imageWidth&&(edx=-ex+imageWidth+w,ex=imageWidth),ey>imageHeight&&(edy=-ey+imageHeight+h,ey=imageHeight),x<1&&(edy=2-x,x=1),y<1&&(edy=2-y,y=1),{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()}},BoundingBox=class extends Box{constructor(left,top,right,bottom,allowNegativeDimensions=!1){super({left,top,right,bottom},allowNegativeDimensions)}};var ObjectDetection=class{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})}},FaceDetection=class extends ObjectDetection{constructor(score,relativeBox,imageDims){super(score,score,"",relativeBox,imageDims)}forSize(width,height){let{score,relativeBox,imageDims}=super.forSize(width,height);return new FaceDetection(score,relativeBox,imageDims)}};function iou(box1,box2,isIOU=!0){let width=Math.max(0,Math.min(box1.right,box2.right)-Math.max(box1.left,box2.left)),height=Math.max(0,Math.min(box1.bottom,box2.bottom)-Math.max(box1.top,box2.top)),interSection=width*height;return isIOU?interSection/(box1.area+box2.area-interSection):interSection/Math.min(box1.area,box2.area)}function minBbox(pts){let xs=pts.map(pt=>pt.x),ys=pts.map(pt=>pt.y),minX=xs.reduce((min,x)=>x<min?x:min,Infinity),minY=ys.reduce((min,y)=>y<min?y:min,Infinity),maxX=xs.reduce((max,x)=>max<x?x:max,0),maxY=ys.reduce((max,y)=>max<y?y:max,0);return new BoundingBox(minX,minY,maxX,maxY)}function nonMaxSuppression(boxes,scores,iouThreshold,isIOU=!0){let indicesSortedByScore=scores.map((score,boxIndex)=>({score,boxIndex})).sort((c1,c2)=>c1.score-c2.score).map(c=>c.boxIndex),pick=[];for(;indicesSortedByScore.length>0;){let curr=indicesSortedByScore.pop();pick.push(curr);let indices=indicesSortedByScore,outputs=[];for(let i=0;i<indices.length;i++){let idx=indices[i],currBox=boxes[curr],idxBox=boxes[idx];outputs.push(iou(currBox,idxBox,isIOU))}indicesSortedByScore=indicesSortedByScore.filter((_,j)=>outputs[j]<=iouThreshold)}return pick}var tf2=__toModule(require_tfjs_esm());function normalize(x,meanRgb){return tf2.tidy(()=>{let[r,g,b]=meanRgb,avg_r=tf2.fill([...x.shape.slice(0,3),1],r,"float32"),avg_g=tf2.fill([...x.shape.slice(0,3),1],g,"float32"),avg_b=tf2.fill([...x.shape.slice(0,3),1],b,"float32"),avg_rgb=tf2.concat([avg_r,avg_g,avg_b],3);return tf2.sub(x,avg_rgb)})}var tf3=__toModule(require_tfjs_esm());function padToSquare(imgTensor,isCenterImage=!1){return tf3.tidy(()=>{let[height,width]=imgTensor.shape.slice(1);if(height===width)return imgTensor;let dimDiff=Math.abs(height-width),paddingAmount=Math.round(dimDiff*(isCenterImage?.5:1)),paddingAxis=height>width?2:1,createPaddingTensor=paddingAmount2=>{let paddingTensorShape=imgTensor.shape.slice();return paddingTensorShape[paddingAxis]=paddingAmount2,tf3.fill(paddingTensorShape,0,"float32")},paddingTensorAppend=createPaddingTensor(paddingAmount),remainingPaddingAmount=dimDiff-paddingTensorAppend.shape[paddingAxis],paddingTensorPrepend=isCenterImage&&remainingPaddingAmount?createPaddingTensor(remainingPaddingAmount):null,tensorsToStack=[paddingTensorPrepend,imgTensor,paddingTensorAppend].filter(t=>!!t).map(t=>tf3.cast(t,"float32"));return tf3.concat(tensorsToStack,paddingAxis)})}function shuffleArray(inputArray){let array=inputArray.slice();for(let i=array.length-1;i>0;i--){let j=Math.floor(Math.random()*(i+1)),x=array[i];array[i]=array[j],array[j]=x}return array}function sigmoid(x){return 1/(1+Math.exp(-x))}function inverseSigmoid(x){return Math.log(x/(1-x))}var Rect=class extends Box{constructor(x,y,width,height,allowNegativeDimensions=!1){super({x,y,width,height},allowNegativeDimensions)}},relX=.5,relY=.43,relScale=.45,FaceLandmarks=class{constructor(relativeFaceLandmarkPositions,imgDims,shift=new Point(0,0)){let{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){let box=detection instanceof FaceDetection?detection.box.floor():new Box(detection);return this.shiftBy(box.x,box.y).align(null,options)}let{useDlibAlignment,minBoxPadding}=Object.assign({},{useDlibAlignment:!1,minBoxPadding:.2},options);return useDlibAlignment?this.alignDlib():this.alignMinBbox(minBoxPadding)}alignDlib(){let centers=this.getRefPointsForAlignment(),[leftEyeCenter,rightEyeCenter,mouthCenter]=centers,distToMouth=pt=>mouthCenter.sub(pt).magnitude(),eyeToMouthDist=(distToMouth(leftEyeCenter)+distToMouth(rightEyeCenter))/2,size=Math.floor(eyeToMouthDist/relScale),refPoint=getCenterPoint(centers),x=Math.floor(Math.max(0,refPoint.x-relX*size)),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){let box=minBbox(this.positions);return box.pad(box.width*padding,box.height*padding)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var FaceLandmarks5=class extends FaceLandmarks{getRefPointsForAlignment(){let pts=this.positions;return[pts[0],pts[1],getCenterPoint([pts[3],pts[4]])]}};var FaceLandmarks68=class 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)}};var FaceMatch=class{constructor(label,distance){this._label=label,this._distance=distance}get label(){return this._label}get distance(){return this._distance}toString(withDistance=!0){return`${this.label}${withDistance?` (${round(this.distance)})`:""}`}};var LabeledBox=class extends Box{static assertIsValidLabeledBox(box,callee){if(Box.assertIsValidBox(box,callee),!isValidNumber(box.label))throw new Error(`${callee} - expected property label (${box.label}) to be a number`)}constructor(box,label){super(box);this._label=label}get label(){return this._label}};var LabeledFaceDescriptors=class{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){let descriptors=json.descriptors.map(d=>new Float32Array(d));return new LabeledFaceDescriptors(json.label,descriptors)}};var PredictedBox=class extends LabeledBox{static assertIsValidPredictedBox(box,callee){if(LabeledBox.assertIsValidLabeledBox(box,callee),!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]`)}constructor(box,label,score,classScore){super(box,label);this._score=score,this._classScore=classScore}get score(){return this._score}get classScore(){return this._classScore}};function isWithFaceDetection(obj){return obj.detection instanceof FaceDetection}function extendWithFaceDetection(sourceObj,detection){let extension={detection};return Object.assign({},sourceObj,extension)}function createBrowserEnv(){let fetch=window.fetch||function(){throw new Error("fetch - missing fetch implementation for browser environment")},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}}function createFileSystem(fs){let requireFsError="";if(!fs)try{fs=require("fs")}catch(err){requireFsError=err.toString()}let 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}}function createNodejsEnv(){let Canvas=global.Canvas||global.HTMLCanvasElement,Image=global.Image||global.HTMLImageElement,createCanvasElement=function(){if(Canvas)return new Canvas;throw new Error("createCanvasElement - missing Canvas implementation for nodejs environment")},createImageElement=function(){if(Image)return new Image;throw new Error("createImageElement - missing Image implementation for nodejs environment")},fetch=global.fetch||function(){throw new Error("fetch - missing fetch implementation for nodejs environment")},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}}function isBrowser(){return typeof window=="object"&&typeof document!="undefined"&&typeof HTMLImageElement!="undefined"&&typeof HTMLCanvasElement!="undefined"&&typeof HTMLVideoElement!="undefined"&&typeof ImageData!="undefined"&&typeof CanvasRenderingContext2D!="undefined"}var isNodejs=__toModule(require_isNodejs()),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(),!environment)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{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}var env={getEnv,setEnv,initialize,createBrowserEnv,createFileSystem,createNodejsEnv,monkeyPatch,isBrowser,isNodejs:isNodejs.isNodejs};initialize();function resolveInput(arg){return!env.isNodejs()&&typeof arg=="string"?document.getElementById(arg):arg}function getContext2dOrThrow(canvasArg){let{Canvas,CanvasRenderingContext2D:CanvasRenderingContext2D2}=env.getEnv();if(canvasArg instanceof CanvasRenderingContext2D2)return canvasArg;let canvas=resolveInput(canvasArg);if(!(canvas instanceof Canvas))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let ctx=canvas.getContext("2d");if(!ctx)throw new Error("resolveContext2d - canvas 2d context is null");return ctx}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={}));var DrawTextFieldOptions=class{constructor(options={}){let{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}},DrawTextField=class{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){let{padding}=this.options;return this.text.map(l=>ctx.measureText(l).width).reduce((w0,w1)=>w0<w1?w1:w0,0)+2*padding}measureHeight(){let{fontSize,padding}=this.options;return this.text.length*fontSize+2*padding}getUpperLeft(ctx,canvasDims){let{anchorPosition}=this.options,isShiftLeft=anchorPosition===AnchorPosition.BOTTOM_RIGHT||anchorPosition===AnchorPosition.TOP_RIGHT,isShiftTop=anchorPosition===AnchorPosition.BOTTOM_LEFT||anchorPosition===AnchorPosition.BOTTOM_RIGHT,textFieldWidth=this.measureWidth(ctx),textFieldHeight=this.measureHeight(),x=isShiftLeft?this.anchor.x-textFieldWidth:this.anchor.x,y=isShiftTop?this.anchor.y-textFieldHeight:this.anchor.y;if(canvasDims){let{width,height}=canvasDims,newX=Math.max(Math.min(x,width-textFieldWidth),0),newY=Math.max(Math.min(y,height-textFieldHeight),0);return{x:newX,y:newY}}return{x,y}}draw(canvasArg){let canvas=resolveInput(canvasArg),ctx=getContext2dOrThrow(canvas),{backgroundColor,fontColor,fontSize,fontStyle,padding}=this.options;ctx.font=`${fontSize}px ${fontStyle}`;let maxTextWidth=this.measureWidth(ctx),textHeight=this.measureHeight();ctx.fillStyle=backgroundColor;let upperLeft=this.getUpperLeft(ctx,canvas);ctx.fillRect(upperLeft.x,upperLeft.y,maxTextWidth,textHeight),ctx.fillStyle=fontColor,this.text.forEach((textLine,i)=>{let x=padding+upperLeft.x,y=padding+upperLeft.y+(i+1)*fontSize;ctx.fillText(textLine,x,y)})}},DrawBoxOptions=class{constructor(options={}){let{boxColor,lineWidth,label,drawLabelOptions}=options;this.boxColor=boxColor||"rgba(0, 0, 255, 1)",this.lineWidth=lineWidth||2,this.label=label;let defaultDrawLabelOptions={anchorPosition:AnchorPosition.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new DrawTextFieldOptions(Object.assign({},defaultDrawLabelOptions,drawLabelOptions))}},DrawBox=class{constructor(box,options={}){this.box=new Box(box),this.options=new DrawBoxOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{boxColor,lineWidth}=this.options,{x,y,width,height}=this.box;ctx.strokeStyle=boxColor,ctx.lineWidth=lineWidth,ctx.strokeRect(x,y,width,height);let{label}=this.options;label&&new DrawTextField([label],{x:x-lineWidth/2,y},this.options.drawLabelOptions).draw(canvasArg)}};function drawDetections(canvasArg,detections){let detectionsArray=Array.isArray(detections)?detections:[detections];detectionsArray.forEach(det=>{let score=det instanceof FaceDetection?det.score:isWithFaceDetection(det)?det.detection.score:void 0,box=det instanceof FaceDetection?det.box:isWithFaceDetection(det)?det.detection.box:new Box(det),label=score?`${round(score)}`:void 0;new DrawBox(box,{label}).draw(canvasArg)})}var tf18=__toModule(require_tfjs_esm());function isMediaLoaded(media){let{Image,Video}=env.getEnv();return media instanceof Image&&media.complete||media instanceof Video&&media.readyState>=3}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)})}function bufferToImage(buf){return new Promise((resolve,reject)=>{if(!(buf instanceof Blob))return reject("bufferToImage - expected buf to be of type: Blob");let reader=new FileReader;reader.onload=()=>{if(typeof reader.result!="string")return reject("bufferToImage - expected reader.result to be a string, in onload");let img=env.getEnv().createImageElement();img.onload=()=>resolve(img),img.onerror=reject,img.src=reader.result},reader.onerror=reject,reader.readAsDataURL(buf)})}function getMediaDimensions(input){let{Image,Video}=env.getEnv();return input instanceof Image?new Dimensions(input.naturalWidth,input.naturalHeight):input instanceof Video?new Dimensions(input.videoWidth,input.videoHeight):new Dimensions(input.width,input.height)}function createCanvas({width,height}){let{createCanvasElement}=env.getEnv(),canvas=createCanvasElement();return canvas.width=width,canvas.height=height,canvas}function createCanvasFromMedia(media,dims){let{ImageData:ImageData2}=env.getEnv();if(!(media instanceof ImageData2)&&!isMediaLoaded(media))throw new Error("createCanvasFromMedia - media has not finished loading yet");let{width,height}=dims||getMediaDimensions(media),canvas=createCanvas({width,height});return media instanceof ImageData2?getContext2dOrThrow(canvas).putImageData(media,0,0):getContext2dOrThrow(canvas).drawImage(media,0,0,width,height),canvas}var tf4=__toModule(require_tfjs_esm());async function imageTensorToCanvas(imgTensor,canvas){let targetCanvas=canvas||env.getEnv().createCanvasElement(),[height,width,numChannels]=imgTensor.shape.slice(isTensor4D(imgTensor)?1:0),imgTensor3D=tf4.tidy(()=>imgTensor.as3D(height,width,numChannels).toInt());return await tf4.browser.toPixels(imgTensor3D,targetCanvas),imgTensor3D.dispose(),targetCanvas}function isMediaElement(input){let{Image,Canvas,Video}=env.getEnv();return input instanceof Image||input instanceof Canvas||input instanceof Video}var tf5=__toModule(require("@tensorflow/tfjs-core"));function imageToSquare(input,inputSize,centerImage=!1){let{Image,Canvas}=env.getEnv();if(!(input instanceof Image||input instanceof Canvas))throw new Error("imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement");let dims=getMediaDimensions(input),scale2=inputSize/Math.max(dims.height,dims.width),width=scale2*dims.width,height=scale2*dims.height,targetCanvas=createCanvas({width:inputSize,height:inputSize}),inputCanvas=input instanceof Canvas?input:createCanvasFromMedia(input),offset=Math.abs(width-height)/2,dx=centerImage&&width<height?offset:0,dy=centerImage&&height<width?offset:0;return getContext2dOrThrow(targetCanvas).drawImage(inputCanvas,dx,dy,width,height),targetCanvas}var NetInput=class{constructor(inputs,treatAsBatchInput=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;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)){let 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}let 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");let width=this.getInputWidth(batchIdx),height=this.getInputHeight(batchIdx);return computeReshapedDimensions({width,height},this.inputSize)}toBatchTensor(inputSize,isCenterInputs=!0){return this._inputSize=inputSize,tf5.tidy(()=>{let inputTensors=range(this.batchSize,0,1).map(batchIdx=>{let input=this.getInput(batchIdx);if(input instanceof tf5.Tensor){let imgTensor=isTensor4D(input)?input:input.expandDims();return imgTensor=padToSquare(imgTensor,isCenterInputs),(imgTensor.shape[1]!==inputSize||imgTensor.shape[2]!==inputSize)&&(imgTensor=tf5.image.resizeBilinear(imgTensor,[inputSize,inputSize])),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}`)}),batchTensor=tf5.stack(inputTensors.map(t=>tf5.cast(t,"float32"))).as4D(this.batchSize,inputSize,inputSize,3);return batchTensor})}};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");let getIdxHint=idx=>Array.isArray(inputs)?` at input index ${idx}:`:"",inputArray=inputArgArray.map(resolveInput);return inputArray.forEach((input,i)=>{if(!isMediaElement(input)&&!isTensor3D(input)&&!isTensor4D(input))throw typeof inputArgArray[i]=="string"?new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`):new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`);if(isTensor4D(input)){let batchSize=input.shape[0];if(batchSize!==1)throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)}}),await Promise.all(inputArray.map(input=>isMediaElement(input)&&awaitMediaLoaded(input))),new NetInput(inputArray,Array.isArray(inputs))}async function extractFaces(input,detections){let{Canvas}=env.getEnv(),canvas=input;if(!(input instanceof Canvas)){let netInput=await toNetInput(input);if(netInput.batchSize>1)throw new Error("extractFaces - batchSize > 1 not supported");let tensorOrCanvas=netInput.getInput(0);canvas=tensorOrCanvas instanceof Canvas?tensorOrCanvas:await imageTensorToCanvas(tensorOrCanvas)}let ctx=getContext2dOrThrow(canvas),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(canvas.width,canvas.height).box.floor():det).map(box=>box.clipAtImageBorders(canvas.width,canvas.height));return boxes.map(({x,y,width,height})=>{let faceImg=createCanvas({width,height});return getContext2dOrThrow(faceImg).putImageData(ctx.getImageData(x,y,width,height),0,0),faceImg})}var tf6=__toModule(require_tfjs_esm());async function extractFaceTensors(imageTensor,detections){if(!isTensor3D(imageTensor)&&!isTensor4D(imageTensor))throw new Error("extractFaceTensors - expected image tensor to be 3D or 4D");if(isTensor4D(imageTensor)&&imageTensor.shape[0]>1)throw new Error("extractFaceTensors - batchSize > 1 not supported");return tf6.tidy(()=>{let[imgHeight,imgWidth,numChannels]=imageTensor.shape.slice(isTensor4D(imageTensor)?1:0),boxes=detections.map(det=>det instanceof FaceDetection?det.forSize(imgWidth,imgHeight).box:det).map(box=>box.clipAtImageBorders(imgWidth,imgHeight)),faceTensors=boxes.map(({x,y,width,height})=>tf6.slice3d(imageTensor.as3D(imgHeight,imgWidth,numChannels),[y,x,0],[height,width,numChannels]));return faceTensors})}async function fetchOrThrow(url,init){let fetch=env.getEnv().fetch,res=await fetch(url,init);if(!(res.status<400))throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`);return res}async function fetchImage(uri){let res=await fetchOrThrow(uri),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: ${res.url}`);return bufferToImage(blob)}async function fetchJson(uri){return(await fetchOrThrow(uri)).json()}async function fetchNetWeights(uri){return new Float32Array(await(await fetchOrThrow(uri)).arrayBuffer())}var tf7=__toModule(require_tfjs_esm());function getModelUris(uri,defaultModelName){let defaultManifestFilename=`${defaultModelName}-weights_manifest.json`;if(!uri)return{modelBaseUri:"",manifestUri:defaultManifestFilename};if(uri==="/")return{modelBaseUri:"/",manifestUri:`/${defaultManifestFilename}`};let protocol=uri.startsWith("http://")?"http://":uri.startsWith("https://")?"https://":"";uri=uri.replace(protocol,"");let parts=uri.split("/").filter(s=>s),manifestFile=uri.endsWith(".json")?parts[parts.length-1]:defaultManifestFilename,modelBaseUri=protocol+(uri.endsWith(".json")?parts.slice(0,parts.length-1):parts).join("/");return modelBaseUri=uri.startsWith("/")?`/${modelBaseUri}`:modelBaseUri,{modelBaseUri,manifestUri:modelBaseUri==="/"?`/${manifestFile}`:`${modelBaseUri}/${manifestFile}`}}async function loadWeightMap(uri,defaultModelName){let{manifestUri,modelBaseUri}=getModelUris(uri,defaultModelName),manifest=await fetchJson(manifestUri);return tf7.io.loadWeights(manifest,modelBaseUri)}function matchDimensions(input,reference,useMediaDimensions=!1){let{width,height}=useMediaDimensions?getMediaDimensions(reference):reference;return input.width=width,input.height=height,{width,height}}var tf15=__toModule(require_tfjs_esm()),tf8=__toModule(require_tfjs_esm()),NeuralNetwork=class{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){let{obj,objProp}=this.traversePropertyPath(paramPath);return obj[objProp]}reassignParamFromPath(paramPath,tensor2){let{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})=>{let tensor2=tf8.tensor(variable.dataSync());variable.dispose(),this.reassignParamFromPath(path,tensor2)})}dispose(throwOnRedispose=!0){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`);let 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`);let{readFile}=env.getEnv(),{manifestUri,modelBaseUri}=getModelUris(filePath,this.getDefaultModelName()),fetchWeightsFromDisk=filePaths=>Promise.all(filePaths.map(filePath2=>readFile(filePath2).then(buf=>buf.buffer))),loadWeights=tf8.io.weightsLoaderFactory(fetchWeightsFromDisk),manifest=JSON.parse((await readFile(manifestUri)).toString()),weightMap=await loadWeights(manifest,modelBaseUri);this.loadFromWeightMap(weightMap)}loadFromWeightMap(weightMap){let{paramMappings,params}=this.extractParamsFromWeigthMap(weightMap);this._paramMappings=paramMappings,this._params=params}extractWeights(weights){let{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");let 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}),{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}}},tf10=__toModule(require_tfjs_esm()),tf9=__toModule(require_tfjs_esm());function depthwiseSeparableConv(x,params,stride){return tf9.tidy(()=>{let out=tf9.separableConv2d(x,params.depthwise_filter,params.pointwise_filter,stride,"same");return out=tf9.add(out,params.bias),out})}function denseBlock3(x,denseBlockParams,isFirstLayer=!1){return tf10.tidy(()=>{let out1=tf10.relu(isFirstLayer?tf10.add(tf10.conv2d(x,denseBlockParams.conv0.filters,[2,2],"same"),denseBlockParams.conv0.bias):depthwiseSeparableConv(x,denseBlockParams.conv0,[2,2])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,out3)))})}function denseBlock4(x,denseBlockParams,isFirstLayer=!1,isScaleDown=!0){return tf10.tidy(()=>{let 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])),out2=depthwiseSeparableConv(out1,denseBlockParams.conv1,[1,1]),in3=tf10.relu(tf10.add(out1,out2)),out3=depthwiseSeparableConv(in3,denseBlockParams.conv2,[1,1]),in4=tf10.relu(tf10.add(out1,tf10.add(out2,out3))),out4=depthwiseSeparableConv(in4,denseBlockParams.conv3,[1,1]);return tf10.relu(tf10.add(out1,tf10.add(out2,tf10.add(out3,out4))))})}var tf11=__toModule(require_tfjs_esm());function convLayer(x,params,padding="same",withRelu=!1){return tf11.tidy(()=>{let out=tf11.add(tf11.conv2d(x,params.filters,[1,1],padding),params.bias);return withRelu?tf11.relu(out):out})}function disposeUnusedWeightTensors(weightMap,paramMappings){Object.keys(weightMap).forEach(path=>{paramMappings.some(pm=>pm.originalPath===path)||weightMap[path].dispose()})}var tf12=__toModule(require_tfjs_esm());function extractConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,filterSize,mappedPrefix){let filters=tf12.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf12.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}}var tf13=__toModule(require_tfjs_esm());function extractFCParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let fc_weights=tf13.tensor2d(extractWeights(channelsIn*channelsOut),[channelsIn,channelsOut]),fc_bias=tf13.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/bias`}),{weights:fc_weights,bias:fc_bias}}}var tf14=__toModule(require_tfjs_esm()),SeparableConvParams=class{constructor(depthwise_filter,pointwise_filter,bias){this.depthwise_filter=depthwise_filter;this.pointwise_filter=pointwise_filter;this.bias=bias}};function extractSeparableConvParamsFactory(extractWeights,paramMappings){return function(channelsIn,channelsOut,mappedPrefix){let depthwise_filter=tf14.tensor4d(extractWeights(3*3*channelsIn),[3,3,channelsIn,1]),pointwise_filter=tf14.tensor4d(extractWeights(channelsIn*channelsOut),[1,1,channelsIn,channelsOut]),bias=tf14.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/depthwise_filter`},{paramPath:`${mappedPrefix}/pointwise_filter`},{paramPath:`${mappedPrefix}/bias`}),new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function loadSeparableConvParamsFactory(extractWeightEntry){return function(prefix){let depthwise_filter=extractWeightEntry(`${prefix}/depthwise_filter`,4),pointwise_filter=extractWeightEntry(`${prefix}/pointwise_filter`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return new SeparableConvParams(depthwise_filter,pointwise_filter,bias)}}function extractWeightEntryFactory(weightMap,paramMappings){return function(originalPath,paramRank,mappedPath){let tensor2=weightMap[originalPath];if(!isTensor(tensor2,paramRank))throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);return paramMappings.push({originalPath,paramPath:mappedPath||originalPath}),tensor2}}function extractWeightsFactory(weights){let remainingWeights=weights;function extractWeights(numWeights){let ret=remainingWeights.slice(0,numWeights);return remainingWeights=remainingWeights.slice(numWeights),ret}function getRemainingWeights(){return remainingWeights}return{extractWeights,getRemainingWeights}}function extractorsFactory(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv0`):extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/conv0`),conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv1`),conv22=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer=!1){let{conv0,conv1,conv2:conv22}=extractDenseBlock3Params(channelsIn,channelsOut,mappedPrefix,isFirstLayer),conv3=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParams(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock4Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock4Params(3,32,"dense0",!0),dense1=extractDenseBlock4Params(32,64,"dense1"),dense2=extractDenseBlock4Params(64,128,"dense2"),dense3=extractDenseBlock4Params(128,256,"dense3");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2,dense3}}}function loadConvParamsFactory(extractWeightEntry){return function(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}}function loadParamsFactory(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractDenseBlock3Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`);return{conv0,conv1,conv2:conv22}}function extractDenseBlock4Params(prefix,isFirstLayer=!1){let conv0=isFirstLayer?extractConvParams(`${prefix}/conv0`):extractSeparableConvParams(`${prefix}/conv0`),conv1=extractSeparableConvParams(`${prefix}/conv1`),conv22=extractSeparableConvParams(`${prefix}/conv2`),conv3=extractSeparableConvParams(`${prefix}/conv3`);return{conv0,conv1,conv2:conv22,conv3}}return{extractDenseBlock3Params,extractDenseBlock4Params}}function extractParamsFromWeigthMap(weightMap){let paramMappings=[],{extractDenseBlock4Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock4Params("dense0",!0),dense1:extractDenseBlock4Params("dense1"),dense2:extractDenseBlock4Params("dense2"),dense3:extractDenseBlock4Params("dense3")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var FaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("FaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceFeatureExtractor - load model before inference");return tf15.tidy(()=>{let batchTensor=tf15.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf15.scalar(255)),out=denseBlock4(normalized,params.dense0,!0);return out=denseBlock4(out,params.dense1),out=denseBlock4(out,params.dense2),out=denseBlock4(out,params.dense3),out=tf15.avgPool(out,[7,7],[2,2],"valid"),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)}},tf17=__toModule(require_tfjs_esm()),tf16=__toModule(require_tfjs_esm());function fullyConnectedLayer(x,params){return tf16.tidy(()=>tf16.add(tf16.matMul(x,params.weights),params.bias))}function extractParams3(weights,channelsIn,channelsOut){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),fc=extractFCParams(channelsIn,channelsOut,"fc");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc}}}function extractParamsFromWeigthMap3(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:extractFcParams("fc")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function seperateWeightMaps(weightMap){let featureExtractorMap={},classifierMap={};return Object.keys(weightMap).forEach(key=>{let map=key.startsWith("fc")?classifierMap:featureExtractorMap;map[key]=weightMap[key]}),{featureExtractorMap,classifierMap}}var FaceProcessor=class extends NeuralNetwork{constructor(_name,faceFeatureExtractor){super(_name);this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf17.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input;return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0],-1),params.fc)})}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams3(weights,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap3(classifierMap)}extractParams(weights){let cIn=this.getClassifierChannelsIn(),cOut=this.getClassifierChannelsOut(),classifierWeightSize=cOut*cIn+cOut,featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}},FACE_EXPRESSION_LABELS=["neutral","happy","sad","angry","fearful","disgusted","surprised"],FaceExpressions=class{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)}},FaceExpressionNet=class 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){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),probabilitesByBatch=await Promise.all(tf18.unstack(out).map(async t=>{let data=await t.data();return t.dispose(),data}));out.dispose();let predictionsByBatch=probabilitesByBatch.map(probabilites=>new FaceExpressions(probabilites));return netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function isWithFaceExpressions(obj){return obj.expressions instanceof FaceExpressions}function extendWithFaceExpressions(sourceObj,expressions){let extension={expressions};return Object.assign({},sourceObj,extension)}function drawFaceExpressions(canvasArg,faceExpressions,minConfidence=.1,textFieldAnchor){let faceExpressionsArray=Array.isArray(faceExpressions)?faceExpressions:[faceExpressions];faceExpressionsArray.forEach(e=>{let 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");let sorted=expr.asSortedArray(),resultsToDisplay=sorted.filter(expr2=>expr2.probability>minConfidence),anchor=isWithFaceDetection(e)?e.detection.box.bottomLeft:textFieldAnchor||new Point(0,0),drawTextField=new DrawTextField(resultsToDisplay.map(expr2=>`${expr2.expression} (${round(expr2.probability)})`),anchor);drawTextField.draw(canvasArg)})}function isWithFaceLandmarks(obj){return isWithFaceDetection(obj)&&obj.landmarks instanceof FaceLandmarks&&obj.unshiftedLandmarks instanceof FaceLandmarks&&obj.alignedRect instanceof FaceDetection}function extendWithFaceLandmarks(sourceObj,unshiftedLandmarks){let{box:shift}=sourceObj.detection,landmarks=unshiftedLandmarks.shiftBy(shift.x,shift.y),rect=landmarks.align(),{imageDims}=sourceObj.detection,alignedRect=new FaceDetection(sourceObj.detection.score,rect.rescale(imageDims.reverse()),imageDims),extension={landmarks,unshiftedLandmarks,alignedRect};return Object.assign({},sourceObj,extension)}var DrawFaceLandmarksOptions=class{constructor(options={}){let{drawLines=!0,drawPoints=!0,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)"}},DrawFaceLandmarks=class{constructor(faceLandmarks,options={}){this.faceLandmarks=faceLandmarks,this.options=new DrawFaceLandmarksOptions(options)}draw(canvasArg){let ctx=getContext2dOrThrow(canvasArg),{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(),!0),drawContour(ctx,this.faceLandmarks.getRightEye(),!0),drawContour(ctx,this.faceLandmarks.getMouth(),!0)),drawPoints){ctx.strokeStyle=pointColor,ctx.fillStyle=pointColor;let 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){let faceLandmarksArray=Array.isArray(faceLandmarks)?faceLandmarks:[faceLandmarks];faceLandmarksArray.forEach(f=>{let 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)})}var tf20=__toModule(require_tfjs_esm()),tf19=__toModule(require_tfjs_esm());function extractorsFactory3(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings),extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);function extractReductionBlockParams(channelsIn,channelsOut,mappedPrefix){let separable_conv0=extractSeparableConvParams(channelsIn,channelsOut,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channelsOut,channelsOut,`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(channels,mappedPrefix){let separable_conv0=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(channels,channels,`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParams5(weights,numMainBlocks){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=extractorsFactory3(extractWeights,paramMappings),entry_flow_conv_in=extractConvParams(3,32,3,"entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams(32,64,"entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams(64,128,"entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(128,`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams(128,256,"exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams(256,512,"exit_flow/separable_conv"),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: ${getRemainingWeights().length}`);return{paramMappings,params:{entry_flow,middle_flow,exit_flow}}}function loadParamsFactory3(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings),extractConvParams=loadConvParamsFactory(extractWeightEntry),extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);function extractReductionBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),expansion_conv=extractConvParams(`${mappedPrefix}/expansion_conv`);return{separable_conv0,separable_conv1,expansion_conv}}function extractMainBlockParams(mappedPrefix){let separable_conv0=extractSeparableConvParams(`${mappedPrefix}/separable_conv0`),separable_conv1=extractSeparableConvParams(`${mappedPrefix}/separable_conv1`),separable_conv2=extractSeparableConvParams(`${mappedPrefix}/separable_conv2`);return{separable_conv0,separable_conv1,separable_conv2}}return{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}}function extractParamsFromWeigthMap5(weightMap,numMainBlocks){let paramMappings=[],{extractConvParams,extractSeparableConvParams,extractReductionBlockParams,extractMainBlockParams}=loadParamsFactory3(weightMap,paramMappings),entry_flow_conv_in=extractConvParams("entry_flow/conv_in"),entry_flow_reduction_block_0=extractReductionBlockParams("entry_flow/reduction_block_0"),entry_flow_reduction_block_1=extractReductionBlockParams("entry_flow/reduction_block_1"),entry_flow={conv_in:entry_flow_conv_in,reduction_block_0:entry_flow_reduction_block_0,reduction_block_1:entry_flow_reduction_block_1},middle_flow={};range(numMainBlocks,0,1).forEach(idx=>{middle_flow[`main_block_${idx}`]=extractMainBlockParams(`middle_flow/main_block_${idx}`)});let exit_flow_reduction_block=extractReductionBlockParams("exit_flow/reduction_block"),exit_flow_separable_conv=extractSeparableConvParams("exit_flow/separable_conv"),exit_flow={reduction_block:exit_flow_reduction_block,separable_conv:exit_flow_separable_conv};return disposeUnusedWeightTensors(weightMap,paramMappings),{params:{entry_flow,middle_flow,exit_flow},paramMappings}}function conv(x,params,stride){return tf19.add(tf19.conv2d(x,params.filters,stride,"same"),params.bias)}function reductionBlock(x,params,isActivateInput=!0){let out=isActivateInput?tf19.relu(x):x;return out=depthwiseSeparableConv(out,params.separable_conv0,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=tf19.maxPool(out,[3,3],[2,2],"same"),out=tf19.add(out,conv(x,params.expansion_conv,[2,2])),out}function mainBlock(x,params){let out=depthwiseSeparableConv(tf19.relu(x),params.separable_conv0,[1,1]);return out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv1,[1,1]),out=depthwiseSeparableConv(tf19.relu(out),params.separable_conv2,[1,1]),out=tf19.add(out,x),out}var TinyXception=class extends NeuralNetwork{constructor(numMainBlocks){super("TinyXception");this._numMainBlocks=numMainBlocks}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyXception - load model before inference");return tf19.tidy(()=>{let batchTensor=tf19.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf19.scalar(256)),out=tf19.relu(conv(normalized,params.entry_flow.conv_in,[2,2]));return out=reductionBlock(out,params.entry_flow.reduction_block_0,!1),out=reductionBlock(out,params.entry_flow.reduction_block_1),range(this._numMainBlocks,0,1).forEach(idx=>{out=mainBlock(out,params.middle_flow[`main_block_${idx}`])}),out=reductionBlock(out,params.exit_flow.reduction_block),out=tf19.relu(depthwiseSeparableConv(out,params.exit_flow.separable_conv,[1,1])),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)}};function extractParams7(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),extractFCParams=extractFCParamsFactory(extractWeights,paramMappings),age=extractFCParams(512,1,"fc/age"),gender=extractFCParams(512,2,"fc/gender");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{fc:{age,gender}}}}function extractParamsFromWeigthMap7(weightMap){let paramMappings=[],extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractFcParams(prefix){let weights=extractWeightEntry(`${prefix}/weights`,2),bias=extractWeightEntry(`${prefix}/bias`,1);return{weights,bias}}let params={fc:{age:extractFcParams("fc/age"),gender:extractFcParams("fc/gender")}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var Gender;(function(Gender2){Gender2.FEMALE="female",Gender2.MALE="male"})(Gender||(Gender={}));var AgeGenderNet=class extends NeuralNetwork{constructor(faceFeatureExtractor=new TinyXception(2)){super("AgeGenderNet");this._faceFeatureExtractor=faceFeatureExtractor}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(input){let{params}=this;if(!params)throw new Error(`${this._name} - load model before inference`);return tf20.tidy(()=>{let bottleneckFeatures=input instanceof NetInput?this.faceFeatureExtractor.forwardInput(input):input,pooled=tf20.avgPool(bottleneckFeatures,[7,7],[2,2],"valid").as2D(bottleneckFeatures.shape[0],-1),age=fullyConnectedLayer(pooled,params.fc.age).as1D(),gender=fullyConnectedLayer(pooled,params.fc.gender);return{age,gender}})}forwardInput(input){return tf20.tidy(()=>{let{age,gender}=this.runNet(input);return{age,gender:tf20.softmax(gender)}})}async forward(input){return this.forwardInput(await toNetInput(input))}async predictAgeAndGender(input){let netInput=await toNetInput(input),out=await this.forwardInput(netInput),ages=tf20.unstack(out.age),genders=tf20.unstack(out.gender),ageAndGenderTensors=ages.map((ageTensor,i)=>({ageTensor,genderTensor:genders[i]})),predictionsByBatch=await Promise.all(ageAndGenderTensors.map(async({ageTensor,genderTensor})=>{let age=(await ageTensor.data())[0],probMale=(await genderTensor.data())[0],isMale=probMale>.5,gender=isMale?Gender.MALE:Gender.FEMALE,genderProbability=isMale?probMale:1-probMale;return ageTensor.dispose(),genderTensor.dispose(),{age,gender,genderProbability}}));return out.age.dispose(),out.gender.dispose(),netInput.isBatchInput?predictionsByBatch:predictionsByBatch[0]}getDefaultModelName(){return"age_gender_model"}dispose(throwOnRedispose=!0){this.faceFeatureExtractor.dispose(throwOnRedispose),super.dispose(throwOnRedispose)}loadClassifierParams(weights){let{params,paramMappings}=this.extractClassifierParams(weights);this._params=params,this._paramMappings=paramMappings}extractClassifierParams(weights){return extractParams7(weights)}extractParamsFromWeigthMap(weightMap){let{featureExtractorMap,classifierMap}=seperateWeightMaps(weightMap);return this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap),extractParamsFromWeigthMap7(classifierMap)}extractParams(weights){let classifierWeightSize=512*1+1+(512*2+2),featureExtractorWeights=weights.slice(0,weights.length-classifierWeightSize),classifierWeights=weights.slice(weights.length-classifierWeightSize);return this.faceFeatureExtractor.extractWeights(featureExtractorWeights),this.extractClassifierParams(classifierWeights)}};var tf21=__toModule(require_tfjs_esm()),FaceLandmark68NetBase=class extends FaceProcessor{postProcess(output,inputSize,originalDimensions){let inputDimensions=originalDimensions.map(({width,height})=>{let scale2=inputSize/Math.max(height,width);return{width:width*scale2,height:height*scale2}}),batchSize=inputDimensions.length;return tf21.tidy(()=>{let createInterleavedTensor=(fillX,fillY)=>tf21.stack([tf21.fill([68],fillX,"float32"),tf21.fill([68],fillY,"float32")],1).as2D(1,136).as1D(),getPadding=(batchIdx,cond)=>{let{width,height}=inputDimensions[batchIdx];return cond(width,height)?Math.abs(width-height)/2:0},getPaddingX=batchIdx=>getPadding(batchIdx,(w,h)=>w<h),getPaddingY=batchIdx=>getPadding(batchIdx,(w,h)=>h<w),landmarkTensors=output.mul(tf21.fill([batchSize,136],inputSize,"float32")).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(()=>{let 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){let netInput=await toNetInput(input),landmarkTensors=tf21.tidy(()=>tf21.unstack(this.forwardInput(netInput))),landmarksForBatch=await Promise.all(landmarkTensors.map(async(landmarkTensor,batchIdx)=>{let landmarksArray=Array.from(await landmarkTensor.data()),xCoords=landmarksArray.filter((_,i)=>isEven(i)),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)})}));return landmarkTensors.forEach(t=>t.dispose()),netInput.isBatchInput?landmarksForBatch:landmarksForBatch[0]}getClassifierChannelsOut(){return 136}},FaceLandmark68Net=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new FaceFeatureExtractor){super("FaceLandmark68Net",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var tf22=__toModule(require_tfjs_esm());function extractParamsFromWeigthMapTiny(weightMap){let paramMappings=[],{extractDenseBlock3Params}=loadParamsFactory(weightMap,paramMappings),params={dense0:extractDenseBlock3Params("dense0",!0),dense1:extractDenseBlock3Params("dense1"),dense2:extractDenseBlock3Params("dense2")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}function extractParamsTiny(weights){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractDenseBlock3Params}=extractorsFactory(extractWeights,paramMappings),dense0=extractDenseBlock3Params(3,32,"dense0",!0),dense1=extractDenseBlock3Params(32,64,"dense1"),dense2=extractDenseBlock3Params(64,128,"dense2");if(getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{paramMappings,params:{dense0,dense1,dense2}}}var TinyFaceFeatureExtractor=class extends NeuralNetwork{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(input){let{params}=this;if(!params)throw new Error("TinyFaceFeatureExtractor - load model before inference");return tf22.tidy(()=>{let batchTensor=tf22.cast(input.toBatchTensor(112,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf22.scalar(255)),out=denseBlock3(normalized,params.dense0,!0);return out=denseBlock3(out,params.dense1),out=denseBlock3(out,params.dense2),out=tf22.avgPool(out,[14,14],[2,2],"valid"),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)}},FaceLandmark68TinyNet=class extends FaceLandmark68NetBase{constructor(faceFeatureExtractor=new TinyFaceFeatureExtractor){super("FaceLandmark68TinyNet",faceFeatureExtractor)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},FaceLandmarkNet=class extends FaceLandmark68Net{};var tf27=__toModule(require_tfjs_esm()),tf24=__toModule(require_tfjs_esm()),tf23=__toModule(require_tfjs_esm());function scale(x,params){return tf23.add(tf23.mul(x,params.weights),params.biases)}function convLayer2(x,params,strides,withRelu,padding="same"){let{filters,bias}=params.conv,out=tf24.conv2d(x,filters,strides,padding);return out=tf24.add(out,bias),out=scale(out,params.scale),withRelu?tf24.relu(out):out}function conv2(x,params){return convLayer2(x,params,[1,1],!0)}function convNoRelu(x,params){return convLayer2(x,params,[1,1],!1)}function convDown(x,params){return convLayer2(x,params,[2,2],!0,"valid")}var tf25=__toModule(require_tfjs_esm());function extractorsFactory5(extractWeights,paramMappings){function extractFilterValues(numFilterValues,numFilters,filterSize){let weights=extractWeights(numFilterValues),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: ${filterSize}`);return tf25.tidy(()=>tf25.transpose(tf25.tensor4d(weights,[numFilters,depth,filterSize,filterSize]),[2,3,1,0]))}function extractConvParams(numFilterValues,numFilters,filterSize,mappedPrefix){let filters=extractFilterValues(numFilterValues,numFilters,filterSize),bias=tf25.tensor1d(extractWeights(numFilters));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/bias`}),{filters,bias}}function extractScaleLayerParams(numWeights,mappedPrefix){let weights=tf25.tensor1d(extractWeights(numWeights)),biases=tf25.tensor1d(extractWeights(numWeights));return paramMappings.push({paramPath:`${mappedPrefix}/weights`},{paramPath:`${mappedPrefix}/biases`}),{weights,biases}}function extractConvLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix){let conv3=extractConvParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv`),scale2=extractScaleLayerParams(numFilters,`${mappedPrefix}/scale`);return{conv:conv3,scale:scale2}}function extractResidualLayerParams(numFilterValues,numFilters,filterSize,mappedPrefix,isDown=!1){let conv1=extractConvLayerParams((isDown?.5:1)*numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv1`),conv22=extractConvLayerParams(numFilterValues,numFilters,filterSize,`${mappedPrefix}/conv2`);return{conv1,conv2:conv22}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParams9(weights){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory5(extractWeights,paramMappings),conv32_down=extractConvLayerParams(4704,32,7,"conv32_down"),conv32_1=extractResidualLayerParams(9216,32,3,"conv32_1"),conv32_2=extractResidualLayerParams(9216,32,3,"conv32_2"),conv32_3=extractResidualLayerParams(9216,32,3,"conv32_3"),conv64_down=extractResidualLayerParams(36864,64,3,"conv64_down",!0),conv64_1=extractResidualLayerParams(36864,64,3,"conv64_1"),conv64_2=extractResidualLayerParams(36864,64,3,"conv64_2"),conv64_3=extractResidualLayerParams(36864,64,3,"conv64_3"),conv128_down=extractResidualLayerParams(147456,128,3,"conv128_down",!0),conv128_1=extractResidualLayerParams(147456,128,3,"conv128_1"),conv128_2=extractResidualLayerParams(147456,128,3,"conv128_2"),conv256_down=extractResidualLayerParams(589824,256,3,"conv256_down",!0),conv256_1=extractResidualLayerParams(589824,256,3,"conv256_1"),conv256_2=extractResidualLayerParams(589824,256,3,"conv256_2"),conv256_down_out=extractResidualLayerParams(589824,256,3,"conv256_down_out"),fc=tf25.tidy(()=>tf25.transpose(tf25.tensor2d(extractWeights(256*128),[128,256]),[1,0]));if(paramMappings.push({paramPath:"fc"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);let params={conv32_down,conv32_1,conv32_2,conv32_3,conv64_down,conv64_1,conv64_2,conv64_3,conv128_down,conv128_1,conv128_2,conv256_down,conv256_1,conv256_2,conv256_down_out,fc};return{params,paramMappings}}function extractorsFactory6(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractScaleLayerParams(prefix){let weights=extractWeightEntry(`${prefix}/scale/weights`,1),biases=extractWeightEntry(`${prefix}/scale/biases`,1);return{weights,biases}}function extractConvLayerParams(prefix){let filters=extractWeightEntry(`${prefix}/conv/filters`,4),bias=extractWeightEntry(`${prefix}/conv/bias`,1),scale2=extractScaleLayerParams(prefix);return{conv:{filters,bias},scale:scale2}}function extractResidualLayerParams(prefix){return{conv1:extractConvLayerParams(`${prefix}/conv1`),conv2:extractConvLayerParams(`${prefix}/conv2`)}}return{extractConvLayerParams,extractResidualLayerParams}}function extractParamsFromWeigthMap9(weightMap){let paramMappings=[],{extractConvLayerParams,extractResidualLayerParams}=extractorsFactory6(weightMap,paramMappings),conv32_down=extractConvLayerParams("conv32_down"),conv32_1=extractResidualLayerParams("conv32_1"),conv32_2=extractResidualLayerParams("conv32_2"),conv32_3=extractResidualLayerParams("conv32_3"),conv64_down=extractResidualLayerParams("conv64_down"),conv64_1=extractResidualLayerParams("conv64_1"),conv64_2=extractResidualLayerParams("conv64_2"),conv64_3=extractResidualLayerParams("conv64_3"),conv128_down=extractResidualLayerParams("conv128_down"),conv128_1=extractResidualLayerParams("conv128_1"),conv128_2=extractResidualLayerParams("conv128_2"),conv256_down=extractResidualLayerParams("conv256_down"),conv256_1=extractResidualLayerParams("conv256_1"),conv256_2=extractResidualLayerParams("conv256_2"),conv256_down_out=extractResidualLayerParams("conv256_down_out"),fc=weightMap.fc;if(paramMappings.push({originalPath:"fc",paramPath:"fc"}),!isTensor2D(fc))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`);let 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 disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf26=__toModule(require_tfjs_esm());function residual(x,params){let out=conv2(x,params.conv1);return out=convNoRelu(out,params.conv2),out=tf26.add(out,x),out=tf26.relu(out),out}function residualDown(x,params){let out=convDown(x,params.conv1);out=convNoRelu(out,params.conv2);let pooled=tf26.avgPool(x,2,2,"valid"),zeros2=tf26.zeros(pooled.shape),isPad=pooled.shape[3]!==out.shape[3],isAdjustShape=pooled.shape[1]!==out.shape[1]||pooled.shape[2]!==out.shape[2];if(isAdjustShape){let padShapeX=[...out.shape];padShapeX[1]=1;let zerosW=tf26.zeros(padShapeX);out=tf26.concat([out,zerosW],1);let padShapeY=[...out.shape];padShapeY[2]=1;let zerosH=tf26.zeros(padShapeY);out=tf26.concat([out,zerosH],2)}return pooled=isPad?tf26.concat([pooled,zeros2],3):pooled,out=tf26.add(pooled,out),out=tf26.relu(out),out}var FaceRecognitionNet=class extends NeuralNetwork{constructor(){super("FaceRecognitionNet")}forwardInput(input){let{params}=this;if(!params)throw new Error("FaceRecognitionNet - load model before inference");return tf27.tidy(()=>{let batchTensor=tf27.cast(input.toBatchTensor(150,!0),"float32"),meanRgb=[122.782,117.001,104.298],normalized=normalize(batchTensor,meanRgb).div(tf27.scalar(256)),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);let globalAvg=out.mean([1,2]),fullyConnected=tf27.matMul(globalAvg,params.fc);return fullyConnected})}async forward(input){return this.forwardInput(await toNetInput(input))}async computeFaceDescriptor(input){let netInput=await toNetInput(input),faceDescriptorTensors=tf27.tidy(()=>tf27.unstack(this.forwardInput(netInput))),faceDescriptorsForBatch=await Promise.all(faceDescriptorTensors.map(t=>t.data()));return faceDescriptorTensors.forEach(t=>t.dispose()),netInput.isBatchInput?faceDescriptorsForBatch:faceDescriptorsForBatch[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap9(weightMap)}extractParams(weights){return extractParams9(weights)}};function createFaceRecognitionNet(weights){let net=new FaceRecognitionNet;return net.extractWeights(weights),net}function extendWithFaceDescriptor(sourceObj,descriptor){let extension={descriptor};return Object.assign({},sourceObj,extension)}function isWithAge(obj){return typeof obj.age=="number"}function extendWithAge(sourceObj,age){let extension={age};return Object.assign({},sourceObj,extension)}function isWithGender(obj){return(obj.gender===Gender.MALE||obj.gender===Gender.FEMALE)&&isValidProbablitiy(obj.genderProbability)}function extendWithGender(sourceObj,gender,genderProbability){let extension={gender,genderProbability};return Object.assign({},sourceObj,extension)}var tf34=__toModule(require_tfjs_esm()),tf28=__toModule(require_tfjs_esm());function extractorsFactory7(extractWeights,paramMappings){function extractDepthwiseConvParams(numChannels,mappedPrefix){let filters=tf28.tensor4d(extractWeights(3*3*numChannels),[3,3,numChannels,1]),batch_norm_scale=tf28.tensor1d(extractWeights(numChannels)),batch_norm_offset=tf28.tensor1d(extractWeights(numChannels)),batch_norm_mean=tf28.tensor1d(extractWeights(numChannels)),batch_norm_variance=tf28.tensor1d(extractWeights(numChannels));return 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`}),{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance}}function extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,isPointwiseConv){let filters=tf28.tensor4d(extractWeights(channelsIn*channelsOut*filterSize*filterSize),[filterSize,filterSize,channelsIn,channelsOut]),bias=tf28.tensor1d(extractWeights(channelsOut));return paramMappings.push({paramPath:`${mappedPrefix}/filters`},{paramPath:`${mappedPrefix}/${isPointwiseConv?"batch_norm_offset":"bias"}`}),{filters,bias}}function extractPointwiseConvParams(channelsIn,channelsOut,filterSize,mappedPrefix){let{filters,bias}=extractConvParams(channelsIn,channelsOut,filterSize,mappedPrefix,!0);return{filters,batch_norm_offset:bias}}function extractConvPairParams(channelsIn,channelsOut,mappedPrefix){let depthwise_conv=extractDepthwiseConvParams(channelsIn,`${mappedPrefix}/depthwise_conv`),pointwise_conv=extractPointwiseConvParams(channelsIn,channelsOut,1,`${mappedPrefix}/pointwise_conv`);return{depthwise_conv,pointwise_conv}}function extractMobilenetV1Params(){let conv_0=extractPointwiseConvParams(3,32,3,"mobilenetv1/conv_0"),conv_1=extractConvPairParams(32,64,"mobilenetv1/conv_1"),conv_2=extractConvPairParams(64,128,"mobilenetv1/conv_2"),conv_3=extractConvPairParams(128,128,"mobilenetv1/conv_3"),conv_4=extractConvPairParams(128,256,"mobilenetv1/conv_4"),conv_5=extractConvPairParams(256,256,"mobilenetv1/conv_5"),conv_6=extractConvPairParams(256,512,"mobilenetv1/conv_6"),conv_7=extractConvPairParams(512,512,"mobilenetv1/conv_7"),conv_8=extractConvPairParams(512,512,"mobilenetv1/conv_8"),conv_9=extractConvPairParams(512,512,"mobilenetv1/conv_9"),conv_10=extractConvPairParams(512,512,"mobilenetv1/conv_10"),conv_11=extractConvPairParams(512,512,"mobilenetv1/conv_11"),conv_12=extractConvPairParams(512,1024,"mobilenetv1/conv_12"),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(){let conv_0=extractPointwiseConvParams(1024,256,1,"prediction_layer/conv_0"),conv_1=extractPointwiseConvParams(256,512,3,"prediction_layer/conv_1"),conv_2=extractPointwiseConvParams(512,128,1,"prediction_layer/conv_2"),conv_3=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_3"),conv_4=extractPointwiseConvParams(256,128,1,"prediction_layer/conv_4"),conv_5=extractPointwiseConvParams(128,256,3,"prediction_layer/conv_5"),conv_6=extractPointwiseConvParams(256,64,1,"prediction_layer/conv_6"),conv_7=extractPointwiseConvParams(64,128,3,"prediction_layer/conv_7"),box_encoding_0_predictor=extractConvParams(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor_0=extractConvParams(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),box_encoding_1_predictor=extractConvParams(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor_1=extractConvParams(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),box_encoding_2_predictor=extractConvParams(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor_2=extractConvParams(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),box_encoding_3_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor_3=extractConvParams(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),box_encoding_4_predictor=extractConvParams(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor_4=extractConvParams(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),box_encoding_5_predictor=extractConvParams(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor_5=extractConvParams(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),box_predictor_0={box_encoding_predictor:box_encoding_0_predictor,class_predictor:class_predictor_0},box_predictor_1={box_encoding_predictor:box_encoding_1_predictor,class_predictor:class_predictor_1},box_predictor_2={box_encoding_predictor:box_encoding_2_predictor,class_predictor:class_predictor_2},box_predictor_3={box_encoding_predictor:box_encoding_3_predictor,class_predictor:class_predictor_3},box_predictor_4={box_encoding_predictor:box_encoding_4_predictor,class_predictor:class_predictor_4},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){let paramMappings=[],{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory7(extractWeights,paramMappings),mobilenetv1=extractMobilenetV1Params(),prediction_layer=extractPredictionLayerParams(),extra_dim=tf28.tensor3d(extractWeights(5118*4),[1,5118,4]),output_layer={extra_dim};if(paramMappings.push({paramPath:"output_layer/extra_dim"}),getRemainingWeights().length!==0)throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`);return{params:{mobilenetv1,prediction_layer,output_layer},paramMappings}}function extractorsFactory8(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractPointwiseConvParams(prefix,idx,mappedPrefix){let filters=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/weights`,4,`${mappedPrefix}/filters`),batch_norm_offset=extractWeightEntry(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`,1,`${mappedPrefix}/batch_norm_offset`);return{filters,batch_norm_offset}}function extractConvPairParams(idx){let mappedPrefix=`mobilenetv1/conv_${idx}`,prefixDepthwiseConv=`MobilenetV1/Conv2d_${idx}_depthwise`,mappedPrefixDepthwiseConv=`${mappedPrefix}/depthwise_conv`,mappedPrefixPointwiseConv=`${mappedPrefix}/pointwise_conv`,filters=extractWeightEntry(`${prefixDepthwiseConv}/depthwise_weights`,4,`${mappedPrefixDepthwiseConv}/filters`),batch_norm_scale=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/gamma`,1,`${mappedPrefixDepthwiseConv}/batch_norm_scale`),batch_norm_offset=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/beta`,1,`${mappedPrefixDepthwiseConv}/batch_norm_offset`),batch_norm_mean=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_mean`,1,`${mappedPrefixDepthwiseConv}/batch_norm_mean`),batch_norm_variance=extractWeightEntry(`${prefixDepthwiseConv}/BatchNorm/moving_variance`,1,`${mappedPrefixDepthwiseConv}/batch_norm_variance`);return{depthwise_conv:{filters,batch_norm_scale,batch_norm_offset,batch_norm_mean,batch_norm_variance},pointwise_conv:extractPointwiseConvParams("MobilenetV1",idx,mappedPrefixPointwiseConv)}}function extractMobilenetV1Params(){return{conv_0:extractPointwiseConvParams("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:extractConvPairParams(1),conv_2:extractConvPairParams(2),conv_3:extractConvPairParams(3),conv_4:extractConvPairParams(4),conv_5:extractConvPairParams(5),conv_6:extractConvPairParams(6),conv_7:extractConvPairParams(7),conv_8:extractConvPairParams(8),conv_9:extractConvPairParams(9),conv_10:extractConvPairParams(10),conv_11:extractConvPairParams(11),conv_12:extractConvPairParams(12),conv_13:extractConvPairParams(13)}}function extractConvParams(prefix,mappedPrefix){let filters=extractWeightEntry(`${prefix}/weights`,4,`${mappedPrefix}/filters`),bias=extractWeightEntry(`${prefix}/biases`,1,`${mappedPrefix}/bias`);return{filters,bias}}function extractBoxPredictorParams(idx){let box_encoding_predictor=extractConvParams(`Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${idx}/box_encoding_predictor`),class_predictor=extractConvParams(`Prediction/BoxPredictor_${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){let paramMappings=[],{extractMobilenetV1Params,extractPredictionLayerParams}=extractorsFactory8(weightMap,paramMappings),extra_dim=weightMap["Output/extra_dim"];if(paramMappings.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!isTensor3D(extra_dim))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`);let params={mobilenetv1:extractMobilenetV1Params(),prediction_layer:extractPredictionLayerParams(),output_layer:{extra_dim}};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}var tf30=__toModule(require_tfjs_esm()),tf29=__toModule(require_tfjs_esm());function pointwiseConvLayer(x,params,strides){return tf29.tidy(()=>{let out=tf29.conv2d(x,params.filters,strides,"same");return out=tf29.add(out,params.batch_norm_offset),tf29.clipByValue(out,0,6)})}var epsilon=.0010000000474974513;function depthwiseConvLayer(x,params,strides){return tf30.tidy(()=>{let out=tf30.depthwiseConv2d(x,params.filters,strides,"same");return out=tf30.batchNorm(out,params.batch_norm_mean,params.batch_norm_variance,params.batch_norm_offset,params.batch_norm_scale,epsilon),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,out=pointwiseConvLayer(x,params.conv_0,[2,2]),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];if(convPairParams.forEach((param,i)=>{let layerIdx=i+1,depthwiseConvStrides=getStridesForLayerIdx(layerIdx);out=depthwiseConvLayer(out,param.depthwise_conv,depthwiseConvStrides),out=pointwiseConvLayer(out,param.pointwise_conv,[1,1]),layerIdx===11&&(conv11=out)}),conv11===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out,conv11}})}function nonMaxSuppression2(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){let numBoxes=boxes.shape[0],outputSize=Math.min(maxOutputSize,numBoxes),candidates=scores.map((score,boxIndex)=>({score,boxIndex})).filter(c=>c.score>scoreThreshold).sort((c1,c2)=>c2.score-c1.score),suppressFunc=x=>x<=iouThreshold?1:0,selected=[];return candidates.forEach(c=>{if(selected.length>=outputSize)return;let originalScore=c.score;for(let j=selected.length-1;j>=0;--j){let iou3=IOU(boxes,c.boxIndex,selected[j]);if(iou3===0)continue;if(c.score*=suppressFunc(iou3),c.score<=scoreThreshold)break}originalScore===c.score&&selected.push(c.boxIndex)}),selected}function IOU(boxes,i,j){let boxesData=boxes.arraySync(),yminI=Math.min(boxesData[i][0],boxesData[i][2]),xminI=Math.min(boxesData[i][1],boxesData[i][3]),ymaxI=Math.max(boxesData[i][0],boxesData[i][2]),xmaxI=Math.max(boxesData[i][1],boxesData[i][3]),yminJ=Math.min(boxesData[j][0],boxesData[j][2]),xminJ=Math.min(boxesData[j][1],boxesData[j][3]),ymaxJ=Math.max(boxesData[j][0],boxesData[j][2]),xmaxJ=Math.max(boxesData[j][1],boxesData[j][3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}var tf31=__toModule(require_tfjs_esm());function getCenterCoordinatesAndSizesLayer(x){let vec=tf31.unstack(tf31.transpose(x,[1,0])),sizes=[tf31.sub(vec[2],vec[0]),tf31.sub(vec[3],vec[1])],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){let{sizes,centers}=getCenterCoordinatesAndSizesLayer(x0),vec=tf31.unstack(tf31.transpose(x1,[1,0])),div0_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[2],tf31.scalar(5))),sizes[0]),tf31.scalar(2)),add0_out=tf31.add(tf31.mul(tf31.div(vec[0],tf31.scalar(10)),sizes[0]),centers[0]),div1_out=tf31.div(tf31.mul(tf31.exp(tf31.div(vec[3],tf31.scalar(5))),sizes[1]),tf31.scalar(2)),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(()=>{let batchSize=boxPredictions.shape[0],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]);let scoresAndClasses=tf31.sigmoid(tf31.slice(classPredictions,[0,0,1],[-1,-1,-1])),scores=tf31.slice(scoresAndClasses,[0,0,0],[-1,-1,1]);scores=tf31.reshape(scores,[batchSize,scores.shape[1]]);let boxesByBatch=tf31.unstack(boxes),scoresByBatch=tf31.unstack(scores);return{boxes:boxesByBatch,scores:scoresByBatch}})}var tf33=__toModule(require_tfjs_esm()),tf32=__toModule(require_tfjs_esm());function boxPredictionLayer(x,params){return tf32.tidy(()=>{let batchSize=x.shape[0],boxPredictionEncoding=tf32.reshape(convLayer(x,params.box_encoding_predictor),[batchSize,-1,1,4]),classPrediction=tf32.reshape(convLayer(x,params.class_predictor),[batchSize,-1,3]);return{boxPredictionEncoding,classPrediction}})}function predictionLayer(x,conv11,params){return tf33.tidy(()=>{let conv0=pointwiseConvLayer(x,params.conv_0,[1,1]),conv1=pointwiseConvLayer(conv0,params.conv_1,[2,2]),conv22=pointwiseConvLayer(conv1,params.conv_2,[1,1]),conv3=pointwiseConvLayer(conv22,params.conv_3,[2,2]),conv4=pointwiseConvLayer(conv3,params.conv_4,[1,1]),conv5=pointwiseConvLayer(conv4,params.conv_5,[2,2]),conv6=pointwiseConvLayer(conv5,params.conv_6,[1,1]),conv7=pointwiseConvLayer(conv6,params.conv_7,[2,2]),boxPrediction0=boxPredictionLayer(conv11,params.box_predictor_0),boxPrediction1=boxPredictionLayer(x,params.box_predictor_1),boxPrediction2=boxPredictionLayer(conv1,params.box_predictor_2),boxPrediction3=boxPredictionLayer(conv3,params.box_predictor_3),boxPrediction4=boxPredictionLayer(conv5,params.box_predictor_4),boxPrediction5=boxPredictionLayer(conv7,params.box_predictor_5),boxPredictions=tf33.concat([boxPrediction0.boxPredictionEncoding,boxPrediction1.boxPredictionEncoding,boxPrediction2.boxPredictionEncoding,boxPrediction3.boxPredictionEncoding,boxPrediction4.boxPredictionEncoding,boxPrediction5.boxPredictionEncoding],1),classPredictions=tf33.concat([boxPrediction0.classPrediction,boxPrediction1.classPrediction,boxPrediction2.classPrediction,boxPrediction3.classPrediction,boxPrediction4.classPrediction,boxPrediction5.classPrediction],1);return{boxPredictions,classPredictions}})}var SsdMobilenetv1Options=class{constructor({minConfidence,maxResults}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=minConfidence||.5,this._maxResults=maxResults||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}},SsdMobilenetv1=class extends NeuralNetwork{constructor(){super("SsdMobilenetv1")}forwardInput(input){let{params}=this;if(!params)throw new Error("SsdMobilenetv1 - load model before inference");return tf34.tidy(()=>{let batchTensor=tf34.cast(input.toBatchTensor(512,!1),"float32"),x=tf34.sub(tf34.mul(batchTensor,tf34.scalar(.007843137718737125)),tf34.scalar(1)),features=mobileNetV1(x,params.mobilenetv1),{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={}){let{maxResults,minConfidence}=new SsdMobilenetv1Options(options),netInput=await toNetInput(input),{boxes:_boxes,scores:_scores}=this.forwardInput(netInput),boxes=_boxes[0],scores=_scores[0];for(let i=1;i<_boxes.length;i++)_boxes[i].dispose(),_scores[i].dispose();let scoresData=Array.from(await scores.data()),iouThreshold=.5,indices=nonMaxSuppression2(boxes,scoresData,maxResults,iouThreshold,minConfidence),reshapedDims=netInput.getReshapedInputDimensions(0),inputSize=netInput.inputSize,padX=inputSize/reshapedDims.width,padY=inputSize/reshapedDims.height,boxesData=boxes.arraySync(),results=indices.map(idx=>{let[top,bottom]=[Math.max(0,boxesData[idx][0]),Math.min(1,boxesData[idx][2])].map(val=>val*padY),[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)})});return boxes.dispose(),scores.dispose(),results}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(weightMap){return extractParamsFromWeigthMap11(weightMap)}extractParams(weights){return extractParams11(weights)}};function createSsdMobilenetv1(weights){let net=new SsdMobilenetv1;return net.extractWeights(weights),net}function createFaceDetectionNet(weights){return createSsdMobilenetv1(weights)}var FaceDetectionNet=class extends SsdMobilenetv1{},IOU_THRESHOLD=.4,BOX_ANCHORS=[new Point(.738768,.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)],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)],MEAN_RGB_SEPARABLE=[117.001,114.697,97.404],DEFAULT_MODEL_NAME="tiny_yolov2_model",DEFAULT_MODEL_NAME_SEPARABLE_CONV="tiny_yolov2_separable_conv_model",tf39=__toModule(require_tfjs_esm()),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: ${JSON.stringify(config2.meanRgb)}`)}var tf36=__toModule(require_tfjs_esm()),tf35=__toModule(require_tfjs_esm());function leaky(x){return tf35.tidy(()=>{let min=tf35.mul(x,tf35.scalar(.10000000149011612));return tf35.add(tf35.relu(tf35.sub(x,min)),min)})}function convWithBatchNorm(x,params){return tf36.tidy(()=>{let out=tf36.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf36.conv2d(out,params.conv.filters,[1,1],"valid"),out=tf36.sub(out,params.bn.sub),out=tf36.mul(out,params.bn.truediv),out=tf36.add(out,params.conv.bias),leaky(out)})}var tf37=__toModule(require_tfjs_esm());function depthwiseSeparableConv3(x,params){return tf37.tidy(()=>{let out=tf37.pad(x,[[0,0],[1,1],[1,1],[0,0]]);return out=tf37.separableConv2d(out,params.depthwise_filter,params.pointwise_filter,[1,1],"valid"),out=tf37.add(out,params.bias),leaky(out)})}var tf38=__toModule(require_tfjs_esm());function extractorsFactory9(extractWeights,paramMappings){let extractConvParams=extractConvParamsFactory(extractWeights,paramMappings);function extractBatchNormParams(size,mappedPrefix){let sub6=tf38.tensor1d(extractWeights(size)),truediv=tf38.tensor1d(extractWeights(size));return paramMappings.push({paramPath:`${mappedPrefix}/sub`},{paramPath:`${mappedPrefix}/truediv`}),{sub:sub6,truediv}}function extractConvWithBatchNormParams(channelsIn,channelsOut,mappedPrefix){let conv3=extractConvParams(channelsIn,channelsOut,3,`${mappedPrefix}/conv`),bn=extractBatchNormParams(channelsOut,`${mappedPrefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=extractSeparableConvParamsFactory(extractWeights,paramMappings);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParams13(weights,config2,boxEncodingSize,filterSizes){let{extractWeights,getRemainingWeights}=extractWeightsFactory(weights),paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory9(extractWeights,paramMappings),params;if(config2.withSeparableConvs){let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=config2.isFirstLayerConv2d?extractConvParams(s0,s1,3,"conv0"):extractSeparableConvParams(s0,s1,"conv0"),conv1=extractSeparableConvParams(s1,s2,"conv1"),conv22=extractSeparableConvParams(s2,s3,"conv2"),conv3=extractSeparableConvParams(s3,s4,"conv3"),conv4=extractSeparableConvParams(s4,s5,"conv4"),conv5=extractSeparableConvParams(s5,s6,"conv5"),conv6=s7?extractSeparableConvParams(s6,s7,"conv6"):void 0,conv7=s8?extractSeparableConvParams(s7,s8,"conv7"):void 0,conv8=extractConvParams(s8||s7||s6,5*boxEncodingSize,1,"conv8");params={conv0,conv1,conv2:conv22,conv3,conv4,conv5,conv6,conv7,conv8}}else{let[s0,s1,s2,s3,s4,s5,s6,s7,s8]=filterSizes,conv0=extractConvWithBatchNormParams(s0,s1,"conv0"),conv1=extractConvWithBatchNormParams(s1,s2,"conv1"),conv22=extractConvWithBatchNormParams(s2,s3,"conv2"),conv3=extractConvWithBatchNormParams(s3,s4,"conv3"),conv4=extractConvWithBatchNormParams(s4,s5,"conv4"),conv5=extractConvWithBatchNormParams(s5,s6,"conv5"),conv6=extractConvWithBatchNormParams(s6,s7,"conv6"),conv7=extractConvWithBatchNormParams(s7,s8,"conv7"),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: ${getRemainingWeights().length}`);return{params,paramMappings}}function extractorsFactory10(weightMap,paramMappings){let extractWeightEntry=extractWeightEntryFactory(weightMap,paramMappings);function extractBatchNormParams(prefix){let sub6=extractWeightEntry(`${prefix}/sub`,1),truediv=extractWeightEntry(`${prefix}/truediv`,1);return{sub:sub6,truediv}}function extractConvParams(prefix){let filters=extractWeightEntry(`${prefix}/filters`,4),bias=extractWeightEntry(`${prefix}/bias`,1);return{filters,bias}}function extractConvWithBatchNormParams(prefix){let conv3=extractConvParams(`${prefix}/conv`),bn=extractBatchNormParams(`${prefix}/bn`);return{conv:conv3,bn}}let extractSeparableConvParams=loadSeparableConvParamsFactory(extractWeightEntry);return{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}}function extractParamsFromWeigthMap13(weightMap,config2){let paramMappings=[],{extractConvParams,extractConvWithBatchNormParams,extractSeparableConvParams}=extractorsFactory10(weightMap,paramMappings),params;if(config2.withSeparableConvs){let 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")};return disposeUnusedWeightTensors(weightMap,paramMappings),{params,paramMappings}}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={}));var TinyYolov2Options=class{constructor({inputSize,scoreThreshold}={}){this._name="TinyYolov2Options";if(this._inputSize=inputSize||416,this._scoreThreshold=scoreThreshold||.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}},TinyYolov2Base2=class 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);return 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),convLayer(out,params.conv8,"valid",!1)}runMobilenet(x,params){let out=this.config.isFirstLayerConv2d?leaky(convLayer(x,params.conv0,"valid",!1)):depthwiseSeparableConv3(x,params.conv0);return 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,convLayer(out,params.conv8,"valid",!1)}forwardInput(input,inputSize){let{params}=this;if(!params)throw new Error("TinyYolov2 - load model before inference");return tf39.tidy(()=>{let batchTensor=tf39.cast(input.toBatchTensor(inputSize,!1),"float32");return batchTensor=this.config.meanRgb?normalize(batchTensor,this.config.meanRgb):batchTensor,batchTensor=batchTensor.div(tf39.scalar(256)),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={}){let{inputSize,scoreThreshold}=new TinyYolov2Options(forwardParams),netInput=await toNetInput(input),out=await this.forwardInput(netInput,inputSize),out0=tf39.tidy(()=>tf39.unstack(out)[0].expandDims()),inputDimensions={width:netInput.getInputWidth(0),height:netInput.getInputHeight(0)},results=await this.extractBoxes(out0,netInput.getReshapedInputDimensions(0),scoreThreshold);out.dispose(),out0.dispose();let boxes=results.map(res=>res.box),scores=results.map(res=>res.score),classScores=results.map(res=>res.classScore),classNames=results.map(res=>this.config.classes[res.label]),indices=nonMaxSuppression(boxes.map(box=>box.rescale(inputSize)),scores,this.config.iouThreshold,!0),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){let filterSizes=this.config.filterSizes||TinyYolov2Base2.DEFAULT_FILTER_SIZES,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){let{width,height}=inputBlobDimensions,inputSize=Math.max(width,height),correctionFactorX=inputSize/width,correctionFactorY=inputSize/height,numCells=outputTensor.shape[1],numBoxes=this.config.anchors.length,[boxesTensor,scoresTensor,classScoresTensor]=tf39.tidy(()=>{let reshaped=outputTensor.reshape([numCells,numCells,numBoxes,this.boxEncodingSize]),boxes=reshaped.slice([0,0,0,0],[numCells,numCells,numBoxes,4]),scores=reshaped.slice([0,0,0,4],[numCells,numCells,numBoxes,1]),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]}),results=[],scoresData=await scoresTensor.array(),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++){let score=sigmoid(scoresData[row][col][anchor][0]);if(!scoreThreshold||score>scoreThreshold){let ctX=(col+sigmoid(boxesData[row][col][anchor][0]))/numCells*correctionFactorX,ctY=(row+sigmoid(boxesData[row][col][anchor][1]))/numCells*correctionFactorY,width2=Math.exp(boxesData[row][col][anchor][2])*this.config.anchors[anchor].x/numCells*correctionFactorX,height2=Math.exp(boxesData[row][col][anchor][3])*this.config.anchors[anchor].y/numCells*correctionFactorY,x=ctX-width2/2,y=ctY-height2/2,pos={row,col,anchor},{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})}}return boxesTensor.dispose(),scoresTensor.dispose(),classScoresTensor.dispose(),results}async extractPredictedClass(classesTensor,pos){let{row,col,anchor}=pos,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=TinyYolov2Base2;TinyYolov2Base.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var TinyYolov2=class extends TinyYolov2Base{constructor(withSeparableConvs=!0){let config2=Object.assign({},{withSeparableConvs,iouThreshold:IOU_THRESHOLD,classes:["face"]},withSeparableConvs?{anchors:BOX_ANCHORS_SEPARABLE,meanRgb:MEAN_RGB_SEPARABLE}:{anchors:BOX_ANCHORS,withClassScores:!0});super(config2)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){let 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)}};function createTinyYolov2(weights,withSeparableConvs=!0){let net=new TinyYolov2(withSeparableConvs);return net.extractWeights(weights),net}var TinyFaceDetectorOptions=class extends TinyYolov2Options{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},ComposableTask=class{async then(onfulfilled){return onfulfilled(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},tf41=__toModule(require_tfjs_esm()),tf40=__toModule(require_tfjs_esm());async function extractAllFacesAndComputeResults(parentResults,input,computeResults,extractedFaces,getRectForAlignment=({alignedRect})=>alignedRect){let faceBoxes=parentResults.map(parentResult=>isWithFaceLandmarks(parentResult)?getRectForAlignment(parentResult):parentResult.detection),faces=extractedFaces||(input instanceof tf40.Tensor?await extractFaceTensors(input,faceBoxes):await extractFaces(input,faceBoxes)),results=await computeResults(faces);return faces.forEach(f=>f instanceof tf40.Tensor&&f.dispose()),results}async function extractSingleFaceAndComputeResult(parentResult,input,computeResult,extractedFaces,getRectForAlignment){return extractAllFacesAndComputeResults([parentResult],input,async faces=>computeResult(faces[0]),extractedFaces,getRectForAlignment)}var IOU_THRESHOLD2=.4,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)],MEAN_RGB=[117.001,114.697,97.404],TinyFaceDetector=class extends TinyYolov2Base{constructor(){let config2={withSeparableConvs:!0,iouThreshold:IOU_THRESHOLD2,classes:["face"],anchors:BOX_ANCHORS2,meanRgb:MEAN_RGB,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(config2)}get anchors(){return this.config.anchors}async locateFaces(input,forwardParams){let 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)}},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},ssdMobilenetv1=(input,options)=>nets.ssdMobilenetv1.locateFaces(input,options),tinyFaceDetector=(input,options)=>nets.tinyFaceDetector.locateFaces(input,options),tinyYolov23=(input,options)=>nets.tinyYolov2.locateFaces(input,options),detectFaceLandmarks=input=>nets.faceLandmark68Net.detectLandmarks(input),detectFaceLandmarksTiny=input=>nets.faceLandmark68TinyNet.detectLandmarks(input),computeFaceDescriptor=input=>nets.faceRecognitionNet.computeFaceDescriptor(input),recognizeFaceExpressions=input=>nets.faceExpressionNet.predictExpressions(input),predictAgeAndGender=input=>nets.ageGenderNet.predictAgeAndGender(input),loadSsdMobilenetv1Model=url=>nets.ssdMobilenetv1.load(url),loadTinyFaceDetectorModel=url=>nets.tinyFaceDetector.load(url),loadTinyYolov2Model=url=>nets.tinyYolov2.load(url),loadFaceLandmarkModel=url=>nets.faceLandmark68Net.load(url),loadFaceLandmarkTinyModel=url=>nets.faceLandmark68TinyNet.load(url),loadFaceRecognitionModel=url=>nets.faceRecognitionNet.load(url),loadFaceExpressionModel=url=>nets.faceExpressionNet.load(url),loadAgeGenderModel=url=>nets.ageGenderNet.load(url),loadFaceDetectionModel=loadSsdMobilenetv1Model,locateFaces=ssdMobilenetv1,detectLandmarks=detectFaceLandmarks,PredictFaceExpressionsTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResults=await this.parentTask,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)}},PredictSingleFaceExpressionsTask=class extends PredictFaceExpressionsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let faceExpressions=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceExpressionNet.predictExpressions(face),this.extractedFaces);return extendWithFaceExpressions(parentResult,faceExpressions)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this,this.input)}},PredictAllFaceExpressionsWithFaceAlignmentTask=class extends PredictAllFaceExpressionsTask{withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleFaceExpressionsWithFaceAlignmentTask=class extends PredictSingleFaceExpressionsTask{withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},PredictAgeAndGenderTaskBase=class extends ComposableTask{constructor(parentTask,input,extractedFaces){super();this.parentTask=parentTask;this.input=input;this.extractedFaces=extractedFaces}},PredictAllAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResults=await this.parentTask,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)=>{let{age,gender,genderProbability}=ageAndGenderByFace[i];return extendWithAge(extendWithGender(parentResult,gender,genderProbability),age)})}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this,this.input)}},PredictSingleAgeAndGenderTask=class extends PredictAgeAndGenderTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{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)}},PredictAllAgeAndGenderWithFaceAlignmentTask=class extends PredictAllAgeAndGenderTask{withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptors(){return new ComputeAllFaceDescriptorsTask(this,this.input)}},PredictSingleAgeAndGenderWithFaceAlignmentTask=class extends PredictSingleAgeAndGenderTask{withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},ComputeFaceDescriptorsTaskBase=class extends ComposableTask{constructor(parentTask,input){super();this.parentTask=parentTask;this.input=input}},ComputeAllFaceDescriptorsTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResults=await this.parentTask,descriptors=await extractAllFacesAndComputeResults(parentResults,this.input,faces=>Promise.all(faces.map(face=>nets.faceRecognitionNet.computeFaceDescriptor(face))),null,parentResult=>parentResult.landmarks.align(null,{useDlibAlignment:!0}));return descriptors.map((descriptor,i)=>extendWithFaceDescriptor(parentResults[i],descriptor))}withFaceExpressions(){return new PredictAllFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderWithFaceAlignmentTask(this,this.input)}},ComputeSingleFaceDescriptorTask=class extends ComputeFaceDescriptorsTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let descriptor=await extractSingleFaceAndComputeResult(parentResult,this.input,face=>nets.faceRecognitionNet.computeFaceDescriptor(face),null,parentResult2=>parentResult2.landmarks.align(null,{useDlibAlignment:!0}));return extendWithFaceDescriptor(parentResult,descriptor)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}},DetectFaceLandmarksTaskBase=class 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}},DetectAllFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResults=await this.parentTask,detections=parentResults.map(res=>res.detection),faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,detections):await extractFaces(this.input,detections),faceLandmarksByFace=await Promise.all(faces.map(face=>this.landmarkNet.detectLandmarks(face)));return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),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)}},DetectSingleFaceLandmarksTask=class extends DetectFaceLandmarksTaskBase{async run(){let parentResult=await this.parentTask;if(!parentResult)return;let{detection}=parentResult,faces=this.input instanceof tf41.Tensor?await extractFaceTensors(this.input,[detection]):await extractFaces(this.input,[detection]),landmarks=await this.landmarkNet.detectLandmarks(faces[0]);return faces.forEach(f=>f instanceof tf41.Tensor&&f.dispose()),extendWithFaceLandmarks(parentResult,landmarks)}withFaceExpressions(){return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this,this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this,this.input)}withFaceDescriptor(){return new ComputeSingleFaceDescriptorTask(this,this.input)}},DetectFacesTaskBase=class extends ComposableTask{constructor(input,options=new SsdMobilenetv1Options){super();this.input=input;this.options=options}},DetectAllFacesTask=class extends DetectFacesTaskBase{async run(){let{input,options}=this,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=>{let detections=await this.run();return res(detections.map(detection=>extendWithFaceDetection({},detection)))})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(),this.input)}},DetectSingleFaceTask=class extends DetectFacesTaskBase{async run(){let faceDetections=await new DetectAllFacesTask(this.input,this.options),faceDetectionWithHighestScore=faceDetections[0];return faceDetections.forEach(faceDetection=>{faceDetection.score>faceDetectionWithHighestScore.score&&(faceDetectionWithHighestScore=faceDetection)}),faceDetectionWithHighestScore}runAndExtendWithFaceDetection(){return new Promise(async res=>{let detection=await this.run();return res(detection?extendWithFaceDetection({},detection):void 0)})}withFaceLandmarks(useTinyLandmarkNet=!1){return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(),this.input,useTinyLandmarkNet)}withFaceExpressions(){return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(),this.input)}};function detectSingleFace(input,options=new SsdMobilenetv1Options){return new DetectSingleFaceTask(input,options)}function detectAllFaces(input,options=new SsdMobilenetv1Options){return new DetectAllFacesTask(input,options)}async function allFacesSsdMobilenetv1(input,minConfidence){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input,new SsdMobilenetv1Options(minConfidence?{minConfidence}:{})).withFaceLandmarks().withFaceDescriptors()}async function allFacesTinyYolov2(input,forwardParams={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await detectAllFaces(input,new TinyYolov2Options(forwardParams)).withFaceLandmarks().withFaceDescriptors()}var allFaces=allFacesSsdMobilenetv1;function euclideanDistance(arr1,arr2){if(arr1.length!==arr2.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let desc1=Array.from(arr1),desc2=Array.from(arr2);return Math.sqrt(desc1.map((val,i)=>val-desc2[i]).reduce((res,diff)=>res+Math.pow(diff,2),0))}var FaceMatcher=class{constructor(inputs,distanceThreshold=.6){this._distanceThreshold=distanceThreshold;let inputArray=Array.isArray(inputs)?inputs:[inputs];if(!inputArray.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let count=1,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){let 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){let labeledDescriptors=json.labeledDescriptors.map(ld=>LabeledFaceDescriptors.fromJSON(ld));return new FaceMatcher(labeledDescriptors,json.distanceThreshold)}};function createTinyFaceDetector(weights){let net=new TinyFaceDetector;return net.extractWeights(weights),net}function resizeResults(results,dimensions){let{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)){let resizedDetection=results.detection.forSize(width,height),resizedLandmarks=results.unshiftedLandmarks.forSize(resizedDetection.box.width,resizedDetection.box.height);return extendWithFaceLandmarks(extendWithFaceDetection(results,resizedDetection),resizedLandmarks)}return isWithFaceDetection(results)?extendWithFaceDetection(results,results.detection.forSize(width,height)):results instanceof FaceLandmarks||results instanceof FaceDetection?results.forSize(width,height):results}var version="0.8.9",node=typeof process!="undefined",browser3=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",version2={faceapi:version,node,browser:browser3};
 //# sourceMappingURL=face-api.node.js.map
diff --git a/dist/face-api.node.js.map b/dist/face-api.node.js.map
index a9a3b4a..e584db6 100644
--- a/dist/face-api.node.js.map
+++ b/dist/face-api.node.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["../node_modules/node-fetch/lib/index.mjs", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/util_base.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/environment.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/global_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/profiler.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tape.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/types.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/engine.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/device_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/flags.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/types.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/print.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/progress.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/http.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/io/io.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/math.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/serialization.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/test_util.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/version.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/globals.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/add.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/div.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js", "../node_modules/@tensorflow/tfjs/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js", 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slicedBuffer;\n\t\treturn blob;\n\t}\n}\n\nObject.defineProperties(Blob.prototype, {\n\tsize: { enumerable: true },\n\ttype: { enumerable: true },\n\tslice: { enumerable: true }\n});\n\nObject.defineProperty(Blob.prototype, Symbol.toStringTag, {\n\tvalue: 'Blob',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\n/**\n * fetch-error.js\n *\n * FetchError interface for operational errors\n */\n\n/**\n * Create FetchError instance\n *\n * @param   String      message      Error message for human\n * @param   String      type         Error type for machine\n * @param   String      systemError  For Node.js system error\n * @return  FetchError\n */\nfunction FetchError(message, type, systemError) {\n  Error.call(this, message);\n\n  this.message = message;\n  this.type = type;\n\n  // when err.type is `system`, err.code contains system error code\n  if (systemError) {\n    this.code = this.errno = systemError.code;\n  }\n\n  // hide custom error implementation details 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consumeBody.call(this).then(function (buffer) {\n\t\t\ttry {\n\t\t\t\treturn JSON.parse(buffer.toString());\n\t\t\t} catch (err) {\n\t\t\t\treturn Body.Promise.reject(new FetchError(`invalid json response body at ${_this2.url} reason: ${err.message}`, 'invalid-json'));\n\t\t\t}\n\t\t});\n\t},\n\n\t/**\n  * Decode response as text\n  *\n  * @return  Promise\n  */\n\ttext() {\n\t\treturn consumeBody.call(this).then(function (buffer) {\n\t\t\treturn buffer.toString();\n\t\t});\n\t},\n\n\t/**\n  * Decode response as buffer (non-spec api)\n  *\n  * @return  Promise\n  */\n\tbuffer() {\n\t\treturn consumeBody.call(this);\n\t},\n\n\t/**\n  * Decode response as text, while automatically detecting the encoding and\n  * trying to decode to UTF-8 (non-spec api)\n  *\n  * @return  Promise\n  */\n\ttextConverted() {\n\t\tvar _this3 = this;\n\n\t\treturn consumeBody.call(this).then(function (buffer) {\n\t\t\treturn convertBody(buffer, _this3.headers);\n\t\t});\n\t}\n};\n\n// In browsers, all properties are enumerable.\nObject.defineProperties(Body.prototype, {\n\tbody: { enumerable: true },\n\tbodyUsed: { enumerable: true },\n\tarrayBuffer: { enumerable: true },\n\tblob: { enumerable: true },\n\tjson: { enumerable: true },\n\ttext: { enumerable: true }\n});\n\nBody.mixIn = function (proto) {\n\tfor (const name of Object.getOwnPropertyNames(Body.prototype)) {\n\t\t// istanbul ignore else: future proof\n\t\tif (!(name in proto)) {\n\t\t\tconst desc = Object.getOwnPropertyDescriptor(Body.prototype, name);\n\t\t\tObject.defineProperty(proto, name, desc);\n\t\t}\n\t}\n};\n\n/**\n * Consume and convert an entire Body to a Buffer.\n *\n * Ref: https://fetch.spec.whatwg.org/#concept-body-consume-body\n *\n * @return  Promise\n */\nfunction consumeBody() {\n\tvar _this4 = this;\n\n\tif (this[INTERNALS].disturbed) {\n\t\treturn Body.Promise.reject(new TypeError(`body used already for: ${this.url}`));\n\t}\n\n\tthis[INTERNALS].disturbed = true;\n\n\tif (this[INTERNALS].error) {\n\t\treturn Body.Promise.reject(this[INTERNALS].error);\n\t}\n\n\tlet body = this.body;\n\n\t// body is null\n\tif (body === null) {\n\t\treturn Body.Promise.resolve(Buffer.alloc(0));\n\t}\n\n\t// body is blob\n\tif (isBlob(body)) {\n\t\tbody = body.stream();\n\t}\n\n\t// body is buffer\n\tif (Buffer.isBuffer(body)) {\n\t\treturn Body.Promise.resolve(body);\n\t}\n\n\t// istanbul ignore if: should never happen\n\tif (!(body instanceof Stream)) {\n\t\treturn Body.Promise.resolve(Buffer.alloc(0));\n\t}\n\n\t// body is stream\n\t// get ready to actually consume the body\n\tlet accum = [];\n\tlet accumBytes = 0;\n\tlet abort = false;\n\n\treturn new Body.Promise(function (resolve, reject) {\n\t\tlet resTimeout;\n\n\t\t// allow timeout on slow response body\n\t\tif (_this4.timeout) {\n\t\t\tresTimeout = setTimeout(function () {\n\t\t\t\tabort = true;\n\t\t\t\treject(new FetchError(`Response timeout while trying to fetch ${_this4.url} (over ${_this4.timeout}ms)`, 'body-timeout'));\n\t\t\t}, _this4.timeout);\n\t\t}\n\n\t\t// handle stream errors\n\t\tbody.on('error', function (err) {\n\t\t\tif (err.name === 'AbortError') {\n\t\t\t\t// if the request was aborted, reject with this Error\n\t\t\t\tabort = true;\n\t\t\t\treject(err);\n\t\t\t} else {\n\t\t\t\t// other errors, such as incorrect content-encoding\n\t\t\t\treject(new FetchError(`Invalid response body while trying to fetch ${_this4.url}: ${err.message}`, 'system', err));\n\t\t\t}\n\t\t});\n\n\t\tbody.on('data', function (chunk) {\n\t\t\tif (abort || chunk === null) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif (_this4.size && accumBytes + chunk.length > _this4.size) {\n\t\t\t\tabort = true;\n\t\t\t\treject(new FetchError(`content size at ${_this4.url} over limit: ${_this4.size}`, 'max-size'));\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\taccumBytes += chunk.length;\n\t\t\taccum.push(chunk);\n\t\t});\n\n\t\tbody.on('end', function () {\n\t\t\tif (abort) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tclearTimeout(resTimeout);\n\n\t\t\ttry {\n\t\t\t\tresolve(Buffer.concat(accum, accumBytes));\n\t\t\t} catch (err) {\n\t\t\t\t// handle streams that have accumulated too much data (issue #414)\n\t\t\t\treject(new FetchError(`Could not create Buffer from response body for ${_this4.url}: ${err.message}`, 'system', err));\n\t\t\t}\n\t\t});\n\t});\n}\n\n/**\n * Detect buffer encoding and convert to target encoding\n * ref: http://www.w3.org/TR/2011/WD-html5-20110113/parsing.html#determining-the-character-encoding\n *\n * @param   Buffer  buffer    Incoming buffer\n * @param   String  encoding  Target encoding\n * @return  String\n */\nfunction convertBody(buffer, headers) {\n\tif (typeof convert !== 'function') {\n\t\tthrow new Error('The package `encoding` must be installed to use the textConverted() function');\n\t}\n\n\tconst ct = headers.get('content-type');\n\tlet charset = 'utf-8';\n\tlet res, str;\n\n\t// header\n\tif (ct) {\n\t\tres = /charset=([^;]*)/i.exec(ct);\n\t}\n\n\t// no charset in content type, peek at response body for at most 1024 bytes\n\tstr = buffer.slice(0, 1024).toString();\n\n\t// html5\n\tif (!res && str) {\n\t\tres = /<meta.+?charset=(['\"])(.+?)\\1/i.exec(str);\n\t}\n\n\t// html4\n\tif (!res && str) {\n\t\tres = /<meta[\\s]+?http-equiv=(['\"])content-type\\1[\\s]+?content=(['\"])(.+?)\\2/i.exec(str);\n\t\tif (!res) {\n\t\t\tres = /<meta[\\s]+?content=(['\"])(.+?)\\1[\\s]+?http-equiv=(['\"])content-type\\3/i.exec(str);\n\t\t\tif (res) {\n\t\t\t\tres.pop(); // drop last quote\n\t\t\t}\n\t\t}\n\n\t\tif (res) {\n\t\t\tres = /charset=(.*)/i.exec(res.pop());\n\t\t}\n\t}\n\n\t// xml\n\tif (!res && str) {\n\t\tres = /<\\?xml.+?encoding=(['\"])(.+?)\\1/i.exec(str);\n\t}\n\n\t// found charset\n\tif (res) {\n\t\tcharset = res.pop();\n\n\t\t// prevent decode issues when sites use incorrect encoding\n\t\t// ref: https://hsivonen.fi/encoding-menu/\n\t\tif (charset === 'gb2312' || charset === 'gbk') {\n\t\t\tcharset = 'gb18030';\n\t\t}\n\t}\n\n\t// turn raw buffers into a single utf-8 buffer\n\treturn convert(buffer, 'UTF-8', charset).toString();\n}\n\n/**\n * Detect a URLSearchParams object\n * ref: https://github.com/bitinn/node-fetch/issues/296#issuecomment-307598143\n *\n * @param   Object  obj     Object to detect by type or brand\n * @return  String\n */\nfunction isURLSearchParams(obj) {\n\t// Duck-typing as a necessary condition.\n\tif (typeof obj !== 'object' || typeof obj.append !== 'function' || typeof obj.delete !== 'function' || typeof obj.get !== 'function' || typeof obj.getAll !== 'function' || typeof obj.has !== 'function' || typeof obj.set !== 'function') {\n\t\treturn false;\n\t}\n\n\t// Brand-checking and more duck-typing as optional condition.\n\treturn obj.constructor.name === 'URLSearchParams' || Object.prototype.toString.call(obj) === '[object URLSearchParams]' || typeof obj.sort === 'function';\n}\n\n/**\n * Check if `obj` is a W3C `Blob` object (which `File` inherits from)\n * @param  {*} obj\n * @return {boolean}\n */\nfunction isBlob(obj) {\n\treturn typeof obj === 'object' && typeof obj.arrayBuffer === 'function' && typeof obj.type === 'string' && typeof obj.stream === 'function' && typeof obj.constructor === 'function' && typeof obj.constructor.name === 'string' && /^(Blob|File)$/.test(obj.constructor.name) && /^(Blob|File)$/.test(obj[Symbol.toStringTag]);\n}\n\n/**\n * Clone body given Res/Req instance\n *\n * @param   Mixed  instance  Response or Request instance\n * @return  Mixed\n */\nfunction clone(instance) {\n\tlet p1, p2;\n\tlet body = instance.body;\n\n\t// don't allow cloning a used body\n\tif (instance.bodyUsed) {\n\t\tthrow new Error('cannot clone body after it is used');\n\t}\n\n\t// check that body is a stream and not form-data object\n\t// note: we can't clone the form-data object without having it as a dependency\n\tif (body instanceof Stream && typeof body.getBoundary !== 'function') {\n\t\t// tee instance body\n\t\tp1 = new PassThrough();\n\t\tp2 = new PassThrough();\n\t\tbody.pipe(p1);\n\t\tbody.pipe(p2);\n\t\t// set instance body to teed body and return the other teed body\n\t\tinstance[INTERNALS].body = p1;\n\t\tbody = p2;\n\t}\n\n\treturn body;\n}\n\n/**\n * Performs the operation \"extract a `Content-Type` value from |object|\" as\n * specified in the specification:\n * https://fetch.spec.whatwg.org/#concept-bodyinit-extract\n *\n * This function assumes that instance.body is present.\n *\n * @param   Mixed  instance  Any options.body input\n */\nfunction extractContentType(body) {\n\tif (body === null) {\n\t\t// body is null\n\t\treturn null;\n\t} else if (typeof body === 'string') {\n\t\t// body is string\n\t\treturn 'text/plain;charset=UTF-8';\n\t} else if (isURLSearchParams(body)) {\n\t\t// body is a URLSearchParams\n\t\treturn 'application/x-www-form-urlencoded;charset=UTF-8';\n\t} else if (isBlob(body)) {\n\t\t// body is blob\n\t\treturn body.type || null;\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\treturn null;\n\t} else if (Object.prototype.toString.call(body) === '[object ArrayBuffer]') {\n\t\t// body is ArrayBuffer\n\t\treturn null;\n\t} else if (ArrayBuffer.isView(body)) {\n\t\t// body is ArrayBufferView\n\t\treturn null;\n\t} else if (typeof body.getBoundary === 'function') {\n\t\t// detect form data input from form-data module\n\t\treturn `multipart/form-data;boundary=${body.getBoundary()}`;\n\t} else if (body instanceof Stream) {\n\t\t// body is stream\n\t\t// can't really do much about this\n\t\treturn null;\n\t} else {\n\t\t// Body constructor defaults other things to string\n\t\treturn 'text/plain;charset=UTF-8';\n\t}\n}\n\n/**\n * The Fetch Standard treats this as if \"total bytes\" is a property on the body.\n * For us, we have to explicitly get it with a function.\n *\n * ref: https://fetch.spec.whatwg.org/#concept-body-total-bytes\n *\n * @param   Body    instance   Instance of Body\n * @return  Number?            Number of bytes, or null if not possible\n */\nfunction getTotalBytes(instance) {\n\tconst body = instance.body;\n\n\n\tif (body === null) {\n\t\t// body is null\n\t\treturn 0;\n\t} else if (isBlob(body)) {\n\t\treturn body.size;\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\treturn body.length;\n\t} else if (body && typeof body.getLengthSync === 'function') {\n\t\t// detect form data input from form-data module\n\t\tif (body._lengthRetrievers && body._lengthRetrievers.length == 0 || // 1.x\n\t\tbody.hasKnownLength && body.hasKnownLength()) {\n\t\t\t// 2.x\n\t\t\treturn body.getLengthSync();\n\t\t}\n\t\treturn null;\n\t} else {\n\t\t// body is stream\n\t\treturn null;\n\t}\n}\n\n/**\n * Write a Body to a Node.js WritableStream (e.g. http.Request) object.\n *\n * @param   Body    instance   Instance of Body\n * @return  Void\n */\nfunction writeToStream(dest, instance) {\n\tconst body = instance.body;\n\n\n\tif (body === null) {\n\t\t// body is null\n\t\tdest.end();\n\t} else if (isBlob(body)) {\n\t\tbody.stream().pipe(dest);\n\t} else if (Buffer.isBuffer(body)) {\n\t\t// body is buffer\n\t\tdest.write(body);\n\t\tdest.end();\n\t} else {\n\t\t// body is stream\n\t\tbody.pipe(dest);\n\t}\n}\n\n// expose Promise\nBody.Promise = global.Promise;\n\n/**\n * headers.js\n *\n * Headers class offers convenient helpers\n */\n\nconst invalidTokenRegex = /[^\\^_`a-zA-Z\\-0-9!#$%&'*+.|~]/;\nconst invalidHeaderCharRegex = /[^\\t\\x20-\\x7e\\x80-\\xff]/;\n\nfunction validateName(name) {\n\tname = `${name}`;\n\tif (invalidTokenRegex.test(name) || name === '') {\n\t\tthrow new TypeError(`${name} is not a legal HTTP header name`);\n\t}\n}\n\nfunction validateValue(value) {\n\tvalue = `${value}`;\n\tif (invalidHeaderCharRegex.test(value)) {\n\t\tthrow new TypeError(`${value} is not a legal HTTP header value`);\n\t}\n}\n\n/**\n * Find the key in the map object given a header name.\n *\n * Returns undefined if not found.\n *\n * @param   String  name  Header name\n * @return  String|Undefined\n */\nfunction find(map, name) {\n\tname = name.toLowerCase();\n\tfor (const key in map) {\n\t\tif (key.toLowerCase() === name) {\n\t\t\treturn key;\n\t\t}\n\t}\n\treturn undefined;\n}\n\nconst MAP = Symbol('map');\nclass Headers {\n\t/**\n  * Headers class\n  *\n  * @param   Object  headers  Response headers\n  * @return  Void\n  */\n\tconstructor() {\n\t\tlet init = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : undefined;\n\n\t\tthis[MAP] = Object.create(null);\n\n\t\tif (init instanceof Headers) {\n\t\t\tconst rawHeaders = init.raw();\n\t\t\tconst headerNames = Object.keys(rawHeaders);\n\n\t\t\tfor (const headerName of headerNames) {\n\t\t\t\tfor (const value of rawHeaders[headerName]) {\n\t\t\t\t\tthis.append(headerName, value);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn;\n\t\t}\n\n\t\t// We don't worry about converting prop to ByteString here as append()\n\t\t// will handle it.\n\t\tif (init == null) ; else if (typeof init === 'object') {\n\t\t\tconst method = init[Symbol.iterator];\n\t\t\tif (method != null) {\n\t\t\t\tif (typeof method !== 'function') {\n\t\t\t\t\tthrow new TypeError('Header pairs must be iterable');\n\t\t\t\t}\n\n\t\t\t\t// sequence<sequence<ByteString>>\n\t\t\t\t// Note: per spec we have to first exhaust the lists then process them\n\t\t\t\tconst pairs = [];\n\t\t\t\tfor (const pair of init) {\n\t\t\t\t\tif (typeof pair !== 'object' || typeof pair[Symbol.iterator] !== 'function') {\n\t\t\t\t\t\tthrow new TypeError('Each header pair must be iterable');\n\t\t\t\t\t}\n\t\t\t\t\tpairs.push(Array.from(pair));\n\t\t\t\t}\n\n\t\t\t\tfor (const pair of pairs) {\n\t\t\t\t\tif (pair.length !== 2) {\n\t\t\t\t\t\tthrow new TypeError('Each header pair must be a name/value tuple');\n\t\t\t\t\t}\n\t\t\t\t\tthis.append(pair[0], pair[1]);\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// record<ByteString, ByteString>\n\t\t\t\tfor (const key of Object.keys(init)) {\n\t\t\t\t\tconst value = init[key];\n\t\t\t\t\tthis.append(key, value);\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tthrow new TypeError('Provided initializer must be an object');\n\t\t}\n\t}\n\n\t/**\n  * Return combined header value given name\n  *\n  * @param   String  name  Header name\n  * @return  Mixed\n  */\n\tget(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key === undefined) {\n\t\t\treturn null;\n\t\t}\n\n\t\treturn this[MAP][key].join(', ');\n\t}\n\n\t/**\n  * Iterate over all headers\n  *\n  * @param   Function  callback  Executed for each item with parameters (value, name, thisArg)\n  * @param   Boolean   thisArg   `this` context for callback function\n  * @return  Void\n  */\n\tforEach(callback) {\n\t\tlet thisArg = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : undefined;\n\n\t\tlet pairs = getHeaders(this);\n\t\tlet i = 0;\n\t\twhile (i < pairs.length) {\n\t\t\tvar _pairs$i = pairs[i];\n\t\t\tconst name = _pairs$i[0],\n\t\t\t      value = _pairs$i[1];\n\n\t\t\tcallback.call(thisArg, value, name, this);\n\t\t\tpairs = getHeaders(this);\n\t\t\ti++;\n\t\t}\n\t}\n\n\t/**\n  * Overwrite header values given name\n  *\n  * @param   String  name   Header name\n  * @param   String  value  Header value\n  * @return  Void\n  */\n\tset(name, value) {\n\t\tname = `${name}`;\n\t\tvalue = `${value}`;\n\t\tvalidateName(name);\n\t\tvalidateValue(value);\n\t\tconst key = find(this[MAP], name);\n\t\tthis[MAP][key !== undefined ? key : name] = [value];\n\t}\n\n\t/**\n  * Append a value onto existing header\n  *\n  * @param   String  name   Header name\n  * @param   String  value  Header value\n  * @return  Void\n  */\n\tappend(name, value) {\n\t\tname = `${name}`;\n\t\tvalue = `${value}`;\n\t\tvalidateName(name);\n\t\tvalidateValue(value);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key !== undefined) {\n\t\t\tthis[MAP][key].push(value);\n\t\t} else {\n\t\t\tthis[MAP][name] = [value];\n\t\t}\n\t}\n\n\t/**\n  * Check for header name existence\n  *\n  * @param   String   name  Header name\n  * @return  Boolean\n  */\n\thas(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\treturn find(this[MAP], name) !== undefined;\n\t}\n\n\t/**\n  * Delete all header values given name\n  *\n  * @param   String  name  Header name\n  * @return  Void\n  */\n\tdelete(name) {\n\t\tname = `${name}`;\n\t\tvalidateName(name);\n\t\tconst key = find(this[MAP], name);\n\t\tif (key !== undefined) {\n\t\t\tdelete this[MAP][key];\n\t\t}\n\t}\n\n\t/**\n  * Return raw headers (non-spec api)\n  *\n  * @return  Object\n  */\n\traw() {\n\t\treturn this[MAP];\n\t}\n\n\t/**\n  * Get an iterator on keys.\n  *\n  * @return  Iterator\n  */\n\tkeys() {\n\t\treturn createHeadersIterator(this, 'key');\n\t}\n\n\t/**\n  * Get an iterator on values.\n  *\n  * @return  Iterator\n  */\n\tvalues() {\n\t\treturn createHeadersIterator(this, 'value');\n\t}\n\n\t/**\n  * Get an iterator on entries.\n  *\n  * This is the default iterator of the Headers object.\n  *\n  * @return  Iterator\n  */\n\t[Symbol.iterator]() {\n\t\treturn createHeadersIterator(this, 'key+value');\n\t}\n}\nHeaders.prototype.entries = Headers.prototype[Symbol.iterator];\n\nObject.defineProperty(Headers.prototype, Symbol.toStringTag, {\n\tvalue: 'Headers',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nObject.defineProperties(Headers.prototype, {\n\tget: { enumerable: true },\n\tforEach: { enumerable: true },\n\tset: { enumerable: true },\n\tappend: { enumerable: true },\n\thas: { enumerable: true },\n\tdelete: { enumerable: true },\n\tkeys: { enumerable: true },\n\tvalues: { enumerable: true },\n\tentries: { enumerable: true }\n});\n\nfunction getHeaders(headers) {\n\tlet kind = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 'key+value';\n\n\tconst keys = Object.keys(headers[MAP]).sort();\n\treturn keys.map(kind === 'key' ? function (k) {\n\t\treturn k.toLowerCase();\n\t} : kind === 'value' ? function (k) {\n\t\treturn headers[MAP][k].join(', ');\n\t} : function (k) {\n\t\treturn [k.toLowerCase(), headers[MAP][k].join(', ')];\n\t});\n}\n\nconst INTERNAL = Symbol('internal');\n\nfunction createHeadersIterator(target, kind) {\n\tconst iterator = Object.create(HeadersIteratorPrototype);\n\titerator[INTERNAL] = {\n\t\ttarget,\n\t\tkind,\n\t\tindex: 0\n\t};\n\treturn iterator;\n}\n\nconst HeadersIteratorPrototype = Object.setPrototypeOf({\n\tnext() {\n\t\t// istanbul ignore if\n\t\tif (!this || Object.getPrototypeOf(this) !== HeadersIteratorPrototype) {\n\t\t\tthrow new TypeError('Value of `this` is not a HeadersIterator');\n\t\t}\n\n\t\tvar _INTERNAL = this[INTERNAL];\n\t\tconst target = _INTERNAL.target,\n\t\t      kind = _INTERNAL.kind,\n\t\t      index = _INTERNAL.index;\n\n\t\tconst values = getHeaders(target, kind);\n\t\tconst len = values.length;\n\t\tif (index >= len) {\n\t\t\treturn {\n\t\t\t\tvalue: undefined,\n\t\t\t\tdone: true\n\t\t\t};\n\t\t}\n\n\t\tthis[INTERNAL].index = index + 1;\n\n\t\treturn {\n\t\t\tvalue: values[index],\n\t\t\tdone: false\n\t\t};\n\t}\n}, Object.getPrototypeOf(Object.getPrototypeOf([][Symbol.iterator]())));\n\nObject.defineProperty(HeadersIteratorPrototype, Symbol.toStringTag, {\n\tvalue: 'HeadersIterator',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\n/**\n * Export the Headers object in a form that Node.js can consume.\n *\n * @param   Headers  headers\n * @return  Object\n */\nfunction exportNodeCompatibleHeaders(headers) {\n\tconst obj = Object.assign({ __proto__: null }, headers[MAP]);\n\n\t// http.request() only supports string as Host header. This hack makes\n\t// specifying custom Host header possible.\n\tconst hostHeaderKey = find(headers[MAP], 'Host');\n\tif (hostHeaderKey !== undefined) {\n\t\tobj[hostHeaderKey] = obj[hostHeaderKey][0];\n\t}\n\n\treturn obj;\n}\n\n/**\n * Create a Headers object from an object of headers, ignoring those that do\n * not conform to HTTP grammar productions.\n *\n * @param   Object  obj  Object of headers\n * @return  Headers\n */\nfunction createHeadersLenient(obj) {\n\tconst headers = new Headers();\n\tfor (const name of Object.keys(obj)) {\n\t\tif (invalidTokenRegex.test(name)) {\n\t\t\tcontinue;\n\t\t}\n\t\tif (Array.isArray(obj[name])) {\n\t\t\tfor (const val of obj[name]) {\n\t\t\t\tif (invalidHeaderCharRegex.test(val)) {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t\tif (headers[MAP][name] === undefined) {\n\t\t\t\t\theaders[MAP][name] = [val];\n\t\t\t\t} else {\n\t\t\t\t\theaders[MAP][name].push(val);\n\t\t\t\t}\n\t\t\t}\n\t\t} else if (!invalidHeaderCharRegex.test(obj[name])) {\n\t\t\theaders[MAP][name] = [obj[name]];\n\t\t}\n\t}\n\treturn headers;\n}\n\nconst INTERNALS$1 = Symbol('Response internals');\n\n// fix an issue where \"STATUS_CODES\" aren't a named export for node <10\nconst STATUS_CODES = http.STATUS_CODES;\n\n/**\n * Response class\n *\n * @param   Stream  body  Readable stream\n * @param   Object  opts  Response options\n * @return  Void\n */\nclass Response {\n\tconstructor() {\n\t\tlet body = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;\n\t\tlet opts = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n\t\tBody.call(this, body, opts);\n\n\t\tconst status = opts.status || 200;\n\t\tconst headers = new Headers(opts.headers);\n\n\t\tif (body != null && !headers.has('Content-Type')) {\n\t\t\tconst contentType = extractContentType(body);\n\t\t\tif (contentType) {\n\t\t\t\theaders.append('Content-Type', contentType);\n\t\t\t}\n\t\t}\n\n\t\tthis[INTERNALS$1] = {\n\t\t\turl: opts.url,\n\t\t\tstatus,\n\t\t\tstatusText: opts.statusText || STATUS_CODES[status],\n\t\t\theaders,\n\t\t\tcounter: opts.counter\n\t\t};\n\t}\n\n\tget url() {\n\t\treturn this[INTERNALS$1].url || '';\n\t}\n\n\tget status() {\n\t\treturn this[INTERNALS$1].status;\n\t}\n\n\t/**\n  * Convenience property representing if the request ended normally\n  */\n\tget ok() {\n\t\treturn this[INTERNALS$1].status >= 200 && this[INTERNALS$1].status < 300;\n\t}\n\n\tget redirected() {\n\t\treturn this[INTERNALS$1].counter > 0;\n\t}\n\n\tget statusText() {\n\t\treturn this[INTERNALS$1].statusText;\n\t}\n\n\tget headers() {\n\t\treturn this[INTERNALS$1].headers;\n\t}\n\n\t/**\n  * Clone this response\n  *\n  * @return  Response\n  */\n\tclone() {\n\t\treturn new Response(clone(this), {\n\t\t\turl: this.url,\n\t\t\tstatus: this.status,\n\t\t\tstatusText: this.statusText,\n\t\t\theaders: this.headers,\n\t\t\tok: this.ok,\n\t\t\tredirected: this.redirected\n\t\t});\n\t}\n}\n\nBody.mixIn(Response.prototype);\n\nObject.defineProperties(Response.prototype, {\n\turl: { enumerable: true },\n\tstatus: { enumerable: true },\n\tok: { enumerable: true },\n\tredirected: { enumerable: true },\n\tstatusText: { enumerable: true },\n\theaders: { enumerable: true },\n\tclone: { enumerable: true }\n});\n\nObject.defineProperty(Response.prototype, Symbol.toStringTag, {\n\tvalue: 'Response',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nconst INTERNALS$2 = Symbol('Request internals');\n\n// fix an issue where \"format\", \"parse\" aren't a named export for node <10\nconst parse_url = Url.parse;\nconst format_url = Url.format;\n\nconst streamDestructionSupported = 'destroy' in Stream.Readable.prototype;\n\n/**\n * Check if a value is an instance of Request.\n *\n * @param   Mixed   input\n * @return  Boolean\n */\nfunction isRequest(input) {\n\treturn typeof input === 'object' && typeof input[INTERNALS$2] === 'object';\n}\n\nfunction isAbortSignal(signal) {\n\tconst proto = signal && typeof signal === 'object' && Object.getPrototypeOf(signal);\n\treturn !!(proto && proto.constructor.name === 'AbortSignal');\n}\n\n/**\n * Request class\n *\n * @param   Mixed   input  Url or Request instance\n * @param   Object  init   Custom options\n * @return  Void\n */\nclass Request {\n\tconstructor(input) {\n\t\tlet init = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n\t\tlet parsedURL;\n\n\t\t// normalize input\n\t\tif (!isRequest(input)) {\n\t\t\tif (input && input.href) {\n\t\t\t\t// in order to support Node.js' Url objects; though WHATWG's URL objects\n\t\t\t\t// will fall into this branch also (since their `toString()` will return\n\t\t\t\t// `href` property anyway)\n\t\t\t\tparsedURL = parse_url(input.href);\n\t\t\t} else {\n\t\t\t\t// coerce input to a string before attempting to parse\n\t\t\t\tparsedURL = parse_url(`${input}`);\n\t\t\t}\n\t\t\tinput = {};\n\t\t} else {\n\t\t\tparsedURL = parse_url(input.url);\n\t\t}\n\n\t\tlet method = init.method || input.method || 'GET';\n\t\tmethod = method.toUpperCase();\n\n\t\tif ((init.body != null || isRequest(input) && input.body !== null) && (method === 'GET' || method === 'HEAD')) {\n\t\t\tthrow new TypeError('Request with GET/HEAD method cannot have body');\n\t\t}\n\n\t\tlet inputBody = init.body != null ? init.body : isRequest(input) && input.body !== null ? clone(input) : null;\n\n\t\tBody.call(this, inputBody, {\n\t\t\ttimeout: init.timeout || input.timeout || 0,\n\t\t\tsize: init.size || input.size || 0\n\t\t});\n\n\t\tconst headers = new Headers(init.headers || input.headers || {});\n\n\t\tif (inputBody != null && !headers.has('Content-Type')) {\n\t\t\tconst contentType = extractContentType(inputBody);\n\t\t\tif (contentType) {\n\t\t\t\theaders.append('Content-Type', contentType);\n\t\t\t}\n\t\t}\n\n\t\tlet signal = isRequest(input) ? input.signal : null;\n\t\tif ('signal' in init) signal = init.signal;\n\n\t\tif (signal != null && !isAbortSignal(signal)) {\n\t\t\tthrow new TypeError('Expected signal to be an instanceof AbortSignal');\n\t\t}\n\n\t\tthis[INTERNALS$2] = {\n\t\t\tmethod,\n\t\t\tredirect: init.redirect || input.redirect || 'follow',\n\t\t\theaders,\n\t\t\tparsedURL,\n\t\t\tsignal\n\t\t};\n\n\t\t// node-fetch-only options\n\t\tthis.follow = init.follow !== undefined ? init.follow : input.follow !== undefined ? input.follow : 20;\n\t\tthis.compress = init.compress !== undefined ? init.compress : input.compress !== undefined ? input.compress : true;\n\t\tthis.counter = init.counter || input.counter || 0;\n\t\tthis.agent = init.agent || input.agent;\n\t}\n\n\tget method() {\n\t\treturn this[INTERNALS$2].method;\n\t}\n\n\tget url() {\n\t\treturn format_url(this[INTERNALS$2].parsedURL);\n\t}\n\n\tget headers() {\n\t\treturn this[INTERNALS$2].headers;\n\t}\n\n\tget redirect() {\n\t\treturn this[INTERNALS$2].redirect;\n\t}\n\n\tget signal() {\n\t\treturn this[INTERNALS$2].signal;\n\t}\n\n\t/**\n  * Clone this request\n  *\n  * @return  Request\n  */\n\tclone() {\n\t\treturn new Request(this);\n\t}\n}\n\nBody.mixIn(Request.prototype);\n\nObject.defineProperty(Request.prototype, Symbol.toStringTag, {\n\tvalue: 'Request',\n\twritable: false,\n\tenumerable: false,\n\tconfigurable: true\n});\n\nObject.defineProperties(Request.prototype, {\n\tmethod: { enumerable: true },\n\turl: { enumerable: true },\n\theaders: { enumerable: true },\n\tredirect: { enumerable: true },\n\tclone: { enumerable: true },\n\tsignal: { enumerable: true }\n});\n\n/**\n * Convert a Request to Node.js http request options.\n *\n * @param   Request  A Request instance\n * @return  Object   The options object to be passed to http.request\n */\nfunction getNodeRequestOptions(request) {\n\tconst parsedURL = request[INTERNALS$2].parsedURL;\n\tconst headers = new Headers(request[INTERNALS$2].headers);\n\n\t// fetch step 1.3\n\tif (!headers.has('Accept')) {\n\t\theaders.set('Accept', '*/*');\n\t}\n\n\t// Basic fetch\n\tif (!parsedURL.protocol || !parsedURL.hostname) {\n\t\tthrow new TypeError('Only absolute URLs are supported');\n\t}\n\n\tif (!/^https?:$/.test(parsedURL.protocol)) {\n\t\tthrow new TypeError('Only HTTP(S) protocols are supported');\n\t}\n\n\tif (request.signal && request.body instanceof Stream.Readable && !streamDestructionSupported) {\n\t\tthrow new Error('Cancellation of streamed requests with AbortSignal is not supported in node < 8');\n\t}\n\n\t// HTTP-network-or-cache fetch steps 2.4-2.7\n\tlet contentLengthValue = null;\n\tif (request.body == null && /^(POST|PUT)$/i.test(request.method)) {\n\t\tcontentLengthValue = '0';\n\t}\n\tif (request.body != null) {\n\t\tconst totalBytes = getTotalBytes(request);\n\t\tif (typeof totalBytes === 'number') {\n\t\t\tcontentLengthValue = String(totalBytes);\n\t\t}\n\t}\n\tif (contentLengthValue) {\n\t\theaders.set('Content-Length', contentLengthValue);\n\t}\n\n\t// HTTP-network-or-cache fetch step 2.11\n\tif (!headers.has('User-Agent')) {\n\t\theaders.set('User-Agent', 'node-fetch/1.0 (+https://github.com/bitinn/node-fetch)');\n\t}\n\n\t// HTTP-network-or-cache fetch step 2.15\n\tif (request.compress && !headers.has('Accept-Encoding')) {\n\t\theaders.set('Accept-Encoding', 'gzip,deflate');\n\t}\n\n\tlet agent = request.agent;\n\tif (typeof agent === 'function') {\n\t\tagent = agent(parsedURL);\n\t}\n\n\tif (!headers.has('Connection') && !agent) {\n\t\theaders.set('Connection', 'close');\n\t}\n\n\t// HTTP-network fetch step 4.2\n\t// chunked encoding is handled by Node.js\n\n\treturn Object.assign({}, parsedURL, {\n\t\tmethod: request.method,\n\t\theaders: exportNodeCompatibleHeaders(headers),\n\t\tagent\n\t});\n}\n\n/**\n * abort-error.js\n *\n * AbortError interface for cancelled requests\n */\n\n/**\n * Create AbortError instance\n *\n * @param   String      message      Error message for human\n * @return  AbortError\n */\nfunction AbortError(message) {\n  Error.call(this, message);\n\n  this.type = 'aborted';\n  this.message = message;\n\n  // hide custom error implementation details from end-users\n  Error.captureStackTrace(this, this.constructor);\n}\n\nAbortError.prototype = Object.create(Error.prototype);\nAbortError.prototype.constructor = AbortError;\nAbortError.prototype.name = 'AbortError';\n\n// fix an issue where \"PassThrough\", \"resolve\" aren't a named export for node <10\nconst PassThrough$1 = Stream.PassThrough;\nconst resolve_url = Url.resolve;\n\n/**\n * Fetch function\n *\n * @param   Mixed    url   Absolute url or Request instance\n * @param   Object   opts  Fetch options\n * @return  Promise\n */\nfunction fetch(url, opts) {\n\n\t// allow custom promise\n\tif (!fetch.Promise) {\n\t\tthrow new Error('native promise missing, set fetch.Promise to your favorite alternative');\n\t}\n\n\tBody.Promise = fetch.Promise;\n\n\t// wrap http.request into fetch\n\treturn new fetch.Promise(function (resolve, reject) {\n\t\t// build request object\n\t\tconst request = new Request(url, opts);\n\t\tconst options = getNodeRequestOptions(request);\n\n\t\tconst send = (options.protocol === 'https:' ? https : http).request;\n\t\tconst signal = request.signal;\n\n\t\tlet response = null;\n\n\t\tconst abort = function abort() {\n\t\t\tlet error = new AbortError('The user aborted a request.');\n\t\t\treject(error);\n\t\t\tif (request.body && request.body instanceof Stream.Readable) {\n\t\t\t\trequest.body.destroy(error);\n\t\t\t}\n\t\t\tif (!response || !response.body) return;\n\t\t\tresponse.body.emit('error', error);\n\t\t};\n\n\t\tif (signal && signal.aborted) {\n\t\t\tabort();\n\t\t\treturn;\n\t\t}\n\n\t\tconst abortAndFinalize = function abortAndFinalize() {\n\t\t\tabort();\n\t\t\tfinalize();\n\t\t};\n\n\t\t// send request\n\t\tconst req = send(options);\n\t\tlet reqTimeout;\n\n\t\tif (signal) {\n\t\t\tsignal.addEventListener('abort', abortAndFinalize);\n\t\t}\n\n\t\tfunction finalize() {\n\t\t\treq.abort();\n\t\t\tif (signal) signal.removeEventListener('abort', abortAndFinalize);\n\t\t\tclearTimeout(reqTimeout);\n\t\t}\n\n\t\tif (request.timeout) {\n\t\t\treq.once('socket', function (socket) {\n\t\t\t\treqTimeout = setTimeout(function () {\n\t\t\t\t\treject(new FetchError(`network timeout at: ${request.url}`, 'request-timeout'));\n\t\t\t\t\tfinalize();\n\t\t\t\t}, request.timeout);\n\t\t\t});\n\t\t}\n\n\t\treq.on('error', function (err) {\n\t\t\treject(new FetchError(`request to ${request.url} failed, reason: ${err.message}`, 'system', err));\n\t\t\tfinalize();\n\t\t});\n\n\t\treq.on('response', function (res) {\n\t\t\tclearTimeout(reqTimeout);\n\n\t\t\tconst headers = createHeadersLenient(res.headers);\n\n\t\t\t// HTTP fetch step 5\n\t\t\tif (fetch.isRedirect(res.statusCode)) {\n\t\t\t\t// HTTP fetch step 5.2\n\t\t\t\tconst location = headers.get('Location');\n\n\t\t\t\t// HTTP fetch step 5.3\n\t\t\t\tconst locationURL = location === null ? null : resolve_url(request.url, location);\n\n\t\t\t\t// HTTP fetch step 5.5\n\t\t\t\tswitch (request.redirect) {\n\t\t\t\t\tcase 'error':\n\t\t\t\t\t\treject(new FetchError(`uri requested responds with a redirect, redirect mode is set to error: ${request.url}`, 'no-redirect'));\n\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\treturn;\n\t\t\t\t\tcase 'manual':\n\t\t\t\t\t\t// node-fetch-specific step: make manual redirect a bit easier to use by setting the Location header value to the resolved URL.\n\t\t\t\t\t\tif (locationURL !== null) {\n\t\t\t\t\t\t\t// handle corrupted header\n\t\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\t\theaders.set('Location', locationURL);\n\t\t\t\t\t\t\t} catch (err) {\n\t\t\t\t\t\t\t\t// istanbul ignore next: nodejs server prevent invalid response headers, we can't test this through normal request\n\t\t\t\t\t\t\t\treject(err);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'follow':\n\t\t\t\t\t\t// HTTP-redirect fetch step 2\n\t\t\t\t\t\tif (locationURL === null) {\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 5\n\t\t\t\t\t\tif (request.counter >= request.follow) {\n\t\t\t\t\t\t\treject(new FetchError(`maximum redirect reached at: ${request.url}`, 'max-redirect'));\n\t\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 6 (counter increment)\n\t\t\t\t\t\t// Create a new Request object.\n\t\t\t\t\t\tconst requestOpts = {\n\t\t\t\t\t\t\theaders: new Headers(request.headers),\n\t\t\t\t\t\t\tfollow: request.follow,\n\t\t\t\t\t\t\tcounter: request.counter + 1,\n\t\t\t\t\t\t\tagent: request.agent,\n\t\t\t\t\t\t\tcompress: request.compress,\n\t\t\t\t\t\t\tmethod: request.method,\n\t\t\t\t\t\t\tbody: request.body,\n\t\t\t\t\t\t\tsignal: request.signal,\n\t\t\t\t\t\t\ttimeout: request.timeout,\n\t\t\t\t\t\t\tsize: request.size\n\t\t\t\t\t\t};\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 9\n\t\t\t\t\t\tif (res.statusCode !== 303 && request.body && getTotalBytes(request) === null) {\n\t\t\t\t\t\t\treject(new FetchError('Cannot follow redirect with body being a readable stream', 'unsupported-redirect'));\n\t\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 11\n\t\t\t\t\t\tif (res.statusCode === 303 || (res.statusCode === 301 || res.statusCode === 302) && request.method === 'POST') {\n\t\t\t\t\t\t\trequestOpts.method = 'GET';\n\t\t\t\t\t\t\trequestOpts.body = undefined;\n\t\t\t\t\t\t\trequestOpts.headers.delete('content-length');\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// HTTP-redirect fetch step 15\n\t\t\t\t\t\tresolve(fetch(new Request(locationURL, requestOpts)));\n\t\t\t\t\t\tfinalize();\n\t\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// prepare response\n\t\t\tres.once('end', function () {\n\t\t\t\tif (signal) signal.removeEventListener('abort', abortAndFinalize);\n\t\t\t});\n\t\t\tlet body = res.pipe(new PassThrough$1());\n\n\t\t\tconst response_options = {\n\t\t\t\turl: request.url,\n\t\t\t\tstatus: res.statusCode,\n\t\t\t\tstatusText: res.statusMessage,\n\t\t\t\theaders: headers,\n\t\t\t\tsize: request.size,\n\t\t\t\ttimeout: request.timeout,\n\t\t\t\tcounter: request.counter\n\t\t\t};\n\n\t\t\t// HTTP-network fetch step 12.1.1.3\n\t\t\tconst codings = headers.get('Content-Encoding');\n\n\t\t\t// HTTP-network fetch step 12.1.1.4: handle content codings\n\n\t\t\t// in following scenarios we ignore compression support\n\t\t\t// 1. compression support is disabled\n\t\t\t// 2. HEAD request\n\t\t\t// 3. no Content-Encoding header\n\t\t\t// 4. no content response (204)\n\t\t\t// 5. content not modified response (304)\n\t\t\tif (!request.compress || request.method === 'HEAD' || codings === null || res.statusCode === 204 || res.statusCode === 304) {\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// For Node v6+\n\t\t\t// Be less strict when decoding compressed responses, since sometimes\n\t\t\t// servers send slightly invalid responses that are still accepted\n\t\t\t// by common browsers.\n\t\t\t// Always using Z_SYNC_FLUSH is what cURL does.\n\t\t\tconst zlibOptions = {\n\t\t\t\tflush: zlib.Z_SYNC_FLUSH,\n\t\t\t\tfinishFlush: zlib.Z_SYNC_FLUSH\n\t\t\t};\n\n\t\t\t// for gzip\n\t\t\tif (codings == 'gzip' || codings == 'x-gzip') {\n\t\t\t\tbody = body.pipe(zlib.createGunzip(zlibOptions));\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// for deflate\n\t\t\tif (codings == 'deflate' || codings == 'x-deflate') {\n\t\t\t\t// handle the infamous raw deflate response from old servers\n\t\t\t\t// a hack for old IIS and Apache servers\n\t\t\t\tconst raw = res.pipe(new PassThrough$1());\n\t\t\t\traw.once('data', function (chunk) {\n\t\t\t\t\t// see http://stackoverflow.com/questions/37519828\n\t\t\t\t\tif ((chunk[0] & 0x0F) === 0x08) {\n\t\t\t\t\t\tbody = body.pipe(zlib.createInflate());\n\t\t\t\t\t} else {\n\t\t\t\t\t\tbody = body.pipe(zlib.createInflateRaw());\n\t\t\t\t\t}\n\t\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\t\tresolve(response);\n\t\t\t\t});\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// for br\n\t\t\tif (codings == 'br' && typeof zlib.createBrotliDecompress === 'function') {\n\t\t\t\tbody = body.pipe(zlib.createBrotliDecompress());\n\t\t\t\tresponse = new Response(body, response_options);\n\t\t\t\tresolve(response);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// otherwise, use response as-is\n\t\t\tresponse = new Response(body, response_options);\n\t\t\tresolve(response);\n\t\t});\n\n\t\twriteToStream(req, request);\n\t});\n}\n/**\n * Redirect code matching\n *\n * @param   Number   code  Status code\n * @return  Boolean\n */\nfetch.isRedirect = function (code) {\n\treturn code === 301 || code === 302 || code === 303 || code === 307 || code === 308;\n};\n\n// expose Promise\nfetch.Promise = global.Promise;\n\nexport default fetch;\nexport { Headers, Request, Response, FetchError };\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n    constructor(backend, dataMover) {\n        this.backend = backend;\n        this.dataMover = dataMover;\n        this.data = new WeakMap();\n        this.dataIdsCount = 0;\n    }\n    get(dataId) {\n        if (!this.data.has(dataId)) {\n            this.dataMover.moveData(this.backend, dataId);\n        }\n        return this.data.get(dataId);\n    }\n    set(dataId, value) {\n        this.dataIdsCount++;\n        this.data.set(dataId, value);\n    }\n    has(dataId) {\n        return this.data.has(dataId);\n    }\n    delete(dataId) {\n        this.dataIdsCount--;\n        return this.data.delete(dataId);\n    }\n    numDataIds() {\n        return this.dataIdsCount;\n    }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n    time(f) {\n        return notYetImplemented('time');\n    }\n    read(dataId) {\n        return notYetImplemented('read');\n    }\n    readSync(dataId) {\n        return notYetImplemented('readSync');\n    }\n    numDataIds() {\n        return notYetImplemented('numDataIds');\n    }\n    disposeData(dataId) {\n        return notYetImplemented('disposeData');\n    }\n    write(values, shape, dtype) {\n        return notYetImplemented('write');\n    }\n    move(dataId, values, shape, dtype) {\n        return notYetImplemented('move');\n    }\n    memory() {\n        return notYetImplemented('memory');\n    }\n    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n    floatPrecision() {\n        return notYetImplemented('floatPrecision');\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        return notYetImplemented('batchMatMul');\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedBatchMatMul');\n    }\n    slice(x, begin, size) {\n        return notYetImplemented('slice');\n    }\n    stridedSlice(x, begin, end, strides) {\n        return notYetImplemented('stridedSlice');\n    }\n    unstack(x, axis) {\n        return notYetImplemented('unstack');\n    }\n    reverse(a, axis) {\n        return notYetImplemented('reverse');\n    }\n    concat(tensors, axis) {\n        return notYetImplemented('concat');\n    }\n    neg(a) {\n        return notYetImplemented('neg');\n    }\n    add(a, b) {\n        return notYetImplemented('add');\n    }\n    addN(tensors) {\n        return notYetImplemented('addN');\n    }\n    subtract(a, b) {\n        return notYetImplemented('subtract');\n    }\n    multiply(a, b) {\n        return notYetImplemented('multiply');\n    }\n    realDivide(a, b) {\n        return notYetImplemented('realDivide');\n    }\n    floorDiv(a, b) {\n        return notYetImplemented('floorDiv');\n    }\n    sum(x, axes) {\n        return notYetImplemented('sum');\n    }\n    prod(x, axes) {\n        return notYetImplemented('prod');\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        return notYetImplemented('unsortedSegmentSum');\n    }\n    argMin(x, axis) {\n        return notYetImplemented('argMin');\n    }\n    argMax(x, axis) {\n        return notYetImplemented('argMax');\n    }\n    equal(a, b) {\n        return notYetImplemented('equal');\n    }\n    notEqual(a, b) {\n        return notYetImplemented('notEqual');\n    }\n    less(a, b) {\n        return notYetImplemented('less');\n    }\n    lessEqual(a, b) {\n        return notYetImplemented('lessEqual');\n    }\n    greater(a, b) {\n        return notYetImplemented('greater');\n    }\n    greaterEqual(a, b) {\n        return notYetImplemented('greaterEqual');\n    }\n    logicalNot(a) {\n        return notYetImplemented('logicalNot');\n    }\n    logicalAnd(a, b) {\n        return notYetImplemented('logicalAnd');\n    }\n    logicalOr(a, b) {\n        return notYetImplemented('logicalOr');\n    }\n    where(condition) {\n        return notYetImplemented('where');\n    }\n    select(condition, a, b) {\n        return notYetImplemented('select');\n    }\n    topk(x, k, sorted) {\n        return notYetImplemented('topk');\n    }\n    min(x, axes) {\n        return notYetImplemented('min');\n    }\n    minimum(a, b) {\n        return notYetImplemented('minimum');\n    }\n    mod(a, b) {\n        return notYetImplemented('mod');\n    }\n    max(x, axes) {\n        return notYetImplemented('max');\n    }\n    maximum(a, b) {\n        return notYetImplemented('maximum');\n    }\n    all(x, axes) {\n        return notYetImplemented('all');\n    }\n    any(x, axes) {\n        return notYetImplemented('any');\n    }\n    squaredDifference(a, b) {\n        return notYetImplemented('squaredDifference');\n    }\n    ceil(x) {\n        return notYetImplemented('ceil');\n    }\n    floor(x) {\n        return notYetImplemented('floor');\n    }\n    round(x) {\n        return notYetImplemented('round');\n    }\n    sign(x) {\n        return notYetImplemented('sign');\n    }\n    isNaN(x) {\n        return notYetImplemented('isNaN');\n    }\n    isInf(x) {\n        return notYetImplemented('isInf');\n    }\n    isFinite(x) {\n        return notYetImplemented('isFinite');\n    }\n    pow(a, b) {\n        return notYetImplemented('pow');\n    }\n    exp(x) {\n        return notYetImplemented('exp');\n    }\n    expm1(x) {\n        return notYetImplemented('expm1');\n    }\n    softmax(x, dim) {\n        return notYetImplemented('softmax');\n    }\n    log(x) {\n        return notYetImplemented('log');\n    }\n    log1p(x) {\n        return notYetImplemented('log1p');\n    }\n    sqrt(x) {\n        return notYetImplemented('sqrt');\n    }\n    rsqrt(x) {\n        return notYetImplemented('rsqrt');\n    }\n    square(x) {\n        return notYetImplemented('square');\n    }\n    reciprocal(x) {\n        return notYetImplemented('reciprocal');\n    }\n    relu(x) {\n        return notYetImplemented('relu');\n    }\n    relu6(x) {\n        return notYetImplemented('relu6');\n    }\n    prelu(x, a) {\n        return notYetImplemented('prelu');\n    }\n    elu(x) {\n        return notYetImplemented('elu');\n    }\n    eluDer(dy, y) {\n        return notYetImplemented('eluDer');\n    }\n    selu(x) {\n        return notYetImplemented('selu');\n    }\n    int(x) {\n        return notYetImplemented('int');\n    }\n    clip(x, min, max) {\n        return notYetImplemented('clip');\n    }\n    abs(x) {\n        return notYetImplemented('abs');\n    }\n    complexAbs(x) {\n        return notYetImplemented('complexAbs');\n    }\n    sigmoid(x) {\n        return notYetImplemented('sigmoid');\n    }\n    softplus(x) {\n        return notYetImplemented('softplus');\n    }\n    sin(x) {\n        return notYetImplemented('sin');\n    }\n    cos(x) {\n        return notYetImplemented('cos');\n    }\n    tan(x) {\n        return notYetImplemented('tan');\n    }\n    asin(x) {\n        return notYetImplemented('asin');\n    }\n    acos(x) {\n        return notYetImplemented('acos');\n    }\n    atan(x) {\n        return notYetImplemented('atan');\n    }\n    atan2(a, b) {\n        return notYetImplemented('atan2');\n    }\n    sinh(x) {\n        return notYetImplemented('sinh');\n    }\n    cosh(x) {\n        return notYetImplemented('cosh');\n    }\n    tanh(x) {\n        return notYetImplemented('tanh');\n    }\n    asinh(x) {\n        return notYetImplemented('asinh');\n    }\n    acosh(x) {\n        return notYetImplemented('acosh');\n    }\n    atanh(x) {\n        return notYetImplemented('atanh');\n    }\n    erf(x) {\n        return notYetImplemented('erf');\n    }\n    step(x, alpha) {\n        return notYetImplemented('step');\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedConv2d');\n    }\n    conv2d(x, filter, convInfo) {\n        return notYetImplemented('conv2d');\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv2dDerInput');\n    }\n    conv2dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv2dDerFilter');\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        return notYetImplemented('fusedDepthwiseConv2D');\n    }\n    depthwiseConv2D(input, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2D');\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerInput');\n    }\n    depthwiseConv2DDerFilter(x, dY, convInfo) {\n        return notYetImplemented('depthwiseConv2DDerFilter');\n    }\n    conv3d(x, filter, convInfo) {\n        return notYetImplemented('conv3d');\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        return notYetImplemented('conv3dDerInput');\n    }\n    conv3dDerFilter(x, dY, convInfo) {\n        return notYetImplemented('conv3dDerFilter');\n    }\n    maxPool(x, convInfo) {\n        return notYetImplemented('maxPool');\n    }\n    maxPoolBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPoolBackprop');\n    }\n    avgPool(x, convInfo) {\n        return notYetImplemented('avgPool');\n    }\n    avgPoolBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPoolBackprop');\n    }\n    avgPool3d(x, convInfo) {\n        return notYetImplemented('avgPool3d');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        return notYetImplemented('avgPool3dBackprop');\n    }\n    maxPool3d(x, convInfo) {\n        return notYetImplemented('maxPool3d');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        return notYetImplemented('maxPool3dBackprop');\n    }\n    reshape(x, shape) {\n        return notYetImplemented('reshape');\n    }\n    cast(x, dtype) {\n        return notYetImplemented('cast');\n    }\n    tile(x, reps) {\n        return notYetImplemented('tile');\n    }\n    pad(x, paddings, constantValue) {\n        return notYetImplemented('pad');\n    }\n    transpose(x, perm) {\n        return notYetImplemented('transpose');\n    }\n    gather(x, indices, axis) {\n        return notYetImplemented('gather');\n    }\n    gatherND(x, indices) {\n        return notYetImplemented('gatherND');\n    }\n    scatterND(indices, updates, shape) {\n        return notYetImplemented('scatterND');\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        return notYetImplemented('batchToSpaceND');\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        return notYetImplemented('spaceToBatchND');\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        return notYetImplemented('resizeBilinear');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeBilinearBackprop');\n    }\n    resizeNearestNeighbor(x, newHEight, newWidth, alignCorners) {\n        return notYetImplemented('resizeNearestNeighbor');\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        return notYetImplemented('resizeNearestNeighborBackprop');\n    }\n    batchNorm(x, mean, variance, offset, scale, varianceEpsilon) {\n        return notYetImplemented('batchNorm');\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        return notYetImplemented('localResponseNormalization4D');\n    }\n    LRNGrad(dy, inputImage, outputImage, radius, bias, alpha, beta) {\n        return notYetImplemented('LRNGrad');\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        return notYetImplemented('multinomial');\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        return notYetImplemented('oneHot');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        return notYetImplemented('cumsum');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        return notYetImplemented('nonMaxSuppression');\n    }\n    fft(x) {\n        return notYetImplemented('fft');\n    }\n    ifft(x) {\n        return notYetImplemented('ifft');\n    }\n    complex(real, imag) {\n        return notYetImplemented('complex');\n    }\n    real(input) {\n        return notYetImplemented('real');\n    }\n    imag(input) {\n        return notYetImplemented('imag');\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        return notYetImplemented('cropAndResize');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        return notYetImplemented('depthToSpace');\n    }\n    // Aligns with the \"SplitV\" kernel in TensorFlow.\n    split(value, sizeSplits, axis) {\n        return notYetImplemented('split');\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        return notYetImplemented('sparseToDense');\n    }\n    diag(x) {\n        return notYetImplemented('diag');\n    }\n    fill(shape, value, dtype) {\n        return notYetImplemented('fill');\n    }\n    onesLike(x) {\n        return notYetImplemented('onesLike');\n    }\n    zerosLike(x) {\n        return notYetImplemented('zerosLike');\n    }\n    linspace(start, stop, num) {\n        return notYetImplemented('linspace');\n    }\n    dispose() {\n        return notYetImplemented('dispose');\n    }\n}\nfunction notYetImplemented(kernelName) {\n    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n        `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n    let counter = array.length;\n    let temp = 0;\n    let index = 0;\n    // While there are elements in the array\n    while (counter > 0) {\n        // Pick a random index\n        index = (Math.random() * counter) | 0;\n        // Decrease counter by 1\n        counter--;\n        // And swap the last element with it\n        temp = array[counter];\n        array[counter] = array[index];\n        array[index] = temp;\n    }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n    return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n    return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n    let sum = 0;\n    for (let i = 0; i < arr.length; i++) {\n        sum += arr[i];\n    }\n    return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n    const r = Math.random();\n    return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n    let result = 0;\n    for (let i = 0; i < a.length; i++) {\n        const diff = Number(a[i]) - Number(b[i]);\n        result += diff * diff;\n    }\n    return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n    if (!expr) {\n        throw new Error(typeof msg === 'string' ? msg : msg());\n    }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n    assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n    assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n    if (result == null) {\n        result = [];\n    }\n    if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n        for (let i = 0; i < arr.length; ++i) {\n            flatten(arr[i], result, skipTypedArray);\n        }\n    }\n    else {\n        result.push(arr);\n    }\n    return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n    if (shape.length === 0) {\n        // Scalar.\n        return 1;\n    }\n    let size = shape[0];\n    for (let i = 1; i < shape.length; i++) {\n        size *= shape[i];\n    }\n    return size;\n}\nexport function isScalarShape(shape) {\n    return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n    if (n1 === n2) {\n        return true;\n    }\n    if (n1 == null || n2 == null) {\n        return false;\n    }\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function isInt(a) {\n    return a % 1 === 0;\n}\nexport function tanh(x) {\n    // tslint:disable-next-line:no-any\n    if (Math.tanh != null) {\n        // tslint:disable-next-line:no-any\n        return Math.tanh(x);\n    }\n    if (x === Infinity) {\n        return 1;\n    }\n    else if (x === -Infinity) {\n        return -1;\n    }\n    else {\n        const e2x = Math.exp(2 * x);\n        return (e2x - 1) / (e2x + 1);\n    }\n}\nexport function sizeToSquarishShape(size) {\n    const width = Math.ceil(Math.sqrt(size));\n    return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n    const shuffledIndices = new Uint32Array(n);\n    for (let i = 0; i < n; ++i) {\n        shuffledIndices[i] = i;\n    }\n    shuffle(shuffledIndices);\n    return shuffledIndices;\n}\nexport function rightPad(a, size) {\n    if (size <= a.length) {\n        return a;\n    }\n    return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n    return new Promise((resolve, reject) => {\n        let tryCount = 0;\n        const tryFn = () => {\n            if (checkFn()) {\n                resolve();\n                return;\n            }\n            tryCount++;\n            const nextBackoff = delayFn(tryCount);\n            if (maxCounter != null && tryCount >= maxCounter) {\n                reject();\n                return;\n            }\n            setTimeout(tryFn, nextBackoff);\n        };\n        tryFn();\n    });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n    let shapeProd = 1;\n    let implicitIdx = -1;\n    for (let i = 0; i < shape.length; ++i) {\n        if (shape[i] >= 0) {\n            shapeProd *= shape[i];\n        }\n        else if (shape[i] === -1) {\n            if (implicitIdx !== -1) {\n                throw Error(`Shapes can only have 1 implicit size. ` +\n                    `Found -1 at dim ${implicitIdx} and dim ${i}`);\n            }\n            implicitIdx = i;\n        }\n        else if (shape[i] < 0) {\n            throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n        }\n    }\n    if (implicitIdx === -1) {\n        if (size > 0 && size !== shapeProd) {\n            throw Error(`Size(${size}) must match the product of shape ${shape}`);\n        }\n        return shape;\n    }\n    if (shapeProd === 0) {\n        throw Error(`Cannot infer the missing size in [${shape}] when ` +\n            `there are 0 elements`);\n    }\n    if (size % shapeProd !== 0) {\n        throw Error(`The implicit shape can't be a fractional number. ` +\n            `Got ${size} / ${shapeProd}`);\n    }\n    const newShape = shape.slice();\n    newShape[implicitIdx] = size / shapeProd;\n    return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n    const rank = shape.length;\n    // Normalize input\n    axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n    // Check for valid range\n    assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n        `got axis ${axis}`);\n    // Check for only integers\n    assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n        `got axis ${axis}`);\n    // Handle negative axis.\n    return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n    const newShape = [];\n    const keptDims = [];\n    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n    const axes = (axis == null || isEmptyArray) ?\n        null :\n        parseAxisParam(axis, shape).sort();\n    let j = 0;\n    for (let i = 0; i < shape.length; ++i) {\n        if (axes != null) {\n            if (axes[j] === i && shape[i] !== 1) {\n                throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n            }\n            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n                newShape.push(shape[i]);\n                keptDims.push(i);\n            }\n            if (axes[j] <= i) {\n                j++;\n            }\n        }\n        if (shape[i] !== 1) {\n            newShape.push(shape[i]);\n            keptDims.push(i);\n        }\n    }\n    return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function getArrayFromDType(dtype, size) {\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else if (dtype === 'string') {\n        values = new Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n        }\n    }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n    return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n        dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n    if (newType === 'complex64') {\n        return false;\n    }\n    if (newType === 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n        return false;\n    }\n    if (newType === 'bool' && oldType === 'bool') {\n        return false;\n    }\n    return true;\n}\nexport function isTypedArray(a) {\n    return a instanceof Float32Array || a instanceof Int32Array ||\n        a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n    if (dtype === 'float32' || dtype === 'int32') {\n        return 4;\n    }\n    else if (dtype === 'complex64') {\n        return 8;\n    }\n    else if (dtype === 'bool') {\n        return 1;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n    if (arr == null) {\n        return 0;\n    }\n    let bytes = 0;\n    arr.forEach(x => bytes += x.length);\n    return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n    return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n    return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n    return typeof value === 'number';\n}\nexport function inferDtype(values) {\n    if (Array.isArray(values)) {\n        return inferDtype(values[0]);\n    }\n    if (values instanceof Float32Array) {\n        return 'float32';\n    }\n    else if (values instanceof Int32Array || values instanceof Uint8Array) {\n        return 'int32';\n    }\n    else if (isNumber(values)) {\n        return 'float32';\n    }\n    else if (isString(values)) {\n        return 'string';\n    }\n    else if (isBoolean(values)) {\n        return 'bool';\n    }\n    return 'float32';\n}\nexport function isFunction(f) {\n    return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n    for (let i = start; i < size; ++i) {\n        if (size % i === 0) {\n            return i;\n        }\n    }\n    return size;\n}\nexport function computeStrides(shape) {\n    const rank = shape.length;\n    if (rank < 2) {\n        return [];\n    }\n    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n    // strides.\n    const strides = new Array(rank - 1);\n    strides[rank - 2] = shape[rank - 1];\n    for (let i = rank - 3; i >= 0; --i) {\n        strides[i] = strides[i + 1] * shape[i + 1];\n    }\n    return strides;\n}\nfunction createNestedArray(offset, shape, a) {\n    const ret = new Array();\n    if (shape.length === 1) {\n        const d = shape[0];\n        for (let i = 0; i < d; i++) {\n            ret[i] = a[offset + i];\n        }\n    }\n    else {\n        const d = shape[0];\n        const rest = shape.slice(1);\n        const len = rest.reduce((acc, c) => acc * c);\n        for (let i = 0; i < d; i++) {\n            ret[i] = createNestedArray(offset + i * len, rest, a);\n        }\n    }\n    return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a) {\n    if (shape.length === 0) {\n        // Scalar type should return a single number.\n        return a[0];\n    }\n    const size = shape.reduce((acc, c) => acc * c);\n    if (size === 0) {\n        // A tensor with shape zero should be turned into empty list.\n        return [];\n    }\n    if (size !== a.length) {\n        throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n    }\n    return createNestedArray(0, shape, a);\n}\nexport function makeOnesTypedArray(size, dtype) {\n    const array = makeZerosTypedArray(size, dtype);\n    for (let i = 0; i < array.length; i++) {\n        array[i] = 1;\n    }\n    return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        return new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n    const size = shape.reduce((prev, curr) => prev * curr, 1);\n    if (dtype == null || dtype === 'float32') {\n        return toNestedArray(shape, new Float32Array(size));\n    }\n    else if (dtype === 'int32') {\n        return toNestedArray(shape, new Int32Array(size));\n    }\n    else if (dtype === 'bool') {\n        return toNestedArray(shape, new Uint8Array(size));\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n    shape.forEach(dimSize => {\n        assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n    });\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n    if (rank === 0) {\n        return 0;\n    }\n    else if (rank === 1) {\n        return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n        index += strides[i] * locs[i];\n    }\n    return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n    if (rank === 0) {\n        return [];\n    }\n    else if (rank === 1) {\n        return [index];\n    }\n    const locs = new Array(rank);\n    for (let i = 0; i < locs.length - 1; ++i) {\n        locs[i] = Math.floor(index / strides[i]);\n        index -= locs[i] * strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n}\n/**\n * This method asserts whether an object is a Promise instance.\n * @param object\n */\n// tslint:disable-next-line: no-any\nexport function isPromise(object) {\n    //  We chose to not use 'obj instanceOf Promise' for two reasons:\n    //  1. It only reliably works for es6 Promise, not other Promise\n    //  implementations.\n    //  2. It doesn't work with framework that uses zone.js. zone.js monkey patch\n    //  the async calls, so it is possible the obj (patched) is comparing to a\n    //  pre-patched Promise.\n    return object && object.then && typeof object.then === 'function';\n}\n//# sourceMappingURL=util_base.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isPromise } from './util_base';\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n    // tslint:disable-next-line: no-any\n    constructor(global) {\n        this.global = global;\n        this.flags = {};\n        this.flagRegistry = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    setPlatform(platformName, platform) {\n        if (this.platform != null) {\n            console.warn(`Platform ${this.platformName} has already been set. ` +\n                `Overwriting the platform with ${platform}.`);\n        }\n        this.platformName = platformName;\n        this.platform = platform;\n    }\n    registerFlag(flagName, evaluationFn, setHook) {\n        this.flagRegistry[flagName] = { evaluationFn, setHook };\n        // Override the flag value from the URL. This has to happen here because the\n        // environment is initialized before flags get registered.\n        if (this.urlFlags[flagName] != null) {\n            const flagValue = this.urlFlags[flagName];\n            console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n            this.set(flagName, flagValue);\n        }\n    }\n    async getAsync(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        this.flags[flagName] = await this.evaluateFlag(flagName);\n        return this.flags[flagName];\n    }\n    get(flagName) {\n        if (flagName in this.flags) {\n            return this.flags[flagName];\n        }\n        const flagValue = this.evaluateFlag(flagName);\n        if (isPromise(flagValue)) {\n            throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n                `Please use getAsync() instead.`);\n        }\n        this.flags[flagName] = flagValue;\n        return this.flags[flagName];\n    }\n    getNumber(flagName) {\n        return this.get(flagName);\n    }\n    getBool(flagName) {\n        return this.get(flagName);\n    }\n    getFlags() {\n        return this.flags;\n    }\n    // For backwards compatibility.\n    get features() {\n        return this.flags;\n    }\n    set(flagName, value) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n        }\n        this.flags[flagName] = value;\n        if (this.flagRegistry[flagName].setHook != null) {\n            this.flagRegistry[flagName].setHook(value);\n        }\n    }\n    evaluateFlag(flagName) {\n        if (this.flagRegistry[flagName] == null) {\n            throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n        }\n        return this.flagRegistry[flagName].evaluationFn();\n    }\n    setFlags(flags) {\n        this.flags = Object.assign({}, flags);\n    }\n    reset() {\n        this.flags = {};\n        this.urlFlags = {};\n        this.populateURLFlags();\n    }\n    populateURLFlags() {\n        if (typeof this.global === 'undefined' ||\n            typeof this.global.location === 'undefined' ||\n            typeof this.global.location.search === 'undefined') {\n            return;\n        }\n        const urlParams = getQueryParams(this.global.location.search);\n        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n            keyValues.forEach(keyValue => {\n                const [key, value] = keyValue.split(':');\n                this.urlFlags[key] = parseValue(key, value);\n            });\n        }\n    }\n}\nexport function getQueryParams(queryString) {\n    const params = {};\n    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n        decodeParam(params, t[0], t[1]);\n        return t.join('=');\n    });\n    return params;\n}\nfunction decodeParam(params, name, value) {\n    params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n    value = value.toLowerCase();\n    if (value === 'true' || value === 'false') {\n        return value === 'true';\n    }\n    else if (`${+value}` === value) {\n        return +value;\n    }\n    throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n    return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n    ENV = environment;\n}\n//# sourceMappingURL=environment.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n    if (globalNameSpace == null) {\n        // tslint:disable-next-line:no-any\n        let ns;\n        if (typeof (window) !== 'undefined') {\n            ns = window;\n        }\n        else if (typeof (global) !== 'undefined') {\n            ns = global;\n        }\n        else if (typeof (process) !== 'undefined') {\n            ns = process;\n        }\n        else if (typeof (self) !== 'undefined') {\n            ns = self;\n        }\n        else {\n            throw new Error('Could not find a global object');\n        }\n        globalNameSpace = ns;\n    }\n    return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n    const ns = getGlobalNamespace();\n    if (ns._tfGlobals == null) {\n        ns._tfGlobals = new Map();\n    }\n    return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n    const globalMap = getGlobalMap();\n    if (globalMap.has(key)) {\n        return globalMap.get(key);\n    }\n    else {\n        const singleton = init();\n        globalMap.set(key, singleton);\n        return globalMap.get(key);\n    }\n}\n//# sourceMappingURL=global_util.js.map", "export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const Div = 'Div';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNBackprop = 'LRNBackprop';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const MirrorPad = 'MirrorPad';\nexport const Mod = 'Mod';\nexport const Multiply = 'Multiply';\nexport const Negate = 'Negate';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const SelectV2 = 'SelectV2';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const Sub = 'Sub';\nexport const SparseToDense = 'SparseToDense';\nexport const StridedSlice = 'StridedSlice';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n    return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n    const it = kernelRegistry.entries();\n    const result = [];\n    while (true) {\n        const { done, value } = it.next();\n        if (done) {\n            break;\n        }\n        const [key, config] = value;\n        const [backend,] = key.split('_');\n        if (backend === backendName) {\n            result.push(config);\n        }\n    }\n    return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n    const { kernelName, backendName } = config;\n    const key = makeKey(kernelName, backendName);\n    if (kernelRegistry.has(key)) {\n        console.warn(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is already registered`);\n    }\n    kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n    const { kernelName } = config;\n    if (gradRegistry.has(kernelName)) {\n        // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n        // to debug mode.\n        if (env().getBool('DEBUG')) {\n            console.warn(`Overriding the gradient for '${kernelName}'`);\n        }\n    }\n    gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n    const key = makeKey(kernelName, backendName);\n    if (!kernelRegistry.has(key)) {\n        throw new Error(`The kernel '${kernelName}' for backend ` +\n            `'${backendName}' is not registered`);\n    }\n    kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n    if (!gradRegistry.has(kernelName)) {\n        throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n    }\n    gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n    const kernels = getKernelsForBackend(registeredBackendName);\n    kernels.forEach(kernelConfig => {\n        const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n        registerKernel(newKernelConfig);\n    });\n}\nfunction makeKey(kernelName, backendName) {\n    return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport * as base from './util_base';\nexport * from './util_base';\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n    if (dtype === 'string') {\n        return encodeString(value);\n    }\n    return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a, dtype) {\n    return (a instanceof Float32Array && dtype === 'float32') ||\n        (a instanceof Int32Array && dtype === 'int32') ||\n        (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function toTypedArray(a, dtype) {\n    if (dtype === 'string') {\n        throw new Error('Cannot convert a string[] to a TypedArray');\n    }\n    if (Array.isArray(a)) {\n        a = base.flatten(a);\n    }\n    if (env().getBool('DEBUG')) {\n        base.checkConversionForErrors(a, dtype);\n    }\n    if (noConversionNeeded(a, dtype)) {\n        return a;\n    }\n    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n        return new Float32Array(a);\n    }\n    else if (dtype === 'int32') {\n        return new Int32Array(a);\n    }\n    else if (dtype === 'bool') {\n        const bool = new Uint8Array(a.length);\n        for (let i = 0; i < bool.length; ++i) {\n            if (Math.round(a[i]) !== 0) {\n                bool[i] = 1;\n            }\n        }\n        return bool;\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n    return env().platform.now();\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n    return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n    encoding = encoding || 'utf-8';\n    return env().platform.decode(bytes, encoding);\n}\n//# sourceMappingURL=util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\nexport class Profiler {\n    constructor(backendTimer, logger) {\n        this.backendTimer = backendTimer;\n        this.logger = logger;\n        if (logger == null) {\n            this.logger = new Logger();\n        }\n    }\n    profileKernel(kernelName, inputs, f) {\n        let outputs;\n        const holdResultWrapperFn = () => {\n            outputs = f();\n        };\n        const timer = this.backendTimer.time(holdResultWrapperFn);\n        for (let i = 0; i < outputs.length; i++) {\n            const output = outputs[i];\n            // Dangling promise here because we don't want to propagate up\n            // asynchronicity.\n            output.data().then(tensorVals => {\n                checkComputationForErrors(tensorVals, output.dtype, kernelName);\n            });\n        }\n        const kernelProfile = {\n            kernelName,\n            outputs,\n            inputs,\n            timeMs: timer.then(timing => timing.kernelMs),\n            extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n                timing.getExtraProfileInfo() :\n                '')\n        };\n        return kernelProfile;\n    }\n    logKernelProfile(kernelProfile) {\n        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n        outputs.forEach(result => {\n            Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n            });\n        });\n    }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n    if (dtype !== 'float32') {\n        // Only floating point computations will generate NaN values\n        return false;\n    }\n    for (let i = 0; i < vals.length; i++) {\n        const num = vals[i];\n        if (isNaN(num) || !isFinite(num)) {\n            // Throwing custom exception so behavior is testable.\n            console.warn(`Found ${num} in the result of '${kernelName}'`);\n            return true;\n        }\n    }\n    return false;\n}\nexport class Logger {\n    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n        const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n            timeMs['error'];\n        const paddedName = util.rightPad(name, 25);\n        const rank = result.rank;\n        const size = result.size;\n        const shape = util.rightPad(result.shape.toString(), 14);\n        let inputShapesDescription = '';\n        for (const name in inputs) {\n            const input = inputs[name];\n            if (input != null) {\n                // The input might be a non-tensor (e.g HTMLImageElement), in which case\n                // we claim the output shape as input shape.\n                const inputShape = input.shape || result.shape;\n                const inputRank = inputShape.length;\n                inputShapesDescription +=\n                    `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n            }\n        }\n        console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n    }\n}\n//# sourceMappingURL=profiler.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n    // Forward pass to compute all the nodes and Tensors that are transitively a\n    // function of x.\n    const tensorsFromX = {};\n    const nodesFromX = {};\n    for (let i = 0; i < xs.length; i++) {\n        tensorsFromX[xs[i].id] = true;\n    }\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        for (const inputName in nodeInputs) {\n            const input = nodeInputs[inputName];\n            let anyInputFromX = false;\n            for (let j = 0; j < xs.length; j++) {\n                if (tensorsFromX[input.id]) {\n                    node.outputs.forEach(output => tensorsFromX[output.id] = true);\n                    anyInputFromX = true;\n                    nodesFromX[node.id] = true;\n                    break;\n                }\n            }\n            if (anyInputFromX) {\n                break;\n            }\n        }\n    }\n    // Backward pass to find all of the nodes and Tensors that lead to y.\n    const tensorsLeadToY = {};\n    tensorsLeadToY[y.id] = true;\n    const nodesToY = {};\n    for (let i = tape.length - 1; i >= 0; i--) {\n        const node = tape[i];\n        const nodeInputs = node.inputs;\n        // If any of the outputs lead to y, mark all of the inputs as leading to y.\n        for (let j = 0; j < node.outputs.length; j++) {\n            if (tensorsLeadToY[node.outputs[j].id]) {\n                for (const inputName in nodeInputs) {\n                    tensorsLeadToY[nodeInputs[inputName].id] = true;\n                    nodesToY[node.id] = true;\n                }\n                break;\n            }\n        }\n    }\n    // Return the paths that come from x and lead to y.\n    const filteredTape = [];\n    for (let i = 0; i < tape.length; i++) {\n        const node = tape[i];\n        if (nodesFromX[node.id] && nodesToY[node.id]) {\n            // Prune the inputs from the node that aren't a function of x.\n            const prunedInputs = {};\n            for (const inputName in node.inputs) {\n                const nodeInput = node.inputs[inputName];\n                if (tensorsFromX[nodeInput.id]) {\n                    prunedInputs[inputName] = nodeInput;\n                }\n            }\n            // Copy the node and overwrite inputsAndArgs to the pruned version.\n            const prunedNode = Object.assign({}, node);\n            prunedNode.inputs = prunedInputs;\n            prunedNode.outputs = node.outputs;\n            filteredTape.push(prunedNode);\n        }\n    }\n    return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n    // Walk the tape backward and keep a map of Tensor to its gradient.\n    for (let i = filteredTape.length - 1; i >= 0; i--) {\n        const node = filteredTape[i];\n        const dys = [];\n        node.outputs.forEach(o => {\n            const gradTensor = tensorAccumulatedGradientMap[o.id];\n            if (gradTensor != null) {\n                dys.push(gradTensor);\n            }\n            else {\n                // This particular output is not in the back-propagation subgraph, so it\n                // does not affect the final output, thus we put null for its dy.\n                dys.push(null);\n            }\n        });\n        if (node.gradient == null) {\n            throw new Error(`Cannot compute gradient: gradient function not found ` +\n                `for ${node.kernelName}.`);\n        }\n        // Backprop dy through this node and accumulate gradients over the inputs.\n        const inputGradients = node.gradient(dys);\n        for (const inputName in node.inputs) {\n            if (!(inputName in inputGradients)) {\n                throw new Error(`Cannot backprop through input ${inputName}. ` +\n                    `Available gradients found: ${Object.keys(inputGradients)}.`);\n            }\n            // Call the gradient function.\n            const dx = tidy(() => inputGradients[inputName]());\n            if (dx.dtype !== 'float32') {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n            }\n            const x = node.inputs[inputName];\n            if (!util.arraysEqual(dx.shape, x.shape)) {\n                throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n                    `'${inputName}' has shape '${dx.shape}', which does not match ` +\n                    `the shape of the input '${x.shape}'`);\n            }\n            if (tensorAccumulatedGradientMap[x.id] == null) {\n                tensorAccumulatedGradientMap[x.id] = dx;\n            }\n            else {\n                const curGradient = tensorAccumulatedGradientMap[x.id];\n                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n                curGradient.dispose();\n            }\n        }\n    }\n}\n//# sourceMappingURL=tape.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n    const strides = computeStrides(shape);\n    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n    const rank = shape.length;\n    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n    const lines = ['Tensor'];\n    if (verbose) {\n        lines.push(`  dtype: ${dtype}`);\n        lines.push(`  rank: ${rank}`);\n        lines.push(`  shape: [${shape}]`);\n        lines.push(`  values:`);\n    }\n    lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n    return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n    const n = sizeFromShape(shape);\n    const numCols = strides[strides.length - 1];\n    const padPerCol = new Array(numCols).fill(0);\n    const rank = shape.length;\n    const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n    if (rank > 1) {\n        for (let row = 0; row < n / numCols; row++) {\n            const offset = row * numCols;\n            for (let j = 0; j < numCols; j++) {\n                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n            }\n        }\n    }\n    return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n    let valStr;\n    if (Array.isArray(val)) {\n        valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n            `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n    }\n    else if (isString(val)) {\n        valStr = `'${val}'`;\n    }\n    else if (dtype === 'bool') {\n        valStr = boolNumToString(val);\n    }\n    else {\n        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n    }\n    return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n    return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n    const storagePerElement = dtype === 'complex64' ? 2 : 1;\n    const size = shape[0];\n    const rank = shape.length;\n    if (rank === 0) {\n        if (dtype === 'complex64') {\n            const complexTuple = createComplexTuples(vals);\n            return [valToString(complexTuple[0], 0, dtype)];\n        }\n        if (dtype === 'bool') {\n            return [boolNumToString(vals[0])];\n        }\n        return [vals[0].toString()];\n    }\n    if (rank === 1) {\n        if (size > FORMAT_LIMIT_NUM_VALS) {\n            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n            let firstVals = Array.from(vals.slice(0, firstValsSize));\n            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n            if (dtype === 'complex64') {\n                firstVals = createComplexTuples(firstVals);\n                lastVals = createComplexTuples(lastVals);\n            }\n            return [\n                '[' +\n                    firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                        .join(', ') +\n                    ', ..., ' +\n                    lastVals\n                        .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n                        .join(', ') +\n                    ']'\n            ];\n        }\n        const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n            Array.from(vals);\n        return [\n            '[' +\n                displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n                    .join(', ') +\n                ']'\n        ];\n    }\n    // The array is rank 2 or more.\n    const subshape = shape.slice(1);\n    const substrides = strides.slice(1);\n    const stride = strides[0] * storagePerElement;\n    const lines = [];\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n        for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n        }\n        lines.push('...');\n        for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    else {\n        for (let i = 0; i < size; i++) {\n            const start = i * stride;\n            const end = start + stride;\n            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n        }\n    }\n    const sep = rank === 2 ? ',' : '';\n    lines[0] = '[' + lines[0] + sep;\n    for (let i = 1; i < lines.length - 1; i++) {\n        lines[i] = ' ' + lines[i] + sep;\n    }\n    let newLineSep = ',\\n';\n    for (let i = 2; i < rank; i++) {\n        newLineSep += '\\n';\n    }\n    lines[lines.length - 1] =\n        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n    return lines;\n}\nfunction createComplexTuples(vals) {\n    const complexTuples = [];\n    for (let i = 0; i < vals.length; i += 2) {\n        complexTuples.push([vals[i], vals[i + 1]]);\n    }\n    return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n    constructor(shape, dtype, values) {\n        this.dtype = dtype;\n        this.shape = shape.slice();\n        this.size = util.sizeFromShape(shape);\n        if (values != null) {\n            const n = values.length;\n            util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n                `inferred by the shape '${this.size}'.`);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n                `a TensorBuffer for the real and imaginary parts separately and ` +\n                `call tf.complex(real, imag).`);\n        }\n        this.values = values || util.getArrayFromDType(dtype, this.size);\n        this.strides = computeStrides(shape);\n    }\n    /**\n     * Sets a value in the buffer at a given location.\n     *\n     * @param value The value to set.\n     * @param locs  The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    set(value, ...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n        const index = this.locToIndex(locs);\n        this.values[index] = value;\n    }\n    /**\n     * Returns the value in the buffer at the provided location.\n     *\n     * @param locs The location indices.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    get(...locs) {\n        if (locs.length === 0) {\n            locs = [0];\n        }\n        let i = 0;\n        for (const loc of locs) {\n            if (loc < 0 || loc >= this.shape[i]) {\n                const msg = `Requested out of range element at ${locs}. ` +\n                    `  Buffer shape=${this.shape}`;\n                throw new Error(msg);\n            }\n            i++;\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return this.values[index];\n    }\n    locToIndex(locs) {\n        if (this.rank === 0) {\n            return 0;\n        }\n        else if (this.rank === 1) {\n            return locs[0];\n        }\n        let index = locs[locs.length - 1];\n        for (let i = 0; i < locs.length - 1; ++i) {\n            index += this.strides[i] * locs[i];\n        }\n        return index;\n    }\n    indexToLoc(index) {\n        if (this.rank === 0) {\n            return [];\n        }\n        else if (this.rank === 1) {\n            return [index];\n        }\n        const locs = new Array(this.shape.length);\n        for (let i = 0; i < locs.length - 1; ++i) {\n            locs[i] = Math.floor(index / this.strides[i]);\n            index -= locs[i] * this.strides[i];\n        }\n        locs[locs.length - 1] = index;\n        return locs;\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Creates an immutable `tf.Tensor` object from the buffer.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Creation'}\n     */\n    toTensor() {\n        return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n    }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n    trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n    opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n    deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n    constructor(shape, dtype, dataId, id) {\n        /** Whether this tensor has been globally kept. */\n        this.kept = false;\n        this.isDisposedInternal = false;\n        this.shape = shape.slice();\n        this.dtype = dtype || 'float32';\n        this.size = util.sizeFromShape(shape);\n        this.strides = computeStrides(shape);\n        this.dataId = dataId;\n        this.id = id;\n        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n    }\n    get rank() {\n        return this.shape.length;\n    }\n    /**\n     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async buffer() {\n        const vals = await this.data();\n        return opHandler.buffer(this.shape, this.dtype, vals);\n    }\n    /**\n     * Returns a `tf.TensorBuffer` that holds the underlying data.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    bufferSync() {\n        return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * asynchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async array() {\n        const vals = await this.data();\n        return toNestedArray(this.shape, vals);\n    }\n    /**\n     * Returns the tensor data as a nested array. The transfer of data is done\n     * synchronously.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    arraySync() {\n        return toNestedArray(this.shape, this.dataSync());\n    }\n    /**\n     * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n     * promise of `TypedArray` that resolves when the computation has finished.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    async data() {\n        this.throwIfDisposed();\n        const data = trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            const bytes = await data;\n            try {\n                return bytes.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /**\n     * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n     * UI thread until the values are ready, which can cause performance issues.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dataSync() {\n        this.throwIfDisposed();\n        const data = trackerFn().readSync(this.dataId);\n        if (this.dtype === 'string') {\n            try {\n                return data.map(b => util.decodeString(b));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode the string bytes into utf-8. ' +\n                    'To get the original bytes, call tensor.bytes().');\n            }\n        }\n        return data;\n    }\n    /** Returns the underlying bytes of the tensor's data. */\n    async bytes() {\n        this.throwIfDisposed();\n        const data = await trackerFn().read(this.dataId);\n        if (this.dtype === 'string') {\n            return data;\n        }\n        else {\n            return new Uint8Array(data.buffer);\n        }\n    }\n    /**\n     * Disposes `tf.Tensor` from memory.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    dispose() {\n        if (this.isDisposed) {\n            return;\n        }\n        trackerFn().disposeTensor(this);\n        this.isDisposedInternal = true;\n    }\n    get isDisposed() {\n        return this.isDisposedInternal;\n    }\n    throwIfDisposed() {\n        if (this.isDisposed) {\n            throw new Error(`Tensor is disposed.`);\n        }\n    }\n    /**\n     * Prints the `tf.Tensor`. See `tf.print` for details.\n     *\n     * @param verbose Whether to print verbose information about the tensor,\n     *    including dtype and size.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    print(verbose = false) {\n        return opHandler.print(this, verbose);\n    }\n    /**\n     * Returns a copy of the tensor. See `tf.clone` for details.\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    clone() {\n        this.throwIfDisposed();\n        return opHandler.clone(this);\n    }\n    /**\n     * Returns a human-readable description of the tensor. Useful for logging.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    toString(verbose = false) {\n        const vals = this.dataSync();\n        return tensorToString(vals, this.shape, this.dtype, verbose);\n    }\n    cast(dtype) {\n        this.throwIfDisposed();\n        return opHandler.cast(this, dtype);\n    }\n    variable(trainable = true, name, dtype) {\n        this.throwIfDisposed();\n        return trackerFn().makeVariable(this, trainable, name, dtype);\n    }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n    value: (instance) => {\n        // Implementation note: we should use properties of the object that will be\n        // defined before the constructor body has finished executing (methods).\n        // This is because when this code is transpiled by babel, babel will call\n        // classCallCheck before the constructor body is run.\n        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n        return !!instance && instance.data != null && instance.dataSync != null &&\n            instance.throwIfDisposed != null;\n    }\n});\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n    constructor(initialValue, trainable, name, tensorId) {\n        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n        this.trainable = trainable;\n        this.name = name;\n    }\n    /**\n     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n     * the same shape and dtype as the old `tf.Tensor`.\n     *\n     * @param newValue New tensor to be assigned to this variable.\n     *\n     * @doc {heading: 'Tensors', subheading: 'Classes'}\n     */\n    assign(newValue) {\n        if (newValue.dtype !== this.dtype) {\n            throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n                `previous value (${this.dtype}) must match`);\n        }\n        if (!util.arraysEqual(newValue.shape, this.shape)) {\n            throw new Error(`shape of the new value (${newValue.shape}) and ` +\n                `previous value (${this.shape}) must match`);\n        }\n        trackerFn().disposeTensor(this);\n        this.dataId = newValue.dataId;\n        trackerFn().incRef(this, null /* backend */);\n    }\n    dispose() {\n        trackerFn().disposeVariable(this);\n        this.isDisposedInternal = true;\n    }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance instanceof Tensor && instance.assign != null &&\n            instance.assign instanceof Function;\n    }\n});\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n    Rank[\"R0\"] = \"R0\";\n    Rank[\"R1\"] = \"R1\";\n    Rank[\"R2\"] = \"R2\";\n    Rank[\"R3\"] = \"R3\";\n    Rank[\"R4\"] = \"R4\";\n    Rank[\"R5\"] = \"R5\";\n    Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n    UpcastInt32AndMap[\"float32\"] = \"float32\";\n    UpcastInt32AndMap[\"int32\"] = \"int32\";\n    UpcastInt32AndMap[\"bool\"] = \"int32\";\n    UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n    UpcastBoolAndMap[\"float32\"] = \"float32\";\n    UpcastBoolAndMap[\"int32\"] = \"int32\";\n    UpcastBoolAndMap[\"bool\"] = \"bool\";\n    UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n    UpcastFloat32AndMap[\"float32\"] = \"float32\";\n    UpcastFloat32AndMap[\"int32\"] = \"float32\";\n    UpcastFloat32AndMap[\"bool\"] = \"float32\";\n    UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n    UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n    UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n    UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n    'float32': UpcastFloat32AndMap,\n    'int32': UpcastInt32AndMap,\n    'bool': UpcastBoolAndMap,\n    'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n    if (typeA === 'string' || typeB === 'string') {\n        if (typeA === 'string' && typeB === 'string') {\n            return 'string';\n        }\n        throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n    }\n    return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n    return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n    if (a.dtype === b.dtype) {\n        return [a, b];\n    }\n    const dtype = upcastType(a.dtype, b.dtype);\n    return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n    assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n        ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n    return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n    const list = [];\n    const seen = new Set();\n    walkTensorContainer(result, list, seen);\n    return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n    if (container == null) {\n        return;\n    }\n    if (container instanceof Tensor) {\n        list.push(container);\n        return;\n    }\n    if (!isIterable(container)) {\n        return;\n    }\n    // Iteration over keys works also for arrays.\n    const iterable = container;\n    for (const k in iterable) {\n        const val = iterable[k];\n        if (!seen.has(val)) {\n            seen.add(val);\n            walkTensorContainer(val, list, seen);\n        }\n    }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n    return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nclass EngineState {\n    constructor() {\n        // Public since optimizers will use it.\n        this.registeredVariables = {};\n        this.nextTapeNodeId = 0;\n        this.numBytes = 0;\n        this.numTensors = 0;\n        this.numStringTensors = 0;\n        this.numDataBuffers = 0;\n        // Number of nested tf.grad() statements when computing higher-order\n        // gradients. E.g. `1` for first-order gradients and `2` for second-order\n        // gradients. Used to track if the tape should be removed after a backprop.\n        this.gradientDepth = 0;\n        // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n        // off the tape.\n        this.kernelDepth = 0;\n        this.scopeStack = [];\n        /**\n         * Keeps track of the number of data moves during a kernel execution. We\n         * maintain a stack since kernels can call other kernels, recursively.\n         */\n        this.numDataMovesStack = [];\n        this.nextScopeId = 0;\n        this.tensorInfo = new WeakMap();\n        this.profiling = false;\n        this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };\n    }\n    dispose() {\n        for (const variableName in this.registeredVariables) {\n            this.registeredVariables[variableName].dispose();\n        }\n    }\n}\nexport class Engine {\n    constructor(ENV) {\n        this.ENV = ENV;\n        this.registry = {};\n        this.registryFactory = {};\n        this.pendingBackendInitId = 0;\n        this.state = new EngineState();\n    }\n    async ready() {\n        if (this.pendingBackendInit != null) {\n            return this.pendingBackendInit.then(() => { });\n        }\n        if (this.backendInstance != null) {\n            return;\n        }\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const success = await this.initializeBackend(backendName).success;\n            if (success) {\n                await this.setBackend(backendName);\n                return;\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    get backend() {\n        if (this.pendingBackendInit != null) {\n            throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n                `sure to await tf.ready() or await tf.setBackend() before calling ` +\n                `other methods`);\n        }\n        if (this.backendInstance == null) {\n            const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n            if (asyncInit) {\n                throw new Error(`The highest priority backend '${name}' has not yet been ` +\n                    `initialized. Make sure to await tf.ready() or ` +\n                    `await tf.setBackend() before calling other methods`);\n            }\n            this.setBackend(name);\n        }\n        return this.backendInstance;\n    }\n    backendNames() {\n        return Object.keys(this.registryFactory);\n    }\n    findBackend(backendName) {\n        if (!(backendName in this.registry)) {\n            // If the backend hasn't been initialized but we have a registry entry for\n            // it, initialize it and return it.\n            if (backendName in this.registryFactory) {\n                const { asyncInit } = this.initializeBackend(backendName);\n                if (asyncInit) {\n                    // Backend is not ready yet.\n                    return null;\n                }\n            }\n            else {\n                return null;\n            }\n        }\n        return this.registry[backendName];\n    }\n    findBackendFactory(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            return null;\n        }\n        return this.registryFactory[backendName].factory;\n    }\n    registerBackend(backendName, factory, priority = 1) {\n        if (backendName in this.registryFactory) {\n            console.warn(`${backendName} backend was already registered. ` +\n                `Reusing existing backend factory.`);\n            return false;\n        }\n        this.registryFactory[backendName] = { factory, priority };\n        return true;\n    }\n    async setBackend(backendName) {\n        if (this.registryFactory[backendName] == null) {\n            throw new Error(`Backend name '${backendName}' not found in registry`);\n        }\n        this.backendName = backendName;\n        if (this.registry[backendName] == null) {\n            this.backendInstance = null;\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            const result = asyncInit ? await success : success;\n            if (!result) {\n                return false;\n            }\n        }\n        this.backendInstance = this.registry[backendName];\n        this.setupRegisteredKernels();\n        // Reset the profiler.\n        this.profiler = new Profiler(this.backendInstance);\n        return true;\n    }\n    setupRegisteredKernels() {\n        const kernels = getKernelsForBackend(this.backendName);\n        kernels.forEach(kernel => {\n            if (kernel.setupFunc != null) {\n                kernel.setupFunc(this.backendInstance);\n            }\n        });\n    }\n    disposeRegisteredKernels(backendName) {\n        const kernels = getKernelsForBackend(backendName);\n        kernels.forEach(kernel => {\n            if (kernel.disposeFunc != null) {\n                kernel.disposeFunc(this.registry[backendName]);\n            }\n        });\n    }\n    /**\n     * Initializes a backend by looking up the backend name in the factory\n     * registry and calling the factory method. Returns a boolean representing\n     * whether the initialization of the backend suceeded. Throws an error if\n     * there is no backend in the factory registry.\n     */\n    initializeBackend(backendName) {\n        const registryFactoryEntry = this.registryFactory[backendName];\n        if (registryFactoryEntry == null) {\n            throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n        }\n        try {\n            const backend = registryFactoryEntry.factory();\n            /* Test if the factory returns a promise.\n            Done in a more liberal way than\n            previous 'Promise.resolve(backend)===backend'\n            as we needed to account for custom Promise\n            implementations (e.g. Angular) */\n            if (backend && !(backend instanceof KernelBackend)\n                && typeof backend.then === 'function') {\n                const promiseId = ++this.pendingBackendInitId;\n                const success = backend\n                    .then(backendInstance => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.registry[backendName] = backendInstance;\n                    this.pendingBackendInit = null;\n                    return true;\n                })\n                    .catch(err => {\n                    // Outdated promise. Another backend was set in the meantime.\n                    if (promiseId < this.pendingBackendInitId) {\n                        return false;\n                    }\n                    this.pendingBackendInit = null;\n                    console.warn(`Initialization of backend ${backendName} failed`);\n                    console.warn(err.stack || err.message);\n                    return false;\n                });\n                this.pendingBackendInit = success;\n                return { success, asyncInit: true };\n            }\n            else {\n                this.registry[backendName] = backend;\n                return { success: true, asyncInit: false };\n            }\n        }\n        catch (err) {\n            console.warn(`Initialization of backend ${backendName} failed`);\n            console.warn(err.stack || err.message);\n            return { success: false, asyncInit: false };\n        }\n    }\n    removeBackend(backendName) {\n        if (!(backendName in this.registryFactory)) {\n            throw new Error(`${backendName} backend not found in registry`);\n        }\n        if (this.backendName === backendName && this.pendingBackendInit != null) {\n            // There is a pending promise of the backend we want to remove. Make it\n            // obsolete.\n            this.pendingBackendInitId++;\n        }\n        if (backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        delete this.registryFactory[backendName];\n        // Unset the backend if it is active.\n        if (this.backendName === backendName) {\n            this.pendingBackendInit = null;\n            this.backendName = null;\n            this.backendInstance = null;\n        }\n    }\n    getSortedBackends() {\n        if (Object.keys(this.registryFactory).length === 0) {\n            throw new Error('No backend found in registry.');\n        }\n        return Object.keys(this.registryFactory).sort((a, b) => {\n            // Highest priority comes first.\n            return this.registryFactory[b].priority -\n                this.registryFactory[a].priority;\n        });\n    }\n    initializeBackendsAndReturnBest() {\n        const sortedBackends = this.getSortedBackends();\n        for (let i = 0; i < sortedBackends.length; i++) {\n            const backendName = sortedBackends[i];\n            const { success, asyncInit } = this.initializeBackend(backendName);\n            if (asyncInit || success) {\n                return { name: backendName, asyncInit };\n            }\n        }\n        throw new Error(`Could not initialize any backends, all backend initializations ` +\n            `failed.`);\n    }\n    moveData(backend, dataId) {\n        const info = this.state.tensorInfo.get(dataId);\n        const srcBackend = info.backend;\n        const values = this.readSync(dataId);\n        // Delete the tensor from the old backend and move it to the new\n        // backend.\n        srcBackend.disposeData(dataId);\n        info.backend = backend;\n        backend.move(dataId, values, info.shape, info.dtype);\n        if (this.shouldCheckForMemLeaks()) {\n            // Track the number of moves during a kernel execution to correctly\n            // detect memory leaks.\n            this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n        }\n    }\n    tidy(nameOrFn, fn) {\n        let name = null;\n        if (fn == null) {\n            // Called with only 1 argument.\n            if (typeof nameOrFn !== 'function') {\n                throw new Error('Please provide a function to tidy()');\n            }\n            fn = nameOrFn;\n        }\n        else {\n            // Called with 2 arguments.\n            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n                throw new Error('When calling with two arguments, the first argument ' +\n                    'to tidy() must be a string');\n            }\n            if (typeof fn !== 'function') {\n                throw new Error('When calling with two arguments, the 2nd argument ' +\n                    'to tidy() must be a function');\n            }\n            name = nameOrFn;\n            // TODO(nsthorat,smilkov): Do operation logging and performance\n            // profiling.\n        }\n        let result;\n        return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n            result = fn();\n            if (result instanceof Promise) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            return result;\n        });\n    }\n    scopedRun(start, end, f) {\n        start();\n        try {\n            const res = f();\n            end();\n            return res;\n        }\n        catch (ex) {\n            end();\n            throw ex;\n        }\n    }\n    nextTensorId() {\n        return Engine.nextTensorId++;\n    }\n    nextVariableId() {\n        return Engine.nextVariableId++;\n    }\n    /**\n     * This method is called instead of the public-facing tensor.clone() when\n     * saving a tensor for backwards pass. It makes sure to add the clone\n     * operation to the tape regardless of being called inside a kernel\n     * execution.\n     *\n     * This method will go away once all kernels are modularized since we won't\n     * need to turn off the tape inside runKernel().\n     */\n    clone(x) {\n        const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n        const inputs = { x };\n        const grad = (dy) => ({\n            x: () => {\n                const dtype = 'float32';\n                const gradInputs = { x: dy };\n                const attrs = { dtype };\n                return ENGINE.runKernelFunc(backend => backend.cast(dy, dtype), gradInputs, null /* grad */, Cast, attrs);\n            }\n        });\n        const saved = [];\n        this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n        return y;\n    }\n    /**\n     * Execute a kernel with the given name and return the output tensor.\n     *\n     * @param kernelName The name of the kernel to execute.\n     * @param inputs A map of input names to tensors.\n     * @param attrs A map of attribute names to their values. An attribute is a\n     *     primitive (non-tensor) input to the kernel.\n     * @param inputsToSave A list of tensors, inputs to save for the backprop\n     *     computation.\n     * @param outputsToSave A list of booleans, specifying which output to save\n     *     for the backprop computation. These are booleans since the output\n     * tensors are not visible to the user.\n     */\n    runKernel(kernelName, inputs, attrs, inputsToSave, outputsToSave) {\n        const forwardFunc = null;\n        const backwardsFunc = null;\n        // Call runKernel as a stop-gap until we modularize all kernels.\n        // Once we modularize all kernels, we will remove the existing\n        // `runKernelFunc`.\n        return this.runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave);\n    }\n    shouldCheckForMemLeaks() {\n        return this.ENV.getBool('IS_TEST');\n    }\n    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n        const numDataIdsAfter = this.backend.numDataIds();\n        // Count the number of data ids associated with the result of the kernel.\n        let numOutputDataIds = 0;\n        outInfos.forEach(info => {\n            // Complex numbers allocate 3 data ids, one for 'real', one for\n            // 'imaginary', and one for the container that holds the former two.\n            numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n        });\n        // Account for the number of moves during kernel execution. A \"data move\"\n        // can happen in the middle of a kernel execution, placing a new (key,value)\n        // pair in the data storage. Since data moves have net zero effect (we\n        // always remove the data from the old backend), we have to cancel them out\n        // when detecting memory leaks.\n        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n        if (dataIdsLeaked > 0) {\n            throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n                `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n        }\n    }\n    /**\n     * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n     *     only for kernels that are not yet fully modularized.\n     */\n    runKernelFunc(forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave, outputsToSave) {\n        let outputs;\n        let saved = [];\n        const isTapeOn = this.isTapeOn();\n        if (kernelName == null) {\n            kernelName =\n                this.state.activeScope != null ? this.state.activeScope.name : '';\n        }\n        const startingBytecount = this.state.numBytes;\n        const startingNumTensors = this.state.numTensors;\n        if (this.shouldCheckForMemLeaks()) {\n            this.state.numDataMovesStack.push(0);\n        }\n        let kernelFunc;\n        const kernel = getKernel(kernelName, this.backendName);\n        let out;\n        if (kernel != null) {\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n                const outInfos = Array.isArray(out) ? out : [out];\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n                }\n                const outTensors = outInfos.map(({ dataId, shape, dtype }) => this.makeTensorFromDataId(dataId, shape, dtype));\n                // Save the inputs and outputs.\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (isTapeOn) {\n                    let tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n                    if (tensorsToSave == null) {\n                        // Fallback for ops that call runKernelFunc and pass in\n                        // inputsToSave and outputsToSave. Currently this is the set of ops\n                        // with kernel support in the WASM backend. Once those ops and\n                        // respective gradients are modularised we can remove this path.\n                        if (outputsToSave == null) {\n                            outputsToSave = [];\n                        }\n                        const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n                        tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n                    }\n                    saved = this.saveTensorsForBackwardMode(tensorsToSave);\n                }\n                return outTensors;\n            };\n        }\n        else {\n            const saveFunc = (tensors) => {\n                // Do not save unless we are recording to the tape. Otherwise it would\n                // cause a mem leak since we would never run backprop, which disposes\n                // the kept tensors.\n                if (!isTapeOn) {\n                    return;\n                }\n                saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n            };\n            kernelFunc = () => {\n                const numDataIdsBefore = this.backend.numDataIds();\n                out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n                const outs = (Array.isArray(out) ? out : [out]);\n                if (this.shouldCheckForMemLeaks()) {\n                    this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n                }\n                return outs;\n            };\n        }\n        // Stop recording to a tape when running a kernel.\n        let kernelProfile;\n        this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n            if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n                outputs = kernelFunc();\n            }\n            else {\n                kernelProfile = this.profiler.profileKernel(kernelName, inputs, () => kernelFunc());\n                if (this.ENV.getBool('DEBUG')) {\n                    this.profiler.logKernelProfile(kernelProfile);\n                }\n                outputs = kernelProfile.outputs;\n            }\n        });\n        if (isTapeOn) {\n            this.addTapeNode(kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n        }\n        if (this.state.profiling) {\n            this.state.activeProfile.kernels.push({\n                name: kernelName,\n                bytesAdded: this.state.numBytes - startingBytecount,\n                totalBytesSnapshot: this.state.numBytes,\n                tensorsAdded: this.state.numTensors - startingNumTensors,\n                totalTensorsSnapshot: this.state.numTensors,\n                inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n                outputShapes: outputs.map(item => item.shape),\n                kernelTimeMs: kernelProfile.timeMs,\n                extraInfo: kernelProfile.extraInfo\n            });\n        }\n        return (Array.isArray(out) ? outputs : outputs[0]);\n    }\n    /**\n     * Saves tensors used in forward mode for use in backward mode.\n     *\n     * @param tensors the list of tensors to save.\n     */\n    saveTensorsForBackwardMode(tensors) {\n        const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n        return saved;\n    }\n    /**\n     * Returns a list of tensors to save for a given gradient calculation.\n     *\n     * Returns undefined if their is no registered gradient for this kernel in the\n     * gradient registry.\n     *\n     * @param kernelName name of kernel to look up gradient for.\n     * @param inputs a map of input tensors.\n     * @param outputs an array of output tensors from forward mode of kernel.\n     */\n    getTensorsForGradient(kernelName, inputs, outputs) {\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            const inputsToSave = gradConfig.inputsToSave || [];\n            const outputsToSave = gradConfig.outputsToSave || [];\n            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n            // specified in inputsToSave will be saved.\n            let inputTensorsToSave;\n            if (gradConfig.saveAllInputs) {\n                util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n                inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n            }\n            else {\n                inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n            }\n            const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n            return inputTensorsToSave.concat(outputTensorsToSave);\n        }\n        // TODO(yassogba) throw exception here once all runkernelFunc calls with\n        // inputsToSave/outputsToSave are removed\n        return null;\n    }\n    /**\n     * Internal method used by public APIs for tensor creation. Makes a new\n     * tensor with the provided shape, dtype and values. It always\n     * creates a new data id and writes the values to the underlying backend.\n     */\n    makeTensor(values, shape, dtype, backend) {\n        if (values == null) {\n            throw new Error('Values passed to engine.makeTensor() are null');\n        }\n        dtype = dtype || 'float32';\n        backend = backend || this.backend;\n        let backendVals = values;\n        if (dtype === 'string' && util.isString(values[0])) {\n            backendVals = values.map(d => util.encodeString(d));\n        }\n        const dataId = backend.write(backendVals, shape, dtype);\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        // Count bytes for string tensors.\n        if (dtype === 'string') {\n            const info = this.state.tensorInfo.get(dataId);\n            const newBytes = bytesFromStringArray(backendVals);\n            this.state.numBytes += newBytes - info.bytes;\n            info.bytes = newBytes;\n        }\n        return t;\n    }\n    /**\n     * Internal method used by backends. Makes a new tensor\n     * that is a wrapper around an existing data id. It doesn't create\n     * a new data id, only increments the ref count used in memory tracking.\n     */\n    makeTensorFromDataId(dataId, shape, dtype, backend) {\n        dtype = dtype || 'float32';\n        const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n        this.incRef(t, backend);\n        return t;\n    }\n    makeVariable(initialValue, trainable = true, name, dtype) {\n        name = name || this.nextVariableId().toString();\n        if (dtype != null && dtype !== initialValue.dtype) {\n            initialValue = initialValue.cast(dtype);\n        }\n        const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n        if (this.state.registeredVariables[v.name] != null) {\n            throw new Error(`Variable with name ${v.name} was already registered`);\n        }\n        this.state.registeredVariables[v.name] = v;\n        this.incRef(v, this.backend);\n        return v;\n    }\n    incRef(a, backend) {\n        const refCount = this.state.tensorInfo.has(a.dataId) ?\n            this.state.tensorInfo.get(a.dataId).refCount :\n            0;\n        this.state.numTensors++;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors++;\n        }\n        if (refCount === 0) {\n            this.state.numDataBuffers++;\n            // Bytes for complex numbers are counted by their components. Bytes for\n            // string tensors are counted when writing values.\n            let bytes = 0;\n            if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n                bytes = a.size * util.bytesPerElement(a.dtype);\n            }\n            this.state.tensorInfo.set(a.dataId, {\n                backend: backend || this.backend,\n                dtype: a.dtype,\n                shape: a.shape,\n                bytes,\n                refCount: 0\n            });\n            this.state.numBytes += bytes;\n        }\n        this.state.tensorInfo.get(a.dataId).refCount++;\n        if (!(a instanceof Variable)) {\n            this.track(a);\n        }\n    }\n    disposeTensor(a) {\n        if (!this.state.tensorInfo.has(a.dataId)) {\n            return;\n        }\n        this.state.numTensors--;\n        if (a.dtype === 'string') {\n            this.state.numStringTensors--;\n        }\n        const info = this.state.tensorInfo.get(a.dataId);\n        const refCount = info.refCount;\n        if (refCount <= 1) {\n            // Don't count bytes for complex numbers as they are counted by their\n            // components.\n            if (a.dtype !== 'complex64') {\n                this.state.numBytes -= info.bytes;\n            }\n            this.state.numDataBuffers--;\n            info.backend.disposeData(a.dataId);\n            this.state.tensorInfo.delete(a.dataId);\n        }\n        else {\n            this.state.tensorInfo.get(a.dataId).refCount--;\n        }\n        // TODO(nsthorat): Construct an error and save the stack trace for\n        // debugging when in debug mode. Creating a stack trace is too expensive\n        // to do unconditionally.\n    }\n    disposeVariables() {\n        for (const varName in this.state.registeredVariables) {\n            const v = this.state.registeredVariables[varName];\n            this.disposeVariable(v);\n        }\n    }\n    disposeVariable(v) {\n        this.disposeTensor(v);\n        if (this.state.registeredVariables[v.name] != null) {\n            delete this.state.registeredVariables[v.name];\n        }\n    }\n    memory() {\n        const info = this.backend.memory();\n        info.numTensors = this.state.numTensors;\n        info.numDataBuffers = this.state.numDataBuffers;\n        info.numBytes = this.state.numBytes;\n        if (this.state.numStringTensors > 0) {\n            info.unreliable = true;\n            if (info.reasons == null) {\n                info.reasons = [];\n            }\n            info.reasons.push('Memory usage by string tensors is approximate ' +\n                '(2 bytes per character)');\n        }\n        return info;\n    }\n    async profile(query) {\n        this.state.profiling = true;\n        const startBytes = this.state.numBytes;\n        const startNumTensors = this.state.numTensors;\n        this.state.activeProfile.kernels = [];\n        this.state.activeProfile.result = await query();\n        this.state.profiling = false;\n        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n        this.state.activeProfile.newTensors =\n            this.state.numTensors - startNumTensors;\n        for (const kernel of this.state.activeProfile.kernels) {\n            kernel.kernelTimeMs = await kernel.kernelTimeMs;\n            kernel.extraInfo = await kernel.extraInfo;\n        }\n        return this.state.activeProfile;\n    }\n    isTapeOn() {\n        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n    }\n    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n        const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n        const gradConfig = getGradient(kernelName);\n        if (gradConfig != null) {\n            gradientsFunc = gradConfig.gradFunc;\n        }\n        if (gradientsFunc != null) {\n            tapeNode.gradient = (dys) => {\n                // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n                // the backprop graph to the user as null instead of zeros\n                dys = dys.map((dy, i) => {\n                    if (dy == null) {\n                        const output = outputs[i];\n                        const vals = util.makeZerosTypedArray(output.size, output.dtype);\n                        return this.makeTensor(vals, output.shape, output.dtype);\n                    }\n                    return dy;\n                });\n                // Grad functions of ops with single outputs expect a dy, while ops\n                // with multiple outputs expect dys (array of dy).\n                return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n            };\n        }\n        this.state.activeTape.push(tapeNode);\n    }\n    keep(result) {\n        result.kept = true;\n        return result;\n    }\n    startTape() {\n        if (this.state.gradientDepth === 0) {\n            this.state.activeTape = [];\n        }\n        this.state.gradientDepth++;\n    }\n    endTape() {\n        this.state.gradientDepth--;\n    }\n    /**\n     * Start a scope. Use this with endScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    startScope(name) {\n        const scopeInfo = {\n            track: [],\n            name: 'unnamed scope',\n            id: this.state.nextScopeId++\n        };\n        if (name) {\n            scopeInfo.name = name;\n        }\n        this.state.scopeStack.push(scopeInfo);\n        this.state.activeScope = scopeInfo;\n    }\n    /**\n     * End a scope. Use this with startScope() to achieve the same functionality\n     * as scope() without the need for a function closure.\n     */\n    endScope(result) {\n        const tensorsToTrackInParent = getTensorsInContainer(result);\n        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n        // Dispose the arrays tracked in this scope.\n        for (let i = 0; i < this.state.activeScope.track.length; i++) {\n            const tensor = this.state.activeScope.track[i];\n            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n                tensor.dispose();\n            }\n        }\n        const oldScope = this.state.scopeStack.pop();\n        this.state.activeScope = this.state.scopeStack.length === 0 ?\n            null :\n            this.state.scopeStack[this.state.scopeStack.length - 1];\n        // Track the current result in the parent scope.\n        tensorsToTrackInParent.forEach(tensor => {\n            // Only track the tensor if was allocated in the inner scope and is not\n            // globally kept.\n            if (!tensor.kept && tensor.scopeId === oldScope.id) {\n                this.track(tensor);\n            }\n        });\n    }\n    /**\n     * Returns gradients of `f` with respect to each of the `xs`. The gradients\n     * returned are of the same length as `xs`, but some might be null if `f`\n     * was not a function of that `x`. It also takes optional dy to multiply the\n     * gradient, which defaults to `1`.\n     */\n    gradients(f, xs, dy, allowNoGradients = false) {\n        util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n        if (dy != null && dy.dtype !== 'float32') {\n            throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n        }\n        const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n        util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n        // Filter out the nodes that don't connect x => y.\n        const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n            throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n                'that the f you passed encloses all operations that lead from x ' +\n                'to y.');\n        }\n        return this.tidy('backward', () => {\n            const accumulatedGradientMap = {};\n            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n            // Backprop gradients through the filtered nodes.\n            backpropagateGradients(accumulatedGradientMap, filteredTape, \n            // Pass the tidy function to avoid circular dep with `tape.ts`.\n            f => this.tidy(f), \n            // Pass an add function to avoide a circular dep with `tape.ts`.\n            add);\n            const grads = xs.map(x => accumulatedGradientMap[x.id]);\n            if (this.state.gradientDepth === 0) {\n                // This means that we are not computing higher-order gradients\n                // and can clean up the tape.\n                this.state.activeTape.forEach(node => {\n                    for (const tensor of node.saved) {\n                        tensor.dispose();\n                    }\n                });\n                this.state.activeTape = null;\n            }\n            return { value: y, grads };\n        });\n    }\n    customGrad(f) {\n        util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n        return (...inputs) => {\n            util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n                'tensors');\n            let res;\n            const inputMap = {};\n            inputs.forEach((input, i) => {\n                inputMap[i] = input;\n            });\n            return this.runKernelFunc((_, save) => {\n                res = f(...[...inputs, save]);\n                util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n                util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n                return res.value;\n            }, inputMap, (dy, saved) => {\n                const gradRes = res.gradFunc(dy, saved);\n                const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n                util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n                util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n                const gradMap = {};\n                grads.forEach((grad, i) => {\n                    gradMap[i] = () => grad;\n                });\n                return gradMap;\n            });\n        };\n    }\n    readSync(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.readSync(dataId);\n    }\n    read(dataId) {\n        // Route the read to the correct backend.\n        const info = this.state.tensorInfo.get(dataId);\n        return info.backend.read(dataId);\n    }\n    async time(query) {\n        const start = now();\n        const timingInfo = await this.backend.time(query);\n        timingInfo.wallMs = now() - start;\n        return timingInfo;\n    }\n    /**\n     * Tracks a Tensor in the current scope to be automatically cleaned up\n     * when the current scope ends, and returns the value.\n     *\n     * @param result The Tensor to track in the current scope.\n     */\n    track(result) {\n        if (this.state.activeScope != null) {\n            result.scopeId = this.state.activeScope.id;\n            this.state.activeScope.track.push(result);\n        }\n        return result;\n    }\n    get registeredVariables() {\n        return this.state.registeredVariables;\n    }\n    /**\n     * Resets the engine state. Removes all backends but does not remove\n     * registered backend factories.\n     */\n    reset() {\n        // Make any pending promise obsolete.\n        this.pendingBackendInitId++;\n        this.state.dispose();\n        this.ENV.reset();\n        this.state = new EngineState();\n        for (const backendName in this.registry) {\n            this.disposeRegisteredKernels(backendName);\n            this.registry[backendName].dispose();\n            delete this.registry[backendName];\n        }\n        this.backendName = null;\n        this.backendInstance = null;\n        this.pendingBackendInit = null;\n    }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n    const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n    return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n    const ns = getGlobalNamespace();\n    if (ns._tfengine == null) {\n        const environment = new Environment(ns);\n        ns._tfengine = new Engine(environment);\n    }\n    setEnvironmentGlobal(ns._tfengine.ENV);\n    // Tell the current tensor interface that the global engine is responsible\n    // for tracking.\n    setTensorTracker(() => ns._tfengine);\n    return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n    // We duplicate Add here to avoid a circular dependency with add.ts.\n    const inputs = { a, b };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.add(a, b);\n        save([a, b]);\n        return res;\n    }, inputs, null /* gradient */, Add);\n}\n//# sourceMappingURL=engine.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n    return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile() {\n    if (_isNavigatorDefined()) {\n        // tslint:disable-next-line:no-any\n        const a = navigator.userAgent || navigator.vendor || window.opera;\n        // tslint:disable-next-line:max-line-length\n        return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n            .test(a) ||\n            // tslint:disable-next-line:max-line-length\n            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n                .test(a.substr(0, 4));\n    }\n    return false;\n}\nexport function isBrowser() {\n    return (typeof window !== 'undefined' && window.document != null) ||\n        //@ts-ignore\n        (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n    if (debugValue) {\n        console.warn('Debugging mode is ON. The output of every math call will ' +\n            'be downloaded to CPU and checked for NaNs. ' +\n            'This significantly impacts performance.');\n    }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n    (typeof process.versions !== 'undefined') &&\n    (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n    navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n    /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n//# sourceMappingURL=flags.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n    let firstElem = val;\n    if (isTypedArray(val)) {\n        return dtype === 'string' ? [] : [val.length];\n    }\n    if (!Array.isArray(val)) {\n        return []; // Scalar.\n    }\n    const shape = [];\n    while (Array.isArray(firstElem) ||\n        isTypedArray(firstElem) && dtype !== 'string') {\n        shape.push(firstElem.length);\n        firstElem = firstElem[0];\n    }\n    if (Array.isArray(val) &&\n        env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n        deepAssertShapeConsistency(val, shape, []);\n    }\n    return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n    indices = indices || [];\n    if (!(Array.isArray(val)) && !isTypedArray(val)) {\n        assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n        return;\n    }\n    assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n        `but is an array of ${val.length} elements`);\n    assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n        `elements, but has ${val.length} elements`);\n    const subShape = shape.slice(1);\n    for (let i = 0; i < val.length; ++i) {\n        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n    }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n    if (expectedDtype == null) {\n        return;\n    }\n    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n        expectedDtype === 'numeric' && actualDType === 'string') {\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n            `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n    }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n    if (x instanceof Tensor) {\n        assertDtype(parseAsDtype, x.dtype, argName, functionName);\n        return x;\n    }\n    let inferredDtype = inferDtype(x);\n    // If the user expects a bool/int/float, use that info to update the\n    // inferredDtype when it is not a string.\n    if (inferredDtype !== 'string' &&\n        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n        inferredDtype = parseAsDtype;\n    }\n    assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n    if ((x == null) ||\n        (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n            typeof x !== 'boolean' && typeof x !== 'string')) {\n        const type = x == null ? 'null' : x.constructor.name;\n        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n            `Tensor or TensorLike, but got '${type}'`);\n    }\n    const inferredShape = inferShape(x, inferredDtype);\n    if (!isTypedArray(x) && !Array.isArray(x)) {\n        x = [x];\n    }\n    const skipTypedArray = true;\n    const values = inferredDtype !== 'string' ?\n        toTypedArray(x, inferredDtype) :\n        flatten(x, [], skipTypedArray);\n    return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n    if (!Array.isArray(arg)) {\n        throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n            '`Tensor[]` or `TensorLike[]`');\n    }\n    const tensors = arg;\n    return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName), parseAsDtype);\n}\n//# sourceMappingURL=tensor_util_env.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { isPromise } from '../util';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n    const keys = Object.keys(f);\n    if (keys.length !== 1) {\n        throw new Error(`Please provide an object with a single key ` +\n            `(operation name) mapping to a function. Got an object with ` +\n            `${keys.length} keys.`);\n    }\n    let opName = keys[0];\n    const fn = f[opName];\n    // Strip the underscore from the end of the function name.\n    if (opName.endsWith('_')) {\n        opName = opName.substring(0, opName.length - 1);\n    }\n    // add an __op suffix to distinguish ops from kernels in tf.profile\n    opName = opName + OP_SCOPE_SUFFIX;\n    // tslint:disable-next-line:no-any\n    const f2 = (...args) => {\n        ENGINE.startScope(opName);\n        try {\n            const result = fn(...args);\n            if (isPromise(result)) {\n                console.error('Cannot return a Promise inside of tidy.');\n            }\n            ENGINE.endScope(result);\n            return result;\n        }\n        catch (ex) {\n            ENGINE.endScope(null);\n            throw ex;\n        }\n    };\n    Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n    // tslint:disable-next-line:no-any\n    return f2;\n}\n//# sourceMappingURL=operation.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n    const $real = convertToTensor(real, 'real', 'complex');\n    const $imag = convertToTensor(imag, 'imag', 'complex');\n    util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n        `must match in call to tf.complex().`);\n    const forward = (backend) => {\n        return backend.complex($real, $imag);\n    };\n    const inputs = { real: $real, imag: $imag };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Complex);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n    if (dtype == null) {\n        dtype = inferDtype(values);\n    }\n    if (dtype === 'complex64') {\n        throw new Error(`Cannot construct a complex64 tensor directly. ` +\n            `Please use tf.complex(real, imag).`);\n    }\n    if (!isTypedArray(values) && !Array.isArray(values) &&\n        typeof values !== 'number' && typeof values !== 'boolean' &&\n        typeof values !== 'string') {\n        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n            'an array of numbers/booleans/strings, or a TypedArray');\n    }\n    if (shape != null) {\n        assertNonNegativeIntegerDimensions(shape);\n        const providedSize = sizeFromShape(shape);\n        const inferredSize = sizeFromShape(inferredShape);\n        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n        for (let i = 0; i < inferredShape.length; ++i) {\n            const inferred = inferredShape[i];\n            const flatDimsDontMatch = i === inferredShape.length - 1 ?\n                inferred !== sizeFromShape(shape.slice(i)) :\n                true;\n            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n                `(${inferredShape}) does not match the provided ` +\n                `shape (${shape}). `);\n        }\n    }\n    if (!isTypedArray(values) && !Array.isArray(values)) {\n        values = [values];\n    }\n    shape = shape || inferredShape;\n    values = dtype !== 'string' ?\n        toTypedArray(values, dtype) :\n        flatten(values, [], true);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n    const inferredShape = inferShape(values, dtype);\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n    'float32': 4,\n    'float16': 2,\n    'int32': 4,\n    'uint16': 2,\n    'uint8': 1,\n    'bool': 1,\n    'complex64': 8\n};\n//# sourceMappingURL=types.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n    // TODO(adarob, cais): Support quantization.\n    const specs = [];\n    const dataPromises = [];\n    const names = Array.isArray(tensors) ?\n        tensors.map(tensor => tensor.name) :\n        Object.keys(tensors);\n    for (let i = 0; i < names.length; ++i) {\n        const name = names[i];\n        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n            t.dtype !== 'string' && t.dtype !== 'complex64') {\n            throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n        }\n        const spec = { name, shape: t.shape, dtype: t.dtype };\n        if (t.dtype === 'string') {\n            const utf8bytes = new Promise(async (resolve) => {\n                const vals = await t.bytes();\n                const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n                    NUM_BYTES_STRING_LENGTH * vals.length;\n                const bytes = new Uint8Array(totalNumBytes);\n                let offset = 0;\n                for (let i = 0; i < vals.length; i++) {\n                    const val = vals[i];\n                    const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n                    bytes.set(bytesOfLength, offset);\n                    offset += NUM_BYTES_STRING_LENGTH;\n                    bytes.set(val, offset);\n                    offset += val.length;\n                }\n                resolve(bytes);\n            });\n            dataPromises.push(utf8bytes);\n        }\n        else {\n            dataPromises.push(t.data());\n        }\n        if (group != null) {\n            spec.group = group;\n        }\n        specs.push(spec);\n    }\n    const tensorValues = await Promise.all(dataPromises);\n    return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n    // TODO(adarob, cais): Support quantization.\n    const out = {};\n    let float16Decode;\n    let offset = 0;\n    for (const spec of specs) {\n        const name = spec.name;\n        const dtype = spec.dtype;\n        const shape = spec.shape;\n        const size = sizeFromShape(shape);\n        let values;\n        if ('quantization' in spec) {\n            const quantization = spec.quantization;\n            if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                if (!('min' in quantization && 'scale' in quantization)) {\n                    throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n                        `doesn't have corresponding metadata min and scale.`);\n                }\n            }\n            else if (quantization.dtype === 'float16') {\n                if (dtype !== 'float32') {\n                    throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n                        `which only supports weights of type float32 not ${dtype}.`);\n                }\n            }\n            else {\n                throw new Error(`Weight ${spec.name} has unknown ` +\n                    `quantization dtype ${quantization.dtype}. ` +\n                    `Supported quantization dtypes are: ` +\n                    `'uint8', 'uint16', and 'float16'.`);\n            }\n            const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n            const quantizedArray = (quantization.dtype === 'uint8') ?\n                new Uint8Array(byteBuffer) :\n                new Uint16Array(byteBuffer);\n            if (dtype === 'float32') {\n                if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n                    values = new Float32Array(quantizedArray.length);\n                    for (let i = 0; i < quantizedArray.length; i++) {\n                        const v = quantizedArray[i];\n                        values[i] = v * quantization.scale + quantization.min;\n                    }\n                }\n                else if (quantization.dtype === 'float16') {\n                    if (float16Decode === undefined) {\n                        float16Decode = getFloat16Decoder();\n                    }\n                    values = float16Decode(quantizedArray);\n                }\n                else {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type float32.`);\n                }\n            }\n            else if (dtype === 'int32') {\n                if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n                    throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n                        `for weight type int32.`);\n                }\n                values = new Int32Array(quantizedArray.length);\n                for (let i = 0; i < quantizedArray.length; i++) {\n                    const v = quantizedArray[i];\n                    values[i] = Math.round(v * quantization.scale + quantization.min);\n                }\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * quantizationSizeFactor;\n        }\n        else if (dtype === 'string') {\n            const size = sizeFromShape(spec.shape);\n            values = [];\n            for (let i = 0; i < size; i++) {\n                const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n                offset += NUM_BYTES_STRING_LENGTH;\n                const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n                values.push(bytes);\n                offset += byteLength;\n            }\n        }\n        else {\n            const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n            const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n            if (dtype === 'float32') {\n                values = new Float32Array(byteBuffer);\n            }\n            else if (dtype === 'int32') {\n                values = new Int32Array(byteBuffer);\n            }\n            else if (dtype === 'bool') {\n                values = new Uint8Array(byteBuffer);\n            }\n            else if (dtype === 'complex64') {\n                values = new Float32Array(byteBuffer);\n                const real = new Float32Array(values.length / 2);\n                const image = new Float32Array(values.length / 2);\n                for (let i = 0; i < real.length; i++) {\n                    real[i] = values[i * 2];\n                    image[i] = values[i * 2 + 1];\n                }\n                const realTensor = tensor(real, shape, 'float32');\n                const imageTensor = tensor(image, shape, 'float32');\n                out[name] = complex(realTensor, imageTensor);\n                realTensor.dispose();\n                imageTensor.dispose();\n            }\n            else {\n                throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n            }\n            offset += size * dtypeFactor;\n        }\n        if (dtype !== 'complex64') {\n            out[name] = tensor(values, shape, dtype);\n        }\n    }\n    return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n    // TODO(adarob, cais): Support quantization.\n    if (xs === null) {\n        throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n    }\n    let totalByteLength = 0;\n    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n    // can have a different byte length from that of the `TypedArray` itself,\n    // for example, when the `TypedArray` is created from an offset in an\n    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n    // the `TypedArray` in byte length. If an element of `xs` does not show\n    // this property, a new `TypedArray` that satisfy this property will be\n    // constructed and pushed into `normalizedXs`.\n    const normalizedXs = [];\n    xs.forEach((x) => {\n        totalByteLength += x.byteLength;\n        // tslint:disable:no-any\n        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n            new x.constructor(x));\n        if (!(x instanceof Float32Array || x instanceof Int32Array ||\n            x instanceof Uint8Array)) {\n            throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n        }\n        // tslint:enable:no-any\n    });\n    const y = new Uint8Array(totalByteLength);\n    let offset = 0;\n    normalizedXs.forEach((x) => {\n        y.set(new Uint8Array(x.buffer), offset);\n        offset += x.byteLength;\n    });\n    return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n        typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n    if (useNodeBuffer) {\n        return Buffer.byteLength(str);\n    }\n    return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n    if (useNodeBuffer) {\n        return Buffer.from(buffer).toString('base64');\n    }\n    const buf = new Uint8Array(buffer);\n    let s = '';\n    for (let i = 0, l = buf.length; i < l; i++) {\n        s += String.fromCharCode(buf[i]);\n    }\n    return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n    if (useNodeBuffer) {\n        const buf = Buffer.from(str, 'base64');\n        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n    }\n    const s = atob(str);\n    const buffer = new Uint8Array(s.length);\n    for (let i = 0; i < s.length; ++i) {\n        buffer.set([s.charCodeAt(i)], i);\n    }\n    return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n    if (buffers.length === 1) {\n        return buffers[0];\n    }\n    let totalByteLength = 0;\n    buffers.forEach((buffer) => {\n        totalByteLength += buffer.byteLength;\n    });\n    const temp = new Uint8Array(totalByteLength);\n    let offset = 0;\n    buffers.forEach((buffer) => {\n        temp.set(new Uint8Array(buffer), offset);\n        offset += buffer.byteLength;\n    });\n    return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n    const SEPARATOR = '/';\n    path = path.trim();\n    while (path.endsWith(SEPARATOR)) {\n        path = path.slice(0, path.length - 1);\n    }\n    const items = path.split(SEPARATOR);\n    return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n        throw new Error('Expected JSON model topology, received ArrayBuffer.');\n    }\n    return {\n        dateSaved: new Date(),\n        modelTopologyType: 'JSON',\n        modelTopologyBytes: modelArtifacts.modelTopology == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n        weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n            0 :\n            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n        weightDataBytes: modelArtifacts.weightData == null ?\n            0 :\n            modelArtifacts.weightData.byteLength,\n    };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n    const convertMantissa = (i) => {\n        let m = i << 13;\n        let e = 0;\n        while ((m & 0x00800000) === 0) {\n            e -= 0x00800000;\n            m <<= 1;\n        }\n        m &= ~0x00800000;\n        e += 0x38800000;\n        return m | e;\n    };\n    const mantisaTable = new Uint32Array(2048);\n    mantisaTable[0] = 0;\n    for (let i = 1; i < 1024; i++) {\n        mantisaTable[i] = convertMantissa(i);\n    }\n    for (let i = 1024; i < 2048; i++) {\n        mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n    }\n    return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n    const exponentTable = new Uint32Array(64);\n    exponentTable[0] = 0;\n    exponentTable[31] = 0x47800000;\n    exponentTable[32] = 0x80000000;\n    exponentTable[63] = 0xc7800000;\n    for (let i = 1; i < 31; i++) {\n        exponentTable[i] = i << 23;\n    }\n    for (let i = 33; i < 63; i++) {\n        exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n    }\n    return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n    const offsetTable = new Uint32Array(64);\n    for (let i = 0; i < 64; i++) {\n        offsetTable[i] = 1024;\n    }\n    offsetTable[0] = offsetTable[32] = 0;\n    return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n    // Algorithm is based off of\n    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n    // Cache lookup tables\n    const mantisaTable = computeFloat16MantisaTable();\n    const exponentTable = computeFloat16ExponentTable();\n    const offsetTable = computeFloat16OffsetTable();\n    return (quantizedArray) => {\n        const buffer = new ArrayBuffer(4 * quantizedArray.length);\n        const bufferUint32View = new Uint32Array(buffer);\n        for (let index = 0; index < quantizedArray.length; index++) {\n            const float16Bits = quantizedArray[index];\n            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n                exponentTable[float16Bits >> 10];\n            bufferUint32View[index] = float32Bits;\n        }\n        return new Float32Array(buffer);\n    };\n}\n//# sourceMappingURL=io_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n    constructor() {\n        this.saveRouters = [];\n        this.loadRouters = [];\n    }\n    static getInstance() {\n        if (IORouterRegistry.instance == null) {\n            IORouterRegistry.instance = new IORouterRegistry();\n        }\n        return IORouterRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerSaveRouter(saveRouter) {\n        IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n    }\n    /**\n     * Register a load-handler router.\n     *\n     * @param loadRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `load` method defined or `null`.\n     */\n    static registerLoadRouter(loadRouter) {\n        IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n    }\n    /**\n     * Look up IOHandler for saving, given a URL-like string.\n     *\n     * @param url\n     * @returns If only one match is found, an instance of IOHandler with the\n     * `save` method defined. If no match is found, `null`.\n     * @throws Error, if more than one match is found.\n     */\n    static getSaveHandlers(url) {\n        return IORouterRegistry.getHandlers(url, 'save');\n    }\n    /**\n     * Look up IOHandler for loading, given a URL-like string.\n     *\n     * @param url\n     * @param loadOptions Optional, custom load options.\n     * @returns All valid handlers for `url`, given the currently registered\n     *   handler routers.\n     */\n    static getLoadHandlers(url, loadOptions) {\n        return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n    }\n    static getHandlers(url, handlerType, loadOptions) {\n        const validHandlers = [];\n        const routers = handlerType === 'load' ?\n            IORouterRegistry.getInstance().loadRouters :\n            IORouterRegistry.getInstance().saveRouters;\n        routers.forEach(router => {\n            const handler = router(url, loadOptions);\n            if (handler !== null) {\n                validHandlers.push(handler);\n            }\n        });\n        return validHandlers;\n    }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n    const idbFactory = getIndexedDBFactory();\n    return new Promise((resolve, reject) => {\n        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n        deleteRequest.onsuccess = () => resolve();\n        deleteRequest.onerror = error => reject(error);\n    });\n}\nfunction getIndexedDBFactory() {\n    if (!env().getBool('IS_BROWSER')) {\n        // TODO(cais): Add more info about what IOHandler subtypes are available.\n        //   Maybe point to a doc page on the web and/or automatically determine\n        //   the available IOHandlers and print them in the error message.\n        throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n            'is not a web browser.');\n    }\n    // tslint:disable-next-line:no-any\n    const theWindow = typeof window === 'undefined' ? self : window;\n    const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n        theWindow.shimIndexedDB;\n    if (factory == null) {\n        throw new Error('The current browser does not appear to support IndexedDB.');\n    }\n    return factory;\n}\nfunction setUpDatabase(openRequest) {\n    const db = openRequest.result;\n    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n    constructor(modelPath) {\n        this.indexedDB = getIndexedDBFactory();\n        if (modelPath == null || !modelPath) {\n            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n    }\n    async save(modelArtifacts) {\n        // TODO(cais): Support saving GraphDef models.\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        return this.databaseAction(this.modelPath, modelArtifacts);\n    }\n    async load() {\n        return this.databaseAction(this.modelPath);\n    }\n    /**\n     * Perform database action to put model artifacts into or read model artifacts\n     * from IndexedDB object store.\n     *\n     * Whether the action is put or get depends on whether `modelArtifacts` is\n     * specified. If it is specified, the action will be put; otherwise the action\n     * will be get.\n     *\n     * @param modelPath A unique string path for the model.\n     * @param modelArtifacts If specified, it will be the model artifacts to be\n     *   stored in IndexedDB.\n     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n     *   of `ModelArtifacts`, if the action is get.\n     */\n    databaseAction(modelPath, modelArtifacts) {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                if (modelArtifacts == null) {\n                    // Read model out from object store.\n                    const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                    const getRequest = modelStore.get(this.modelPath);\n                    getRequest.onsuccess = () => {\n                        if (getRequest.result == null) {\n                            db.close();\n                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n                                `in IndexedDB.`));\n                        }\n                        else {\n                            resolve(getRequest.result.modelArtifacts);\n                        }\n                    };\n                    getRequest.onerror = error => {\n                        db.close();\n                        return reject(getRequest.error);\n                    };\n                    modelTx.oncomplete = () => db.close();\n                }\n                else {\n                    // Put model into object store.\n                    const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n                    // First, put ModelArtifactsInfo into info store.\n                    const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                    const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n                    let modelTx;\n                    putInfoRequest.onsuccess = () => {\n                        // Second, put model data into model store.\n                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                        const putModelRequest = modelStore.put({\n                            modelPath: this.modelPath,\n                            modelArtifacts,\n                            modelArtifactsInfo\n                        });\n                        putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n                        putModelRequest.onerror = error => {\n                            // If the put-model request fails, roll back the info entry as\n                            // well.\n                            infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                            const deleteInfoRequest = infoStore.delete(this.modelPath);\n                            deleteInfoRequest.onsuccess = () => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                            deleteInfoRequest.onerror = error => {\n                                db.close();\n                                return reject(putModelRequest.error);\n                            };\n                        };\n                    };\n                    putInfoRequest.onerror = error => {\n                        db.close();\n                        return reject(putInfoRequest.error);\n                    };\n                    infoTx.oncomplete = () => {\n                        if (modelTx == null) {\n                            db.close();\n                        }\n                        else {\n                            modelTx.oncomplete = () => db.close();\n                        }\n                    };\n                }\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n    return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n        key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n        key;\n}\nexport class BrowserIndexedDBManager {\n    constructor() {\n        this.indexedDB = getIndexedDBFactory();\n    }\n    async listModels() {\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n                const store = tx.objectStore(INFO_STORE_NAME);\n                // tslint:disable:max-line-length\n                // Need to cast `store` as `any` here because TypeScript's DOM\n                // library does not have the `getAll()` method even though the\n                // method is supported in the latest version of most mainstream\n                // browsers:\n                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n                // tslint:enable:max-line-length\n                // tslint:disable-next-line:no-any\n                const getAllInfoRequest = store.getAll();\n                getAllInfoRequest.onsuccess = () => {\n                    const out = {};\n                    for (const item of getAllInfoRequest.result) {\n                        out[item.modelPath] = item.modelArtifactsInfo;\n                    }\n                    resolve(out);\n                };\n                getAllInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getAllInfoRequest.error);\n                };\n                tx.oncomplete = () => db.close();\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        return new Promise((resolve, reject) => {\n            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n            openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n            openRequest.onsuccess = () => {\n                const db = openRequest.result;\n                const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n                const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n                const getInfoRequest = infoStore.get(path);\n                let modelTx;\n                getInfoRequest.onsuccess = () => {\n                    if (getInfoRequest.result == null) {\n                        db.close();\n                        return reject(new Error(`Cannot find model with path '${path}' ` +\n                            `in IndexedDB.`));\n                    }\n                    else {\n                        // First, delete the entry in the info store.\n                        const deleteInfoRequest = infoStore.delete(path);\n                        const deleteModelData = () => {\n                            // Second, delete the entry in the model store.\n                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n                            const deleteModelRequest = modelStore.delete(path);\n                            deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n                            deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n                        };\n                        // Proceed with deleting model data regardless of whether deletion\n                        // of info data succeeds or not.\n                        deleteInfoRequest.onsuccess = deleteModelData;\n                        deleteInfoRequest.onerror = error => {\n                            deleteModelData();\n                            db.close();\n                            return reject(getInfoRequest.error);\n                        };\n                    }\n                };\n                getInfoRequest.onerror = error => {\n                    db.close();\n                    return reject(getInfoRequest.error);\n                };\n                infoTx.oncomplete = () => {\n                    if (modelTx == null) {\n                        db.close();\n                    }\n                    else {\n                        modelTx.oncomplete = () => db.close();\n                    }\n                };\n            };\n            openRequest.onerror = error => reject(openRequest.error);\n        });\n    }\n}\n//# sourceMappingURL=indexed_db.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n            'unavailable in the current environment.');\n    }\n    const LS = window.localStorage;\n    const purgedModelPaths = [];\n    for (let i = 0; i < LS.length; ++i) {\n        const key = LS.key(i);\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        if (key.startsWith(prefix) && key.length > prefix.length) {\n            LS.removeItem(key);\n            const modelName = getModelPathFromKey(key);\n            if (purgedModelPaths.indexOf(modelName) === -1) {\n                purgedModelPaths.push(modelName);\n            }\n        }\n    }\n    return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n    return {\n        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n        modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n    };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n    const items = key.split(PATH_SEPARATOR);\n    if (items.length < 3) {\n        throw new Error(`Invalid key format: ${key}`);\n    }\n    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n        key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n        key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n    constructor(modelPath) {\n        if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n            typeof window.localStorage === 'undefined') {\n            // TODO(cais): Add more info about what IOHandler subtypes are\n            // available.\n            //   Maybe point to a doc page on the web and/or automatically determine\n            //   the available IOHandlers and print them in the error message.\n            throw new Error('The current environment does not support local storage.');\n        }\n        this.LS = window.localStorage;\n        if (modelPath == null || !modelPath) {\n            throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n        }\n        this.modelPath = modelPath;\n        this.keys = getModelKeys(this.modelPath);\n    }\n    /**\n     * Save model artifacts to browser local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @param modelArtifacts The model artifacts to be stored.\n     * @returns An instance of SaveResult.\n     */\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const topology = JSON.stringify(modelArtifacts.modelTopology);\n            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n            const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n            try {\n                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n                this.LS.setItem(this.keys.topology, topology);\n                this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n                this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n                this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n                    format: modelArtifacts.format,\n                    generatedBy: modelArtifacts.generatedBy,\n                    convertedBy: modelArtifacts.convertedBy,\n                    userDefinedMetadata: modelArtifacts.userDefinedMetadata\n                }));\n                return { modelArtifactsInfo };\n            }\n            catch (err) {\n                // If saving failed, clean up all items saved so far.\n                this.LS.removeItem(this.keys.info);\n                this.LS.removeItem(this.keys.topology);\n                this.LS.removeItem(this.keys.weightSpecs);\n                this.LS.removeItem(this.keys.weightData);\n                this.LS.removeItem(this.keys.modelMetadata);\n                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n                    `size quota being exceeded is a possible cause of this failure: ` +\n                    `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n                    `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n                    `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n            }\n        }\n    }\n    /**\n     * Load a model from local storage.\n     *\n     * See the documentation to `browserLocalStorage` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model (if loading succeeds).\n     */\n    async load() {\n        const info = JSON.parse(this.LS.getItem(this.keys.info));\n        if (info == null) {\n            throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n        }\n        if (info.modelTopologyType !== 'JSON') {\n            throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n                'topology yet.');\n        }\n        const out = {};\n        // Load topology.\n        const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n        if (topology == null) {\n            throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n                `is missing.`);\n        }\n        out.modelTopology = topology;\n        // Load weight specs.\n        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n        if (weightSpecs == null) {\n            throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n                `are missing.`);\n        }\n        out.weightSpecs = weightSpecs;\n        // Load meta-data fields.\n        const metadataString = this.LS.getItem(this.keys.modelMetadata);\n        if (metadataString != null) {\n            const metadata = JSON.parse(metadataString);\n            out.format = metadata['format'];\n            out.generatedBy = metadata['generatedBy'];\n            out.convertedBy = metadata['convertedBy'];\n            out.userDefinedMetadata = metadata['userDefinedMetadata'];\n        }\n        // Load weight data.\n        const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n        if (weightDataBase64 == null) {\n            throw new Error(`In local storage, the binary weight values of model ` +\n                `'${this.modelPath}' are missing.`);\n        }\n        out.weightData = base64StringToArrayBuffer(weightDataBase64);\n        return out;\n    }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n    return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n    constructor() {\n        assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n        assert(typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n        this.LS = window.localStorage;\n    }\n    async listModels() {\n        const out = {};\n        const prefix = PATH_PREFIX + PATH_SEPARATOR;\n        const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n        for (let i = 0; i < this.LS.length; ++i) {\n            const key = this.LS.key(i);\n            if (key.startsWith(prefix) && key.endsWith(suffix)) {\n                const modelPath = getModelPathFromKey(key);\n                out[modelPath] = JSON.parse(this.LS.getItem(key));\n            }\n        }\n        return out;\n    }\n    async removeModel(path) {\n        path = maybeStripScheme(path);\n        const keys = getModelKeys(path);\n        if (this.LS.getItem(keys.info) == null) {\n            throw new Error(`Cannot find model at path '${path}'`);\n        }\n        const info = JSON.parse(this.LS.getItem(keys.info));\n        this.LS.removeItem(keys.info);\n        this.LS.removeItem(keys.topology);\n        this.LS.removeItem(keys.weightSpecs);\n        this.LS.removeItem(keys.weightData);\n        return info;\n    }\n}\n//# sourceMappingURL=local_storage.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n    constructor() {\n        this.managers = {};\n    }\n    static getInstance() {\n        if (ModelStoreManagerRegistry.instance == null) {\n            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n        }\n        return ModelStoreManagerRegistry.instance;\n    }\n    /**\n     * Register a save-handler router.\n     *\n     * @param saveRouter A function that maps a URL-like string onto an instance\n     * of `IOHandler` with the `save` method defined or `null`.\n     */\n    static registerManager(scheme, manager) {\n        assert(scheme != null, () => 'scheme must not be undefined or null.');\n        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n        }\n        assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n        const registry = ModelStoreManagerRegistry.getInstance();\n        assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n        registry.managers[scheme] = manager;\n    }\n    static getManager(scheme) {\n        const manager = this.getInstance().managers[scheme];\n        if (manager == null) {\n            throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n        }\n        return manager;\n    }\n    static getSchemes() {\n        return Object.keys(this.getInstance().managers);\n    }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n        throw new Error(`The url string provided does not contain a scheme. ` +\n            `Supported schemes are: ` +\n            `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n    }\n    return {\n        scheme: url.split(URL_SCHEME_SUFFIX)[0],\n        path: url.split(URL_SCHEME_SUFFIX)[1],\n    };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n    assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n    const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n    assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n    assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `load handlers for source URL ${sourceURL}.`);\n    const loadHandler = loadHandlers[0];\n    const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n    assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n        `URL ${destURL}.`);\n    assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n        `save handlers for destination URL ${destURL}.`);\n    const saveHandler = saveHandlers[0];\n    const sourceScheme = parseURL(sourceURL).scheme;\n    const sourcePath = parseURL(sourceURL).path;\n    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n    const modelArtifacts = await loadHandler.load();\n    // If moving within the same storage medium, remove the old model as soon as\n    // the loading is done. Without doing this, it is possible that the combined\n    // size of the two models will cause the cloning to fail.\n    if (deleteSource && sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    const saveResult = await saveHandler.save(modelArtifacts);\n    // If moving between mediums, the deletion is done after the save succeeds.\n    // This guards against the case in which saving to the destination medium\n    // fails.\n    if (deleteSource && !sameMedium) {\n        await ModelStoreManagerRegistry.getManager(sourceScheme)\n            .removeModel(sourcePath);\n    }\n    return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels() {\n    const schemes = ModelStoreManagerRegistry.getSchemes();\n    const out = {};\n    for (const scheme of schemes) {\n        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n        for (const path in schemeOut) {\n            const url = scheme + URL_SCHEME_SUFFIX + path;\n            out[url] = schemeOut[path];\n        }\n    }\n    return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n    const schemeAndPath = parseURL(url);\n    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n    return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n    const deleteSource = false;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n    const deleteSource = true;\n    return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n    fetch(path, init) {\n        return fetch(path, init);\n    }\n    now() {\n        return performance.now();\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n        }\n        if (this.textEncoder == null) {\n            this.textEncoder = new TextEncoder();\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        return new TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_BROWSER')) {\n    env().setPlatform('browser', new PlatformBrowser());\n    // Register LocalStorage IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n    }\n    catch (err) {\n    }\n    // Register IndexedDB IOHandler\n    try {\n        ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n    }\n    catch (err) {\n    }\n}\n//# sourceMappingURL=platform_browser.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n    // tslint:disable-next-line:no-require-imports\n    importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n    systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n    systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n    return systemFetch;\n}\nexport class PlatformNode {\n    constructor() {\n        // tslint:disable-next-line:no-require-imports\n        this.util = require('util');\n        // According to the spec, the built-in encoder can do only UTF-8 encoding.\n        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n        this.textEncoder = new this.util.TextEncoder();\n    }\n    fetch(path, requestInits) {\n        if (env().global.fetch != null) {\n            return env().global.fetch(path, requestInits);\n        }\n        if (systemFetch == null) {\n            systemFetch = getNodeFetch.importFetch();\n        }\n        return systemFetch(path, requestInits);\n    }\n    now() {\n        const time = process.hrtime();\n        return time[0] * 1000 + time[1] / 1000000;\n    }\n    encode(text, encoding) {\n        if (encoding !== 'utf-8' && encoding !== 'utf8') {\n            throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n        }\n        return this.textEncoder.encode(text);\n    }\n    decode(bytes, encoding) {\n        if (bytes.length === 0) {\n            return '';\n        }\n        return new this.util.TextDecoder(encoding).decode(bytes);\n    }\n}\nif (env().get('IS_NODE')) {\n    env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n    dtype = dtype || 'float32';\n    util.assertNonNegativeIntegerDimensions(shape);\n    return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n    const $x = convertToTensor(x, 'x', 'cast');\n    // Sanity checks.\n    if (!util.isValidDtype(dtype)) {\n        throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n    }\n    if (dtype === 'string' && $x.dtype !== 'string' ||\n        dtype !== 'string' && $x.dtype === 'string') {\n        throw new Error('Only strings can be casted to strings');\n    }\n    const inputs = { x: $x };\n    const attrs = { dtype };\n    return ENGINE.runKernelFunc(backend => backend.cast($x, dtype), inputs, null /* grad */, Cast, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n    const $x = convertToTensor(x, 'x', 'clone', null);\n    const forward = () => ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype);\n    const inputs = { x: $x };\n    // Note this op is called tf.identity in python. Hence the kernel name used\n    // here.\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Identity);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n    console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n    buffer,\n    cast,\n    clone,\n    print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n    return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n    constructor(fileNamePrefix) {\n        if (!env().getBool('IS_BROWSER')) {\n            // TODO(cais): Provide info on what IOHandlers are available under the\n            //   current environment.\n            throw new Error('browserDownloads() cannot proceed because the current environment ' +\n                'is not a browser.');\n        }\n        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n        }\n        if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n        }\n        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n        this.weightDataFileName =\n            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n    }\n    async save(modelArtifacts) {\n        if (typeof (document) === 'undefined') {\n            throw new Error('Browser downloads are not supported in ' +\n                'this environment since `document` is not present');\n        }\n        const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        else {\n            const weightsManifest = [{\n                    paths: ['./' + this.weightDataFileName],\n                    weights: modelArtifacts.weightSpecs\n                }];\n            const modelTopologyAndWeightManifest = {\n                modelTopology: modelArtifacts.modelTopology,\n                format: modelArtifacts.format,\n                generatedBy: modelArtifacts.generatedBy,\n                convertedBy: modelArtifacts.convertedBy,\n                weightsManifest\n            };\n            const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n            // If anchor elements are not provided, create them without attaching them\n            // to parents, so that the downloaded file names can be controlled.\n            const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                this.jsonAnchor;\n            jsonAnchor.download = this.modelTopologyFileName;\n            jsonAnchor.href = modelTopologyAndWeightManifestURL;\n            // Trigger downloads by evoking a click event on the download anchors.\n            // When multiple downloads are started synchronously, Firefox will only\n            // save the last one.\n            await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n            if (modelArtifacts.weightData != null) {\n                const weightDataAnchor = this.weightDataAnchor == null ?\n                    document.createElement('a') :\n                    this.weightDataAnchor;\n                weightDataAnchor.download = this.weightDataFileName;\n                weightDataAnchor.href = weightsURL;\n                await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n            }\n            return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n        }\n    }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n    constructor(files) {\n        if (files == null || files.length < 1) {\n            throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n                `but received ${files}`);\n        }\n        this.files = files;\n    }\n    async load() {\n        const jsonFile = this.files[0];\n        const weightFiles = this.files.slice(1);\n        return new Promise((resolve, reject) => {\n            const jsonReader = new FileReader();\n            jsonReader.onload = (event) => {\n                // tslint:disable-next-line:no-any\n                const modelJSON = JSON.parse(event.target.result);\n                const modelTopology = modelJSON.modelTopology;\n                if (modelTopology == null) {\n                    reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                if (weightFiles.length === 0) {\n                    resolve({ modelTopology });\n                }\n                const weightsManifest = modelJSON.weightsManifest;\n                if (weightsManifest == null) {\n                    reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n                    return;\n                }\n                let pathToFile;\n                try {\n                    pathToFile =\n                        this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n                }\n                catch (err) {\n                    reject(err);\n                    return;\n                }\n                const weightSpecs = [];\n                const paths = [];\n                const perFileBuffers = [];\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        paths.push(path);\n                        perFileBuffers.push(null);\n                    });\n                    weightSpecs.push(...weightsGroup.weights);\n                });\n                weightsManifest.forEach(weightsGroup => {\n                    weightsGroup.paths.forEach(path => {\n                        const weightFileReader = new FileReader();\n                        weightFileReader.onload = (event) => {\n                            // tslint:disable-next-line:no-any\n                            const weightData = event.target.result;\n                            const index = paths.indexOf(path);\n                            perFileBuffers[index] = weightData;\n                            if (perFileBuffers.indexOf(null) === -1) {\n                                resolve({\n                                    modelTopology,\n                                    weightSpecs,\n                                    weightData: concatenateArrayBuffers(perFileBuffers),\n                                    format: modelJSON.format,\n                                    generatedBy: modelJSON.generatedBy,\n                                    convertedBy: modelJSON.convertedBy,\n                                    userDefinedMetadata: modelJSON.userDefinedMetadata\n                                });\n                            }\n                        };\n                        weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n                        weightFileReader.readAsArrayBuffer(pathToFile[path]);\n                    });\n                });\n            };\n            jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n                `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n                `Keras-style tf.Model artifacts only.`);\n            jsonReader.readAsText(jsonFile);\n        });\n    }\n    /**\n     * Check the compatibility between weights manifest and weight files.\n     */\n    checkManifestAndWeightFiles(manifest, files) {\n        const basenames = [];\n        const fileNames = files.map(file => basename(file.name));\n        const pathToFile = {};\n        for (const group of manifest) {\n            group.paths.forEach(path => {\n                const pathBasename = basename(path);\n                if (basenames.indexOf(pathBasename) !== -1) {\n                    throw new Error(`Duplicate file basename found in weights manifest: ` +\n                        `'${pathBasename}'`);\n                }\n                basenames.push(pathBasename);\n                if (fileNames.indexOf(pathBasename) === -1) {\n                    throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n                }\n                else {\n                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n                }\n            });\n        }\n        if (basenames.length !== files.length) {\n            throw new Error(`Mismatch in the number of files in weights manifest ` +\n                `(${basenames.length}) and the number of weight files provided ` +\n                `(${files.length}).`);\n        }\n        return pathToFile;\n    }\n}\nexport const browserDownloadsRouter = (url) => {\n    if (!env().getBool('IS_BROWSER')) {\n        return null;\n    }\n    else {\n        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n        }\n        else {\n            return null;\n        }\n    }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n    return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n    return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n    checkPromises(promises);\n    startFraction = startFraction == null ? 0 : startFraction;\n    endFraction = endFraction == null ? 1 : endFraction;\n    checkFraction(startFraction, endFraction);\n    let resolvedPromise = 0;\n    const registerMonitor = (promise) => {\n        promise.then(value => {\n            const fraction = startFraction +\n                ++resolvedPromise / promises.length * (endFraction - startFraction);\n            // pass fraction as parameter to callback function.\n            onProgress(fraction);\n            return value;\n        });\n        return promise;\n    };\n    function checkPromises(promises) {\n        assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n    }\n    function checkFraction(startFraction, endFraction) {\n        assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n        assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n        assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n    }\n    return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n    if (loadOptions == null) {\n        loadOptions = {};\n    }\n    const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n        loadOptions.fetchFunc;\n    // Create the requests for all of the weights in parallel.\n    const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n    const fetchStartFraction = 0;\n    const fetchEndFraction = 0.5;\n    const responses = loadOptions.onProgress == null ?\n        await Promise.all(requests) :\n        await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n    const bufferPromises = responses.map(response => response.arrayBuffer());\n    const bufferStartFraction = 0.5;\n    const bufferEndFraction = 1;\n    const buffers = loadOptions.onProgress == null ?\n        await Promise.all(bufferPromises) :\n        await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n    return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n    // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n    // single weight from a group, the whole group will be fetched. At a future\n    // date, we should support fetching only the individual shards within a\n    // group that are needed to reconstruct the requested weight.\n    // TODO(cais): Use `decodeWeights` for implementation.\n    const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n    const loadWeights = weightsLoaderFactory(fetchWeights);\n    return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n    return async (manifest, filePathPrefix = '', weightNames) => {\n        // Collect all the groups, weights, and their relative offsets to be\n        // fetched.\n        const groupIndicesToFetchMap = manifest.map(() => false);\n        const groupWeightsToFetch = {};\n        const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n        const allManifestWeightNames = [];\n        manifest.forEach((manifestGroupConfig, groupIndex) => {\n            let groupOffset = 0;\n            manifestGroupConfig.weights.forEach(weightsEntry => {\n                const rawDtype = ('quantization' in weightsEntry) ?\n                    weightsEntry.quantization.dtype :\n                    weightsEntry.dtype;\n                const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n                    util.sizeFromShape(weightsEntry.shape);\n                const enqueueWeightsForFetchingFn = () => {\n                    groupIndicesToFetchMap[groupIndex] = true;\n                    if (groupWeightsToFetch[groupIndex] == null) {\n                        groupWeightsToFetch[groupIndex] = [];\n                    }\n                    groupWeightsToFetch[groupIndex].push({\n                        manifestEntry: weightsEntry,\n                        groupOffset,\n                        sizeBytes: weightsBytes\n                    });\n                };\n                if (weightNames != null) {\n                    weightNames.forEach((weightName, weightIndex) => {\n                        if (weightName === weightsEntry.name) {\n                            enqueueWeightsForFetchingFn();\n                            weightsFound[weightIndex] = true;\n                        }\n                    });\n                }\n                else {\n                    enqueueWeightsForFetchingFn();\n                }\n                allManifestWeightNames.push(weightsEntry.name);\n                groupOffset += weightsBytes;\n            });\n        });\n        if (!weightsFound.every(found => found)) {\n            const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n            throw new Error(`Could not find weights in manifest with names: ` +\n                `${weightsNotFound.join(', ')}. \\n` +\n                `Manifest JSON has weights with names: ` +\n                `${allManifestWeightNames.join(', ')}.`);\n        }\n        // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n        // IDs.\n        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n            if (shouldFetch) {\n                accumulator.push(i);\n            }\n            return accumulator;\n        }, []);\n        const fetchUrls = [];\n        groupIndicesToFetch.forEach(i => {\n            manifest[i].paths.forEach(filepath => {\n                const fetchUrl = filePathPrefix +\n                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n                fetchUrls.push(fetchUrl);\n            });\n        });\n        const buffers = await fetchWeightsFunction(fetchUrls);\n        const weightsTensorMap = {};\n        let bufferIndexOffset = 0;\n        groupIndicesToFetch.forEach(i => {\n            const numBuffers = manifest[i].paths.length;\n            let groupBytes = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                groupBytes += buffers[bufferIndexOffset + i].byteLength;\n            }\n            // Create a buffer for the whole group.\n            const groupBuffer = new ArrayBuffer(groupBytes);\n            const groupByteBuffer = new Uint8Array(groupBuffer);\n            let groupBufferOffset = 0;\n            for (let i = 0; i < numBuffers; i++) {\n                const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n                groupByteBuffer.set(buffer, groupBufferOffset);\n                groupBufferOffset += buffer.byteLength;\n            }\n            const weightsEntries = groupWeightsToFetch[i];\n            weightsEntries.forEach(weightsEntry => {\n                const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n                const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n                for (const name in nameToTensorMap) {\n                    weightsTensorMap[name] = nameToTensorMap[name];\n                }\n            });\n            bufferIndexOffset += numBuffers;\n        });\n        return weightsTensorMap;\n    };\n}\n//# sourceMappingURL=weights_loader.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n    constructor(path, loadOptions) {\n        this.DEFAULT_METHOD = 'POST';\n        if (loadOptions == null) {\n            loadOptions = {};\n        }\n        this.weightPathPrefix = loadOptions.weightPathPrefix;\n        this.onProgress = loadOptions.onProgress;\n        this.weightUrlConverter = loadOptions.weightUrlConverter;\n        if (loadOptions.fetchFunc != null) {\n            assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n                '`fetch` (see ' +\n                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n            this.fetch = loadOptions.fetchFunc;\n        }\n        else {\n            this.fetch = env().platform.fetch;\n        }\n        assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n        if (Array.isArray(path)) {\n            assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n                `(actual length is ${path.length}).`);\n        }\n        this.path = path;\n        if (loadOptions.requestInit != null &&\n            loadOptions.requestInit.body != null) {\n            throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n        }\n        this.requestInit = loadOptions.requestInit || {};\n    }\n    async save(modelArtifacts) {\n        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n                'in binary formats yet.');\n        }\n        const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n        init.body = new FormData();\n        const weightsManifest = [{\n                paths: ['./model.weights.bin'],\n                weights: modelArtifacts.weightSpecs,\n            }];\n        const modelTopologyAndWeightManifest = {\n            modelTopology: modelArtifacts.modelTopology,\n            format: modelArtifacts.format,\n            generatedBy: modelArtifacts.generatedBy,\n            convertedBy: modelArtifacts.convertedBy,\n            userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n            weightsManifest\n        };\n        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n        if (modelArtifacts.weightData != null) {\n            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n        }\n        const response = await this.fetch(this.path, init);\n        if (response.ok) {\n            return {\n                modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n                responses: [response],\n            };\n        }\n        else {\n            throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n                `${response.status}.`);\n        }\n    }\n    /**\n     * Load model artifacts via HTTP request(s).\n     *\n     * See the documentation to `tf.io.http` for details on the saved\n     * artifacts.\n     *\n     * @returns The loaded model artifacts (if loading succeeds).\n     */\n    async load() {\n        const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n        if (!modelConfigRequest.ok) {\n            throw new Error(`Request to ${this.path} failed with status code ` +\n                `${modelConfigRequest.status}. Please verify this URL points to ` +\n                `the model JSON of the model to load.`);\n        }\n        let modelConfig;\n        try {\n            modelConfig = await modelConfigRequest.json();\n        }\n        catch (e) {\n            let message = `Failed to parse model JSON of response from ${this.path}.`;\n            // TODO(nsthorat): Remove this after some time when we're comfortable that\n            // .pb files are mostly gone.\n            if (this.path.endsWith('.pb')) {\n                message += ' Your path contains a .pb file extension. ' +\n                    'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n                    'in favor of .json models. You can re-convert your Python ' +\n                    'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n                    'or you can convert your.pb models with the \\'pb2json\\'' +\n                    'NPM script in the tensorflow/tfjs-converter repository.';\n            }\n            else {\n                message += ' Please make sure the server is serving valid ' +\n                    'JSON for this request.';\n            }\n            throw new Error(message);\n        }\n        const modelTopology = modelConfig.modelTopology;\n        const weightsManifest = modelConfig.weightsManifest;\n        const generatedBy = modelConfig.generatedBy;\n        const convertedBy = modelConfig.convertedBy;\n        const format = modelConfig.format;\n        const userDefinedMetadata = modelConfig.userDefinedMetadata;\n        // We do not allow both modelTopology and weightsManifest to be missing.\n        if (modelTopology == null && weightsManifest == null) {\n            throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n                `topology or manifest for weights.`);\n        }\n        let weightSpecs;\n        let weightData;\n        if (weightsManifest != null) {\n            const results = await this.loadWeights(weightsManifest);\n            [weightSpecs, weightData] = results;\n        }\n        const artifacts = {\n            modelTopology,\n            weightSpecs,\n            weightData,\n            userDefinedMetadata,\n            generatedBy,\n            convertedBy,\n            format\n        };\n        const initializer = modelConfig.modelInitializer;\n        if (initializer) {\n            artifacts.modelInitializer = initializer;\n        }\n        return artifacts;\n    }\n    async loadWeights(weightsManifest) {\n        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n        const [prefix, suffix] = parseUrl(weightPath);\n        const pathPrefix = this.weightPathPrefix || prefix;\n        const weightSpecs = [];\n        for (const entry of weightsManifest) {\n            weightSpecs.push(...entry.weights);\n        }\n        const fetchURLs = [];\n        const urlPromises = [];\n        for (const weightsGroup of weightsManifest) {\n            for (const path of weightsGroup.paths) {\n                if (this.weightUrlConverter != null) {\n                    urlPromises.push(this.weightUrlConverter(path));\n                }\n                else {\n                    fetchURLs.push(pathPrefix + path + suffix);\n                }\n            }\n        }\n        if (this.weightUrlConverter) {\n            fetchURLs.push(...await Promise.all(urlPromises));\n        }\n        const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n            requestInit: this.requestInit,\n            fetchFunc: this.fetch,\n            onProgress: this.onProgress\n        });\n        return [weightSpecs, concatenateArrayBuffers(buffers)];\n    }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n    const lastSlash = url.lastIndexOf('/');\n    const lastSearchParam = url.lastIndexOf('?');\n    const prefix = url.substring(0, lastSlash);\n    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n    return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n    if (typeof fetch === 'undefined' &&\n        (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n    }\n    else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n            isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        }\n        else {\n            isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n            return http(url, loadOptions);\n        }\n    }\n    return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n    return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n    return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n    constructor(modelArtifacts) {\n        this.modelArtifacts = modelArtifacts;\n    }\n    async load() {\n        return this.modelArtifacts;\n    }\n}\nclass PassthroughSaver {\n    constructor(saveHandler) {\n        this.saveHandler = saveHandler;\n    }\n    async save(modelArtifacts) {\n        return this.saveHandler(modelArtifacts);\n    }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n    if (arguments.length === 1) {\n        const isModelArtifacts = modelArtifacts.modelTopology != null ||\n            modelArtifacts.weightSpecs != null;\n        if (isModelArtifacts) {\n            return new PassthroughLoader(modelArtifacts);\n        }\n        else {\n            // Legacy support: with only modelTopology.\n            // TODO(cais): Remove this deprecated API.\n            console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n                'The argument should be of type ModelArtifacts. ' +\n                'The multi-argument signature of tf.io.fromMemory() has been ' +\n                'deprecated and will be removed in a future release.');\n            return new PassthroughLoader({ modelTopology: modelArtifacts });\n        }\n    }\n    else {\n        // Legacy support.\n        // TODO(cais): Remove this deprecated API.\n        console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n            'The argument should be of type ModelArtifacts. ' +\n            'The multi-argument signature of tf.io.fromMemory() has been ' +\n            'deprecated and will be removed in a future release.');\n        return new PassthroughLoader({\n            modelTopology: modelArtifacts,\n            weightSpecs,\n            weightData,\n            trainingConfig\n        });\n    }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n    return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\nimport { browserFiles } from './browser_files';\nimport { browserHTTPRequest, http, isHTTPScheme } from './http';\nimport { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';\nimport { fromMemory, withSaveHandler } from './passthrough';\nimport { getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter } from './router_registry';\nimport { loadWeights, weightsLoaderFactory } from './weights_loader';\nexport { copyModel, listModels, moveModel, removeModel } from './model_management';\nexport { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, http, isHTTPScheme, loadWeights, registerLoadRouter, registerSaveRouter, weightsLoaderFactory, withSaveHandler };\n//# sourceMappingURL=io.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reshape } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_(x, shape) {\n    const $x = convertToTensor(x, 'x', 'reshape', null);\n    const inputs = { x: $x };\n    const attrs = { shape };\n    const forward = (backend, save) => {\n        shape = util.inferFromImplicitShape(shape, $x.size);\n        util.assert($x.size === util.sizeFromShape(shape), () => 'new shape and old shape must have the same number of elements.');\n        save([$x]);\n        return backend.reshape($x, shape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Reshape, attrs);\n}\nexport const reshape = op({ reshape_ });\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n    let $a = convertToTensor(a, 'a', 'matMul');\n    let $b = convertToTensor(b, 'b', 'matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        save([$a, $b]);\n        const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n        const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n        const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n        const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n        const outerDimsA = $a.shape.slice(0, -2);\n        const outerDimsB = $b.shape.slice(0, -2);\n        const batchDimA = util.sizeFromShape(outerDimsA);\n        const batchDimB = util.sizeFromShape(outerDimsB);\n        const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n        util.assert($a.rank >= 2 && $b.rank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the ` +\n            `same or at least one input batch dimension must be 1. Got input ` +\n            `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n        util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n            `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n            `${$b.shape} and transposeA=${transposeA}` +\n            ` and transposeB=${transposeB} must match.`);\n        const outShapeOuterDims = batchDimA > batchDimB ? outerDimsA : outerDimsB;\n        const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n        const a3D = transposeA ?\n            reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n            reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n        const b3D = transposeB ?\n            reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n            reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n        const res3d = backend.batchMatMul(a3D, b3D, transposeA, transposeB);\n        return reshape(res3d, outShape);\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = { transposeA, transposeB };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BatchMatMul, attrs);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n    if (depth < 2) {\n        throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n    }\n    const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n    const outShape = [...$indices.shape, depth];\n    const forward = (backend, save) => {\n        save([$indices]);\n        return reshape(backend.oneHot(reshape($indices, [$indices.size]), depth, onValue, offValue), outShape);\n    };\n    const inputs = { indices: $indices };\n    const attrs = { depth, onValue, offValue };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OneHot, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n    const $x = convertToTensor(x, 'x', 'transpose');\n    if (perm == null) {\n        perm = $x.shape.map((s, i) => i).reverse();\n    }\n    util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of perm ${perm}.`);\n    perm.forEach(axis => {\n        util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n    });\n    if ($x.rank <= 1) {\n        return $x.clone();\n    }\n    const inputs = { x: $x };\n    const attrs = { perm };\n    return ENGINE.runKernelFunc(backend => backend.transpose($x, perm), inputs, null /* gradient */, Transpose, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n    const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n    const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n        `but got ${numClasses}`);\n    util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n    util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n        `but got ${$predictions.rank}`);\n    util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n        `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n        `Labels and predictions should have the same number of elements.`);\n    util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n        `${numClasses}`);\n    // TODO(cais): In the future, if oneHot supports tensors inputs for\n    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n    const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n    const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n    const oneHotLabelsT = transpose(oneHotLabels);\n    const product = matMul(oneHotLabelsT, oneHotPredictions);\n    return cast(product, 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Exports under the tf.math.* namespace.\n */\nimport { confusionMatrix } from './ops/confusion_matrix';\nexport { confusionMatrix };\n//# sourceMappingURL=math.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 3) {\n        throw new Error('tensor3d() requires shape to have three numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor3d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n    // Sanity checks.\n    if (numChannels > 4) {\n        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n    }\n    if (pixels == null) {\n        throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n    }\n    let isPixelData = false;\n    let isImageData = false;\n    let isVideo = false;\n    let isImage = false;\n    let isCanvasLike = false;\n    if (pixels.data instanceof Uint8Array) {\n        isPixelData = true;\n    }\n    else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n        isImageData = true;\n    }\n    else if (typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement) {\n        isVideo = true;\n    }\n    else if (typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement) {\n        isImage = true;\n        // tslint:disable-next-line: no-any\n    }\n    else if (pixels.getContext != null) {\n        isCanvasLike = true;\n    }\n    else {\n        throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n            `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n            `in browser, or OffscreenCanvas, ImageData in webworker` +\n            ` or {data: Uint32Array, width: number, height: number}, ` +\n            `but was ${pixels.constructor.name}`);\n    }\n    if (isVideo) {\n        const HAVE_CURRENT_DATA_READY_STATE = 2;\n        if (isVideo &&\n            pixels.readyState <\n                HAVE_CURRENT_DATA_READY_STATE) {\n            throw new Error('The video element has not loaded data yet. Please wait for ' +\n                '`loadeddata` event on the <video> element.');\n        }\n    }\n    // If the current backend has 'FromPixels' registered, it has a more\n    // efficient way of handling pixel uploads, so we call that.\n    const kernel = getKernel(FromPixels, ENGINE.backendName);\n    if (kernel != null) {\n        const inputs = { pixels };\n        const attrs = { numChannels };\n        return ENGINE.runKernel(FromPixels, inputs, attrs);\n    }\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    let vals;\n    if (isCanvasLike) {\n        vals =\n            // tslint:disable-next-line:no-any\n            pixels.getContext('2d').getImageData(0, 0, width, height).data;\n    }\n    else if (isImageData || isPixelData) {\n        vals = pixels.data;\n    }\n    else if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n    }\n    let values;\n    if (numChannels === 4) {\n        values = new Int32Array(vals);\n    }\n    else {\n        const numPixels = width * height;\n        values = new Int32Array(numPixels * numChannels);\n        for (let i = 0; i < numPixels; i++) {\n            for (let channel = 0; channel < numChannels; ++channel) {\n                values[i * numChannels + channel] = vals[i * 4 + channel];\n            }\n        }\n    }\n    const outShape = [height, width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n}\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(img, canvas) {\n    let $img = convertToTensor(img, 'img', 'toPixels');\n    if (!(img instanceof Tensor)) {\n        // Assume int32 if user passed a native array.\n        const originalImgTensor = $img;\n        $img = cast(originalImgTensor, 'int32');\n        originalImgTensor.dispose();\n    }\n    if ($img.rank !== 2 && $img.rank !== 3) {\n        throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n    }\n    const [height, width] = $img.shape.slice(0, 2);\n    const depth = $img.rank === 2 ? 1 : $img.shape[2];\n    if (depth > 4 || depth === 2) {\n        throw new Error(`toPixels only supports depth of size ` +\n            `1, 3 or 4 but got ${depth}`);\n    }\n    if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n        throw new Error(`Unsupported type for toPixels: ${$img.dtype}.` +\n            ` Please use float32 or int32 tensors.`);\n    }\n    const data = await $img.data();\n    const multiplier = $img.dtype === 'float32' ? 255 : 1;\n    const bytes = new Uint8ClampedArray(width * height * 4);\n    for (let i = 0; i < height * width; ++i) {\n        const rgba = [0, 0, 0, 255];\n        for (let d = 0; d < depth; d++) {\n            const value = data[i * depth + d];\n            if ($img.dtype === 'float32') {\n                if (value < 0 || value > 1) {\n                    throw new Error(`Tensor values for a float32 Tensor must be in the ` +\n                        `range [0 - 1] but encountered ${value}.`);\n                }\n            }\n            else if ($img.dtype === 'int32') {\n                if (value < 0 || value > 255) {\n                    throw new Error(`Tensor values for a int32 Tensor must be in the ` +\n                        `range [0 - 255] but encountered ${value}.`);\n                }\n            }\n            if (depth === 1) {\n                rgba[0] = value * multiplier;\n                rgba[1] = value * multiplier;\n                rgba[2] = value * multiplier;\n            }\n            else {\n                rgba[d] = value * multiplier;\n            }\n        }\n        const j = i * 4;\n        bytes[j + 0] = Math.round(rgba[0]);\n        bytes[j + 1] = Math.round(rgba[1]);\n        bytes[j + 2] = Math.round(rgba[2]);\n        bytes[j + 3] = Math.round(rgba[3]);\n    }\n    if (canvas != null) {\n        canvas.width = width;\n        canvas.height = height;\n        const ctx = canvas.getContext('2d');\n        const imageData = new ImageData(bytes, width, height);\n        ctx.putImageData(imageData, 0, 0);\n    }\n    if ($img !== img) {\n        $img.dispose();\n    }\n    return bytes;\n}\nexport const fromPixels = op({ fromPixels_ });\n//# sourceMappingURL=browser.js.map", "import { computeStrides } from '../util';\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(tensor, indices) {\n    if (tensor.rank < 1) {\n        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +\n            ` but the rank was ${tensor.rank}.`);\n    }\n    if (indices.rank < 1) {\n        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error('tf.gatherND() expects the indices to be int32 type,' +\n            ` but the dtype was ${indices.dtype}.`);\n    }\n    if (indices.shape[indices.rank - 1] > tensor.rank) {\n        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +\n            `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n    }\n    if (tensor.size === 0) {\n        throw new Error('Requested more than 0 entries, but input is empty.' +\n            ` Input shape: ${tensor.shape}.`);\n    }\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n    // The result shape is\n    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n    let nResult = 1;\n    for (let i = 0; i < indicesShape.length - 1; ++i) {\n        nResult *= indicesShape[i];\n    }\n    const inputShape = tensor.shape;\n    const resultShape = indicesShape.slice();\n    resultShape.pop();\n    let sliceSize = 1;\n    for (let i = sliceRank; i < tensor.rank; ++i) {\n        sliceSize *= inputShape[i];\n        resultShape.push(inputShape[i]);\n    }\n    const strides = [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n        1].slice(0, sliceRank);\n    return [resultShape, nResult, sliceSize, strides];\n}\n//# sourceMappingURL=gather_nd_util.js.map", "import { computeStrides, sizeFromShape } from '../util';\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(shape, indices, updates) {\n    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n        `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n        `, indices.shape: ${indices.shape}, shape: ${shape}` +\n        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n    if (updates.rank < batchDim) {\n        throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n    }\n    if (shape.length < sliceDim + (updates.rank - batchDim)) {\n        throw new Error(shapeError +\n            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n    }\n    if (updates.rank !== batchDim + shape.length - sliceDim) {\n        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n    }\n    for (let d = 0; d < batchDim; ++d) {\n        if (updates.shape[d] !== indices.shape[d]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n        }\n    }\n    for (let d = 0; d < updates.rank - batchDim; ++d) {\n        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n            throw new Error(shapeError +\n                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n        }\n    }\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(updates, indices, shape) {\n    if (indices.rank < 1) {\n        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +\n            ` but the rank was ${indices.rank}.`);\n    }\n    if (updates.rank < 1) {\n        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +\n            ` but the rank was ${updates.rank}.`);\n    }\n    if (indices.dtype !== 'int32') {\n        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n    }\n    if (shape.length < 1) {\n        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n    }\n    if (shape.length === 0) {\n        if (indices.size === 0) {\n            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n        }\n        if (updates.size === 0) {\n            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n        }\n    }\n    validateUpdateShape(shape, indices, updates);\n}\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(updates, indices, shape) {\n    // Calculate the number of dimensions in indices\n    const indicesRank = indices.shape.length;\n    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n    // Calculate the number of elements that make up each slice of our updated\n    // tensor. This allows us to work with flattened tensors and copy over whole\n    // slices at a time.\n    const totalNd = shape.length;\n    let sliceSize = 1;\n    for (let i = sliceRank; i < totalNd; ++i) {\n        sliceSize *= shape[i];\n    }\n    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n    const outputSize = sizeFromShape(shape);\n    return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n//# sourceMappingURL=scatter_nd_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsValid(input, begin, size) {\n    const inputRank = input.shape.length;\n    util.assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n        `match the rank of the array (${inputRank}).`);\n    util.assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n        `match the rank of the array (${inputRank}).`);\n    for (let i = 0; i < inputRank; ++i) {\n        util.assert(begin[i] + size[i] <= input.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);\n    }\n}\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask) {\n    const axes = [];\n    let axis = 0;\n    while (mask > 0) {\n        if (mask & 1) {\n            axes.push(axis);\n        }\n        mask /= 2;\n        axis++;\n    }\n    return axes;\n}\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(begin, end, strides) {\n    const size = [];\n    for (let axis = 0; axis < begin.length; axis++) {\n        size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n    }\n    return size;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n    const newStrides = [...strides];\n    for (let i = newStrides.length; i < inputShape.length; i++) {\n        newStrides.push(1);\n    }\n    for (let i = 0; i < numElidedAxes; i++) {\n        if (i === 0) {\n            newStrides[ellipsisInsertionIndex] = 1;\n        }\n        else {\n            newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */, 1 /* element to add */);\n            newStrides.pop();\n        }\n    }\n    return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n    if (normalizedAxis <= ellipsisInsertionIndex) {\n        return normalizedAxis;\n    }\n    return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n    const elidedAxes = [];\n    for (let i = 0; i < numElidedAxes; i++) {\n        elidedAxes.push(ellipsisInsertionIndex + i);\n    }\n    return elidedAxes;\n}\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n    const inputRank = inputShape.length;\n    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n    if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n        const fullIndex = ellipsisAxes[0];\n        // The ellipsis applies to the masked index as well as any dimensions\n        // that are interpolated.\n        const numElidedAxes = numInterpolatedAxes + 1;\n        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n        normalizedStrides =\n            stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n    }\n    else {\n        for (let axis = 0; axis < inputRank; axis++) {\n            normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n            normalizedEnd[axis] =\n                stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n            normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n        }\n    }\n    return {\n        begin: normalizedBegin,\n        end: normalizedEnd,\n        strides: normalizedStrides\n    };\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = 0;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalBegin[originalAxis];\n            if (beginMask & 1 << originalAxis) {\n                originalValue = 0;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    return newIndices;\n}\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n    const newIndices = [...inputShape];\n    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n    for (let axis = 0; axis < newIndices.length; axis++) {\n        if (elidedAxes.indexOf(axis) > -1) {\n            newIndices[axis] = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n            let originalValue = originalEnd[originalAxis];\n            if (endMask & 1 << originalAxis) {\n                originalValue = Number.MAX_SAFE_INTEGER;\n            }\n            newIndices[axis] = originalValue;\n        }\n    }\n    for (let i = 0; i < newIndices.length; i++) {\n        // Handle negative indices\n        const axisSize = inputShape[i];\n        if (newIndices[i] < 0) {\n            newIndices[i] += axisSize;\n        }\n        newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n    }\n    return newIndices;\n}\nexport function stridesForAxis(strides, axis, ellipsisMask) {\n    let stride = strides[axis];\n    if (ellipsisMask & (1 << axis) || stride == null) {\n        stride = 1;\n    }\n    return stride;\n}\nexport function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let start = startIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or the begin index is not set\n    // for the axis.\n    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n        if (stride > 0) {\n            // Forward iteration - use the first element. These values will get\n            // clamped below (Note: We could have set them to 0 and axis_size-1, but\n            // use lowest() and max() to maintain symmetry with StopForAxis())\n            start = Number.MIN_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the last element.\n            start = Number.MAX_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (start < 0) {\n        start += axisSize;\n    }\n    // Clamping\n    start = util.clamp(0, start, axisSize - 1);\n    return start;\n}\nexport function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n    // Begin with the specified index\n    let stop = stopIndices[axis];\n    const stride = strides[axis] || 1;\n    // Check the axis bit from right of masked axes, or if the stop index is not\n    // set for this axis.\n    if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n        if (stride > 0) {\n            // Forward iteration - use the last element. These values will get\n            // clamped below\n            stop = Number.MAX_SAFE_INTEGER;\n        }\n        else {\n            // Backward iteration - use the first element.\n            stop = Number.MIN_SAFE_INTEGER;\n        }\n    }\n    // Handle negative indices\n    const axisSize = inputShape[axis];\n    if (stop < 0) {\n        stop += axisSize;\n    }\n    // Clamping\n    // Because the end index points one past the last element, we need slightly\n    // different clamping ranges depending on the direction.\n    if (stride > 0) {\n        // Forward iteration\n        stop = util.clamp(0, stop, axisSize);\n    }\n    else {\n        // Backward iteration\n        stop = util.clamp(-1, stop, axisSize - 1);\n    }\n    return stop;\n}\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(shape, begin, size) {\n    // Index of the first axis that has size > 1.\n    let firstNonOneAxis = size.length;\n    for (let i = 0; i < size.length; i++) {\n        if (size[i] > 1) {\n            firstNonOneAxis = i;\n            break;\n        }\n    }\n    for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n        if (begin[i] > 0 || size[i] !== shape[i]) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function computeFlatOffset(begin, strides) {\n    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n    for (let i = 0; i < begin.length - 1; i++) {\n        flatOffset += begin[i] * strides[i];\n    }\n    return flatOffset;\n}\nexport function parseSliceParams(x, begin, size) {\n    // The following logic allows for more ergonomic calls.\n    let begin_;\n    const xRank = x.shape.length;\n    if (typeof begin === 'number') {\n        begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n    }\n    else if (begin.length < xRank) {\n        begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n    }\n    else {\n        begin_ = begin.slice();\n    }\n    begin_.forEach(d => {\n        util.assert(d !== -1, () => 'slice() does not support negative begin indexing.');\n    });\n    let size_;\n    if (size == null) {\n        size_ = new Array(xRank).fill(-1);\n    }\n    else if (typeof size === 'number') {\n        size_ = [size, ...new Array(xRank - 1).fill(-1)];\n    }\n    else if (size.length < xRank) {\n        size_ = size.concat(new Array(xRank - size.length).fill(-1));\n    }\n    else {\n        size_ = size;\n    }\n    size_ = size_.map((d, i) => {\n        if (d >= 0) {\n            return d;\n        }\n        else {\n            util.assert(d === -1, () => `Negative size values should be exactly -1 but got ` +\n                `${d} for the slice() size at index ${i}.`);\n            return x.shape[i] - begin_[i];\n        }\n    });\n    return [begin_, size_];\n}\n//# sourceMappingURL=slice_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from './util';\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport class Serializable {\n    /**\n     * Return the class name for this class to use in serialization contexts.\n     *\n     * Generally speaking this will be the same thing that constructor.name\n     * would have returned.  However, the class name needs to be robust\n     * against minification for serialization/deserialization to work properly.\n     *\n     * There's also places such as initializers.VarianceScaling, where\n     * implementation details between different languages led to different\n     * class hierarchies and a non-leaf node is used for serialization purposes.\n     */\n    getClassName() {\n        return this.constructor\n            .className;\n    }\n    /**\n     * Creates an instance of T from a ConfigDict.\n     *\n     * This works for most descendants of serializable.  A few need to\n     * provide special handling.\n     * @param cls A Constructor for the class to instantiate.\n     * @param config The Configuration for the object.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n    constructor() {\n        this.classNameMap = {};\n    }\n    /**\n     * Returns the singleton instance of the map.\n     */\n    static getMap() {\n        if (SerializationMap.instance == null) {\n            SerializationMap.instance = new SerializationMap();\n        }\n        return SerializationMap.instance;\n    }\n    /**\n     * Registers the class as serializable.\n     */\n    static register(cls) {\n        SerializationMap.getMap().classNameMap[cls.className] =\n            [cls, cls.fromConfig];\n    }\n}\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass(cls) {\n    assert(cls.className != null, () => `Class being registered does not have the static className ` +\n        `property defined.`);\n    assert(typeof cls.className === 'string', () => `className is required to be a string, but got type ` +\n        typeof cls.className);\n    assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, ` +\n        `which is disallowed.`);\n    SerializationMap.register(cls);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { inferShape } from './tensor_util_env';\nimport { arraysEqual, flatten, isString, isTypedArray } from './util';\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\nexport function expectArraysClose(actual, expected, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon));\n}\nexport function testEpsilon() {\n    return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n        TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n    let checkClassType = true;\n    if (isTypedArray(actual) || isTypedArray(expected)) {\n        checkClassType = false;\n    }\n    if (isTypedArray(actual) && isTypedArray(expected)) {\n        checkClassType = true;\n    }\n    if (checkClassType) {\n        const aType = actual.constructor.name;\n        const bType = expected.constructor.name;\n        if (aType !== bType) {\n            throw new Error(`Arrays are of different type. Actual: ${aType}. ` +\n                `Expected: ${bType}`);\n        }\n    }\n    if (Array.isArray(actual) && Array.isArray(expected)) {\n        const actualShape = inferShape(actual);\n        const expectedShape = inferShape(expected);\n        if (!arraysEqual(actualShape, expectedShape)) {\n            throw new Error(`Arrays have different shapes. ` +\n                `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n        }\n    }\n    const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n    const expectedFlat = isTypedArray(expected) ?\n        expected :\n        flatten(expected);\n    if (actualFlat.length !== expectedFlat.length) {\n        throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` +\n            `expected: ${expectedFlat.length}.\\n` +\n            `Actual:   ${actualFlat}.\\n` +\n            `Expected: ${expectedFlat}.`);\n    }\n    for (let i = 0; i < expectedFlat.length; ++i) {\n        const a = actualFlat[i];\n        const e = expectedFlat[i];\n        if (!predicate(a, e)) {\n            throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n                `Actual:   ${actualFlat}.\\n` +\n                `Expected: ${expectedFlat}.`);\n        }\n    }\n}\nexport function expectPromiseToFail(fn, done) {\n    fn().then(() => done.fail(), () => done());\n}\nexport function expectArraysEqual(actual, expected) {\n    const exp = typeof expected === 'string' || typeof expected === 'number' ||\n        typeof expected === 'boolean' ?\n        [expected] :\n        expected;\n    if (isString(actual) || isString(actual[0]) ||\n        isString(expected) || isString(expected[0])) {\n        // tslint:disable-next-line: triple-equals\n        return expectArraysPredicate(actual, exp, (a, b) => a == b);\n    }\n    return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nexport function expectNumbersClose(a, e, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    if (!areClose(a, e, epsilon)) {\n        throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n    }\n}\nfunction areClose(a, e, epsilon) {\n    if (!isFinite(a) && !isFinite(e)) {\n        return true;\n    }\n    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n        return false;\n    }\n    return true;\n}\nexport function expectValuesInRange(actual, low, high) {\n    for (let i = 0; i < actual.length; i++) {\n        if (actual[i] < low || actual[i] > high) {\n            throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n        }\n    }\n}\nexport function expectArrayBuffersEqual(actual, expected) {\n    // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n    // a Float32Array solves this issue.\n    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n//# sourceMappingURL=test_util.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { setDeprecationWarningFn } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode() {\n    env().set('PROD', true);\n}\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode() {\n    env().set('DEBUG', true);\n}\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings() {\n    env().set('DEPRECATION_WARNINGS_ENABLED', false);\n    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg) {\n    if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n        console.warn(msg + ' You can disable deprecation warnings with ' +\n            'tf.disableDeprecationWarnings().');\n    }\n}\nsetDeprecationWarningFn(deprecationWarn);\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables() {\n    ENGINE.disposeVariables();\n}\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine() {\n    return ENGINE;\n}\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory() {\n    return ENGINE.memory();\n}\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f) {\n    return ENGINE.profile(f);\n}\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy(nameOrFn, fn) {\n    return ENGINE.tidy(nameOrFn, fn);\n}\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n}\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep(result) {\n    return ENGINE.keep(result);\n}\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f) {\n    return ENGINE.time(f);\n}\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName) {\n    return ENGINE.setBackend(backendName);\n}\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready() {\n    return ENGINE.ready();\n}\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend() {\n    return ENGINE.backendName;\n}\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name) {\n    ENGINE.removeBackend(name);\n}\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name) {\n    return ENGINE.findBackend(name);\n}\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name) {\n    return ENGINE.findBackendFactory(name);\n}\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(name, factory, priority = 1) {\n    return ENGINE.registerBackend(name, factory, priority);\n}\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend() {\n    return ENGINE.backend;\n}\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName, platform) {\n    env().setPlatform(platformName, platform);\n}\n//# sourceMappingURL=globals.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Add } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_(a, b) {\n    let $a = convertToTensor(a, 'a', 'add');\n    let $b = convertToTensor(b, 'b', 'add');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.add($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Add);\n}\nexport const add = op({ add_ });\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FloorDiv } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_(a, b) {\n    let $a = convertToTensor(a, 'a', 'floorDiv');\n    let $b = convertToTensor(b, 'b', 'floorDiv');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.floorDiv($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FloorDiv);\n}\nexport const floorDiv = op({ floorDiv_ });\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Div } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { floorDiv } from './floorDiv';\nimport { op } from './operation';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_(a, b) {\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n        return floorDiv($a, $b);\n    }\n    const forward = (backend, save) => {\n        const res = backend.realDivide($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Div, attrs);\n}\nexport const div = op({ div_ });\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Multiply } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mul');\n    let $b = convertToTensor(b, 'b', 'mul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.multiply($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Multiply);\n}\nexport const mul = op({ mul_ });\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Abs } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_(x) {\n    const $x = convertToTensor(x, 'x', 'abs');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'complex64') {\n            return backend.complexAbs($x);\n        }\n        return backend.abs($x);\n    }, inputs, null /* grad */, Abs);\n}\nexport const abs = op({ abs_ });\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_(x) {\n    const $x = convertToTensor(x, 'x', 'acos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acos);\n}\nexport const acos = op({ acos_ });\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Acosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_(x) {\n    const $x = convertToTensor(x, 'x', 'acosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.acosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Acosh);\n}\nexport const acosh = op({ acosh_ });\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AddN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_(tensors) {\n    util.assert(Array.isArray(tensors), () => 'The argument passed to tf.addN() must be a list of tensors');\n    util.assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ` +\n        `${tensors.length}`);\n    const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n    const firstTensor = $tensors[0];\n    $tensors.forEach(t => {\n        if (t.dtype !== firstTensor.dtype) {\n            throw new Error('All tensors passed to tf.addN() must have the same dtype');\n        }\n    });\n    $tensors.forEach(t => {\n        if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n            throw new Error('All tensors passed to tf.addN() must have the same shape');\n        }\n    });\n    const forward = (backend, save) => {\n        const res = backend.addN($tensors);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, AddN);\n}\nexport const addN = op({ addN_ });\n//# sourceMappingURL=add_n.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes, rank) {\n    for (let i = 0; i < axes.length; ++i) {\n        if (axes[axes.length - i - 1] !== rank - 1 - i) {\n            return false;\n        }\n    }\n    return true;\n}\nexport function combineLocations(outputLoc, reduceLoc, axes) {\n    const rank = outputLoc.length + reduceLoc.length;\n    const loc = [];\n    let outIdx = 0;\n    let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            loc.push(outputLoc[outIdx++]);\n        }\n        else {\n            loc.push(reduceLoc[reduceIdx++]);\n        }\n    }\n    return loc;\n}\nexport function computeOutAndReduceShapes(aShape, axes) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (axes.indexOf(dim) === -1) {\n            outShape.push(aShape[dim]);\n        }\n    }\n    const reduceShape = axes.map(dim => aShape[dim]);\n    return [outShape, reduceShape];\n}\nexport function expandShapeToKeepDim(shape, axes) {\n    const reduceSubShape = axes.map(x => 1);\n    return combineLocations(shape, reduceSubShape, axes);\n}\nexport function assertAxesAreInnerMostDims(msg, axes, rank) {\n    util.assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. ` +\n        `Got axes ${axes} and rank-${rank} input.`);\n}\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes, rank) {\n    if (axesAreInnerMostDims(axes, rank)) {\n        return null;\n    }\n    const result = [];\n    for (let i = 0; i < rank; ++i) {\n        if (axes.indexOf(i) === -1) {\n            result.push(i);\n        }\n    }\n    axes.forEach(axis => result.push(axis));\n    return result;\n}\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes) {\n    return axes.map((axis, i) => [i, axis])\n        .sort((a, b) => a[1] - b[1])\n        .map(x => x[0]);\n}\nexport function getInnerMostAxes(numAxes, rank) {\n    const res = [];\n    for (let i = rank - numAxes; i < rank; ++i) {\n        res.push(i);\n    }\n    return res;\n}\n//# sourceMappingURL=axis_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { All } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'all', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.all($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, All, attrs);\n}\nexport const all = op({ all_ });\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Any } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'any', 'bool');\n    const forward = (backend) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = getInnerMostAxes(axes.length, $x.rank);\n        }\n        const res = backend.any($x, axes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(res.shape, origAxes);\n            return reshape(res, newShape);\n        }\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Any, attrs);\n}\n// tslint:disable-next-line:variable-name\nexport const any = op({ any_ });\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMax');\n    const forward = (backend, save) => {\n        save([$x]);\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMax($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMax, attrs);\n}\nexport const argMax = op({ argMax_ });\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ArgMin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_(x, axis = 0) {\n    let $x = convertToTensor(x, 'x', 'argMin');\n    const forward = (backend, save) => {\n        save([$x]);\n        if (axis == null) {\n            axis = 0;\n        }\n        let axes = util.parseAxisParam(axis, $x.shape);\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        if (permutedAxes != null) {\n            $x = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        return backend.argMin($x, axes[0]);\n    };\n    const inputs = { x: $x };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ArgMin, attrs);\n}\nexport const argMin = op({ argMin_ });\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_(x) {\n    const $x = convertToTensor(x, 'x', 'asin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asin);\n}\nexport const asin = op({ asin_ });\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Asinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_(x) {\n    const $x = convertToTensor(x, 'x', 'asinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.asinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Asinh);\n}\nexport const asinh = op({ asinh_ });\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_(x) {\n    const $x = convertToTensor(x, 'x', 'atan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atan);\n}\nexport const atan = op({ atan_ });\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atan2 } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_(a, b) {\n    let $a = convertToTensor(a, 'a', 'atan2');\n    let $b = convertToTensor(b, 'b', 'atan2');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.atan2($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Atan2);\n}\nexport const atan2 = op({ atan2_ });\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Atanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_(x) {\n    const $x = convertToTensor(x, 'x', 'atanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.atanh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Atanh);\n}\nexport const atanh = op({ atanh_ });\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = 'NHWC', dilations) {\n    // `computerConv2DInfo` require filterShape to be in the dimension of:\n    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n    // outDepth, it should have the same depth as the input.\n    // Input shape: [batch, height, width, inChannels]\n    const inputChannels = inputShape[3];\n    const $filterShape = [...filterShape, inputChannels];\n    const $dataFormat = convertConv2DDataFormat(dataFormat);\n    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\nexport function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'channelsLast') {\n    const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n    let filterShape;\n    if (dataFormat === 'channelsLast') {\n        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n    }\n    else if (dataFormat === 'channelsFirst') {\n        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);\n}\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = 'NDHWC') {\n    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n    let filterShape;\n    let $dataFormat;\n    if (dataFormat === 'NDHWC') {\n        $dataFormat = 'channelsLast';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n    }\n    else if (dataFormat === 'NCDHW') {\n        $dataFormat = 'channelsFirst';\n        filterShape =\n            [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);\n}\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = 'channelsLast') {\n    let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideHeight, strideWidth] = parseTupleParam(strides);\n    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inHeight,\n        inWidth,\n        inChannels,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideHeight,\n        strideWidth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = 'channelsLast', roundingMode) {\n    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n    if (dataFormat === 'channelsLast') {\n        [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n    }\n    else if (dataFormat === 'channelsFirst') {\n        [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n    let outShape;\n    if (dataFormat === 'channelsFirst') {\n        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n    }\n    else if (dataFormat === 'channelsLast') {\n        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n    }\n    return {\n        batchSize,\n        dataFormat,\n        inDepth,\n        inHeight,\n        inWidth,\n        inChannels,\n        outDepth,\n        outHeight,\n        outWidth,\n        outChannels,\n        padInfo,\n        strideDepth,\n        strideHeight,\n        strideWidth,\n        filterDepth,\n        filterHeight,\n        filterWidth,\n        effectiveFilterDepth,\n        effectiveFilterHeight,\n        effectiveFilterWidth,\n        dilationDepth,\n        dilationHeight,\n        dilationWidth,\n        inShape,\n        outShape,\n        filterShape\n    };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputRows = inShape[0];\n    const inputCols = inShape[1];\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n    if (zeroPad == null) {\n        zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n    }\n    const inputDepth = inShape[0];\n    const inputRows = inShape[1];\n    const inputCols = inShape[2];\n    const outputDepths = conditionalRound((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputDepths), () => `The output # of depths (${outputDepths}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputRows), () => `The output # of rows (${outputRows}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    const outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n    util.assert(util.isInt(outputCols), () => `The output # of columns (${outputCols}) must be an integer. ` +\n        `Change the stride and/or zero pad parameters`);\n    return [outputDepths, outputRows, outputCols, outChannels];\n}\nexport function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n    if (typeof param === 'number') {\n        return [param, param, param];\n    }\n    if (param.length === 2) {\n        return [param[0], param[1], 1];\n    }\n    return param;\n}\nfunction parse3TupleParam(param) {\n    return typeof param === 'number' ? [param, param, param] : param;\n}\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize, dilation) {\n    if (dilation <= 1) {\n        return filterSize;\n    }\n    return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n    let padInfo;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };\n        const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n        outHeight = outShape[0];\n        outWidth = outShape[1];\n    }\n    else if (pad === 'same') {\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else if (typeof pad === 'object') {\n        const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n        const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n        const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n        const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n        const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n            'VALID' :\n            'EXPLICIT';\n        padInfo = { top, bottom, left, right, type: padType };\n        outHeight = conditionalRound((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n        outWidth = conditionalRound((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n    let padInfo;\n    let outDepth;\n    let outHeight;\n    let outWidth;\n    if (typeof pad === 'number') {\n        const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n        padInfo = {\n            top: pad,\n            bottom: pad,\n            left: pad,\n            right: pad,\n            front: pad,\n            back: pad,\n            type: padType\n        };\n        const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad, roundingMode);\n        outDepth = outShape[0];\n        outHeight = outShape[1];\n        outWidth = outShape[2];\n    }\n    else if (pad === 'same') {\n        outDepth = Math.ceil(inDepth / strideDepth);\n        outHeight = Math.ceil(inHeight / strideHeight);\n        outWidth = Math.ceil(inWidth / strideWidth);\n        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n        const front = Math.floor(padAlongDepth / 2);\n        const back = padAlongDepth - front;\n        const top = Math.floor(padAlongHeight / 2);\n        const bottom = padAlongHeight - top;\n        const left = Math.floor(padAlongWidth / 2);\n        const right = padAlongWidth - left;\n        padInfo = { top, bottom, left, right, front, back, type: 'SAME' };\n    }\n    else if (pad === 'valid') {\n        padInfo = {\n            top: 0,\n            bottom: 0,\n            left: 0,\n            right: 0,\n            front: 0,\n            back: 0,\n            type: 'VALID'\n        };\n        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n    }\n    else {\n        throw Error(`Unknown padding parameter: ${pad}`);\n    }\n    return { padInfo, outDepth, outHeight, outWidth };\n}\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(value, roundingMode) {\n    if (!roundingMode) {\n        return value;\n    }\n    switch (roundingMode) {\n        case 'round':\n            // used for Caffe Conv\n            return Math.round(value);\n        case 'ceil':\n            // used for Caffe Pool\n            return Math.ceil(value);\n        case 'floor':\n            return Math.floor(value);\n        default:\n            throw new Error(`Unknown roundingMode ${roundingMode}`);\n    }\n}\nexport function tupleValuesAreOne(param) {\n    const [dimA, dimB, dimC] = parseTupleParam(param);\n    return dimA === 1 && dimB === 1 && dimC === 1;\n}\nexport function eitherStridesOrDilationsAreOne(strides, dilations) {\n    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat) {\n    if (dataFormat === 'NHWC') {\n        return 'channelsLast';\n    }\n    else if (dataFormat === 'NCHW') {\n        return 'channelsFirst';\n    }\n    else {\n        throw new Error(`Unknown dataFormat ${dataFormat}`);\n    }\n}\n//# sourceMappingURL=conv_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n    const dilations = 1;\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        save([x4D]);\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            return x4D.clone();\n        }\n        return backend.avgPool(x4D, convInfo);\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool, attrs);\n    res = cast(res, $x.dtype);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPool = op({ avgPool_ });\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { AvgPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        save([x5D]);\n        return backend.avgPool3d(x5D, convInfo);\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3D, attrs);\n    res = cast(res, x5D.dtype);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3d = op({ avgPool3d_ });\n//# sourceMappingURL=avg_pool_3d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nexport function assertParamsConsistent(shapes, axis) {\n    const rank = shapes[0].length;\n    shapes.forEach((shape, i) => {\n        util.assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n    });\n    util.assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n    const firstShape = shapes[0];\n    shapes.forEach((shape, i) => {\n        for (let r = 0; r < rank; r++) {\n            util.assert((r === axis) || (shape[r] === firstShape[r]), () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n                `does not match the shape of the rest (${firstShape}) ` +\n                `along the non-concatenated axis ${i}.`);\n        }\n    });\n}\nexport function computeOutShape(shapes, axis) {\n    const outputShape = shapes[0].slice();\n    for (let i = 1; i < shapes.length; i++) {\n        outputShape[axis] += shapes[i][axis];\n    }\n    return outputShape;\n}\n//# sourceMappingURL=concat_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Concat } from '../kernel_names';\nimport { convertToTensorArray } from '../tensor_util_env';\nimport { assert, parseAxisParam, sizeFromShape } from '../util';\nimport { assertParamsConsistent, computeOutShape } from './concat_util';\nimport { op } from './operation';\nimport { tensor } from './tensor';\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_(tensors, axis = 0) {\n    assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n    let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n    if ($tensors[0].dtype === 'complex64') {\n        $tensors.forEach(tensor => {\n            if (tensor.dtype !== 'complex64') {\n                throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n            }\n        });\n    }\n    const forward = (backend, save) => {\n        const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n        const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n        if (sizeFromShape(outShape) === 0) {\n            return tensor([], outShape);\n        }\n        // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n        $tensors = $tensors.filter(t => t.size > 0);\n        if ($tensors.length === 1) {\n            return $tensors[0];\n        }\n        const shapes = $tensors.map(t => t.shape);\n        assertParamsConsistent(shapes, $axis);\n        const res = backend.concat($tensors, $axis);\n        save($tensors);\n        return res;\n    };\n    const inputs = $tensors;\n    const attr = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Concat, attr);\n}\nexport const concat = op({ concat_ });\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sigmoid } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'sigmoid');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sigmoid($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({ sigmoid_ });\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Slice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as slice_util from './slice_util';\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice');\n    if ($x.rank === 0) {\n        throw new Error('Slicing scalar is not possible');\n    }\n    const forward = (backend, save) => {\n        const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n        slice_util.assertParamsValid($x, begin_, size_);\n        save([$x]);\n        return backend.slice($x, begin_, size_);\n    };\n    const inputs = { x: $x };\n    const attrs = { begin, size };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Slice, attrs);\n}\nexport const slice = op({ slice_ });\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tanh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_(x) {\n    const $x = convertToTensor(x, 'x', 'tanh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.tanh($x);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Tanh);\n}\nexport const tanh = op({ tanh_ });\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { add } from './add';\nimport { concat } from './concat';\nimport { matMul } from './mat_mul';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { slice } from './slice';\nimport { tanh } from './tanh';\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n    const $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n    const $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n    const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n    const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n    const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n    const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n    const combined = concat([$data, $h], 1);\n    const weighted = matMul(combined, $lstmKernel);\n    const res = add(weighted, $lstmBias);\n    // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n    const batchSize = res.shape[0];\n    const sliceCols = res.shape[1] / 4;\n    const sliceSize = [batchSize, sliceCols];\n    const i = slice(res, [0, 0], sliceSize);\n    const j = slice(res, [0, sliceCols], sliceSize);\n    const f = slice(res, [0, sliceCols * 2], sliceSize);\n    const o = slice(res, [0, sliceCols * 3], sliceSize);\n    const newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f))));\n    const newH = mul(tanh(newC), sigmoid(o));\n    return [newC, newH];\n}\nexport const basicLSTMCell = op({ basicLSTMCell_ });\n//# sourceMappingURL=basic_lstm_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchToSpaceND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_(x, blockShape, crops) {\n    const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n    const prod = blockShape.reduce((a, b) => a * b);\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n    util.assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n    util.assert($x.shape[0] % prod === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` +\n        `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n    const forward = backend => {\n        return backend.batchToSpaceND($x, blockShape, crops);\n    };\n    const inputs = { x: $x };\n    const attrs = { blockShape, crops };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, BatchToSpaceND, attrs);\n}\nexport const batchToSpaceND = op({ batchToSpaceND_ });\n//# sourceMappingURL=batch_to_space_nd.js.map", "import { reshape } from './reshape';\nexport function xAs4D(x) {\n    let x4D;\n    if (x.rank === 0 || x.rank === 1) {\n        x4D = reshape(x, [1, 1, 1, x.size]);\n    }\n    else if (x.rank === 2) {\n        x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    else {\n        x4D = x;\n    }\n    return x4D;\n}\n//# sourceMappingURL=batchnorm_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { FusedBatchNorm } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { xAs4D } from './batchnorm_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($mean.rank === $variance.rank, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert($offset == null || $mean.rank === $offset.rank, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert($scale == null || $mean.rank === $scale.rank, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    const x4D = xAs4D($x);\n    const forward = (backend, save) => {\n        save([x4D, $mean, $variance, $scale]);\n        return backend.batchNorm(x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset), as1DOr4D($scale), varianceEpsilon);\n    };\n    const inputs = {\n        x: x4D,\n        scale: $scale,\n        offset: $offset,\n        mean: $mean,\n        variance: $variance\n    };\n    const attrs = { varianceEpsilon };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, FusedBatchNorm, attrs);\n    return reshape(res, $x.shape);\n}\nfunction as1DOr4D(x) {\n    if (x == null) {\n        return null;\n    }\n    if (x.rank === 0) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [x.size]);\n    }\n    else if (x.rank === 1) {\n        return x;\n    }\n    else if (x.rank === 2) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n    }\n    else if (x.rank === 3) {\n        // tslint:disable-next-line:no-unnecessary-type-assertion\n        return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    return x;\n}\nexport const batchNorm = op({ batchNorm_ });\n//# sourceMappingURL=batchnorm.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm2d = op({ batchNorm2d_ });\n//# sourceMappingURL=batchnorm2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm3d = op({ batchNorm3d_ });\n//# sourceMappingURL=batchnorm3d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { batchNorm } from './batchnorm';\nimport { op } from './operation';\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {\n    const $x = convertToTensor(x, 'x', 'batchNorm');\n    const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n    const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n    let $scale;\n    if (scale != null) {\n        $scale = convertToTensor(scale, 'scale', 'batchNorm');\n    }\n    let $offset;\n    if (offset != null) {\n        $offset = convertToTensor(offset, 'offset', 'batchNorm');\n    }\n    util.assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n        `${$x.rank}.`);\n    util.assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n        `got rank ${$mean.rank}.`);\n    util.assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n        `but got rank ${$variance.rank}.`);\n    if ($scale != null) {\n        util.assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n    }\n    if ($offset != null) {\n        util.assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n    }\n    return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nexport const batchNorm4d = op({ batchNorm4d_ });\n//# sourceMappingURL=batchnorm4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BroadcastTo } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_(x, shape) {\n    let input = convertToTensor(x, 'broadcastTo', 'x');\n    const xShape = input.shape;\n    if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n        throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n    }\n    if (shape.length < input.rank) {\n        throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);\n    }\n    if (shape.length > input.rank) {\n        const newShape = input.shape.slice();\n        while (newShape.length < shape.length) {\n            newShape.unshift(1);\n        }\n        input = reshape(input, newShape);\n    }\n    const inputShape = input.shape;\n    const reps = Array.from(shape);\n    for (let i = shape.length - 1; i >= 0; i--) {\n        if (inputShape[i] === shape[i]) {\n            reps[i] = 1;\n        }\n        else if (input.shape[i] !== 1) {\n            throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n        }\n    }\n    const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n    if (axes.length === 0) {\n        return clone(input);\n    }\n    const forward = (backend) => backend.tile(input, reps);\n    const inputs = { x: input };\n    const attrs = { shape, inputShape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, BroadcastTo, attrs);\n}\nexport const broadcastTo = op({ broadcastTo_ });\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Ceil } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_(x) {\n    const $x = convertToTensor(x, 'x', 'ceil');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.ceil($x), inputs, null /* grad */, Ceil);\n}\nexport const ceil = op({ ceil_ });\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ClipByValue } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n    const $x = convertToTensor(x, 'x', 'clipByValue');\n    util.assert((clipValueMin <= clipValueMax), () => `Error in clip: min (${clipValueMin}) must be ` +\n        `less than or equal to max (${clipValueMax}).`);\n    const inputs = { x: $x };\n    const attrs = { clipValueMin, clipValueMax };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, ClipByValue, attrs);\n}\nexport const clipByValue = op({ clipByValue_ });\n//# sourceMappingURL=clip_by_value.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors) {\n    return concat(tensors, 0 /* axis */);\n}\nexport const concat1d = op({ concat1d_ });\n//# sourceMappingURL=concat_1d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat2d = op({ concat2d_ });\n//# sourceMappingURL=concat_2d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat3d = op({ concat3d_ });\n//# sourceMappingURL=concat_3d.js.map", "import { concat } from './concat';\nimport { op } from './operation';\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(tensors, axis) {\n    return concat(tensors, axis);\n}\nexport const concat4d = op({ concat4d_ });\n//# sourceMappingURL=concat_4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    util.assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const forward = (backend, save) => {\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2d(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2D, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2d = op({ conv2d_ });\n//# sourceMappingURL=conv2d.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_(x, filter, stride, pad, dataFormat = 'NWC', dilation = 1, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv1d');\n    const $filter = convertToTensor(filter, 'filter', 'conv1d');\n    let x3D = $x;\n    let reshapedTo3D = false;\n    if ($x.rank === 2) {\n        reshapedTo3D = true;\n        x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n    }\n    util.assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n        `input depth for filter ${$filter.shape[1]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n        `Got stride ${stride} and dilation '${dilation}'`);\n    util.assert(dataFormat === 'NWC', () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n    const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n    const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n    const strides = [1, stride];\n    const dilations = [1, dilation];\n    const conv2dDataFormat = 'NHWC';\n    const res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);\n    if (reshapedTo3D) {\n        return reshape(res, [res.shape[2], res.shape[3]]);\n    }\n    return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nexport const conv1d = op({ conv1d_ });\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape4D = xShape;\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n        xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n    }\n    util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n        `${xShape4D.length}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n        `rank ${dy4D.rank}`);\n    util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n        `rank ${filter.rank}`);\n    const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[2]}.`);\n    util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[3]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n        save([dy4D, filter]);\n        return res;\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv2DBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const conv2DBackpropInput = op({ conv2DBackpropInput_ });\n//# sourceMappingURL=conv2d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv2DBackpropInput } from './conv2d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n    return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\nexport const conv2dTranspose = op({ conv2dTranspose_ });\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { eitherStridesOrDilationsAreOne } from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_(x, filter, strides, pad, dataFormat = 'NDHWC', dilations = [1, 1, 1]) {\n    const $x = convertToTensor(x, 'x', 'conv3d');\n    const $filter = convertToTensor(filter, 'filter', 'conv3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n    util.assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n        `input depth for filter ${$filter.shape[3]}.`);\n    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);\n        const res = backend.conv3d(x5D, $filter, convInfo);\n        save([x5D, $filter]);\n        return res;\n    };\n    const inputs = { x: x5D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Conv3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3d = op({ conv3d_ });\n//# sourceMappingURL=conv3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropInputV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad) {\n    util.assert(xShape.length === dy.rank, () => `Length of inShape ` +\n        `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n    let xShape5D = xShape;\n    let dy5D = dy;\n    let reshapedTo5D = false;\n    if (dy.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n    }\n    const inDepth = xShape5D[4];\n    const outDepth = dy5D.shape[4];\n    util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n        `${xShape5D.length}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n        `rank ${dy5D.rank}`);\n    util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n        `rank ${filter.rank}`);\n    util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n        `match input depth for filter ${filter.shape[3]}.`);\n    util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n        `match output depth for filter ${filter.shape[4]}.`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);\n        return backend.conv3dDerInput(dy5D, filter, convInfo);\n    };\n    const inputs = { dy: dy5D, filter };\n    const attrs = { pad, strides, inputShape: xShape5D };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropInputV2, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const conv3DBackpropInput = op({ conv3DBackpropInput_ });\n//# sourceMappingURL=conv3d_backprop_input.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport { conv3DBackpropInput } from './conv3d_backprop_input';\nimport { op } from './operation';\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad) {\n    const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n    const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n    return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\nexport const conv3dTranspose = op({ conv3dTranspose_ });\n//# sourceMappingURL=conv3d_transpose.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cos } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_(x) {\n    const $x = convertToTensor(x, 'x', 'cos');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cos($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cos);\n}\nexport const cos = op({ cos_ });\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cosh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_(x) {\n    const $x = convertToTensor(x, 'x', 'cosh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.cosh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Cosh);\n}\nexport const cosh = op({ cosh_ });\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cumsum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation } from './axis_util';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_(x, axis = 0, exclusive = false, reverse = false) {\n    const $x = convertToTensor(x, 'x', 'cumsum');\n    const forward = (backend, save) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n        if (permutation != null) {\n            const reversePermutation = getUndoAxesPermutation(permutation);\n            value = transpose(value, reversePermutation);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, exclusive, reverse };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Cumsum, attrs);\n}\nexport const cumsum = op({ cumsum_ });\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthToSpace } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(x, blockSize, dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'depthToSpace');\n    const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n    util.assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n    util.assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n    util.assert((inputDepth % (blockSize * blockSize) === 0), () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n    const forward = backend => backend.depthToSpace($x, blockSize, dataFormat);\n    const inputs = { x: $x };\n    const attrs = { blockSize, dataFormat };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, DepthToSpace, attrs);\n}\nexport const depthToSpace = op({ depthToSpace_ });\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_(x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1];\n        }\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n        save([x4D, $filter]);\n        return res;\n    };\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, DepthwiseConv2dNative, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2d = op({ depthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Diag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x) {\n    const $x = convertToTensor(x, 'x', 'diag');\n    const forward = backend => {\n        const flat = reshape($x, [$x.size]);\n        const result = backend.diag(flat);\n        const outShape = [...x.shape, ...x.shape];\n        return reshape(result, outShape);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Diag);\n}\nexport const diag = op({ diag_ });\n//# sourceMappingURL=diag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'dilation2d');\n    const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n    util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n        `${$x.rank}.`);\n    util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n        `${$filter.rank}.`);\n    util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n        reshapedTo4D = true;\n    }\n    const inputs = { x: x4D, filter: $filter };\n    const attrs = { strides, pad, dilations };\n    const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const dilation2d = op({ dilation2d_ });\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(inShape, outShape) {\n    const inRank = inShape.length;\n    const dims = [];\n    for (let i = 0; i < inRank; i++) {\n        const dim = inRank - 1 - i;\n        const a = inShape[dim] || 1;\n        const b = outShape[outShape.length - 1 - i] || 1;\n        if (b > 1 && a === 1) {\n            dims.unshift(dim);\n        }\n    }\n    return dims;\n}\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(inShape, outShape) {\n    const result = [];\n    for (let i = 0; i < outShape.length; i++) {\n        const inDim = inShape[inShape.length - i - 1];\n        const outAxis = outShape.length - i - 1;\n        const outDim = outShape[outAxis];\n        if (inDim == null || (inDim === 1 && outDim > 1)) {\n            result.unshift(outAxis);\n        }\n    }\n    return result;\n}\nexport function assertAndGetBroadcastShape(shapeA, shapeB) {\n    const result = [];\n    const l = Math.max(shapeA.length, shapeB.length);\n    for (let i = 0; i < l; i++) {\n        let a = shapeA[shapeA.length - i - 1];\n        if (a == null) {\n            a = 1;\n        }\n        let b = shapeB[shapeB.length - i - 1];\n        if (b == null) {\n            b = 1;\n        }\n        if (a === 1) {\n            result.unshift(b);\n        }\n        else if (b === 1) {\n            result.unshift(a);\n        }\n        else if (a !== b) {\n            const errMsg = `Operands could not be broadcast together with shapes ` +\n                `${shapeA} and ${shapeB}.`;\n            throw Error(errMsg);\n        }\n        else {\n            result.unshift(a);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=broadcast_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Equal } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_(a, b) {\n    let $a = convertToTensor(a, 'a', 'equal');\n    let $b = convertToTensor(b, 'b', 'equal');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.equal($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null, Equal);\n}\nexport const equal = op({ equal_ });\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SelectV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch } from '../util';\nimport { broadcastTo } from './broadcast_to';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_(condition, a, b) {\n    const $a = convertToTensor(a, 'a', 'where');\n    const $b = convertToTensor(b, 'b', 'where');\n    const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n    // TODO: move this logic to forward function when the broadcastTo op is\n    // implemented in WASM.\n    // Find the broadcastable shape for $a and $b.\n    const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n    const $broadcastedA = broadcastTo($a, broadcastShape);\n    const $broadcastedB = broadcastTo($b, broadcastShape);\n    if ($condition.rank === 1) {\n        // If condition rank is 1, then the first dimension must match the size of\n        // condition.\n        assert($condition.shape[0] === $a.shape[0], () => 'The first dimension of `a` must match the size of `condition`.');\n    }\n    if ($condition.rank !== 1) {\n        // A must have the same shape as condition.\n        assertShapesMatch($condition.shape, $broadcastedB.shape, 'Error in where: ');\n    }\n    const forward = (backend, save) => {\n        const res = backend.select($condition, $broadcastedA, $broadcastedB);\n        save([$condition]);\n        return res;\n    };\n    const inputs = {\n        condition: $condition,\n        t: $broadcastedA,\n        e: $broadcastedB\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SelectV2);\n}\nexport const where = op({ where_ });\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ZerosLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_(x) {\n    const $x = convertToTensor(x, 'x', 'zerosLike');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.zerosLike($x), inputs, null /* grad */, ZerosLike);\n}\nexport const zerosLike = op({ zerosLike_ });\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { div } from './div';\nimport { equal } from './equal';\nimport { op } from './operation';\nimport { where } from './where';\nimport { zerosLike } from './zeros_like';\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_(a, b) {\n    // TODO: Make this into its own kernel.\n    let $a = convertToTensor(a, 'a', 'div');\n    let $b = convertToTensor(b, 'b', 'div');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const divResult = div($a, $b);\n    const zeros = zerosLike(divResult);\n    const bEqualsZero = equal($b, zeros);\n    return where(bEqualsZero, zeros, divResult);\n}\nexport const divNoNan = op({ divNoNan_ });\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1, t2) {\n    const $t1 = convertToTensor(t1, 't1', 'dot');\n    const $t2 = convertToTensor(t2, 't2', 'dot');\n    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n        `${$t1.rank} and ${$t2.rank}.`);\n    const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n    const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n    util.assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ` +\n        `${t1Inner} and ${t2Inner}.`);\n    if ($t1.rank === 1 && $t2.rank === 1) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, []);\n    }\n    else if ($t1.rank === 1 && $t2.rank === 2) {\n        const t12D = reshape($t1, [1, -1]);\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul(t12D, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else if ($t1.rank === 2 && $t2.rank === 1) {\n        const t22D = reshape($t2, [-1, 1]);\n        const t1t2 = matMul($t1, t22D);\n        return reshape(t1t2, [t1t2.size]);\n    }\n    else {\n        const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n        const t1t2 = matMul($t1, t22D);\n        return t1t2;\n    }\n}\nexport const dot = op({ dot_ });\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_(x) {\n    const $x = convertToTensor(x, 'x', 'elu');\n    const forward = (backend, save) => {\n        const y = backend.elu($x);\n        save([y]);\n        return y;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Elu);\n}\nexport const elu = op({ elu_ });\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Erf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_(x) {\n    let $x = convertToTensor(x, 'x', 'erf');\n    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', () => 'Input dtype must be `int32` or `float32`.');\n    if ($x.dtype === 'int32') {\n        $x = cast($x, 'float32');\n    }\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.erf($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Erf);\n}\nexport const erf = op({ erf_ });\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Exp } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_(x) {\n    const $x = convertToTensor(x, 'x', 'exp');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.exp($x);\n        save([res]);\n        return res;\n    }, inputs, null /* grad */, Exp);\n}\nexport const exp = op({ exp_ });\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_(x, axis = 0) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n    util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n    const newShape = $x.shape.slice();\n    if (axis < 0) {\n        // Negative value is counted from the tail of rank.\n        util.assert(-($x.rank + 1) <= axis, () => `Axis must be in the interval [${-($x.rank + 1)}, ${$x.rank}]`);\n        axis = $x.rank + axis + 1;\n    }\n    newShape.splice(axis, 0, 1);\n    return reshape($x, newShape);\n}\nexport const expandDims = op({ expandDims_ });\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Expm1 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_(x) {\n    const $x = convertToTensor(x, 'x', 'expm1');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.expm1($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Expm1);\n}\nexport const expm1 = op({ expm1_ });\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tile } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_(x, reps) {\n    const parseAs = null;\n    const $x = convertToTensor(x, 'x', 'tile', parseAs);\n    util.assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n        `must match length of reps ${reps}.`);\n    const forward = (backend, save) => {\n        const res = backend.tile($x, reps);\n        save([$x]);\n        return res;\n    };\n    const inputsToSave = [$x];\n    const inputs = { x: $x };\n    const attrs = { reps };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Tile, attrs, inputsToSave);\n}\nexport const tile = op({ tile_ });\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { tile } from './tile';\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(numRows, numColumns, batchShape, dtype = 'float32') {\n    if (numColumns == null) {\n        numColumns = numRows;\n    }\n    const buff = buffer([numRows, numColumns], dtype);\n    const n = numRows <= numColumns ? numRows : numColumns;\n    for (let i = 0; i < n; ++i) {\n        buff.set(1, i, i);\n    }\n    const out = reshape(buff.toTensor(), [numRows, numColumns]);\n    if (batchShape == null) {\n        return out;\n    }\n    else {\n        if (batchShape.length === 1) {\n            return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n        }\n        else if (batchShape.length === 2) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n        }\n        else if (batchShape.length === 3) {\n            // tslint:disable-next-line:no-unnecessary-type-assertion\n            return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n                batchShape[0], batchShape[1], batchShape[2], 1, 1\n            ]);\n        }\n        else {\n            throw new Error(`eye() currently supports only 1D and 2D ` +\n                // tslint:disable-next-line:no-any\n                `batchShapes, but received ${batchShape.length}D.`);\n        }\n    }\n}\nexport const eye = op({ eye_ });\n//# sourceMappingURL=eye.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Fill } from '../kernel_names';\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill(shape, value, dtype) {\n    const attrs = { shape, value, dtype };\n    return ENGINE.runKernelFunc(backend => backend.fill(shape, value, dtype), {}, null, Fill, attrs);\n}\nexport { fill };\n//# sourceMappingURL=fill.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Floor } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_(x) {\n    const $x = convertToTensor(x, 'x', 'floor');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.floor($x), inputs, null /* grad */, Floor);\n}\nexport const floor = op({ floor_ });\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport { nearestDivisor } from '../util';\nexport const PARALLELIZE_THRESHOLD = 30;\nexport function computeOptimalWindowSize(inSize) {\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        return inSize;\n    }\n    return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n//# sourceMappingURL=reduce_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nearestDivisor } from '../util';\nimport { PARALLELIZE_THRESHOLD } from './reduce_util';\nexport function segOpComputeOptimalWindowSize(inSize, numSegments) {\n    let done = false;\n    let res;\n    if (inSize <= PARALLELIZE_THRESHOLD) {\n        res = inSize;\n        done = true;\n    }\n    else {\n        res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n    }\n    while (!done) {\n        if (res > numSegments || res === inSize) {\n            done = true;\n        }\n        else {\n            res = nearestDivisor(inSize, res + 1);\n        }\n    }\n    return res;\n}\nexport function computeOutShape(aShape, axis, numSegments) {\n    const outShape = [];\n    const rank = aShape.length;\n    for (let dim = 0; dim < rank; dim++) {\n        if (dim !== axis) {\n            outShape.push(aShape[dim]);\n        }\n        else {\n            outShape.push(numSegments);\n        }\n    }\n    return outShape;\n}\nexport function collectGatherOpShapeInfo(x, indices, axis) {\n    const dimSize = x.shape[axis];\n    const outputShape = [];\n    let batchSize = 1;\n    let sliceSize = 1;\n    for (let i = 0; i < axis; i++) {\n        outputShape.push(x.shape[i]);\n        batchSize *= x.shape[i];\n    }\n    for (let i = 0; i < indices.rank; i++) {\n        outputShape.push(indices.shape[i]);\n    }\n    for (let i = axis + 1; i < x.rank; i++) {\n        outputShape.push(x.shape[i]);\n        sliceSize *= x.shape[i];\n    }\n    return { batchSize, sliceSize, dimSize, outputShape };\n}\n//# sourceMappingURL=segment_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { collectGatherOpShapeInfo } from './segment_util';\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_(x, indices, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'gather');\n    const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n    const inputs = { x: $x, indices: $indices };\n    const attrs = { axis };\n    const forward = (backend, save) => {\n        const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n        const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n        const res = backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n        save([$x, $indices]);\n        return reshape(res, shapeInfo.outputShape);\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GatherV2, attrs);\n}\nexport const gather = op({ gather_ });\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Greater } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greater');\n    let $b = convertToTensor(b, 'b', 'greater');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.greater($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Greater);\n}\nexport const greater = op({ greater_ });\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GreaterEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'greaterEqual');\n    let $b = convertToTensor(b, 'b', 'greaterEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.greaterEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, GreaterEqual);\n}\nexport const greaterEqual = op({ greaterEqual_ });\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Imag } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_(input) {\n    const $input = convertToTensor(input, 'input', 'imag');\n    const forward = (backend) => {\n        return backend.imag($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Imag);\n}\nexport const imag = op({ imag_ });\n//# sourceMappingURL=imag.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsFinite } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_(x) {\n    const $x = convertToTensor(x, 'x', 'isFinite');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isFinite($x), inputs, null /* grad */, IsFinite);\n}\nexport const isFinite = op({ isFinite_ });\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsInf } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_(x) {\n    const $x = convertToTensor(x, 'x', 'isInf');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.isInf($x), inputs, null /* grad */, IsInf);\n}\nexport const isInf = op({ isInf_ });\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { IsNan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_(x) {\n    const $x = convertToTensor(x, 'x', 'isNaN');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.isNaN($x), inputs, null /* grad */, IsNan);\n}\nexport const isNaN = op({ isNaN_ });\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Maximum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'maximum');\n    let $b = convertToTensor(b, 'b', 'maximum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.maximum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Maximum);\n}\nexport const maximum = op({ maximum_ });\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isTypedArray } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(value, dtype) {\n    if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n        dtype !== 'complex64') {\n        throw new Error('Error creating a new Scalar: value must be a primitive ' +\n            '(number|boolean|string)');\n    }\n    if (dtype === 'string' && isTypedArray(value) &&\n        !(value instanceof Uint8Array)) {\n        throw new Error('When making a scalar from encoded string, ' +\n            'the value must be `Uint8Array`.');\n    }\n    const shape = [];\n    const inferredShape = [];\n    return makeTensor(value, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { maximum } from './maximum';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { scalar } from './scalar';\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_(x, alpha = 0.2) {\n    const $x = convertToTensor(x, 'x', 'leakyRelu');\n    return maximum(mul(scalar(alpha), $x), $x);\n}\nexport const leakyRelu = op({ leakyRelu_ });\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Less } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_(a, b) {\n    let $a = convertToTensor(a, 'a', 'less');\n    let $b = convertToTensor(b, 'b', 'less');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = backend => backend.less($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Less);\n}\nexport const less = op({ less_ });\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LessEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'lessEqual');\n    let $b = convertToTensor(b, 'b', 'lessEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.lessEqual($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LessEqual);\n}\nexport const lessEqual = op({ lessEqual_ });\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LinSpace } from '../kernel_names';\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start, stop, num) {\n    if (num <= 0) {\n        throw new Error('The number of values should be positive.');\n    }\n    const attrs = { start, stop, num };\n    return ENGINE.runKernelFunc(backend => backend.linspace(start, stop, num), {} /* inputs */, null /* grad */, LinSpace, attrs);\n}\n//# sourceMappingURL=linspace.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRN } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n    util.assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n    util.assert(util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` +\n        `integer but got depthRadius ${depthRadius}.`);\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    const forward = (backend, save) => {\n        const y = backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta);\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { depthRadius, bias, alpha, beta };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRN, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    else {\n        return res;\n    }\n}\nexport const localResponseNormalization = op({ localResponseNormalization_ });\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_(x) {\n    const $x = convertToTensor(x, 'x', 'log');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log);\n}\nexport const log = op({ log_ });\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Log1p } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_(x) {\n    const $x = convertToTensor(x, 'x', 'log1p');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.log1p($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Log1p);\n}\nexport const log1p = op({ log1p_ });\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { Tensor, Variable } from './tensor';\nimport { convertToTensor, convertToTensorArray } from './tensor_util_env';\nimport * as util from './util';\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n    return (x, dy) => {\n        // x can be of any dtype, thus null as the last argument.\n        const $x = convertToTensor(x, 'x', 'tf.grad', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f($x), [$x], $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                    'returned by f(x)');\n            }\n            checkGrads(grads);\n            return grads[0];\n        });\n    };\n}\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args), () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n        // args can be of any dtype, thus null as the last argument.\n        const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n        const $dy = (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n        return ENGINE.tidy(() => {\n            const { value, grads } = ENGINE.gradients(() => f(...$args), $args, $dy);\n            if ($dy != null) {\n                util.assertShapesMatch(value.shape, $dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                    'match the shape returned by f([x1,...])');\n            }\n            checkGrads(grads);\n            return grads;\n        });\n    };\n}\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrad(f) must be a function');\n    return (x, dy) => {\n        util.assert(x instanceof Tensor, () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n        const { grads, value } = ENGINE.gradients(() => f(x), [x], dy);\n        checkGrads(grads);\n        return { grad: grads[0], value };\n    };\n}\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads(f) {\n    util.assert(util.isFunction(f), () => 'The f passed in valueAndGrads(f) must be a function');\n    return (args, dy) => {\n        util.assert(Array.isArray(args) && args.every(arg => arg instanceof Tensor), () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n        util.assert(dy == null || dy instanceof Tensor, () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n        const res = ENGINE.gradients(() => f(...args), args, dy);\n        if (dy != null) {\n            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n        }\n        checkGrads(res.grads);\n        return res;\n    };\n}\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f, varList) {\n    util.assert(util.isFunction(f), () => 'The f passed in variableGrads(f) must be a function');\n    util.assert(varList == null ||\n        Array.isArray(varList) && varList.every(v => v instanceof Variable), () => 'The varList passed in variableGrads(f, varList) must be an array ' +\n        'of variables');\n    const specifiedVarList = varList != null;\n    if (!specifiedVarList) {\n        // Get all of the trainable variables.\n        varList = [];\n        for (const varName in ENGINE.registeredVariables) {\n            varList.push(ENGINE.registeredVariables[varName]);\n        }\n    }\n    const specifiedNonTrainable = specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n    // Prune non-trainable variables.\n    const originalVarCount = varList.length;\n    varList = varList.filter(variable => variable.trainable);\n    util.assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to ` +\n        `be trainable, but none of the ${originalVarCount} variables is ` +\n        `trainable.`);\n    const allowNoGradients = true;\n    const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);\n    util.assert(grads.some(g => g != null), () => 'Cannot find a connection between any variable and the result of ' +\n        'the loss function y=f(x). Please make sure the operations that ' +\n        'use variables are inside the function f passed to minimize().');\n    util.assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n        `returned a rank-${value.rank} tensor`);\n    const namedGrads = {};\n    varList.forEach((v, i) => {\n        if (grads[i] != null) {\n            namedGrads[v.name] = grads[i];\n        }\n    });\n    if (specifiedNonTrainable != null) {\n        // If varList is explicitly provided and contains non-trainable values,\n        // add them to the returned gradients with `null` values.\n        specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n    }\n    return { value, grads: namedGrads };\n}\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad(f) {\n    return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads) {\n    const numNullGradients = grads.filter(g => g == null).length;\n    if (numNullGradients > 0) {\n        throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n    }\n}\nexport { customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };\n//# sourceMappingURL=gradients.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Negate } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_(x) {\n    const $x = convertToTensor(x, 'x', 'neg');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.neg($x), inputs, null /* grad */, Negate);\n}\nexport const neg = op({ neg_ });\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softplus } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_(x) {\n    const $x = convertToTensor(x, 'x', 'softplus');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.softplus($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Softplus);\n}\nexport const softplus = op({ softplus_ });\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../gradients';\nimport { convertToTensor } from '../tensor_util_env';\nimport { mul } from './mul';\nimport { neg } from './neg';\nimport { op } from './operation';\nimport { sigmoid } from './sigmoid';\nimport { softplus } from './softplus';\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_(x) {\n    const $x = convertToTensor(x, 'x', 'logSigmoid');\n    // Use a custom gradient to maintain previous implementation.\n    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n    // directly\n    const customOp = customGrad((x) => {\n        // TODO(yassogba) we can remove the chained softplus call here only\n        // after backends have modualrized softplus at which point we can call\n        // engine runKernel(..., Sotfplus, ...) directly.\n        const value = neg(softplus(neg(x)));\n        const gradFunc = (dy) => {\n            const derX = mul(dy, sigmoid(neg(x)));\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const logSigmoid = op({ logSigmoid_ });\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Max } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'max');\n    const forward = (backend, save) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n            maxInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n            maxInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, util.parseAxisParam(axis, $x.shape));\n            res = reshape(res, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { reductionIndices: axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Max, attrs);\n}\nexport const max = op({ max_ });\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sub } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_(a, b) {\n    let $a = convertToTensor(a, 'a', 'sub');\n    let $b = convertToTensor(b, 'b', 'sub');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.subtract($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sub);\n}\nexport const sub = op({ sub_ });\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'sum');\n    if ($x.dtype === 'bool') {\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.sum(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Sum, attrs);\n}\nexport const sum = op({ sum_ });\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogSoftmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_(logits, axis = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n    if (axis === -1) {\n        axis = $logits.rank - 1;\n    }\n    if (axis !== $logits.rank - 1) {\n        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and axis was ${axis}`);\n    }\n    const forward = (backend, save) => {\n        const keepDims = true;\n        const xMax = max(logits, axis, true);\n        const shifted = sub(logits, xMax);\n        const value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n        save([value]);\n        return value;\n    };\n    const inputs = { logits: $logits };\n    const attrs = { axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LogSoftmax, attrs);\n}\nexport const logSoftmax = op({ logSoftmax_ });\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { add } from './add';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { exp } from './exp';\nimport { log } from './log';\nimport { max } from './max';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { sub } from './sub';\nimport { sum } from './sum';\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'logSumExp');\n    const axes = parseAxisParam(axis, $x.shape);\n    const xMax = max($x, axes, true /* keepDims */);\n    const a = sub($x, xMax);\n    const b = exp(a);\n    const c = sum(b, axes);\n    const d = log(c);\n    const res = add(reshape(xMax, d.shape), d);\n    if (keepDims) {\n        const newShape = expandShapeToKeepDim(res.shape, axes);\n        return reshape(res, newShape);\n    }\n    return res;\n}\nexport const logSumExp = op({ logSumExp_ });\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalAnd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalAnd($a, $b), inputs, null /* grad */, LogicalAnd);\n}\nexport const logicalAnd = op({ logicalAnd_ });\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalNot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_(x) {\n    const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.logicalNot($x), inputs, null /* grad */, LogicalNot);\n}\nexport const logicalNot = op({ logicalNot_ });\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LogicalOr } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(backend => backend.logicalOr($a, $b), inputs, null /* grad */, LogicalOr);\n}\nexport const logicalOr = op({ logicalOr_ });\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { logicalAnd } from './logical_and';\nimport { logicalNot } from './logical_not';\nimport { logicalOr } from './logical_or';\nimport { op } from './operation';\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_(a, b) {\n    const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n    const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    // x ^ y = (x | y) & ~(x & y)\n    return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nexport const logicalXor = op({ logicalXor_ });\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_(x, filterSize, strides, pad, dimRoundingMode) {\n    const $x = convertToTensor(x, 'x', 'maxPool');\n    const dilations = 1;\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        const convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        let y;\n        if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n            util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n            y = x4D.clone();\n        }\n        else {\n            y = backend.maxPool(x4D, convInfo);\n        }\n        save([x4D, y]);\n        return y;\n    };\n    const inputs = { x: x4D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const maxPool = op({ maxPool_ });\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { deprecationWarn } from '../globals';\nimport { MaxPool3D } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad, dimRoundingMode, dataFormat = 'NDHWC', dilations) {\n    if (dilations == null) {\n        dilations = [1, 1, 1];\n    }\n    else {\n        deprecationWarn('dilations is deprecated, this field will be gone in ' +\n            'v3.0.0.');\n    }\n    const $x = convertToTensor(x, 'x', 'maxPool3d');\n    let x5D = $x;\n    let reshapedTo5D = false;\n    if ($x.rank === 4) {\n        reshapedTo5D = true;\n        x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n    util.assert(dataFormat === 'NDHWC', () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n        `but got dataFormat of ${dataFormat}`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = (backend, save) => {\n        if (dilations == null) {\n            dilations = [1, 1, 1];\n        }\n        const convInfo = conv_util.computePool3DInfo(x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);\n        const y = backend.maxPool3d(x5D, convInfo);\n        save([x5D, y]);\n        return y;\n    };\n    const inputs = { x: x5D };\n    const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat, dilations };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3D, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3d = op({ maxPool3d_ });\n//# sourceMappingURL=max_pool_3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolWithArgmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {\n    const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n    const inputs = { x: $x };\n    const attrs = { filterSize, strides, pad, includeBatchInIndex };\n    const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n    return { result: result[0], indexes: result[1] };\n}\nexport const maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n//# sourceMappingURL=max_pool_with_argmax.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeZerosTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = zeros(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=zeros.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { makeOnesTypedArray, sizeFromShape } from '../util';\nimport { complex } from './complex';\nimport { zeros } from './zeros';\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones(shape, dtype = 'float32') {\n    if (dtype === 'complex64') {\n        const real = ones(shape, 'float32');\n        const imag = zeros(shape, 'float32');\n        return complex(real, imag);\n    }\n    const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n    return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=ones.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { customGrad } from '../gradients';\nimport { Mean } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam, sizeFromShape } from '../util';\nimport { computeOutAndReduceShapes } from './axis_util';\nimport { cast } from './cast';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { ones } from './ones';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sum } from './sum';\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'mean');\n    const axes = parseAxisParam(axis, $x.shape);\n    const shapes = computeOutAndReduceShapes($x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    const forward = () => {\n        const reduceSizeScalar = scalar(reduceSize);\n        // Cast if needed.\n        const xReduce = reduceSizeScalar.dtype === $x.dtype ?\n            $x :\n            cast($x, reduceSizeScalar.dtype);\n        const res = div(xReduce, reduceSizeScalar);\n        return sum(res, axis, keepDims);\n    };\n    // Use a custom gradient to bypass 2 gradient backprops since mean is used\n    // extremely often.\n    const customOp = customGrad((x) => {\n        const value = ENGINE.runKernelFunc(forward, inputs, null /* grad */, Mean, attrs);\n        const gradFunc = (dy) => {\n            const expandedDyShape = x.shape.slice();\n            axes.forEach(axis => {\n                expandedDyShape[axis] = 1;\n            });\n            const expandedDy = reshape(dy, expandedDyShape);\n            const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n            return derX;\n        };\n        return { value, gradFunc };\n    });\n    return customOp($x);\n}\nexport const mean = op({ mean_ });\n//# sourceMappingURL=mean.js.map", "import { ENGINE } from '../engine';\nimport { Min } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport * as axis_util from './axis_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_(x, axis = null, keepDims = false) {\n    const $x = convertToTensor(x, 'x', 'min');\n    const forward = (backend, save) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n            minInput = transpose($x, permutedAxes);\n            axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n            minInput.dispose();\n        }\n        let res = y;\n        if (keepDims) {\n            const expandedShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n            res = reshape(y, expandedShape);\n            y.dispose();\n        }\n        save([$x, res]);\n        return res;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Min, attrs);\n}\nexport const min = op({ min_ });\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Minimum } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_(a, b) {\n    let $a = convertToTensor(a, 'a', 'minimum');\n    let $b = convertToTensor(b, 'b', 'minimum');\n    [$a, $b] = makeTypesMatch($a, $b);\n    if ($a.dtype === 'bool') {\n        $a = cast($a, 'int32');\n        $b = cast($b, 'int32');\n    }\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.minimum($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Minimum);\n}\nexport const minimum = op({ minimum_ });\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MirrorPad } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` using mirror padding.\n *\n * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.\n *\n * ```js\n * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);\n * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * In \"reflect\" mode, the padded regions do not include the borders,\n * while in \"symmetric\" mode the padded regions do include the borders.\n * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,\n * then the output is `[1, 2, 3, 2, 1]` in \"reflect\" mode, and\n * `[1, 2, 3, 3, 2]` in \"symmetric\" mode.\n * If `mode` is \"reflect\" then both `paddings[D, 0]` and `paddings[D, 1]`\n * must be no greater than `x.shape[D] - 1`. If mode is \"symmetric\"\n * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than\n * `x.shape[D]`\n * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction mirrorPad_(x, paddings, mode) {\n    util.assert(mode === 'reflect' || mode === 'symmetric', () => `Invalid mode. Mode must be either reflect or symmetric. ` +\n        `Got ${mode}.`);\n    const $x = convertToTensor(x, 'x', 'mirrorPad');\n    if ($x.rank === 0) {\n        throw new Error('mirrorPad(scalar) is not defined. ' +\n            'Pass non-scalar to mirrorPad');\n    }\n    util.assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. ` +\n        `Got ${paddings.length}.`);\n    const shapeOffset = mode === 'reflect' ? 1 : 0;\n    for (let i = 0; i < $x.rank; i++) {\n        util.assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n        util.assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&\n            paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal ` +\n            `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` +\n            `shape ${$x.shape}`);\n    }\n    const attrs = { paddings, mode };\n    const inputs = { x: $x };\n    return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nexport const mirrorPad = op({ mirrorPad_ });\n//# sourceMappingURL=mirror_pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Mod } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_(a, b) {\n    let $a = convertToTensor(a, 'a', 'mod');\n    let $b = convertToTensor(b, 'b', 'mod');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const forward = (backend, save) => {\n        const res = backend.mod($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Mod);\n}\nexport const mod = op({ mod_ });\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_(x) {\n    const $x = convertToTensor(x, 'x', 'square');\n    const attrs = {};\n    const inputsToSave = [$x];\n    const outputsToSave = [];\n    return ENGINE.runKernelFunc((backend, save) => {\n        save([$x]);\n        return backend.square($x);\n    }, { x: $x }, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\nexport const square = op({ square_ });\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim } from './axis_util';\nimport { cast } from './cast';\nimport { mean } from './mean';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { square } from './square';\nimport { sub } from './sub';\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(x, axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'moments');\n    const axes = parseAxisParam(axis, x.shape);\n    const xMean = mean(x, axes, keepDims);\n    let keepDimsShape = xMean.shape;\n    if (!keepDims) {\n        keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n    }\n    const devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n    const variance = mean(devSquared, axes, keepDims);\n    return { mean: xMean, variance };\n}\nexport const moments = op({ moments_ });\n//# sourceMappingURL=moments.js.map", "import { convertToTensor, convertToTensorArray } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(lstmCells, data, c, h) {\n    const $data = convertToTensor(data, 'data', 'multiRNNCell');\n    const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n    const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n    let input = $data;\n    const newStates = [];\n    for (let i = 0; i < lstmCells.length; i++) {\n        const output = lstmCells[i](input, $c[i], $h[i]);\n        newStates.push(output[0]);\n        newStates.push(output[1]);\n        input = output[1];\n    }\n    const newC = [];\n    const newH = [];\n    for (let i = 0; i < newStates.length; i += 2) {\n        newC.push(newStates[i]);\n        newH.push(newStates[i + 1]);\n    }\n    return [newC, newH];\n}\nexport const multiRNNCell = op({ multiRNNCell_ });\n//# sourceMappingURL=multi_rnn_cell.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n    const $logits = convertToTensor(logits, 'logits', 'multinomial');\n    const numOutcomes = $logits.size;\n    const origRank = $logits.rank;\n    if (numOutcomes < 2) {\n        throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` +\n            `${numOutcomes}.`);\n    }\n    if (origRank > 2) {\n        throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n    }\n    seed = seed || Math.random();\n    const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n    const res = ENGINE.runKernelFunc(backend => backend.multinomial(logits2D, normalized, numSamples, seed), { logits2D });\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nexport const multinomial = op({ multinomial_ });\n//# sourceMappingURL=multinomial.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { NotEqual } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_(a, b) {\n    let $a = convertToTensor(a, 'a', 'notEqual');\n    let $b = convertToTensor(b, 'b', 'notEqual');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend) => backend.notEqual($a, $b);\n    const inputs = { a: $a, b: $b };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, NotEqual);\n}\nexport const notEqual = op({ notEqual_ });\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Real } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_(input) {\n    const $input = convertToTensor(input, 'input', 'real');\n    const forward = (backend) => {\n        return backend.real($input);\n    };\n    const inputs = { input: $input };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Real);\n}\nexport const real = op({ real_ });\n//# sourceMappingURL=real.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OnesLike } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { complex } from './complex';\nimport { imag } from './imag';\nimport { op } from './operation';\nimport { real } from './real';\nimport { zerosLike } from './zeros_like';\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_(x) {\n    const $x = convertToTensor(x, 'x', 'onesLike');\n    const forward = (backend, save) => {\n        if ($x.dtype === 'complex64') {\n            const r = onesLike(real($x));\n            const i = zerosLike(imag($x));\n            return complex(r, i);\n        }\n        return backend.onesLike($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, OnesLike);\n}\nexport const onesLike = op({ onesLike_ });\n//# sourceMappingURL=ones_like.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { matMul } from './mat_mul';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(v1, v2) {\n    const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n    const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n    util.assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n        `${$v1.rank} and ${$v2.rank}.`);\n    const v12D = reshape($v1, [-1, 1]);\n    const v22D = reshape($v2, [1, -1]);\n    return matMul(v12D, v22D);\n}\nexport const outerProduct = op({ outerProduct_ });\n//# sourceMappingURL=outer_product.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { PadV2 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,\n * refer to `tf.mirrorPad`\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_(x, paddings, constantValue = 0) {\n    const $x = convertToTensor(x, 'x', 'pad');\n    if ($x.rank === 0) {\n        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n    }\n    const forward = (backend, save) => {\n        save([$x]);\n        return backend.pad($x, paddings, constantValue);\n    };\n    const attrs = { paddings, constantValue };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, PadV2, attrs);\n}\nexport const pad = op({ pad_ });\n//# sourceMappingURL=pad.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2, () => 'Invalid number of paddings. Must be length of 2.');\n    return pad(x, [paddings], constantValue);\n}\nexport const pad1d = op({ pad1d_ });\n//# sourceMappingURL=pad1d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 2 && paddings[0].length === 2 &&\n        paddings[1].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad2d = op({ pad2d_ });\n//# sourceMappingURL=pad2d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 3 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad3d = op({ pad3d_ });\n//# sourceMappingURL=pad3d.js.map", "import { assert } from '../util';\nimport { op } from './operation';\nimport { pad } from './pad';\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(x, paddings, constantValue = 0) {\n    assert(paddings.length === 4 && paddings[0].length === 2 &&\n        paddings[1].length === 2 && paddings[2].length === 2 &&\n        paddings[3].length === 2, () => 'Invalid number of paddings. Must be length of 2 each.');\n    return pad(x, paddings, constantValue);\n}\nexport const pad4d = op({ pad4d_ });\n//# sourceMappingURL=pad4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SpaceToBatchND } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_(x, blockShape, paddings) {\n    const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n    util.assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n    util.assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n    util.assert($x.shape.reduce((a, b, i) => {\n        if (i > 0 && i <= blockShape.length) {\n            return a &&\n                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                    blockShape[i - 1] ===\n                    0);\n        }\n        return a;\n    }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n    const forward = backend => backend.spaceToBatchND($x, blockShape, paddings);\n    const inputs = { x: $x };\n    const attrs = { blockShape, paddings };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, SpaceToBatchND, attrs);\n}\nexport const spaceToBatchND = op({ spaceToBatchND_ });\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { avgPool } from './avg_pool';\nimport { batchToSpaceND } from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport { maxPool } from './max_pool';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { spaceToBatchND } from './space_to_batch_nd';\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_(input, windowShape, poolingType, pad, dilations, strides) {\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    if (strides == null) {\n        strides = 1;\n    }\n    if (pad === 0) {\n        pad = 'valid';\n    }\n    const $x = convertToTensor(input, 'x', 'maxPool');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in pool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);\n    const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n    // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n    // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n    let basePadding;\n    if (pad === 'same') {\n        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n    }\n    else {\n        basePadding = [[0, 0], [0, 0]];\n    }\n    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n    const convertedPad = isDilationOne ? pad : 'valid';\n    const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n    const forwardOp = poolingType === 'avg' ?\n        () => avgPool(convertedX, windowShape, strides, convertedPad) :\n        () => maxPool(convertedX, windowShape, strides, convertedPad);\n    const y = forwardOp();\n    const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n    const padStart = basePadding.map(b => b[0]);\n    const origPadEnd = basePadding.map(b => b[1]);\n    const fullInputShape = inputShape.concat(padStart, origPadEnd);\n    const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n    const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n    const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n    const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n    return [paddings, crops];\n}\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n    // Spatial dimensions of the filters and the upsampled filters in which we\n    // introduce (rate - 1) zeros between consecutive filter values.\n    const dilatedFilterShape = filterShape.map((s, i) => {\n        return s + (s - 1) * (dilation[i] - 1);\n    });\n    const padExtraShape = dilatedFilterShape.map(s => s - 1);\n    // When padding is odd, we pad more at end, following the same\n    // convention as conv2d.\n    const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n    const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n    return padExtraShape.map((_, i) => {\n        return [padExtraStart[i], padExtraEnd[i]];\n    });\n}\nexport const pool = op({ pool_ });\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Pow } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_(base, exp) {\n    let $base = convertToTensor(base, 'base', 'pow');\n    let $exp = convertToTensor(exp, 'exp', 'pow');\n    [$base, $exp] = makeTypesMatch($base, $exp);\n    const inputs = { a: $base, b: $exp };\n    const forward = (backend, save) => {\n        const y = backend.pow($base, $exp);\n        save([$base, $exp, y]);\n        return y;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Pow);\n}\nexport const pow = op({ pow_ });\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prelu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_(x, alpha) {\n    const $x = convertToTensor(x, 'x', 'prelu');\n    const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n    const forward = (backend, save) => {\n        const res = backend.prelu($x, $alpha);\n        save([$x, $alpha]);\n        return res;\n    };\n    const inputs = { x: $x, alpha: $alpha };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prelu);\n}\nexport const prelu = op({ prelu_ });\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Prod } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes } from './axis_util';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { transpose } from './transpose';\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_(x, axis = null, keepDims = false) {\n    let $x = convertToTensor(x, 'x', 'prod');\n    if ($x.dtype === 'bool') {\n        // bool is not an allowed type for the underlying kernel.\n        $x = cast($x, 'int32');\n    }\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        const permutation = getAxesPermutation(axes, $x.rank);\n        let reductionAxes = axes;\n        let permutedX = $x;\n        if (permutation != null) {\n            permutedX = transpose($x, permutation);\n            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n        }\n        let value = backend.prod(permutedX, reductionAxes);\n        if (keepDims) {\n            const newShape = expandShapeToKeepDim(value.shape, axes);\n            value = reshape(value, newShape);\n        }\n        return value;\n    };\n    const inputs = { x: $x };\n    const attrs = { axis, keepDims };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Prod, attrs);\n}\nexport const prod = op({ prod_ });\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { sizeFromShape } from '../util';\nimport { op } from './operation';\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_(shape, randFunction, dtype) {\n    const size = sizeFromShape(shape);\n    let values = null;\n    if (dtype == null || dtype === 'float32') {\n        values = new Float32Array(size);\n    }\n    else if (dtype === 'int32') {\n        values = new Int32Array(size);\n    }\n    else if (dtype === 'bool') {\n        values = new Uint8Array(size);\n    }\n    else {\n        throw new Error(`Unknown data type ${dtype}`);\n    }\n    for (let i = 0; i < size; i++) {\n        values[i] = randFunction();\n    }\n    return ENGINE.makeTensor(values, shape, dtype);\n}\nexport const rand = op({ rand_ });\n//# sourceMappingURL=rand.js.map", "// 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", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as seedrandom from 'seedrandom';\nimport { expectNumbersClose, testEpsilon } from '../test_util';\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss {\n    constructor(mean, stdDeviation, dtype, truncated, seed) {\n        this.mean = mean;\n        this.stdDev = stdDeviation;\n        this.dtype = dtype;\n        this.nextVal = NaN;\n        this.truncated = truncated;\n        if (this.truncated) {\n            this.upper = this.mean + this.stdDev * 2;\n            this.lower = this.mean - this.stdDev * 2;\n        }\n        const seedValue = seed ? seed : Math.random();\n        this.random = seedrandom.alea(seedValue.toString());\n    }\n    /** Returns next sample from a Gaussian distribution. */\n    nextValue() {\n        if (!isNaN(this.nextVal)) {\n            const value = this.nextVal;\n            this.nextVal = NaN;\n            return value;\n        }\n        let resultX, resultY;\n        let isValid = false;\n        while (!isValid) {\n            let v1, v2, s;\n            do {\n                v1 = 2 * this.random() - 1;\n                v2 = 2 * this.random() - 1;\n                s = v1 * v1 + v2 * v2;\n            } while (s >= 1 || s === 0);\n            const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n            resultX = this.mean + this.stdDev * v1 * mul;\n            resultY = this.mean + this.stdDev * v2 * mul;\n            if (!this.truncated || this.isValidTruncated(resultX)) {\n                isValid = true;\n            }\n        }\n        if (!this.truncated || this.isValidTruncated(resultY)) {\n            this.nextVal = this.convertValue(resultY);\n        }\n        return this.convertValue(resultX);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype == null || this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n    /** Returns true if less than 2-standard-deviations from the mean. */\n    isValidTruncated(value) {\n        return value <= this.upper && value >= this.lower;\n    }\n}\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma {\n    constructor(alpha, beta, dtype, seed) {\n        this.alpha = alpha;\n        this.beta = 1 / beta; // convert rate to scale parameter\n        this.dtype = dtype;\n        const seedValue = seed ? seed : Math.random();\n        this.randu = seedrandom.alea(seedValue.toString());\n        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n        if (alpha < 1) {\n            this.d = alpha + (2 / 3);\n        }\n        else {\n            this.d = alpha - (1 / 3);\n        }\n        this.c = 1 / Math.sqrt(9 * this.d);\n    }\n    /** Returns next sample from a gamma distribution. */\n    nextValue() {\n        let x2, v0, v1, x, u, v;\n        while (true) {\n            do {\n                x = this.randn.nextValue();\n                v = 1 + (this.c * x);\n            } while (v <= 0);\n            v *= v * v;\n            x2 = x * x;\n            v0 = 1 - (0.331 * x2 * x2);\n            v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n            u = this.randu();\n            if (u < v0 || Math.log(u) < v1) {\n                break;\n            }\n        }\n        v = (1 / this.beta) * this.d * v;\n        if (this.alpha < 1) {\n            v *= Math.pow(this.randu(), 1 / this.alpha);\n        }\n        return this.convertValue(v);\n    }\n    /** Handles proper rounding for non-floating-point numbers. */\n    convertValue(value) {\n        if (this.dtype === 'float32') {\n            return value;\n        }\n        return Math.round(value);\n    }\n}\nexport class UniformRandom {\n    constructor(min = 0, max = 1, dtype, seed) {\n        /** Handles proper rounding for non floating point numbers. */\n        this.canReturnFloat = () => (this.dtype == null || this.dtype === 'float32');\n        this.min = min;\n        this.range = max - min;\n        this.dtype = dtype;\n        if (seed == null) {\n            seed = Math.random();\n        }\n        if (typeof seed === 'number') {\n            seed = seed.toString();\n        }\n        if (!this.canReturnFloat() && this.range <= 1) {\n            throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);\n        }\n        this.random = seedrandom.alea(seed);\n    }\n    convertValue(value) {\n        if (this.canReturnFloat()) {\n            return value;\n        }\n        return Math.round(value);\n    }\n    nextValue() {\n        return this.convertValue(this.min + this.range * this.random());\n    }\n}\nexport function jarqueBeraNormalityTest(values) {\n    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n    const n = values.length;\n    const s = skewness(values);\n    const k = kurtosis(values);\n    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n    const CHI_SQUARE_2DEG = 5.991;\n    if (jb > CHI_SQUARE_2DEG) {\n        throw new Error(`Invalid p-value for JB: ${jb}`);\n    }\n}\nexport function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {\n    if (epsilon == null) {\n        epsilon = testEpsilon();\n    }\n    const actualMean = mean(actual);\n    expectNumbersClose(actualMean, expectedMean, epsilon);\n    expectNumbersClose(standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\nfunction mean(values) {\n    let sum = 0;\n    for (let i = 0; i < values.length; i++) {\n        sum += values[i];\n    }\n    return sum / values.length;\n}\nfunction standardDeviation(values, mean) {\n    let squareDiffSum = 0;\n    for (let i = 0; i < values.length; i++) {\n        const diff = values[i] - mean;\n        squareDiffSum += diff * diff;\n    }\n    return Math.sqrt(squareDiffSum / values.length);\n}\nfunction kurtosis(values) {\n    // https://en.wikipedia.org/wiki/Kurtosis\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum4 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum4 += Math.pow(v, 4);\n    }\n    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\nfunction skewness(values) {\n    // https://en.wikipedia.org/wiki/Skewness\n    const valuesMean = mean(values);\n    const n = values.length;\n    let sum2 = 0;\n    let sum3 = 0;\n    for (let i = 0; i < n; i++) {\n        const v = values[i] - valuesMean;\n        sum2 += Math.pow(v, 2);\n        sum3 += Math.pow(v, 3);\n    }\n    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n//# sourceMappingURL=rand_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { RandGamma } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_(shape, alpha, beta = 1, dtype = 'float32', seed) {\n    if (beta == null) {\n        beta = 1;\n    }\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const rgamma = new RandGamma(alpha, beta, dtype, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = rgamma.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomGamma = op({ randomGamma_ });\n//# sourceMappingURL=random_gamma.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type ${dtype}`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomNormal = op({ randomNormal_ });\n//# sourceMappingURL=random_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { UniformRandom } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = 'float32', seed) {\n    const res = buffer(shape, dtype);\n    const random = new UniformRandom(minval, maxval, null, seed);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = random.nextValue();\n    }\n    return res.toTensor();\n}\nexport const randomUniform = op({ randomUniform_ });\n//# sourceMappingURL=random_uniform.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values, dtype) {\n    assertNonNull(values);\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 1) {\n        throw new Error('tensor1d() requires values to be a flat/TypedArray');\n    }\n    const shape = null;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Range } from '../kernel_names';\nimport { makeZerosTypedArray } from '../util';\nimport { tensor1d } from './tensor1d';\nimport { zeros } from './zeros';\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(start, stop, step = 1, dtype = 'float32') {\n    if (step === 0) {\n        throw new Error('Cannot have a step of zero');\n    }\n    const forward = () => {\n        const sameStartStop = start === stop;\n        const increasingRangeNegativeStep = start < stop && step < 0;\n        const decreasingRangePositiveStep = stop < start && step > 1;\n        if (sameStartStop || increasingRangeNegativeStep ||\n            decreasingRangePositiveStep) {\n            return zeros([0], dtype);\n        }\n        const numElements = Math.abs(Math.ceil((stop - start) / step));\n        const values = makeZerosTypedArray(numElements, dtype);\n        if (stop < start && step === 1) {\n            // Auto adjust the step's sign if it hasn't been set\n            // (or was set to 1)\n            step = -1;\n        }\n        values[0] = start;\n        for (let i = 1; i < values.length; i++) {\n            values[i] = values[i - 1] + step;\n        }\n        return tensor1d(values, dtype);\n    };\n    const attrs = { start, stop, step, dtype };\n    return ENGINE.runKernelFunc(forward, {} /* inputs */, null /* grad */, Range, attrs);\n}\n//# sourceMappingURL=range.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reciprocal } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_(x) {\n    const $x = convertToTensor(x, 'x', 'reciprocal');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.reciprocal($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({ reciprocal_ });\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { cast } from './cast';\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_(x) {\n    const $x = convertToTensor(x, 'x', 'relu');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu);\n}\nexport const relu = op({ relu_ });\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Relu6 } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_(x) {\n    const $x = convertToTensor(x, 'x', 'relu6');\n    const forward = (backend, save) => {\n        save([$x]);\n        if ($x.dtype === 'bool') {\n            return cast($x, 'int32');\n        }\n        return backend.relu6($x);\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Relu6);\n}\nexport const relu6 = op({ relu6_ });\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Reverse } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { clone } from './clone';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    const forward = (backend) => {\n        const axes = parseAxisParam(axis, $x.shape);\n        if ($x.rank === 0) {\n            return clone($x);\n        }\n        const res = backend.reverse($x, axes);\n        return reshape(res, $x.shape);\n    };\n    const inputs = { x: $x };\n    const attrs = { dims: axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, Reverse, attrs);\n}\nexport const reverse = op({ reverse_ });\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n    return reverse($x, 0);\n}\nexport const reverse1d = op({ reverse1d_ });\n//# sourceMappingURL=reverse_1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse2d = op({ reverse2d_ });\n//# sourceMappingURL=reverse_2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse3d = op({ reverse3d_ });\n//# sourceMappingURL=reverse_3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { reverse } from './reverse';\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'reverse');\n    util.assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n    return reverse($x, axis);\n}\nexport const reverse4d = op({ reverse4d_ });\n//# sourceMappingURL=reverse_4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Round } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_(x) {\n    const $x = convertToTensor(x, 'x', 'round');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend) => backend.round($x), inputs, null /* grad */, Round);\n}\nexport const round = op({ round_ });\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Rsqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'rsqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.rsqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({ rsqrt_ });\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Selu } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_(x) {\n    const $x = convertToTensor(x, 'x', 'selu');\n    const forward = (backend, save) => {\n        const res = backend.selu($x);\n        save([$x]);\n        return res;\n    };\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Selu);\n}\nexport const selu = op({ selu_ });\n//# sourceMappingURL=selu.js.map", "import { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { conv2d } from './conv2d';\nimport { depthwiseConv2d } from './depthwise_conv2d';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [1, 1], dataFormat = 'NHWC') {\n    const $x = convertToTensor(x, 'x', 'separableConv2d');\n    const $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n    const $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    if (dataFormat === 'NCHW') {\n        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +\n            'NHWC is supported');\n    }\n    util.assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n        `got rank ${$depthwiseFilter.rank}.`);\n    util.assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter ` +\n        ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n    util.assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise ` +\n        `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n    const inChannels = $depthwiseFilter.shape[2];\n    const channelMultiplier = $depthwiseFilter.shape[3];\n    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter ` +\n        `must be ${inChannels * channelMultiplier}, ` +\n        `but got ${$pointwiseFilter.shape[2]}.`);\n    const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n    const pointwiseStride = 1;\n    const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const separableConv2d = op({ separableConv2d_ });\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(x, y) {\n    const $x = convertToTensor(x, 'x', 'setdiff1d');\n    const $y = convertToTensor(y, 'y', 'setdiff1d');\n    util.assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n    util.assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n    util.assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n    const xVals = await $x.data();\n    const yVals = await $y.data();\n    const ySet = new Set(yVals);\n    let outputSize = 0;\n    for (let i = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            outputSize++;\n        }\n    }\n    const buffer = new TensorBuffer([outputSize], $x.dtype);\n    const indices = new TensorBuffer([outputSize], 'int32');\n    for (let i = 0, p = 0; i < xVals.length; i++) {\n        if (!ySet.has(xVals[i])) {\n            buffer.values[p] = xVals[i];\n            indices.values[p] = i;\n            p++;\n        }\n    }\n    return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n//# sourceMappingURL=setdiff1d_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sign } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_(x) {\n    const $x = convertToTensor(x, 'x', 'sign');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc(backend => backend.sign($x), inputs, null /* grad */, Sign);\n}\nexport const sign = op({ sign_ });\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sin } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_(x) {\n    const $x = convertToTensor(x, 'x', 'sin');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sin($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sin);\n}\nexport const sin = op({ sin_ });\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sinh } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_(x) {\n    const $x = convertToTensor(x, 'x', 'sinh');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sinh($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sinh);\n}\nexport const sinh = op({ sinh_ });\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice1d');\n    util.assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, [begin], [size]);\n}\nexport const slice1d = op({ slice1d_ });\n//# sourceMappingURL=slice1d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice2d');\n    util.assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice2d = op({ slice2d_ });\n//# sourceMappingURL=slice2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice3d');\n    util.assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice3d = op({ slice3d_ });\n//# sourceMappingURL=slice3d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\nimport { slice } from './slice';\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(x, begin, size) {\n    const $x = convertToTensor(x, 'x', 'slice4d');\n    util.assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n    return slice($x, begin, size);\n}\nexport const slice4d = op({ slice4d_ });\n//# sourceMappingURL=slice4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Softmax } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_(logits, dim = -1) {\n    const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n    if (dim === -1) {\n        dim = $logits.rank - 1;\n    }\n    if (dim !== $logits.rank - 1) {\n        throw Error('Softmax along a non-last dimension is not yet supported. ' +\n            `Logits was rank ${$logits.rank} and dim was ${dim}`);\n    }\n    const inputs = { logits: $logits };\n    const attrs = { dim };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n    }, inputs, null /* grad */, Softmax, attrs);\n}\nexport const softmax = op({ softmax_ });\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.fft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = input.as2D(batch, innerDimensionSize);\n        const result = backend.fft(input2D);\n        return result.reshape(input.shape);\n    }, inputs, null /* gradient */, FFT);\n}\nexport const fft = op({ fft_ });\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { IFFT } from '../../kernel_names';\nimport { assert } from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input) {\n    assert(input.dtype === 'complex64', () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n        `but got ${input.dtype}.`);\n    const inputs = { input };\n    return ENGINE.runKernelFunc(backend => {\n        // Collapse all outer dimensions to a single batch dimension.\n        const innerDimensionSize = input.shape[input.shape.length - 1];\n        const batch = input.size / innerDimensionSize;\n        const input2D = reshape(input, [batch, innerDimensionSize]);\n        const result = backend.ifft(input2D);\n        return reshape(result, input.shape);\n    }, inputs, null /* gradient */, IFFT);\n}\nexport const ifft = op({ ifft_ });\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { reverse } from '../reverse';\nimport { scalar } from '../scalar';\nimport { slice } from '../slice';\nimport { ifft } from './ifft';\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input) {\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let ret;\n    if (innerDimensionSize <= 2) {\n        const complexInput = reshape(input, [batch, innerDimensionSize]);\n        ret = ifft(complexInput);\n    }\n    else {\n        // The length of unique components of the DFT of a real-valued signal\n        // is 2 * (input_len - 1)\n        const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n        const realInput = reshape(real(input), [batch, innerDimensionSize]);\n        const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n        const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n        const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n        const r = concat([realInput, realConjugate], 1);\n        const i = concat([imagInput, imagConjugate], 1);\n        const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n        ret = ifft(complexInput);\n    }\n    ret = real(ret);\n    // reshape the result if the input is 3D tensor.\n    if (input.rank === 3 && input.shape[0] !== 0) {\n        const temp = ret;\n        const batch = input.shape[0];\n        ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n        temp.dispose();\n    }\n    return ret;\n}\nexport const irfft = op({ irfft_ });\n//# sourceMappingURL=irfft.js.map", "import { assert } from '../util';\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n    let splitSizes = [];\n    if (typeof (numOrSizeSplits) === 'number') {\n        assert(x.shape[axis] % numOrSizeSplits === 0, () => 'Number of splits must evenly divide the axis.');\n        splitSizes =\n            new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n    }\n    else {\n        const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n            if (value === -1) {\n                count += 1;\n            }\n            return count;\n        }, 0);\n        assert(numOfNegs <= 1, () => 'There should be only one negative value in split array.');\n        const negIndex = numOrSizeSplits.indexOf(-1);\n        // Allow the number of split array to be -1, which indicates the rest\n        // of dimension is allocated to that split.\n        if (negIndex !== -1) {\n            const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n            numOrSizeSplits[negIndex] = x.shape[axis] - total;\n        }\n        assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => 'The sum of sizes must match the size of the axis dimension.');\n        splitSizes = numOrSizeSplits;\n    }\n    return splitSizes;\n}\n//# sourceMappingURL=split_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SplitV } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { op } from './operation';\nimport { prepareSplitSize } from './split_util';\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_(x, numOrSizeSplits, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'split');\n    const forward = (backend, _) => {\n        const $axis = parseAxisParam(axis, $x.shape)[0];\n        const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n        return backend.split($x, splitSizes, $axis);\n    };\n    const inputs = { x: $x };\n    const attr = { numOrSizeSplits, axis };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SplitV, attr);\n}\nexport const split = op({ split_ });\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../../util';\nimport { complex } from '../complex';\nimport { concat } from '../concat';\nimport { imag } from '../imag';\nimport { op } from '../operation';\nimport { real } from '../real';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { split } from '../split';\nimport { zeros } from '../zeros';\nimport { zerosLike } from '../zeros_like';\nimport { fft } from './fft';\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input, fftLength) {\n    assert(input.dtype === 'float32', () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n    let innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n    let adjustedInput;\n    if (fftLength != null && fftLength < innerDimensionSize) {\n        // Need to crop\n        const begin = input.shape.map(v => 0);\n        const size = input.shape.map(v => v);\n        size[input.shape.length - 1] = fftLength;\n        adjustedInput = slice(input, begin, size);\n        innerDimensionSize = fftLength;\n    }\n    else if (fftLength != null && fftLength > innerDimensionSize) {\n        // Need to pad with zeros\n        const zerosShape = input.shape.map(v => v);\n        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n        adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n        innerDimensionSize = fftLength;\n    }\n    else {\n        adjustedInput = input;\n    }\n    // Complement the input with zero imaginary numbers.\n    const zerosInput = zerosLike(adjustedInput);\n    const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n    const ret = fft(complexInput);\n    // Exclude complex conjugations. These conjugations are put symmetrically.\n    const half = Math.floor(innerDimensionSize / 2) + 1;\n    const realValues = real(ret);\n    const imagValues = imag(ret);\n    const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n    const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n    const outputShape = adjustedInput.shape.slice();\n    outputShape[adjustedInput.shape.length - 1] = half;\n    return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nexport const rfft = op({ rfft_ });\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Sqrt } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_(x) {\n    const $x = convertToTensor(x, 'x', 'sqrt');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.sqrt($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Sqrt);\n}\nexport const sqrt = op({ sqrt_ });\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SquaredDifference } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertAndGetBroadcastShape } from './broadcast_util';\nimport { op } from './operation';\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_(a, b) {\n    let $a = convertToTensor(a, 'a', 'squaredDifference');\n    let $b = convertToTensor(b, 'b', 'squaredDifference');\n    [$a, $b] = makeTypesMatch($a, $b);\n    assertAndGetBroadcastShape($a.shape, $b.shape);\n    const forward = (backend, save) => {\n        const res = backend.squaredDifference($a, $b);\n        save([$a, $b]);\n        return res;\n    };\n    const inputs = { a: $a, b: $b };\n    const attrs = {};\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, SquaredDifference, attrs);\n}\nexport const squaredDifference = op({ squaredDifference_ });\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { squeezeShape } from '../util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_(x, axis) {\n    const $x = convertToTensor(x, 'x', 'squeeze');\n    return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nexport const squeeze = op({ squeeze_ });\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensorArray } from '../tensor_util_env';\nimport * as util from '../util';\nimport { concat } from './concat';\nimport { expandDims } from './expand_dims';\nimport { op } from './operation';\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_(tensors, axis = 0) {\n    const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n    util.assert($tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n    if ($tensors.length === 1) {\n        return expandDims($tensors[0], axis);\n    }\n    const rank = $tensors[0].rank;\n    const shape = $tensors[0].shape;\n    const dtype = $tensors[0].dtype;\n    util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n    $tensors.forEach(t => {\n        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');\n        util.assert(dtype === t.dtype, () => 'All tensors passed to stack must have matching dtypes');\n    });\n    const expandedTensors = $tensors.map(t => expandDims(t, axis));\n    // Stack exists in the TensorFlow C++ API\n    // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n    // in\n    // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n    // Therefore we are treating it like a high-level op rather than\n    // creating a dedicated stack kernel.\n    return concat(expandedTensors, axis);\n}\nexport const stack = op({ stack_ });\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Step } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_(x, alpha = 0.0) {\n    const $x = convertToTensor(x, 'x', 'step');\n    const inputs = { x: $x };\n    const attrs = { alpha };\n    return ENGINE.runKernelFunc(backend => backend.step($x, alpha), inputs, null /* grad */, Step, attrs);\n}\nexport const step = op({ step_ });\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { StridedSlice } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nimport { slice } from './slice';\nimport { computeOutShape, getNormalizedAxes, maskToAxes } from './slice_util';\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n    let $x = convertToTensor(x, 'x', 'stridedSlice');\n    const forward = (backend) => {\n        if (strides == null) {\n            strides = new Array(begin.length);\n        }\n        const ellipsisAxes = maskToAxes(ellipsisMask);\n        if (ellipsisAxes.length > 1) {\n            throw new Error('Multiple ellipses in slice is not allowed.');\n        }\n        if (ellipsisMask !== 0 && newAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and newAxisMask is not yet supported.');\n        }\n        if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n            throw new Error('Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n        }\n        const numInterpolatedAxes = $x.rank - begin.length;\n        // Expand the dims of x based on the newAxisMask.\n        const expandAxes = maskToAxes(newAxisMask);\n        const newShape = $x.shape.slice();\n        expandAxes.forEach(axis => {\n            begin[axis] = 0;\n            end[axis] = 1;\n            newShape.splice(axis, 0, 1);\n        });\n        $x = reshape($x, newShape);\n        const { begin: normalizedBegin, end: normalizedEnd, strides: normalizedStrides } = getNormalizedAxes($x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask);\n        begin = normalizedBegin;\n        end = normalizedEnd;\n        strides = normalizedStrides;\n        const shrinkAxes = maskToAxes(shrinkAxisMask);\n        // Adjust the ends based on the shrink mask.\n        shrinkAxes.forEach(axis => {\n            end[axis] = begin[axis] + 1;\n            strides[axis] = 1;\n        });\n        // Figure out the output shape.\n        const size = computeOutShape(begin, end, strides);\n        // Remove the axes based on shrinkMask.\n        const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n        const nonStrided = strides.every(v => v === 1);\n        if (nonStrided) {\n            return reshape(slice($x, begin, size), outShape);\n        }\n        const res = backend.stridedSlice($x, begin, end, strides);\n        return reshape(res, outShape);\n    };\n    const inputs = { x: $x };\n    const attrs = {\n        begin,\n        end,\n        strides,\n        beginMask,\n        endMask,\n        ellipsisMask,\n        newAxisMask,\n        shrinkAxisMask\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, StridedSlice, attrs);\n}\nexport const stridedSlice = op({ stridedSlice_ });\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Tan } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_(x) {\n    const $x = convertToTensor(x, 'x', 'tan');\n    const inputs = { x: $x };\n    return ENGINE.runKernelFunc((backend, save) => {\n        const res = backend.tan($x);\n        save([$x]);\n        return res;\n    }, inputs, null /* grad */, Tan);\n}\nexport const tan = op({ tan_ });\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 2) {\n        throw new Error('tensor2d() requires shape to have two numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor2d() requires shape to be provided when `values` ' +\n            'are a flat/TypedArray');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor2d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 4) {\n        throw new Error('tensor4d() requires shape to have four numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor4d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor4d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 5) {\n        throw new Error('tensor5d() requires shape to have five numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n        throw new Error('tensor5d() requires values to be ' +\n            'number[][][][][] or flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor5d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor5d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(values, shape, dtype) {\n    assertNonNull(values);\n    if (shape != null && shape.length !== 6) {\n        throw new Error('tensor6d() requires shape to have six numbers');\n    }\n    const inferredShape = inferShape(values, dtype);\n    if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n        throw new Error('tensor6d() requires values to be number[][][][][][] or ' +\n            'flat/TypedArray');\n    }\n    if (inferredShape.length === 1 && shape == null) {\n        throw new Error('tensor6d() requires shape to be provided when `values` ' +\n            'are a flat array');\n    }\n    shape = shape ||\n        inferredShape;\n    return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor6d.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { TopK } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_(x, k = 1, sorted = true) {\n    const $x = convertToTensor(x, 'x', 'topk');\n    if ($x.rank === 0) {\n        throw new Error('topk() expects the input to be of rank 1 or higher');\n    }\n    const lastDim = $x.shape[$x.shape.length - 1];\n    if (k > lastDim) {\n        throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n            `but got ${k}`);\n    }\n    const inputs = { x: $x };\n    const attrs = { k, sorted };\n    const [values, indices] = ENGINE.runKernelFunc(b => b.topk($x, k, sorted), inputs, null /* grad */, TopK, attrs);\n    return { values, indices };\n}\nexport const topk = op({ topk_ });\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from './buffer';\nimport { op } from './operation';\nimport { MPRandGauss } from './rand_util';\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {\n    if (dtype != null && dtype === 'bool') {\n        throw new Error(`Unsupported data type $ { dtype }`);\n    }\n    const randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n    const res = buffer(shape, dtype);\n    for (let i = 0; i < res.values.length; i++) {\n        res.values[i] = randGauss.nextValue();\n    }\n    return res.toTensor();\n}\nexport const truncatedNormal = op({ truncatedNormal_ });\n//# sourceMappingURL=truncated_normal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unique } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert } from '../util';\nimport { op } from './operation';\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_(x, axis = 0) {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'unique', null);\n    assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n    const inputs = { x: $x };\n    const attrs = { axis };\n    const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n    return { values, indices };\n}\nexport const unique = op({ unique_ });\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, isInt } from '../util';\nimport { op } from './operation';\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n    const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n    const $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n    assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n    const inputs = { x: $x, segmentIds: $segmentIds };\n    const attrs = { numSegments };\n    const forward = (backend, save) => {\n        const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n        save([$segmentIds]);\n        return res;\n    };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, UnsortedSegmentSum, attrs);\n}\nexport const unsortedSegmentSum = op({ unsortedSegmentSum_ });\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Unpack } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x, axis = 0) {\n    const $x = convertToTensor(x, 'x', 'unstack');\n    util.assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n    if (axis < 0) {\n        axis += $x.shape.length;\n    }\n    const inputs = { value: $x };\n    const attrs = { axis };\n    const forward = (backend) => backend.unstack($x, axis);\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, Unpack, attrs);\n}\nexport const unstack = op({ unstack_ });\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable(initialValue, trainable = true, name, dtype) {\n    return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n//# sourceMappingURL=variable.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the Where kernel shared between cpu and webgl */\nimport { buffer } from '../ops/buffer';\nexport function whereImpl(condShape, condVals) {\n    const indices = [];\n    for (let i = 0; i < condVals.length; i++) {\n        if (condVals[i]) {\n            indices.push(i);\n        }\n    }\n    const inBuffer = buffer(condShape, 'int32');\n    const out = buffer([indices.length, condShape.length], 'int32');\n    for (let i = 0; i < indices.length; i++) {\n        const loc = inBuffer.indexToLoc(indices[i]);\n        const offset = i * condShape.length;\n        out.values.set(loc, offset);\n    }\n    return out.toTensor();\n}\n//# sourceMappingURL=where_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { whereImpl } from '../backends/where_impl';\nimport { convertToTensor } from '../tensor_util_env';\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition) {\n    const $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n    const vals = await $condition.data();\n    const res = whereImpl($condition.shape, vals);\n    if (condition !== $condition) {\n        $condition.dispose();\n    }\n    return res;\n}\nexport const whereAsync = whereAsync_;\n//# sourceMappingURL=where_async.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { gather } from './gather';\nimport { reshape } from './reshape';\nimport { squeeze } from './squeeze';\nimport { whereAsync } from './where_async';\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(tensor, mask, axis) {\n    const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n    const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n    const axisFrom = axis == null ? 0 : axis;\n    const maskDim = $mask.rank;\n    const tensorShape = $tensor.shape;\n    util.assert(maskDim > 0, () => 'mask cannot be scalar');\n    util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n    let leadingSize = 1;\n    for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n        leadingSize *= tensorShape[i];\n    }\n    const targetTensorShape = tensorShape.slice(0, axisFrom)\n        .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n    const reshapedTensor = reshape($tensor, targetTensorShape);\n    const reshapedMask = reshape($mask, [-1]);\n    const positivePositions = await whereAsync(reshapedMask);\n    const indices = squeeze(positivePositions, [1]);\n    const res = gather(reshapedTensor, indices, axisFrom);\n    // Ensure no memory leak.\n    if (tensor !== $tensor) {\n        $tensor.dispose();\n    }\n    if (mask !== $mask) {\n        $mask.dispose();\n    }\n    indices.dispose();\n    reshapedTensor.dispose();\n    reshapedMask.dispose();\n    positivePositions.dispose();\n    return res;\n}\nexport const booleanMaskAsync = booleanMaskAsync_;\n//# sourceMappingURL=boolean_mask.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport { assertShapesMatch } from '../util';\nimport { equal } from './equal';\nimport { greater } from './greater';\nimport { greaterEqual } from './greater_equal';\nimport { less } from './less';\nimport { lessEqual } from './less_equal';\nimport { notEqual } from './not_equal';\nimport { op } from './operation';\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'notEqualStrict');\n    const $b = convertToTensor(b, 'b', 'notEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n    return notEqual($a, $b);\n}\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessStrict');\n    const $b = convertToTensor(b, 'b', 'lessStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n    return less($a, $b);\n}\nfunction equalStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'equalStrict');\n    const $b = convertToTensor(b, 'b', 'equalStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n    return equal($a, $b);\n}\nfunction lessEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n    const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n    return lessEqual($a, $b);\n}\nfunction greaterStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterStrict');\n    const $b = convertToTensor(b, 'b', 'greaterStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n    return greater($a, $b);\n}\nfunction greaterEqualStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n    const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n    assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n    return greaterEqual($a, $b);\n}\nexport const equalStrict = op({ equalStrict_ });\nexport const greaterEqualStrict = op({ greaterEqualStrict_ });\nexport const greaterStrict = op({ greaterStrict_ });\nexport const lessEqualStrict = op({ lessEqualStrict_ });\nexport const lessStrict = op({ lessStrict_ });\nexport const notEqualStrict = op({ notEqualStrict_ });\n//# sourceMappingURL=compare.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../globals';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { maximum } from './maximum';\nimport { minimum } from './minimum';\nimport { mod } from './mod';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { squaredDifference } from './squared_difference';\nimport { sub } from './sub';\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'addStrict');\n    const $b = convertToTensor(b, 'b', 'addStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n    return add($a, $b);\n}\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'subStrict');\n    const $b = convertToTensor(b, 'b', 'subStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n    return sub($a, $b);\n}\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_(base, exp) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n    return pow(base, exp);\n}\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'mul');\n    const $b = convertToTensor(b, 'b', 'mul');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n    return mul($a, $b);\n}\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'div');\n    const $b = convertToTensor(b, 'b', 'div');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n    return div($a, $b);\n}\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'modStrict');\n    const $b = convertToTensor(b, 'b', 'modStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n    return mod($a, $b);\n}\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'minimumStrict');\n    const $b = convertToTensor(b, 'b', 'minimumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n    return minimum($a, $b);\n}\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'maximumStrict');\n    const $b = convertToTensor(b, 'b', 'maximumStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n    return maximum($a, $b);\n}\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_(a, b) {\n    deprecationWarn('strict variants of ops have been deprecated ' +\n        'and will be removed in future');\n    const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n    const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n    return squaredDifference($a, $b);\n}\nexport const addStrict = op({ addStrict_ });\nexport const divStrict = op({ divStrict_ });\nexport const maximumStrict = op({ maximumStrict_ });\nexport const minimumStrict = op({ minimumStrict_ });\nexport const modStrict = op({ modStrict_ });\nexport const mulStrict = op({ mulStrict_ });\nexport const powStrict = op({ powStrict_ });\nexport const squaredDifferenceStrict = op({ squaredDifferenceStrict_ });\nexport const subStrict = op({ subStrict_ });\n//# sourceMappingURL=binary_ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { parseAxisParam } from '../util';\nimport { abs } from './abs';\nimport * as axis_util from './axis_util';\nimport { max } from './max';\nimport { min } from './min';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { reshape } from './reshape';\nimport { scalar } from './scalar';\nimport { sqrt } from './sqrt';\nimport { square } from './square';\nimport { sum } from './sum';\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(x, ord = 'euclidean', axis = null, keepDims = false) {\n    x = convertToTensor(x, 'x', 'norm');\n    const norm = normImpl(x, ord, axis);\n    let keepDimsShape = norm.shape;\n    if (keepDims) {\n        const axes = parseAxisParam(axis, x.shape);\n        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n    }\n    return reshape(norm, keepDimsShape);\n}\nfunction normImpl(x, p, axis = null) {\n    if (x.rank === 0) {\n        return abs(x);\n    }\n    // consider vector when no axis is specified\n    if (x.rank !== 1 && axis === null) {\n        return normImpl(reshape(x, [-1]), p, axis);\n    }\n    // vector\n    if (x.rank === 1 || typeof axis === 'number' ||\n        Array.isArray(axis) && axis.length === 1) {\n        if (p === 1) {\n            return sum(abs(x), axis);\n        }\n        if (p === Infinity) {\n            return max(abs(x), axis);\n        }\n        if (p === -Infinity) {\n            return min(abs(x), axis);\n        }\n        if (p === 'euclidean' || p === 2) {\n            // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n            return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    // matrix (assumption axis[0] < axis[1])\n    if (Array.isArray(axis) && axis.length === 2) {\n        if (p === 1) {\n            return max(sum(abs(x), axis[0]), axis[1] - 1);\n        }\n        if (p === Infinity) {\n            return max(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === -Infinity) {\n            return min(sum(abs(x), axis[1]), axis[0]);\n        }\n        if (p === 'fro' || p === 'euclidean') {\n            // norm(x) = sqrt(sum(pow(x, 2)))\n            return sqrt(sum(square(x), axis));\n        }\n        throw new Error(`Error in norm: invalid ord value: ${p}`);\n    }\n    throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nexport const norm = op({ norm_ });\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assertTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { pow } from './pow';\nimport { scalar } from './scalar';\nimport { sub } from './sub';\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_(v, x, decay, step, zeroDebias = true) {\n    const $v = convertToTensor(v, 'v', 'movingAverage');\n    const $x = convertToTensor(x, 'x', 'movingAverage');\n    const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n    assertTypesMatch($v, $x);\n    util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n    const one = scalar(1);\n    const oneMinusDecay = sub(one, $decay);\n    let update = mul(sub($x, $v), oneMinusDecay);\n    if (zeroDebias) {\n        util.assert(step != null, () => 'When using zeroDebias: true, step is required.');\n        const $step = convertToTensor(step, 'step', 'movingAverage');\n        update = div(update, sub(one, pow($decay, $step)));\n    }\n    return add($v, update);\n}\nexport const movingAverage = op({ movingAverage_ });\n//# sourceMappingURL=moving_average.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ScatterNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_(indices, updates, shape) {\n    const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n    const $updates = convertToTensor(updates, 'updates', 'scatterND');\n    scatter_nd_util.validateInput($updates, $indices, shape);\n    const forward = (backend) => {\n        return backend.scatterND($indices, $updates, shape);\n    };\n    const inputs = { indices: $indices, updates: $updates };\n    const attrs = { shape };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, ScatterNd, attrs);\n}\nexport const scatterND = op({ scatterND_ });\n//# sourceMappingURL=scatter_nd.js.map", "/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {\n    if (sparseIndices.dtype !== 'int32') {\n        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +\n            ` but the dtype was ${sparseIndices.dtype}.`);\n    }\n    if (sparseIndices.rank > 2) {\n        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +\n            ` but got shape ${sparseIndices.shape}.`);\n    }\n    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n    if (outputShape.length !== numDims) {\n        throw new Error('outputShape has incorrect number of elements:,' +\n            ` ${outputShape.length}, should be: ${numDims}.`);\n    }\n    const numValues = sparseValues.size;\n    if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n        throw new Error('sparseValues has incorrect shape ' +\n            `${sparseValues.shape}, should be [] or [${numElems}]`);\n    }\n    if (sparseValues.dtype !== defaultValues.dtype) {\n        throw new Error('sparseValues.dtype must match defaultValues.dtype');\n    }\n}\n//# sourceMappingURL=sparse_to_dense_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { SparseToDense } from '../kernel_names';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n    const $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n    const $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n    const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);\n    const inputs = {\n        sparseIndices: $sparseIndices,\n        sparseValues: $sparseValues,\n        defaultValue: $defaultValue\n    };\n    const attrs = { outputShape };\n    return ENGINE.runKernelFunc(backend => backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue), inputs, null /* grad */, SparseToDense, attrs);\n}\nexport const sparseToDense = op({ sparseToDense_ });\n//# sourceMappingURL=sparse_to_dense.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { GatherNd } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x, indices) {\n    const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n    const $x = convertToTensor(x, 'x', 'gatherND');\n    const forward = (backend) => {\n        return backend.gatherND($x, $indices);\n    };\n    const inputs = { params: $x, indices: $indices };\n    return ENGINE.runKernelFunc(forward, inputs, null /* gradient */, GatherNd);\n}\nexport const gatherND = op({ gatherND_ });\n//# sourceMappingURL=gather_nd.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x, noiseShape) {\n    if (noiseShape == null) {\n        return x.shape.slice();\n    }\n    if (util.arraysEqual(x.shape, noiseShape)) {\n        return noiseShape;\n    }\n    if (x.shape.length === noiseShape.length) {\n        const newDimension = [];\n        for (let i = 0; i < x.shape.length; i++) {\n            if (noiseShape[i] == null && x.shape[i] != null) {\n                newDimension.push(x.shape[i]);\n            }\n            else {\n                newDimension.push(noiseShape[i]);\n            }\n        }\n        return newDimension;\n    }\n    return noiseShape;\n}\n//# sourceMappingURL=dropout_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { add } from './add';\nimport { div } from './div';\nimport { getNoiseShape } from './dropout_util';\nimport { floor } from './floor';\nimport { mul } from './mul';\nimport { op } from './operation';\nimport { randomUniform } from './random_uniform';\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(x, rate, noiseShape, seed) {\n    const $x = convertToTensor(x, 'x', 'dropout');\n    util.assert($x.dtype === 'float32', () => `x has to be a floating point tensor since it's going to be ` +\n        `scaled, but got a ${$x.dtype} tensor instead.`);\n    util.assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n    if (rate === 0) {\n        return x instanceof Tensor ? $x.clone() : $x;\n    }\n    const $noiseShape = getNoiseShape($x, noiseShape);\n    const keepProb = 1 - rate;\n    const multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb);\n    return mul($x, multiplier);\n}\nexport const dropout = op({ dropout_ });\n//# sourceMappingURL=dropout.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tensor1d } from './tensor1d';\nexport function enclosingPowerOfTwo(value) {\n    // Return 2**N for integer N such that 2**N >= value.\n    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\nexport function cosineWindow(windowLength, a, b) {\n    const even = 1 - windowLength % 2;\n    const newValues = new Float32Array(windowLength);\n    for (let i = 0; i < windowLength; ++i) {\n        const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n        newValues[i] = a - b * Math.cos(cosArg);\n    }\n    return tensor1d(newValues, 'float32');\n}\n//# sourceMappingURL=signal_ops_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';\nimport { tensor } from './tensor';\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_(predictions, targets, k = 1) {\n    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n    const $targets = convertToTensor(targets, 'targets', 'inTopK');\n    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n        `but got ${$predictions.rank}`);\n    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +\n        `targets rank, but got predictions rank ` +\n        `${$predictions.rank} and targets rank ${$targets.rank}`);\n    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +\n        'except the last dimension.');\n    const lastDim = $predictions.shape[$predictions.shape.length - 1];\n    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n        `dimension (${lastDim}), but got ${k}`);\n    const predictionsVals = await $predictions.data();\n    const targetsVals = await $targets.data();\n    // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n    // and look up topK along lastDim.\n    const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n    const precision = getTypedArrayFromDType('bool', batch);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = predictionsVals.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        precision[b] = 0;\n        for (let i = 0; i < k; i++) {\n            if (valAndInd[i].index === targetsVals[b]) {\n                precision[b] = 1;\n                break;\n            }\n        }\n    }\n    if (predictions !== $predictions) {\n        $predictions.dispose();\n    }\n    if (targets !== $targets) {\n        $targets.dispose();\n    }\n    // Output precision has the same shape as targets.\n    return tensor(precision, $targets.shape, 'bool');\n}\nexport const inTopKAsync = inTopKAsync_;\n//# sourceMappingURL=in_top_k.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv2DBackpropFilter } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n        `${x4D.shape}.`);\n    util.assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n        `${dy4D.shape}.`);\n    util.assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n        `${filterShape}.`);\n    const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n    const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n    util.assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n        `match input depth in filter (${filterShape[2]}.`);\n    util.assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n        `match output depth for filter (${filterShape[3]}).`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const dilations = 1;\n        const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n        const convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);\n        return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dataFormat, dimRoundingMode, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv2DBackpropFilter, attrs);\n}\nexport const conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n//# sourceMappingURL=conv2d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as broadcast_util from './broadcast_util';\nimport { elu } from './elu';\nimport { mul } from './mul';\nimport { prelu } from './prelu';\nimport { relu } from './relu';\nimport { relu6 } from './relu6';\nimport { reshape } from './reshape';\nimport { step } from './step';\nimport { sum } from './sum';\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(dy, y, activation) {\n    if (activation == null || activation === 'linear') {\n        return dy;\n    }\n    if (activation === 'relu') {\n        return mul(dy, step(y));\n    }\n    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);\n}\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(bias, dyActivation) {\n    let res = dyActivation;\n    const reduceAxes = broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n    if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n    }\n    return reshape(res, bias.shape);\n}\nexport function applyActivation(x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return x;\n    }\n    else if (activation === 'relu') {\n        return relu(x);\n    }\n    else if (activation === 'elu') {\n        return elu(x);\n    }\n    else if (activation === 'relu6') {\n        return relu6(x);\n    }\n    else if (activation === 'prelu') {\n        return prelu(x, preluActivationWeights);\n    }\n    throw new Error(`Unknown fused activation ${activation}.`);\n}\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth, activation) => {\n    const gradientMode = gradientDepth > 0;\n    return !gradientMode || activation === 'linear';\n};\n//# sourceMappingURL=fused_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { conv2d as unfusedConv2d } from '../conv2d';\nimport { conv2DBackpropFilter } from '../conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    activation = activation || 'linear';\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'conv2d');\n    const $filter = convertToTensor(filter, 'filter', 'conv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n        `${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n        `${$filter.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n        `input depth for filter ${$filter.shape[2]}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    util.assert(dataFormat === 'NHWC', () => `Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');\n    }\n    const grad = (dy, saved) => {\n        const [$filter, x4D, y, $bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n        const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n        const der = [xDer, filterDer];\n        if ($bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            der.push(biasDer);\n        }\n        return der;\n    };\n    const forward = (backend) => {\n        const res = backend.fusedConv2d({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const conv2d = op({ fusedConv2d_ });\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropFilter } from '../kernel_names';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations = [1, 1], dimRoundingMode) {\n    let x4D = x;\n    if (x.rank === 3) {\n        x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n    }\n    let dy4D = dy;\n    if (dy4D.rank === 3) {\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        return backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n    };\n    const inputs = { x: x4D, dy: dy4D };\n    const attrs = { strides, pad, dimRoundingMode, dilations, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropFilter, attrs);\n}\nexport const depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { DepthwiseConv2dNativeBackpropInput } from '../kernel_names';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations = [1, 1], dimRoundingMode) {\n    let dy4D = dy;\n    let reshapedTo4D = false;\n    if (dy.rank === 3) {\n        reshapedTo4D = true;\n        dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computeConv2DInfo(xShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n        return backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n    };\n    const inputs = { dy: dy4D, filter };\n    const attrs = { strides, pad, dimRoundingMode, dilations, inputShape: xShape };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, DepthwiseConv2dNativeBackpropInput, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n//# sourceMappingURL=depthwise_conv2d_native_backprop_input.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { FusedDepthwiseConv2D } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport { depthwiseConv2d as unfusedDepthwiseConv2d } from '../depthwise_conv2d';\nimport { depthwiseConv2dNativeBackpropFilter } from '../depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../depthwise_conv2d_native_backprop_input';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedDepthwiseConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n    const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n    let x4D = $x;\n    let reshapedTo4D = false;\n    if ($x.rank === 3) {\n        reshapedTo4D = true;\n        x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    }\n    util.assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n        `rank ${x4D.rank}.`);\n    util.assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n        `but got rank ${$filter.rank}.`);\n    util.assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels ` +\n        `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n        `filter ${$filter.shape[2]}.`);\n    if (dilations == null) {\n        dilations = [1, 1];\n    }\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n        `be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n        [$bias] = makeTypesMatch($bias, $x);\n        broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n    }\n    const grad = (dy, saved) => {\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n        const [$filter, x4D, y, bias] = saved;\n        const dyActivation = getFusedDyActivation(dy, y, activation);\n        const xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode);\n        const filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode);\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [xDer, filterDer, biasDer];\n        }\n        return [xDer, filterDer];\n    };\n    const forward = (backend) => {\n        const res = backend.fusedDepthwiseConv2D({\n            input: x4D,\n            filter: $filter,\n            convInfo,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return res;\n    };\n    const inputs = {\n        x: x4D,\n        filter: $filter,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { strides, pad, dataFormat, dilations, dimRoundingMode, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((x4D, filter, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOp(x4D, $filter);\n    }\n    else {\n        const customOpWithBias = customGrad((x4D, filter, bias, save) => {\n            let res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, FusedDepthwiseConv2D, attrs);\n            save([filter, x4D, res, bias]);\n            if (reshapedTo4D) {\n                res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n            }\n            return { value: res, gradFunc: grad };\n        });\n        return customOpWithBias(x4D, $filter, $bias);\n    }\n}\nexport const depthwiseConv2d = op({ fusedDepthwiseConv2d_ });\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { customGrad } from '../../gradients';\nimport { _FusedMatMul } from '../../kernel_names';\nimport { makeTypesMatch } from '../../tensor_util';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { add } from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';\nimport { matMul as unfusedMatMul } from '../mat_mul';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = 'linear', preluActivationWeights }) {\n    if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n        let result = unfusedMatMul(a, b, transposeA, transposeB);\n        if (bias != null) {\n            result = add(result, bias);\n        }\n        return applyActivation(result, activation, preluActivationWeights);\n    }\n    let $a = convertToTensor(a, 'a', 'fused matMul');\n    let $b = convertToTensor(b, 'b', 'fused matMul');\n    [$a, $b] = makeTypesMatch($a, $b);\n    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, () => `Error in fused matMul: inputs must have the same rank of at least ` +\n        `2, got ranks ${$a.rank} and ${$b.rank}.`);\n    util.assert(util.arraysEqual(outerDimsA, outerDimsB), () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n        `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} must match.`);\n    util.assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n        `${$b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n    let $bias;\n    if (bias != null) {\n        $bias = convertToTensor(bias, 'bias', 'fused matMul');\n        [$bias] = makeTypesMatch($bias, $a);\n        broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n    }\n    let $preluActivationWeights;\n    if (preluActivationWeights != null) {\n        $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul');\n    }\n    const grad = (dy, saved) => {\n        const [a3D, b3D, y, $bias] = saved;\n        // we reshape dy because the result of the forward is not\n        // necessarily going to be a 3d tensor due to a reshape done at the end of\n        // the customOp.\n        const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);\n        let aDer;\n        let bDer;\n        if (!transposeA && !transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, true, false);\n        }\n        else if (!transposeA && transposeB) {\n            aDer = unfusedMatMul(dyActivation, b3D, false, false);\n            bDer = unfusedMatMul(dyActivation, a3D, true, false);\n        }\n        else if (transposeA && !transposeB) {\n            aDer = unfusedMatMul(b3D, dyActivation, false, true);\n            bDer = unfusedMatMul(a3D, dyActivation, false, false);\n        }\n        else {\n            aDer = unfusedMatMul(b3D, dyActivation, true, true);\n            bDer = unfusedMatMul(dyActivation, a3D, true, true);\n        }\n        if (bias != null) {\n            const biasDer = getFusedBiasGradient($bias, dyActivation);\n            return [aDer, bDer, biasDer];\n        }\n        else {\n            return [aDer, bDer];\n        }\n    };\n    const forward = (backend) => {\n        const y = backend.fusedBatchMatMul({\n            a: a3D,\n            b: b3D,\n            transposeA,\n            transposeB,\n            bias: $bias,\n            activation,\n            preluActivationWeights: $preluActivationWeights\n        });\n        return y;\n    };\n    const inputs = {\n        a: a3D,\n        b: b3D,\n        bias: $bias,\n        preluActivationWeights: $preluActivationWeights\n    };\n    const attrs = { transposeA, transposeB, activation };\n    // Depending on the the params passed in we will have different number of\n    // inputs and thus a a different number of elements in the gradient.\n    if (bias == null) {\n        const customOp = customGrad((a3D, b3D, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOp(a3D, b3D);\n    }\n    else {\n        const customOpWithBias = customGrad((a3D, b3D, $bias, save) => {\n            const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, _FusedMatMul, attrs);\n            save([a3D, b3D, res, $bias]);\n            return { value: reshape(res, outShape), gradFunc: grad };\n        });\n        return customOpWithBias(a3D, b3D, $bias);\n    }\n}\nexport const matMul = op({ fusedMatMul_ });\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from './fused/conv2d';\nimport { depthwiseConv2d } from './fused/depthwise_conv2d';\nimport { matMul } from './fused/mat_mul';\nexport { conv2d, depthwiseConv2d, matMul };\n//# sourceMappingURL=fused_ops.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({ hammingWindow_ });\n//# sourceMappingURL=hamming_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { op } from '../operation';\nimport { cosineWindow } from '../signal_ops_util';\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength) {\n    return cosineWindow(windowLength, 0.5, 0.5);\n}\nexport const hannWindow = op({ hannWindow_ });\n//# sourceMappingURL=hann_window.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { fill } from '../fill';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { tensor2d } from '../tensor2d';\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {\n    let start = 0;\n    const output = [];\n    while (start + frameLength <= signal.size) {\n        output.push(slice(signal, start, frameLength));\n        start += frameStep;\n    }\n    if (padEnd) {\n        while (start < signal.size) {\n            const padLen = (start + frameLength) - signal.size;\n            const pad = concat([\n                slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n            ]);\n            output.push(pad);\n            start += frameStep;\n        }\n    }\n    if (output.length === 0) {\n        return tensor2d([], [0, frameLength]);\n    }\n    return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({ frame_ });\n//# sourceMappingURL=frame.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../concat';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { enclosingPowerOfTwo } from '../signal_ops_util';\nimport { slice } from '../slice';\nimport { rfft } from '../spectral/rfft';\nimport { frame } from './frame';\nimport { hannWindow } from './hann_window';\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(signal, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n    if (fftLength == null) {\n        fftLength = enclosingPowerOfTwo(frameLength);\n    }\n    const framedSignal = frame(signal, frameLength, frameStep);\n    const windowedSignal = mul(framedSignal, windowFn(frameLength));\n    const output = [];\n    for (let i = 0; i < framedSignal.shape[0]; i++) {\n        output.push(rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n    }\n    return concat(output);\n}\nexport const stft = op({ stft_ });\n//# sourceMappingURL=stft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { CropAndResize } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {\n    const $image = convertToTensor(image, 'image', 'cropAndResize');\n    const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n    const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n    method = method || 'bilinear';\n    extrapolationValue = extrapolationValue || 0;\n    const numBoxes = $boxes.shape[0];\n    util.assert($image.rank === 4, () => 'Error in cropAndResize: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n        `but had shape ${$boxes.shape}.`);\n    util.assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n        `length ${cropSize.length}.`);\n    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n    util.assert(method === 'bilinear' || method === 'nearest', () => `method must be bilinear or nearest, but was ${method}`);\n    const forward = (backend) => backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n    const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n    const attrs = { method, extrapolationValue, cropSize };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, CropAndResize, attrs);\n    return res;\n}\nexport const cropAndResize = op({ cropAndResize_ });\n//# sourceMappingURL=crop_and_resize.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { FlipLeftRight } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image) {\n    const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n    util.assert($image.rank === 4, () => 'Error in flipLeftRight: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n    return res;\n}\nexport const flipLeftRight = op({ flipLeftRight_ });\n//# sourceMappingURL=flip_left_right.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { RotateWithOffset } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {\n    const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n    util.assert($image.rank === 4, () => 'Error in rotateWithOffset: image must be rank 4,' +\n        `but got rank ${$image.rank}.`);\n    const inputs = { image: $image };\n    const attrs = { radians, fillValue, center };\n    const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n    return res;\n}\nexport const rotateWithOffset = op({ rotateWithOffset_ });\n//# sourceMappingURL=rotate_with_offset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from '../util';\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    if (iouThreshold == null) {\n        iouThreshold = 0.5;\n    }\n    if (scoreThreshold == null) {\n        scoreThreshold = Number.NEGATIVE_INFINITY;\n    }\n    if (softNmsSigma == null) {\n        softNmsSigma = 0.0;\n    }\n    const numBoxes = boxes.shape[0];\n    maxOutputSize = Math.min(maxOutputSize, numBoxes);\n    util.assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n    util.assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n    util.assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n    util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n    util.assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n        `but was ${scores.shape[0]}`);\n    util.assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n    return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\nexport { nonMaxSuppSanityCheck };\n//# sourceMappingURL=nonmax_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV3 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n    return ENGINE.runKernelFunc(b => b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold), { boxes: $boxes, scores: $scores }, null /* grad */, NonMaxSuppressionV3, attrs);\n}\nexport const nonMaxSuppression = op({ nonMaxSuppression_ });\n//# sourceMappingURL=non_max_suppression.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert(arr, element, comparator) {\n    const index = binarySearch(arr, element, comparator);\n    const insertionPoint = index < 0 ? -(index + 1) : index;\n    arr.splice(insertionPoint, 0, element);\n}\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch(arr, target, comparator) {\n    return binarySearch_(arr, target, comparator || defaultComparator);\n}\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator(a, b) {\n    return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n    let left = 0;\n    let right = arr.length;\n    let middle = 0;\n    let found = false;\n    while (left < right) {\n        middle = left + ((right - left) >>> 1);\n        const compareResult = comparator(target, arr[middle]);\n        if (compareResult > 0) {\n            left = middle + 1;\n        }\n        else {\n            right = middle;\n            // If compareResult is 0, the value is found. We record it is found,\n            // and then keep looking because there may be duplicate.\n            found = !compareResult;\n        }\n    }\n    return found ? left : -left - 1;\n}\n//# sourceMappingURL=array_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { binaryInsert } from './array_util';\nexport function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */)\n        .selectedIndices;\n}\nexport function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true\n    /* returnValidOutputs */ );\n}\nexport function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n    // The list is sorted in ascending order, so that we can always pop the\n    // candidate with the largest score in O(1) time.\n    const candidates = [];\n    for (let i = 0; i < scores.length; i++) {\n        if (scores[i] > scoreThreshold) {\n            candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n        }\n    }\n    candidates.sort(ascendingComparator);\n    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n    // before.\n    const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n    const selectedIndices = [];\n    const selectedScores = [];\n    while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n        const candidate = candidates.pop();\n        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n        if (originalScore < scoreThreshold) {\n            break;\n        }\n        // Overlapping boxes are likely to have similar scores, therefore we\n        // iterate through the previously selected boxes backwards in order to\n        // see if candidate's score should be suppressed. We use\n        // suppressBeginIndex to track and ensure a candidate can be suppressed\n        // by a selected box no more than once. Also, if the overlap exceeds\n        // iouThreshold, we simply ignore the candidate.\n        let ignoreCandidate = false;\n        for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n            if (iou >= iouThreshold) {\n                ignoreCandidate = true;\n                break;\n            }\n            candidate.score =\n                candidate.score * suppressWeight(iouThreshold, scale, iou);\n            if (candidate.score <= scoreThreshold) {\n                break;\n            }\n        }\n        // At this point, if `candidate.score` has not dropped below\n        // `scoreThreshold`, then we know that we went through all of the\n        // previous selections and can safely update `suppressBeginIndex` to the\n        // end of the selected array. Then we can re-insert the candidate with\n        // the updated score and suppressBeginIndex back in the candidate list.\n        // If on the other hand, `candidate.score` has dropped below the score\n        // threshold, we will not add it back to the candidates list.\n        candidate.suppressBeginIndex = selectedIndices.length;\n        if (!ignoreCandidate) {\n            // Candidate has passed all the tests, and is not suppressed, so\n            // select the candidate.\n            if (candidate.score === originalScore) {\n                selectedIndices.push(boxIndex);\n                selectedScores.push(candidate.score);\n            }\n            else if (candidate.score > scoreThreshold) {\n                // Candidate's score is suppressed but is still high enough to be\n                // considered, so add back to the candidates list.\n                binaryInsert(candidates, candidate, ascendingComparator);\n            }\n        }\n    }\n    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n    const validOutputs = selectedIndices.length;\n    const elemsToPad = maxOutputSize - validOutputs;\n    if (padToMaxOutputSize && elemsToPad > 0) {\n        selectedIndices.push(...new Array(elemsToPad).fill(0));\n        selectedScores.push(...new Array(elemsToPad).fill(0.0));\n    }\n    const result = { selectedIndices: tensor1d(selectedIndices, 'int32') };\n    if (returnScoresTensor) {\n        result['selectedScores'] = tensor1d(selectedScores, 'float32');\n    }\n    if (returnValidOutputs) {\n        result['validOutputs'] = scalar(validOutputs, 'int32');\n    }\n    return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n    const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n    const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n    const yminI = Math.min(iCoord[0], iCoord[2]);\n    const xminI = Math.min(iCoord[1], iCoord[3]);\n    const ymaxI = Math.max(iCoord[0], iCoord[2]);\n    const xmaxI = Math.max(iCoord[1], iCoord[3]);\n    const yminJ = Math.min(jCoord[0], jCoord[2]);\n    const xminJ = Math.min(jCoord[1], jCoord[3]);\n    const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n    const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n    const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n    if (areaI <= 0 || areaJ <= 0) {\n        return 0.0;\n    }\n    const intersectionYmin = Math.max(yminI, yminJ);\n    const intersectionXmin = Math.max(xminI, xminJ);\n    const intersectionYmax = Math.min(ymaxI, ymaxJ);\n    const intersectionXmax = Math.min(xmaxI, xmaxJ);\n    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\n    return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold, scale, iou) {\n    const weight = Math.exp(scale * iou * iou);\n    return iou <= iouThreshold ? weight : 0.0;\n}\nfunction ascendingComparator(c1, c2) {\n    // For objects with same scores, we make the object with the larger index go\n    // first. In an array that pops from the end, this means that the object with\n    // the smaller index will be popped first. This ensures the same output as\n    // the TensorFlow python version.\n    return (c1.score - c2.score) ||\n        ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n//# sourceMappingURL=non_max_suppression_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV3Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n    maxOutputSize = inputs.maxOutputSize;\n    iouThreshold = inputs.iouThreshold;\n    scoreThreshold = inputs.scoreThreshold;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n//# sourceMappingURL=non_max_suppression_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV5 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n    const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n    return { selectedIndices: result[0], selectedScores: result[1] };\n}\nexport const nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n//# sourceMappingURL=non_max_suppression_with_score.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV5Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    maxOutputSize = params.maxOutputSize;\n    iouThreshold = params.iouThreshold;\n    scoreThreshold = params.scoreThreshold;\n    softNmsSigma = params.softNmsSigma;\n    const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n    const boxesVals = boxesAndScores[0];\n    const scoresVals = boxesAndScores[1];\n    // We call a cpu based impl directly with the typedarray data  here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n//# sourceMappingURL=non_max_suppression_with_score_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { NonMaxSuppressionV4 } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\nimport { op } from '../operation';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const inputs = { boxes: $boxes, scores: $scores };\n    const attrs = {\n        maxOutputSize: $maxOutputSize,\n        iouThreshold: $iouThreshold,\n        scoreThreshold: $scoreThreshold,\n        padToMaxOutputSize\n    };\n    const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n    return { selectedIndices: result[0], validOutputs: result[1] };\n}\nexport const nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n//# sourceMappingURL=non_max_suppression_padded.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { nonMaxSuppressionV4Impl } from '../../backends/non_max_suppression_impl';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { nonMaxSuppSanityCheck } from '../nonmax_util';\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n    const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n    const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n    const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */);\n    const $maxOutputSize = params.maxOutputSize;\n    const $iouThreshold = params.iouThreshold;\n    const $scoreThreshold = params.scoreThreshold;\n    const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n    // We call a cpu based impl directly with the typedarray data here rather\n    // than a kernel because all kernels are synchronous (and thus cannot await\n    // .data()).\n    const res = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n    if ($boxes !== boxes) {\n        $boxes.dispose();\n    }\n    if ($scores !== scores) {\n        $scores.dispose();\n    }\n    return res;\n}\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n//# sourceMappingURL=non_max_suppression_padded_async.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeBilinear } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * Bilinear resize a single 3D image or a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeBilinear');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n        `${size}.`);\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeBilinear, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeBilinear = op({ resizeBilinear_ });\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { ResizeNearestNeighbor } from '../../kernel_names';\nimport { convertToTensor } from '../../tensor_util_env';\nimport * as util from '../../util';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_(images, size, alignCorners = false) {\n    const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n    util.assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n        `rank ${$images.rank}.`);\n    util.assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n        `${size}.`);\n    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', () => '`images` must have `int32` or `float32` as dtype');\n    let batchImages = $images;\n    let reshapedTo4D = false;\n    if ($images.rank === 3) {\n        reshapedTo4D = true;\n        batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n    }\n    const [newHeight, newWidth] = size;\n    const inputs = { images: batchImages };\n    const attrs = { alignCorners, size };\n    const forward = (backend, save) => {\n        save([batchImages]);\n        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);\n    };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* gradient */, ResizeNearestNeighbor, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assert } from '../../util';\nimport { greaterEqual } from '../greater_equal';\nimport { lessEqual } from '../less_equal';\nimport { logicalAnd } from '../logical_and';\nimport { op } from '../operation';\nimport { range } from '../range';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\nimport { zeros } from '../zeros';\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_(a, numLower, numUpper) {\n    assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    const $a = convertToTensor(a, 'a', 'bandPart');\n    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n    const shape = $a.shape;\n    const [M, N] = $a.shape.slice(-2);\n    if (!(numLower <= M)) {\n        throw new Error(`bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    }\n    if (!(numUpper <= N)) {\n        throw new Error(`bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    }\n    if (numLower < 0) {\n        numLower = M;\n    }\n    if (numUpper < 0) {\n        numUpper = N;\n    }\n    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n    const j = range(0, N, 1, 'int32');\n    const ij = sub(i, j);\n    const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32')));\n    const zero = zeros([M, N], $a.dtype);\n    return reshape(stack(unstack(reshape($a, [-1, M, N]))\n        .map(mat => where(inBand, mat, zero))), shape);\n}\nexport const bandPart = op({ bandPart_ });\n//# sourceMappingURL=band_part.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { assert } from '../../util';\nimport { div } from '../div';\nimport { mul } from '../mul';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { split } from '../split';\nimport { squeeze } from '../squeeze';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs) {\n    let inputIsTensor2D;\n    if (Array.isArray(xs)) {\n        inputIsTensor2D = false;\n        assert(xs != null && xs.length > 0, () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n        const dim = xs[0].shape[0];\n        for (let i = 1; i < xs.length; ++i) {\n            assert(xs[i].shape[0] === dim, () => 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n                `(${xs[i].shape[0]} vs. ${dim})`);\n        }\n    }\n    else {\n        inputIsTensor2D = true;\n        xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n    }\n    assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n        `number of dimensions (${xs[0].shape[0]}).`);\n    const ys = [];\n    const xs1d = xs;\n    for (let i = 0; i < xs.length; ++i) {\n        ys.push(ENGINE.tidy(() => {\n            let x = xs1d[i];\n            if (i > 0) {\n                for (let j = 0; j < i; ++j) {\n                    const proj = mul(sum(mul(ys[j], x)), ys[j]);\n                    x = sub(x, proj);\n                }\n            }\n            return div(x, norm(x, 'euclidean'));\n        }));\n    }\n    if (inputIsTensor2D) {\n        return stack(ys, 0);\n    }\n    else {\n        return ys;\n    }\n}\nexport const gramSchmidt = op({ gramSchmidt_ });\n//# sourceMappingURL=gram_schmidt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../../engine';\nimport { dispose } from '../../globals';\nimport { assert } from '../../util';\nimport { clone } from '../clone';\nimport { concat } from '../concat';\nimport { div } from '../div';\nimport { eye } from '../eye';\nimport { greater } from '../greater';\nimport { matMul } from '../mat_mul';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { norm } from '../norm';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { slice } from '../slice';\nimport { stack } from '../stack';\nimport { sub } from '../sub';\nimport { tensor2d } from '../tensor2d';\nimport { transpose } from '../transpose';\nimport { unstack } from '../unstack';\nimport { where } from '../where';\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x, fullMatrices = false) {\n    assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n    if (x.rank === 2) {\n        return qr2d(x, fullMatrices);\n    }\n    else {\n        // Rank > 2.\n        // TODO(cais): Below we split the input into individual 2D tensors,\n        //   perform QR decomposition on them and then stack the results back\n        //   together. We should explore whether this can be parallelized.\n        const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n            .reduce((value, prev) => value * prev);\n        const x2ds = unstack(reshape(x, [\n            outerDimsProd, x.shape[x.shape.length - 2],\n            x.shape[x.shape.length - 1]\n        ]), 0);\n        const q2ds = [];\n        const r2ds = [];\n        x2ds.forEach(x2d => {\n            const [q2d, r2d] = qr2d(x2d, fullMatrices);\n            q2ds.push(q2d);\n            r2ds.push(r2d);\n        });\n        const q = reshape(stack(q2ds, 0), x.shape);\n        const r = reshape(stack(r2ds, 0), x.shape);\n        return [q, r];\n    }\n}\nfunction qr2d(x, fullMatrices = false) {\n    return ENGINE.tidy(() => {\n        assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n        const m = x.shape[0];\n        const n = x.shape[1];\n        let q = eye(m); // Orthogonal transform so far.\n        let r = clone(x); // Transformed matrix so far.\n        const one2D = tensor2d([[1]], [1, 1]);\n        let w = clone(one2D);\n        const iters = m >= n ? n : m;\n        for (let j = 0; j < iters; ++j) {\n            // This tidy within the for-loop ensures we clean up temporary\n            // tensors as soon as they are no longer needed.\n            const rTemp = r;\n            const wTemp = w;\n            const qTemp = q;\n            [w, r, q] = ENGINE.tidy(() => {\n                // Find H = I - tau * w * w', to put zeros below R(j, j).\n                const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n                const normX = norm(rjEnd1);\n                const rjj = slice(r, [j, j], [1, 1]);\n                // The sign() function returns 0 on 0, which causes division by zero.\n                const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n                const u1 = sub(rjj, mul(s, normX));\n                const wPre = div(rjEnd1, u1);\n                if (wPre.shape[0] === 1) {\n                    w = clone(one2D);\n                }\n                else {\n                    w = concat([\n                        one2D,\n                        slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n                    ], 0);\n                }\n                const tau = neg(div(matMul(s, u1), normX));\n                // -- R := HR, Q := QH.\n                const rjEndAll = slice(r, [j, 0], [m - j, n]);\n                const tauTimesW = mul(tau, w);\n                const wT = transpose(w);\n                if (j === 0) {\n                    r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                }\n                else {\n                    const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n                    r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n                }\n                const tawTimesWT = transpose(tauTimesW);\n                const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n                if (j === 0) {\n                    q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                }\n                else {\n                    const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n                    q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n                }\n                return [w, r, q];\n            });\n            dispose([rTemp, wTemp, qTemp]);\n        }\n        if (!fullMatrices && m > n) {\n            q = slice(q, [0, 0], [m, n]);\n            r = slice(r, [0, 0], [n, n]);\n        }\n        return [q, r];\n    });\n}\nexport const qr = op({ qr_ });\n//# sourceMappingURL=qr.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Reduction;\n(function (Reduction) {\n    Reduction[Reduction[\"NONE\"] = 0] = \"NONE\";\n    Reduction[Reduction[\"MEAN\"] = 1] = \"MEAN\";\n    Reduction[Reduction[\"SUM\"] = 2] = \"SUM\";\n    Reduction[Reduction[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n//# sourceMappingURL=loss_ops_utils.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { Reduction } from '../loss_ops_utils';\nimport { mean } from '../mean';\nimport { mul } from '../mul';\nimport { notEqual } from '../not_equal';\nimport { ones } from '../ones';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sum } from '../sum';\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_(losses, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n    }\n    const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n    if (reduction === Reduction.NONE) {\n        return weightedLoss;\n    }\n    if (reduction === Reduction.SUM) {\n        return sum(weightedLoss);\n    }\n    if (reduction === Reduction.MEAN) {\n        if ($weights == null) {\n            return mean(weightedLoss);\n        }\n        else {\n            const broadcastFactor = $losses.size / $weights.size;\n            const result = div(sum(weightedLoss), sum($weights));\n            return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                result;\n        }\n    }\n    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n        if ($weights == null) {\n            return div(sum(weightedLoss), scalar($losses.size));\n        }\n        else {\n            const broadcastedWeights = mul($weights, ones($losses.shape));\n            const numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n            return div(sum(weightedLoss), numNonZeros);\n        }\n    }\n    throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({ computeWeightedLoss_ });\n//# sourceMappingURL=compute_weighted_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n    const $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n    const losses = abs(sub($labels, $predictions));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const absoluteDifference = op({ absoluteDifference_ });\n//# sourceMappingURL=absolute_difference.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n    const $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n    const one = scalar(1);\n    const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({ cosineDistance_ });\n//# sourceMappingURL=cosine_distance.js.map", "import { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n    const one = scalar(1);\n    // Convert binary labels to (-1, 1)\n    $labels = sub(mul(scalar(2), $labels), one);\n    const losses = relu(sub(one, mul($labels, $predictions)));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({ hingeLoss_ });\n//# sourceMappingURL=hinge_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { Reduction } from '../loss_ops_utils';\nimport { minimum } from '../minimum';\nimport { mul } from '../mul';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { square } from '../square';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'huberLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n    const deltaScalar = scalar(delta);\n    const error = abs(sub($predictions, $labels));\n    const quadratic = minimum(error, deltaScalar);\n    const linear = sub(error, quadratic);\n    const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({ huberLoss_ });\n//# sourceMappingURL=huber_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { log } from '../log';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'logLoss');\n    const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'logLoss');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n    const one = scalar(1);\n    const epsilonScalar = scalar(epsilon);\n    const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n    const l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n    const losses = sub(l1, l2);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({ logLoss_ });\n//# sourceMappingURL=log_loss.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { Reduction } from '../loss_ops_utils';\nimport { op } from '../operation';\nimport { squaredDifference } from '../squared_difference';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n    }\n    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n    const losses = squaredDifference($labels, $predictions);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({ meanSquaredError_ });\n//# sourceMappingURL=mean_squared_error.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { abs } from '../abs';\nimport { add } from '../add';\nimport { exp } from '../exp';\nimport { log1p } from '../log1p';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { relu } from '../relu';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { computeWeightedLoss } from './compute_weighted_loss';\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n    const $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n    assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n    /**\n     * Implementation Details:\n     *\n     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n     *   = (1 - z) * x + log(1 + exp(-x))\n     *   = x - x * z + log(1 + exp(-x))\n     *\n     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n     *     x - x * z + log(1 + exp(-x))\n     *   = log(exp(x)) - x * z + log(1 + exp(-x))\n     *   = - x * z + log(1 + exp(x))\n     *\n     * Hence, to ensure stability and avoid overflow, the implementation uses\n     * this equivalent formulation:\n     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n     */\n    const maxOutput = relu($logits);\n    const outputXTarget = mul($logits, $labels);\n    const sigmoidOutput = log1p(exp(neg(abs($logits))));\n    return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n    }\n    assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const half = scalar(0.5);\n        $multiClassLabels =\n            add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n    }\n    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n//# sourceMappingURL=sigmoid_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { customGrad } from '../../gradients';\nimport { convertToTensor } from '../../tensor_util_env';\nimport { assertShapesMatch } from '../../util';\nimport { add } from '../add';\nimport { expandShapeToKeepDim } from '../axis_util';\nimport { cast } from '../cast';\nimport { div } from '../div';\nimport { exp } from '../exp';\nimport { logSumExp } from '../log_sum_exp';\nimport { Reduction } from '../loss_ops_utils';\nimport { mul } from '../mul';\nimport { neg } from '../neg';\nimport { op } from '../operation';\nimport { reshape } from '../reshape';\nimport { scalar } from '../scalar';\nimport { sub } from '../sub';\nimport { sum } from '../sum';\nimport { computeWeightedLoss } from './compute_weighted_loss';\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n    if (dim === -1) {\n        dim = logits.rank - 1;\n    }\n    if (dim !== logits.rank - 1) {\n        throw Error(`Softmax cross entropy along a non-last dimension is not yet ` +\n            `supported. Labels / logits was rank ${logits.rank} ` +\n            `and dim was ${dim}`);\n    }\n    // Use a custom gradient for numerical stability.\n    const customOp = customGrad((labels, logits, save) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n        const costVector = neg(mul(logResult, labels));\n        const value = sum(costVector, [dim]);\n        const gradFunc = (dy, saved) => {\n            const [labels, logResult] = saved;\n            const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n            return [\n                mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))),\n                mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))),\n            ];\n        };\n        return { value, gradFunc };\n    });\n    return customOp(labels, logits);\n}\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    let $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n    const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n    let $weights = null;\n    if (weights != null) {\n        $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n    }\n    assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n    if (labelSmoothing > 0) {\n        const labelSmoothingScalar = scalar(labelSmoothing);\n        const one = scalar(1);\n        const numClasses = scalar($onehotLabels.shape[1]);\n        $onehotLabels =\n            add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n    }\n    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n    return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n//# sourceMappingURL=softmax_cross_entropy.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Modularized ops.\nexport { abs } from './abs';\nexport { acos } from './acos';\nexport { acosh } from './acosh';\nexport { add } from './add';\nexport { addN } from './add_n';\nexport { all } from './all';\nexport { any } from './any';\nexport { argMax } from './arg_max';\nexport { argMin } from './arg_min';\nexport { asin } from './asin';\nexport { asinh } from './asinh';\nexport { atan } from './atan';\nexport { atan2 } from './atan2';\nexport { atanh } from './atanh';\nexport { avgPool } from './avg_pool';\nexport { avgPool3d } from './avg_pool_3d';\nexport { basicLSTMCell } from './basic_lstm_cell';\nexport { batchToSpaceND } from './batch_to_space_nd';\nexport { batchNorm } from './batchnorm';\nexport { batchNorm2d } from './batchnorm2d';\nexport { batchNorm3d } from './batchnorm3d';\nexport { batchNorm4d } from './batchnorm4d';\nexport { broadcastTo } from './broadcast_to';\nexport { buffer } from './buffer';\nexport { cast } from './cast';\nexport { ceil } from './ceil';\nexport { clipByValue } from './clip_by_value';\nexport { clone } from './clone';\nexport { complex } from './complex';\nexport { concat } from './concat';\nexport { concat1d } from './concat_1d';\nexport { concat2d } from './concat_2d';\nexport { concat3d } from './concat_3d';\nexport { concat4d } from './concat_4d';\nexport { conv1d } from './conv1d';\nexport { conv2d } from './conv2d';\nexport { conv2dTranspose } from './conv2d_transpose';\nexport { conv3d } from './conv3d';\nexport { conv3dTranspose } from './conv3d_transpose';\nexport { cos } from './cos';\nexport { cosh } from './cosh';\nexport { cumsum } from './cumsum';\nexport { depthToSpace } from './depth_to_space';\nexport { depthwiseConv2d } from './depthwise_conv2d';\nexport { diag } from './diag';\nexport { dilation2d } from './dilation2d';\nexport { div } from './div';\nexport { divNoNan } from './div_no_nan';\nexport { dot } from './dot';\nexport { elu } from './elu';\nexport { equal } from './equal';\nexport { erf } from './erf';\nexport { exp } from './exp';\nexport { expandDims } from './expand_dims';\nexport { expm1 } from './expm1';\nexport { eye } from './eye';\nexport { fill } from './fill';\nexport { floor } from './floor';\nexport { floorDiv } from './floorDiv';\nexport { gather } from './gather';\nexport { greater } from './greater';\nexport { greaterEqual } from './greater_equal';\nexport { imag } from './imag';\nexport { isFinite } from './is_finite';\nexport { isInf } from './is_inf';\nexport { isNaN } from './is_nan';\nexport { leakyRelu } from './leaky_relu';\nexport { less } from './less';\nexport { lessEqual } from './less_equal';\nexport { linspace } from './linspace';\nexport { localResponseNormalization } from './local_response_normalization';\nexport { log } from './log';\nexport { log1p } from './log1p';\nexport { logSigmoid } from './log_sigmoid';\nexport { logSoftmax } from './log_softmax';\nexport { logSumExp } from './log_sum_exp';\nexport { logicalAnd } from './logical_and';\nexport { logicalNot } from './logical_not';\nexport { logicalOr } from './logical_or';\nexport { logicalXor } from './logical_xor';\nexport { matMul } from './mat_mul';\nexport { max } from './max';\nexport { maxPool } from './max_pool';\nexport { maxPool3d } from './max_pool_3d';\nexport { maxPoolWithArgmax } from './max_pool_with_argmax';\nexport { maximum } from './maximum';\nexport { mean } from './mean';\nexport { min } from './min';\nexport { minimum } from './minimum';\nexport { mirrorPad } from './mirror_pad';\nexport { mod } from './mod';\nexport { moments } from './moments';\nexport { mul } from './mul';\nexport { multiRNNCell } from './multi_rnn_cell';\nexport { multinomial } from './multinomial';\nexport { neg } from './neg';\nexport { notEqual } from './not_equal';\nexport { oneHot } from './one_hot';\nexport { ones } from './ones';\nexport { onesLike } from './ones_like';\nexport { outerProduct } from './outer_product';\nexport { pad } from './pad';\nexport { pad1d } from './pad1d';\nexport { pad2d } from './pad2d';\nexport { pad3d } from './pad3d';\nexport { pad4d } from './pad4d';\nexport { pool } from './pool';\nexport { pow } from './pow';\nexport { prelu } from './prelu';\nexport { print } from './print';\nexport { prod } from './prod';\nexport { rand } from './rand';\nexport { randomGamma } from './random_gamma';\nexport { randomNormal } from './random_normal';\nexport { randomUniform } from './random_uniform';\nexport { range } from './range';\nexport { real } from './real';\nexport { reciprocal } from './reciprocal';\nexport { relu } from './relu';\nexport { relu6 } from './relu6';\nexport { reshape } from './reshape';\nexport { reverse } from './reverse';\nexport { reverse1d } from './reverse_1d';\nexport { reverse2d } from './reverse_2d';\nexport { reverse3d } from './reverse_3d';\nexport { reverse4d } from './reverse_4d';\nexport { round } from './round';\nexport { rsqrt } from './rsqrt';\nexport { scalar } from './scalar';\nexport { selu } from './selu';\nexport { separableConv2d } from './separable_conv2d';\nexport { setdiff1dAsync } from './setdiff1d_async';\nexport { sigmoid } from './sigmoid';\nexport { sign } from './sign';\nexport { sin } from './sin';\nexport { sinh } from './sinh';\nexport { slice } from './slice';\nexport { slice1d } from './slice1d';\nexport { slice2d } from './slice2d';\nexport { slice3d } from './slice3d';\nexport { slice4d } from './slice4d';\nexport { softmax } from './softmax';\nexport { softplus } from './softplus';\nexport { spaceToBatchND } from './space_to_batch_nd';\nexport { fft } from './spectral/fft';\nexport { ifft } from './spectral/ifft';\nexport { irfft } from './spectral/irfft';\nexport { rfft } from './spectral/rfft';\nexport { split } from './split';\nexport { sqrt } from './sqrt';\nexport { square } from './square';\nexport { squaredDifference } from './squared_difference';\nexport { squeeze } from './squeeze';\nexport { stack } from './stack';\nexport { step } from './step';\nexport { stridedSlice } from './strided_slice';\nexport { sub } from './sub';\nexport { sum } from './sum';\nexport { tan } from './tan';\nexport { tanh } from './tanh';\nexport { tensor } from './tensor';\nexport { tensor1d } from './tensor1d';\nexport { tensor2d } from './tensor2d';\nexport { tensor3d } from './tensor3d';\nexport { tensor4d } from './tensor4d';\nexport { tensor5d } from './tensor5d';\nexport { tensor6d } from './tensor6d';\nexport { tile } from './tile';\nexport { topk } from './topk';\nexport { truncatedNormal } from './truncated_normal';\nexport { unique } from './unique';\nexport { unsortedSegmentSum } from './unsorted_segment_sum';\nexport { unstack } from './unstack';\nexport { variable } from './variable';\nexport { where } from './where';\nexport { whereAsync } from './where_async';\nexport { zeros } from './zeros';\nexport { zerosLike } from './zeros_like';\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\nexport { op, OP_SCOPE_SUFFIX } from './operation';\nimport { rfft } from './spectral/rfft';\nimport { fft } from './spectral/fft';\nimport { ifft } from './spectral/ifft';\nimport { irfft } from './spectral/irfft';\nconst spectral = {\n    fft,\n    ifft,\n    rfft,\n    irfft\n};\nimport * as fused from './fused_ops';\nimport { hammingWindow } from './signal/hamming_window';\nimport { hannWindow } from './signal/hann_window';\nimport { frame } from './signal/frame';\nimport { stft } from './signal/stft';\nconst signal = {\n    hammingWindow,\n    hannWindow,\n    frame,\n    stft,\n};\n// Image Ops namespace\nimport { cropAndResize } from './image/crop_and_resize';\nimport { flipLeftRight } from './image/flip_left_right';\nimport { rotateWithOffset } from './image/rotate_with_offset';\nimport { nonMaxSuppression } from './image/non_max_suppression';\nimport { nonMaxSuppressionAsync } from './image/non_max_suppression_async';\nimport { nonMaxSuppressionWithScore } from './image/non_max_suppression_with_score';\nimport { nonMaxSuppressionWithScoreAsync } from './image/non_max_suppression_with_score_async';\nimport { nonMaxSuppressionPadded } from './image/non_max_suppression_padded';\nimport { nonMaxSuppressionPaddedAsync } from './image/non_max_suppression_padded_async';\nimport { resizeBilinear } from './image/resize_bilinear';\nimport { resizeNearestNeighbor } from './image/resize_nearest_neighbor';\nconst image = {\n    flipLeftRight,\n    resizeNearestNeighbor,\n    resizeBilinear,\n    rotateWithOffset,\n    cropAndResize,\n    nonMaxSuppression,\n    nonMaxSuppressionAsync,\n    nonMaxSuppressionWithScore,\n    nonMaxSuppressionWithScoreAsync,\n    nonMaxSuppressionPadded,\n    nonMaxSuppressionPaddedAsync\n};\n// linalg namespace\nimport { bandPart } from './linalg/band_part';\nimport { gramSchmidt } from './linalg/gram_schmidt';\nimport { qr } from './linalg/qr';\nconst linalg = {\n    bandPart,\n    gramSchmidt,\n    qr\n};\n// losses namespace;\nimport { absoluteDifference } from './losses/absolute_difference';\nimport { computeWeightedLoss } from './losses/compute_weighted_loss';\nimport { cosineDistance } from './losses/cosine_distance';\nimport { hingeLoss } from './losses/hinge_loss';\nimport { huberLoss } from './losses/huber_loss';\nimport { logLoss } from './losses/log_loss';\nimport { meanSquaredError } from './losses/mean_squared_error';\nimport { sigmoidCrossEntropy } from './losses/sigmoid_cross_entropy';\nimport { softmaxCrossEntropy } from './losses/softmax_cross_entropy';\nconst losses = {\n    absoluteDifference,\n    computeWeightedLoss,\n    cosineDistance,\n    hingeLoss,\n    huberLoss,\n    logLoss,\n    meanSquaredError,\n    sigmoidCrossEntropy,\n    softmaxCrossEntropy\n};\n// Second level exports.\nexport { image, linalg, losses, spectral, fused, signal };\n//# sourceMappingURL=ops.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dispose } from '../globals';\nimport { variableGrads } from '../gradients';\nimport { scalar } from '../ops/ops';\nimport { Serializable } from '../serialization';\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport class Optimizer extends Serializable {\n    /**\n     * Executes `f()` and minimizes the scalar output of `f()` by computing\n     * gradients of y with respect to the list of trainable variables provided by\n     * `varList`. If no list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to minimize.\n     * @param returnCost Whether to return the scalar cost value produced by\n     * executing `f()`.\n     * @param varList An optional list of variables to update. If specified, only\n     * the trainable variables in varList will be updated by minimize. Defaults to\n     * all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    minimize(f, returnCost = false, varList) {\n        const { value, grads } = this.computeGradients(f, varList);\n        if (varList != null) {\n            const gradArray = varList.map(v => ({ name: v.name, tensor: grads[v.name] }));\n            this.applyGradients(gradArray);\n        }\n        else {\n            this.applyGradients(grads);\n        }\n        // Dispose gradients.\n        dispose(grads);\n        if (returnCost) {\n            return value;\n        }\n        else {\n            value.dispose();\n            return null;\n        }\n    }\n    /**\n     * The number of iterations that this optimizer instance has been invoked for.\n     */\n    get iterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return this.iterations_;\n    }\n    incrementIterations() {\n        this.iterations_ = this.iterations + 1;\n    }\n    /**\n     * Executes f() and computes the gradient of the scalar output of f() with\n     * respect to the list of trainable variables provided by `varList`. If no\n     * list is provided, it defaults to all trainable variables.\n     *\n     * @param f The function to execute and whose output to use for computing\n     * gradients with respect to variables.\n     * @param varList An optional list of variables to compute gradients with\n     * respect to. If specified, only the trainable variables in varList will have\n     * gradients computed with respect to. Defaults to all trainable variables.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers'}\n     */\n    computeGradients(f, varList) {\n        return variableGrads(f, varList);\n    }\n    /**\n     * Dispose the variables (if any) owned by this optimizer instance.\n     */\n    dispose() {\n        if (this.iterations_ != null) {\n            dispose(this.iterations_);\n        }\n    }\n    async saveIterations() {\n        if (this.iterations_ == null) {\n            this.iterations_ = 0;\n        }\n        return {\n            name: 'iter',\n            // TODO(cais): Use 'int64' type when available.\n            tensor: scalar(this.iterations_, 'int32')\n        };\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for this optimizer yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error(`setWeights() is not implemented for this optimizer class ` +\n            `${this.getClassName()}`);\n    }\n    /**\n     * Extract the first element of the weight values and set it\n     * as the iterations counter variable of this instance of optimizer.\n     *\n     * @param weightValues\n     * @returns Weight values with the first element consumed and excluded.\n     */\n    async extractIterations(weightValues) {\n        this.iterations_ = (await weightValues[0].tensor.data())[0];\n        return weightValues.slice(1);\n    }\n}\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n    value: (instance) => {\n        return instance.minimize != null && instance.computeGradients != null &&\n            instance.applyGradients != null;\n    }\n});\n//# sourceMappingURL=optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/ops';\nimport { square } from '../ops/square';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n    constructor(learningRate, rho, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.rho = rho;\n        this.epsilon = epsilon;\n        this.accumulatedGrads = [];\n        this.accumulatedUpdates = [];\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedGrads[i] == null) {\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accum_grad`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedUpdates[i] == null) {\n                this.accumulatedUpdates[i] = {\n                    originalName: `${name}/accum_var`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);\n                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                accumulatedUpdate.assign(newAccumulatedUpdate);\n                const newValue = add(mul(updates, -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedUpdates != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n            dispose(this.accumulatedUpdates.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedGrads =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedUpdates =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'rho': this.rho,\n            'epsilon': this.epsilon\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['rho'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdadeltaOptimizer.className = 'Adadelta'; // Name matters for Python compatibility.\nregisterClass(AdadeltaOptimizer);\n//# sourceMappingURL=adadelta_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { fill } from '../ops/fill';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n    constructor(learningRate, initialAccumulatorValue = 0.1) {\n        super();\n        this.learningRate = learningRate;\n        this.initialAccumulatorValue = initialAccumulatorValue;\n        this.accumulatedGrads = [];\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulatedGrads[i] == null) {\n                const trainable = false;\n                this.accumulatedGrads[i] = {\n                    originalName: `${name}/accumulator`,\n                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedGrad = this.accumulatedGrads[i].variable;\n            tidy(() => {\n                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n                accumulatedGrad.assign(newAccumulatedGrad);\n                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedGrads != null) {\n            dispose(this.accumulatedGrads.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'initialAccumulatorValue': this.initialAccumulatorValue,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['initialAccumulatorValue']);\n    }\n}\n/** @nocollapse */\nAdagradOptimizer.className = 'Adagrad'; // Note: Name matters for Python compatibility.\nregisterClass(AdagradOptimizer);\n//# sourceMappingURL=adagrad_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedSecondMoment = [];\n        tidy(() => {\n            // accB* will be updated by batch.\n            this.accBeta1 = scalar(beta1).variable();\n            this.accBeta2 = scalar(beta2).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const oneMinusAccBeta2 = sub(1, this.accBeta2);\n            varNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                if (this.accumulatedSecondMoment[i] == null) {\n                    this.accumulatedSecondMoment[i] = {\n                        originalName: `${name}/v`,\n                        variable: tidy(() => zerosLike(value).variable(trainable))\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const secondMoment = this.accumulatedSecondMoment[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n                firstMoment.assign(newFirstMoment);\n                secondMoment.assign(newSecondMoment);\n                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n                value.assign(newValue);\n            });\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n            this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.accBeta2.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedSecondMoment != null) {\n            dispose(this.accumulatedSecondMoment.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        tidy(() => {\n            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n        });\n        const variableCount = weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedFirstMoment =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedSecondMoment =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);\n    }\n}\n/** @nocollapse */\nAdamOptimizer.className = 'Adam'; // Note: Name matters for Python compatibility.\nregisterClass(AdamOptimizer);\n//# sourceMappingURL=adam_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { abs } from '../ops/abs';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { maximum } from '../ops/maximum';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\nexport class AdamaxOptimizer extends Optimizer {\n    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {\n        super();\n        this.learningRate = learningRate;\n        this.beta1 = beta1;\n        this.beta2 = beta2;\n        this.epsilon = epsilon;\n        this.decay = decay;\n        this.accumulatedFirstMoment = [];\n        this.accumulatedWeightedInfNorm = [];\n        tidy(() => {\n            this.iteration = scalar(0).variable();\n            this.accBeta1 = scalar(beta1).variable();\n        });\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        tidy(() => {\n            const oneMinusAccBeta1 = sub(1, this.accBeta1);\n            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n            variableNames.forEach((name, i) => {\n                const value = ENGINE.registeredVariables[name];\n                const trainable = false;\n                if (this.accumulatedFirstMoment[i] == null) {\n                    this.accumulatedFirstMoment[i] = {\n                        originalName: `${name}/m`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                if (this.accumulatedWeightedInfNorm[i] == null) {\n                    this.accumulatedWeightedInfNorm[i] = {\n                        originalName: `${name}/v`,\n                        variable: zerosLike(value).variable(trainable)\n                    };\n                }\n                const gradient = Array.isArray(variableGradients) ?\n                    variableGradients[i].tensor :\n                    variableGradients[name];\n                if (gradient == null) {\n                    return;\n                }\n                const firstMoment = this.accumulatedFirstMoment[i].variable;\n                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n                const ut0 = mul(weightedInfNorm, this.beta2);\n                const ut1 = abs(gradient);\n                const newWeightedInfNorm = maximum(ut0, ut1);\n                firstMoment.assign(newFirstMoment);\n                weightedInfNorm.assign(newWeightedInfNorm);\n                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);\n                value.assign(newValue);\n            });\n            this.iteration.assign(add(this.iteration, 1));\n            this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.accBeta1.dispose();\n        this.iteration.dispose();\n        if (this.accumulatedFirstMoment != null) {\n            dispose(this.accumulatedFirstMoment.map(v => v.variable));\n        }\n        if (this.accumulatedWeightedInfNorm != null) {\n            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        throw new Error('getWeights() is not implemented for Adamax yet.');\n    }\n    async setWeights(weightValues) {\n        throw new Error('setWeights() is not implemented for Adamax yet.');\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'beta1': this.beta1,\n            'beta2': this.beta2,\n            'epsilon': this.epsilon,\n            'decay': this.decay\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);\n    }\n}\n/** @nocollapse */\nAdamaxOptimizer.className = 'Adamax'; // Note: Name matters for Python compatbility.\nregisterClass(AdamaxOptimizer);\n//# sourceMappingURL=adamax_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { keep, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n    constructor(learningRate) {\n        super();\n        this.learningRate = learningRate;\n        this.setLearningRate(learningRate);\n    }\n    applyGradients(variableGradients) {\n        const varNames = Array.isArray(variableGradients) ?\n            variableGradients.map(v => v.name) :\n            Object.keys(variableGradients);\n        varNames.forEach((name, i) => {\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const value = ENGINE.registeredVariables[name];\n            tidy(() => {\n                const newValue = add(mul(this.c, gradient), value);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    /**\n     * Sets the learning rate of the optimizer.\n     */\n    setLearningRate(learningRate) {\n        this.learningRate = learningRate;\n        if (this.c != null) {\n            this.c.dispose();\n        }\n        this.c = keep(scalar(-learningRate));\n    }\n    dispose() {\n        this.c.dispose();\n    }\n    async getWeights() {\n        return [await this.saveIterations()];\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        if (weightValues.length !== 0) {\n            throw new Error('SGD optimizer does not have settable weights.');\n        }\n    }\n    getConfig() {\n        return { 'learningRate': this.learningRate };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate']);\n    }\n}\n/** @nocollapse */\nSGDOptimizer.className = 'SGD'; // Note: Name matters for Python compatibility.\nregisterClass(SGDOptimizer);\n//# sourceMappingURL=sgd_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { SGDOptimizer } from './sgd_optimizer';\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n    constructor(learningRate, momentum, useNesterov = false) {\n        super(learningRate);\n        this.learningRate = learningRate;\n        this.momentum = momentum;\n        this.useNesterov = useNesterov;\n        this.accumulations = [];\n        this.m = scalar(this.momentum);\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            if (this.accumulations[i] == null) {\n                const trainable = false;\n                this.accumulations[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const accumulation = this.accumulations[i].variable;\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            tidy(() => {\n                let newValue;\n                const newAccumulation = add(mul(this.m, accumulation), gradient);\n                if (this.useNesterov) {\n                    newValue = add(mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n                }\n                else {\n                    newValue = add(mul(this.c, newAccumulation), value);\n                }\n                accumulation.assign(newAccumulation);\n                value.assign(newValue);\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        this.m.dispose();\n        if (this.accumulations != null) {\n            dispose(this.accumulations.map(v => v.variable));\n        }\n    }\n    /**\n     * Sets the momentum of the optimizer.\n     *\n     * @param momentum\n     */\n    setMomentum(momentum) {\n        this.momentum = momentum;\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        return [await this.saveIterations()].concat(this.accumulations.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const trainable = false;\n        this.accumulations = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'momentum': this.momentum,\n            'useNesterov': this.useNesterov\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['momentum'], config['useNesterov']);\n    }\n}\n/** @nocollapse */\nMomentumOptimizer.className = 'Momentum'; // Name matters for Python compatibility.\nregisterClass(MomentumOptimizer);\n//# sourceMappingURL=momentum_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { dispose, tidy } from '../globals';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { zerosLike } from '../ops/zeros_like';\nimport { registerClass } from '../serialization';\nimport { Optimizer } from './optimizer';\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false) {\n        super();\n        this.learningRate = learningRate;\n        this.decay = decay;\n        this.momentum = momentum;\n        this.epsilon = epsilon;\n        this.accumulatedMeanSquares = [];\n        this.accumulatedMoments = [];\n        this.accumulatedMeanGrads = [];\n        this.centered = centered;\n        if (epsilon == null) {\n            this.epsilon = ENGINE.backend.epsilon();\n        }\n        if (learningRate == null) {\n            throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n        }\n    }\n    applyGradients(variableGradients) {\n        const variableNames = Array.isArray(variableGradients) ?\n            variableGradients.map(item => item.name) :\n            Object.keys(variableGradients);\n        variableNames.forEach((name, i) => {\n            const value = ENGINE.registeredVariables[name];\n            const trainable = false;\n            if (this.accumulatedMeanSquares[i] == null) {\n                this.accumulatedMeanSquares[i] = {\n                    originalName: `${name}/rms`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMoments[i] == null) {\n                this.accumulatedMoments[i] = {\n                    originalName: `${name}/momentum`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            if (this.accumulatedMeanGrads[i] == null && this.centered) {\n                this.accumulatedMeanGrads[i] = {\n                    originalName: `${name}/mg`,\n                    variable: tidy(() => zerosLike(value).variable(trainable))\n                };\n            }\n            const gradient = Array.isArray(variableGradients) ?\n                variableGradients[i].tensor :\n                variableGradients[name];\n            if (gradient == null) {\n                return;\n            }\n            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n            const accumulatedMoments = this.accumulatedMoments[i].variable;\n            tidy(() => {\n                const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                if (this.centered) {\n                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n                    // Centered gradient\n                    const newAccumulatedMeanGrad = add(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n                    const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add(square(newAccumulatedMeanGrad), this.epsilon))));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), gradContribution);\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n                else {\n                    // Plain gradient\n                    const newAccumulatedMeanSquare = add(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n                    const newAccumulatedMoments = add(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n                    accumulatedMoments.assign(newAccumulatedMoments);\n                    const newValue = sub(value, newAccumulatedMoments);\n                    value.assign(newValue);\n                }\n            });\n        });\n        this.incrementIterations();\n    }\n    dispose() {\n        if (this.accumulatedMeanSquares != null) {\n            dispose(this.accumulatedMeanSquares.map(v => v.variable));\n        }\n        if (this.accumulatedMeanGrads != null && this.centered) {\n            dispose(this.accumulatedMeanGrads.map(v => v.variable));\n        }\n        if (this.accumulatedMoments != null) {\n            dispose(this.accumulatedMoments.map(v => v.variable));\n        }\n    }\n    async getWeights() {\n        // Order matters for Python compatibility.\n        const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n        if (this.centered) {\n            variables.push(...this.accumulatedMeanGrads);\n        }\n        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));\n    }\n    async setWeights(weightValues) {\n        weightValues = await this.extractIterations(weightValues);\n        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n        const trainable = false;\n        this.accumulatedMeanSquares =\n            weightValues.slice(0, variableCount).map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        this.accumulatedMoments =\n            weightValues.slice(variableCount, variableCount * 2)\n                .map(v => ({\n                originalName: v.name,\n                variable: v.tensor.variable(trainable)\n            }));\n        if (this.centered) {\n            this.accumulatedMeanGrads =\n                weightValues.slice(variableCount * 2, variableCount * 3)\n                    .map(v => ({\n                    originalName: v.name,\n                    variable: v.tensor.variable(trainable)\n                }));\n        }\n    }\n    getConfig() {\n        return {\n            'learningRate': this.learningRate,\n            'decay': this.decay,\n            'momentum': this.momentum,\n            'epsilon': this.epsilon,\n            'centered': this.centered\n        };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);\n    }\n}\n/** @nocollapse */\nRMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.\nregisterClass(RMSPropOptimizer);\n//# sourceMappingURL=rmsprop_optimizer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AdadeltaOptimizer } from './adadelta_optimizer';\nimport { AdagradOptimizer } from './adagrad_optimizer';\nimport { AdamOptimizer } from './adam_optimizer';\nimport { AdamaxOptimizer } from './adamax_optimizer';\nimport { MomentumOptimizer } from './momentum_optimizer';\nimport { RMSPropOptimizer } from './rmsprop_optimizer';\nimport { SGDOptimizer } from './sgd_optimizer';\nexport class OptimizerConstructors {\n    /**\n     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n     *\n     * ```js\n     * // Fit a quadratic function by learning the coefficients a, b, c.\n     * const xs = tf.tensor1d([0, 1, 2, 3]);\n     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n     *\n     * const a = tf.scalar(Math.random()).variable();\n     * const b = tf.scalar(Math.random()).variable();\n     * const c = tf.scalar(Math.random()).variable();\n     *\n     * // y = a * x^2 + b * x + c.\n     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n     * const loss = (pred, label) => pred.sub(label).square().mean();\n     *\n     * const learningRate = 0.01;\n     * const optimizer = tf.train.sgd(learningRate);\n     *\n     * // Train the model.\n     * for (let i = 0; i < 10; i++) {\n     *   optimizer.minimize(() => loss(f(xs), ys));\n     * }\n     *\n     * // Make predictions.\n     * console.log(\n     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n     * const preds = f(xs).dataSync();\n     * preds.forEach((pred, i) => {\n     *   console.log(`x: ${i}, pred: ${pred}`);\n     * });\n     * ```\n     *\n     * @param learningRate The learning rate to use for the SGD algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static sgd(learningRate) {\n        return new SGDOptimizer(learningRate);\n    }\n    /**\n     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n     * descent.\n     *\n     * See\n     * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n     * http://proceedings.mlr.press/v28/sutskever13.pdf)\n     *\n     * @param learningRate The learning rate to use for the Momentum gradient\n     * descent algorithm.\n     * @param momentum The momentum to use for the momentum gradient descent\n     * algorithm.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static momentum(learningRate, momentum, useNesterov = false) {\n        return new MomentumOptimizer(learningRate, momentum, useNesterov);\n    }\n    /**\n     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n     * descent. This implementation uses plain momentum and is not centered\n     * version of RMSProp.\n     *\n     * See\n     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n     *\n     * @param learningRate The learning rate to use for the RMSProp gradient\n     * descent algorithm.\n     * @param decay The discounting factor for the history/coming gradient.\n     * @param momentum The momentum to use for the RMSProp gradient descent\n     * algorithm.\n     * @param epsilon Small value to avoid zero denominator.\n     * @param centered If true, gradients are normalized by the estimated\n     * variance of the gradient.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {\n        return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);\n    }\n    /**\n     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adam gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {\n        return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n     *\n     * @param learningRate The learning rate to use for the Adadelta gradient\n     * descent algorithm.\n     * @param rho The learning rate decay over each update.\n     * @param epsilon A constant epsilon used to better condition the grad\n     * update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adadelta(learningRate = .001, rho = .95, epsilon = null) {\n        return new AdadeltaOptimizer(learningRate, rho, epsilon);\n    }\n    /**\n     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n     *\n     * @param learningRate The learning rate to use for the Adamax gradient\n     * descent algorithm.\n     * @param beta1 The exponential decay rate for the 1st moment estimates.\n     * @param beta2 The exponential decay rate for the 2nd moment estimates.\n     * @param epsilon A small constant for numerical stability.\n     * @param decay The learning rate decay over each update.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {\n        return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n    }\n    /**\n     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n     * See\n     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n     * or\n     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n     *\n     * @param learningRate The learning rate to use for the Adagrad gradient\n     * descent algorithm.\n     * @param initialAccumulatorValue Starting value for the accumulators, must be\n     * positive.\n     *\n     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n     */\n    static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n        return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n    }\n}\n//# sourceMappingURL=optimizer_constructors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// So typings can propagate.\nimport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nimport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nimport { AdamOptimizer } from './optimizers/adam_optimizer';\nimport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nimport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nimport { OptimizerConstructors } from './optimizers/optimizer_constructors';\nimport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nimport { SGDOptimizer } from './optimizers/sgd_optimizer';\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n    RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\nexport const train = {\n    sgd: OptimizerConstructors.sgd,\n    momentum: OptimizerConstructors.momentum,\n    adadelta: OptimizerConstructors.adadelta,\n    adagrad: OptimizerConstructors.adagrad,\n    rmsprop: OptimizerConstructors.rmsprop,\n    adamax: OptimizerConstructors.adamax,\n    adam: OptimizerConstructors.adam\n};\n//# sourceMappingURL=train.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst delayCallback = (() => {\n    if (typeof requestAnimationFrame !== 'undefined') {\n        return requestAnimationFrame;\n    }\n    else if (typeof setImmediate !== 'undefined') {\n        return setImmediate;\n    }\n    return (f) => f(); // no delays\n})();\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame() {\n    return new Promise(resolve => delayCallback(() => resolve()));\n}\nexport { nextFrame };\n//# sourceMappingURL=browser_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Returns the image center in pixels.\nexport function getImageCenter(center, imageHeight, imageWidth) {\n    const centerX = imageWidth * (typeof center === 'number' ? center : center[0]);\n    const centerY = imageHeight * (typeof center === 'number' ? center : center[1]);\n    return [centerX, centerY];\n}\n//# sourceMappingURL=rotate_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {\n    let reshaped = [];\n    if (batchToSpace) {\n        reshaped = reshaped.concat(blockShape.slice(0));\n        reshaped.push(inputShape[0] / prod);\n        reshaped = reshaped.concat(inputShape.slice(1));\n    }\n    else {\n        reshaped = reshaped.concat(inputShape[0]);\n        const spatialLength = blockShape.length;\n        for (let i = 0; i < spatialLength; ++i) {\n            reshaped =\n                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n        }\n        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n    }\n    return reshaped;\n}\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n    const permuted = [];\n    if (batchToSpace) {\n        permuted.push(blockShapeRank);\n        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n            if (i <= 2 * blockShapeRank) {\n                permuted.push(i);\n                permuted.push(i - (blockShapeRank + 1));\n            }\n            else {\n                permuted.push(i);\n            }\n        }\n    }\n    else {\n        const permutedBeforeBatch = [];\n        const permutedAfterBatch = [];\n        for (let i = 1; i < reshapedRank; ++i) {\n            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n                permutedAfterBatch.push(i);\n            }\n            else {\n                permutedBeforeBatch.push(i);\n            }\n        }\n        permuted.push(...permutedBeforeBatch);\n        permuted.push(0);\n        permuted.push(...permutedAfterBatch);\n    }\n    return permuted;\n}\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {\n    const reshapedPermuted = [];\n    if (batchToSpace) {\n        reshapedPermuted.push(inputShape[0] / prod);\n    }\n    else {\n        reshapedPermuted.push(inputShape[0] * prod);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n        if (i <= blockShape.length) {\n            if (batchToSpace) {\n                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n            }\n            else {\n                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n            }\n        }\n        else {\n            reshapedPermuted.push(inputShape[i]);\n        }\n    }\n    return reshapedPermuted;\n}\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(crops, blockShape) {\n    const sliceBeginCoords = [0];\n    for (let i = 0; i < blockShape; ++i) {\n        sliceBeginCoords.push(crops[i][0]);\n    }\n    return sliceBeginCoords;\n}\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(uncroppedShape, crops, blockShape) {\n    const sliceSize = uncroppedShape.slice(0, 1);\n    for (let i = 0; i < blockShape; ++i) {\n        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n    }\n    return sliceSize;\n}\n//# sourceMappingURL=array_ops_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n//# sourceMappingURL=selu_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n//# sourceMappingURL=erf_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nexport function warn(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.warn(...msg);\n    }\n}\nexport function log(...msg) {\n    if (!env().getBool('IS_TEST')) {\n        console.log(...msg);\n    }\n}\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(real, imag) {\n    if (real.length !== imag.length) {\n        throw new Error(`Cannot merge real and imag arrays of different lengths. real:` +\n            `${real.length}, imag: ${imag.length}.`);\n    }\n    const result = new Float32Array(real.length * 2);\n    for (let i = 0; i < result.length; i += 2) {\n        result[i] = real[i / 2];\n        result[i + 1] = imag[i / 2];\n    }\n    return result;\n}\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex) {\n    const real = new Float32Array(complex.length / 2);\n    const imag = new Float32Array(complex.length / 2);\n    for (let i = 0; i < complex.length; i += 2) {\n        real[i / 2] = complex[i];\n        imag[i / 2] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex) {\n    const len = Math.ceil(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 0; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex) {\n    const len = Math.floor(complex.length / 4);\n    const real = new Float32Array(len);\n    const imag = new Float32Array(len);\n    for (let i = 2; i < complex.length; i += 4) {\n        real[Math.floor(i / 4)] = complex[i];\n        imag[Math.floor(i / 4)] = complex[i + 1];\n    }\n    return { real, imag };\n}\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(complex, index) {\n    const real = complex[index * 2];\n    const imag = complex[index * 2 + 1];\n    return { real, imag };\n}\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(data, real, imag, index) {\n    data[index * 2] = real;\n    data[index * 2 + 1] = imag;\n}\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(n, inverse) {\n    const real = new Float32Array(n / 2);\n    const imag = new Float32Array(n / 2);\n    for (let i = 0; i < Math.ceil(n / 2); i++) {\n        const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n        real[i] = Math.cos(x);\n        imag[i] = Math.sin(x);\n    }\n    return { real, imag };\n}\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(k, n, inverse) {\n    const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n    const real = Math.cos(x);\n    const imag = Math.sin(x);\n    return { real, imag };\n}\n//# sourceMappingURL=complex_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { cast } from '../ops/cast';\nimport { scalar } from '../ops/scalar';\nimport { tensor1d } from '../ops/tensor1d';\nimport { zeros } from '../ops/zeros';\nimport { hasEncodingLoss, makeZerosTypedArray } from '../util';\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/reduce_util';\nimport * as slice_util from '../ops/slice_util';\nexport { slice_util };\nexport { upcastType } from '../types';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\nimport * as segment_util from '../ops/segment_util';\nexport { segment_util };\nexport function castTensor(x, dtype, backend) {\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return x.clone();\n        }\n        const zerosTensor = zeros(x.shape);\n        const floatX = cast(x, 'float32');\n        const result = backend.complex(floatX, zerosTensor);\n        zerosTensor.dispose();\n        floatX.dispose();\n        return result;\n    }\n    if (!hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype);\n    }\n    if (x.dtype === 'complex64') {\n        const real = backend.real(x);\n        const result = cast(real, dtype);\n        real.dispose();\n        return result;\n    }\n    if (dtype === 'int32') {\n        return backend.int(x);\n    }\n    else if (dtype === 'bool') {\n        const zero = scalar(0, x.dtype);\n        const result = backend.notEqual(x, zero);\n        zero.dispose();\n        return result;\n    }\n    else {\n        throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n    }\n}\nexport function reshapeTensor(x, shape) {\n    return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype);\n}\nexport function linspaceImpl(start, stop, num) {\n    const step = (stop - start) / (num - 1);\n    const values = makeZerosTypedArray(num, 'float32');\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n        values[i] = values[i - 1] + step;\n    }\n    return tensor1d(values, 'float32');\n}\n//# sourceMappingURL=backend_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { slice } from '../ops/slice';\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split(x, sizeSplits, axis) {\n    const begin = new Array(x.rank).fill(0);\n    const size = x.shape.slice();\n    return sizeSplits.map(s => {\n        const sliceSize = [...size];\n        sliceSize[axis] = s;\n        const sliceT = slice(x, begin, sliceSize);\n        begin[axis] += s;\n        return sliceT;\n    });\n}\n//# sourceMappingURL=split_shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\nimport { buffer } from '../ops/buffer';\nexport function tile(xBuf, reps) {\n    const newShape = new Array(xBuf.rank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = xBuf.shape[i] * reps[i];\n    }\n    const result = buffer(newShape, xBuf.dtype);\n    for (let i = 0; i < result.values.length; ++i) {\n        const newLoc = result.indexToLoc(i);\n        const originalLoc = new Array(xBuf.rank);\n        for (let j = 0; j < originalLoc.length; j++) {\n            originalLoc[j] = newLoc[j] % xBuf.shape[j];\n        }\n        const originalIndex = xBuf.locToIndex(originalLoc);\n        result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor();\n}\n//# sourceMappingURL=tile_impl.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/** An implementation of the TopK kernel shared between webgl and cpu. */\nimport { tensor } from '../ops/tensor';\nimport { getTypedArrayFromDType } from '../util';\nexport function topkImpl(x, xShape, xDtype, k, sorted) {\n    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n    const lastDim = xShape[xShape.length - 1];\n    const [batch, size] = [x.length / lastDim, lastDim];\n    const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n    const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n    for (let b = 0; b < batch; b++) {\n        const offset = b * size;\n        const vals = x.subarray(offset, offset + size);\n        const valAndInd = [];\n        for (let i = 0; i < vals.length; i++) {\n            valAndInd.push({ value: vals[i], index: i });\n        }\n        valAndInd.sort((a, b) => b.value - a.value);\n        const outOffset = b * k;\n        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n        for (let i = 0; i < k; i++) {\n            topKVals[i] = valAndInd[i].value;\n            topKIndices[i] = valAndInd[i].index;\n        }\n    }\n    // Reshape back to the original input shape, except that the last\n    // dimension is k.\n    const outputShape = xShape.slice();\n    outputShape[outputShape.length - 1] = k;\n    return [\n        tensor(allTopKVals, outputShape, xDtype),\n        tensor(allTopKIndices, outputShape, 'int32')\n    ];\n}\n//# sourceMappingURL=topk_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl } from './non_max_suppression_impl';\nexport { split } from './split_shared';\nexport { tile } from './tile_impl';\nexport { topkImpl } from './topk_impl';\nexport { whereImpl } from './where_impl';\n//# sourceMappingURL=kernel_impls.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is tfjs-core without auto registration of things like flags,\n// gradients, chained ops or the opHandler. See base_side_effects.ts for parts\n// tfjs core that are required side effects.\n/**\n * @fileoverview\n * @suppress {partialAlias} Optimization disabled due to passing the module\n * object into a function below:\n *\n *   import * as ops from './ops/ops';\n *   setOpHandler(ops);\n */\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as browser from './ops/browser';\nimport * as gather_util from './ops/gather_nd_util';\nimport * as scatter_util from './ops/scatter_nd_util';\nimport * as slice_util from './ops/slice_util';\nimport * as serialization from './serialization';\nimport * as tensor_util from './tensor_util';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport { version } from './version';\n// Optimizers.\nexport { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';\nexport { AdagradOptimizer } from './optimizers/adagrad_optimizer';\nexport { AdamOptimizer } from './optimizers/adam_optimizer';\nexport { AdamaxOptimizer } from './optimizers/adamax_optimizer';\nexport { MomentumOptimizer } from './optimizers/momentum_optimizer';\nexport { Optimizer } from './optimizers/optimizer';\nexport { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';\nexport { SGDOptimizer } from './optimizers/sgd_optimizer';\nexport { Tensor, TensorBuffer, Variable } from './tensor';\nexport { Rank, sumOutType, upcastType } from './types';\nexport * from './ops/ops';\nexport { Reduction } from './ops/loss_ops_utils';\nexport * from './train';\nexport * from './globals';\nexport * from './kernel_registry';\nexport { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';\nexport { Environment, env, ENV } from './environment';\nexport { version as version_core };\n// Top-level method exports.\nexport { nextFrame } from './browser_util';\n// Second level exports.\nimport * as backend_util from './backends/backend_util';\nimport * as device_util from './device_util';\nexport { browser, io, math, serialization, test_util, util, backend_util, tensor_util, slice_util, gather_util, scatter_util, device_util };\nimport * as kernel_impls from './backends/kernel_impls';\nexport { kernel_impls };\n// Backend specific.\nexport { KernelBackend, DataStorage } from './backends/backend';\n// Export all kernel names / info.\nexport * from './kernel_names';\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const absGradConfig = {\n    kernelName: Abs,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, step(cast(x, 'float32'), -1)) };\n    }\n};\n//# sourceMappingURL=Abs_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acosGradConfig = {\n    kernelName: Acos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = square(cast(x, 'float32'));\n                const b = sqrt(sub(scalar(1), a));\n                return neg(div(dy, b));\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const acoshGradConfig = {\n    kernelName: Acosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(sub(square(cast(x, 'float32')), 1));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Acosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const addGradConfig = {\n    kernelName: Add,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Add_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AddN } from '../kernel_names';\nexport const addNGradConfig = {\n    kernelName: AddN,\n    saveAllInputs: true,\n    gradFunc: (dy, saved) => {\n        const ders = {};\n        saved.forEach((_, i) => {\n            ders[i] = () => dy.clone();\n        });\n        return ders;\n    }\n};\n//# sourceMappingURL=AddN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMax } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMaxGradConfig = {\n    kernelName: ArgMax,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ArgMin } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const argMinGradConfig = {\n    kernelName: ArgMin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => zerosLike(x) };\n    }\n};\n//# sourceMappingURL=ArgMin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const asinGradConfig = {\n    kernelName: Asin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32'))))) };\n    }\n};\n//# sourceMappingURL=Asin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { scalar } from '../ops/scalar';\nimport { sqrt } from '../ops/sqrt';\nimport { square } from '../ops/square';\nexport const asinhGradConfig = {\n    kernelName: Asinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n                return div(dy, a);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Asinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const atan2GradConfig = {\n    kernelName: Atan2,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const d = add(square(a), square(b));\n            let res = mul(dy, div(b, d));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            const d = add(square(a), square(b));\n            let res = neg(mul(dy, div(a, d)));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Atan2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const atanGradConfig = {\n    kernelName: Atan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(square(cast(x, 'float32')), 1)) };\n    }\n};\n//# sourceMappingURL=Atan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nimport { scalar } from '../ops/scalar';\nexport const atanhGradConfig = {\n    kernelName: Atanh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, sub(scalar(1), square(cast(x, 'float32')))) };\n    }\n};\n//# sourceMappingURL=Atanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_(dy, input, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, AvgPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const avgPool3dBackprop = op({ avgPool3dBackprop_ });\n//# sourceMappingURL=avg_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool3D } from '../kernel_names';\nimport { avgPool3dBackprop } from '../ops/avg_pool_3d_backprop';\nexport const avgPool3DGradConfig = {\n    kernelName: AvgPool3D,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => avgPool3dBackprop(dy, x, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { AvgPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_(dy, input, filterSize, strides, pad) {\n    const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n    let input4D = $input;\n    let dy4D = $dy;\n    let reshapedTo4D = false;\n    if ($input.rank === 3) {\n        reshapedTo4D = true;\n        input4D =\n            reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n        dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n    }\n    util.assert(dy4D.rank === 4, () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n        `${dy4D.rank}.`);\n    util.assert(input4D.rank === 4, () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n        `${input4D.rank}.`);\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n        return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n    };\n    const inputs = { dy: dy4D, input: input4D };\n    const attrs = { filterSize, strides, pad };\n    const res = ENGINE.runKernelFunc(forward, inputs, null, AvgPoolBackprop, attrs);\n    if (reshapedTo4D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n    }\n    return res;\n}\nexport const avgPoolBackprop = op({ avgPoolBackprop_ });\n//# sourceMappingURL=avg_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool } from '../kernel_names';\nimport { avgPoolBackprop } from '../ops/avg_pool_backprop';\nexport const avgPoolGradConfig = {\n    kernelName: AvgPool,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => avgPoolBackprop(dy, x, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=AvgPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul } from '../kernel_names';\nimport { matMul } from '../ops/mat_mul';\nexport const batchMatMulGradConfig = {\n    kernelName: BatchMatMul,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved, attrs) => {\n        const [a, b] = saved;\n        const { transposeA, transposeB } = attrs;\n        if (!transposeA && !transposeB) {\n            return {\n                a: () => matMul(dy, b, false, true),\n                b: () => matMul(a, dy, true, false)\n            };\n        }\n        else if (!transposeA && transposeB) {\n            return {\n                a: () => matMul(dy, b, false, false),\n                b: () => matMul(dy, a, true, false)\n            };\n        }\n        else if (transposeA && !transposeB) {\n            return {\n                a: () => matMul(b, dy, false, true),\n                b: () => matMul(a, dy, false, false)\n            };\n        }\n        else {\n            return {\n                a: () => matMul(b, dy, true, true),\n                b: () => matMul(dy, a, true, true)\n            };\n        }\n    }\n};\n//# sourceMappingURL=BatchMatMul_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchToSpaceND } from '../kernel_names';\nimport { spaceToBatchND } from '../ops/space_to_batch_nd';\nexport const batchToSpaceNDGradConfig = {\n    kernelName: BatchToSpaceND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, crops } = attrs;\n        return { x: () => spaceToBatchND(dy, blockShape, crops) };\n    }\n};\n//# sourceMappingURL=BatchToSpaceND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BroadcastTo } from '../kernel_names';\nimport { sum } from '../ops/sum';\nexport const broadcastToGradConfig = {\n    kernelName: BroadcastTo,\n    gradFunc: (dy, saved, attrs) => {\n        const broadCastToAttrs = attrs;\n        const inputShape = broadCastToAttrs.inputShape;\n        const outputShape = broadCastToAttrs.shape;\n        const reps = Array.from(outputShape);\n        for (let i = inputShape.length - 1; i >= 0; i--) {\n            if (inputShape[i] === outputShape[i]) {\n                reps[i] = 1;\n            }\n            else if (inputShape[i] !== 1) {\n                throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n            }\n        }\n        const axes = [];\n        for (let i = 0; i < reps.length; i++) {\n            if (reps[i] > 1) {\n                axes.push(i);\n            }\n        }\n        return { x: () => sum(dy, axes, true /* keepDims */) };\n    }\n};\n//# sourceMappingURL=BroadcastTo_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cast } from '../kernel_names';\nexport const castGradConfig = {\n    kernelName: Cast,\n    gradFunc: (dy) => {\n        return { x: () => dy.clone() };\n    }\n};\n//# sourceMappingURL=Cast_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const ceilGradConfig = {\n    kernelName: Ceil,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Ceil_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '../kernel_names';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { lessEqual } from '../ops/less_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const clipByValueGradConfig = {\n    kernelName: ClipByValue,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { clipValueMin, clipValueMax } = attrs;\n        return {\n            x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy)),\n        };\n    }\n};\n//# sourceMappingURL=ClipByValue_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Concat } from '../kernel_names';\nimport { split } from '../ops/split';\nimport { parseAxisParam } from '../util';\nexport const concatGradConfig = {\n    kernelName: Concat,\n    saveAllInputs: true,\n    gradFunc: (dy, saved, attrs) => {\n        const shapes = saved.map(t => t.shape);\n        const { axis } = attrs;\n        const $axis = parseAxisParam(axis, saved[0].shape)[0];\n        const sizeSplits = shapes.map(s => s[$axis]);\n        const derTensors = split(dy, sizeSplits, $axis);\n        return derTensors.map(t => () => t);\n    }\n};\n//# sourceMappingURL=Concat_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2D } from '../kernel_names';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nimport { conv2DBackpropInput } from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv2DGradConfig = {\n    kernelName: Conv2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x4D, $filter] = saved;\n        const { dilations, strides, pad, dataFormat } = attrs;\n        util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n        return {\n            x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n            filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n        };\n    }\n};\n//# sourceMappingURL=Conv2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv2DBackpropInput } from '../kernel_names';\nimport { conv2d } from '../ops/conv2d';\nimport { conv2DBackpropFilter } from '../ops/conv2d_backprop_filter';\nexport const conv2DBackpropInputGradConfig = {\n    kernelName: Conv2DBackpropInput,\n    inputsToSave: ['dy', 'filter'],\n    gradFunc: (ddx, saved, attrs) => {\n        const [dy, filter] = saved;\n        const { strides, pad, dataFormat, dimRoundingMode } = attrs;\n        return {\n            dy: () => conv2d(ddx, filter, strides, pad, dataFormat, 1 /* dilations */, dimRoundingMode),\n            filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=Conv2DBackpropInput_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Conv3DBackpropFilterV2 } from '../kernel_names';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad) {\n    let x5D = x;\n    if (x.rank === 4) {\n        x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n    }\n    let dy5D = dy;\n    if (dy5D.rank === 4) {\n        dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    }\n    util.assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n        `${x5D.shape}.`);\n    util.assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n        `${dy5D.shape}.`);\n    util.assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n        `${filterShape}.`);\n    util.assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n        `match input depth in filter (${filterShape[3]}.`);\n    util.assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n        `match output depth for filter (${filterShape[4]}).`);\n    const forward = backend => {\n        const dilations = 1;\n        const convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);\n        return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n    };\n    const inputs = { x: x5D, dy: dy5D };\n    const attrs = { strides, pad, filterShape };\n    return ENGINE.runKernelFunc(forward, inputs, null, Conv3DBackpropFilterV2, attrs);\n}\nexport const conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n//# sourceMappingURL=conv3d_backprop_filter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Conv3D } from '../kernel_names';\nimport { conv3DBackpropFilter } from '../ops/conv3d_backprop_filter';\nimport { conv3DBackpropInput } from '../ops/conv3d_backprop_input';\nimport { tupleValuesAreOne } from '../ops/conv_util';\nimport * as util from '../util';\nexport const conv3DGradConfig = {\n    kernelName: Conv3D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad } = attrs;\n        util.assert(tupleValuesAreOne(dilations), () => 'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n        const [x5D, $filter] = saved;\n        return {\n            x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad),\n            filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=Conv3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { sin } from '../ops/sin';\nexport const cosGradConfig = {\n    kernelName: Cos,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(neg(sin(cast(x, 'float32'))), dy) };\n    }\n};\n//# sourceMappingURL=Cos_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { sinh } from '../ops/sinh';\nexport const coshGradConfig = {\n    kernelName: Cosh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(sinh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Cosh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cumsum } from '../kernel_names';\nimport { getAxesPermutation } from '../ops/axis_util';\nimport { cumsum } from '../ops/cumsum';\nimport { transpose } from '../ops/transpose';\nexport const cumsumGradConfig = {\n    kernelName: Cumsum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { axis, exclusive, reverse } = attrs;\n        return {\n            x: () => {\n                const permutation = getAxesPermutation([axis], x.rank);\n                let out = cumsum(dy, axis, exclusive, !reverse);\n                if (permutation != null) {\n                    out = transpose(out, permutation);\n                }\n                return out;\n            }\n        };\n    }\n};\n//# sourceMappingURL=Cumsum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { DepthwiseConv2dNative } from '../kernel_names';\nimport * as conv_util from '../ops/conv_util';\nimport { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';\nimport { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';\nimport * as util from '../util';\nexport const depthwiseConv2dNativeGradConfig = {\n    kernelName: DepthwiseConv2dNative,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const { dilations, strides, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1] : dilations;\n        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n        const [x, filter] = saved;\n        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n        if (dimRoundingMode != null) {\n            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n        }\n        return {\n            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad, dilations, dimRoundingMode),\n            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad, dilations, dimRoundingMode),\n        };\n    }\n};\n//# sourceMappingURL=DepthwiseConv2dNative_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropInput } from '../kernel_names';\nexport const dilation2dGradConfig = {\n    kernelName: Dilation2D,\n    inputsToSave: ['x', 'filter'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, filter] = saved;\n        const inputInputs = { x, filter, dy };\n        const filterInputs = { x, filter, dy };\n        return {\n            x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n            filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n        };\n    }\n};\n//# sourceMappingURL=Dilation2D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const divGradConfig = {\n    kernelName: Div,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Div_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Elu, EluGrad } from '../kernel_names';\nexport const eluGradConfig = {\n    kernelName: Elu,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        const backPropKernelFunc = (backend) => {\n            return backend.eluDer(dy, y);\n        };\n        const inputs = { dy, y };\n        return {\n            x: () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* grad */, EluGrad)\n        };\n    }\n};\n//# sourceMappingURL=Elu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Erf } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const erfGradConfig = {\n    kernelName: Erf,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n        return { x: () => mul(dy, a) };\n    }\n};\n//# sourceMappingURL=Erf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '../kernel_names';\nimport { mul } from '../ops/mul';\nexport const expGradConfig = {\n    kernelName: Exp,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, y) };\n    }\n};\n//# sourceMappingURL=Exp_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nexport const expm1GradConfig = {\n    kernelName: Expm1,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, exp(x)) };\n    }\n};\n//# sourceMappingURL=Expm1_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const floorGradConfig = {\n    kernelName: Floor,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Floor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FloorDiv } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { square } from '../ops/square';\nimport { sum } from '../ops/sum';\nexport const floorDivGradConfig = {\n    kernelName: FloorDiv,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = div(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            let res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = reshape(sum(res, reduceAxes), b.shape);\n            }\n            const tmp = square(b);\n            return neg(div(res, cast(tmp, 'float32')));\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=FloorDiv_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { rsqrt } from '../ops/rsqrt';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { tile } from '../ops/tile';\nexport const fusedBatchNormGradConfig = {\n    kernelName: FusedBatchNorm,\n    inputsToSave: ['x', 'mean', 'variance', 'scale'],\n    gradFunc: (dy, saved, attrs) => {\n        const { varianceEpsilon } = attrs;\n        const [x, mean, variance, scale] = saved;\n        const scaleValue = scale == null ? scalar(1) : scale;\n        const reductionAxes = getReductionAxes(mean.shape, x.shape);\n        const tileShape = [];\n        if (mean.rank === 1) {\n            for (let i = 0; i < x.shape.length - 1; ++i) {\n                tileShape.push(x.shape[i]);\n            }\n            tileShape.push(1);\n        }\n        const xMinusMean = sub(x, mean);\n        const dyTimesScaleValue = mul(dy, scaleValue);\n        const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n        const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n        const derX = () => {\n            if (mean.rank === 1) {\n                return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);\n            }\n            else {\n                return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n            }\n        };\n        const derMean = () => {\n            let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                meanDer = sum(meanDer, reductionAxes);\n            }\n            return reshape(meanDer, mean.shape);\n        };\n        const derVariance = () => {\n            let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n            if (mean.rank === 1) {\n                varianceDer = sum(varianceDer, reductionAxes);\n            }\n            return reshape(varianceDer, mean.shape);\n        };\n        const derScale = () => {\n            const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n            let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n            if (mean.rank === 1) {\n                scaleDer = sum(scaleDer, reductionAxes);\n            }\n            return reshape(scaleDer, mean.shape);\n        };\n        const derOffset = () => {\n            let offsetDer = dy;\n            if (mean.rank === 1) {\n                offsetDer = sum(offsetDer, reductionAxes);\n            }\n            return reshape(offsetDer, mean.shape);\n        };\n        return {\n            x: derX,\n            mean: derMean,\n            variance: derVariance,\n            scale: derScale,\n            offset: derOffset\n        };\n    }\n};\n//# sourceMappingURL=FusedBatchNorm_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GatherV2 } from '../kernel_names';\nimport { getUndoAxesPermutation } from '../ops/axis_util';\nimport { reshape } from '../ops/reshape';\nimport { transpose } from '../ops/transpose';\nimport { unsortedSegmentSum } from '../ops/unsorted_segment_sum';\nimport { parseAxisParam } from '../util';\nexport const gatherGradConfig = {\n    kernelName: GatherV2,\n    inputsToSave: ['x', 'indices'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, indices] = saved;\n        const { axis } = attrs;\n        const parsedAxis = parseAxisParam(axis, x.shape)[0];\n        const derX = () => {\n            const paramsShape = x.shape;\n            const indicesSize = indices.size;\n            const outerShape = paramsShape.slice(0, parsedAxis);\n            const outerDims = outerShape.length;\n            const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n            const innerDims = innerShape.length;\n            const outerAxesIndices = arrayRange(0, outerDims);\n            const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n            const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n            const values = reshape(dy, valuesShape);\n            const reshapedIndices = reshape(indices, [indicesSize]);\n            const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n            const valuesTranspose = transpose(values, transposeDims);\n            let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n            const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n            paramsGrad = transpose(paramsGrad, invertTransposeDims);\n            return paramsGrad;\n        };\n        return { x: derX, indices: () => indices };\n    }\n};\nfunction arrayRange(start, stop) {\n    const result = [];\n    for (let i = start; i < stop; ++i) {\n        result.push(i);\n    }\n    return result;\n}\nfunction arrayConcat(arrays) {\n    const result = [];\n    for (let i = 0; i < arrays.length; ++i) {\n        for (let j = 0; j < arrays[i].length; ++j) {\n            result.push(arrays[i][j]);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=GatherV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { GreaterEqual } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const greaterEqualGradConfig = {\n    kernelName: GreaterEqual,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        return { a: () => zerosLike(a), b: () => zerosLike(b) };\n    }\n};\n//# sourceMappingURL=GreaterEqual_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '../kernel_names';\nimport { cast } from '../ops/cast';\nexport const identityGradConfig = {\n    kernelName: Identity,\n    gradFunc: (dy) => {\n        return { x: () => cast(dy, 'float32') };\n    }\n};\n//# sourceMappingURL=Identity_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isFiniteGradConfig = {\n    kernelName: IsFinite,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsFinite_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isInfGradConfig = {\n    kernelName: IsInf,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsInf_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const isNanGradConfig = {\n    kernelName: IsNan,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=IsNan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { div } from '../ops/div';\nexport const log1pGradConfig = {\n    kernelName: Log1p,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, add(x, 1)) };\n    }\n};\n//# sourceMappingURL=Log1p_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nexport const logGradConfig = {\n    kernelName: Log,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, cast(x, 'float32')) };\n    }\n};\n//# sourceMappingURL=Log_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogSoftmax } from '../kernel_names';\nimport { exp } from '../ops/exp';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const logSoftmaxGradConfig = {\n    kernelName: LogSoftmax,\n    inputsToSave: [],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [value] = saved;\n        const { axis } = attrs;\n        return {\n            logits: () => {\n                const keepDims = true;\n                const softmax = exp(value);\n                return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n            }\n        };\n    }\n};\n//# sourceMappingURL=LogSoftmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { LRNBackprop } from '../kernel_names';\nimport { op } from './operation';\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n    const forward = backend => backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n    const inputs = { x, y, dy };\n    const attrs = { depthRadius, bias, alpha, beta };\n    return ENGINE.runKernelFunc(forward, inputs, null /* grad */, LRNBackprop, attrs);\n}\nexport const localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n//# sourceMappingURL=local_response_normalization_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LRN } from '../kernel_names';\nimport { localResponseNormalizationBackprop } from '../ops/local_response_normalization_backprop';\nexport const lrnGradConfig = {\n    kernelName: LRN,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { depthRadius, bias, alpha, beta } = attrs;\n        return {\n            x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n        };\n    }\n};\n//# sourceMappingURL=LRN_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as axis_util from '../ops/axis_util';\nimport { cast } from '../ops/cast';\nimport { equal } from '../ops/equal';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax(dy, y, xOrig, origAxes) {\n    if (y.rank < xOrig.rank) {\n        y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes));\n    }\n    if (dy.rank < xOrig.rank) {\n        dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes));\n    }\n    return {\n        x: () => {\n            const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n            return dx;\n        }\n    };\n}\n//# sourceMappingURL=min_max_grad_util.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '../kernel_names';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const maxGradConfig = {\n    kernelName: Max,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const maxAttrs = attrs;\n        const { reductionIndices } = maxAttrs;\n        const x = saved[0];\n        const y = saved[1];\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n        return {\n            x: () => {\n                return maxGrad['x']();\n            }\n        };\n    }\n};\n//# sourceMappingURL=Max_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Maximum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { less } from '../ops/less';\nimport { mul } from '../ops/mul';\nexport const maximumGradConfig = {\n    kernelName: Maximum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(less(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Maximum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPool3DBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\nimport { reshape } from './reshape';\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_(dy, input, output, filterSize, strides, dilations = [1, 1, 1], pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n    let dy5D = $dy;\n    let input5D = $input;\n    let output5D = $output;\n    let reshapedTo5D = false;\n    if ($input.rank === 4) {\n        reshapedTo5D = true;\n        dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n        input5D = reshape($input, [\n            1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n        ]);\n        output5D = reshape($output, [\n            1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n        ]);\n    }\n    util.assert(dy5D.rank === 5, () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n        `${dy5D.rank}.`);\n    util.assert(input5D.rank === 5, () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n        `${input5D.rank}.`);\n    util.assert(output5D.rank === 5, () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n        `${output5D.rank}.`);\n    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n        `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool3DInfo(input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n        return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n    };\n    const inputs = { dy: dy5D, input: input5D, output: output5D };\n    const attrs = { filterSize, strides, dilations, pad, dimRoundingMode };\n    const res = ENGINE.runKernelFunc(forward, inputs, null /* grad */, MaxPool3DBackprop, attrs);\n    if (reshapedTo5D) {\n        return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n    }\n    return res;\n}\nexport const maxPool3dBackprop = op({ maxPool3dBackprop_ });\n//# sourceMappingURL=max_pool_3d_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool3D } from '../kernel_names';\nimport { maxPool3dBackprop } from '../ops/max_pool_3d_backprop';\nexport const maxPool3DGradConfig = {\n    kernelName: MaxPool3D,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, dilations, pad, dimRoundingMode } = attrs;\n        const $dilations = dilations == null ? [1, 1, 1] : dilations;\n        return {\n            x: () => maxPool3dBackprop(dy, x, y, filterSize, strides, $dilations, pad, dimRoundingMode)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool3D_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { MaxPoolBackprop } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport { op } from './operation';\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {\n    const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n    const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n    const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n    util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n        `(${$dy.rank})`);\n    util.assert($dy.rank === 4, () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n        `${$dy.rank}.`);\n    util.assert($input.rank === 4, () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n        `${$input.rank}.`);\n    if (dimRoundingMode != null) {\n        util.assert(util.isInt(pad), () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n    const forward = backend => {\n        const convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n        return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n    };\n    const inputs = { dy: $dy, input: $input, output: $output };\n    const attrs = { filterSize, strides, pad, dimRoundingMode };\n    return ENGINE.runKernelFunc(forward, inputs, null, MaxPoolBackprop, attrs);\n}\nexport const maxPoolBackprop = op({ maxPoolBackprop_ });\n//# sourceMappingURL=max_pool_backprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPool } from '../kernel_names';\nimport { maxPoolBackprop } from '../ops/max_pool_backprop';\nexport const maxPoolGradConfig = {\n    kernelName: MaxPool,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [x, y] = saved;\n        const { filterSize, strides, pad } = attrs;\n        return {\n            x: () => maxPoolBackprop(dy, x, y, filterSize, strides, pad)\n        };\n    }\n};\n//# sourceMappingURL=MaxPool_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Min } from '../kernel_names';\nimport * as util from '../util';\nimport { gradForMinAndMax } from './min_max_grad_util';\nexport const minGradConfig = {\n    kernelName: Min,\n    inputsToSave: ['x'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const minAttrs = attrs;\n        const { axis } = minAttrs;\n        const [x, y] = saved;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n        return {\n            x: () => {\n                return minGrad['x']();\n            }\n        };\n    }\n};\n//# sourceMappingURL=Min_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Minimum } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nexport const minimumGradConfig = {\n    kernelName: Minimum,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n        const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Minimum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MirrorPad } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const mirrorPadGradConfig = {\n    kernelName: MirrorPad,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=MirrorPad_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Mod } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { div } from '../ops/div';\nimport { floor } from '../ops/floor';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const modGradConfig = {\n    kernelName: Mod,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(dy, reduceAxes), a.shape);\n            }\n            return dy;\n        };\n        const derB = () => {\n            const res = mul(dy, neg(floor(div(a, b))));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Mod_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '../kernel_names';\nimport { assertAndGetBroadcastShape, getReductionAxes } from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const multiplyGradConfig = {\n    kernelName: Multiply,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            const res = mul(dy, cast(b, 'float32'));\n            const reduceAxes = getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), a.shape);\n            }\n            return res;\n        };\n        const derB = () => {\n            const res = mul(dy, cast(a, 'float32'));\n            const reduceAxes = getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                return reshape(sum(res, reduceAxes), b.shape);\n            }\n            return res;\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Multiply_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Negate } from '../kernel_names';\nimport { neg } from '../ops/neg';\nexport const negateGradConfig = {\n    kernelName: Negate,\n    gradFunc: (dy) => {\n        return { x: () => neg(dy) };\n    }\n};\n//# sourceMappingURL=Negate_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OneHot } from '../kernel_names';\nimport { zeros } from '../ops/zeros';\nexport const oneHotGradConfig = {\n    kernelName: OneHot,\n    inputsToSave: ['indices'],\n    gradFunc: (dy, saved) => {\n        const indices = saved[0];\n        return { indices: () => zeros(indices.shape, 'float32') };\n    }\n};\n//# sourceMappingURL=OneHot_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { OnesLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const onesLikeGradConfig = {\n    kernelName: OnesLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=OnesLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2 } from '../kernel_names';\nimport { slice } from '../ops/slice';\nexport const padV2GradConfig = {\n    kernelName: PadV2,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        // Pad introduces values around the original tensor, so the gradient\n        // slices the original shape out of the gradient.\n        const x = saved[0];\n        const { paddings } = attrs;\n        const begin = paddings.map(p => p[0]);\n        return { x: () => slice(dy, begin, x.shape) };\n    }\n};\n//# sourceMappingURL=PadV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pow } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { cast } from '../ops/cast';\nimport { greater } from '../ops/greater';\nimport { log } from '../ops/log';\nimport { mul } from '../ops/mul';\nimport { pow } from '../ops/pow';\nimport { reshape } from '../ops/reshape';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const powGradConfig = {\n    kernelName: Pow,\n    inputsToSave: ['a', 'b'],\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [a, b, y] = saved;\n        const base = a;\n        const exp = b;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n        const derBase = () => {\n            const expFloat = cast(exp, 'float32');\n            let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n            const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, base.shape);\n        };\n        const derExp = () => {\n            const condition = greater(base, 0);\n            const logBase = where(condition, log(base), zerosLike(base));\n            let res = mul(dy, mul(y, logBase));\n            const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, exp.shape);\n        };\n        return { a: derBase, b: derExp };\n    }\n};\n//# sourceMappingURL=Pow_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '../kernel_names';\nimport { getReductionAxes } from '../ops/broadcast_util';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const preluGradConfig = {\n    kernelName: Prelu,\n    inputsToSave: ['x', 'alpha'],\n    gradFunc: (dy, saved) => {\n        const [x, alpha] = saved;\n        const mask = greater(x, 0);\n        return {\n            x: () => where(mask, dy, mul(dy, alpha)),\n            alpha: () => {\n                let res = where(mask, zerosLike(dy), mul(dy, x));\n                const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n                if (reduceAxes.length > 0) {\n                    res = sum(res, reduceAxes);\n                }\n                return reshape(res, alpha.shape);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Prelu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { neg } from '../ops/neg';\nimport { square } from '../ops/square';\nexport const reciprocalGradConfig = {\n    kernelName: Reciprocal,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, neg(square(x))) };\n    }\n};\n//# sourceMappingURL=Reciprocal_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { lessEqual } from '../ops/less_equal';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const relu6GradConfig = {\n    kernelName: Relu6,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        const mask = mul(lessEqual(x, 6), step(x));\n        return { x: () => mul(dy, cast(mask, 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu6_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nimport { step } from '../ops/step';\nexport const reluGradConfig = {\n    kernelName: Relu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, cast(step(x), 'float32')) };\n    }\n};\n//# sourceMappingURL=Relu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape } from '../kernel_names';\nimport { reshape } from '../ops/reshape';\nexport const reshapeGradConfig = {\n    kernelName: Reshape,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => reshape(dy, x.shape) };\n    }\n};\n//# sourceMappingURL=Reshape_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeBilinear, ResizeBilinearGrad } from '../kernel_names';\nexport const resizeBilinearGradConfig = {\n    kernelName: ResizeBilinear,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeBilinearBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeBilinearGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeBilinear_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { ResizeNearestNeighbor, ResizeNearestNeighborGrad } from '../kernel_names';\nexport const resizeNearestNeighborGradConfig = {\n    kernelName: ResizeNearestNeighbor,\n    inputsToSave: ['images'],\n    gradFunc: (dy, saved, attrs) => {\n        const [images] = saved;\n        const backPropKernelFunc = (backend) => {\n            const { alignCorners } = attrs;\n            return backend.resizeNearestNeighborBackprop(dy, images, alignCorners);\n        };\n        const inputs = { images };\n        const imagesDer = () => ENGINE.runKernelFunc(backPropKernelFunc, inputs, null /* gradient */, ResizeNearestNeighborGrad, attrs);\n        return { images: imagesDer };\n    }\n};\n//# sourceMappingURL=ResizeNearestNeighbor_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reverse } from '../kernel_names';\nimport { reverse } from '../ops/reverse';\nimport { parseAxisParam } from '../util';\nexport const reverseGradConfig = {\n    kernelName: Reverse,\n    gradFunc: (dy, saved, attrs) => {\n        const { dims } = attrs;\n        const axes = parseAxisParam(dims, dy.shape);\n        return { x: () => reverse(dy, axes) };\n    }\n};\n//# sourceMappingURL=Reverse_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const roundGradConfig = {\n    kernelName: Round,\n    gradFunc: (dy) => {\n        // TODO(nsthorat): Let gradients be null for cases where we want to stop\n        // backpropgation.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Round_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '../kernel_names';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { neg } from '../ops/neg';\nimport { pow } from '../ops/pow';\nexport const rsqrtGradConfig = {\n    kernelName: Rsqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n    }\n};\n//# sourceMappingURL=Rsqrt_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SelectV2 } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { logicalNot } from '../ops/logical_not';\nimport { mul } from '../ops/mul';\nimport { zerosLike } from '../ops/zeros_like';\nexport const selectV2PoolGradConfig = {\n    kernelName: SelectV2,\n    inputsToSave: ['condition'],\n    gradFunc: (dy, saved) => {\n        const [condition] = saved;\n        return {\n            // TODO(julianoks): Return null for condition gradient\n            // when backprop supports it.\n            condition: () => cast(zerosLike(condition), 'float32'),\n            t: () => mul(dy, cast(condition, dy.dtype)),\n            e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n        };\n    }\n};\n//# sourceMappingURL=SelectV2_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Selu } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { exp } from '../ops/exp';\nimport { greater } from '../ops/greater';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { SELU_SCALE, SELU_SCALEALPHA } from '../ops/selu_util';\nimport { where } from '../ops/where';\nexport const seluGradConfig = {\n    kernelName: Selu,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return {\n            x: () => {\n                const mask = greater(x, scalar(0));\n                const scaleAlpha = scalar(SELU_SCALEALPHA);\n                const scale = scalar(SELU_SCALE);\n                const greaterThanZeroDer = mul(dy, scale);\n                const lessEqualZeroDer = mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n                return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n            }\n        };\n    }\n};\n//# sourceMappingURL=Selu_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const sigmoidGradConfig = {\n    kernelName: Sigmoid,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n    }\n};\n//# sourceMappingURL=Sigmoid_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const signGradConfig = {\n    kernelName: Sign,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Sign_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cos } from '../ops/cos';\nimport { mul } from '../ops/mul';\nexport const sinGradConfig = {\n    kernelName: Sin,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cos(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sin_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { cosh } from '../ops/cosh';\nimport { mul } from '../ops/mul';\nexport const sinhGradConfig = {\n    kernelName: Sinh,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(cosh(cast(x, 'float32')), dy) };\n    }\n};\n//# sourceMappingURL=Sinh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice } from '../kernel_names';\nimport { pad } from '../ops/pad';\nimport { parseSliceParams } from '../ops/slice_util';\nexport const sliceGradConfig = {\n    kernelName: Slice,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { begin, size } = attrs;\n        const inputShape = x.shape;\n        const [begin_, size_] = parseSliceParams(x, begin, size);\n        // Create an Nx2 padding where the first column represents how many\n        // zeros are prepended (at start) for each dimension, and the second\n        // column indicates how many zeros are appended (at end).\n        // The number of zeros to append is the shape of the input\n        // elementwise-subtracted by both the begin vector and sizes vector.\n        const paddings = [];\n        for (let i = 0; i < dy.rank; i++) {\n            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n        }\n        return { x: () => pad(dy, paddings) };\n    }\n};\n//# sourceMappingURL=Slice_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softmax } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sub } from '../ops/sub';\nimport { sum } from '../ops/sum';\nexport const softmaxGradConfig = {\n    kernelName: Softmax,\n    outputsToSave: [true],\n    gradFunc: (dy, saved, attrs) => {\n        const [y] = saved;\n        const { dim } = attrs;\n        const keepDims = true;\n        const dyTimesY = mul(dy, y);\n        return {\n            logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n        };\n    }\n};\n//# sourceMappingURL=Softmax_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { sigmoid } from '../ops/sigmoid';\nexport const softplusGradConfig = {\n    kernelName: Softplus,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, sigmoid(x)) };\n    }\n};\n//# sourceMappingURL=Softplus_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SpaceToBatchND } from '../kernel_names';\nimport { batchToSpaceND } from '../ops/batch_to_space_nd';\nexport const spaceToBatchNDGradConfig = {\n    kernelName: SpaceToBatchND,\n    gradFunc: (dy, saved, attrs) => {\n        const { blockShape, paddings } = attrs;\n        return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n    }\n};\n//# sourceMappingURL=SpaceToBatchND_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SplitV } from '../kernel_names';\nimport { concat } from '../ops/concat';\nexport const splitVGradConfig = {\n    kernelName: SplitV,\n    gradFunc: (dy, saved, attrs) => {\n        const { axis } = attrs;\n        return { x: () => concat(dy, axis) };\n    }\n};\n//# sourceMappingURL=SplitV_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { div } from '../ops/div';\nimport { mul } from '../ops/mul';\nimport { sqrt } from '../ops/sqrt';\nexport const sqrtGradConfig = {\n    kernelName: Sqrt,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2)) };\n    }\n};\n//# sourceMappingURL=Sqrt_grad.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '../kernel_names';\nimport { cast } from '../ops/cast';\nimport { mul } from '../ops/mul';\nexport const squareGradConfig = {\n    kernelName: Square,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => mul(dy, mul(cast(x, 'float32'), 2)) };\n    }\n};\n//# sourceMappingURL=Square_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { sub } from '../ops/sub';\nexport const squaredDifferenceGradConfig = {\n    kernelName: SquaredDifference,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const two = scalar(2);\n        const derA = () => mul(dy, mul(two, sub(a, b)));\n        const derB = () => mul(dy, mul(two, sub(b, a)));\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=SquaredDifference_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const stepGradConfig = {\n    kernelName: Step,\n    gradFunc: (dy) => {\n        // TODO(manrajgrover): Return null for gradients when backprop supports\n        // it.\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=Step_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '../kernel_names';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport { neg } from '../ops/neg';\nimport { reshape } from '../ops/reshape';\nimport { sum } from '../ops/sum';\nexport const subGradConfig = {\n    kernelName: Sub,\n    inputsToSave: ['a', 'b'],\n    gradFunc: (dy, saved) => {\n        const [a, b] = saved;\n        const outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const derA = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(res, a.shape);\n        };\n        const derB = () => {\n            let res = dy;\n            const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n            if (reduceAxes.length > 0) {\n                res = sum(res, reduceAxes);\n            }\n            return reshape(neg(res), b.shape);\n        };\n        return { a: derA, b: derB };\n    }\n};\n//# sourceMappingURL=Sub_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sum } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { ones } from '../ops/ones';\nimport { reshape } from '../ops/reshape';\nimport { parseAxisParam } from '../util';\nexport const sumGradConfig = {\n    kernelName: Sum,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const expandedDyShape = x.shape.slice();\n        const { axis } = attrs;\n        const axes = parseAxisParam(axis, x.shape);\n        axes.forEach(axis => {\n            expandedDyShape[axis] = 1;\n        });\n        const expandedDy = reshape(dy, expandedDyShape);\n        const derX = mul(expandedDy, ones(x.shape, 'float32'));\n        return { x: () => derX };\n    }\n};\n//# sourceMappingURL=Sum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '../kernel_names';\nimport { cos } from '../ops/cos';\nimport { div } from '../ops/div';\nimport { square } from '../ops/square';\nexport const tanGradConfig = {\n    kernelName: Tan,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved) => {\n        const [x] = saved;\n        return { x: () => div(dy, square(cos(x))) };\n    }\n};\n//# sourceMappingURL=Tan_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '../kernel_names';\nimport { mul } from '../ops/mul';\nimport { scalar } from '../ops/scalar';\nimport { square } from '../ops/square';\nimport { sub } from '../ops/sub';\nexport const tanhGradConfig = {\n    kernelName: Tanh,\n    outputsToSave: [true],\n    gradFunc: (dy, saved) => {\n        const [y] = saved;\n        return { x: () => mul(sub(scalar(1), square(y)), dy) };\n    }\n};\n//# sourceMappingURL=Tanh_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tile } from '../kernel_names';\nimport { add } from '../ops/add';\nimport { slice } from '../ops/slice';\nimport { zerosLike } from '../ops/zeros_like';\nexport const tileGradConfig = {\n    kernelName: Tile,\n    inputsToSave: ['x'],\n    gradFunc: (dy, saved, attrs) => {\n        const [x] = saved;\n        const { reps } = attrs;\n        const derX = () => {\n            let xGrad = zerosLike(x);\n            // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n            // slicing.\n            if (x.rank === 1) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n                }\n            }\n            else if (x.rank === 2) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                            x.shape[0], x.shape[1]\n                        ]));\n                    }\n                }\n            }\n            else if (x.rank === 3) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            xGrad =\n                                add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n                        }\n                    }\n                }\n            }\n            else if (x.rank === 4) {\n                for (let i = 0; i < reps[0]; ++i) {\n                    for (let j = 0; j < reps[1]; ++j) {\n                        for (let k = 0; k < reps[2]; ++k) {\n                            for (let l = 0; l < reps[3]; ++l) {\n                                xGrad =\n                                    add(xGrad, slice(dy, [\n                                        i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                                        l * x.shape[3]\n                                    ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n                            }\n                        }\n                    }\n                }\n            }\n            else {\n                throw new Error(`Gradient for tile operation is not implemented for rank-` +\n                    `${x.rank} tensors yet.`);\n            }\n            return xGrad;\n        };\n        return { x: derX };\n    },\n};\n//# sourceMappingURL=Tile_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '../kernel_names';\nimport * as axis_util from '../ops/axis_util';\nimport { transpose } from '../ops/transpose';\nexport const transposeGradConfig = {\n    kernelName: Transpose,\n    gradFunc: (dy, saved, attrs) => {\n        const transposeAttrs = attrs;\n        const { perm } = transposeAttrs;\n        const undoPerm = axis_util.getUndoAxesPermutation(perm);\n        return { x: () => transpose(dy, undoPerm) };\n    }\n};\n//# sourceMappingURL=Transpose_grad.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unpack } from '../kernel_names';\nimport { stack } from '../ops/stack';\nexport const unpackGradConfig = {\n    kernelName: Unpack,\n    gradFunc: (dy, saved, attrs) => {\n        const unpackAttrs = attrs;\n        const { axis } = unpackAttrs;\n        return { value: () => stack(dy, axis) };\n    }\n};\n//# sourceMappingURL=Unpack_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnsortedSegmentSum } from '../kernel_names';\nimport { expandDims } from '../ops/expand_dims';\nimport { gather } from '../ops/gather';\nimport { greaterEqual } from '../ops/greater_equal';\nimport { logicalAnd } from '../ops/logical_and';\nimport { maximum } from '../ops/maximum';\nimport { ones } from '../ops/ones';\nimport { scalar } from '../ops/scalar';\nimport { where } from '../ops/where';\nimport { zerosLike } from '../ops/zeros_like';\nexport const unsortedSegmentSumGradConfig = {\n    kernelName: UnsortedSegmentSum,\n    inputsToSave: ['segmentIds'],\n    gradFunc: (dy, saved) => {\n        const [segmentIds] = saved;\n        const derX = () => {\n            return gatherDropNegatives(dy, segmentIds);\n        };\n        return { x: derX };\n    }\n};\nfunction gatherDropNegatives(x, indices) {\n    // Helper function for unsorted segment ops. Gathers params for\n    // positive segment ids and gathers 0 for inputs with negative segment id.\n    // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n    const zeroClippedIndices = maximum(indices, zerosLike(indices));\n    const gathered = gather(x, zeroClippedIndices);\n    let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n    const numIters = gathered.rank - isPositive.rank;\n    for (let i = 0; i < numIters; ++i) {\n        isPositive = expandDims(isPositive, i + 1);\n    }\n    isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n    const zeroSlice = zerosLike(gathered);\n    return where(isPositive, gathered, zeroSlice);\n}\n//# sourceMappingURL=UnsortedSegmentSum_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ZerosLike } from '../kernel_names';\nimport { zerosLike } from '../ops/zeros_like';\nexport const zerosLikeGradConfig = {\n    kernelName: ZerosLike,\n    gradFunc: (dy) => {\n        return { x: () => zerosLike(dy) };\n    }\n};\n//# sourceMappingURL=ZerosLike_grad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { absGradConfig } from './gradients/Abs_grad';\nimport { acosGradConfig } from './gradients/Acos_grad';\nimport { acoshGradConfig } from './gradients/Acosh_grad';\nimport { addGradConfig } from './gradients/Add_grad';\nimport { addNGradConfig } from './gradients/AddN_grad';\nimport { argMaxGradConfig } from './gradients/ArgMax_grad';\nimport { argMinGradConfig } from './gradients/ArgMin_grad';\nimport { asinGradConfig } from './gradients/Asin_grad';\nimport { asinhGradConfig } from './gradients/Asinh_grad';\nimport { atan2GradConfig } from './gradients/Atan2_grad';\nimport { atanGradConfig } from './gradients/Atan_grad';\nimport { atanhGradConfig } from './gradients/Atanh_grad';\nimport { avgPool3DGradConfig } from './gradients/AvgPool3D_grad';\nimport { avgPoolGradConfig } from './gradients/AvgPool_grad';\nimport { batchMatMulGradConfig } from './gradients/BatchMatMul_grad';\nimport { batchToSpaceNDGradConfig } from './gradients/BatchToSpaceND_grad';\nimport { broadcastToGradConfig } from './gradients/BroadcastTo_grad';\nimport { castGradConfig } from './gradients/Cast_grad';\nimport { ceilGradConfig } from './gradients/Ceil_grad';\nimport { clipByValueGradConfig } from './gradients/ClipByValue_grad';\nimport { concatGradConfig } from './gradients/Concat_grad';\nimport { conv2DGradConfig } from './gradients/Conv2D_grad';\nimport { conv2DBackpropInputGradConfig } from './gradients/Conv2DBackpropInput_grad';\nimport { conv3DGradConfig } from './gradients/Conv3D_grad';\nimport { cosGradConfig } from './gradients/Cos_grad';\nimport { coshGradConfig } from './gradients/Cosh_grad';\nimport { cumsumGradConfig } from './gradients/Cumsum_grad';\nimport { depthwiseConv2dNativeGradConfig } from './gradients/DepthwiseConv2dNative_grad';\nimport { dilation2dGradConfig } from './gradients/Dilation2D_grad';\nimport { divGradConfig } from './gradients/Div_grad';\nimport { eluGradConfig } from './gradients/Elu_grad';\nimport { erfGradConfig } from './gradients/Erf_grad';\nimport { expGradConfig } from './gradients/Exp_grad';\nimport { expm1GradConfig } from './gradients/Expm1_grad';\nimport { floorGradConfig } from './gradients/Floor_grad';\nimport { floorDivGradConfig } from './gradients/FloorDiv_grad';\nimport { fusedBatchNormGradConfig } from './gradients/FusedBatchNorm_grad';\nimport { gatherGradConfig } from './gradients/GatherV2_grad';\nimport { greaterEqualGradConfig } from './gradients/GreaterEqual_grad';\nimport { identityGradConfig } from './gradients/Identity_grad';\nimport { isFiniteGradConfig } from './gradients/IsFinite_grad';\nimport { isInfGradConfig } from './gradients/IsInf_grad';\nimport { isNanGradConfig } from './gradients/IsNan_grad';\nimport { log1pGradConfig } from './gradients/Log1p_grad';\nimport { logGradConfig } from './gradients/Log_grad';\nimport { logSoftmaxGradConfig } from './gradients/LogSoftmax_grad';\nimport { lrnGradConfig } from './gradients/LRN_grad';\nimport { maxGradConfig } from './gradients/Max_grad';\nimport { maximumGradConfig } from './gradients/Maximum_grad';\nimport { maxPool3DGradConfig } from './gradients/MaxPool3D_grad';\nimport { maxPoolGradConfig } from './gradients/MaxPool_grad';\nimport { minGradConfig } from './gradients/Min_grad';\nimport { minimumGradConfig } from './gradients/Minimum_grad';\nimport { mirrorPadGradConfig } from './gradients/MirrorPad_grad';\nimport { modGradConfig } from './gradients/Mod_grad';\nimport { multiplyGradConfig } from './gradients/Multiply_grad';\nimport { negateGradConfig } from './gradients/Negate_grad';\nimport { oneHotGradConfig } from './gradients/OneHot_grad';\nimport { onesLikeGradConfig } from './gradients/OnesLike_grad';\nimport { padV2GradConfig } from './gradients/PadV2_grad';\nimport { powGradConfig } from './gradients/Pow_grad';\nimport { preluGradConfig } from './gradients/Prelu_grad';\nimport { reciprocalGradConfig } from './gradients/Reciprocal_grad';\nimport { relu6GradConfig } from './gradients/Relu6_grad';\nimport { reluGradConfig } from './gradients/Relu_grad';\nimport { reshapeGradConfig } from './gradients/Reshape_grad';\nimport { resizeBilinearGradConfig } from './gradients/ResizeBilinear_grad';\nimport { resizeNearestNeighborGradConfig } from './gradients/ResizeNearestNeighbor_grad';\nimport { reverseGradConfig } from './gradients/Reverse_grad';\nimport { roundGradConfig } from './gradients/Round_grad';\nimport { rsqrtGradConfig } from './gradients/Rsqrt_grad';\nimport { selectV2PoolGradConfig } from './gradients/SelectV2_grad';\nimport { seluGradConfig } from './gradients/Selu_grad';\nimport { sigmoidGradConfig } from './gradients/Sigmoid_grad';\nimport { signGradConfig } from './gradients/Sign_grad';\nimport { sinGradConfig } from './gradients/Sin_grad';\nimport { sinhGradConfig } from './gradients/Sinh_grad';\nimport { sliceGradConfig } from './gradients/Slice_grad';\nimport { softmaxGradConfig } from './gradients/Softmax_grad';\nimport { softplusGradConfig } from './gradients/Softplus_grad';\nimport { spaceToBatchNDGradConfig } from './gradients/SpaceToBatchND_grad';\nimport { splitVGradConfig } from './gradients/SplitV_grad';\nimport { sqrtGradConfig } from './gradients/Sqrt_grad';\nimport { squareGradConfig } from './gradients/Square_grad';\nimport { squaredDifferenceGradConfig } from './gradients/SquaredDifference_grad';\nimport { stepGradConfig } from './gradients/Step_grad';\nimport { subGradConfig } from './gradients/Sub_grad';\nimport { sumGradConfig } from './gradients/Sum_grad';\nimport { tanGradConfig } from './gradients/Tan_grad';\nimport { tanhGradConfig } from './gradients/Tanh_grad';\nimport { tileGradConfig } from './gradients/Tile_grad';\nimport { transposeGradConfig } from './gradients/Transpose_grad';\nimport { unpackGradConfig } from './gradients/Unpack_grad';\nimport { unsortedSegmentSumGradConfig } from './gradients/UnsortedSegmentSum_grad';\nimport { zerosLikeGradConfig } from './gradients/ZerosLike_grad';\nimport { registerGradient } from './kernel_registry';\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs = [\n    absGradConfig,\n    acosGradConfig,\n    acoshGradConfig,\n    addGradConfig,\n    addNGradConfig,\n    argMaxGradConfig,\n    argMinGradConfig,\n    asinGradConfig,\n    asinhGradConfig,\n    atan2GradConfig,\n    atanGradConfig,\n    atanhGradConfig,\n    avgPool3DGradConfig,\n    avgPoolGradConfig,\n    batchMatMulGradConfig,\n    batchToSpaceNDGradConfig,\n    broadcastToGradConfig,\n    castGradConfig,\n    ceilGradConfig,\n    clipByValueGradConfig,\n    concatGradConfig,\n    conv2DBackpropInputGradConfig,\n    conv2DGradConfig,\n    conv3DGradConfig,\n    cosGradConfig,\n    coshGradConfig,\n    cumsumGradConfig,\n    depthwiseConv2dNativeGradConfig,\n    dilation2dGradConfig,\n    divGradConfig,\n    eluGradConfig,\n    erfGradConfig,\n    expGradConfig,\n    expm1GradConfig,\n    floorDivGradConfig,\n    floorGradConfig,\n    fusedBatchNormGradConfig,\n    gatherGradConfig,\n    greaterEqualGradConfig,\n    identityGradConfig,\n    isFiniteGradConfig,\n    isInfGradConfig,\n    isNanGradConfig,\n    log1pGradConfig,\n    logGradConfig,\n    logSoftmaxGradConfig,\n    lrnGradConfig,\n    maxGradConfig,\n    maxGradConfig,\n    maximumGradConfig,\n    maxPool3DGradConfig,\n    maxPoolGradConfig,\n    minGradConfig,\n    minimumGradConfig,\n    mirrorPadGradConfig,\n    modGradConfig,\n    multiplyGradConfig,\n    negateGradConfig,\n    oneHotGradConfig,\n    onesLikeGradConfig,\n    padV2GradConfig,\n    padV2GradConfig,\n    powGradConfig,\n    preluGradConfig,\n    reciprocalGradConfig,\n    relu6GradConfig,\n    reluGradConfig,\n    reshapeGradConfig,\n    resizeBilinearGradConfig,\n    resizeNearestNeighborGradConfig,\n    reverseGradConfig,\n    roundGradConfig,\n    rsqrtGradConfig,\n    selectV2PoolGradConfig,\n    seluGradConfig,\n    sigmoidGradConfig,\n    signGradConfig,\n    sinGradConfig,\n    sinhGradConfig,\n    sliceGradConfig,\n    softmaxGradConfig,\n    softplusGradConfig,\n    spaceToBatchNDGradConfig,\n    spaceToBatchNDGradConfig,\n    splitVGradConfig,\n    splitVGradConfig,\n    sqrtGradConfig,\n    squaredDifferenceGradConfig,\n    squareGradConfig,\n    stepGradConfig,\n    subGradConfig,\n    sumGradConfig,\n    tanGradConfig,\n    tanhGradConfig,\n    tileGradConfig,\n    transposeGradConfig,\n    unpackGradConfig,\n    unsortedSegmentSumGradConfig,\n    zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n    registerGradient(gradientConfig);\n}\n//# sourceMappingURL=register_all_gradients.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { abs } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.abs = function () {\n    this.throwIfDisposed();\n    return abs(this);\n};\n//# sourceMappingURL=abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acos = function () {\n    this.throwIfDisposed();\n    return acos(this);\n};\n//# sourceMappingURL=acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { acosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.acosh = function () {\n    this.throwIfDisposed();\n    return acosh(this);\n};\n//# sourceMappingURL=acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { addStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function (x) {\n    this.throwIfDisposed();\n    return addStrict(this, x);\n};\n//# sourceMappingURL=add_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { add } from '../../ops/add';\nimport { Tensor } from '../../tensor';\nTensor.prototype.add = function (b) {\n    this.throwIfDisposed();\n    return add(this, b);\n};\n//# sourceMappingURL=add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { all } from '../../ops/all';\nimport { Tensor } from '../../tensor';\nTensor.prototype.all = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return all(this, axis, keepDims);\n};\n//# sourceMappingURL=all.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { any } from '../../ops/any';\nimport { Tensor } from '../../tensor';\nTensor.prototype.any = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return any(this, axis, keepDims);\n};\n//# sourceMappingURL=any.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMax } from '../../ops/arg_max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMax = function (axis) {\n    this.throwIfDisposed();\n    return argMax(this, axis);\n};\n//# sourceMappingURL=arg_max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { argMin } from '../../ops/arg_min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.argMin = function (axis) {\n    this.throwIfDisposed();\n    return argMin(this, axis);\n};\n//# sourceMappingURL=arg_min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nimport { assert } from '../../util';\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function () {\n    this.throwIfDisposed();\n    assert(this.size === 1, () => 'The array must have only 1 element.');\n    return reshape(this, []);\n};\n//# sourceMappingURL=as_scalar.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=as_type.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=as1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function (rows, columns) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns]);\n};\n//# sourceMappingURL=as2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function (rows, columns, depth) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth]);\n};\n//# sourceMappingURL=as3d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function (rows, columns, depth, depth2) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2]);\n};\n//# sourceMappingURL=as4d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {\n    this.throwIfDisposed();\n    return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n//# sourceMappingURL=as5d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asin = function () {\n    this.throwIfDisposed();\n    return asin(this);\n};\n//# sourceMappingURL=asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { asinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.asinh = function () {\n    this.throwIfDisposed();\n    return asinh(this);\n};\n//# sourceMappingURL=asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan = function () {\n    this.throwIfDisposed();\n    return atan(this);\n};\n//# sourceMappingURL=atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { atan2 } from '../../ops/atan2';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atan2 = function (b) {\n    this.throwIfDisposed();\n    return atan2(this, b);\n};\n//# sourceMappingURL=atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { atanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.atanh = function () {\n    this.throwIfDisposed();\n    return atanh(this);\n};\n//# sourceMappingURL=atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { avgPool } from '../../ops/avg_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=avg_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchToSpaceND } from '../../ops/batch_to_space_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchToSpaceND = function (blockShape, crops) {\n    this.throwIfDisposed();\n    return batchToSpaceND(this, blockShape, crops);\n};\n//# sourceMappingURL=batch_to_space_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { batchNorm } from '../../ops/batchnorm';\nimport { Tensor } from '../../tensor';\nTensor.prototype.batchNorm = function (mean, variance, offset, scale, varianceEpsilon) {\n    this.throwIfDisposed();\n    return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n//# sourceMappingURL=batchnorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { broadcastTo } from '../../ops/broadcast_to';\nimport { Tensor } from '../../tensor';\nTensor.prototype.broadcastTo = function (shape) {\n    this.throwIfDisposed();\n    return broadcastTo(this, shape);\n};\n//# sourceMappingURL=broadcast_to.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cast = function (dtype) {\n    this.throwIfDisposed();\n    return cast(this, dtype);\n};\n//# sourceMappingURL=cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ceil } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ceil = function () {\n    this.throwIfDisposed();\n    return ceil(this);\n};\n//# sourceMappingURL=ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { clipByValue } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.clipByValue = function (min, max) {\n    this.throwIfDisposed();\n    return clipByValue(this, min, max);\n};\n//# sourceMappingURL=clip_by_value.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat } from '../../ops/concat';\nimport { Tensor } from '../../tensor';\nTensor.prototype.concat = function (x, axis) {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n        x = [x];\n    }\n    return concat([this, ...x], axis);\n};\n//# sourceMappingURL=concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv1d } from '../../ops/conv1d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n};\n//# sourceMappingURL=conv1d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2dTranspose } from '../../ops/conv2d_transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d_transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { conv2d } from '../../ops/conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cos } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cos = function () {\n    this.throwIfDisposed();\n    return cos(this);\n};\n//# sourceMappingURL=cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cosh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cosh = function () {\n    this.throwIfDisposed();\n    return cosh(this);\n};\n//# sourceMappingURL=cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { cumsum } from '../../ops/cumsum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.cumsum = function (axis, exclusive, reverse) {\n    this.throwIfDisposed();\n    return cumsum(this, axis, exclusive, reverse);\n};\n//# sourceMappingURL=cumsum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthToSpace } from '../../ops/depth_to_space';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthToSpace = function (blockSize, dataFormat) {\n    this.throwIfDisposed();\n    return depthToSpace(this, blockSize, dataFormat);\n};\n//# sourceMappingURL=depth_to_space.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { deprecationWarn } from '../../globals';\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2D_deprecated.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { depthwiseConv2d } from '../../ops/depthwise_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.depthwiseConv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {\n    this.throwIfDisposed();\n    return depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n};\n//# sourceMappingURL=depthwise_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dilation2d } from '../../ops/dilation2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dilation2d = function (filter, strides, pad, dilations, dataFormat) {\n    this.throwIfDisposed();\n    return dilation2d(this, filter, strides, pad, dilations, dataFormat);\n};\n//# sourceMappingURL=dilation2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { divNoNan } from '../../ops/div_no_nan';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divNoNan = function (b) {\n    this.throwIfDisposed();\n    return divNoNan(this, b);\n};\n//# sourceMappingURL=div_no_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { divStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.divStrict = function (x) {\n    this.throwIfDisposed();\n    return divStrict(this, x);\n};\n//# sourceMappingURL=div_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { div } from '../../ops/div';\nimport { Tensor } from '../../tensor';\nTensor.prototype.div = function (b) {\n    this.throwIfDisposed();\n    return div(this, b);\n};\n//# sourceMappingURL=div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { dot } from '../../ops/dot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.dot = function (b) {\n    this.throwIfDisposed();\n    return dot(this, b);\n};\n//# sourceMappingURL=dot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../../ops/elu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.elu = function () {\n    this.throwIfDisposed();\n    return elu(this);\n};\n//# sourceMappingURL=elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { equalStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function (x) {\n    this.throwIfDisposed();\n    return equalStrict(this, x);\n};\n//# sourceMappingURL=equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { equal } from '../../ops/equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.equal = function (b) {\n    this.throwIfDisposed();\n    return equal(this, b);\n};\n//# sourceMappingURL=equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { erf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.erf = function () {\n    this.throwIfDisposed();\n    return erf(this);\n};\n//# sourceMappingURL=erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { exp } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.exp = function () {\n    this.throwIfDisposed();\n    return exp(this);\n};\n//# sourceMappingURL=exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { expandDims } from '../../ops/expand_dims';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expandDims = function (axis) {\n    this.throwIfDisposed();\n    return expandDims(this, axis);\n};\n//# sourceMappingURL=expand_dims.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { expm1 } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.expm1 = function () {\n    this.throwIfDisposed();\n    return expm1(this);\n};\n//# sourceMappingURL=expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { fft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.fft = function () {\n    this.throwIfDisposed();\n    return fft(this);\n};\n//# sourceMappingURL=fft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function () {\n    this.throwIfDisposed();\n    return reshape(this, [this.size]);\n};\n//# sourceMappingURL=flatten.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { floor } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floor = function () {\n    this.throwIfDisposed();\n    return floor(this);\n};\n//# sourceMappingURL=floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { floorDiv } from '../../ops/floorDiv';\nimport { Tensor } from '../../tensor';\nTensor.prototype.floorDiv = function (b) {\n    this.throwIfDisposed();\n    return floorDiv(this, b);\n};\n//# sourceMappingURL=floorDiv.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { gather } from '../../ops/gather';\nimport { Tensor } from '../../tensor';\nTensor.prototype.gather = function (indices, axis) {\n    this.throwIfDisposed();\n    return gather(this, indices, axis);\n};\n//# sourceMappingURL=gather.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterEqualStrict(this, x);\n};\n//# sourceMappingURL=greater_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greaterEqual } from '../../ops/greater_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greaterEqual = function (b) {\n    this.throwIfDisposed();\n    return greaterEqual(this, b);\n};\n//# sourceMappingURL=greater_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { greaterStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function (x) {\n    this.throwIfDisposed();\n    return greaterStrict(this, x);\n};\n//# sourceMappingURL=greater_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { greater } from '../../ops/greater';\nimport { Tensor } from '../../tensor';\nTensor.prototype.greater = function (b) {\n    this.throwIfDisposed();\n    return greater(this, b);\n};\n//# sourceMappingURL=greater.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { ifft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.ifft = function () {\n    this.throwIfDisposed();\n    return ifft(this);\n};\n//# sourceMappingURL=ifft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { irfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.irfft = function () {\n    this.throwIfDisposed();\n    return irfft(this);\n};\n//# sourceMappingURL=irfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isFinite } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isFinite = function () {\n    this.throwIfDisposed();\n    return isFinite(this);\n};\n//# sourceMappingURL=is_finite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isInf } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isInf = function () {\n    this.throwIfDisposed();\n    return isInf(this);\n};\n//# sourceMappingURL=is_inf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { isNaN } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.isNaN = function () {\n    this.throwIfDisposed();\n    return isNaN(this);\n};\n//# sourceMappingURL=is_nan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { leakyRelu } from '../../ops/leaky_relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.leakyRelu = function (alpha) {\n    this.throwIfDisposed();\n    return leakyRelu(this, alpha);\n};\n//# sourceMappingURL=leaky_relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return lessEqualStrict(this, x);\n};\n//# sourceMappingURL=less_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { lessEqual } from '../../ops/less_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessEqual = function (b) {\n    this.throwIfDisposed();\n    return lessEqual(this, b);\n};\n//# sourceMappingURL=less_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { lessStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.lessStrict = function (x) {\n    this.throwIfDisposed();\n    return lessStrict(this, x);\n};\n//# sourceMappingURL=less_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { less } from '../../ops/less';\nimport { Tensor } from '../../tensor';\nTensor.prototype.less = function (b) {\n    this.throwIfDisposed();\n    return less(this, b);\n};\n//# sourceMappingURL=less.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { localResponseNormalization } from '../../ops/local_response_normalization';\nimport { Tensor } from '../../tensor';\nTensor.prototype.localResponseNormalization = function (depthRadius, bias, alpha, beta) {\n    this.throwIfDisposed();\n    return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n//# sourceMappingURL=local_response_normalization.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSigmoid = function () {\n    this.throwIfDisposed();\n    return logSigmoid(this);\n};\n//# sourceMappingURL=log_sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { logSoftmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSoftmax = function (axis) {\n    this.throwIfDisposed();\n    return logSoftmax(this, axis);\n};\n//# sourceMappingURL=log_softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logSumExp } from '../../ops/log_sum_exp';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logSumExp = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return logSumExp(this, axis, keepDims);\n};\n//# sourceMappingURL=log_sum_exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log = function () {\n    this.throwIfDisposed();\n    return log(this);\n};\n//# sourceMappingURL=log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { log1p } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.log1p = function () {\n    this.throwIfDisposed();\n    return log1p(this);\n};\n//# sourceMappingURL=log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalAnd } from '../../ops/logical_and';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalAnd = function (b) {\n    this.throwIfDisposed();\n    return logicalAnd(this, b);\n};\n//# sourceMappingURL=logical_and.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalNot } from '../../ops/logical_not';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalNot = function () {\n    this.throwIfDisposed();\n    return logicalNot(this);\n};\n//# sourceMappingURL=logical_not.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalOr } from '../../ops/logical_or';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalOr = function (b) {\n    this.throwIfDisposed();\n    return logicalOr(this, b);\n};\n//# sourceMappingURL=logical_or.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { logicalXor } from '../../ops/logical_xor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.logicalXor = function (b) {\n    this.throwIfDisposed();\n    return logicalXor(this, b);\n};\n//# sourceMappingURL=logical_xor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { matMul } from '../../ops/mat_mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.matMul = function (b, transposeA, transposeB) {\n    this.throwIfDisposed();\n    return matMul(this, b, transposeA, transposeB);\n};\n//# sourceMappingURL=mat_mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maxPool } from '../../ops/max_pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {\n    this.throwIfDisposed();\n    return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n//# sourceMappingURL=max_pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { max } from '../../ops/max';\nimport { Tensor } from '../../tensor';\nTensor.prototype.max = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return max(this, axis, keepDims);\n};\n//# sourceMappingURL=max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { maximumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function (x) {\n    this.throwIfDisposed();\n    return maximumStrict(this, x);\n};\n//# sourceMappingURL=maximum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { maximum } from '../../ops/maximum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.maximum = function (b) {\n    this.throwIfDisposed();\n    return maximum(this, b);\n};\n//# sourceMappingURL=maximum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mean } from '../../ops/mean';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mean = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return mean(this, axis, keepDims);\n};\n//# sourceMappingURL=mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { min } from '../../ops/min';\nimport { Tensor } from '../../tensor';\nTensor.prototype.min = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return min(this, axis, keepDims);\n};\n//# sourceMappingURL=min.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { minimumStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function (x) {\n    this.throwIfDisposed();\n    return minimumStrict(this, x);\n};\n//# sourceMappingURL=minimum_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { minimum } from '../../ops/minimum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.minimum = function (b) {\n    this.throwIfDisposed();\n    return minimum(this, b);\n};\n//# sourceMappingURL=minimum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mirrorPad } from '../../ops/mirror_pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mirrorPad = function (paddings, mode) {\n    this.throwIfDisposed();\n    return mirrorPad(this, paddings, mode);\n};\n//# sourceMappingURL=mirror_pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { modStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function (x) {\n    this.throwIfDisposed();\n    return modStrict(this, x);\n};\n//# sourceMappingURL=mod_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mod } from '../../ops/mod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mod = function (b) {\n    this.throwIfDisposed();\n    return mod(this, b);\n};\n//# sourceMappingURL=mod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { mulStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function (x) {\n    this.throwIfDisposed();\n    return mulStrict(this, x);\n};\n//# sourceMappingURL=mul_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { mul } from '../../ops/mul';\nimport { Tensor } from '../../tensor';\nTensor.prototype.mul = function (b) {\n    this.throwIfDisposed();\n    return mul(this, b);\n};\n//# sourceMappingURL=mul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { neg } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.neg = function () {\n    this.throwIfDisposed();\n    return neg(this);\n};\n//# sourceMappingURL=neg.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { norm } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.norm = function (ord, axis, keepDims) {\n    this.throwIfDisposed();\n    return norm(this, ord, axis, keepDims);\n};\n//# sourceMappingURL=norm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { notEqualStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function (x) {\n    this.throwIfDisposed();\n    return notEqualStrict(this, x);\n};\n//# sourceMappingURL=not_equal_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { notEqual } from '../../ops/not_equal';\nimport { Tensor } from '../../tensor';\nTensor.prototype.notEqual = function (b) {\n    this.throwIfDisposed();\n    return notEqual(this, b);\n};\n//# sourceMappingURL=not_equal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { oneHot } from '../../ops/one_hot';\nimport { Tensor } from '../../tensor';\nTensor.prototype.oneHot = function (depth, onValue = 1, offValue = 0) {\n    this.throwIfDisposed();\n    return oneHot(this, depth, onValue, offValue);\n};\n//# sourceMappingURL=one_hot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { onesLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.onesLike = function () {\n    this.throwIfDisposed();\n    return onesLike(this);\n};\n//# sourceMappingURL=ones_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pad } from '../../ops/pad';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pad = function (paddings, constantValue) {\n    this.throwIfDisposed();\n    return pad(this, paddings, constantValue);\n};\n//# sourceMappingURL=pad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pool } from '../../ops/pool';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {\n    this.throwIfDisposed();\n    return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n//# sourceMappingURL=pool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { powStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function (exp) {\n    this.throwIfDisposed();\n    return powStrict(this, exp);\n};\n//# sourceMappingURL=pow_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { pow } from '../../ops/pow';\nimport { Tensor } from '../../tensor';\nTensor.prototype.pow = function (exp) {\n    this.throwIfDisposed();\n    return pow(this, exp);\n};\n//# sourceMappingURL=pow.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prelu } from '../../ops/prelu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prelu = function (alpha) {\n    this.throwIfDisposed();\n    return prelu(this, alpha);\n};\n//# sourceMappingURL=prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { prod } from '../../ops/prod';\nimport { Tensor } from '../../tensor';\nTensor.prototype.prod = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return prod(this, axis, keepDims);\n};\n//# sourceMappingURL=prod.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { reciprocal } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reciprocal = function () {\n    this.throwIfDisposed();\n    return reciprocal(this);\n};\n//# sourceMappingURL=reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu } from '../../ops/relu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu = function () {\n    this.throwIfDisposed();\n    return relu(this);\n};\n//# sourceMappingURL=relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { relu6 } from '../../ops/relu6';\nimport { Tensor } from '../../tensor';\nTensor.prototype.relu6 = function () {\n    this.throwIfDisposed();\n    return relu6(this);\n};\n//# sourceMappingURL=relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function (x) {\n    this.throwIfDisposed();\n    return reshape(this, x.shape);\n};\n//# sourceMappingURL=reshape_as.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reshape } from '../../ops/reshape';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reshape = function (shape) {\n    this.throwIfDisposed();\n    return reshape(this, shape);\n};\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeBilinear } from '../../ops/image/resize_bilinear';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeBilinear(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_bilinear.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { resizeNearestNeighbor } from '../../ops/image/resize_nearest_neighbor';\nimport { Tensor } from '../../tensor';\nTensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {\n    this.throwIfDisposed();\n    return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n//# sourceMappingURL=resize_nearest_neighbor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { reverse } from '../../ops/reverse';\nimport { Tensor } from '../../tensor';\nTensor.prototype.reverse = function (axis) {\n    this.throwIfDisposed();\n    return reverse(this, axis);\n};\n//# sourceMappingURL=reverse.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rfft } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rfft = function () {\n    this.throwIfDisposed();\n    return rfft(this);\n};\n//# sourceMappingURL=rfft.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { round } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.round = function () {\n    this.throwIfDisposed();\n    return round(this);\n};\n//# sourceMappingURL=round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { rsqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.rsqrt = function () {\n    this.throwIfDisposed();\n    return rsqrt(this);\n};\n//# sourceMappingURL=rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { selu } from '../../ops/selu';\nimport { Tensor } from '../../tensor';\nTensor.prototype.selu = function () {\n    this.throwIfDisposed();\n    return selu(this);\n};\n//# sourceMappingURL=selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { separableConv2d } from '../../ops/separable_conv2d';\nimport { Tensor } from '../../tensor';\nTensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {\n    this.throwIfDisposed();\n    return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);\n};\n//# sourceMappingURL=separable_conv2d.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sigmoid } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sigmoid = function () {\n    this.throwIfDisposed();\n    return sigmoid(this);\n};\n//# sourceMappingURL=sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sign } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sign = function () {\n    this.throwIfDisposed();\n    return sign(this);\n};\n//# sourceMappingURL=sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sin } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sin = function () {\n    this.throwIfDisposed();\n    return sin(this);\n};\n//# sourceMappingURL=sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sinh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sinh = function () {\n    this.throwIfDisposed();\n    return sinh(this);\n};\n//# sourceMappingURL=sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { slice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.slice = function (begin, size) {\n    this.throwIfDisposed();\n    return slice(this, begin, size);\n};\n//# sourceMappingURL=slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softmax } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softmax = function (dim) {\n    this.throwIfDisposed();\n    return softmax(this, dim);\n};\n//# sourceMappingURL=softmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { softplus } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.softplus = function () {\n    this.throwIfDisposed();\n    return softplus(this);\n};\n//# sourceMappingURL=softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { spaceToBatchND } from '../../ops/space_to_batch_nd';\nimport { Tensor } from '../../tensor';\nTensor.prototype.spaceToBatchND = function (blockShape, paddings) {\n    this.throwIfDisposed();\n    return spaceToBatchND(this, blockShape, paddings);\n};\n//# sourceMappingURL=space_to_batch_nd.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { split } from '../../ops/split';\nimport { Tensor } from '../../tensor';\nTensor.prototype.split = function (numOrSizeSplits, axis) {\n    this.throwIfDisposed();\n    return split(this, numOrSizeSplits, axis);\n};\n//# sourceMappingURL=split.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { sqrt } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sqrt = function () {\n    this.throwIfDisposed();\n    return sqrt(this);\n};\n//# sourceMappingURL=sqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { square } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.square = function () {\n    this.throwIfDisposed();\n    return square(this);\n};\n//# sourceMappingURL=square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squaredDifference } from '../../ops/squared_difference';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squaredDifference = function (b) {\n    this.throwIfDisposed();\n    return squaredDifference(this, b);\n};\n//# sourceMappingURL=squared_difference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { squaredDifferenceStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function (x) {\n    this.throwIfDisposed();\n    return squaredDifferenceStrict(this, x);\n};\n//# sourceMappingURL=squared_difference_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { squeeze } from '../../ops/squeeze';\nimport { Tensor } from '../../tensor';\nTensor.prototype.squeeze = function (axis) {\n    this.throwIfDisposed();\n    return squeeze(this, axis);\n};\n//# sourceMappingURL=squeeze.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { stack } from '../../ops/stack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stack = function (x, axis) {\n    this.throwIfDisposed();\n    const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n    return stack(tensorsToBeStacked, axis);\n};\n//# sourceMappingURL=stack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { step } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.step = function (alpha) {\n    this.throwIfDisposed();\n    return step(this, alpha);\n};\n//# sourceMappingURL=step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { stridedSlice } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n    this.throwIfDisposed();\n    return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n//# sourceMappingURL=strided_slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { subStrict } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function (x) {\n    this.throwIfDisposed();\n    return subStrict(this, x);\n};\n//# sourceMappingURL=sub_strict.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sub } from '../../ops/sub';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sub = function (b) {\n    this.throwIfDisposed();\n    return sub(this, b);\n};\n//# sourceMappingURL=sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { sum } from '../../ops/sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.sum = function (axis, keepDims) {\n    this.throwIfDisposed();\n    return sum(this, axis, keepDims);\n};\n//# sourceMappingURL=sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tan } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tan = function () {\n    this.throwIfDisposed();\n    return tan(this);\n};\n//# sourceMappingURL=tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { tanh } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tanh = function () {\n    this.throwIfDisposed();\n    return tanh(this);\n};\n//# sourceMappingURL=tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tile } from '../../ops/tile';\nimport { Tensor } from '../../tensor';\nTensor.prototype.tile = function (reps) {\n    this.throwIfDisposed();\n    return tile(this, reps);\n};\n//# sourceMappingURL=tile.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function () {\n    this.throwIfDisposed();\n    return cast(this, 'bool');\n};\n//# sourceMappingURL=to_bool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function () {\n    this.throwIfDisposed();\n    return cast(this, 'float32');\n};\n//# sourceMappingURL=to_float.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { cast } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function () {\n    this.throwIfDisposed();\n    return cast(this, 'int32');\n};\n//# sourceMappingURL=to_int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { topk } from '../../ops/topk';\nimport { Tensor } from '../../tensor';\nTensor.prototype.topk = function (k, sorted) {\n    this.throwIfDisposed();\n    return topk(this, k, sorted);\n};\n//# sourceMappingURL=topk.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { transpose } from '../../ops/transpose';\nimport { Tensor } from '../../tensor';\nTensor.prototype.transpose = function (perm) {\n    this.throwIfDisposed();\n    return transpose(this, perm);\n};\n//# sourceMappingURL=transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unique } from '../../ops/unique';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unique = function (axis) {\n    this.throwIfDisposed();\n    return unique(this, axis);\n};\n//# sourceMappingURL=unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unsortedSegmentSum } from '../../ops/unsorted_segment_sum';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {\n    this.throwIfDisposed();\n    return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n//# sourceMappingURL=unsorted_segment_sum.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { unstack } from '../../ops/unstack';\nimport { Tensor } from '../../tensor';\nTensor.prototype.unstack = function (axis) {\n    this.throwIfDisposed();\n    return unstack(this, axis);\n};\n//# sourceMappingURL=unstack.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { where } from '../../ops/where';\nimport { Tensor } from '../../tensor';\nTensor.prototype.where = function (condition, x) {\n    this.throwIfDisposed();\n    return where(condition, this, x);\n};\n//# sourceMappingURL=where.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// TODO update import path once op is modularized.\nimport { zerosLike } from '../../ops/ops';\nimport { Tensor } from '../../tensor';\nTensor.prototype.zerosLike = function () {\n    this.throwIfDisposed();\n    return zerosLike(this);\n};\n//# sourceMappingURL=zeros_like.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './abs';\nimport './acos';\nimport './acosh';\nimport './add_strict';\nimport './add';\nimport './all';\nimport './any';\nimport './arg_max';\nimport './arg_min';\nimport './as_scalar';\nimport './as_type';\nimport './as1d';\nimport './as2d';\nimport './as3d';\nimport './as4d';\nimport './as5d';\nimport './asin';\nimport './asinh';\nimport './atan';\nimport './atan2';\nimport './atanh';\nimport './avg_pool';\nimport './batch_to_space_nd';\nimport './batchnorm';\nimport './broadcast_to';\nimport './cast';\nimport './ceil';\nimport './clip_by_value';\nimport './concat';\nimport './conv1d';\nimport './conv2d_transpose';\nimport './conv2d';\nimport './cos';\nimport './cosh';\nimport './cumsum';\nimport './depth_to_space';\nimport './depthwise_conv2D_deprecated';\nimport './depthwise_conv2d';\nimport './dilation2d';\nimport './div_no_nan';\nimport './div_strict';\nimport './div';\nimport './dot';\nimport './elu';\nimport './equal_strict';\nimport './equal';\nimport './erf';\nimport './exp';\nimport './expand_dims';\nimport './expm1';\nimport './fft';\nimport './flatten';\nimport './floor';\nimport './floorDiv';\nimport './gather';\nimport './greater_equal_strict';\nimport './greater_equal';\nimport './greater_strict';\nimport './greater';\nimport './ifft';\nimport './irfft';\nimport './is_finite';\nimport './is_inf';\nimport './is_nan';\nimport './leaky_relu';\nimport './less_equal_strict';\nimport './less_equal';\nimport './less_strict';\nimport './less';\nimport './local_response_normalization';\nimport './log_sigmoid';\nimport './log_softmax';\nimport './log_sum_exp';\nimport './log';\nimport './log1p';\nimport './logical_and';\nimport './logical_not';\nimport './logical_or';\nimport './logical_xor';\nimport './mat_mul';\nimport './max_pool';\nimport './max';\nimport './maximum_strict';\nimport './maximum';\nimport './mean';\nimport './min';\nimport './minimum_strict';\nimport './minimum';\nimport './mirror_pad';\nimport './mod_strict';\nimport './mod';\nimport './mul_strict';\nimport './mul';\nimport './neg';\nimport './norm';\nimport './not_equal_strict';\nimport './not_equal';\nimport './one_hot';\nimport './ones_like';\nimport './pad';\nimport './pool';\nimport './pow_strict';\nimport './pow';\nimport './prelu';\nimport './prod';\nimport './reciprocal';\nimport './relu';\nimport './relu6';\nimport './reshape_as';\nimport './reshape';\nimport './resize_bilinear';\nimport './resize_nearest_neighbor';\nimport './reverse';\nimport './rfft';\nimport './round';\nimport './rsqrt';\nimport './selu';\nimport './separable_conv2d';\nimport './sigmoid';\nimport './sign';\nimport './sin';\nimport './sinh';\nimport './slice';\nimport './softmax';\nimport './softplus';\nimport './space_to_batch_nd';\nimport './split';\nimport './sqrt';\nimport './square';\nimport './squared_difference';\nimport './squared_difference_strict';\nimport './squeeze';\nimport './stack';\nimport './step';\nimport './strided_slice';\nimport './sub_strict';\nimport './sub';\nimport './sum';\nimport './tan';\nimport './tanh';\nimport './tile';\nimport './to_bool';\nimport './to_float';\nimport './to_int';\nimport './topk';\nimport './transpose';\nimport './unique';\nimport './unsorted_segment_sum';\nimport './unstack';\nimport './where';\nimport './zeros_like';\n//# sourceMappingURL=register_all_chained_ops.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code.\nimport './base_side_effects';\n// All exports from this package should be in base.\nexport * from './base';\n// Register all the gradients.\nimport './register_all_gradients';\n// Import all op chainers and add type info to Tensor.\nimport './public/chained_ops/register_all_chained_ops';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { backend } from '@tensorflow/tfjs-core';\nlet _epsilon;\n/**\n * Returns the value of the fuzz factor used in numeric expressions.\n */\nexport function epsilon() {\n    if (_epsilon == null) {\n        _epsilon = backend().epsilon();\n    }\n    return _epsilon;\n}\n/**\n * Sets the value of the fuzz factor used in numeric expressions.\n * @param e New value of epsilon.\n */\nexport function setEpsilon(e) {\n    _epsilon = e;\n}\n/**\n * Returns the default image data format convention.\n */\nexport function imageDataFormat() {\n    return 'channelsLast';\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Explicit error types.\n *\n * See the following link for more information about why the code includes\n * calls to setPrototypeOf:\n *\n * https://github.com/Microsoft/TypeScript-wiki/blob/master/Breaking-Changes.md#extending-built-ins-like-error-array-and-map-may-no-longer-work\n */\n// tslint:enable\n/**\n * Equivalent of Python's AttributeError.\n */\nexport class AttributeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AttributeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's RuntimeError.\n */\nexport class RuntimeError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, RuntimeError.prototype);\n    }\n}\n/**\n * Equivalent of Python's ValueError.\n */\nexport class ValueError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, ValueError.prototype);\n    }\n}\n/**\n * Equivalent of Python's NotImplementedError.\n */\nexport class NotImplementedError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, NotImplementedError.prototype);\n    }\n}\n/**\n * Equivalent of Python's AssertionError.\n */\nexport class AssertionError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, AssertionError.prototype);\n    }\n}\n/**\n * Equivalent of Python's IndexError.\n */\nexport class IndexError extends Error {\n    constructor(message) {\n        super(message);\n        // Set the prototype explicitly.\n        Object.setPrototypeOf(this, IndexError.prototype);\n    }\n}\n//# sourceMappingURL=errors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: utils/generic_utils.py */\nimport { util } from '@tensorflow/tfjs-core';\nimport { AssertionError, ValueError } from '../errors';\n// tslint:enable\n/**\n * If `value` is an Array, equivalent to Python's `value * numValues`.\n * If `value` is not an Array, equivalent to Python's `[value] * numValues`\n */\n// tslint:disable-next-line:no-any\nexport function pyListRepeat(value, numValues) {\n    if (Array.isArray(value)) {\n        // tslint:disable-next-line:no-any\n        let newArray = [];\n        for (let i = 0; i < numValues; i++) {\n            newArray = newArray.concat(value);\n        }\n        return newArray;\n    }\n    else {\n        const newArray = new Array(numValues);\n        newArray.fill(value);\n        return newArray;\n    }\n}\nexport function assert(val, message) {\n    if (!val) {\n        throw new AssertionError(message);\n    }\n}\n/**\n * Count the number of elements of the `array` that are equal to `reference`.\n */\nexport function count(array, refernce) {\n    let counter = 0;\n    for (const item of array) {\n        if (item === refernce) {\n            counter++;\n        }\n    }\n    return counter;\n}\n/**\n * If an array is of length 1, just return the first element. Otherwise, return\n * the full array.\n * @param tensors\n */\nexport function singletonOrArray(xs) {\n    if (xs.length === 1) {\n        return xs[0];\n    }\n    return xs;\n}\n/**\n * Normalizes a list/tensor into a list.\n *\n * If a tensor is passed, we return\n * a list of size 1 containing the tensor.\n *\n * @param x target object to be normalized.\n */\n// tslint:disable-next-line:no-any\nexport function toList(x) {\n    if (Array.isArray(x)) {\n        return x;\n    }\n    return [x];\n}\n/**\n * Generate a UID for a list\n */\n// tslint:disable-next-line:no-any\nexport function objectListUid(objs) {\n    const objectList = toList(objs);\n    let retVal = '';\n    for (const obj of objectList) {\n        if (obj.id == null) {\n            throw new ValueError(`Object ${obj} passed to objectListUid without an id`);\n        }\n        if (retVal !== '') {\n            retVal = retVal + ', ';\n        }\n        retVal = `${retVal}${Math.abs(obj.id)}`;\n    }\n    return retVal;\n}\n/**\n * Converts string to snake-case.\n * @param name\n */\nexport function toSnakeCase(name) {\n    const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, '$1_$2');\n    const insecure = intermediate.replace(/([a-z])([A-Z])/g, '$1_$2').toLowerCase();\n    /*\n     If the class is private the name starts with \"_\" which is not secure\n     for creating scopes. We prefix the name with \"private\" in this case.\n     */\n    if (insecure[0] !== '_') {\n        return insecure;\n    }\n    return 'private' + insecure;\n}\nexport function toCamelCase(identifier) {\n    // quick return for empty string or single character strings\n    if (identifier.length <= 1) {\n        return identifier;\n    }\n    // Check for the underscore indicating snake_case\n    if (identifier.indexOf('_') === -1) {\n        return identifier;\n    }\n    return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\n// tslint:disable-next-line:no-any\nlet _GLOBAL_CUSTOM_OBJECTS = {};\nexport function serializeKerasObject(instance) {\n    if (instance === null || instance === undefined) {\n        return null;\n    }\n    const dict = {};\n    dict['className'] = instance.getClassName();\n    dict['config'] = instance.getConfig();\n    return dict;\n}\n/**\n * Replace ndarray-style scalar objects in serialization objects with numbers.\n *\n * Background: In some versions of tf.keras, certain scalar values in the HDF5\n * model save file can be serialized as: `{'type': 'ndarray', 'value': num}`,\n * where in `num` is a plain number. This method converts such serialization\n * to a `number`.\n *\n * @param config The keras-format serialization object to be processed\n *   (in place).\n */\nfunction convertNDArrayScalarsInConfig(config) {\n    if (config == null || typeof config !== 'object') {\n        return;\n    }\n    else if (Array.isArray(config)) {\n        config.forEach(configItem => convertNDArrayScalarsInConfig(configItem));\n    }\n    else {\n        const fields = Object.keys(config);\n        for (const field of fields) {\n            const value = config[field];\n            if (value != null && typeof value === 'object') {\n                if (!Array.isArray(value) && value['type'] === 'ndarray' &&\n                    typeof value['value'] === 'number') {\n                    config[field] = value['value'];\n                }\n                else {\n                    convertNDArrayScalarsInConfig(value);\n                }\n            }\n        }\n    }\n}\n/**\n * Deserialize a saved Keras Object\n * @param identifier either a string ID or a saved Keras dictionary\n * @param moduleObjects a list of Python class names to object constructors\n * @param customObjects a list of Python class names to object constructors\n * @param printableModuleName debug text for the object being reconstituted\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns a TensorFlow.js Layers object\n */\n// tslint:disable:no-any\nexport function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = 'object', fastWeightInit = false) {\n    // tslint:enable\n    if (typeof identifier === 'string') {\n        const functionName = identifier;\n        let fn;\n        if (functionName in customObjects) {\n            fn = customObjects[functionName];\n        }\n        else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n            fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n        }\n        else {\n            fn = moduleObjects[functionName];\n            if (fn == null) {\n                throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. ` +\n                    `This may be due to one of the following reasons:\\n` +\n                    `1. The ${printableModuleName} is defined in Python, in which ` +\n                    `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                    `code.\\n` +\n                    `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                    `but is not registered properly with ` +\n                    `tf.serialization.registerClass().`);\n                // TODO(cais): Add link to tutorial page on custom layers.\n            }\n        }\n        return fn;\n    }\n    else {\n        // In this case we are dealing with a Keras config dictionary.\n        const config = identifier;\n        if (config['className'] == null || config['config'] == null) {\n            throw new ValueError(`${printableModuleName}: Improper config format: ` +\n                `${JSON.stringify(config)}.\\n` +\n                `'className' and 'config' must set.`);\n        }\n        const className = config['className'];\n        let cls, fromConfig;\n        if (className in customObjects) {\n            [cls, fromConfig] = customObjects[className];\n        }\n        else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n            [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS['className'];\n        }\n        else if (className in moduleObjects) {\n            [cls, fromConfig] = moduleObjects[className];\n        }\n        if (cls == null) {\n            throw new ValueError(`Unknown ${printableModuleName}: ${className}. ` +\n                `This may be due to one of the following reasons:\\n` +\n                `1. The ${printableModuleName} is defined in Python, in which ` +\n                `case it needs to be ported to TensorFlow.js or your JavaScript ` +\n                `code.\\n` +\n                `2. The custom ${printableModuleName} is defined in JavaScript, ` +\n                `but is not registered properly with ` +\n                `tf.serialization.registerClass().`);\n            // TODO(cais): Add link to tutorial page on custom layers.\n        }\n        if (fromConfig != null) {\n            // Porting notes: Instead of checking to see whether fromConfig accepts\n            // customObjects, we create a customObjects dictionary and tack it on to\n            // config['config'] as config['config'].customObjects. Objects can use it,\n            // if they want.\n            // tslint:disable-next-line:no-any\n            const customObjectsCombined = {};\n            for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n                customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n            }\n            for (const key of Object.keys(customObjects)) {\n                customObjectsCombined[key] = customObjects[key];\n            }\n            // Add the customObjects to config\n            const nestedConfig = config['config'];\n            nestedConfig['customObjects'] = customObjectsCombined;\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            convertNDArrayScalarsInConfig(config['config']);\n            const returnObj = fromConfig(cls, config['config'], customObjects, fastWeightInit);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n        else {\n            // Then `cls` may be a function returning a class.\n            // In this case by convention `config` holds\n            // the kwargs of the function.\n            const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n            for (const key of Object.keys(customObjects)) {\n                _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n            }\n            // In python this is **config['config'], for tfjs-layers we require\n            // classes that use this fall-through construction method to take\n            // a config interface that mimics the expansion of named parameters.\n            const returnObj = new cls(config['config']);\n            _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n            return returnObj;\n        }\n    }\n}\n/**\n * Compares two numbers for sorting.\n * @param a\n * @param b\n */\nexport function numberCompare(a, b) {\n    return (a < b) ? -1 : ((a > b) ? 1 : 0);\n}\n/**\n * Comparison of two numbers for reverse sorting.\n * @param a\n * @param b\n */\nexport function reverseNumberCompare(a, b) {\n    return -1 * numberCompare(a, b);\n}\n/**\n * Convert a string into the corresponding DType.\n * @param dtype\n * @returns An instance of DType.\n */\nexport function stringToDType(dtype) {\n    switch (dtype) {\n        case 'float32':\n            return 'float32';\n        default:\n            throw new ValueError(`Invalid dtype: ${dtype}`);\n    }\n}\n/**\n * Test the element-by-element equality of two Arrays of strings.\n * @param xs First array of strings.\n * @param ys Second array of strings.\n * @returns Wether the two arrays are all equal, element by element.\n */\nexport function stringsEqual(xs, ys) {\n    if (xs == null || ys == null) {\n        return xs === ys;\n    }\n    if (xs.length !== ys.length) {\n        return false;\n    }\n    for (let i = 0; i < xs.length; ++i) {\n        if (xs[i] !== ys[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Get the unique elements of an array.\n * @param xs Array.\n * @returns An Array consisting of the unique elements in `xs`.\n */\nexport function unique(xs) {\n    if (xs == null) {\n        return xs;\n    }\n    const out = [];\n    // TODO(cais): Maybe improve performance by sorting.\n    for (const x of xs) {\n        if (out.indexOf(x) === -1) {\n            out.push(x);\n        }\n    }\n    return out;\n}\n/**\n * Determine if an Object is empty (i.e., does not have own properties).\n * @param obj Object\n * @returns Whether the Object is empty.\n * @throws ValueError: If object is `null` or `undefined`.\n */\nexport function isObjectEmpty(obj) {\n    if (obj == null) {\n        throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n    }\n    for (const key in obj) {\n        if (obj.hasOwnProperty(key)) {\n            return false;\n        }\n    }\n    return true;\n}\n/**\n * Helper function used to build type union/enum run-time checkers.\n * @param values The list of allowed values.\n * @param label A string name for the type\n * @param value The value to test.\n * @throws ValueError: If the value is not in values nor `undefined`/`null`.\n */\nexport function checkStringTypeUnionValue(values, label, value) {\n    if (value == null) {\n        return;\n    }\n    if (values.indexOf(value) < 0) {\n        throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n    }\n}\n/**\n * Helper function for verifying the types of inputs.\n *\n * Ensures that the elements of `x` are all of type `expectedType`.\n * Also verifies that the length of `x` is within bounds.\n *\n * @param x Object to test.\n * @param expectedType The string expected type of all of the elements in the\n * Array.\n * @param minLength Return false if x.length is less than this.\n * @param maxLength Return false if x.length is greater than this.\n * @returns true if and only if `x` is an `Array<expectedType>` with\n * length >= `minLength` and <= `maxLength`.\n */\n// tslint:disable:no-any\nexport function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n    assert(minLength >= 0);\n    assert(maxLength >= minLength);\n    return (Array.isArray(x) && x.length >= minLength && x.length <= maxLength &&\n        x.every(e => typeof e === expectedType));\n}\n// tslint:enable:no-any\n/**\n * Assert that a value or an array of value are positive integer.\n *\n * @param value The value being asserted on. May be a single number or an array\n *   of numbers.\n * @param name Name of the value, used to make the error message.\n */\nexport function assertPositiveInteger(value, name) {\n    if (Array.isArray(value)) {\n        util.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n        value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n    }\n    else {\n        util.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ` +\n            `${formatAsFriendlyString(value)}.`);\n    }\n}\n/**\n * Format a value into a display-friendly, human-readable fashion.\n *\n * - `null` is formatted as `'null'`\n * - Strings are formated with flanking pair of quotes.\n * - Arrays are formatted with flanking pair of square brackets.\n *\n * @param value The value to display.\n * @return Formatted string.\n */\n// tslint:disable-next-line:no-any\nexport function formatAsFriendlyString(value) {\n    if (value === null) {\n        return 'null';\n    }\n    else if (Array.isArray(value)) {\n        return '[' + value.map(v => formatAsFriendlyString(v)).join(',') + ']';\n    }\n    else if (typeof value === 'string') {\n        return `\"${value}\"`;\n    }\n    else {\n        return `${value}`;\n    }\n}\n/**\n * Returns a function `f2` (decorator) which wraps the original function\n * `f`. `f2` guarantees that `f` can be called at most once\n * every `waitMs` ms. If `f2` is called more often, it will return\n * the last returned result of `f`.\n *\n * @param f The original function `f` to wrap.\n * @param waitMs The time between two consecutive calls to `f` in ms.\n */\nexport function debounce(f, waitMs) {\n    let lastTime = util.now();\n    let lastResult;\n    const f2 = (...args) => {\n        const now = util.now();\n        if (now - lastTime < waitMs) {\n            return lastResult;\n        }\n        lastTime = now;\n        lastResult = f(...args);\n        return lastResult;\n    };\n    return f2;\n}\n/**\n * Returns the fusable activation given a layers identifier.\n *\n * @param activationName The layers identifier string.\n * @return The name of the fusable activation.\n */\nexport function mapActivationToFusedKernel(activationName) {\n    if (activationName === 'relu') {\n        return 'relu';\n    }\n    if (activationName === 'linear') {\n        return 'linear';\n    }\n    if (activationName === 'elu') {\n        return 'elu';\n    }\n    return null;\n}\n/**\n * Returns the cartesian product of sets of values.\n * This works the same as itertools.product in Python.\n *\n * Example:\n *\n * filters = [128, 256, 512]\n * paddings = ['same', 'valid']\n *\n * product = [ [128, 'same'], [128, 'valid'], [256, 'same'], [256, 'valid'],\n * [512, 'same'], [512, 'valid']]\n *\n * @param arrayOfValues List/array of values.\n * @return The cartesian product.\n */\nexport function getCartesianProductOfValues(...arrayOfValues) {\n    assert(arrayOfValues.length > 0, 'arrayOfValues is empty');\n    for (const values of arrayOfValues) {\n        assert(Array.isArray(values), 'one of the values is not an array');\n        assert(values.length > 0, 'one of the values is empty');\n    }\n    return arrayOfValues.reduce((products, values) => {\n        if (products.length === 0) {\n            return values.map(value => [value]);\n        }\n        return values\n            .map(value => {\n            return products.map((prevValue) => [...prevValue, value]);\n        })\n            .reduce((flattenedProduct, unflattenedProduct) => {\n            return flattenedProduct.concat(unflattenedProduct);\n        }, []);\n    }, []);\n}\n//# sourceMappingURL=generic_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/contraints.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\n/**\n * Helper function used by many of the Constraints to find the L2Norms.\n */\nfunction calcL2Norms(w, axis) {\n    return tidy(() => tfc.sqrt(tfc.sum(tfc.mul(w, w), axis, true)));\n}\n/**\n * Base class for functions that impose constraints on weight values\n *\n * @doc {\n *   heading: 'Constraints',\n *   subheading: 'Classes',\n *   namespace: 'constraints'\n * }\n */\nexport class Constraint extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\nexport class MaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMaxValue = 2;\n        this.defaultAxis = 0;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.clipByValue(norms, 0, this.maxValue);\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return { maxValue: this.maxValue, axis: this.axis };\n    }\n}\n/** @nocollapse */\nMaxNorm.className = 'MaxNorm';\nserialization.registerClass(MaxNorm);\nexport class UnitNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultAxis = 0;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => tfc.div(w, tfc.add(epsilon(), calcL2Norms(w, this.axis))));\n    }\n    getConfig() {\n        return { axis: this.axis };\n    }\n}\n/** @nocollapse */\nUnitNorm.className = 'UnitNorm';\nserialization.registerClass(UnitNorm);\nexport class NonNeg extends Constraint {\n    apply(w) {\n        return tfc.relu(w);\n    }\n}\n/** @nocollapse */\nNonNeg.className = 'NonNeg';\nserialization.registerClass(NonNeg);\nexport class MinMaxNorm extends Constraint {\n    constructor(args) {\n        super();\n        this.defaultMinValue = 0.0;\n        this.defaultMaxValue = 1.0;\n        this.defaultRate = 1.0;\n        this.defaultAxis = 0;\n        this.minValue =\n            args.minValue != null ? args.minValue : this.defaultMinValue;\n        this.maxValue =\n            args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n        this.rate = args.rate != null ? args.rate : this.defaultRate;\n        this.axis = args.axis != null ? args.axis : this.defaultAxis;\n    }\n    apply(w) {\n        return tidy(() => {\n            const norms = calcL2Norms(w, this.axis);\n            const desired = tfc.add(tfc.mul(this.rate, tfc.clipByValue(norms, this.minValue, this.maxValue)), tfc.mul(1.0 - this.rate, norms));\n            return tfc.mul(w, tfc.div(desired, tfc.add(epsilon(), norms)));\n        });\n    }\n    getConfig() {\n        return {\n            minValue: this.minValue,\n            maxValue: this.maxValue,\n            rate: this.rate,\n            axis: this.axis\n        };\n    }\n}\n/** @nocollapse */\nMinMaxNorm.className = 'MinMaxNorm';\nserialization.registerClass(MinMaxNorm);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'maxNorm': 'MaxNorm',\n    'minMaxNorm': 'MinMaxNorm',\n    'nonNeg': 'NonNeg',\n    'unitNorm': 'UnitNorm'\n};\nexport function serializeConstraint(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeConstraint(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'constraint');\n}\nexport function getConstraint(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeConstraint(config);\n    }\n    else if (identifier instanceof Constraint) {\n        return identifier;\n    }\n    else {\n        return deserializeConstraint(identifier);\n    }\n}\n//# sourceMappingURL=constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { MaxNorm, MinMaxNorm, NonNeg, UnitNorm } from './constraints';\n/**\n * MaxNorm weight constraint.\n *\n * Constrains the weights incident to each hidden unit\n * to have a norm less than or equal to a desired value.\n *\n * References\n *       - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n * Srivastava, Hinton, et al.\n * 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Constraints',namespace: 'constraints'}\n */\nexport function maxNorm(args) {\n    return new MaxNorm(args);\n}\n/**\n * Constrains the weights incident to each hidden unit to have unit norm.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function unitNorm(args) {\n    return new UnitNorm(args);\n}\n/**\n * Constains the weight to be non-negative.\n *\n * @doc {heading: 'Constraints', namespace: 'constraints'}\n */\nexport function nonNeg() {\n    return new NonNeg();\n}\n/** @doc {heading: 'Constraints', namespace: 'constraints'} */\nexport function minMaxNorm(config) {\n    return new MinMaxNorm(config);\n}\n//# sourceMappingURL=exports_constraints.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_DATA_FORMAT_VALUES = ['channelsFirst', 'channelsLast'];\nexport const VALID_PADDING_MODE_VALUES = ['valid', 'same', 'causal'];\nexport const VALID_POOL_MODE_VALUES = ['max', 'avg'];\nexport const VALID_BIDIRECTIONAL_MERGE_MODES = ['sum', 'mul', 'concat', 'ave'];\nexport const VALID_SAMPLE_WEIGHT_MODES = ['temporal'];\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Common functions for TensorFlow.js Layers.\n */\nimport { VALID_DATA_FORMAT_VALUES, VALID_PADDING_MODE_VALUES, VALID_POOL_MODE_VALUES } from './keras_format/common';\nimport { checkStringTypeUnionValue } from './utils/generic_utils';\n// A map from the requested scoped name of a Tensor to the number of Tensors\n// wanting that name so far.  This allows enforcing name uniqueness by appending\n// an incrementing index, e.g. scope/name, scope/name_1, scope/name_2, etc.\nconst nameMap = new Map();\nexport function checkDataFormat(value) {\n    checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, 'DataFormat', value);\n}\nexport function checkPaddingMode(value) {\n    checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, 'PaddingMode', value);\n}\nexport function checkPoolMode(value) {\n    checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, 'PoolMode', value);\n}\nconst _nameScopeStack = [];\nconst _nameScopeDivider = '/';\n/**\n * Enter namescope, which can be nested.\n */\nexport function nameScope(name, fn) {\n    _nameScopeStack.push(name);\n    try {\n        const val = fn();\n        _nameScopeStack.pop();\n        return val;\n    }\n    catch (e) {\n        _nameScopeStack.pop();\n        throw e;\n    }\n}\n/**\n * Get the current namescope as a flat, concatenated string.\n */\nfunction currentNameScopePrefix() {\n    if (_nameScopeStack.length === 0) {\n        return '';\n    }\n    else {\n        return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n    }\n}\n/**\n * Get the name a Tensor (or Variable) would have if not uniqueified.\n * @param tensorName\n * @return Scoped name string.\n */\nexport function getScopedTensorName(tensorName) {\n    if (!isValidTensorName(tensorName)) {\n        throw new Error('Not a valid tensor name: \\'' + tensorName + '\\'');\n    }\n    return currentNameScopePrefix() + tensorName;\n}\n/**\n * Get unique names for Tensors and Variables.\n * @param scopedName The fully-qualified name of the Tensor, i.e. as produced by\n *  `getScopedTensorName()`.\n * @return A unique version of the given fully scoped name.\n *   If this is the first time that the scoped name is seen in this session,\n *   then the given `scopedName` is returned unaltered.  If the same name is\n *   seen again (producing a collision), an incrementing suffix is added to the\n *   end of the name, so it takes the form 'scope/name_1', 'scope/name_2', etc.\n */\nexport function getUniqueTensorName(scopedName) {\n    if (!isValidTensorName(scopedName)) {\n        throw new Error('Not a valid tensor name: \\'' + scopedName + '\\'');\n    }\n    if (!nameMap.has(scopedName)) {\n        nameMap.set(scopedName, 0);\n    }\n    const index = nameMap.get(scopedName);\n    nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n    if (index > 0) {\n        const result = `${scopedName}_${index}`;\n        // Mark the composed name as used in case someone wants\n        // to call getUniqueTensorName(\"name_1\").\n        nameMap.set(result, 1);\n        return result;\n    }\n    else {\n        return scopedName;\n    }\n}\nconst tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\n/**\n * Determine whether a string is a valid tensor name.\n * @param name\n * @returns A Boolean indicating whether `name` is a valid tensor name.\n */\nexport function isValidTensorName(name) {\n    return !!name.match(tensorNameRegex);\n}\n//# sourceMappingURL=common.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Math utility functions.\n *\n * This file contains some frequently used math function that operates on\n * number[] or Float32Array and return a number. Many of these functions are\n * not-so-thick wrappers around TF.js Core functions. But they offer the\n * convenience of\n * 1) not having to convert the inputs into Tensors,\n * 2) not having to convert the returned Tensors to numbers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar, tensor1d } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\n/**\n * Determine if a number is an integer.\n */\nexport function isInteger(x) {\n    return x === parseInt(x.toString(), 10);\n}\n/**\n * Calculate the product of an array of numbers.\n * @param array The array to calculate the product over.\n * @param begin Beginning index, inclusive.\n * @param end Ending index, exclusive.\n * @return The product.\n */\nexport function arrayProd(array, begin, end) {\n    if (begin == null) {\n        begin = 0;\n    }\n    if (end == null) {\n        end = array.length;\n    }\n    let prod = 1;\n    for (let i = begin; i < end; ++i) {\n        prod *= array[i];\n    }\n    return prod;\n}\n/**\n * A helper function transforms the two input types to an instance of Tensor1D,\n * so the return value can be fed directly into various TF.js Core functions.\n * @param array\n */\nfunction toArray1D(array) {\n    array = Array.isArray(array) ? new Float32Array(array) : array;\n    return tensor1d(array);\n}\n/**\n * Compute minimum value.\n * @param array\n * @return minimum value.\n */\nexport function min(array) {\n    return tfc.min(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute maximum value.\n * @param array\n * @return maximum value\n */\nexport function max(array) {\n    return tfc.max(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute sum of array.\n * @param array\n * @return The sum.\n */\nexport function sum(array) {\n    return tfc.sum(toArray1D(array)).dataSync()[0];\n}\n/**\n * Compute mean of array.\n * @param array\n * @return The mean.\n */\nexport function mean(array) {\n    return sum(array) / array.length;\n}\n/**\n * Compute variance of array.\n * @param array\n * @return The variance.\n */\nexport function variance(array) {\n    const demeaned = tfc.sub(toArray1D(array), scalar(mean(array)));\n    const sumSquare = tfc.sum(tfc.mul(demeaned, demeaned)).dataSync()[0];\n    return sumSquare / array.length;\n}\n/**\n * Compute median of array.\n * @param array\n * @return The median value.\n */\nexport function median(array) {\n    const arraySorted = array.slice().sort((a, b) => a - b);\n    const lowIdx = Math.floor((arraySorted.length - 1) / 2);\n    const highIdx = Math.ceil((arraySorted.length - 1) / 2);\n    if (lowIdx === highIdx) {\n        return arraySorted[lowIdx];\n    }\n    return (arraySorted[lowIdx] + arraySorted[highIdx]) / 2;\n}\n/**\n * Generate an array of integers in [begin, end).\n * @param begin Beginning integer, inclusive.\n * @param end Ending integer, exclusive.\n * @returns Range array.\n * @throws ValueError, iff `end` < `begin`.\n */\nexport function range(begin, end) {\n    if (end < begin) {\n        throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n    }\n    const out = [];\n    for (let i = begin; i < end; ++i) {\n        out.push(i);\n    }\n    return out;\n}\n//# sourceMappingURL=math_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * deeplearn.js backend.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { onesLike as coreOnesLike, scalar, tensor1d, tidy, where, zerosLike as coreZerosLike } from '@tensorflow/tfjs-core';\nimport { checkDataFormat } from '../common';\nimport { NotImplementedError, ValueError } from '../errors';\nimport * as math_utils from '../utils/math_utils';\nimport { imageDataFormat } from './common';\n// tslint:enable\n/* Setting and getting backend from deeplearn.js. */\n// Default deeplearn.js backend is WebGL (GPU).\nlet backend = 'webgl';\nexport function setBackend(requestedBackend) {\n    tfc.setBackend(requestedBackend);\n    backend = requestedBackend;\n}\nexport function getBackend() {\n    return backend;\n}\n/**\n * Indicates whether the backend is operating symbolically.\n *\n * This function will be used to determine how to interpret user code. If\n * it returns true, calls to the backend construct a symbolic graph; if\n * it returns false, calls to the backend execute immediately.\n */\nexport function isBackendSymbolic() {\n    return false;\n}\n/**\n * Get the number of elements in a Tensor.\n * @param x The Tensor.\n * @return Number of elements in `x`.\n */\nexport function countParams(x) {\n    const shape = x.shape;\n    if (shape.length > 0) {\n        return shape.reduce((a, b) => a * b);\n    }\n    else {\n        // Scalar.\n        return 1;\n    }\n}\n/**\n * Casts a tensor to a different dtype and returns it.\n * @param x Input tensor.\n * @param dtype String: 'float32'|'int32'|'bool'.\n * @returns Tensor of the specified `dtype`.\n */\nexport function cast(x, dtype) {\n    return x.asType(dtype);\n}\n/**\n * Adds a 1-sized dimension at index \"axis\".\n * @param x Input tensor.\n * @param axis Position where to add the new axis.\n * @returns Result of the dimension expansion.\n */\nexport function expandDims(x, axis = -1) {\n    const outShape = x.shape.slice();\n    if (axis < 0) {\n        axis = outShape.length + axis + 1;\n    }\n    outShape.splice(axis, 0, 1);\n    return x.reshape(outShape);\n}\n/**\n * Repeats a 2D tensor.\n *\n * If `x` has shape `[samples, dim]` and `n` is 2, for example, the output\n * will have shape `[samples, 2, dim]`.\n *\n * @param x Input tensor.\n * @param n Integer, number of times to repeat.\n * @returns The result of the repeat operation.\n * @throws ValueError: If input tensor is not 2D.\n */\nexport function repeat(x, n) {\n    return tidy(() => {\n        if (x.shape.length !== 2) {\n            throw new ValueError(`repeat() expects a rank-2 tensor, but received a ` +\n                `rank-${x.shape.length} tensor.`);\n        }\n        const y = expandDims(x, 1);\n        return tile(y, [1, n, 1]);\n    });\n}\n/**\n * Flatten a Tensor into 1D.\n * @param x Input tensor.\n * @return The result of the flattening `x`.\n */\nexport function flatten(x) {\n    const newShape = [math_utils.arrayProd(x.shape)];\n    return x.reshape(newShape);\n}\n/**\n * Turn a nD tensor into a 2D tensor with same 0th dimension.\n * In other words, it flattens each data samples of a batch.\n *\n * @param x The tensor to flatten. The rank of this tensor is required to be 2\n *   or higher.\n * @return The result of the flattening.\n */\nexport function batchFlatten(x) {\n    if (x.rank <= 1) {\n        throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n    }\n    const newShape = [x.shape[0], math_utils.arrayProd(x.shape, 1)];\n    return x.reshape(newShape);\n}\n/**\n * Do slicing along the first axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the first axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongFirstAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [start, 0], [size, array.shape[1]]);\n            case 3:\n                return tfc.slice3d(array, [start, 0, 0], [size, array.shape[1], array.shape[2]]);\n            case 4:\n                return tfc.slice4d(array, [start, 0, 0, 0], [size, array.shape[1], array.shape[2], array.shape[3]]);\n            case 5:\n                return tfc.slice(array, [start, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4]\n                ]);\n            case 6:\n                return tfc.slice(array, [start, 0, 0, 0, 0, 0], [\n                    size, array.shape[1], array.shape[2], array.shape[3], array.shape[4],\n                    array.shape[5]\n                ]);\n            default:\n                throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the last axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size size of the slice along the last axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongLastAxis(array, start, size) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                return tfc.slice2d(array, [0, start], [array.shape[0], size]);\n            case 3:\n                return tfc.slice3d(array, [0, 0, start], [array.shape[0], array.shape[1], size]);\n            case 4:\n                return tfc.slice4d(array, [0, 0, 0, start], [array.shape[0], array.shape[1], array.shape[2], size]);\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Do slicing along the sepcified axis.\n * @param array input `tf.Tensor`.\n * @param start starting index, inclusive.\n * @param size of the slice along the chosen axis.\n * @param choose an axis.\n * @returns result of the slicing.\n * @throws ValueError: If `array` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function sliceAlongAxis(array, start, size, axis) {\n    return tidy(() => {\n        switch (array.rank) {\n            case 1:\n                return tfc.slice1d(array, start, size);\n            case 2:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 3:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice3d(array, [0, start, 0], [array.shape[0], size, array.shape[2]]);\n                    case 3:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            case 4:\n                switch (axis) {\n                    case 1:\n                        return sliceAlongFirstAxis(array, start, size);\n                    case 2:\n                        return tfc.slice4d(array, [0, start, 0, 0], [array.shape[0], size, array.shape[2], array.shape[3]]);\n                    case 3:\n                        return tfc.slice4d(array, [0, 0, start, 0], [array.shape[0], array.shape[1], size, array.shape[3]]);\n                    case 4:\n                        return sliceAlongLastAxis(array, start, size);\n                    default:\n                        throw new ValueError(`The axis is not within the rank of the tensor ` +\n                            `${axis}`);\n                }\n            default:\n                throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ` +\n                    `${array.rank}`);\n        }\n    });\n}\n/**\n * Concatenates a list of tensors alongside the specified axis.\n * @param tensors `Array` of tensors to concatenate.\n * @param axis Concatenation axis.\n * @returns The result of the concatenation.\n */\nexport function concatenate(tensors, axis = -1) {\n    let rank;\n    if (axis < 0) {\n        rank = tensors[0].rank;\n        if (rank !== 0) {\n            axis = rank;\n        }\n        else {\n            axis = 0;\n        }\n    }\n    if (axis === tensors[0].rank) {\n        // Porting Note: This is necessary because tfc.concat() requires axis to be\n        //   in the interval [-rank, rank).\n        axis = -1;\n    }\n    // Porting Note: Sparse concat is not supported yet.\n    return tfc.concat(tensors, axis);\n}\n/**\n * Concatenate two arrays along the first dimension.\n * @param a The 1st `tf.Tensor` to concatenate.\n * @param b The 2nd `tf.Tensor` to concatenate.\n * @returns Result of the concatenation.\n * @throws ValueError: If `a` is of an unsupported subtype of `tf.Tensor`.\n */\nexport function concatAlongFirstAxis(a, b) {\n    switch (a.rank) {\n        case 1:\n            return tfc.concat1d([a, b]);\n        case 2:\n            return tfc.concat2d([a, b], 0);\n        case 3:\n            return tfc.concat3d([a, b], 0);\n        case 4:\n            return tfc.concat4d([a, b], 0);\n        default:\n            throw new ValueError(`concatAlongFirstAxis() received an unsupported ` +\n                `tensor rank: ${a.rank}`);\n    }\n}\n/**\n * Creates a tensor by tiling `x` by `n`.\n * @param x A tensor.\n * @param n An Array of integers or a single integer. If an Array, the length\n *   must be the same as the number of dimensions in `x`. If a single integer,\n *   it will be treated as an Array of length 1.\n */\nexport function tile(x, n) {\n    if (!Array.isArray(n)) {\n        n = [n];\n    }\n    if (x.rank !== n.length) {\n        throw new ValueError(`The length of input n (${n.length}) does not match ` +\n            `the number of dimensions in input x (${x.rank})`);\n    }\n    return tfc.tile(x, n);\n}\n/* Creation of random tensors. */\n/**\n * Get a tensor with normal distribution of values.\n *\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @return The normal tensor.\n */\nexport function randomNormal(shape, mean = 0.0, stddev = 1.0, dtype, seed) {\n    return tfc.randomNormal(shape, mean, stddev, dtype, seed);\n}\n/* Linear Algebra */\n/**\n * Multiply two tensors and returns the result as a tensor.\n *\n * For 2D tensors, this is equivalent to matrix multiplication (matMul).\n * For tensors of higher ranks, it follows the Theano behavior,\n * (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`).  From the Theano documentation:\n *\n * For N dimensions it is a sum product over the last axis of x and the\n * second-to-last of y:\n *\n * @param a A tensor of at least rank 2.\n * @param b A tensor of at least rank 2.\n * @param activation (optional) A string identifying the activation\n *   function.\n * @return Result of the dot operation.\n */\nexport function dot(a, b, activation, bias) {\n    if ((a.rank < 2) || (b.rank < 2)) {\n        throw new NotImplementedError(`dot requires both inputs to be rank >= 2` +\n            ` but got x shape = ${a.shape} and y shape = ${b.shape}`);\n    }\n    if (b.rank >= 3) {\n        const xLastDim = a.shape.slice(-1)[0];\n        const ySecondLastDim = b.shape.slice(-2)[0];\n        if (xLastDim !== ySecondLastDim) {\n            throw new NotImplementedError(`If rank y >= 3, then the second last dim` +\n                ` of y must equal the last dim of x but got x shape = ${a.shape} and ` +\n                ` y shape = ${b.shape}`);\n        }\n    }\n    // Handle basic 2D x 2D case.\n    if ((a.rank === 2) && (b.rank === 2)) {\n        const transposeA = false;\n        const transposeB = false;\n        // tfc.fused.matMul only fuses certain activation functions. Unsupported\n        // activation functions are treated as 'linear' activations, which is\n        // equivalent to a no-op.\n        return tfc.fused.matMul({\n            a,\n            b: b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        });\n    }\n    else {\n        // Reshape x into the analogous 2D Tensor.\n        const aFirstDims = a.shape.slice(); // Holds all but the last dim of x.\n        const aLastDim = aFirstDims.pop();\n        a = a.reshape([-1, aLastDim]);\n        // Reshape y into the analogous 2D Tensor, and keep track of the\n        // required dimensions to reproduce the output shape.\n        const bShape = b.shape.slice();\n        const bLastDim = bShape.pop();\n        const ySecondLastDim = bShape.pop();\n        const yOtherDims = [...bShape, bLastDim];\n        // permutation should be like [r-2, 0, 1, 2, ... r-4, r-3, r-1]\n        // where r is the rank of y.\n        const perm = Array.from({ length: b.rank }, (_, i) => {\n            if (i === 0) {\n                return b.rank - 2;\n            }\n            else if (i <= b.rank - 2) {\n                return i - 1;\n            }\n            return i;\n        });\n        b = b.transpose(perm).reshape([ySecondLastDim, -1]);\n        // Multiply x and y as 2D Tensors, and then reshape back to original.\n        const outputShape = [...aFirstDims, ...yOtherDims];\n        const transposeA = false;\n        const transposeB = false;\n        return tfc.fused\n            .matMul({\n            a,\n            b,\n            transposeA,\n            transposeB,\n            bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n            activation\n        })\n            .reshape(outputShape);\n    }\n}\n/**\n * Compute the sign Tensor of an input Tensor.\n *\n * Elements of the input `tf.Tensor` that are === 0 are mapped to 0.\n * Elements of the input `tf.Tensor` that are > 0 are mapped to 1.\n * Elements of the input `tf.Tensor` that are < 0 are mapped to -1.\n *\n * @param x Input `tf.Tensor`.\n * @return The sign `tf.Tensor`.\n */\nexport function sign(x) {\n    // TODO(cais): Move to the core.\n    return tidy(() => {\n        const zerosLikeX = coreZerosLike(x);\n        const onesLikeX = coreOnesLike(x);\n        return where(tfc.equal(x, zerosLikeX), zerosLikeX, where(tfc.greater(x, coreZerosLike(x)), onesLikeX, tfc.mul(-1, onesLikeX)));\n    });\n}\n/**\n * Computes the one-hot representation of an integer tensor.\n * @param indices nD integer tensor of shape\n *   `(batch_size, dim1, dim2, ... dim(n-1))`\n * @param numClasses Integer, number of classes to consider.\n * @returns (n + 1)D one hot representation of the input\n *   with shape `(batch_size, dim1, dim2, ... dim(n-1), num_classes)`\n */\nexport function oneHot(indices, numClasses) {\n    return tidy(() => {\n        if (indices.rank !== 1) {\n            throw new Error('Only 1D one-hot tensors are supported in the ' +\n                'deeplearn backend, at present.');\n        }\n        indices = indices.toInt();\n        return tfc.oneHot(indices, numClasses).toFloat();\n    });\n}\n/* Elementary math functions. */\n/**\n * Retrieves the elements of indices `indices` in the tensor `reference`.\n * @param reference A tensor.\n * @param indices An integer tensor of indices or an `Array` of integers.\n * @param axis Axis along which to perform the gather operation.\n * @returns The result of the gathering as a tensor.\n */\nexport function gather(reference, indices, axis) {\n    return tidy(() => {\n        if (Array.isArray(indices)) {\n            indices = tensor1d(indices, 'int32');\n        }\n        else {\n            indices = indices.toInt();\n        }\n        return tfc.gather(reference, indices, axis);\n    });\n}\n/**\n * Element-wise square.\n * @param x Input tensor.\n * @return element-wise x^2\n */\nexport function square(x) {\n    return tfc.mul(x, x);\n}\n/**\n * Element-wise exponentiation.\n *\n * Porting Note: In PyKeras, `a` (the exponent) is a Python integer, which\n *   takes advatnage of the backend's (e.g., TensorFlow's) automatic\n * conversion to tensor. Here we allow `a` to be either a number or a tensor.\n *\n * @param x The base tensor.\n * @param a The exponent, tensor or number. If a number, it is rounded to the\n *   nearest integer and converted to a tensor.\n * @returns A tensor of the same shape as `x`.\n */\nexport function pow(x, a) {\n    return tidy(() => {\n        if (typeof (a) === 'number') {\n            a = scalar(Math.round(a), 'int32');\n        }\n        if (a.dtype !== 'int32') {\n            throw new NotImplementedError(`Non-int32 dtype (${a.dtype}) is not supported by pow() yet`);\n        }\n        return tfc.pow(x, a);\n    });\n}\n/**\n * Reshapes bias tensor according to rank of x.\n */\nfunction reshapeBias(xRank, bias, dataFormat) {\n    const biasShape = bias.shape;\n    if (bias.rank !== 1 && bias.rank !== xRank) {\n        throw new ValueError(`Unexpected bias dimensions: ${bias.rank}` +\n            `; expected it to be 1 or ${xRank}`);\n    }\n    if (xRank === 5) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 4) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1, 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[2], biasShape[0], biasShape[1]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank === 3) {\n        if (dataFormat === 'channelsFirst') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, biasShape[0], 1]);\n            }\n            else {\n                return bias.reshape([1, biasShape[1], biasShape[0]]);\n            }\n        }\n        else if (dataFormat === 'channelsLast') {\n            if (biasShape.length === 1) {\n                return bias.reshape([1, 1, biasShape[0]]);\n            }\n            else {\n                return bias.reshape([1].concat(biasShape));\n            }\n        }\n    }\n    else if (xRank < 3) {\n        return bias;\n    }\n    throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\n/* Neural-network operations. */\n/**\n * Add a bias to a tensor.\n *\n * @param x The tensor to add the bias to.\n * @param bias The bias to add to `x`. Must be 1D or the same rank as `x`.\n * @return Result of the bias adding.\n * @throws ValueError: If the rank of `bias` is incorrect.\n */\nexport function biasAdd(x, bias, dataFormat) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        return x.add(reshapeBias(x.rank, bias, dataFormat));\n    });\n}\n/**\n * Exponential linear unit (ELU).\n * @param x A tensor or variable to compute the activation function for.\n * @param alpha: A scalar, a scaling factor for the negative section.\n * @return Output of the ELU operation.\n */\nexport function elu(x, alpha = 1) {\n    // TODO(cais): Add support for alpha values other than 1.\n    if (alpha !== 1) {\n        throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented ` +\n            `yet.`);\n    }\n    return tfc.elu(x);\n}\n/**\n * Softsign of a tensor.\n *\n * Defined as x / (abs(x) + 1), element-wise.\n *\n * @param x: Input.\n * @returns Output.\n */\nexport function softsign(x) {\n    return tidy(() => tfc.div(x, tfc.abs(x).add(1)));\n}\n/**\n * Sets entries in `x` to zero at random, while scaling the entire tensor.\n *\n * @param x input tensor.\n * @param level fraction of the entries in the tensor that will be set to 0.\n * @param noiseShape shape of randomly generated keep/drop flags, must be\n *   broadcastable to the shape of `x`. Optional.\n * @param seed random seed to ensure determinism. Optional.\n * @returns Result of the dropout operation.\n */\nexport function dropout(x, level, noiseShape, seed) {\n    return tidy(() => tfc.dropout(x, level, noiseShape, seed));\n}\n/**\n * Element-wise, segment-wise linear approximation of sigmoid.\n *\n * Returns `0.` if `x < -2.5`, `1.` if `x > 2.5`.\n * In `-2.5 <= x <= 2.5`, returns `0.2 * x + 0.5`.\n *\n * @param x Input tensor.\n * @returns Output tensor.\n */\nexport function hardSigmoid(x) {\n    return tidy(() => {\n        const y = tfc.add(.5, tfc.mul(.2, x));\n        return tfc.clipByValue(y, 0, 1);\n    });\n}\n/**\n * Invoke `x` in the training phase, and `alt` otherwise.\n *\n * Porting Note: We do not create placeholder tensors for the `training`\n * boolean flag here, because there is no such thing in the TF.js imperative\n * backend.\n *\n * @param x The function to invoke iff `training` is `true`.\n * @param alt The function to invoke iff `training` is `false`.\n * @param training Boolean flag for whether training phase is active.\n * @returns The return value of `x()` if `training` is `true`, or the return\n *   value of `alt()` if `training` is `false`.\n */\nexport function inTrainPhase(x, alt, training = false) {\n    return training ? x() : alt();\n}\n//# sourceMappingURL=tfjs_backend.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport const VALID_FAN_MODE_VALUES = ['fanIn', 'fanOut', 'fanAvg'];\nexport const VALID_DISTRIBUTION_VALUES = ['normal', 'uniform', 'truncatedNormal'];\n// We can't easily extract a string[] from the string union type, but we can\n// recapitulate the list, enforcing at compile time that the values are valid\n// and that we have the right number of them.\n/**\n * A string array of valid Initializer class names.\n *\n * This is guaranteed to match the `InitializerClassName` union type.\n */\nexport const initializerClassNames = [\n    'Zeros', 'Ones', 'Constant', 'RandomNormal', 'RandomUniform',\n    'TruncatedNormal', 'VarianceScaling', 'Orthogonal', 'Identity'\n];\n//# sourceMappingURL=initializer_config.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { eye, linalg, mul, ones, randomUniform, scalar, serialization, tidy, truncatedNormal, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { checkDataFormat } from './common';\nimport { NotImplementedError, ValueError } from './errors';\nimport { VALID_DISTRIBUTION_VALUES, VALID_FAN_MODE_VALUES } from './keras_format/initializer_config';\nimport { checkStringTypeUnionValue, deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nimport { arrayProd } from './utils/math_utils';\nexport function checkFanMode(value) {\n    checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, 'FanMode', value);\n}\nexport function checkDistribution(value) {\n    checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, 'Distribution', value);\n}\n/**\n * Initializer base class.\n *\n * @doc {\n *   heading: 'Initializers', subheading: 'Classes', namespace: 'initializers'}\n */\nexport class Initializer extends serialization.Serializable {\n    fromConfigUsesCustomObjects() {\n        return false;\n    }\n    getConfig() {\n        return {};\n    }\n}\nexport class Zeros extends Initializer {\n    apply(shape, dtype) {\n        return zeros(shape, dtype);\n    }\n}\n/** @nocollapse */\nZeros.className = 'Zeros';\nserialization.registerClass(Zeros);\nexport class Ones extends Initializer {\n    apply(shape, dtype) {\n        return ones(shape, dtype);\n    }\n}\n/** @nocollapse */\nOnes.className = 'Ones';\nserialization.registerClass(Ones);\nexport class Constant extends Initializer {\n    constructor(args) {\n        super();\n        if (typeof args !== 'object') {\n            throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n        }\n        if (args.value === undefined) {\n            throw new ValueError(`config must have value set but got ${args}`);\n        }\n        this.value = args.value;\n    }\n    apply(shape, dtype) {\n        return tidy(() => mul(scalar(this.value), ones(shape, dtype)));\n    }\n    getConfig() {\n        return {\n            value: this.value,\n        };\n    }\n}\n/** @nocollapse */\nConstant.className = 'Constant';\nserialization.registerClass(Constant);\nexport class RandomUniform extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MINVAL = -0.05;\n        this.DEFAULT_MAXVAL = 0.05;\n        this.minval = args.minval || this.DEFAULT_MINVAL;\n        this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        return randomUniform(shape, this.minval, this.maxval, dtype);\n    }\n    getConfig() {\n        return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomUniform.className = 'RandomUniform';\nserialization.registerClass(RandomUniform);\nexport class RandomNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n        }\n        return K.randomNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nRandomNormal.className = 'RandomNormal';\nserialization.registerClass(RandomNormal);\nexport class TruncatedNormal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_MEAN = 0.;\n        this.DEFAULT_STDDEV = 0.05;\n        this.mean = args.mean || this.DEFAULT_MEAN;\n        this.stddev = args.stddev || this.DEFAULT_STDDEV;\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        dtype = dtype || 'float32';\n        if (dtype !== 'float32' && dtype !== 'int32') {\n            throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n        }\n        return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n    }\n    getConfig() {\n        return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n    }\n}\n/** @nocollapse */\nTruncatedNormal.className = 'TruncatedNormal';\nserialization.registerClass(TruncatedNormal);\nexport class Identity extends Initializer {\n    constructor(args) {\n        super();\n        this.gain = args.gain != null ? args.gain : 1.0;\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length !== 2 || shape[0] !== shape[1]) {\n                throw new ValueError('Identity matrix initializer can only be used for' +\n                    ' 2D square matrices.');\n            }\n            else {\n                return mul(this.gain, eye(shape[0]));\n            }\n        });\n    }\n    getConfig() {\n        return { gain: this.gain };\n    }\n}\n/** @nocollapse */\nIdentity.className = 'Identity';\nserialization.registerClass(Identity);\n/**\n * Computes the number of input and output units for a weight shape.\n * @param shape Shape of weight.\n * @param dataFormat data format to use for convolution kernels.\n *   Note that all kernels in Keras are standardized on the\n *   CHANNEL_LAST ordering (even when inputs are set to CHANNEL_FIRST).\n * @return An length-2 array: fanIn, fanOut.\n */\nfunction computeFans(shape, dataFormat = 'channelsLast') {\n    let fanIn;\n    let fanOut;\n    checkDataFormat(dataFormat);\n    if (shape.length === 2) {\n        fanIn = shape[0];\n        fanOut = shape[1];\n    }\n    else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n        if (dataFormat === 'channelsFirst') {\n            const receptiveFieldSize = arrayProd(shape, 2);\n            fanIn = shape[1] * receptiveFieldSize;\n            fanOut = shape[0] * receptiveFieldSize;\n        }\n        else if (dataFormat === 'channelsLast') {\n            const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n            fanIn = shape[shape.length - 2] * receptiveFieldSize;\n            fanOut = shape[shape.length - 1] * receptiveFieldSize;\n        }\n    }\n    else {\n        const shapeProd = arrayProd(shape);\n        fanIn = Math.sqrt(shapeProd);\n        fanOut = Math.sqrt(shapeProd);\n    }\n    return [fanIn, fanOut];\n}\nexport class VarianceScaling extends Initializer {\n    /**\n     * Constructor of VarianceScaling.\n     * @throws ValueError for invalid value in scale.\n     */\n    constructor(args) {\n        super();\n        if (args.scale < 0.0) {\n            throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n        }\n        this.scale = args.scale == null ? 1.0 : args.scale;\n        this.mode = args.mode == null ? 'fanIn' : args.mode;\n        checkFanMode(this.mode);\n        this.distribution =\n            args.distribution == null ? 'normal' : args.distribution;\n        checkDistribution(this.distribution);\n        this.seed = args.seed;\n    }\n    apply(shape, dtype) {\n        const fans = computeFans(shape);\n        const fanIn = fans[0];\n        const fanOut = fans[1];\n        let scale = this.scale;\n        if (this.mode === 'fanIn') {\n            scale /= Math.max(1, fanIn);\n        }\n        else if (this.mode === 'fanOut') {\n            scale /= Math.max(1, fanOut);\n        }\n        else {\n            scale /= Math.max(1, (fanIn + fanOut) / 2);\n        }\n        if (this.distribution === 'normal') {\n            const stddev = Math.sqrt(scale);\n            dtype = dtype || 'float32';\n            if (dtype !== 'float32' && dtype !== 'int32') {\n                throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n            }\n            return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n        }\n        else {\n            const limit = Math.sqrt(3 * scale);\n            return randomUniform(shape, -limit, limit, dtype);\n        }\n    }\n    getConfig() {\n        return {\n            scale: this.scale,\n            mode: this.mode,\n            distribution: this.distribution,\n            seed: this.seed\n        };\n    }\n}\n/** @nocollapse */\nVarianceScaling.className = 'VarianceScaling';\nserialization.registerClass(VarianceScaling);\nexport class GlorotUniform extends VarianceScaling {\n    /**\n     * Constructor of GlorotUniform\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotUniform.className = 'GlorotUniform';\nserialization.registerClass(GlorotUniform);\nexport class GlorotNormal extends VarianceScaling {\n    /**\n     * Constructor of GlorotNormal.\n     * @param scale\n     * @param mode\n     * @param distribution\n     * @param seed\n     */\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanAvg',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, GlorotNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nGlorotNormal.className = 'GlorotNormal';\nserialization.registerClass(GlorotNormal);\nexport class HeNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeNormal.className = 'HeNormal';\nserialization.registerClass(HeNormal);\nexport class HeUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 2.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, HeUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nHeUniform.className = 'HeUniform';\nserialization.registerClass(HeUniform);\nexport class LeCunNormal extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'normal',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunNormal is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunNormal.className = 'LeCunNormal';\nserialization.registerClass(LeCunNormal);\nexport class LeCunUniform extends VarianceScaling {\n    constructor(args) {\n        super({\n            scale: 1.0,\n            mode: 'fanIn',\n            distribution: 'uniform',\n            seed: args == null ? null : args.seed\n        });\n    }\n    getClassName() {\n        // In Python Keras, LeCunUniform is not a class, but a helper method\n        // that creates a VarianceScaling object. Use 'VarianceScaling' as\n        // class name to be compatible with that.\n        return VarianceScaling.className;\n    }\n}\n/** @nocollapse */\nLeCunUniform.className = 'LeCunNormal';\nserialization.registerClass(LeCunUniform);\nexport class Orthogonal extends Initializer {\n    constructor(args) {\n        super();\n        this.DEFAULT_GAIN = 1;\n        this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n        this.seed = args.seed;\n        if (this.seed != null) {\n            throw new NotImplementedError('Random seed is not implemented for Orthogonal Initializer yet.');\n        }\n    }\n    apply(shape, dtype) {\n        return tidy(() => {\n            if (shape.length < 2) {\n                throw new NotImplementedError('Shape must be at least 2D.');\n            }\n            if (shape[0] * shape[1] > 2000) {\n                console.warn(`Orthogonal initializer is being called on a matrix with more ` +\n                    `than 2000 (${shape[0] * shape[1]}) elements: ` +\n                    `Slowness may result.`);\n            }\n            // TODO(cais): Add seed support.\n            const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n            const a = K.randomNormal(normalizedShape, 0, 1, 'float32');\n            let q = linalg.gramSchmidt(a);\n            if (shape[0] > shape[1]) {\n                q = q.transpose();\n            }\n            return mul(this.gain, q);\n        });\n    }\n    getConfig() {\n        return {\n            gain: this.gain,\n            seed: this.seed,\n        };\n    }\n}\n/** @nocollapse */\nOrthogonal.className = 'Orthogonal';\nserialization.registerClass(Orthogonal);\n// Maps the JavaScript-like identifier keys to the corresponding registry\n// symbols.\nexport const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'constant': 'Constant',\n    'glorotNormal': 'GlorotNormal',\n    'glorotUniform': 'GlorotUniform',\n    'heNormal': 'HeNormal',\n    'heUniform': 'HeUniform',\n    'identity': 'Identity',\n    'leCunNormal': 'LeCunNormal',\n    'leCunUniform': 'LeCunUniform',\n    'ones': 'Ones',\n    'orthogonal': 'Orthogonal',\n    'randomNormal': 'RandomNormal',\n    'randomUniform': 'RandomUniform',\n    'truncatedNormal': 'TruncatedNormal',\n    'varianceScaling': 'VarianceScaling',\n    'zeros': 'Zeros'\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'initializer');\n}\nexport function serializeInitializer(initializer) {\n    return serializeKerasObject(initializer);\n}\nexport function getInitializer(identifier) {\n    if (typeof identifier === 'string') {\n        const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        /* We have four 'helper' classes for common initializers that\n        all get serialized as 'VarianceScaling' and shouldn't go through\n        the deserializeInitializer pathway. */\n        if (className === 'GlorotNormal') {\n            return new GlorotNormal();\n        }\n        else if (className === 'GlorotUniform') {\n            return new GlorotUniform();\n        }\n        else if (className === 'HeNormal') {\n            return new HeNormal();\n        }\n        else if (className === 'HeUniform') {\n            return new HeUniform();\n        }\n        else if (className === 'LeCunNormal') {\n            return new LeCunNormal();\n        }\n        else if (className === 'LeCunUniform') {\n            return new LeCunUniform();\n        }\n        else {\n            const config = {};\n            config['className'] = className;\n            config['config'] = {};\n            return deserializeInitializer(config);\n        }\n    }\n    else if (identifier instanceof Initializer) {\n        return identifier;\n    }\n    else {\n        return deserializeInitializer(identifier);\n    }\n}\n//# sourceMappingURL=initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// tslint:disable-next-line:max-line-length\nimport { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';\n/**\n * Initializer that generates tensors initialized to 0.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function zeros() {\n    return new Zeros();\n}\n/**\n * Initializer that generates tensors initialized to 1.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function ones() {\n    return new Ones();\n}\n/**\n * Initializer that generates values initialized to some constant.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function constant(args) {\n    return new Constant(args);\n}\n/**\n * Initializer that generates random values initialized to a uniform\n * distribution.\n *\n * Values will be distributed uniformly between the configured minval and\n * maxval.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomUniform(args) {\n    return new RandomUniform(args);\n}\n/**\n * Initializer that generates random values initialized to a normal\n * distribution.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function randomNormal(args) {\n    return new RandomNormal(args);\n}\n/**\n * Initializer that generates random values initialized to a truncated normal.\n * distribution.\n *\n * These values are similar to values from a `RandomNormal` except that values\n * more than two standard deviations from the mean are discarded and re-drawn.\n * This is the recommended initializer for neural network weights and filters.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function truncatedNormal(args) {\n    return new TruncatedNormal(args);\n}\n/**\n * Initializer that generates the identity matrix.\n * Only use for square 2D matrices.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function identity(args) {\n    return new Identity(args);\n}\n/**\n * Initializer capable of adapting its scale to the shape of weights.\n * With distribution=NORMAL, samples are drawn from a truncated normal\n * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:\n *   - number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config) {\n    return new VarianceScaling(config);\n}\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args) {\n    return new GlorotUniform(args);\n}\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args) {\n    return new GlorotNormal(args);\n}\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args) {\n    return new HeNormal(args);\n}\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args) {\n    return new HeUniform(args);\n}\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args) {\n    return new LeCunNormal(args);\n}\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args) {\n    return new LeCunUniform(args);\n}\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args) {\n    return new Orthogonal(args);\n}\n//# sourceMappingURL=exports_initializers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Utilities related to persistent state in the backend.\n */\n/**\n * An ID to track `tf.SymbolicTensor`s and derived classes.\n * Required in different places in engine/topology.ts to identify unique\n * tensors.\n */\nlet _nextUniqueTensorId = 0;\nexport function getNextUniqueTensorId() {\n    return _nextUniqueTensorId++;\n}\nconst _uidPrefixes = {};\n/**\n * Provides a unique UID given a string prefix.\n *\n * @param prefix\n */\nexport function getUid(prefix = '') {\n    if (!(prefix in _uidPrefixes)) {\n        _uidPrefixes[prefix] = 0;\n    }\n    _uidPrefixes[prefix] += 1;\n    return prefix + _uidPrefixes[prefix].toString();\n}\n//# sourceMappingURL=state.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\n// tslint:enable\n/**\n * Determine whether the input is an Array of Shapes.\n */\nexport function isArrayOfShapes(x) {\n    return Array.isArray(x) && Array.isArray(x[0]);\n}\n/**\n * Special case of normalizing shapes to lists.\n *\n * @param x A shape or list of shapes to normalize into a list of Shapes.\n * @return A list of Shapes.\n */\nexport function normalizeShapeList(x) {\n    if (x.length === 0) {\n        return [];\n    }\n    if (!Array.isArray(x[0])) {\n        return [x];\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly one Tensor.\n * @param xs: A single `tf.Tensor` or an `Array` of `tf.Tensor`s.\n * @return A single `tf.Tensor`. If `xs` is an `Array`, return the first one.\n * @throws ValueError: If `xs` is an `Array` and its length is not 1.\n */\nexport function getExactlyOneTensor(xs) {\n    let x;\n    if (Array.isArray(xs)) {\n        if (xs.length !== 1) {\n            throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n        }\n        x = xs[0];\n    }\n    else {\n        x = xs;\n    }\n    return x;\n}\n/**\n * Helper function to obtain exactly on instance of Shape.\n *\n * @param shapes Input single `Shape` or Array of `Shape`s.\n * @returns If input is a single `Shape`, return it unchanged. If the input is\n *   an `Array` containing exactly one instance of `Shape`, return the instance.\n *   Otherwise, throw a `ValueError`.\n * @throws ValueError: If input is an `Array` of `Shape`s, and its length is not\n *   1.\n */\nexport function getExactlyOneShape(shapes) {\n    if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n        if (shapes.length === 1) {\n            shapes = shapes;\n            return shapes[0];\n        }\n        else {\n            throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n        }\n    }\n    else {\n        return shapes;\n    }\n}\n//# sourceMappingURL=types_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Count the elements in an Array of LayerVariables.\n *\n * @param weights: The LayerVariables of which the constituent numbers are to\n *   be counted.\n * @returns A count of the elements in all the LayerVariables\n */\nexport function countParamsInWeights(weights) {\n    let count = 0;\n    for (const weight of weights) {\n        if (weight.shape.length === 0) {\n            count += 1;\n        }\n        else {\n            count += weight.shape.reduce((a, b) => a * b);\n        }\n    }\n    return count;\n}\n//# sourceMappingURL=variable_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { variableGrads } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId } from './backend/state';\nimport { getScopedTensorName, getUniqueTensorName } from './common';\nimport { NotImplementedError } from './errors';\nconst DEFAULT_VARIABLE_NAME_PREFIX = 'Variable';\n/**\n * A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a\n * dtype and shape, but its value is mutable.  The value is itself represented\n * as a`tf.Tensor`, and can be read with the `read()` method and updated with\n * the `write()` method.\n */\nexport class LayerVariable {\n    /**\n     * Construct Variable from a `tf.Tensor`.\n     *\n     * If not explicitly named, the Variable will be given a name with the\n     * prefix 'Variable'. Variable names are unique. In the case of name\n     * collision, suffixies '_<num>' will be added to the name.\n     *\n     * @param val Initial value of the Variable.\n     * @param name Name of the variable. If `null` or `undefined` is provided, it\n     *   will default a name with the prefix 'Variable'.\n     * @param constraint Optional, projection function to be applied to the\n     * variable after optimize updates\n     * @throws ValueError if `name` is `null` or `undefined`.\n     */\n    constructor(val, dtype = 'float32', name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n        this.dtype = dtype == null ? 'float32' : dtype;\n        this.shape = val.shape;\n        this.id = getNextUniqueTensorId();\n        name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n        this.originalName = getScopedTensorName(name);\n        this.name = getUniqueTensorName(this.originalName);\n        this.trainable_ = trainable;\n        this.constraint = constraint;\n        this.val = tfc.variable(val, this.trainable_, this.name, this.dtype);\n    }\n    /**\n     * Get a snapshot of the Variable's value.\n     *\n     * The returned value is a snapshot of the Variable's value at the time of\n     * the invocation. Future mutations in the value of the tensor will only\n     * be reflected by future calls to this method.\n     */\n    read() {\n        this.assertNotDisposed();\n        return this.val;\n    }\n    /**\n     * Update the value of the Variable.\n     *\n     * @param newVal: The new value to update to. Must be consistent with the\n     *   dtype and shape of the Variable.\n     * @return This Variable.\n     */\n    write(newVal) {\n        // TODO(cais): Once  TF.js Core supports Tensor.dtype, check dtype match.\n        this.assertNotDisposed();\n        checkShapesMatch(this.val, newVal);\n        // Skip updating if this is the exact same tensor.\n        if (this.val.id !== newVal.id) {\n            this.val.assign(newVal);\n            if (this.constraint != null) {\n                this.val.assign(this.constraint.apply(this.val));\n            }\n        }\n        return this;\n    }\n    /**\n     * Dispose this LayersVariable instance from memory.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        this.val.dispose();\n    }\n    assertNotDisposed() {\n        if (this.val.isDisposed) {\n            throw new Error(`LayersVariable ${this.name} is already disposed.`);\n        }\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.trainable_ = trainable;\n        this.val.trainable = trainable;\n    }\n}\nfunction checkShapesMatch(x, y) {\n    if (x.shape.toString() !== y.shape.toString()) {\n        throw new Error('Shape mismatch: ' + JSON.stringify(x.shape) + ' vs. ' +\n            JSON.stringify(y.shape));\n    }\n}\n/**\n * Create a Variable.\n * @param x The initial value of the `Variable`.\n * @param dtype optional, the type of the variable.\n * @param name optional, the name of the variable, default provided by\n * Variable.\n * @param constraint optional, a constraint to be applied after every update.\n * @return The newly instantiated `Variable`.\n */\nexport function variable(x, dtype, name, constraint) {\n    return new LayerVariable(x, dtype, name, true, constraint);\n}\n/**\n * Instantiates an all-zeros Variable and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-zero Variable.\n */\nexport function zerosVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    return new LayerVariable(tfc.zeros(shape), dtype, name);\n}\n/**\n * Instantiates an all-zeros tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function zerosLike(x, dtype, name) {\n    return new LayerVariable(tfc.zerosLike(x), dtype, name);\n}\n/**\n * Instantiates an all-ones tensor and returns it.\n *\n * @param shape Shape of the tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return An all-ones Variable.\n */\nexport function onesVariable(shape, dtype, name) {\n    // TODO(cais): Implement logic for dtype.\n    const allocated = tfc.ones(shape);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiates an all-ones tensor of the same shape as another tensor.\n *\n * @param x The other tensor.\n * @param dtype DType of the tensor.\n * @param name Name of the tensor.\n * @return A newly instantiated Variable.\n */\nexport function onesLike(x, dtype, name) {\n    const allocated = tfc.onesLike(x);\n    return new LayerVariable(allocated, dtype, name);\n}\n/**\n * Instantiate an identity matrix and returns it, as a Variable\n *\n * @param size Number of rows/columns.\n * @param dtype Data type of returned Variable.\n * @param name Name of returned Variable.\n * @return A Variable, an identity matrix.\n */\nexport function eyeVariable(size, dtype, name) {\n    return new LayerVariable(tfc.eye(size), dtype, name);\n}\n/**\n * Get a Variable with uniform distribution of values.\n * @param shape Shape of the tensor.\n * @param minval Lower bound of the uniform distribution.\n * @param maxval Upper bound of the uniform distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The uniform-random Variable.\n */\nexport function randomUniformVariable(shape, minval, maxval, dtype, seed, name = 'randomUniform') {\n    return new LayerVariable(tfc.randomUniform(shape, minval, maxval, dtype), dtype, name);\n}\n/**\n * Get a Variable with truncated-normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function truncatedNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'truncatedNormal') {\n    // TODO(cais): Implement logic for dtype and seed once they are supported\n    // by deeplearn.js.\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.truncatedNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Get a Variable with normal distribution of values.\n * @param shape Shape of the tensor.\n * @param mean mean value of the normal distribution.\n * @param stddev standard deviation of the normal distribution.\n * @param dtype\n * @param seed\n * @param name Optional name.\n * @return The truncated-normal-random Variable.\n */\nexport function randomNormalVariable(shape, mean = 0.0, stddev = 1.0, dtype, seed, name = 'randomNormal') {\n    dtype = dtype || 'float32';\n    if (dtype !== 'float32' && dtype !== 'int32') {\n        throw new NotImplementedError(`randomNormalVariable does not support dType ${dtype}.`);\n    }\n    return new LayerVariable(tfc.randomNormal(shape, mean, stddev, dtype, seed), dtype, name);\n}\n/**\n * Update the value of a Variable.\n * @param x The Variable to be updated.\n * @param xNew The new value to update to.\n * @return The Variable updated.\n */\nexport function update(x, xNew) {\n    return x.write(xNew);\n}\n/**\n * Update the value of a Variable by adding an increment.\n * @param x The Variable to be updated.\n * @param increment The incrment to add to `x`.\n * @return The Variable updated.\n */\nexport function updateAdd(x, increment) {\n    return x.write(tfc.add(x.read(), increment));\n}\n/**\n * Update the value of a Variable by subtracting a decrement.\n * @param x The Variable to be updated.\n * @param decrement The decrement to subtract from `x`.\n * @return The Variable updated.\n */\nexport function updateSub(x, decrement) {\n    return x.write(tfc.sub(x.read(), decrement));\n}\n/**\n * Get the values of an array of Variables.\n *\n * @param tensors An `Array` of `Variable`s to get the values of.\n * @return The values of the inputs, as an `Array` of`tf.Tensor`s.\n */\nexport function batchGetValue(xs) {\n    return xs.map(x => x.read());\n}\n/**\n * Update the value of multiple Variables at once.\n *\n * @param variablesAndValues An `Array`, each element is of type\n *   [Variable, Tensor]. The first item is the\n *   `Variable` of which the value is to be updated. The second item\n *   carries the new value.\n */\nexport function batchSetValue(variablesAndValues) {\n    variablesAndValues.forEach(variableAndValue => {\n        const variable = variableAndValue[0];\n        variable.write(variableAndValue[1]);\n    });\n}\n/**\n * Returns the gradients of `variables` w.r.t. the return value of `lossFn`.\n * @param lossFn A function which returns a Scalar to be used as the function\n *   value (i.e., numerator) for differentiation.\n * @param variables List of variables to be used as the independent variables\n *   (i.e., denominator) for differentiation.\n * @returns An Array of gradients tensors.\n */\nexport function gradients(lossFn, variables) {\n    // TODO(cais): The return type signature can be simplified if deeplearn makes\n    //   the corresponding type public.\n    const variableList = variables.map(variable => variable.read());\n    const valudAndGrads = variableGrads(lossFn, variableList);\n    return variables.map(variable => valudAndGrads.grads[variable.name]);\n}\n//# sourceMappingURL=variables.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getNextUniqueTensorId, getUid } from '../backend/state';\nimport { getScopedTensorName, getUniqueTensorName, nameScope } from '../common';\nimport { AttributeError, NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { getInitializer } from '../initializers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as types_utils from '../utils/types_utils';\nimport * as variable_utils from '../utils/variable_utils';\nimport { batchGetValue, batchSetValue, LayerVariable } from '../variables';\n/**\n * Specifies the ndim, dtype and shape of every input to a layer.\n *\n * Every layer should expose (if appropriate) an `inputSpec` attribute:\n * a list of instances of InputSpec (one per input tensor).\n *\n * A null entry in a shape is compatible with any dimension,\n * a null shape is compatible with any shape.\n */\nexport class InputSpec {\n    constructor(args) {\n        this.dtype = args.dtype;\n        this.shape = args.shape;\n        /*\n          TODO(michaelterry): Could throw error if ndim and shape are both defined\n            (then backport).\n        */\n        if (args.shape != null) {\n            this.ndim = args.shape.length;\n        }\n        else {\n            this.ndim = args.ndim;\n        }\n        this.maxNDim = args.maxNDim;\n        this.minNDim = args.minNDim;\n        this.axes = args.axes || {};\n    }\n}\n/**\n * `tf.SymbolicTensor` is a placeholder for a Tensor without any concrete value.\n *\n * They are most often encountered when building a graph of `Layer`s for a\n * a `tf.LayersModel` and the input data's shape, but not values are known.\n *\n * @doc {heading: 'Models', 'subheading': 'Classes'}\n */\nexport class SymbolicTensor {\n    /**\n     *\n     * @param dtype\n     * @param shape\n     * @param sourceLayer The Layer that produced this symbolic tensor.\n     * @param inputs The inputs passed to sourceLayer's __call__() method.\n     * @param nodeIndex\n     * @param tensorIndex\n     * @param callArgs The keyword arguments passed to the __call__() method.\n     * @param name\n     * @param outputTensorIndex The index of this tensor in the list of outputs\n     *   returned by apply().\n     */\n    constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n        this.dtype = dtype;\n        this.shape = shape;\n        this.sourceLayer = sourceLayer;\n        this.inputs = inputs;\n        this.callArgs = callArgs;\n        this.outputTensorIndex = outputTensorIndex;\n        this.id = getNextUniqueTensorId();\n        if (name != null) {\n            this.originalName = getScopedTensorName(name);\n            this.name = getUniqueTensorName(this.originalName);\n        }\n        this.rank = shape.length;\n    }\n}\nlet _nextNodeID = 0;\n/**\n * A `Node` describes the connectivity between two layers.\n *\n * Each time a layer is connected to some new input,\n * a node is added to `layer.inboundNodes`.\n *\n * Each time the output of a layer is used by another layer,\n * a node is added to `layer.outboundNodes`.\n *\n * `nodeIndices` and `tensorIndices` are basically fine-grained coordinates\n * describing the origin of the `inputTensors`, verifying the following:\n *\n * `inputTensors[i] ==\n * inboundLayers[i].inboundNodes[nodeIndices[i]].outputTensors[\n *   tensorIndices[i]]`\n *\n * A node from layer A to layer B is added to:\n *     A.outboundNodes\n *     B.inboundNodes\n */\nexport class Node {\n    constructor(args, \n    // TODO(michaelterry): Define actual type for this.\n    callArgs) {\n        this.callArgs = callArgs;\n        this.id = _nextNodeID++;\n        /*\n          Layer instance (NOT a list).\n          this is the layer that takes a list of input tensors\n          and turns them into a list of output tensors.\n          the current node will be added to\n          the inboundNodes of outboundLayer.\n        */\n        this.outboundLayer = args.outboundLayer;\n        /*\n            The following 3 properties describe where\n            the input tensors come from: which layers,\n            and for each layer, which node and which\n            tensor output of each node.\n        */\n        // List of layer instances.\n        this.inboundLayers = args.inboundLayers;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.nodeIndices = args.nodeIndices;\n        // List of integers, 1:1 mapping with inboundLayers.\n        this.tensorIndices = args.tensorIndices;\n        /*\n            Following 2 properties:\n            tensor inputs and outputs of outboundLayer.\n        */\n        // List of tensors. 1:1 mapping with inboundLayers.\n        this.inputTensors = args.inputTensors;\n        // List of tensors, created by outboundLayer.call().\n        this.outputTensors = args.outputTensors;\n        /*\n            Following 2 properties: input and output masks.\n            List of tensors, 1:1 mapping with inputTensor.\n        */\n        this.inputMasks = args.inputMasks;\n        // List of tensors, created by outboundLayer.computeMask().\n        this.outputMasks = args.outputMasks;\n        // Following 2 properties: input and output shapes.\n        // List of shape tuples, shapes of inputTensors.\n        this.inputShapes = args.inputShapes;\n        // List of shape tuples, shapes of outputTensors.\n        this.outputShapes = args.outputShapes;\n        // Add nodes to all layers involved.\n        for (const layer of args.inboundLayers) {\n            if (layer != null) {\n                layer.outboundNodes.push(this);\n            }\n        }\n        args.outboundLayer.inboundNodes.push(this);\n    }\n    getConfig() {\n        const inboundNames = [];\n        for (const layer of this.inboundLayers) {\n            if (layer != null) {\n                inboundNames.push(layer.name);\n            }\n            else {\n                inboundNames.push(null);\n            }\n        }\n        return {\n            outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n            inboundLayers: inboundNames,\n            nodeIndices: this.nodeIndices,\n            tensorIndices: this.tensorIndices\n        };\n    }\n}\nlet _nextLayerID = 0;\n/**\n * A layer is a grouping of operations and weights that can be composed to\n * create a `tf.LayersModel`.\n *\n * Layers are constructed by using the functions under the\n * [tf.layers](#Layers-Basic) namespace.\n *\n * @doc {heading: 'Layers', subheading: 'Classes', namespace: 'layers'}\n */\nexport class Layer extends serialization.Serializable {\n    constructor(args = {}) {\n        super();\n        this._callHook = null;\n        this._addedWeightNames = [];\n        // Porting Notes: PyKeras does not have this property in this base Layer\n        //   class. Instead lets Layer subclass set it dynamically and checks the\n        //   value with `hasattr`. In tfjs-layers, we let this be a member of this\n        //   base class.\n        this._stateful = false;\n        this.id = _nextLayerID++;\n        this.activityRegularizer = null;\n        this.inputSpec = null;\n        this.supportsMasking = false;\n        // These properties will be set upon call of this.build()\n        this._trainableWeights = [];\n        this._nonTrainableWeights = [];\n        this._losses = [];\n        this._updates = [];\n        this._built = false;\n        /*\n          These lists will be filled via successive calls\n          to this.addInboundNode().\n         */\n        this.inboundNodes = [];\n        this.outboundNodes = [];\n        let name = args.name;\n        if (!name) {\n            const prefix = this.getClassName();\n            name = generic_utils.toSnakeCase(prefix) + '_' + getUid(prefix);\n        }\n        this.name = name;\n        this.trainable_ = args.trainable == null ? true : args.trainable;\n        if (args.inputShape != null || args.batchInputShape != null) {\n            /*\n              In this case we will later create an input layer\n              to insert before the current layer\n             */\n            let batchInputShape;\n            if (args.batchInputShape != null) {\n                batchInputShape = args.batchInputShape;\n            }\n            else if (args.inputShape != null) {\n                let batchSize = null;\n                if (args.batchSize != null) {\n                    batchSize = args.batchSize;\n                }\n                batchInputShape = [batchSize].concat(args.inputShape);\n            }\n            this.batchInputShape = batchInputShape;\n            // Set dtype.\n            let dtype = args.dtype;\n            if (dtype == null) {\n                dtype = args.inputDType;\n            }\n            if (dtype == null) {\n                dtype = 'float32';\n            }\n            this.dtype = dtype;\n        }\n        if (args.weights != null) {\n            this.initialWeights = args.weights;\n        }\n        else {\n            this.initialWeights = null;\n        }\n        // The value of `_refCount` is initialized to null. When the layer is used\n        // in a symbolic way for the first time, it will be set to 1.\n        this._refCount = null;\n        this.fastWeightInitDuringBuild = false;\n    }\n    /**\n     * Converts a layer and its index to a unique (immutable type) name.\n     * This function is used internally with `this.containerNodes`.\n     * @param layer The layer.\n     * @param nodeIndex The layer's position (e.g. via enumerate) in a list of\n     *   nodes.\n     *\n     * @returns The unique name.\n     */\n    static nodeKey(layer, nodeIndex) {\n        return layer.name + '_ib-' + nodeIndex.toString();\n    }\n    /**\n     * Returns this.inboundNode at index nodeIndex.\n     *\n     * Porting note: This is a replacement for _get_node_attribute_at_index()\n     * @param nodeIndex\n     * @param attrName The name of the attribute related to request for this node.\n     */\n    getNodeAtIndex(nodeIndex, attrName) {\n        if (this.inboundNodes.length === 0) {\n            throw new RuntimeError('The layer has never been called ' +\n                `and thus has no defined ${attrName}.`);\n        }\n        if (this.inboundNodes.length <= nodeIndex) {\n            throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, ` +\n                `but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n        }\n        return this.inboundNodes[nodeIndex];\n    }\n    /**\n     * Retrieves the input tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple inputs).\n     */\n    getInputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer at a given node.\n     *\n     * @param nodeIndex Integer, index of the node from which to retrieve the\n     *   attribute. E.g. `nodeIndex=0` will correspond to the first time the layer\n     *   was called.\n     *\n     * @return A tensor (or list of tensors if the layer has multiple outputs).\n     */\n    getOutputAt(nodeIndex) {\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(nodeIndex, 'output').outputTensors);\n    }\n    // Properties\n    /**\n     * Retrieves the input tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Input tensor or list of input tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get input() {\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer input\" ' +\n                'is ill-defined. ' +\n                'Use `getInputAt(nodeIndex)` instead.');\n        }\n        else if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' is not connected, no input to return.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'input').inputTensors);\n    }\n    /**\n     * Retrieves the output tensor(s) of a layer.\n     *\n     * Only applicable if the layer has exactly one inbound node,\n     * i.e. if it is connected to one incoming layer.\n     *\n     * @return Output tensor or list of output tensors.\n     *\n     * @exception AttributeError if the layer is connected to more than one\n     *   incoming layers.\n     */\n    get output() {\n        if (this.inboundNodes.length === 0) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has no inbound nodes.');\n        }\n        if (this.inboundNodes.length > 1) {\n            throw new AttributeError(`Layer ${this.name}` +\n                ' has multiple inbound nodes, ' +\n                'hence the notion of \"layer output\" ' +\n                'is ill-defined. ' +\n                'Use `getOutputAt(nodeIndex)` instead.');\n        }\n        return generic_utils.singletonOrArray(this.getNodeAtIndex(0, 'output').outputTensors);\n    }\n    get losses() {\n        return this._losses;\n    }\n    /**\n     * Retrieves the Layer's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Layer.loss in PyKeras.\n        //   In PyKeras, Layer.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return this.losses.map(lossFn => lossFn());\n    }\n    get updates() {\n        return this._updates;\n    }\n    get built() {\n        return this._built;\n    }\n    set built(built) {\n        this._built = built;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this._trainableWeights.forEach(w => w.trainable = trainable);\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        if (this.trainable_) {\n            return this._trainableWeights.filter(w => w.trainable);\n        }\n        else {\n            return [];\n        }\n    }\n    set trainableWeights(weights) {\n        this._trainableWeights = weights;\n    }\n    get nonTrainableWeights() {\n        if (this.trainable) {\n            return this._trainableWeights.filter(w => !w.trainable)\n                .concat(this._nonTrainableWeights);\n        }\n        else {\n            return this._trainableWeights.concat(this._nonTrainableWeights);\n        }\n    }\n    set nonTrainableWeights(weights) {\n        this._nonTrainableWeights = weights;\n    }\n    /**\n     * The concatenation of the lists trainableWeights and nonTrainableWeights\n     * (in this order).\n     */\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    get stateful() {\n        return this._stateful;\n    }\n    /**\n     * Reset the states of the layer.\n     *\n     * This method of the base Layer class is essentially a no-op.\n     * Subclasses that are stateful (e.g., stateful RNNs) should override this\n     * method.\n     */\n    resetStates() {\n        if (!this.stateful) {\n            throw new Error('Cannot call the resetStates() method of a non-stateful Layer ' +\n                'object.');\n        }\n    }\n    /**\n     * Checks compatibility between the layer and provided inputs.\n     *\n     * This checks that the tensor(s) `input`\n     * verify the input assumptions of the layer\n     * (if any). If not, exceptions are raised.\n     *\n     * @param inputs Input tensor or list of input tensors.\n     *\n     * @exception ValueError in case of mismatch between\n     *   the provided inputs and the expectations of the layer.\n     */\n    assertInputCompatibility(inputs) {\n        inputs = generic_utils.toList(inputs);\n        if (this.inputSpec == null || this.inputSpec.length === 0) {\n            return;\n        }\n        const inputSpec = generic_utils.toList(this.inputSpec);\n        if (inputs.length !== inputSpec.length) {\n            throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, ` +\n                `but it received ${inputs.length} input tensors. ` +\n                `Input received: ${inputs}`);\n        }\n        for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n            const x = inputs[inputIndex];\n            const spec = inputSpec[inputIndex];\n            if (spec == null) {\n                continue;\n            }\n            // Check ndim.\n            const ndim = x.rank;\n            if (spec.ndim != null) {\n                if (ndim !== spec.ndim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: ` +\n                        `expected ndim=${spec.ndim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.maxNDim != null) {\n                if (ndim > spec.maxNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n                }\n            }\n            if (spec.minNDim != null) {\n                if (ndim < spec.minNDim) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}` +\n                        `: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n                }\n            }\n            // Check dtype.\n            if (spec.dtype != null) {\n                if (x.dtype !== spec.dtype) {\n                    throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} ` +\n                        `: expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n                }\n            }\n            // Check specific shape axes.\n            if (spec.axes) {\n                const xShape = x.shape;\n                for (const key in spec.axes) {\n                    const axis = Number(key);\n                    const value = spec.axes[key];\n                    // Perform Python-style slicing in case axis < 0;\n                    // TODO(cais): Use https://github.com/alvivi/typescript-underscore to\n                    // ensure type safety through Underscore calls.\n                    const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n                    if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n                        throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                            `${this.name}: expected axis ${axis} of input shape to ` +\n                            `have value ${value} but got shape ${xShape}.`);\n                    }\n                }\n            }\n            // Check shape.\n            if (spec.shape != null) {\n                for (let i = 0; i < spec.shape.length; ++i) {\n                    const specDim = spec.shape[i];\n                    const dim = x.shape[i];\n                    if (specDim != null && dim != null) {\n                        if (specDim !== dim) {\n                            throw new ValueError(`Input ${inputIndex} is incompatible with layer ` +\n                                `${this.name}: expected shape=${spec.shape}, ` +\n                                `found shape=${x.shape}.`);\n                        }\n                    }\n                }\n            }\n        }\n    }\n    /**\n     * This is where the layer's logic lives.\n     *\n     * @param inputs Input tensor, or list/tuple of input tensors.\n     * @param kwargs Additional keyword arguments.\n     *\n     * @return A tensor or list/tuple of tensors.\n     */\n    call(inputs, kwargs) {\n        return inputs;\n    }\n    invokeCallHook(inputs, kwargs) {\n        if (this._callHook != null) {\n            this._callHook(inputs, kwargs);\n        }\n    }\n    /**\n     * Set call hook.\n     * This is currently used for testing only.\n     * @param callHook\n     */\n    setCallHook(callHook) {\n        this._callHook = callHook;\n    }\n    /**\n     * Clear call hook.\n     * This is currently used for testing only.\n     */\n    clearCallHook() {\n        this._callHook = null;\n    }\n    /**\n     * Builds or executes a `Layer's logic.\n     *\n     * When called with `tf.Tensor`(s), execute the `Layer`s computation and\n     * return Tensor(s). For example:\n     *\n     * ```js\n     * const denseLayer = tf.layers.dense({\n     *   units: 1,\n     *   kernelInitializer: 'zeros',\n     *   useBias: false\n     * });\n     *\n     * // Invoke the layer's apply() method with a `tf.Tensor` (with concrete\n     * // numeric values).\n     * const input = tf.ones([2, 2]);\n     * const output = denseLayer.apply(input);\n     *\n     * // The output's value is expected to be [[0], [0]], due to the fact that\n     * // the dense layer has a kernel initialized to all-zeros and does not have\n     * // a bias.\n     * output.print();\n     * ```\n     *\n     * When called with `tf.SymbolicTensor`(s), this will prepare the layer for\n     * future execution.  This entails internal book-keeping on shapes of\n     * expected Tensors, wiring layers together, and initializing weights.\n     *\n     * Calling `apply` with `tf.SymbolicTensor`s are typically used during the\n     * building of non-`tf.Sequential` models. For example:\n     *\n     * ```js\n     * const flattenLayer = tf.layers.flatten();\n     * const denseLayer = tf.layers.dense({units: 1});\n     *\n     * // Use tf.layers.input() to obtain a SymbolicTensor as input to apply().\n     * const input = tf.input({shape: [2, 2]});\n     * const output1 = flattenLayer.apply(input);\n     *\n     * // output1.shape is [null, 4]. The first dimension is the undetermined\n     * // batch size. The second dimension comes from flattening the [2, 2]\n     * // shape.\n     * console.log(JSON.stringify(output1.shape));\n     *\n     * // The output SymbolicTensor of the flatten layer can be used to call\n     * // the apply() of the dense layer:\n     * const output2 = denseLayer.apply(output1);\n     *\n     * // output2.shape is [null, 1]. The first dimension is the undetermined\n     * // batch size. The second dimension matches the number of units of the\n     * // dense layer.\n     * console.log(JSON.stringify(output2.shape));\n     *\n     * // The input and output and be used to construct a model that consists\n     * // of the flatten and dense layers.\n     * const model = tf.model({inputs: input, outputs: output2});\n     * ```\n     *\n     * @param inputs a `tf.Tensor` or `tf.SymbolicTensor` or an Array of them.\n     * @param kwargs Additional keyword arguments to be passed to `call()`.\n     *\n     * @return Output of the layer's `call` method.\n     *\n     * @exception ValueError error in case the layer is missing shape information\n     *   for its `build` call.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    // Porting Note: This is a replacement for __call__() in Python.\n    apply(inputs, kwargs) {\n        kwargs = kwargs || {};\n        this.assertNotDisposed();\n        // Ensure inputs are all the same type.\n        const inputsList = generic_utils.toList(inputs);\n        let allAreSymbolic = true;\n        for (const input of inputsList) {\n            if (!(input instanceof SymbolicTensor)) {\n                allAreSymbolic = false;\n                break;\n            }\n        }\n        let noneAreSymbolic = true;\n        for (const input of inputsList) {\n            if (input instanceof SymbolicTensor) {\n                noneAreSymbolic = false;\n                break;\n            }\n        }\n        if (allAreSymbolic === noneAreSymbolic) {\n            throw new ValueError('Arguments to apply() must be all ' +\n                'SymbolicTensors or all Tensors');\n        }\n        // TODO(michaelterry): nameScope() may not be necessary.\n        return nameScope(this.name, () => {\n            // Handle laying building (weight creating, input spec locking).\n            if (!this.built) {\n                /*\n                  Throw exceptions in case the input is not compatible\n                  with the inputSpec specified in the layer constructor.\n                 */\n                this.assertInputCompatibility(inputs);\n                // Collect input shapes to build layer.\n                const inputShapes = [];\n                for (const xElem of generic_utils.toList(inputs)) {\n                    inputShapes.push(xElem.shape);\n                }\n                this.build(generic_utils.singletonOrArray(inputShapes));\n                this.built = true;\n                // Load weights that were specified at layer instantiation.\n                if (this.initialWeights) {\n                    this.setWeights(this.initialWeights);\n                }\n                if (this._refCount === null && noneAreSymbolic) {\n                    // The first use of this layer is a non-symbolic call, set ref count\n                    // to 1 so the Layer can be properly disposed if its dispose() method\n                    // is called.\n                    this._refCount = 1;\n                }\n            }\n            /*\n              Throw exceptions in case the input is not compatible\n              with the inputSpec set at build time.\n            */\n            this.assertInputCompatibility(inputs);\n            // Handle mask propagation.\n            // TODO(michaelterry): Mask propagation not currently implemented.\n            // Actually call the layer, collecting output(s), mask(s), and shape(s).\n            if (noneAreSymbolic) {\n                let output = this.call(inputs, kwargs);\n                // TODO(michaelterry): Compute the outputMask\n                // If the layer returns tensors from its inputs, unmodified,\n                // we copy them to avoid loss of tensor metadata.\n                const outputList = generic_utils.toList(output);\n                const outputListCopy = [];\n                // TODO(michaelterry): This copying may not be necessary given our eager\n                // backend.\n                for (let x of outputList) {\n                    if (inputsList.indexOf(x) !== -1) {\n                        x = x.clone();\n                    }\n                    outputListCopy.push(x);\n                }\n                output = generic_utils.singletonOrArray(outputListCopy);\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                // TODO(michaelterry): Call addInboundNode()?\n                return output;\n            }\n            else {\n                const inputShape = collectInputShape(inputs);\n                const outputShape = this.computeOutputShape(inputShape);\n                let output;\n                const outputDType = guessOutputDType(inputs);\n                this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] :\n                    inputShape);\n                if (outputShape != null && outputShape.length > 0 &&\n                    Array.isArray(outputShape[0])) {\n                    // We have multiple output shapes. Create multiple output tensors.\n                    output = outputShape\n                        .map((shape, index) => new SymbolicTensor(outputDType, shape, this, generic_utils.toList(inputs), kwargs, this.name, index));\n                }\n                else {\n                    output = new SymbolicTensor(outputDType, outputShape, this, generic_utils.toList(inputs), kwargs, this.name);\n                }\n                /*\n                  Add an inbound node to the layer, so that it keeps track\n                  of the call and of all new variables created during the call.\n                  This also updates the layer history of the output tensor(s).\n                  If the input tensor(s) had no previous history,\n                  this does nothing.\n                */\n                this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n                this._refCount++;\n                if (this.activityRegularizer != null) {\n                    throw new NotImplementedError('Layer invocation in the presence of activity ' +\n                        'regularizer(s) is not supported yet.');\n                }\n                return output;\n            }\n        });\n    }\n    /**\n     * Check compatibility between input shape and this layer's batchInputShape.\n     *\n     * Print warning if any incompatibility is found.\n     *\n     * @param inputShape Input shape to be checked.\n     */\n    warnOnIncompatibleInputShape(inputShape) {\n        if (this.batchInputShape == null) {\n            return;\n        }\n        else if (inputShape.length !== this.batchInputShape.length) {\n            console.warn(`The rank of the input tensor provided (shape: ` +\n                `${JSON.stringify(inputShape)}) does not match that of the ` +\n                `batchInputShape (${JSON.stringify(this.batchInputShape)}) ` +\n                `of the layer ${this.name}`);\n        }\n        else {\n            let dimMismatch = false;\n            this.batchInputShape.forEach((dimension, i) => {\n                if (dimension != null && inputShape[i] != null &&\n                    inputShape[i] !== dimension) {\n                    dimMismatch = true;\n                }\n            });\n            if (dimMismatch) {\n                console.warn(`The shape of the input tensor ` +\n                    `(${JSON.stringify(inputShape)}) does not ` +\n                    `match the expectation of layer ${this.name}: ` +\n                    `${JSON.stringify(this.batchInputShape)}`);\n            }\n        }\n    }\n    /**\n     * Retrieves the output shape(s) of a layer.\n     *\n     * Only applicable if the layer has only one inbound node, or if all inbound\n     * nodes have the same output shape.\n     *\n     * @returns Output shape or shapes.\n     * @throws AttributeError: if the layer is connected to more than one incoming\n     *   nodes.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    get outputShape() {\n        if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n            throw new AttributeError(`The layer ${this.name} has never been called and thus has no ` +\n                `defined output shape.`);\n        }\n        const allOutputShapes = [];\n        for (const node of this.inboundNodes) {\n            const shapeString = JSON.stringify(node.outputShapes);\n            if (allOutputShapes.indexOf(shapeString) === -1) {\n                allOutputShapes.push(shapeString);\n            }\n        }\n        if (allOutputShapes.length === 1) {\n            const outputShapes = this.inboundNodes[0].outputShapes;\n            if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) &&\n                outputShapes.length === 1) {\n                return outputShapes[0];\n            }\n            else {\n                return outputShapes;\n            }\n        }\n        else {\n            throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different ` +\n                `output shapes. Hence the notion of \"output shape\" is ill-defined ` +\n                `for the layer.`);\n            // TODO(cais): Implement getOutputShapeAt().\n        }\n    }\n    /**\n     * Counts the total number of numbers (e.g., float32, int32) in the\n     * weights.\n     *\n     * @returns An integer count.\n     * @throws RuntimeError: If the layer is not built yet (in which case its\n     *   weights are not defined yet.)\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    countParams() {\n        if (!this.built) {\n            throw new RuntimeError(`You tried to call countParams() on ${this.name}, ` +\n                `but the layer is not built yet. Build it first by calling ` +\n                `build(batchInputShape).`);\n        }\n        return variable_utils.countParamsInWeights(this.weights);\n    }\n    /**\n     * Creates the layer weights.\n     *\n     * Must be implemented on all layers that have weights.\n     *\n     * Called when apply() is called to construct the weights.\n     *\n     * @param inputShape A `Shape` or array of `Shape` (unused).\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    build(inputShape) {\n        this.built = true;\n    }\n    /**\n     * Returns the current values of the weights of the layer.\n     *\n     * @param trainableOnly Whether to get the values of only trainable weights.\n     * @returns Weight values as an `Array` of `tf.Tensor`s.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getWeights(trainableOnly = false) {\n        return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n    }\n    /**\n     * Sets the weights of the layer, from Tensors.\n     *\n     * @param weights a list of Tensors. The number of arrays and their shape\n     *   must match number of the dimensions of the weights of the layer (i.e.\n     *   it should match the output of `getWeights`).\n     *\n     * @exception ValueError If the provided weights list does not match the\n     *   layer's specifications.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    setWeights(weights) {\n        tidy(() => {\n            const params = this.weights;\n            if (params.length !== weights.length) {\n                // TODO(cais): Restore the following and use `providedWeights`, instead\n                // of `weights` in the error message, once the deeplearn.js bug is\n                // fixed: https://github.com/PAIR-code/deeplearnjs/issues/498 const\n                // providedWeights = JSON.stringify(weights).substr(0, 50);\n                throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" ` +\n                    `with a weight list of length ${weights.length}, ` +\n                    `but the layer was expecting ${params.length} weights. ` +\n                    `Provided weights: ${weights}...`);\n            }\n            if (params.length === 0) {\n                return;\n            }\n            const weightValueTuples = [];\n            const paramValues = batchGetValue(params);\n            for (let i = 0; i < paramValues.length; ++i) {\n                const pv = paramValues[i];\n                const p = params[i];\n                const w = weights[i];\n                if (!util.arraysEqual(pv.shape, w.shape)) {\n                    throw new ValueError(`Layer weight shape ${pv.shape} ` +\n                        `not compatible with provided weight shape ${w.shape}`);\n                }\n                weightValueTuples.push([p, w]);\n            }\n            batchSetValue(weightValueTuples);\n        });\n    }\n    /**\n     * Adds a weight variable to the layer.\n     *\n     * @param name Name of the new weight variable.\n     * @param shape The shape of the weight.\n     * @param dtype The dtype of the weight.\n     * @param initializer An initializer instance.\n     * @param regularizer A regularizer instance.\n     * @param trainable Whether the weight should be trained via backprop or not\n     *   (assuming that the layer itself is also trainable).\n     * @param constraint An optional trainable.\n     * @return The created weight variable.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint) {\n        // Reject duplicate weight names.\n        if (this._addedWeightNames.indexOf(name) !== -1) {\n            throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n        }\n        this._addedWeightNames.push(name);\n        if (dtype == null) {\n            dtype = 'float32';\n        }\n        if (this.fastWeightInitDuringBuild) {\n            initializer = getInitializer('zeros');\n        }\n        const initValue = initializer.apply(shape, dtype);\n        const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n        initValue.dispose();\n        // Request backend not to dispose the weights of the model on scope() exit.\n        if (regularizer != null) {\n            this.addLoss(() => regularizer.apply(weight.read()));\n        }\n        if (trainable == null) {\n            trainable = true;\n        }\n        if (trainable) {\n            this._trainableWeights.push(weight);\n        }\n        else {\n            this._nonTrainableWeights.push(weight);\n        }\n        return weight;\n    }\n    /**\n     * Set the fast-weight-initialization flag.\n     *\n     * In cases where the initialized weight values will be immediately\n     * overwritten by loaded weight values during model loading, setting\n     * the flag to `true` saves unnecessary calls to potentially expensive\n     * initializers and speeds up the loading process.\n     *\n     * @param value Target value of the flag.\n     */\n    setFastWeightInitDuringBuild(value) {\n        this.fastWeightInitDuringBuild = value;\n    }\n    /**\n     * Add losses to the layer.\n     *\n     * The loss may potentionally be conditional on some inputs tensors,\n     * for instance activity losses are conditional on the layer's inputs.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    addLoss(losses) {\n        if (losses == null || Array.isArray(losses) && losses.length === 0) {\n            return;\n        }\n        // Update this.losses\n        losses = generic_utils.toList(losses);\n        if (this._losses !== undefined && this._losses !== null) {\n            this.losses.push(...losses);\n        }\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        if (!this.supportsMasking) {\n            if (mask != null) {\n                if (Array.isArray(mask)) {\n                    mask.forEach(maskElement => {\n                        if (maskElement != null) {\n                            throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                                'but was passed an inputMask.');\n                        }\n                    });\n                }\n                else {\n                    throw new TypeError(`Layer ${this.name} does not support masking, ` +\n                        'but was passed an inputMask.');\n                }\n            }\n            // masking not explicitly supported: return null as mask\n            return null;\n        }\n        // if masking is explictly supported, by default\n        // carry over the input mask\n        return mask;\n    }\n    /**\n     * Internal method to create an inbound node for the layer.\n     *\n     * @param inputTensors List of input tensors.\n     * @param outputTensors List of output tensors.\n     * @param inputMasks List of input masks (a mask can be a tensor, or null).\n     * @param outputMasks List of output masks (a mask can be a tensor, or null).\n     * @param inputShapes List of input shape tuples.\n     * @param outputShapes List of output shape tuples.\n     * @param kwargs Dictionary of keyword arguments that were passed to the\n     *   `call` method of the layer at the call that created the node.\n     */\n    addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n        const inputTensorList = generic_utils.toList(inputTensors);\n        outputTensors = generic_utils.toList(outputTensors);\n        inputMasks = generic_utils.toList(inputMasks);\n        outputMasks = generic_utils.toList(outputMasks);\n        inputShapes = types_utils.normalizeShapeList(inputShapes);\n        outputShapes = types_utils.normalizeShapeList(outputShapes);\n        // Collect input tensor(s) coordinates.\n        const inboundLayers = [];\n        const nodeIndices = [];\n        const tensorIndices = [];\n        for (const x of inputTensorList) {\n            /*\n             * TODO(michaelterry): Keras adds this value to tensors; it's not\n             * clear whether we'll use this or not.\n             */\n            inboundLayers.push(x.sourceLayer);\n            nodeIndices.push(x.nodeIndex);\n            tensorIndices.push(x.tensorIndex);\n        }\n        // Create node, add it to inbound nodes.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers,\n            nodeIndices,\n            tensorIndices,\n            inputTensors: inputTensorList,\n            outputTensors,\n            inputMasks,\n            outputMasks,\n            inputShapes,\n            outputShapes\n        }, kwargs);\n        // Update tensor history\n        for (let i = 0; i < outputTensors.length; i++) {\n            // TODO(michaelterry: _uses_learning_phase not tracked.\n            outputTensors[i].sourceLayer = this;\n            outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n            outputTensors[i].tensorIndex = i;\n        }\n    }\n    /**\n     * Returns the config of the layer.\n     *\n     * A layer config is a TS dictionary (serializable)\n     * containing the configuration of a layer.\n     * The same layer can be reinstantiated later\n     * (without its trained weights) from this configuration.\n     *\n     * The config of a layer does not include connectivity\n     * information, nor the layer class name.  These are handled\n     * by 'Container' (one layer of abstraction above).\n     *\n     * Porting Note: The TS dictionary follows TS naming standrds for\n     * keys, and uses tfjs-layers type-safe Enums.  Serialization methods\n     * should use a helper function to convert to the pythonic storage\n     * standard. (see serialization_utils.convertTsToPythonic)\n     *\n     * @returns TS dictionary of configuration.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    getConfig() {\n        const config = { name: this.name, trainable: this.trainable };\n        if (this.batchInputShape != null) {\n            config['batchInputShape'] = this.batchInputShape;\n        }\n        if (this.dtype != null) {\n            config['dtype'] = this.dtype;\n        }\n        return config;\n    }\n    /**\n     * Dispose the weight variables that this Layer instance holds.\n     *\n     * @returns {number} Number of disposed variables.\n     */\n    disposeWeights() {\n        this.weights.forEach(weight => weight.dispose());\n        return this.weights.length;\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Layer '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose layer's weights.\n     *\n     * This method decrease the reference count of the Layer object by 1.\n     *\n     * A Layer is reference-counted. Its reference count is incremented by 1\n     * the first item its `apply()` method is called and when it becomes a part\n     * of a new `Node` (through calling the `apply()`) method on a\n     * `tf.SymbolicTensor`).\n     *\n     * If the reference count of a Layer becomes 0, all the weights will be\n     * disposed and the underlying memory (e.g., the textures allocated in WebGL)\n     * will be freed.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * weights of the Layer will *not* be disposed.\n     *\n     * After a Layer is disposed, it cannot be used in calls such as `apply()`,\n     * `getWeights()` or `setWeights()` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the Container after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the layer has already\n     *   been disposed.\n     *\n     * @doc {heading: 'Models', 'subheading': 'Classes'}\n     */\n    dispose() {\n        if (!this.built) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been ` +\n                `built yet.`);\n        }\n        if (this._refCount === null) {\n            throw new Error(`Cannot dispose Layer ${this.name} because it has not been used ` +\n                `yet.`);\n        }\n        this.assertNotDisposed();\n        let numDisposedVariables = 0;\n        if (--this._refCount === 0) {\n            numDisposedVariables = this.disposeWeights();\n        }\n        return { refCountAfterDispose: this._refCount, numDisposedVariables };\n    }\n}\n/**\n * Collects the input shape(s) of a list of `tf.Tensor`s or\n * `tf.SymbolicTensor`s.\n *\n * TODO(michaelterry): Update PyKeras docs (backport).\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return List of shape tuples (or single tuple), one tuple per input.\n */\nfunction collectInputShape(inputTensors) {\n    inputTensors =\n        generic_utils.toList(inputTensors);\n    const shapes = [];\n    for (const x of inputTensors) {\n        shapes.push(x.shape);\n    }\n    return generic_utils.singletonOrArray(shapes);\n}\n/**\n * Guesses output dtype based on inputs.\n *\n * At present, just returns 'float32' for any input.\n *\n * @param inputTensors List of input tensors (or single input tensor).\n *\n * @return The guessed DType. At present, always returns 'float32'.\n */\nfunction guessOutputDType(inputTensors) {\n    return 'float32';\n}\n/**\n * Returns the list of input tensors necessary to compute `tensor`.\n *\n * Output will always be a list of tensors (potentially with 1 element).\n *\n * @param tensor The tensor to start from.\n * @param layer Origin layer of the tensor.\n * @param nodeIndex Origin node index of the tensor.\n *\n * @return Array of input tensors.\n */\nexport function getSourceInputs(tensor, layer, nodeIndex) {\n    if (layer == null || (nodeIndex != null && nodeIndex > 0)) {\n        layer = tensor.sourceLayer;\n        nodeIndex = tensor.nodeIndex;\n    }\n    if (layer.inboundNodes.length === 0) {\n        return [tensor];\n    }\n    else {\n        const node = layer.inboundNodes[nodeIndex];\n        if (node.inboundLayers.length === 0) {\n            return node.inputTensors;\n        }\n        else {\n            const sourceTensors = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const previousSources = getSourceInputs(x, layer, nodeIndex);\n                // Avoid input redundancy.\n                for (const x of previousSources) {\n                    if (sourceTensors.indexOf(x) === -1) {\n                        sourceTensors.push(x);\n                    }\n                }\n            }\n            return sourceTensors;\n        }\n    }\n}\n//# sourceMappingURL=topology.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { ValueError } from '../errors';\nimport { Layer, Node, SymbolicTensor } from './topology';\nexport class InputLayer extends Layer {\n    constructor(args) {\n        super({\n            dtype: args.dtype,\n            name: args.name != null ? args.name : getUid('input').toString()\n        });\n        // Normalize config.batchSize and config.sparse\n        if (args.batchSize == null) {\n            args.batchSize = null;\n        }\n        if (args.sparse == null) {\n            args.sparse = false;\n        }\n        this.trainable = false;\n        this.built = true;\n        this.sparse = args.sparse;\n        if (args.inputShape != null && args.batchInputShape != null) {\n            throw new ValueError('Only provide the inputShape OR ' +\n                'batchInputShape argument to inputLayer, not both at the same time.');\n        }\n        let batchInputShape = args.batchInputShape;\n        if (batchInputShape == null) {\n            if (args.inputShape == null) {\n                throw new ValueError('An InputLayer should be passed either a ' +\n                    '`batchInputShape` or an `inputShape`.');\n            }\n            else {\n                batchInputShape = [args.batchSize].concat(args.inputShape);\n            }\n        }\n        else {\n            // TODO(michaelterry): Backport to PyKeras\n            if (args.batchSize != null) {\n                throw new ValueError('Cannot specify batchSize if batchInputShape is ' +\n                    'specified when creating an InputLayer.');\n            }\n        }\n        const dtype = args.dtype || 'float32';\n        this.batchInputShape = batchInputShape;\n        this.dtype = dtype;\n        // TODO(michaelterry): Backport this to PyKeras?\n        this.inputSpec = [{ shape: batchInputShape }];\n        const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n        inputTensor.nodeIndex = 0;\n        inputTensor.tensorIndex = 0;\n        // Create an input node to add to this.outboundNode.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: [inputTensor],\n            outputTensors: [inputTensor],\n            inputMasks: [null],\n            outputMasks: [null],\n            inputShapes: [batchInputShape],\n            outputShapes: [batchInputShape]\n        });\n    }\n    apply(inputs, kwargs) {\n        throw new ValueError('Cannot pass any input to an ' +\n            `InputLayer's apply() method. InputLayer name: ${this.name}`);\n    }\n    dispose() {\n        // dispose() for InputLayer is overridden as no-op.\n        return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n    }\n    getConfig() {\n        return {\n            batchInputShape: this.batchInputShape,\n            dtype: this.dtype,\n            sparse: this.sparse,\n            name: this.name\n        };\n    }\n}\n/** @nocollapse */\nInputLayer.className = 'InputLayer';\nserialization.registerClass(InputLayer);\nexport function Input(config) {\n    if (config.batchShape == null && config.shape == null) {\n        throw new Error('Please provide to Input either a `shape`' +\n            ' or a `batchShape` argument. Note that ' +\n            '`shape` does not include the batch ' +\n            'dimension.');\n    }\n    if (config.batchShape != null && config.shape != null) {\n        // TODO(michaelterry): Backport to PyKeras.\n        throw new ValueError('Please provide either a `shape` or `batchShape` ' +\n            'argument to Input, but not both.');\n    }\n    let batchShape = config.batchShape;\n    if (config.shape != null && batchShape == null) {\n        batchShape = [null].concat(config.shape);\n    }\n    let dtype = config.dtype;\n    if (dtype == null) {\n        dtype = 'float32';\n    }\n    const inputLayer = new InputLayer({\n        batchInputShape: batchShape,\n        name: config.name,\n        dtype,\n        sparse: config.sparse\n    });\n    const outputs = inputLayer.inboundNodes[0].outputTensors;\n    return outputs[0];\n}\n//# sourceMappingURL=input_layer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose } from '@tensorflow/tfjs-core';\n/**\n * Turn any Scalar values in a Logs object into actual number values.\n *\n * @param logs The `Logs` object to be resolved in place.\n */\nexport async function resolveScalarsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    const promises = [];\n    const keys = [];\n    const scalarsToDispose = [];\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            const valueScalar = value;\n            promises.push(valueScalar.data());\n            keys.push(key);\n            scalarsToDispose.push(valueScalar);\n        }\n    }\n    if (promises.length > 0) {\n        const values = await Promise.all(promises);\n        for (let i = 0; i < values.length; ++i) {\n            logs[keys[i]] = values[i][0];\n        }\n        // Dispose the original scalar tensors.\n        dispose(scalarsToDispose);\n    }\n}\n/**\n * Dispose all Tensors in an UnresolvedLogs object.\n *\n * @param logs An `UnresolvedLogs` object potentially containing `tf.Tensor`s in\n *   places where the values can be `tf.Tensor` or `number`.\n */\nexport function disposeTensorsInLogs(logs) {\n    if (logs == null) {\n        return;\n    }\n    for (const key in logs) {\n        const value = logs[key];\n        if (typeof value !== 'number') {\n            value.dispose();\n        }\n    }\n}\n//# sourceMappingURL=logs.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { add, div, keep, mul, nextFrame, tidy, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nimport * as generic_utils from './utils/generic_utils';\n/** Verbosity logging level when fitting a model. */\nexport var ModelLoggingVerbosity;\n(function (ModelLoggingVerbosity) {\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"SILENT\"] = 0] = \"SILENT\";\n    ModelLoggingVerbosity[ModelLoggingVerbosity[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\n/** How often to yield to the main thread when training (in ms). */\nexport const DEFAULT_YIELD_EVERY_MS = 125;\n/**\n * Abstract base class used to build new callbacks.\n *\n * The `logs` dictionary that callback methods take as argument will contain\n * keys for quantities relevant to the current batch or epoch.\n *\n * Currently, the `.fit()` method of the `Sequential` model class\n * will include the following quantities in the `logs` that\n * it passes to its callbacks:\n *\n * onEpochEnd: Logs include `acc` and `loss`, and optionally include `valLoss`\n *   (if validation is enabled in `fit`), and `valAcc` (if validation and\n *   accuracy monitoring are enabled).\n * onBatchBegin: Logs include `size`, the number of samples in the current\n *   batch.\n * onBatchEnd: Logs include `loss`, and optionally `acc` (if accuracy monitoring\n *   is enabled).\n */\nexport class BaseCallback {\n    constructor() {\n        // TODO(michaelterry): This type is a best guess.\n        this.validationData = null;\n    }\n    setParams(params) {\n        this.params = params;\n    }\n    async onEpochBegin(epoch, logs) { }\n    async onEpochEnd(epoch, logs) { }\n    async onBatchBegin(batch, logs) { }\n    async onBatchEnd(batch, logs) { }\n    async onTrainBegin(logs) { }\n    async onTrainEnd(logs) { }\n    // LayersModel needs to call Callback.setModel(), but cannot actually depend\n    // on Callback because that creates a cyclic dependency.  Providing this no-op\n    // method on BaseCallback breaks the cycle: this way LayersModel can depend on\n    // BaseCallback but not on Callback.  The argument is typed as `Container`\n    // (the superclass of LayersModel) to avoid recapitulating the cycle. Callback\n    // overrides this method and enforces that the argument is really a\n    // LayersModel.\n    setModel(model) {\n        // Do nothing. Use Callback instead of BaseCallback to track the model.\n    }\n}\n/**\n * Container abstracting a list of callbacks.\n */\nexport class CallbackList {\n    // TODO(cais): When the need arises, uncomment the following lines and\n    // implement the queue for time values.\n    // private deltaTBatch: number;\n    // private deltaTsBatchBegin: Array<number>;\n    // private deltaTsBatchEnd: Array<number>;\n    /**\n     * Constructor of CallbackList.\n     * @param callbacks Array of `Callback` instances.\n     * @param queueLength Queue length for keeping running statistics over\n     *   callback execution time.\n     */\n    constructor(callbacks, queueLength = 10) {\n        // TODO(cais): Make use of queueLength when implementing the queue for time\n        // values.\n        if (callbacks == null) {\n            callbacks = [];\n        }\n        this.callbacks = callbacks;\n        this.queueLength = queueLength;\n    }\n    append(callback) {\n        this.callbacks.push(callback);\n    }\n    setParams(params) {\n        for (const callback of this.callbacks) {\n            callback.setParams(params);\n        }\n    }\n    setModel(model) {\n        for (const callback of this.callbacks) {\n            callback.setModel(model);\n        }\n    }\n    /**\n     * Called at the start of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochBegin(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochBegin(epoch, logs);\n        }\n    }\n    /**\n     * Called at the end of an epoch.\n     * @param epoch Index of epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onEpochEnd(epoch, logs);\n        }\n    }\n    /**\n     * Called  right before processing a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchBegin(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchBegin(batch, logs);\n        }\n    }\n    /**\n     * Called at the end of a batch.\n     * @param batch Index of batch within the current epoch.\n     * @param logs Dictionary of logs.\n     */\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onBatchEnd(batch, logs);\n        }\n    }\n    /**\n     * Called at the beginning of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainBegin(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainBegin(logs);\n        }\n    }\n    /**\n     * Called at the end of training.\n     * @param logs Dictionary of logs.\n     */\n    async onTrainEnd(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        for (const callback of this.callbacks) {\n            await callback.onTrainEnd(logs);\n        }\n    }\n}\n/**\n * Callback that accumulates epoch averages of metrics.\n *\n * This callback is automatically applied to every LayersModel.\n */\nexport class BaseLogger extends BaseCallback {\n    constructor() {\n        super();\n    }\n    async onEpochBegin(epoch) {\n        this.seen = 0;\n        this.totals = {};\n    }\n    async onBatchEnd(batch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const batchSize = logs['size'] == null ? 0 : logs['size'];\n        this.seen += batchSize;\n        for (const key in logs) {\n            const value = logs[key];\n            if (typeof value === 'number') {\n                if (!this.totals.hasOwnProperty(key)) {\n                    this.totals[key] = 0;\n                }\n                this.totals[key] = this.totals[key] + value * batchSize;\n            }\n            else {\n                let oldTotalsToDispose;\n                if (key in this.totals) {\n                    oldTotalsToDispose = this.totals[key];\n                }\n                else {\n                    this.totals[key] = 0;\n                }\n                const total = tidy(() => add((this.totals[key]), mul(value, batchSize)));\n                this.totals[key] = total;\n                if (oldTotalsToDispose != null) {\n                    oldTotalsToDispose.dispose();\n                }\n            }\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs != null) {\n            for (const key of this.params['metrics']) {\n                if (this.totals[key] == null) {\n                    continue;\n                }\n                if (typeof this.totals[key] === 'number') {\n                    logs[key] = this.totals[key] / this.seen;\n                }\n                else {\n                    tidy(() => {\n                        const log = mul(div(1, this.seen), this.totals[key]);\n                        logs[key] = log;\n                        this.totals[key].dispose();\n                        keep(logs[key]);\n                    });\n                }\n            }\n        }\n    }\n}\n/**\n * Callback that records events into a `History` object. This callback is\n * automatically applied to every TF.js Layers model. The `History` object\n * gets returned by the `fit` method of models.\n */\nexport class History extends BaseCallback {\n    async onTrainBegin(logs) {\n        this.epoch = [];\n        this.history = {};\n    }\n    async onEpochEnd(epoch, logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        this.epoch.push(epoch);\n        for (const key in logs) {\n            if (this.history[key] == null) {\n                this.history[key] = [];\n            }\n            this.history[key].push(logs[key]);\n        }\n    }\n    /**\n     * Await the values of all losses and metrics.\n     */\n    async syncData() {\n        const promises = [];\n        const keys = [];\n        const indices = [];\n        for (const key in this.history) {\n            const valueArray = this.history[key];\n            for (let i = 0; i < valueArray.length; ++i) {\n                if (typeof valueArray[i] !== 'number') {\n                    const valueScalar = valueArray[i];\n                    promises.push(valueScalar.data());\n                    keys.push(key);\n                    indices.push(i);\n                }\n            }\n        }\n        const values = await Promise.all(promises);\n        for (let n = 0; n < values.length; ++n) {\n            const tensorToDispose = this.history[keys[n]][indices[n]];\n            tensorToDispose.dispose();\n            this.history[keys[n]][indices[n]] = values[n][0];\n        }\n    }\n}\n/**\n * Custom callback for training.\n */\nexport class CustomCallback extends BaseCallback {\n    constructor(args, yieldEvery) {\n        super();\n        this.currentEpoch = 0;\n        this.yieldEvery = yieldEvery || 'auto';\n        if (this.yieldEvery === 'auto') {\n            this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n        }\n        if (this.yieldEvery === 'never' && args.onYield != null) {\n            throw new Error('yieldEvery is `never` but you provided an `onYield` callback. ' +\n                'Either change `yieldEvery` or remove the callback');\n        }\n        if (util.isNumber(this.yieldEvery)) {\n            // Decorate `maybeWait` so it will be called at most once every\n            // `yieldEvery` ms.\n            this.maybeWait = generic_utils.debounce(this.maybeWait.bind(this), this.yieldEvery);\n        }\n        this.trainBegin = args.onTrainBegin;\n        this.trainEnd = args.onTrainEnd;\n        this.epochBegin = args.onEpochBegin;\n        this.epochEnd = args.onEpochEnd;\n        this.batchBegin = args.onBatchBegin;\n        this.batchEnd = args.onBatchEnd;\n        this.yield = args.onYield;\n    }\n    async maybeWait(epoch, batch, logs) {\n        const ps = [];\n        if (this.yield != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.yield(epoch, batch, logs));\n        }\n        ps.push(nextFrame());\n        await Promise.all(ps);\n    }\n    async onEpochBegin(epoch, logs) {\n        this.currentEpoch = epoch;\n        if (this.epochBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.epochBegin(epoch, logs);\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        const ps = [];\n        if (this.epochEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.epochEnd(epoch, logs));\n        }\n        if (this.yieldEvery === 'epoch') {\n            ps.push(nextFrame());\n        }\n        await Promise.all(ps);\n    }\n    async onBatchBegin(batch, logs) {\n        if (this.batchBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.batchBegin(batch, logs);\n        }\n    }\n    async onBatchEnd(batch, logs) {\n        const ps = [];\n        if (this.batchEnd != null) {\n            await resolveScalarsInLogs(logs);\n            ps.push(this.batchEnd(batch, logs));\n        }\n        if (this.yieldEvery === 'batch') {\n            ps.push(nextFrame());\n        }\n        else if (util.isNumber(this.yieldEvery)) {\n            ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n        }\n        await Promise.all(ps);\n    }\n    async onTrainBegin(logs) {\n        if (this.trainBegin != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainBegin(logs);\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.trainEnd != null) {\n            await resolveScalarsInLogs(logs);\n            await this.trainEnd(logs);\n        }\n    }\n}\n/**\n * Standardize callbacks or configurations of them to an Array of callbacks.\n */\nexport function standardizeCallbacks(callbacks, yieldEvery) {\n    if (callbacks == null) {\n        callbacks = {};\n    }\n    if (callbacks instanceof BaseCallback) {\n        return [callbacks];\n    }\n    if (Array.isArray(callbacks) && callbacks[0] instanceof BaseCallback) {\n        return callbacks;\n    }\n    // Convert custom callback configs to custom callback objects.\n    const callbackConfigs = generic_utils.toList(callbacks);\n    return callbackConfigs.map(callbackConfig => new CustomCallback(callbackConfig, yieldEvery));\n}\n/**\n * A global registry for callback constructors to be used during\n * LayersModel.fit().\n */\nexport class CallbackConstructorRegistry {\n    /**\n     * Blocks public access to constructor.\n     */\n    constructor() { }\n    /**\n     * Register a tf.LayersModel.fit() callback constructor.\n     *\n     * The registered callback constructor will be used to instantiate\n     * callbacks for every tf.LayersModel.fit() call afterwards.\n     *\n     * @param verbosityLevel Level of verbosity at which the `callbackConstructor`\n     *   is to be reigstered.\n     * @param callbackConstructor A no-arg constructor for `tf.Callback`.\n     * @throws Error, if the same callbackConstructor has been registered before,\n     *   either at the same or a different `verbosityLevel`.\n     */\n    static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n        util.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, ` +\n            `but got ${verbosityLevel}`);\n        CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n        if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n            CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n        }\n        CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n    }\n    static checkForDuplicate(callbackConstructor) {\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const constructors = CallbackConstructorRegistry.constructors[+levelName];\n            constructors.forEach(ctor => {\n                if (ctor === callbackConstructor) {\n                    throw new ValueError('Duplicate callback constructor.');\n                }\n            });\n        }\n    }\n    /**\n     * Clear all registered callback constructors.\n     */\n    static clear() {\n        CallbackConstructorRegistry.constructors = {};\n    }\n    /**\n     * Create callbacks using the registered callback constructors.\n     *\n     * Given `verbosityLevel`, all constructors registered at that level or above\n     * will be called and the instantiated callbacks will be used.\n     *\n     * @param verbosityLevel: Level of verbosity.\n     */\n    static createCallbacks(verbosityLevel) {\n        const constructors = [];\n        for (const levelName in CallbackConstructorRegistry.constructors) {\n            const level = +levelName;\n            if (verbosityLevel >= level) {\n                constructors.push(...CallbackConstructorRegistry.constructors[level]);\n            }\n        }\n        return constructors.map(ctor => new ctor());\n    }\n}\nCallbackConstructorRegistry.constructors = {};\nexport function configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n    const history = new History();\n    const actualCallbacks = [\n        new BaseLogger(), ...CallbackConstructorRegistry.createCallbacks(verbose)\n    ];\n    if (callbacks != null) {\n        actualCallbacks.push(...callbacks);\n    }\n    actualCallbacks.push(history);\n    const callbackList = new CallbackList(actualCallbacks);\n    // TODO(cais): Figure out when this LayersModel instance can have a\n    // dynamically\n    //   set property called 'callback_model' as in PyKeras.\n    callbackList.setParams({\n        epochs,\n        initialEpoch,\n        samples: numTrainSamples,\n        steps: stepsPerEpoch,\n        batchSize,\n        verbose,\n        doValidation,\n        metrics: callbackMetrics,\n    });\n    return { callbackList, history };\n}\n//# sourceMappingURL=base_callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source layers/__init__.py */\nimport { serialization } from '@tensorflow/tfjs-core';\nimport { deserializeKerasObject } from '../utils/generic_utils';\n/**\n * Instantiate a layer from a config dictionary.\n * @param config dict of the form {class_name: str, config: dict}\n * @param customObjects dict mapping class names (or function names)\n *   of custom (non-Keras) objects to class/functions\n * @param fastWeightInit Optional flag to use fast weight initialization\n *   during deserialization. This is applicable to cases in which\n *   the initialization will be immediately overwritten by loaded weight\n *   values. Default: `false`.\n * @returns Layer instance (may be LayersModel, Sequential, Layer...)\n */\nexport function deserialize(config, customObjects = {}, fastWeightInit = false) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'layer', fastWeightInit);\n}\n//# sourceMappingURL=serialization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: losses.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport * as K from './backend/tfjs_backend';\nimport { ValueError } from './errors';\n/**\n * Normalizes a tensor wrt the L2 norm alongside the specified axis.\n * @param x\n * @param axis Axis along which to perform normalization.\n */\nexport function l2Normalize(x, axis) {\n    return tidy(() => {\n        if (x.dtype !== 'float32') {\n            x = x.asType('float32');\n        }\n        const squareSum = tfc.sum(K.square(x), axis, true);\n        const epsilonTensor = tfc.fill(squareSum.shape, epsilon());\n        const norm = tfc.sqrt(tfc.maximum(squareSum, epsilonTensor));\n        return tfc.div(x, norm);\n    });\n}\nexport function meanSquaredError(yTrue, yPred) {\n    return tidy(() => tfc.mean(K.square(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsoluteError(yTrue, yPred) {\n    return tidy(() => tfc.mean(tfc.abs(tfc.sub(yPred, yTrue)), -1));\n}\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return tidy(() => {\n        const diff = tfc.sub(yTrue, yPred);\n        const clippedTrue = tfc.clipByValue(tfc.abs(yTrue), epsilon(), Number.MAX_VALUE);\n        const absResult = tfc.abs(tfc.div(diff, clippedTrue));\n        return tfc.mul(100, tfc.mean(absResult, -1));\n    });\n}\nexport function meanSquaredLogarithmicError(yTrue, yPred) {\n    return tidy(() => {\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n        const firstLog = tfc.log(tfc.add(1, clippedPred));\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n        const secondLog = tfc.log(tfc.add(1, clippedTrue));\n        return tfc.mean(K.square(tfc.sub(firstLog, secondLog)), -1);\n    });\n}\nexport function squaredHinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(K.square(maxResult), -1);\n    });\n}\nexport function hinge(yTrue, yPred) {\n    return tidy(() => {\n        const maxResult = tfc.maximum(0, tfc.sub(1, tfc.mul(yTrue, yPred)));\n        return tfc.mean(maxResult, -1);\n    });\n}\nexport function categoricalHinge(yTrue, yPred) {\n    return tidy(() => {\n        const pos = tfc.sum(tfc.mul(yTrue, yPred), -1);\n        const neg = tfc.max(tfc.mul(tfc.sub(1, yTrue), yPred), -1);\n        return tfc.maximum(0, tfc.add(1, tfc.sub(neg, pos)));\n    });\n}\n/**\n * Logarithm of the hyperbolic cosine of the prediction error.\n *\n * `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and\n * to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly\n * like the mean squared error, but will not be so strongly affected by the\n * occasional wildly incorrect prediction.\n */\nexport function logcosh(yTrue, yPred) {\n    return tidy(() => {\n        const log2 = Math.log(2);\n        const predictionDiff = tfc.sub(yPred, yTrue);\n        const logcoshResult = tfc.sub(tfc.add(predictionDiff, tfc.softplus(tfc.mul(-2, predictionDiff))), log2);\n        return tfc.mean(logcoshResult, -1);\n    });\n}\nexport function categoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        if (fromLogits) {\n            output = tfc.softmax(output);\n        }\n        else {\n            // scale preds so that the class probabilities of each sample sum to 1.\n            const outputSum = tfc.sum(output, output.shape.length - 1, true);\n            output = tfc.div(output, outputSum);\n        }\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        return tfc.neg(tfc.sum(tfc.mul(target.toFloat(), tfc.log(output)), output.shape.length - 1));\n    });\n}\n/**\n * Categorical crossentropy with integer targets.\n *\n * @param target An integer tensor.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n */\nexport function sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n    return tidy(() => {\n        const flatTarget = tfc.floor(K.flatten(target)).toInt();\n        output = tfc.clipByValue(output, epsilon(), 1 - epsilon());\n        const outputShape = output.shape;\n        const oneHotTarget = tfc.oneHot(flatTarget, outputShape[outputShape.length - 1])\n            .reshape(outputShape);\n        return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n    });\n}\n/**\n * From TensorFlow's implementation in nn_impl.py:\n *\n * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n *      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n *    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n *    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n *    = (1 - z) * x + log(1 + exp(-x))\n *    = x - x * z + log(1 + exp(-x))\n * For x < 0, to avoid overflow in exp(-x), we reformulate the above\n *      x - x * z + log(1 + exp(-x))\n *    = log(exp(x)) - x * z + log(1 + exp(-x))\n *    = - x * z + log(1 + exp(x))\n * Hence, to ensure stability and avoid overflow, the implementation uses this\n * equivalent formulation\n *    max(x, 0) - x * z + log(1 + exp(-abs(x)))\n *\n * @param labels The labels.\n * @param logits The logits.\n */\nexport function sigmoidCrossEntropyWithLogits(labels, logits) {\n    if (!util.arraysEqual(labels.shape, logits.shape)) {\n        throw new ValueError(`logits and labels must have the same shape, but got shapes ` +\n            `${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n    }\n    return tidy(() => {\n        // The logistic loss formula from above is\n        //   x - x * z + log(1 + exp(-x))\n        // For x < 0, a more numerically stable formula is\n        //   -x * z + log(1 + exp(x))\n        // Note that these two expressions can be combined into the following:\n        //   max(x, 0) - x * z + log(1 + exp(-abs(x)))\n        const reluLogits = logits.relu();\n        const negAbsLogits = logits.abs().neg();\n        return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p());\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return tidy(() => {\n        let y;\n        y = tfc.clipByValue(yPred, epsilon(), 1 - epsilon());\n        y = tfc.log(tfc.div(y, tfc.sub(1, y)));\n        return tfc.mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n    });\n}\nexport function kullbackLeiblerDivergence(yTrue, yPred) {\n    return tidy(() => {\n        const clippedTrue = tfc.clipByValue(yTrue, epsilon(), 1);\n        const clippedPred = tfc.clipByValue(yPred, epsilon(), 1);\n        return tfc.sum(tfc.mul(yTrue, tfc.log(tfc.div(clippedTrue, clippedPred))), -1);\n    });\n}\nexport function poisson(yTrue, yPred) {\n    return tidy(() => {\n        const logPred = tfc.log(tfc.add(epsilon(), yPred));\n        return tfc.mean(tfc.sub(yPred, tfc.mul(yTrue, logPred)), -1);\n    });\n}\nexport function cosineProximity(yTrue, yPred) {\n    return tidy(() => {\n        const trueNormalized = l2Normalize(yTrue, -1);\n        const predNormalized = l2Normalize(yPred, -1);\n        const trueXPred = tfc.mul(trueNormalized, predNormalized);\n        return tfc.neg(tfc.sum(trueXPred, -1));\n    });\n}\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const msle = meanSquaredLogarithmicError;\nexport const MSLE = meanSquaredLogarithmicError;\nexport const kld = kullbackLeiblerDivergence;\nexport const KLD = kullbackLeiblerDivergence;\nexport const cosine = cosineProximity;\n// TODO(michaelterry): Add deserialize() function.\nexport const lossesMap = {\n    meanSquaredError,\n    meanAbsoluteError,\n    meanAbsolutePercentageError,\n    meanSquaredLogarithmicError,\n    squaredHinge,\n    hinge,\n    categoricalHinge,\n    logcosh,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    binaryCrossentropy,\n    kullbackLeiblerDivergence,\n    poisson,\n    cosineProximity\n};\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function get(identifierOrFn) {\n    if (typeof identifierOrFn === 'string') {\n        if (identifierOrFn in lossesMap) {\n            return lossesMap[identifierOrFn];\n        }\n        let errMsg = `Unknown loss ${identifierOrFn}`;\n        if (identifierOrFn.toLowerCase().includes('softmaxcrossentropy')) {\n            errMsg = `Unknown loss ${identifierOrFn}. ` +\n                'Use \"categoricalCrossentropy\" as the string name for ' +\n                'tf.losses.softmaxCrossEntropy';\n        }\n        throw new ValueError(errMsg);\n    }\n    else {\n        return identifierOrFn;\n    }\n}\n//# sourceMappingURL=losses.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Built-in metrics.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { NotImplementedError, ValueError } from './errors';\nimport { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';\nimport { binaryCrossentropy as lossBinaryCrossentropy } from './losses';\nimport { lossesMap } from './losses';\nimport * as util from './utils/generic_utils';\nexport function binaryAccuracy(yTrue, yPred) {\n    return tidy(() => {\n        const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n    });\n}\nexport function categoricalAccuracy(yTrue, yPred) {\n    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\nfunction truePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(1)).sum().cast('float32');\n    });\n}\nfunction falseNegatives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(1), yPred.equal(0)).sum().cast('float32');\n    });\n}\nfunction falsePositives(yTrue, yPred) {\n    return tidy(() => {\n        return tfc.logicalAnd(yTrue.equal(0), yPred.equal(1)).sum().cast('float32');\n    });\n}\nexport function precision(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fp = falsePositives(yTrue, yPred);\n        const denominator = tp.add(fp);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function recall(yTrue, yPred) {\n    return tidy(() => {\n        const tp = truePositives(yTrue, yPred);\n        const fn = falseNegatives(yTrue, yPred);\n        const denominator = tp.add(fn);\n        return tfc.where(tfc.greater(denominator, 0), tp.div(denominator), 0)\n            .cast('float32');\n    });\n}\nexport function binaryCrossentropy(yTrue, yPred) {\n    return lossBinaryCrossentropy(yTrue, yPred);\n}\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    if (yTrue.rank === yPred.rank) {\n        yTrue = yTrue.squeeze([yTrue.rank - 1]);\n    }\n    yPred = yPred.argMax(-1);\n    if (yPred.dtype !== yTrue.dtype) {\n        yPred = yPred.asType(yTrue.dtype);\n    }\n    return tfc.equal(yTrue, yPred).asType('float32');\n}\nexport function topKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\nexport function sparseTopKCategoricalAccuracy(yTrue, yPred) {\n    throw new NotImplementedError();\n}\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n// TODO(cais, nielsene): Add serialize().\nexport const metricsMap = {\n    binaryAccuracy,\n    categoricalAccuracy,\n    precision,\n    categoricalCrossentropy,\n    sparseCategoricalCrossentropy,\n    mse,\n    MSE,\n    mae,\n    MAE,\n    mape,\n    MAPE,\n    cosine\n};\nexport function get(identifier) {\n    if (typeof identifier === 'string' && identifier in metricsMap) {\n        return metricsMap[identifier];\n    }\n    else if (typeof identifier !== 'string' && identifier != null) {\n        return identifier;\n    }\n    else {\n        throw new ValueError(`Unknown metric ${identifier}`);\n    }\n}\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn) {\n    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n    if (typeof fn === 'string') {\n        return fn;\n    }\n    else {\n        let fnName;\n        for (const key of Object.keys(lossesMap)) {\n            if (lossesMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        for (const key of Object.keys(metricsMap)) {\n            if (metricsMap[key] === fn) {\n                fnName = key;\n                break;\n            }\n        }\n        if (fnName !== undefined) {\n            return fnName;\n        }\n        return fn.name;\n    }\n}\n//# sourceMappingURL=metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Optimizers.\n */\nimport { train } from '@tensorflow/tfjs-core';\nimport { epsilon } from './backend/common';\nimport { ValueError } from './errors';\n// Add (de)serialize()\n// Porting note: This diverges from the PyKeras implementation and may need to\n// change based on (de)serialization requirements.\nexport function getOptimizer(identifier) {\n    const optimizerMap = {\n        'Adagrad': () => train.adagrad(0.01),\n        'Adadelta': () => train.adadelta(1, 0.95, epsilon()),\n        'Adam': () => train.adam(0.001, 0.9, 0.999, epsilon()),\n        'Adamax': () => train.adamax(0.002, 0.9, 0.999, epsilon(), 0),\n        'RMSProp': () => train.rmsprop(0.001, 0.9, 0, epsilon()),\n        'SGD': () => train.sgd(0.01)\n    };\n    optimizerMap['adagrad'] = optimizerMap['Adagrad'];\n    optimizerMap['adadelta'] = optimizerMap['Adadelta'];\n    optimizerMap['adam'] = optimizerMap['Adam'];\n    optimizerMap['adamax'] = optimizerMap['Adamax'];\n    optimizerMap['rmsprop'] = optimizerMap['RMSProp'];\n    optimizerMap['sgd'] = optimizerMap['SGD'];\n    if (identifier in optimizerMap) {\n        return optimizerMap[identifier]();\n    }\n    throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n//# sourceMappingURL=optimizers.js.map", "/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/** Utility functions related to user-defined metadata. */\n// Maximum recommended serialized size for user-defined metadata.\n// Beyond this limit, a warning message will be printed during model loading and\n// saving.\nexport const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\n/**\n * Check validity of user-defined metadata.\n *\n * @param userDefinedMetadata\n * @param modelName Name of the model that the user-defined metadata belongs to.\n *   Used during construction of error messages.\n * @param checkSize Whether to check the size of the metadata is under\n *   recommended limit. Default: `false`. If `true`, will try stringify the\n *   JSON object and print a console warning if the serialzied size is above the\n *   limit.\n * @throws Error if `userDefinedMetadata` is not a plain JSON object.\n */\nexport function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n    if (userDefinedMetadata == null ||\n        typeof userDefinedMetadata !== 'object' ||\n        Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype ||\n        !plainObjectCheck(userDefinedMetadata)) {\n        throw new Error('User-defined metadata is expected to be a JSON object, but is not.');\n    }\n    if (checkSize) {\n        const out = JSON.stringify(userDefinedMetadata);\n        if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n            console.warn(`User-defined metadata of model \"${modelName}\" is too large in ` +\n                `size (length=${out.length} when serialized). It is not ` +\n                `recommended to store such large objects in user-defined metadata. ` +\n                `Please make sure its serialized length is <= ` +\n                `${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n        }\n    }\n}\n/**\n * Check if an input is plain JSON object or any valid subfield of it.\n *\n * @param x The input to be checked.\n * @param assertObject Whether to assert `x` is a JSON object, i.e., reject\n *   cases of arrays and primitives.\n * @return Returns `true` if and only if `x` is a plain JSON object,\n *   a JSON-valid primitive including string, number, boolean and null,\n *   or an array of the said types.\n */\n// tslint:disable-next-line:no-any\nexport function plainObjectCheck(x) {\n    if (x === null) {\n        // Note: typeof `null` is 'object', and `null` is valid in JSON.\n        return true;\n    }\n    else if (typeof x === 'object') {\n        if (Object.getPrototypeOf(x) === Object.prototype) {\n            // `x` is a JavaScript object and its prototype is Object.\n            const keys = Object.keys(x);\n            for (const key of keys) {\n                if (typeof key !== 'string') {\n                    // JSON keys must be strings.\n                    return false;\n                }\n                if (!plainObjectCheck(x[key])) { // Recursive call.\n                    return false;\n                }\n            }\n            return true;\n        }\n        else {\n            // `x` is a JavaScript object but its prototype is not Object.\n            if (Array.isArray(x)) {\n                // `x` is a JavaScript array.\n                for (const item of x) {\n                    if (!plainObjectCheck(item)) { // Recursive call.\n                        return false;\n                    }\n                }\n                return true;\n            }\n            else {\n                // `x` is a JavaScript object and its prototype is not Object,\n                // and it's not an Array. I.e., it's a complex object such as\n                // `Error` and `Date`.\n                return false;\n            }\n        }\n    }\n    else {\n        // `x` is not a JavaScript object or `null`.\n        const xType = typeof x;\n        return xType === 'string' || xType === 'number' || xType === 'boolean';\n    }\n}\n//# sourceMappingURL=user_defined_metadata.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { countParamsInWeights } from './variable_utils';\n/**\n * Print the summary of a LayersModel object.\n *\n * @param model tf.LayersModel instance.\n * @param lineLength Total length of printed lines. Set this to adapt to the\n *   display to different terminal or console sizes.\n * @param positions Relative or absolute positions of log elements in each\n *   line. Each number corresponds to right-most (i.e., ending) position of a\n *   column.\n *   If not provided, defaults to `[0.45, 0.85, 1]` for sequential-like\n *   models and `[0.33, 0.55, 0.67, 1]` for non-sequential like models.\n * @param printFn Print function to use.\n *   It will be called on each line of the summary. You can provide a custom\n *   function in order to capture the string summary. Defaults to `console.log`.\n */\nexport function printSummary(model, lineLength, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    const sequentialLike = isModelSequentialLike(model);\n    // Header names for different log elements.\n    const toDisplay = ['Layer (type)', 'Output shape', 'Param #'];\n    if (sequentialLike) {\n        lineLength = lineLength || 65;\n        positions = positions || [0.45, 0.85, 1];\n    }\n    else {\n        lineLength = lineLength || 98;\n        positions = positions || [0.33, 0.55, 0.67, 1];\n        // Header names for different log elements.\n    }\n    if (positions[positions.length - 1] <= 1) {\n        // `positions` is relative. Convert it to absolute positioning.\n        positions = positions.map(p => Math.floor(lineLength * p));\n    }\n    let relevantNodes;\n    if (!sequentialLike) {\n        toDisplay.push('Receives inputs');\n        relevantNodes = [];\n        for (const depth in model.nodesByDepth) {\n            relevantNodes.push(...model.nodesByDepth[depth]);\n        }\n    }\n    printFn('_'.repeat(lineLength));\n    printRow(toDisplay, positions, printFn);\n    printFn('='.repeat(lineLength));\n    const layers = model.layers;\n    for (let i = 0; i < layers.length; ++i) {\n        if (sequentialLike) {\n            printLayerSummary(layers[i], positions, printFn);\n        }\n        else {\n            printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n        }\n        printFn((i === layers.length - 1 ? '=' : '_').repeat(lineLength));\n    }\n    // tslint:disable-next-line:no-any\n    model.checkTrainableWeightsConsistency();\n    const trainableCount = countTrainableParams(model);\n    const nonTrainableCount = countParamsInWeights(model.nonTrainableWeights);\n    printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n    printFn(`Trainable params: ${trainableCount}`);\n    printFn(`Non-trainable params: ${nonTrainableCount}`);\n    printFn('_'.repeat(lineLength));\n}\nfunction countTrainableParams(model) {\n    let trainableCount;\n    // tslint:disable:no-any\n    if (model.collectedTrainableWeights != null) {\n        trainableCount =\n            countParamsInWeights(model.collectedTrainableWeights);\n    }\n    else {\n        trainableCount = countParamsInWeights(model.trainableWeights);\n    }\n    // tslint:enable:no-any\n    return trainableCount;\n}\nfunction isModelSequentialLike(model) {\n    let sequentialLike = true;\n    const nodesByDepth = [];\n    const nodes = [];\n    for (const depth in model.nodesByDepth) {\n        nodesByDepth.push(model.nodesByDepth[depth]);\n    }\n    for (const depthNodes of nodesByDepth) {\n        if (depthNodes.length > 1 ||\n            depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n            sequentialLike = false;\n            break;\n        }\n        nodes.push(...depthNodes);\n    }\n    if (sequentialLike) {\n        // Search for shared layers.\n        for (const layer of model.layers) {\n            let flag = false;\n            for (const node of layer.inboundNodes) {\n                if (nodes.indexOf(node) !== -1) {\n                    if (flag) {\n                        sequentialLike = false;\n                        break;\n                    }\n                    else {\n                        flag = true;\n                    }\n                }\n            }\n            if (!sequentialLike) {\n                break;\n            }\n        }\n    }\n    return sequentialLike;\n}\nfunction printRow(fields, positions, \n// tslint:disable-next-line:no-any\nprintFn = console.log) {\n    let line = '';\n    for (let i = 0; i < fields.length; ++i) {\n        if (i > 0) {\n            line = line.slice(0, line.length - 1) + ' ';\n        }\n        line += fields[i];\n        line = line.slice(0, positions[i]);\n        line += ' '.repeat(positions[i] - line.length);\n    }\n    printFn(line);\n}\n/**\n * Prints a summary for a single Layer, without connectivity information.\n *\n * @param layer: Layer instance to print.\n */\nfunction printLayerSummary(layer, positions, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const fields = [`${name} (${className})`, outputShape, layer.countParams().toString()];\n    printRow(fields, positions, printFn);\n}\n/**\n * Prints a summary for a single Layer, with connectivity information.\n */\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, \n// tslint:disable-next-line:no-any\nprintFn) {\n    let outputShape;\n    try {\n        outputShape = JSON.stringify(layer.outputShape);\n    }\n    catch (err) {\n        outputShape = 'multiple';\n    }\n    const connections = [];\n    for (const node of layer.inboundNodes) {\n        if (relevantNodes != null && relevantNodes.length > 0 &&\n            relevantNodes.indexOf(node) === -1) {\n            continue;\n        }\n        for (let i = 0; i < node.inboundLayers.length; ++i) {\n            const inboundLayer = node.inboundLayers[i].name;\n            const inboundLayerIndex = node.nodeIndices[i];\n            const inboundTensorIndex = node.tensorIndices[i];\n            connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n        }\n    }\n    const name = layer.name;\n    const className = layer.getClassName();\n    const firstConnection = connections.length === 0 ? '' : connections[0];\n    const fields = [\n        `${name} (${className})`, outputShape, layer.countParams().toString(),\n        firstConnection\n    ];\n    printRow(fields, positions, printFn);\n    for (let i = 1; i < connections.length; ++i) {\n        printRow(['', '', '', connections[i]], positions, printFn);\n    }\n}\n//# sourceMappingURL=layer_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as generic_utils from '../utils/generic_utils';\n// tslint:enable\n/**\n * Test whether a value in an array is the name of a LayersModel or Layer.\n * @param key The key name that the value is found under. Note that the key\n *   may not be at the level immediately above the value, if the value is in a\n *   nested array.\n * @param index Index of the value in the Array that it is found in.\n * @param value The value object.\n * @returns A boolean indicating whether value is a name.\n */\nfunction isArrayItemInputOrOutputName(key, index, value) {\n    return (key === 'inboundNodes' || key === 'outputLayers' ||\n        key === 'inputLayers') &&\n        index === 0 && typeof value === 'string';\n}\n/**\n * Convert a Pythonic config object to TypeScript config object.\n * @param pythonicConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertPythonicToTs(pythonicConfig, key) {\n    if (pythonicConfig === null) {\n        return null;\n    }\n    else if (typeof pythonicConfig === 'string') {\n        return generic_utils.toCamelCase(pythonicConfig);\n    }\n    else if ((typeof pythonicConfig === 'number') ||\n        (typeof pythonicConfig === 'boolean')) {\n        return pythonicConfig;\n    }\n    else if (pythonicConfig instanceof Array) {\n        const tsArray = [];\n        const arrayLength = pythonicConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = pythonicConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                tsArray.push(item);\n            }\n            else {\n                tsArray.push(convertPythonicToTs(item, key));\n            }\n        }\n        return tsArray;\n    }\n    else {\n        const tsDict = {};\n        for (const pythonicKey of Object.keys(pythonicConfig)) {\n            const pythonicValue = pythonicConfig[pythonicKey];\n            if (pythonicKey === 'name' && typeof pythonicValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // camel-case conversion.\n                tsDict[pythonicKey] = pythonicValue;\n            }\n            else {\n                const tsKey = generic_utils.toCamelCase(pythonicKey);\n                tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n            }\n        }\n        return tsDict;\n    }\n}\n/**\n * Convert a TypeScript config object to Python config object.\n * @param tsConfig The config object to convert.\n * @param key Optional key name of the object being converted.\n * @returns Result of the conversion.\n */\nexport function convertTsToPythonic(tsConfig, key) {\n    if (tsConfig === null || tsConfig === undefined) {\n        return null;\n    }\n    else if (typeof tsConfig === 'string') {\n        return generic_utils.toSnakeCase(tsConfig);\n    }\n    else if ((typeof tsConfig === 'number') || (typeof tsConfig === 'boolean')) {\n        return tsConfig;\n    }\n    else if (tsConfig instanceof Array) {\n        const pyArray = [];\n        const arrayLength = tsConfig.length;\n        for (let i = 0; i < arrayLength; ++i) {\n            const item = tsConfig[i];\n            if (isArrayItemInputOrOutputName(key, i, item)) {\n                pyArray.push(item);\n            }\n            else {\n                pyArray.push(convertTsToPythonic(item, key));\n            }\n        }\n        return pyArray;\n    }\n    else {\n        const pyDict = {};\n        for (const tsKey of Object.keys(tsConfig)) {\n            const tsValue = tsConfig[tsKey];\n            const pyKey = generic_utils.toSnakeCase(tsKey);\n            if ((tsKey === 'name' || tsKey === 'className') &&\n                typeof tsValue === 'string') {\n                // Special case the 'name' key with a string value. Name values, such as\n                // the names of LayersModel and Layer instances, should not undergo the\n                // snake-case conversion.\n                pyDict[pyKey] = tsValue;\n            }\n            else {\n                pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n            }\n        }\n        return pyDict;\n    }\n}\n//# sourceMappingURL=serialization_utils.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Executor: Evaluates SymbolicTensor based on feeds.\n */\nimport { cast, dispose, memory, util } from '@tensorflow/tfjs-core';\nimport { ValueError } from '../errors';\nimport { toList } from '../utils/generic_utils';\nimport { InputLayer } from './input_layer';\nimport { SymbolicTensor } from './topology';\n/**\n * Helper function to check the dtype and shape compatibility of a feed value.\n */\nfunction assertFeedCompatibility(key, val) {\n    // Check dtype compatibility.\n    if (key.dtype == null || key.dtype === val.dtype) {\n        //  a.  If types match, return val tensor as is.\n        return val;\n    }\n    try {\n        //  b. Attempt to convert to expected type.\n        return cast(val, key.dtype);\n    }\n    catch (err) {\n        //  c. If conversion fails, return helpful error.\n        throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype ` +\n            `of the key '${key.name}' (${key.dtype}).`);\n    }\n}\n/**\n * FeedDict: A mapping from unique SymbolicTensors to feed values for them.\n * A feed value is a concrete value represented as an `Tensor`.\n */\nexport class FeedDict {\n    /**\n     * Constructor, optionally does copy-construction.\n     * @param feeds An Array of `Feed`s, or another `FeedDict`, in which case\n     *   copy-construction will be performed.\n     */\n    constructor(feeds) {\n        this.id2Value = {};\n        this.id2Mask = {};\n        this.name2Id = {};\n        if (feeds instanceof FeedDict) {\n            for (const id in feeds.id2Value) {\n                this.id2Value[id] = feeds.id2Value[id];\n                if (id in feeds.id2Mask) {\n                    this.id2Mask[id] = feeds.id2Mask[id];\n                }\n            }\n        }\n        else {\n            if (feeds == null) {\n                return;\n            }\n            for (const feed of feeds) {\n                this.add(feed.key, feed.value);\n            }\n        }\n    }\n    /**\n     * Add a key-value pair to the FeedDict.\n     *\n     * @param key The key of the feed.\n     * @param value The value of the tensor feed.\n     * @param mask The value of the mask feed (optional).\n     * @returns This `FeedDict`.\n     * @throws ValueError: If the key `SymbolicTensor` already exists in the\n     *   `FeedDict`.\n     */\n    add(key, value, mask) {\n        if (this.id2Value[key.id] == null) {\n            this.id2Value[key.id] = assertFeedCompatibility(key, value);\n            this.name2Id[key.name] = key.id;\n            if (mask != null) {\n                this.id2Mask[key.id] = mask;\n            }\n        }\n        else {\n            throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n        }\n        return this;\n    }\n    /**\n     * Add a Feed to the FeedDict.\n     * @param feed The new `Feed` to add.\n     * @returns This `FeedDict`.\n     */\n    addFeed(feed) {\n        this.add(feed.key, feed.value);\n    }\n    /**\n     * Probe whether a key already exists in the FeedDict.\n     * @param key\n     */\n    hasKey(key) {\n        return this.id2Value[key.id] != null;\n    }\n    /**\n     * Get all the SymbolicTensor available in this FeedDict.\n     */\n    names() {\n        return Object.keys(this.name2Id);\n    }\n    /**\n     * Get the feed value for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed value.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getValue(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Value[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Value[id];\n        }\n    }\n    /**\n     * Get the feed mask for given key.\n     * @param key The SymbolicTensor, or its name (as a string), of which the\n     *     value is sought.\n     * @returns If `key` exists, the corresponding feed mask.\n     * @throws ValueError: If `key` does not exist in this `FeedDict`.\n     */\n    getMask(key) {\n        if (key instanceof SymbolicTensor) {\n            if (this.id2Value[key.id] == null) {\n                throw new ValueError(`Nonexistent key: ${key.name}`);\n            }\n            else {\n                return this.id2Mask[key.id];\n            }\n        }\n        else {\n            const id = this.name2Id[key];\n            if (id == null) {\n                throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n            }\n            return this.id2Mask[id];\n        }\n    }\n    /** Dispose all mask Tensors held by this object. */\n    disposeMasks() {\n        if (this.id2Mask != null) {\n            dispose(this.id2Mask);\n        }\n    }\n}\n// Cache for topologically sorted SymbolicTensors for given execution\n// targets (i.e., fetches).\nconst cachedSorted = {};\n// Cache for recipient count maps for given execution targets (i.e., fetches).\nconst cachedRecipientCounts = {};\n/**\n * Execute a SymbolicTensor by using concrete feed values.\n *\n * A `SymbolicTensor` object is a node in a computation graph of TF.js\n * Layers. The object is backed by a source layer and input\n * `SymbolicTensor`s to the source layer. This method evaluates\n * the `call()` method of the source layer, using concrete values of the\n * inputs obtained from either\n * * `feedDict`, if the input key exists in `feedDict`, or else,\n * * a recursive call to `execute()` itself.\n *\n * @param x: The `SymbolicTensor` to execute.\n * @param feedDict: The feed values, as base condition of the recursion.\n *   execution.\n * @param kwargs: Optional keyword arguments.\n * @param probe: A probe object (of interface `ExecutionProbe`) used for\n *   testing memory footprint of `execute` calls.\n * @returns Result of the execution.\n * @throws ValueError: If any `SymbolicTensor`s from `InputLayer`s\n *   encountered during the execution lacks a feed value in `feedDict`.\n */\nexport function execute(fetches, feedDict, kwargs, probe) {\n    const training = kwargs == null ? false : kwargs['training'];\n    const arrayFetches = Array.isArray(fetches);\n    const fetchArray = arrayFetches ? fetches : [fetches];\n    const outputNames = fetchArray.map(t => t.name);\n    const finalOutputs = [];\n    const feedNames = feedDict.names();\n    for (const outputName of outputNames) {\n        if (feedNames.indexOf(outputName) !== -1) {\n            finalOutputs.push(feedDict.getValue(outputName));\n        }\n        else {\n            finalOutputs.push(null);\n        }\n    }\n    if (probe != null) {\n        // For optional probing of memory footprint during execution.\n        probe.maxNumTensors = -Infinity;\n        probe.minNumTensors = Infinity;\n    }\n    // Check cache.\n    const fetchAndFeedKey = outputNames.join(',') + '|' + feedDict.names().join(',');\n    let sorted;\n    let recipientCounts;\n    if (cachedSorted[fetchAndFeedKey] == null) {\n        // Cache doesn't contain the desired combination of fetches. Compute\n        // topological sort for the combination for the first time.\n        const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n        sorted = out.sorted;\n        recipientCounts = out.recipientCounts;\n        // Store results in cache for future use.\n        cachedSorted[fetchAndFeedKey] = sorted;\n        cachedRecipientCounts[fetchAndFeedKey] = recipientCounts;\n    }\n    sorted = cachedSorted[fetchAndFeedKey];\n    recipientCounts = {};\n    if (!training) {\n        Object.assign(recipientCounts, cachedRecipientCounts[fetchAndFeedKey]);\n    }\n    const internalFeedDict = new FeedDict(feedDict);\n    // Start iterative execution on the topologically-sorted SymbolicTensors.\n    for (let i = 0; i < sorted.length; ++i) {\n        if (probe != null) {\n            // For optional probing of memory usage during execution.\n            const numTensors = memory().numTensors;\n            if (numTensors > probe.maxNumTensors) {\n                probe.maxNumTensors = numTensors;\n            }\n            if (numTensors < probe.minNumTensors) {\n                probe.minNumTensors = numTensors;\n            }\n        }\n        const symbolic = sorted[i];\n        const srcLayer = symbolic.sourceLayer;\n        if (srcLayer instanceof InputLayer) {\n            continue;\n        }\n        const inputValues = [];\n        const inputMasks = [];\n        const tensorsToDispose = [];\n        let maskExists = false;\n        for (const input of symbolic.inputs) {\n            const value = internalFeedDict.getValue(input);\n            const mask = internalFeedDict.getMask(input);\n            inputValues.push(value);\n            inputMasks.push(mask);\n            if (mask != null) {\n                maskExists = true;\n            }\n            if (!training) {\n                recipientCounts[input.name]--;\n                if (recipientCounts[input.name] === 0 && !feedDict.hasKey(input) &&\n                    outputNames.indexOf(input.name) === -1 && !value.isDisposed &&\n                    input.sourceLayer.stateful !== true) {\n                    tensorsToDispose.push(value);\n                }\n            }\n        }\n        if (maskExists) {\n            kwargs = kwargs || {};\n            kwargs['mask'] = inputMasks[0];\n        }\n        const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n        let outputMask = null;\n        if (srcLayer.supportsMasking) {\n            outputMask = srcLayer.computeMask(inputValues, inputMasks);\n        }\n        const layerOutputs = getNodeOutputs(symbolic);\n        const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n        for (let i = 0; i < outputSymbolicTensors.length; ++i) {\n            if (!internalFeedDict.hasKey(outputSymbolicTensors[i])) {\n                internalFeedDict.add(outputSymbolicTensors[i], outputTensors[i], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n            }\n            const index = outputNames.indexOf(outputSymbolicTensors[i].name);\n            if (index !== -1) {\n                finalOutputs[index] = outputTensors[i];\n            }\n        }\n        if (!training) {\n            // Clean up Tensors that are no longer needed.\n            dispose(tensorsToDispose);\n        }\n    }\n    // NOTE(cais): Unlike intermediate tensors, we don't discard mask\n    // tensors as we go, because these tensors are sometimes passed over a\n    // series of mutliple layers, i.e., not obeying the immediate input\n    // relations in the graph. If this becomes a memory-usage concern,\n    // we can improve this in the future.\n    internalFeedDict.disposeMasks();\n    return arrayFetches ? finalOutputs : finalOutputs[0];\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for an array of fetches.\n *\n * This function calls getTopologicalSortAndRecipientCountsForOneFetch and\n * merges their results.\n *\n * @param fetch The array of fetches requested. Must be a non-empty array.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientCounts: Recipient counts for all SymbolicTensors in `sorted`.\n */\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n    util.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n    let finalSorted = [];\n    let finalRecipientMap = {};\n    if (fetches.length === 1) {\n        // Special-casing 1 fetch for efficiency.\n        const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n        finalSorted = out.sorted;\n        finalRecipientMap = out.recipientMap;\n    }\n    else {\n        const visited = new Set();\n        for (const fetch of fetches) {\n            const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict);\n            // Merge sorted SymbolicTensor Arrays.\n            for (const symbolicTensor of sorted) {\n                if (!visited.has(symbolicTensor.name)) {\n                    finalSorted.push(symbolicTensor);\n                    visited.add(symbolicTensor.name);\n                }\n            }\n            // Merge recipient maps.\n            for (const name in recipientMap) {\n                if (finalRecipientMap[name] == null) {\n                    finalRecipientMap[name] = new Set();\n                }\n                recipientMap[name].forEach(recipient => finalRecipientMap[name].add(recipient));\n            }\n        }\n    }\n    return {\n        sorted: finalSorted,\n        recipientCounts: recipientMap2Counts(finalRecipientMap)\n    };\n}\nfunction recipientMap2Counts(recipientMap) {\n    const recipientCounts = {};\n    for (const name in recipientMap) {\n        recipientCounts[name] = recipientMap[name].size;\n    }\n    return recipientCounts;\n}\n/**\n * Sort the `SymbolicTensor`s topologically, for a single fetch.\n *\n * This helper function processes the upstream SymbolicTensors of a single\n * fetch.\n *\n * @param fetch The single fetch requested.\n * @param feedDict The dictionary of fed values.\n * @returns sorted: Topologically-sorted array of SymbolicTensors.\n *   recipientMap: Recipient names for all SymbolicTensors in `sorted`.\n */\nexport function getTopologicalSortAndRecipientCountsForOneFetch(fetch, feedDict) {\n    const visited = new Set();\n    const sorted = [];\n    const recipientMap = {};\n    // Put keys of the feedDict into visited first, so they don't have to be\n    // walked. This is needed in case where there are feeds for intermediate\n    // SymbolicTensors of the graph.\n    for (const key of feedDict.names()) {\n        visited.add(key);\n    }\n    const stack = [];\n    const marks = [];\n    // Initial population of stack and marks.\n    stack.push(fetch);\n    while (stack.length > 0) {\n        const top = stack[stack.length - 1];\n        if (visited.has(top.name)) {\n            stack.pop();\n            continue;\n        }\n        const topIsMarked = marks[marks.length - 1] === stack.length - 1;\n        if (top.inputs.length === 0 || topIsMarked) {\n            // Input SymbolicTensor or all children have been visited.\n            stack.pop();\n            sorted.push(top);\n            visited.add(top.name);\n            if (topIsMarked) {\n                marks.pop();\n            }\n        }\n        else {\n            // A non-input SymbolicTensor whose upstream SymbolicTensors haven't\n            // been visited yet. Push them onto the stack.\n            marks.push(stack.length - 1);\n            for (const input of top.inputs) {\n                // Increment the recipient count. Note that this needs to happen\n                // regardless of whether the SymbolicTensor has been visited before.\n                if (recipientMap[input.name] == null) {\n                    recipientMap[input.name] = new Set();\n                }\n                recipientMap[input.name].add(top.name);\n                if (visited.has(input.name)) {\n                    continue; // Avoid repeated visits to the same SymbolicTensor.\n                }\n                stack.push(input);\n            }\n        }\n    }\n    return { sorted, recipientMap };\n}\n/**\n * Get the symbolic output tensors of the node to which a given fetch belongs.\n * @param fetch The fetched symbolic tensor.\n * @returns The Array of symbolic tensors output by the node to which `fetch`\n *   belongs.\n */\nfunction getNodeOutputs(fetch) {\n    let layerOutputs;\n    if (fetch.sourceLayer.inboundNodes.length === 1) {\n        layerOutputs = fetch.sourceLayer.output;\n    }\n    else {\n        let nodeIndex = null;\n        for (let i = 0; i < fetch.sourceLayer.inboundNodes.length; ++i) {\n            for (const outputTensor of fetch.sourceLayer.inboundNodes[i]\n                .outputTensors) {\n                if (outputTensor.id === fetch.id) {\n                    nodeIndex = i;\n                    break;\n                }\n            }\n        }\n        layerOutputs = fetch.sourceLayer.getOutputAt(nodeIndex);\n    }\n    return layerOutputs;\n}\n//# sourceMappingURL=executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/engine/topology.py */\nimport { tidy } from '@tensorflow/tfjs-core';\nimport { getUid } from '../backend/state';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize as deserializeLayer } from '../layers/serialization';\nimport * as generic_utils from '../utils/generic_utils';\nimport { convertTsToPythonic } from '../utils/serialization_utils';\nimport * as types_utils from '../utils/types_utils';\nimport { batchSetValue } from '../variables';\nimport { version as layersVersion } from '../version';\nimport { execute, FeedDict } from './executor';\nimport { InputLayer } from './input_layer';\nimport { Layer, Node } from './topology';\n/**\n * A Container is a directed acyclic graph of layers.\n *\n * It is the topological form of a \"model\". A LayersModel\n * is simply a Container with added training routines.\n *\n */\nexport class Container extends Layer {\n    constructor(args) {\n        // No args passed to super's constructor.\n        super({});\n        this.containerNodes = new Set();\n        this.name = args.name;\n        if (this.name == null) {\n            const prefix = this.getClassName().toLowerCase();\n            this.name = getUid(prefix);\n        }\n        this.supportsMasking = false;\n        this.trainable_ = true;\n        // TODO(michaelterry): Initialize perInputLosses/Updates here.\n        // Container-specific properties.\n        if (Array.isArray(args.inputs)) {\n            this.inputs = args.inputs.slice();\n        }\n        else {\n            this.inputs = [args.inputs];\n        }\n        if (Array.isArray(args.outputs)) {\n            this.outputs = args.outputs.slice();\n        }\n        else {\n            this.outputs = [args.outputs];\n        }\n        // Check for redundancy in inputs.\n        if (generic_utils.unique(this.inputs).length !== this.inputs.length) {\n            throw new ValueError('The list of inputs passed to the model is ' +\n                'redundant. All inputs should only appear once. Found: ' +\n                `${this.inputs.map(x => x.name)}`);\n        }\n        // Check for redundancy in outputs.\n        if (generic_utils.unique(this.outputs).length !== this.outputs.length) {\n            console.warn('The list of outputs passed to the model is redundant. ' +\n                'All outputs should only appear once. Found: ' +\n                `${this.outputs.map(x => x.name)}`);\n        }\n        /*\n          List of initial layers (1 to 1 mapping with this.inputs, hence the same\n          layer might appear twice)\n        */\n        this.inputLayers = [];\n        this.inputLayersNodeIndices = [];\n        this.inputLayersTensorIndices = [];\n        /*\n          List of layers (1 to 1 mapping with this.outputs, hence the same layer\n          might appear twice)\n        */\n        this.outputLayers = [];\n        this.outputLayersNodeIndices = [];\n        this.outputLayersTensorIndices = [];\n        /*\n          All layers in order of horizontal graph traversal. Entries are unique.\n          Includes input and output layers.\n        */\n        this.layers = [];\n        /*\n          References to container layers that were constructed internally. We need\n          these to properly dispose of tensors from nested containers.\n        */\n        this.internalContainerRefs = [];\n        // TODO(michaelterry): Determine if caching still needed with eager\n        // backend.\n        /*\n          This is for performance optimization when calling the Container on new\n          inputs. Every time the Container is called on a set on input tensors,\n          we compute the output tensors, output masks and output shapes in one pass,\n          then cache them here. When one of these outputs is queried later,\n          we retrieve it from there instead of recomputing it.\n        */\n        // this.outputTensorCache = {};\n        // this.outputShapeCache = {};\n        // Build this.outputLayers:\n        for (const x of this.outputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            this.outputLayers.push(layer);\n            this.outputLayersNodeIndices.push(nodeIndex);\n            this.outputLayersTensorIndices.push(tensorIndex);\n        }\n        // TODO(michaelterry): Add output mask cache code.\n        // Build this.inputLayers:\n        for (const x of this.inputs) {\n            const layer = x.sourceLayer;\n            const nodeIndex = x.nodeIndex;\n            const tensorIndex = x.tensorIndex;\n            /*\n              It's supposed to be an input layer, so only one node\n              and one tensor output.\n            */\n            generic_utils.assert(nodeIndex === 0, 'input layer has >1 nodes');\n            generic_utils.assert(tensorIndex === 0, 'input layer has >1 tensors');\n            this.inputLayers.push(layer);\n            this.inputLayersNodeIndices.push(nodeIndex);\n            this.inputLayersTensorIndices.push(tensorIndex);\n        }\n        // Build this.inputNames and this.outputNames.\n        this.inputNames = [];\n        this.outputNames = [];\n        this.feedInputShapes = [];\n        this.feedInputNames = [];\n        this.feedOutputNames = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            // Check that layer is an InputLayer.\n            if (!(layer instanceof InputLayer)) {\n                throw new TypeError('Input layers to a LayersModel must be InputLayer objects. ' +\n                    `Received inputs: ${args.inputs}. ` +\n                    `Input ${i} (0-based) originates ` +\n                    `from layer type ${layer.getClassName()}.`);\n            }\n            this.inputNames.push(layer.name);\n            this.feedInputShapes.push(layer.batchInputShape);\n            this.feedInputNames.push(layer.name);\n        }\n        for (const layer of this.outputLayers) {\n            this.outputNames.push(layer.name);\n        }\n        this.internalInputShapes = this.inputs.map(x => x.shape);\n        this.internalOutputShapes = this.outputs.map(x => x.shape);\n        /*\n          Container_nodes: set of nodes included in the graph (not all nodes\n          included in the layers are relevant to the current graph).\n        */\n        // ids of all nodes relevant to the Container:\n        const nodesDepths = {};\n        // To recover nodes from their ID.\n        const nodeIDToNode = {};\n        const layersDepths = {};\n        // To layers from their ID.\n        const layerIDToLayer = {};\n        const layerIndices = {};\n        const nodesInDecreasingDepth = [];\n        /**\n         * Builds a map of the graph of layers.\n         *\n         * This recursively updates the map `layerIndices`,\n         * the list `nodesInDecreasingDepth` and the set `containerNodes`.\n         *\n         * @param tensor Some tensor in a graph.\n         * @param finishedNodes Set of nodes whose subgraphs have been traversed\n         *         completely. Useful to prevent duplicated work.\n         * @param nodesInProgress Set of nodes that are currently active on the\n         *         recursion stack. Useful to detect cycles.\n         * @param layer Layer from which `tensor` comes from. If not provided,\n         *   will be obtained from tensor.sourceLayer.\n         * @param nodeIndex Node index from which `tensor` comes from.\n         * @param tensorIndex TensorIndex from which `tensor` comes from.\n         *\n         * @exception RuntimeError if a cycle is detected.\n         */\n        const buildMapOfGraph = (tensor, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex) => {\n            if (layer == null || nodeIndex == null || tensorIndex == null) {\n                layer = tensor.sourceLayer;\n                nodeIndex = tensor.nodeIndex;\n                tensorIndex = tensor.tensorIndex;\n            }\n            const node = layer.inboundNodes[nodeIndex];\n            // Prevent cycles.\n            if (nodesInProgress.indexOf(node) !== -1) {\n                throw new RuntimeError(`The tensor ${tensor.name} at layer \"${layer.name}\" ` +\n                    'is part of a cycle.');\n            }\n            // Don't repeat work for shared subgraphs\n            if (finishedNodes.indexOf(node) !== -1) {\n                return;\n            }\n            // Update containerNodes.\n            this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n            // Store the traversal order for layer sorting.\n            if (!(layer.id in layerIndices)) {\n                layerIndices[layer.id] = Object.keys(layerIndices).length;\n            }\n            if (nodesInProgress.indexOf(node) === -1) {\n                nodesInProgress.push(node);\n            }\n            // Propagate to all previous tensors connected to this node.\n            const numInboundLayers = node.inboundLayers.length;\n            for (let i = 0; i < numInboundLayers; i++) {\n                const x = node.inputTensors[i];\n                const layer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const tensorIndex = node.tensorIndices[i];\n                buildMapOfGraph(x, finishedNodes, nodesInProgress, layer, nodeIndex, tensorIndex);\n            }\n            finishedNodes.push(node);\n            while (nodesInProgress.indexOf(node) >= 0) {\n                nodesInProgress.splice(nodesInProgress.indexOf(node), 1);\n            }\n            nodesInDecreasingDepth.push(node);\n        };\n        const finishedNodes = [];\n        const nodesInProgress = [];\n        for (const x of this.outputs) {\n            buildMapOfGraph(x, finishedNodes, nodesInProgress);\n        }\n        const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n        for (const node of reversedNodesInDecreasingDepth) {\n            nodeIDToNode[node.id] = node;\n            // If the depth is not set, the node has no outbound nodes (depth 0).\n            if (!(node.id in nodesDepths)) {\n                nodesDepths[node.id] = 0;\n            }\n            let depth = nodesDepths[node.id];\n            // Update the depth of the corresponding layer\n            const previousDepth = (layersDepths[node.outboundLayer.id] == null ?\n                0 :\n                layersDepths[node.outboundLayer.id]);\n            /*\n              If we've seen this layer before at a higher depth, we should use that\n              depth instead of the node depth.  This is necessary for shared layers\n              that have inputs at different depth levels in the graph.\n            */\n            depth = Math.max(depth, previousDepth);\n            layersDepths[node.outboundLayer.id] = depth;\n            layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n            nodesDepths[node.id] = depth;\n            // Update the depth of inbound nodes.\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n                const inboundLayer = node.inboundLayers[i];\n                const nodeIndex = node.nodeIndices[i];\n                const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n                const previousDepth = (nodesDepths[inboundNode.id] == null ? 0 :\n                    nodesDepths[inboundNode.id]);\n                nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth);\n                nodeIDToNode[inboundNode.id] = inboundNode;\n            }\n        }\n        // Build a dict {depth: list of nodes with this depth}\n        const nodesByDepth = {};\n        for (const nodeID in nodesDepths) {\n            const depth = nodesDepths[nodeID];\n            if (!(depth in nodesByDepth)) {\n                nodesByDepth[depth] = [];\n            }\n            nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n        }\n        // Build a dict {depth: list of layers with this depth}\n        const layersByDepth = {};\n        for (const layerID in layersDepths) {\n            const depth = layersDepths[layerID];\n            if (!(depth in layersByDepth)) {\n                layersByDepth[depth] = [];\n            }\n            layersByDepth[depth].push(layerIDToLayer[layerID]);\n        }\n        // Get sorted list of layer depths.\n        let depthKeys = Object.keys(layersByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Set this.layers and this.layersByDepth.\n        this.layers = [];\n        for (const depth of depthKeys) {\n            const layersForDepth = layersByDepth[depth];\n            // Container.layers needs to have a deterministic order:\n            // here we order them by traversal order.\n            layersForDepth.sort((a, b) => {\n                const aIndex = layerIndices[a.id];\n                const bIndex = layerIndices[b.id];\n                if (aIndex < bIndex) {\n                    return -1;\n                }\n                if (aIndex > bIndex) {\n                    return 1;\n                }\n                return 0;\n            });\n            for (const layer of layersForDepth) {\n                if (layer instanceof Container) {\n                    this.internalContainerRefs.push(layer);\n                }\n                this.layers.push(layer);\n            }\n        }\n        this.layersByDepth = layersByDepth;\n        // Get sorted list of node depths;\n        depthKeys = Object.keys(nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Check that all tensors required are computable.\n        // computable_tensors: all tensors in the graph\n        // that can be computed from the inputs provided.\n        const computableTensors = this.inputs.slice();\n        // To provide a better error msg.\n        const layersWithCompleteInput = [];\n        for (const depth of depthKeys) {\n            for (const node of nodesByDepth[depth]) {\n                const layer = node.outboundLayer;\n                if (layer != null) {\n                    for (const x of node.inputTensors) {\n                        if (computableTensors.indexOf(x) === -1) {\n                            throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x}` +\n                                ` at layer \"${layer.name}\". ` +\n                                'The following previous layers were accessed without ' +\n                                `issue: ${layersWithCompleteInput}`);\n                        }\n                    }\n                    for (const x of node.outputTensors) {\n                        computableTensors.push(x);\n                    }\n                    layersWithCompleteInput.push(layer.name);\n                }\n            }\n        }\n        // Set this.containerNodes and this.nodesByDepth.\n        this.nodesByDepth = nodesByDepth;\n        // Ensure name unicity, which will be crucial for serialization\n        // (since serialized nodes refer to layers by their name).\n        const allNames = this.layers.map(x => x.name);\n        for (const name of allNames) {\n            const numOccurrences = allNames.filter(x => x === name).length;\n            if (numOccurrences !== 1) {\n                throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times ` +\n                    'in the model. All layer names should be unique. Layer names: ' +\n                    JSON.stringify(allNames));\n            }\n        }\n        // Layer parameters.\n        // The new container starts with a single inbound node\n        // for its inputs, and no outbound nodes.\n        // Will be appended to by future calls to apply().\n        this.outboundNodes = [];\n        // Will be appended to below, and by future calls to apply().\n        this.inboundNodes = [];\n        // Create the node linking internal inputs to internal outputs.\n        // (This call has side effects.)\n        // tslint:disable-next-line:no-unused-expression\n        new Node({\n            outboundLayer: this,\n            inboundLayers: [],\n            nodeIndices: [],\n            tensorIndices: [],\n            inputTensors: this.inputs,\n            outputTensors: this.outputs,\n            inputMasks: this.inputs.map(x => null),\n            outputMasks: this.outputs.map(x => null),\n            inputShapes: this.inputs.map(x => x.shape),\n            outputShapes: this.outputs.map(x => x.shape)\n        });\n        this.built = true;\n        this._refCount = 1; // The ref count of a container always start at 1.\n    }\n    assertNotDisposed() {\n        if (this._refCount === 0) {\n            throw new Error(`Container '${this.name}' is already disposed.`);\n        }\n    }\n    /**\n     * Attempt to dispose a LayersModel's weights.\n     *\n     * This method decrease the reference count of the LayersModel object by 1.\n     *\n     * A LayersModel is reference-counted. Its reference count is incremented by 1\n     * when it is first constructed and when it is used as a Layer of another\n     * LayersModel.\n     *\n     * If the reference count of a LayersModel becomes 0, the `dispose` method of\n     * all its constituent `Layer`s will be called.\n     *\n     * Note: If the reference count is greater than 0 after the decrement, the\n     * `dispose` method of its constituent `Layer`s will *not* be called.\n     *\n     * After a LayersModel is disposed, it cannot be used in calls such as\n     * 'predict`, `evaluate` or `fit` anymore.\n     *\n     * @returns A DisposeResult Object with the following fields:\n     *   - refCountAfterDispose: The reference count of the LayersModel after this\n     *     `dispose()` call.\n     *   - numDisposedVariables: Number of `tf.Variable`s (i.e., weights) disposed\n     *     during this `dispose()` call.\n     * @throws {Error} If the layer is not built yet, or if the LayersModel has\n     *   already been disposed.\n     */\n    dispose() {\n        this.assertNotDisposed();\n        const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n        if (--this._refCount === 0) {\n            for (const layer of this.layers) {\n                result.numDisposedVariables += layer.dispose().numDisposedVariables;\n            }\n            // Call dispose on each internally created container layer again to ensure\n            // their refCounts hit zero and their tensors are subsequently deleted.\n            for (const container of this.internalContainerRefs) {\n                result.numDisposedVariables += container.dispose().numDisposedVariables;\n            }\n        }\n        result.refCountAfterDispose = this._refCount;\n        return result;\n    }\n    get trainable() {\n        return this.trainable_;\n    }\n    set trainable(trainable) {\n        this.layers.forEach(layer => {\n            // tslint:disable-next-line:no-any\n            layer._trainableWeights\n                .forEach(w => w.trainable = trainable);\n        });\n        this.trainable_ = trainable;\n    }\n    get trainableWeights() {\n        // Porting Note: This check below is to prevent errors where the\n        //   _trainableWeights inherited from the parent class (Layer) gets\n        //   inadvertently used.\n        if (this._trainableWeights.length > 0) {\n            throw new ValueError('Container instance unexpectedly contains _trainableWeights.' +\n                'The trainable weights of a Container are a union of the ' +\n                'trainable weights of its consituent Layers. Its own ' +\n                '_trainableWeights must remain an empty Array.');\n        }\n        if (!this.trainable) {\n            return [];\n        }\n        let weights = [];\n        for (const layer of this.layers) {\n            weights = weights.concat(layer.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const layer of this.layers) {\n            weights.push(...layer.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const layer of this.layers) {\n                trainableWeights.push(...layer.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    get weights() {\n        return this.trainableWeights.concat(this.nonTrainableWeights);\n    }\n    /**\n     * Loads all layer weights from a JSON object.\n     *\n     * Porting Note: HDF5 weight files cannot be directly loaded in JavaScript /\n     *   TypeScript. The utility script at `scripts/pykeras.py` offers means\n     *   to convert them into JSON strings compatible with this method.\n     * Porting Note: TensorFlow.js Layers supports only loading by name currently.\n     *\n     * @param weights A JSON mapping weight names to weight values as nested\n     *   arrays of numbers, or a `NamedTensorMap`, i.e., a JSON mapping weight\n     *   names to `tf.Tensor` objects.\n     * @param strict Require that the provided weights exactly match those\n     *   required by the container.  Default: `true`.  Passing `false` means that\n     *   extra weights and missing weights will be silently ignored.\n     */\n    loadWeights(weights, strict = true) {\n        const nameToWeight = {};\n        let totalWeightsCount = 0;\n        for (const layer of this.layers) {\n            for (const weight of layer.weights) {\n                if (nameToWeight[weight.originalName] != null) {\n                    throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n                }\n                nameToWeight[weight.originalName] = weight;\n                totalWeightsCount++;\n            }\n        }\n        const weightValueTuples = [];\n        for (const name in weights) {\n            // TF 2.2.0 added cell name to the weight name in the format of\n            // layer_name/cell_name/weight_name, we need to remove\n            // the inner cell name.\n            let validatedName = name;\n            if (nameToWeight[name] == null) {\n                const tokens = name.split('/');\n                const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n                validatedName = shortenNameArray.join('/');\n            }\n            if (nameToWeight[validatedName] != null) {\n                weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n            }\n            else if (strict) {\n                throw new ValueError(`Provided weight data has no target variable: ${name}`);\n            }\n            delete nameToWeight[validatedName];\n        }\n        if (strict) {\n            // Check that all weights are set.\n            const unsetNames = [];\n            for (const name in nameToWeight) {\n                unsetNames.push(name);\n            }\n            if (unsetNames.length > 0) {\n                throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ` +\n                    `${unsetNames}`);\n            }\n        }\n        batchSetValue(weightValueTuples);\n    }\n    /**\n     * Util shared between different serialization methods.\n     * @returns LayersModel config with Keras version information added.\n     */\n    updatedConfig() {\n        const theConfig = this.getConfig();\n        const modelConfig = {};\n        modelConfig['className'] = this.getClassName();\n        modelConfig['config'] = theConfig;\n        modelConfig['kerasVersion'] = `tfjs-layers ${layersVersion}`;\n        // TODO(nielsene): Replace something like K.backend() once\n        // possible.\n        modelConfig['backend'] = 'TensorFlow.js';\n        return modelConfig;\n    }\n    /**\n     * Returns a JSON string containing the network configuration.\n     *\n     * To load a network from a JSON save file, use\n     * models.modelFromJSON(jsonString);\n     * @param extraJsonArgs Unused in tfjs-layers, maintained for PyKeras\n     * @param returnString Whether the return value should be stringified\n     *    (default: `true`).\n     * @returns a JSON string if `returnString` (default), or a JSON object if\n     *   `!returnString`.\n     */\n    // tslint:disable-next-line:no-any\n    toJSON(unused, returnString = true) {\n        const modelConfig = convertTsToPythonic(this.updatedConfig());\n        return returnString ? JSON.stringify(modelConfig) : modelConfig;\n    }\n    /**\n     * Call the model on new inputs.\n     *\n     * In this case `call` just reapplies all ops in the graph to the new inputs\n     * (e.g. build a new computational graph from the provided inputs).\n     *\n     * @param inputs A tensor or list of tensors.\n     * @param mask A mask or list of masks. A mask can be either a tensor or null\n     *   (no mask).\n     *\n     * @return A tensor if there is a single output, or a list of tensors if there\n     *   are more than one outputs.\n     */\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            const feedDict = new FeedDict();\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n            return execute(this.outputs, feedDict, kwargs);\n        });\n    }\n    /**\n     * Computes an output mask tensor.\n     *\n     * @param inputs Tensor or list of tensors.\n     * @param mask Tensor or list of tensors.\n     *\n     * @return null or a tensor (or list of tensors, one per output tensor of the\n     * layer).\n     */\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            inputs = generic_utils.toList(inputs);\n            let masks;\n            if (mask == null) {\n                masks = generic_utils.pyListRepeat(null, inputs.length);\n            }\n            else {\n                masks = generic_utils.toList(mask);\n            }\n            // TODO(michaelterry): Add support for mask caching.\n            return this.runInternalGraph(inputs, masks)[1];\n        });\n    }\n    /**\n     * Computes the output shape of the layer.\n     *\n     * Assumes that the layer will be built to match that input shape provided.\n     *\n     * @param inputShape A shape (tuple of integers) or a list of shape tuples\n     *   (one per output tensor of the layer). Shape tuples can include null for\n     *   free dimensions, instead of an integer.\n     */\n    computeOutputShape(inputShape) {\n        const inputShapes = types_utils.normalizeShapeList(inputShape);\n        if (inputShapes.length !== this.inputLayers.length) {\n            throw new ValueError(`Invalid inputShape argument ${inputShape}: ` +\n                `model has ${this.inputLayers.length} tensor inputs.`);\n        }\n        // TODO(michaelterry): Add caching\n        const layersToOutputShapes = {};\n        for (let i = 0; i < inputShapes.length; i++) {\n            const layer = this.inputLayers[i];\n            const inputShape = inputShapes[i];\n            // It's an input layer: computeOutputShape is identity,\n            // and there is only one node and one tensor output.\n            const shapeKey = layer.name + '_0_0';\n            layersToOutputShapes[shapeKey] = inputShape;\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        // Iterate over nodes, by depth level.\n        if (depthKeys.length > 1) {\n            for (const depth of depthKeys) {\n                const nodes = this.nodesByDepth[depth];\n                for (const node of nodes) {\n                    // This is always a single layer, never a list.\n                    const layer = node.outboundLayer;\n                    if (this.inputLayers.map(x => x.id).indexOf(layer.id) !== -1) {\n                        // We've already covered the input layers a few lines above.\n                        continue;\n                    }\n                    // Potentially redundant list, same size of node.inputTensors.\n                    const inputShapes = [];\n                    for (let j = 0; j < node.inboundLayers.length; j++) {\n                        const inboundLayer = node.inboundLayers[j];\n                        const nodeIndex = node.nodeIndices[j];\n                        const tensorIndex = node.tensorIndices[j];\n                        const shapeKey = `${inboundLayer.name}_${nodeIndex}_${tensorIndex}`;\n                        const inputShape = layersToOutputShapes[shapeKey];\n                        inputShapes.push(inputShape);\n                    }\n                    const outputShape = layer.computeOutputShape(generic_utils.singletonOrArray(inputShapes));\n                    const outputShapes = types_utils.normalizeShapeList(outputShape);\n                    const nodeIndex = layer.inboundNodes.indexOf(node);\n                    for (let j = 0; j < outputShapes.length; j++) {\n                        const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n                        layersToOutputShapes[shapeKey] = outputShapes[j];\n                    }\n                }\n            }\n        }\n        // Read final output shapes from layersToOutputShapes.\n        const outputShapes = [];\n        const outputShapeKeys = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n            outputShapeKeys.push(shapeKey);\n        }\n        for (let i = 0; i < outputShapeKeys.length; i++) {\n            const key = outputShapeKeys[i];\n            generic_utils.assert(key in layersToOutputShapes);\n            outputShapes.push(layersToOutputShapes[key]);\n        }\n        // TODO(michaelterry): Update cache\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    /**\n     * Computes output tensors for new inputs.\n     *\n     * Note:\n     *   - Expects `inputs` to be a list (potentially with 1 element).\n     *\n     * @param inputs List of tensors\n     * @param masks List of masks (tensors or null).\n     * @return Three lists: outputTensors, outputMasks, outputShapes\n     */\n    runInternalGraph(inputs, masks) {\n        if (masks == null) {\n            masks = generic_utils.pyListRepeat(null, inputs.length);\n        }\n        // Dictionary mapping reference tensors to tuples\n        // (computed tensor, compute mask)\n        // we assume a 1:1 mapping from tensor to mask\n        // TODO: raise exception when a `.computeMask()` call\n        // does not return a list the same size as `call`\n        const tensorMap = {};\n        for (let i = 0; i < this.inputs.length; ++i) {\n            const x = this.inputs[i];\n            const y = inputs[i];\n            const mask = masks[i];\n            tensorMap[x.id] = [y, mask];\n        }\n        const depthKeys = Object.keys(this.nodesByDepth)\n            .map(x => parseInt(x, 10))\n            .sort(generic_utils.reverseNumberCompare);\n        for (const depth of depthKeys) {\n            const nodes = this.nodesByDepth[depth];\n            for (const node of nodes) {\n                // This is always a single layer, never a list.\n                const layer = node.outboundLayer;\n                const referenceInputTensors = node.inputTensors;\n                const referenceOutputTensors = node.outputTensors;\n                // If all previous input tensors are available in tensorMap,\n                // then call node.inboundLayer on them.\n                // List of tuples [input, mask]:\n                const computedData = new Array();\n                for (const x of referenceInputTensors) {\n                    if (x.id in tensorMap) {\n                        computedData.push(tensorMap[x.id]);\n                    }\n                }\n                if (computedData.length === referenceInputTensors.length) {\n                    // TODO(michaelterry): Add K.name_scope here, if we need it.\n                    let kwargs = {};\n                    let computedTensors;\n                    let computedMasks;\n                    let outputTensors;\n                    let outputMasks;\n                    // call layer\n                    if (node.callArgs != null) {\n                        kwargs = node.callArgs;\n                    }\n                    if (computedData.length === 1) {\n                        const [computedTensor, computedMask] = computedData[0];\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMask;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensor, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensor, computedMask));\n                        computedTensors = [computedTensor];\n                        computedMasks = [computedMask];\n                    }\n                    else {\n                        computedTensors = computedData.map(x => x[0]);\n                        computedMasks = computedData.map(x => x[1]);\n                        if (kwargs['mask'] == null) {\n                            kwargs['mask'] = computedMasks;\n                        }\n                        outputTensors =\n                            generic_utils.toList(layer.call(computedTensors, kwargs));\n                        outputMasks = generic_utils.toList(layer.computeMask(computedTensors, computedMasks));\n                    }\n                    if (layer.activityRegularizer) {\n                        throw new NotImplementedError('LayersModel invocation with concrete Tensor value(s) in the ' +\n                            'presence of activity regularizer(s) is not supported yet.');\n                    }\n                    // TODO(michaelterry): Add model updates and losses\n                    // Update tensor map.\n                    for (let i = 0; i < referenceOutputTensors.length; ++i) {\n                        const x = referenceOutputTensors[i];\n                        const y = outputTensors[i];\n                        const mask = outputMasks[i];\n                        tensorMap[x.id] = [y, mask];\n                    }\n                }\n            }\n        }\n        const outputTensors = [];\n        const outputMasks = [];\n        const outputShapes = [];\n        for (const x of this.outputs) {\n            generic_utils.assert(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n            const [tensor, mask] = tensorMap[x.id];\n            outputShapes.push(tensor.shape);\n            outputTensors.push(tensor);\n            outputMasks.push(mask);\n        }\n        // TODO(michaelterry): Add support for caches.\n        return [outputTensors, outputMasks, outputShapes];\n    }\n    /**\n     * Builds a map of internal node keys to node ordering.\n     * Used in serializaion a node orderings may change as unused nodes are\n     * dropped. Porting Note:  This helper method was pulled out of getConfig to\n     * improve readability.\n     * @param layers An array of Layers in the model.\n     * @returns Map of Node Keys to index order within the layer.\n     */\n    buildNodeConversionMap(layers) {\n        const nodeConversionMap = {};\n        let keptNodes;\n        for (const layer of this.layers) {\n            keptNodes = layer instanceof Container ? 1 : 0;\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                if (this.containerNodes.has(nodeKey)) {\n                    // i.e. we mark it to be saved\n                    nodeConversionMap[nodeKey] = keptNodes;\n                    keptNodes += 1;\n                }\n            }\n        }\n        return nodeConversionMap;\n    }\n    /**\n     * Retrieves a layer based on either its name (unique) or index.\n     *\n     * Indices are based on order of horizontal graph traversal (bottom-up).\n     *\n     * If both `name` and `index` are specified, `index` takes precedence.\n     *\n     * @param name Name of layer.\n     * @param index Index of layer.\n     * @returns A Layer instance.\n     * @throws ValueError: In case of invalid layer name or index.\n     *\n     * @doc {\n     *    heading: 'Layers',\n     *    subheading: 'Classes',\n     *    namespace: 'layers',\n     *    subclasses: ['LayersModel']\n     * }\n     */\n    getLayer(name, index) {\n        if (index != null) {\n            if (this.layers.length <= index) {\n                throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only ` +\n                    `has ${this.layers.length} layer(s).`);\n            }\n            else {\n                return this.layers[index];\n            }\n        }\n        else {\n            if (name == null) {\n                throw new ValueError('Provide either a layer name or layer index');\n            }\n        }\n        for (const layer of this.layers) {\n            if (layer.name === name) {\n                return layer;\n            }\n        }\n        throw new ValueError(`No such layer: ${name}`);\n    }\n    /**\n     * Retrieves the Container's current loss values.\n     *\n     * Used for regularizers during training.\n     */\n    calculateLosses() {\n        // Porting Node: This is an augmentation to Container.loss in PyKeras.\n        //   In PyKeras, Container.loss returns symbolic tensors. Here a concrete\n        //   Tensor (specifically Scalar) values are returned. This is due to the\n        //   imperative backend.\n        return tidy(() => {\n            const losses = [];\n            for (const layer of this.layers) {\n                for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n                    const nodeKey = Container.nodeKey(layer, nodeIndex);\n                    if (this.containerNodes.has(nodeKey)) {\n                        losses.push(...layer.calculateLosses());\n                    }\n                }\n            }\n            // TODO(cais): Add any unconditional model-level losses?\n            return losses;\n        });\n    }\n    getConfig() {\n        const config = { name: this.name };\n        // Build a map from layer unique name (self._node_key)\n        // to the index of the nodes that are saved in the config.\n        // Only nodes in container_nodes are saved.\n        const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n        // Serialize and save the layers in layerConfigs\n        const layerConfigs = [];\n        for (const layer of this.layers) {\n            const layerClassName = layer.getClassName();\n            const layerConfig = layer.getConfig();\n            const filteredInboundNodes = [];\n            for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n                const node = layer.inboundNodes[originalNodeIndex];\n                const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n                let kwargs = {};\n                if (this.containerNodes.has(nodeKey)) {\n                    // The node is relevant to the model:\n                    // add to filteredInboundNodes.\n                    if (node.callArgs) {\n                        try {\n                            JSON.stringify(node.callArgs);\n                            kwargs = node.callArgs;\n                        }\n                        catch (err) {\n                            console.warn(`Layer ${layer.name} was passed ` +\n                                `non-serializable keyword arguments: ` +\n                                `${node.callArgs}. They will not be included ` +\n                                `in the serialized model (and thus will be ` +\n                                `missing at deserialization time).`);\n                            kwargs = {};\n                        }\n                    }\n                    if (node.inboundLayers.length > 0) {\n                        const nodeData = [];\n                        for (let i = 0; i < node.inboundLayers.length; i++) {\n                            const inboundLayer = node.inboundLayers[i];\n                            const nodeIndex = node.nodeIndices[i];\n                            const tensorIndex = node.tensorIndices[i];\n                            const nodeKey = Container.nodeKey(inboundLayer, nodeIndex);\n                            let newNodeIndex = nodeConversionMap[nodeKey];\n                            if (newNodeIndex == null) {\n                                newNodeIndex = 0;\n                            }\n                            nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n                        }\n                        filteredInboundNodes.push(nodeData);\n                    }\n                }\n            }\n            const dict = {};\n            dict['name'] = layer.name;\n            dict['className'] = layerClassName;\n            dict['config'] = layerConfig;\n            dict['inboundNodes'] = filteredInboundNodes;\n            layerConfigs.push(dict);\n        }\n        config['layers'] = layerConfigs;\n        // Gather info about inputs and outputs\n        const modelInputs = [];\n        for (let i = 0; i < this.inputLayers.length; i++) {\n            const layer = this.inputLayers[i];\n            const nodeIndex = this.inputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.inputLayersTensorIndices[i];\n            modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['inputLayers'] = modelInputs;\n        const modelOutputs = [];\n        for (let i = 0; i < this.outputLayers.length; i++) {\n            const layer = this.outputLayers[i];\n            const nodeIndex = this.outputLayersNodeIndices[i];\n            const nodeKey = Container.nodeKey(layer, nodeIndex);\n            if (!this.containerNodes.has(nodeKey)) {\n                continue;\n            }\n            let newNodeIndex = nodeConversionMap[nodeKey];\n            if (newNodeIndex === null || newNodeIndex === undefined) {\n                newNodeIndex = 0;\n            }\n            const tensorIndex = this.outputLayersTensorIndices[i];\n            modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n        }\n        config['outputLayers'] = modelOutputs;\n        return config;\n    }\n    /**\n     * Instantiates a LayersModel from its config (output of `get_config()`).\n     * @param cls the class to create\n     * @param config LayersModel config dictionary.\n     * @param customObjects An optional dictionary of custom objects.\n     * @param fastWeightInit Optional flag to use fast weight initialization\n     *   during deserialization. This is applicable to cases in which\n     *   the initialization will be immediately overwritten by loaded weight\n     *   values. Default: `false`.\n     * @returns A LayersModel instance.\n     * @throws ValueError: In case of improperly formatted config dict.\n     */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        // Layer instances created during\n        // the graph reconstruction process\n        const createdLayers = {};\n        // Dictionary mapping layer instances to\n        // node data that specifies a layer call.\n        // It acts as a queue that maintains any unprocessed\n        // layer call until it becomes possible to process it\n        // (i.e. until the input tensors to the call all exist).\n        const unprocessedNodes = {};\n        function addUnprocessedNode(layer, nodeData) {\n            if (!(layer.name in unprocessedNodes)) {\n                unprocessedNodes[layer.name] = [nodeData];\n            }\n            else {\n                unprocessedNodes[layer.name].push(nodeData);\n            }\n        }\n        function processNode(layer, nodeData) {\n            const inputTensors = [];\n            let kwargs;\n            for (const inputData of nodeData) {\n                const inboundLayerName = inputData[0];\n                const inboundNodeIndex = inputData[1];\n                const inboundTensorIndex = inputData[2];\n                kwargs = inputData[3] == null ?\n                    {} :\n                    inputData[3];\n                if (!(inboundLayerName in createdLayers)) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundLayer = createdLayers[inboundLayerName];\n                if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n                    addUnprocessedNode(layer, nodeData);\n                    return;\n                }\n                const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n                inputTensors.push(inboundNode.outputTensors[inboundTensorIndex]);\n            }\n            // Call layer on its inputs, thus creating the node\n            // and building the layer if needed.\n            // Note: This has Eager vs Graph Implications.\n            if (inputTensors.length > 0) {\n                layer.apply(generic_utils.singletonOrArray(inputTensors), kwargs); // was ** kwargs\n            }\n        }\n        /**\n         * Deserialize a layer, then call it on appropriate inputs.\n         * @param layerData: layer config dict.\n         * @throws ValueError: In case of improperly formatted `layer_data`\n         * dict.\n         */\n        function processLayer(layerData) {\n            const layerName = layerData['name'];\n            // Instantiate layer.\n            const layer = deserializeLayer(layerData, config['customObjects'] != null ?\n                config['customObjects'] :\n                {});\n            layer.setFastWeightInitDuringBuild(fastWeightInit);\n            createdLayers[layerName] = layer;\n            // Gather layer inputs.\n            const inboundNodesData = layerData['inboundNodes'];\n            inboundNodesData.forEach(nodeData => {\n                if (!(nodeData instanceof Array)) {\n                    throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n                }\n                // We don't process nodes (i.e. make layer calls)\n                // on the fly because the inbound node may not yet exist,\n                // in case of layer shared at different topological depths\n                // (e.g.a model such as A(B(A(B(x)))))\n                addUnprocessedNode(layer, nodeData);\n            });\n        }\n        // First, we create all layers and enqueue nodes to be processed.\n        const name = config['name'];\n        const layersFromConfig = config['layers'];\n        for (const layerData of layersFromConfig) {\n            processLayer(layerData);\n        }\n        // Then we process nodes in order of layer depth.\n        // Nodes that cannot yet be processed(if the inbound node\n        // does not yet exist) are re - enqueued, and the process\n        // is repeated until all nodes are processed.\n        while (!generic_utils.isObjectEmpty(unprocessedNodes)) {\n            for (const layerData of layersFromConfig) {\n                const layer = createdLayers[layerData['name']];\n                if (layer.name in unprocessedNodes) {\n                    const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n                    delete unprocessedNodes[layer.name];\n                    for (const nodeData of currentUnprocessedNodesForLayer) {\n                        processNode(layer, nodeData);\n                    }\n                }\n            }\n        }\n        const inputTensors = [];\n        const outputTensors = [];\n        const inputLayersFromConfig = config['inputLayers'];\n        for (const layerData of inputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            inputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        const outputLayersFromConfig = config['outputLayers'];\n        for (const layerData of outputLayersFromConfig) {\n            const layerName = layerData[0];\n            const nodeIndex = layerData[1];\n            const tensorIndex = layerData[2];\n            generic_utils.assert(layerName in createdLayers);\n            const layer = createdLayers[layerName];\n            const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n            outputTensors.push(layerOutputTensors[tensorIndex]);\n        }\n        return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n    }\n    /**\n     * Determine whether the container is stateful.\n     *\n     * Porting Note: this is the equivalent of the stateful @property of\n     *   the Container class in PyKeras.\n     */\n    get stateful() {\n        // Porting Note: This check is to prevent inadvertent setting of the\n        //   _stateful property of the Container instance.\n        if (this._stateful) {\n            throw new ValueError('Container instance unexpectedly has _stateful = true. The ' +\n                'statefulness of a Container is determined by the Layers it ' +\n                'contains. Its _stateful property must remain the default false.');\n        }\n        for (const layer of this.layers) {\n            if (layer.stateful) {\n                return true;\n            }\n        }\n        return false;\n    }\n    /**\n     * Reset the state of all stateful constituent layers (if any).\n     *\n     * Examples of stateful layers include RNN layers whose `stateful` property\n     * is set as `true`.\n     */\n    resetStates() {\n        tidy(() => {\n            this.layers.forEach(layer => {\n                // tslint:disable:no-any\n                if (layer.stateful) {\n                    layer.resetStates();\n                }\n                // tslint:enable:no-any\n            });\n        });\n    }\n}\n//# sourceMappingURL=container.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { dispose, mul, tensor1d, tidy } from '@tensorflow/tfjs-core';\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n    const numOutputs = outputNames.length;\n    if (xWeight == null || (Array.isArray(xWeight) && xWeight.length === 0)) {\n        return outputNames.map(name => null);\n    }\n    if (numOutputs === 1) {\n        if (Array.isArray(xWeight) && xWeight.length === 1) {\n            return xWeight;\n        }\n        else if (typeof xWeight === 'object' && outputNames[0] in xWeight) {\n            return [xWeight[outputNames[0]]];\n        }\n        else {\n            return [xWeight];\n        }\n    }\n    if (Array.isArray(xWeight)) {\n        if (xWeight.length !== numOutputs) {\n            throw new Error(`Provided ${weightType} is an array of ${xWeight.length} ` +\n                `element(s), but the model has ${numOutputs} outputs. ` +\n                `Make sure a set of weights is provided for each model output.`);\n        }\n        return xWeight;\n    }\n    else if (typeof xWeight === 'object' && Object.keys(xWeight).length > 0 &&\n        typeof xWeight[Object.keys(xWeight)[0]] ===\n            'object') {\n        const output = [];\n        outputNames.forEach(outputName => {\n            if (outputName in xWeight) {\n                output.push(xWeight[outputName]);\n            }\n            else {\n                output.push(null);\n            }\n        });\n        return output;\n    }\n    else {\n        throw new Error(`The model has multiple (${numOutputs}) outputs, ` +\n            `so ${weightType} must be either an array with ` +\n            `${numOutputs} elements or an object with ${outputNames} keys. ` +\n            `Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n    }\n}\n/**\n * Standardize class weighting objects.\n *\n * This function takes a single class-weighting object, an array of them,\n * or a map from output name to class-weighting object. It compares it to the\n * output name(s) of the model, base on which it outputs an array of\n * class-weighting objects of which the length matches the number of outputs.\n *\n * @param classWeight Input class-weighting object(s).\n * @param outputNames All output name(s) of the model.\n * @return An array of class-weighting objects. The length of the array matches\n *   the model's number of outputs.\n */\nexport function standardizeClassWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'classWeight');\n}\nexport function standardizeSampleWeights(classWeight, outputNames) {\n    return standardizeSampleOrClassWeights(classWeight, outputNames, 'sampleWeight');\n}\n/**\n * Standardize by-sample and/or by-class weights for training.\n *\n * Note that this function operates on one model output at a time. For a model\n * with multiple outputs, you must call this function multiple times.\n *\n * @param y The target tensor that the by-sample and/or by-class weight is for.\n *     The values of y are assumed to encode the classes, either directly\n *     as an integer index, or as one-hot encoding.\n * @param sampleWeight By-sample weights.\n * @param classWeight By-class weights: an object mapping class indices\n *     (integers) to a weight (float) to apply to the model's loss for the\n *     samples from this class during training. This can be useful to tell the\n *     model to \"pay more attention\" to samples from an under-represented class.\n * @param sampleWeightMode The mode for the sample weights.\n * @return A Promise of weight tensor, of which the size of the first dimension\n *     matches that of `y`.\n */\nexport async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n    if (sampleWeight != null || sampleWeightMode != null) {\n        // TODO(cais): Once 'temporal' mode is implemented, document it in the doc\n        // string.\n        throw new Error('Support sampleWeight is not implemented yet');\n    }\n    if (classWeight != null) {\n        // Apply class weights per sample.\n        const yClasses = tidy(() => {\n            if (y.shape.length === 1) {\n                // Assume class indices.\n                return y.clone();\n            }\n            else if (y.shape.length === 2) {\n                if (y.shape[1] > 1) {\n                    // Assume one-hot encoding of classes.\n                    const axis = 1;\n                    return y.argMax(axis);\n                }\n                else if (y.shape[1] === 1) {\n                    // Class index.\n                    return y.reshape([y.shape[0]]);\n                }\n                else {\n                    throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) ` +\n                        `during handling of class weights. The size is expected to be ` +\n                        `>= 1.`);\n                }\n            }\n            else {\n                throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during ` +\n                    `handling of class weights. The rank is expected to be 1 or 2.`);\n            }\n        });\n        const yClassIndices = Array.from(await yClasses.data());\n        dispose(yClasses);\n        const classSampleWeight = [];\n        yClassIndices.forEach(classIndex => {\n            if (classWeight[classIndex] == null) {\n                throw new Error(`classWeight must contain all classes in the training data. ` +\n                    `The class ${classIndex} exists in the data but not in ` +\n                    `classWeight`);\n            }\n            else {\n                classSampleWeight.push(classWeight[classIndex]);\n            }\n        });\n        return tensor1d(classSampleWeight, 'float32');\n    }\n    else {\n        return null;\n    }\n}\n/**\n * Apply per-sample weights on the loss values from a number of samples.\n *\n * @param losses Loss tensor of shape `[batchSize]`.\n * @param sampleWeights Per-sample weight tensor of shape `[batchSize]`.\n * @returns Tensor of the same shape as`losses`.\n */\nexport function computeWeightedLoss(losses, sampleWeights) {\n    return mul(losses, sampleWeights);\n}\n//# sourceMappingURL=training_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using TensorFlow.js datasets.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { singletonOrArray, toList } from '../utils/generic_utils';\nimport { standardizeClassWeights, standardizeWeights } from './training_utils';\n// Default batch size used during tensor-based validation.\nconst DEFAULT_VALIDATION_BATCH_SIZE = 32;\n/**\n * Standardize the output of a dataset iterator for use by\n * LayersModel.fitDataset().\n *\n * @param model: A `tf.LayersModel` object.\n * @param iteratorOut The output of a dataset iterator. It is required to be\n *   an object of the form `{xs: TensorOrArrayOrMap, ys:\n * TensorOrArrayOrMap}`, where `TensorOrArrayOrMap` is a single `tf.Tensor`,\n * a `tf.Tensor[]`, or a flat map from string names to `tf.Tensor`s.\n * @returns A flat array of `tf.Tensor` objects: the input `tf.Tensor`s\n *   followed by the target `tf.Tensor`s.  When `tf.Tensor`s are provided\n *   as a map, the order in the resulting array is taken from the `inputNames`\n *   and `outputNames` of the model.\n */\nfunction standardizeDataIteratorOutput(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, iteratorOut) {\n    let xs;\n    let ys;\n    const iteratorOutObj = iteratorOut;\n    xs = iteratorOutObj['xs'];\n    ys = iteratorOutObj['ys'];\n    tfc.util.assert(xs != null && ys != null, () => 'A Dataset iterator for fitDataset() is expected to generate ' +\n        'objects of the form `{xs: xVal, ys: yVal}`, where the two ' +\n        'values may be `tf.Tensor`, an array of Tensors, or a map of ' +\n        'string to Tensor.  The provided Dataset instead generates ' +\n        `${iteratorOut}`);\n    const flattenedXs = flattenTensorOrArrayOrMap('input', model.inputNames, xs);\n    const flattenedYs = flattenTensorOrArrayOrMap('output', model.outputNames, ys);\n    const batchSize = flattenedXs[0].shape[0];\n    tfc.util.assert(flattenedXs.length === model.inputs.length, () => `LayersModel has ${model.inputs.length} inputs, but the dataset ` +\n        `provides ${flattenedXs.length} inputs.  (Expected input keys: ` +\n        `${JSON.stringify(model.inputNames)})`);\n    tfc.util.assert(flattenedYs.length === model.outputs.length, () => `LayersModel has ${model.outputs.length} outputs, but the dataset ` +\n        `provides ${flattenedYs.length} outputs.  (Expected output keys: ` +\n        `${JSON.stringify(model.outputNames)})`);\n    for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n        tfc.util.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ` +\n            `${model.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n        tfc.util.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ` +\n            `${model.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; ` +\n            `expected  ${batchSize} based on input ${model.inputNames[0]}.`);\n    }\n    return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n    if (values instanceof tfc.Tensor) {\n        return [values];\n    }\n    else if (Array.isArray(values)) {\n        tfc.util.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n        return values;\n    }\n    else {\n        const result = [];\n        // Check that all the required keys are available.\n        for (const name of names) {\n            if (values[name] == null) {\n                throw new ValueError(`The feature data generated by the dataset lacks the required ` +\n                    `${inputOrOutput} key '${name}'.`);\n            }\n            result.push(values[name]);\n        }\n        return result;\n    }\n}\nfunction standardizeTensorValidationData(data) {\n    if (data.length === 3) {\n        throw new NotImplementedError('Validation with sample weights is not implemented yet.');\n    }\n    return { xs: data[0], ys: data[1] };\n}\nexport async function fitDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n    tfc.util.assert(model.optimizer != null, () => 'You must compile a model before training/testing. Use ' +\n        'LayersModel.compile(modelCompileConfig).');\n    tfc.util.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, ` +\n        `but it is not provided in this call.`);\n    tfc.util.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive ` +\n        `integer, but got ${args.epochs}`);\n    tfc.util.assert(!hasBatchesPerEpoch ||\n        (args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch)), () => `For fitDataset(), config.batchesPerEpoch is expected to be a ` +\n        `positive integer if specified, but got ${args.batchesPerEpoch}`);\n    tfc.util.assert(\n    // tslint:disable-next-line:no-any\n    args['validationSplit'] == null, () => '`validationSplit` is not supported by `fitDataset()`. ' +\n        'Use validationData instead.');\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    try {\n        const doValidation = args.validationData != null;\n        let valXs;\n        let valYs;\n        if (doValidation) {\n            if (isDatasetObject(args.validationData)) {\n                tfc.util.assert(args.validationBatches == null ||\n                    (args.validationBatches > 0 &&\n                        Number.isInteger(args.validationBatches)), () => `For fitDataset() with dataset-based validation, ` +\n                    `config.validationBatches is expected not to be provided, ` +\n                    `or to be a positive integer, ` +\n                    `but got ${args.validationBatches}`);\n            }\n            else {\n                const validationData = standardizeTensorValidationData(args.validationData);\n                valXs = validationData.xs;\n                valYs = validationData.ys;\n            }\n        }\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let callbackMetrics;\n        if (doValidation) {\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const verbose = args.verbose == null ? 1 : args.verbose;\n        const { callbackList, history } = configureCallbacks(callbacks, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, // Batch size determined by the dataset itself.\n        doValidation, callbackMetrics);\n        callbackList.setModel(model);\n        model.history = history;\n        await callbackList.onTrainBegin();\n        model.stopTraining_ = false;\n        let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n        let dataIterator = await dataset.iterator();\n        while (epoch < args.epochs) {\n            const epochLogs = {};\n            await callbackList.onEpochBegin(epoch);\n            let stepsDone = 0;\n            let batchIndex = 0;\n            if (!hasBatchesPerEpoch) {\n                dataIterator = await dataset.iterator();\n            }\n            while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n                const iteratorOut = await dataIterator.next();\n                // If `batchesPerEpoch` is specified, the dataset should not be\n                // exhausted until all epoches are done.\n                if (hasBatchesPerEpoch && iteratorOut.done) {\n                    console.warn('You provided `batchesPerEpoch` as ' +\n                        `${args.batchesPerEpoch}, ` +\n                        'but your dataset iterator ran out of data after ' +\n                        `${stepsDone} batches; ` +\n                        'interrupting training. Make sure that your ' +\n                        'dataset can generate at least `batchesPerEpoch * epochs` ' +\n                        'batches (in this case, ' +\n                        `${args.batchesPerEpoch * args.epochs} batches). ` +\n                        'You may need to use the repeat() function when building ' +\n                        'your dataset.');\n                    break;\n                }\n                if (iteratorOut.value != null) {\n                    const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                    const batchLogs = {};\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = xs[0].shape[0];\n                    await callbackList.onBatchBegin(batchIndex, batchLogs);\n                    const sampleWeights = [];\n                    if (args.classWeight != null) {\n                        const standardClassWeights = standardizeClassWeights(args.classWeight, model.outputNames);\n                        for (let i = 0; i < standardClassWeights.length; ++i) {\n                            sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n                        }\n                    }\n                    // Train on batch.\n                    const ins = xs.concat(ys).concat(sampleWeights);\n                    const outs = trainFunction(ins);\n                    tfc.dispose(ins);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                    }\n                    await callbackList.onBatchEnd(batchIndex, batchLogs);\n                    disposeTensorsInLogs(batchLogs);\n                    batchIndex++;\n                    stepsDone++;\n                }\n                if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch :\n                    iteratorOut.done) {\n                    // Epoch finished. Perform validation.\n                    if (doValidation) {\n                        let valOuts;\n                        if (isDatasetObject(args.validationData)) {\n                            valOuts = toList(await model.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n                        }\n                        else {\n                            valOuts = toList(model.evaluate(valXs, valYs, {\n                                batchSize: args.validationBatchSize == null ?\n                                    DEFAULT_VALIDATION_BATCH_SIZE :\n                                    args.validationBatchSize,\n                                verbose: 0\n                            }));\n                        }\n                        for (let i = 0; i < model.metricsNames.length; ++i) {\n                            epochLogs[`val_${model.metricsNames[i]}`] = valOuts[i];\n                        }\n                    }\n                    // Call `break` to exit one epoch lopp after validation is done. If\n                    // config.batchesPerEpoch is specified, an epoch while loop will\n                    // stop when `stepsDone >= config.batchesPerEpoch`. When\n                    // config.batchesPerEpoch is not provided, the following `break` is\n                    // required to exit the while lopp after dataset is exhausted.\n                    break;\n                }\n                if (model.stopTraining_) {\n                    break;\n                }\n            }\n            await callbackList.onEpochEnd(epoch, epochLogs);\n            epoch++;\n            if (model.stopTraining_) {\n                break;\n            }\n        }\n        await callbackList.onTrainEnd();\n        await model.history.syncData();\n        return model.history;\n    }\n    finally {\n        model.isTraining = false;\n    }\n}\n/** Helper function that determines number of steps (batches) per epoch. */\nfunction getStepsPerEpoch(dataset, args) {\n    // Attempt to determine # of batches in an epoch.\n    let stepsPerEpoch = null;\n    if (args.batchesPerEpoch != null) {\n        stepsPerEpoch = args.batchesPerEpoch;\n    }\n    else if (Number.isFinite(dataset.size)) {\n        stepsPerEpoch = dataset.size;\n    }\n    return stepsPerEpoch;\n}\n// Check if provided object is a Dataset object by checking its .iterator\n// element.\nfunction isDatasetObject(dataset) {\n    return (typeof dataset.iterator === 'function');\n}\n// Check if provided object is a LazyIterator object by checking it's .next\n// element.\nfunction isLazyIteratorObject(iterator) {\n    return (typeof iterator.next === 'function');\n}\nexport async function evaluateDataset(\n// Type `model` as `any` here to avoid circular dependency w/\n// training.ts.\n// tslint:disable-next-line:no-any\nmodel, dataset, args) {\n    args = args || {};\n    const hasBatches = args.batches != null;\n    const f = model.testFunction;\n    let outs = [];\n    if (args.verbose > 0) {\n        throw new NotImplementedError('Verbose mode is not implemented yet.');\n    }\n    tfc.util.assert(!hasBatches || (args.batches > 0 && Number.isInteger(args.batches)), () => 'Test loop expects `batches` to be a positive integer, but ' +\n        `received ${JSON.stringify(args.batches)}`);\n    const dataIterator = isLazyIteratorObject(dataset) ?\n        dataset :\n        await dataset.iterator();\n    // Keeps track of number of examples used in this evaluation.\n    let numExamples = 0;\n    let batch = 0;\n    while (hasBatches ? batch < args.batches : true) {\n        const iteratorOut = await dataIterator.next();\n        outs = tfc.tidy(() => {\n            if (iteratorOut.value) {\n                // TODO(cais): Once real dataset is available, use\n                //   `map(x => standardizeDataIteratorOutput(model, x).map(f)`.\n                const { xs, ys } = standardizeDataIteratorOutput(model, iteratorOut.value);\n                const xsAndYs = xs.concat(ys);\n                const batchOuts = tfc.tidy(() => f(xsAndYs));\n                tfc.dispose(xsAndYs);\n                if (batch === 0) {\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        outs.push(scalar(0));\n                    }\n                }\n                const batchSize = xsAndYs[0].shape[0];\n                for (let i = 0; i < batchOuts.length; ++i) {\n                    const batchOut = batchOuts[i];\n                    const oldScalar = outs[i];\n                    outs[i] =\n                        tfc.tidy(() => tfc.add(outs[i], tfc.mul(batchSize, batchOut)));\n                    if (batch > 0) {\n                        tfc.dispose(oldScalar);\n                    }\n                }\n                tfc.dispose(batchOuts);\n                numExamples += batchSize;\n                ++batch;\n            }\n            return outs;\n        });\n        if (iteratorOut.done) {\n            if (hasBatches) {\n                console.warn('Your dataset iterator ran out of data during evaluateDataset(). ' +\n                    'Interrupting evalution. Make sure that your ' +\n                    'dataset can generate at least `batches` ' +\n                    `batches (in this case, ${args.batches} batches). ` +\n                    'You may need to use the repeat() function when building ' +\n                    'your dataset.');\n            }\n            break;\n        }\n    }\n    for (let i = 0; i < outs.length; ++i) {\n        const oldScalar = outs[i];\n        outs[i] = tfc.div(outs[i], numExamples);\n        tfc.dispose(oldScalar);\n    }\n    return singletonOrArray(outs);\n}\n//# sourceMappingURL=training_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Interfaces and methods for training models using tf.Tensor objects.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport { expandDims, gather, sliceAlongFirstAxis } from '../backend/tfjs_backend';\nimport { configureCallbacks, standardizeCallbacks } from '../base_callbacks';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { disposeTensorsInLogs } from '../logs';\nimport { range } from '../utils/math_utils';\nexport function checkBatchSize(batchSize) {\n    tfc.util.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\n/**\n * Slice a Tensor or an Array of Tensors, by start and stop indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArraysByIndices()` together.\n *\n * @param arrays: the input.\n * @param start: the starting index (inclusive).\n * @param stop: the stopping index (exclusive).\n * @returns The result of the slicing. If `arrays` is an `Array` of\n *   `tf.Tensor`s, the slicing will be applied to all elements of the `Array`\n *   in the same way.\n */\nexport function sliceArrays(arrays, start, stop) {\n    if (arrays == null) {\n        return [null];\n    }\n    else if (Array.isArray(arrays)) {\n        return arrays.map(array => sliceAlongFirstAxis(array, start, stop - start));\n    }\n    else { // Tensor.\n        return sliceAlongFirstAxis(arrays, start, stop - start);\n    }\n}\n/**\n * Slice a Tensor or an Array of Tensors, by random-order indices.\n *\n * Porting Note: The `_slice_arrays` function in PyKeras is covered by this\n *   function and `sliceArrays()` together.\n *\n * @param arrays The input `tf.Tensor` or `Array` of `tf.Tensor`s to slice.\n *   If an `Array` of `tf.Tensor`s, all `tf.Tensor`s will be sliced in the\n *   same fashion.\n * @param indices The indices to use for slicing along the first (batch)\n *   dimension.\n * @returns Result(s) of the slicing.\n */\nexport function sliceArraysByIndices(arrays, indices) {\n    return tfc.tidy(() => {\n        if (arrays == null) {\n            return null;\n        }\n        else if (Array.isArray(arrays)) {\n            return arrays.map(array => sliceArraysByIndices(array, indices));\n        }\n        else {\n            // TODO(cais): indices should be a pre-constructed Tensor1D to avoid\n            //   tensor1d() calls.\n            return gather(arrays, indices.dtype === 'int32' ? indices : indices.toInt());\n        }\n    });\n}\n/**\n * Returns a list of batch indices (tuples of indices).\n * @param size: Integer, total size of the data to slice into batches.\n * @param batchSize: Integer, batch size.\n * @returns An Array of [batchStart, batchEnd] tuples. batchStart is\n *   inclusive; batchEnd is exclusive. I.e., each batch consists of indices x\n *   that satisfy batchStart <= x < batchEnd.\n */\nexport function makeBatches(size, batchSize) {\n    const output = [];\n    let batchStart = 0;\n    let batchEnd = null;\n    while (batchStart < size) {\n        batchEnd = batchStart + batchSize;\n        if (batchEnd >= size) {\n            batchEnd = size;\n        }\n        output.push([batchStart, batchEnd]);\n        batchStart = batchEnd;\n    }\n    return output;\n}\n/**\n * Abstract fit function for `f(ins)`.\n * @param f A Function returning a list of tensors. For training, this\n *   function is expected to perform the updates to the variables.\n * @param ins List of tensors to be fed to `f`.\n * @param outLabels List of strings, display names of the outputs of `f`.\n * @param batchSize Integer batch size or `== null` if unknown. Default : 32.\n * @param epochs Number of times to iterate over the data. Default : 1.\n * @param verbose Verbosity mode: 0, 1, or 2. Default: 1.\n * @param callbacks List of callbacks to be called during training.\n * @param valF Function to call for validation.\n * @param valIns List of tensors to be fed to `valF`.\n * @param shuffle Whether to shuffle the data at the beginning of every\n * epoch. Default : true.\n * @param callbackMetrics List of strings, the display names of the metrics\n *   passed to the callbacks. They should be the concatenation of the\n *   display names of the outputs of `f` and the list of display names\n *   of the outputs of `valF`.\n * @param initialEpoch Epoch at which to start training (useful for\n *   resuming a previous training run). Default : 0.\n * @param stepsPerEpoch Total number of steps (batches on samples) before\n *   declaring one epoch finished and starting the next epoch. Ignored with\n *   the default value of `undefined` or `null`.\n * @param validationSteps Number of steps to run validation for (only if\n *   doing validation from data tensors). Not applicable for tfjs-layers.\n * @returns A `History` object.\n */\nasync function fitLoop(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, f, ins, outLabels, batchSize, epochs, verbose, callbacks, valF, valIns, shuffle, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n    if (batchSize == null) {\n        batchSize = 32;\n    }\n    if (epochs == null) {\n        epochs = 1;\n    }\n    if (shuffle == null) {\n        shuffle = true;\n    }\n    if (initialEpoch == null) {\n        initialEpoch = 0;\n    }\n    // TODO(cais): Change const to let below when implementing validation.\n    let doValidation = false;\n    if (valF != null && valIns != null) {\n        doValidation = true;\n        // TODO(cais): verbose message.\n    }\n    if (validationSteps != null) {\n        doValidation = true;\n        if (stepsPerEpoch == null) {\n            throw new ValueError('Can only use `validationSteps` when doing step-wise training, ' +\n                'i.e., `stepsPerEpoch` must be set.');\n        }\n    }\n    const numTrainSamples = model.checkNumSamples(ins, batchSize, stepsPerEpoch, 'steps_per_epoch');\n    let indexArray;\n    if (numTrainSamples != null) {\n        indexArray = range(0, numTrainSamples);\n    }\n    if (verbose == null) {\n        verbose = 1;\n    }\n    const { callbackList, history } = configureCallbacks(callbacks, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n    callbackList.setModel(model);\n    model.history = history;\n    await callbackList.onTrainBegin();\n    model.stopTraining_ = false;\n    // TODO(cais): Take care of callbacks.validation_data as in PyKeras.\n    // TODO(cais): Pre-convert feeds for performance as in PyKeras.\n    for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n        await callbackList.onEpochBegin(epoch);\n        const epochLogs = {};\n        if (stepsPerEpoch != null) {\n            throw new NotImplementedError('stepsPerEpoch mode is not implemented yet.');\n        }\n        else {\n            if (shuffle === 'batch') {\n                throw new NotImplementedError('batch shuffling is not implemneted yet');\n            }\n            else if (shuffle) {\n                util.shuffle(indexArray);\n            }\n            // Convert the potentially shuffled indices to Tensor1D, to avoid the\n            // cost of repeated creation of Array1Ds later on.\n            const epochIndexArray1D = tensor1d(indexArray);\n            const batches = makeBatches(numTrainSamples, batchSize);\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchLogs = {};\n                await callbackList.onBatchBegin(batchIndex, batchLogs);\n                tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n                    batchLogs['batch'] = batchIndex;\n                    batchLogs['size'] = batchEnd - batchStart;\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const outs = f(insBatch);\n                    for (let i = 0; i < outLabels.length; ++i) {\n                        const label = outLabels[i];\n                        const out = outs[i];\n                        batchLogs[label] = out;\n                        tfc.keep(out);\n                        // TODO(cais): Use scope() to avoid ownership.\n                    }\n                    if (batchIndex === batches.length - 1) { // Last batch.\n                        if (doValidation) {\n                            const valOuts = model.testLoop(valF, valIns, batchSize);\n                            // Porting Notes: In tfjs-layers, valOuts is always an Array.\n                            for (let i = 0; i < outLabels.length; ++i) {\n                                const label = outLabels[i];\n                                const out = valOuts[i];\n                                tfc.keep(out);\n                                // TODO(cais): Use scope() to avoid ownership.\n                                epochLogs['val_' + label] = out;\n                            }\n                        }\n                    }\n                });\n                await callbackList.onBatchEnd(batchIndex, batchLogs);\n                disposeTensorsInLogs(batchLogs);\n                if (model.stopTraining_) {\n                    break;\n                }\n                // TODO(cais): return outs as list of Tensor.\n            }\n            epochIndexArray1D.dispose();\n        }\n        // TODO(cais): Run validation at the end of the epoch.\n        await callbackList.onEpochEnd(epoch, epochLogs);\n        if (model.stopTraining_) {\n            break;\n        }\n    }\n    await callbackList.onTrainEnd();\n    await model.history.syncData();\n    return model.history;\n}\nexport async function fitTensors(\n// Type `model` as `any` here to avoid circular dependency w/ training.ts.\n// tslint:disable-next-line:no-any\nmodel, x, y, args = {}) {\n    if (model.isTraining) {\n        throw new Error('Cannot start training because another fit() call is ongoing.');\n    }\n    model.isTraining = true;\n    let inputs;\n    let targets;\n    let inputValX;\n    let inputValY;\n    let valX;\n    let valY;\n    let sampleWeights;\n    try {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // Validate user data.\n        // TODO(cais): Support sampleWeight.\n        const checkBatchAxis = false;\n        const standardizedOuts = await model.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n        inputs = standardizedOuts[0];\n        targets = standardizedOuts[1];\n        sampleWeights = standardizedOuts[2];\n        // Prepare validation data.\n        let doValidation = false;\n        let valIns;\n        if (args.validationData != null && args.validationData.length > 0) {\n            doValidation = true;\n            if (args.validationData.length === 2) {\n                // config.validationData consists of valX and valY.\n                inputValX = args.validationData[0];\n                inputValY = args.validationData[1];\n            }\n            else if (args.validationData.length === 3) {\n                throw new NotImplementedError('validationData including sample weights is not supported yet.');\n            }\n            else {\n                throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) ` +\n                    `or 3 (valX, valY, valSampleWeight) items; ` +\n                    `${args.validationData} is invalid.`);\n            }\n            const checkBatchAxis = true;\n            const valStandardized = await model.standardizeUserData(inputValX, inputValY, null, /** Unused sample weights. */ null, /** Unused class weights. */ checkBatchAxis, batchSize);\n            valX = valStandardized[0];\n            valY = valStandardized[1];\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSplit != null && args.validationSplit > 0 &&\n            args.validationSplit < 1) {\n            doValidation = true;\n            // Porting Note: In tfjs-layers, inputs[0] is always a Tensor.\n            const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n            const originalBatchSize = inputs[0].shape[0];\n            valX = sliceArrays(inputs, splitAt, originalBatchSize);\n            inputs = sliceArrays(inputs, 0, splitAt);\n            valY = sliceArrays(targets, splitAt, originalBatchSize);\n            targets = sliceArrays(targets, 0, splitAt);\n            // TODO(cais): Once sampleWeights becomes available, slice it to get\n            //   valSampleWeights.\n            valIns = valX.concat(valY);\n            // TODO(cais): Add useLearningPhase data properly.\n        }\n        else if (args.validationSteps != null) {\n            doValidation = true;\n            // TODO(cais): Add useLearningPhase.\n        }\n        const ins = inputs.concat(targets).concat(sampleWeights);\n        model.checkTrainableWeightsConsistency();\n        // TODO(cais): Handle use_learning_phase and learning_phase?\n        // Porting Note: Here we see a key deviation of tfjs-layers from\n        // Keras.\n        //  Due to the imperative nature of tfjs-layers' backend (tfjs-core),\n        //  we do not construct symbolic computation graphs to embody the\n        //  training process. Instead, we define a function that performs the\n        //  training action. In PyKeras, the data (inputs and targets) are fed\n        //  through graph placeholders. In tfjs-layers, the data are fed as\n        //  function arguments. Since the function are defined below in the\n        //  scope, we don't have equivalents of PyKeras's\n        //  `_make_train_funciton`.\n        const trainFunction = model.makeTrainFunction();\n        const outLabels = model.getDedupedMetricsNames();\n        let valFunction;\n        let callbackMetrics;\n        if (doValidation) {\n            model.makeTestFunction();\n            valFunction = model.testFunction;\n            callbackMetrics =\n                outLabels.slice().concat(outLabels.map(n => 'val_' + n));\n        }\n        else {\n            valFunction = null;\n            valIns = [];\n            callbackMetrics = outLabels.slice();\n        }\n        const callbacks = standardizeCallbacks(args.callbacks, args.yieldEvery);\n        const out = await fitLoop(model, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n        return out;\n    }\n    finally {\n        model.isTraining = false;\n        // Memory clean up.\n        disposeNewTensors(inputs, x);\n        disposeNewTensors(targets, y);\n        disposeNewTensors(valX, inputValX);\n        disposeNewTensors(valY, inputValY);\n        if (sampleWeights != null) {\n            tfc.dispose(sampleWeights);\n        }\n    }\n    // TODO(cais): Add value to outLabels.\n}\n/**\n * Ensure tensors all have a rank of at least 2.\n *\n * If a tensor has a rank of 1, it is dimension-expanded to rank 2.\n * If any tensor has a rank of 0 (i.e., is a scalar), an error will be thrown.\n */\nexport function ensureTensorsRank2OrHigher(tensors) {\n    const outs = [];\n    if (tensors instanceof Tensor) {\n        tensors = [tensors];\n    }\n    // Make Tensors at least 2D.\n    for (let i = 0; i < tensors.length; ++i) {\n        const tensor = tensors[i];\n        if (tensor.rank === 1) {\n            outs.push(expandDims(tensor, 1));\n        }\n        else if (tensor.rank === 0) {\n            throw new Error('Expected tensor to be at least 1D, but received a 0D tensor ' +\n                '(scalar).');\n        }\n        else {\n            outs.push(tensor);\n        }\n    }\n    return outs;\n}\n/**\n * Compare a set of tensors with a reference (old) set, discard the ones\n * in the new set that are not present in the reference set.\n *\n * This method is used for memory clenaup during calls such as\n * LayersModel.fit().\n *\n * @param tensors New set which may contain Tensors not present in\n *   `refTensors`.\n * @param refTensors Reference Tensor set.\n */\n// TODO(cais, kangyizhang): Deduplicate with tfjs-data.\nexport function disposeNewTensors(tensors, refTensors) {\n    if (tensors == null) {\n        return;\n    }\n    const oldTensorIds = [];\n    if (refTensors instanceof Tensor) {\n        oldTensorIds.push(refTensors.id);\n    }\n    else if (Array.isArray(refTensors)) {\n        refTensors.forEach(t => oldTensorIds.push(t.id));\n    }\n    else if (refTensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in refTensors) {\n            const oldTensor = refTensors[name];\n            oldTensorIds.push(oldTensor.id);\n        }\n    }\n    const tensorsToDispose = [];\n    if (tensors instanceof Tensor) {\n        if (oldTensorIds.indexOf(tensors.id) === -1) {\n            tensorsToDispose.push(tensors);\n        }\n    }\n    else if (Array.isArray(tensors)) {\n        tensors.forEach(t => {\n            if (oldTensorIds.indexOf(t.id) === -1) {\n                tensorsToDispose.push(t);\n            }\n        });\n    }\n    else if (tensors != null) {\n        // `oldTensors` is a map from string name to Tensor.\n        for (const name in tensors) {\n            const tensor = tensors[name];\n            if (oldTensorIds.indexOf(tensor.id) === -1) {\n                tensorsToDispose.push(tensor);\n            }\n        }\n    }\n    tensorsToDispose.forEach(t => {\n        if (!t.isDisposed) {\n            t.dispose();\n        }\n    });\n}\n//# sourceMappingURL=training_tensors.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original Source: engine/training.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { io, Optimizer, scalar, serialization, Tensor, tensor1d, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { NotImplementedError, RuntimeError, ValueError } from '../errors';\nimport { deserialize } from '../layers/serialization';\nimport * as losses from '../losses';\nimport * as Metrics from '../metrics';\nimport * as optimizers from '../optimizers';\nimport { checkUserDefinedMetadata } from '../user_defined_metadata';\nimport { count, pyListRepeat, singletonOrArray, toCamelCase, toSnakeCase, unique } from '../utils/generic_utils';\nimport { printSummary } from '../utils/layer_utils';\nimport { range } from '../utils/math_utils';\nimport { convertPythonicToTs } from '../utils/serialization_utils';\nimport { version } from '../version';\nimport { Container } from './container';\nimport { execute, FeedDict } from './executor';\nimport { evaluateDataset, fitDataset } from './training_dataset';\nimport { checkBatchSize, disposeNewTensors, ensureTensorsRank2OrHigher, fitTensors, makeBatches, sliceArrays, sliceArraysByIndices } from './training_tensors';\nimport { computeWeightedLoss, standardizeClassWeights, standardizeWeights } from './training_utils';\n/**\n * Helper function for polymorphic input data: 1. singleton Tensor.\n */\nexport function isDataTensor(x) {\n    return x instanceof Tensor;\n}\n/**\n * Helper function for polymorphic input data: 2. Array of Tensor.\n */\nexport function isDataArray(x) {\n    return Array.isArray(x);\n}\n/**\n * Helper function for polymorphic input data: 3. \"dict\" of Tensor.\n */\nexport function isDataDict(x) {\n    return !isDataTensor(x) && !isDataArray(x);\n}\n/**\n * Normalizes inputs and targets provided by users.\n * @param data User-provided input data (polymorphic).\n * @param names An Array of expected Tensor names.\n * @param shapes Optional Array of expected Tensor shapes.\n * @param checkBatchAxis Whether to check that the batch axis of the arrays\n *   match  the expected value found in `shapes`.\n * @param exceptionPrefix String prefix used for exception formatting.\n * @returns List of standardized input Tensors (one Tensor per model input).\n * @throws ValueError: in case of improperly formatted user data.\n */\nexport function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    if (names == null || names.length === 0) {\n        // Check for the case where the model expected no data, but some data got\n        // sent.\n        if (data != null) {\n            let gotUnexpectedData = false;\n            if (isDataArray(data) && data.length > 0) {\n                gotUnexpectedData = true;\n            }\n            else if (isDataDict(data)) {\n                for (const key in data) {\n                    if (data.hasOwnProperty(key)) {\n                        gotUnexpectedData = true;\n                        break;\n                    }\n                }\n            }\n            else {\n                // `data` is a singleton Tensor in this case.\n                gotUnexpectedData = true;\n            }\n            if (gotUnexpectedData) {\n                throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, ` +\n                    `but got ${data}`);\n            }\n        }\n        return [];\n    }\n    if (data == null) {\n        return names.map(name => null);\n    }\n    let arrays;\n    if (isDataDict(data)) {\n        data = data;\n        arrays = [];\n        for (const name of names) {\n            if (data[name] == null) {\n                throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ` +\n                    `${names}`);\n            }\n            arrays.push(data[name]);\n        }\n    }\n    else if (isDataArray(data)) {\n        data = data;\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `model expected. Expected to see ${names.length} Tensor(s), but ` +\n                `instead got the following list of Tensor(s): ${data}`);\n        }\n        arrays = data;\n    }\n    else {\n        data = data;\n        if (names.length > 1) {\n            throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), ` +\n                `but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n        }\n        arrays = [data];\n    }\n    arrays = ensureTensorsRank2OrHigher(arrays);\n    // Check shape compatibility.\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s). but got array with ` +\n                    `shape ${array.shape}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    // Skip the first (batch) axis.\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null && refDim >= 0 && dim !== refDim) {\n                    throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                        `to have shape [${shapes[i]}], but got array with shape ` +\n                        `[${array.shape}].`);\n                }\n            }\n        }\n    }\n    return arrays;\n}\n/**\n * User input validation for Tensors.\n * @param inputs `Array` of `tf.Tensor`s for inputs.\n * @param targets `Array` of `tf.Tensor`s for targets.\n * @param weights Optional `Array` of `tf.Tensor`s for sample weights.\n * @throws ValueError: in case of incorrectly formatted data.\n */\nexport function checkArrayLengths(inputs, targets, weights) {\n    const setX = unique(inputs.map(input => input.shape[0]));\n    setX.sort();\n    const setY = unique(targets.map(target => target.shape[0]));\n    setY.sort();\n    // TODO(cais): Check `weights` as well.\n    if (setX.length > 1) {\n        throw new ValueError(`All input Tensors (x) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(inputs.map(input => input.shape))}`);\n    }\n    if (setY.length > 1) {\n        throw new ValueError(`All target Tensors (y) should have the same number of samples. ` +\n            `Got array shapes: ` +\n            `${JSON.stringify(targets.map(target => target.shape))}`);\n    }\n    if (setX.length > 0 && setY.length > 0 && !util.arraysEqual(setX, setY)) {\n        throw new ValueError(`Input Tensors should have the same number of samples as target ` +\n            `Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target ` +\n            `sample(s).`);\n    }\n}\n/**\n * Validation on the compatibility of targes and loss functions.\n *\n * This helps prevent users from using loss functions incorrectly.\n *\n * @param targets `Array` of `tf.Tensor`s of targets.\n * @param lossFns `Array` of loss functions.\n * @param outputShapes `Array` of shapes of model outputs.\n */\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n    // TODO(cais): Dedicated test coverage?\n    const keyLosses = [\n        losses.meanSquaredError, losses.binaryCrossentropy,\n        losses.categoricalCrossentropy\n    ];\n    for (let i = 0; i < targets.length; ++i) {\n        const y = targets[i];\n        const loss = lossFns[i];\n        const shape = outputShapes[i];\n        if (loss == null) {\n            continue;\n        }\n        if (loss === losses.categoricalCrossentropy) {\n            if (y.shape[y.shape.length - 1] === 1) {\n                throw new ValueError(`You are passing a target array of shape ${y.shape} while using ` +\n                    `a loss 'categorical_crossentropy'. 'categorical_crossentropy'` +\n                    `expects targets to be binary matrices (1s and 0s) of shape ` +\n                    `[samples, classes].`);\n                // TODO(cais): Example code in error message.\n            }\n        }\n        if (keyLosses.indexOf(loss) !== -1) {\n            const slicedYShape = y.shape.slice(1);\n            const slicedShape = shape.slice(1);\n            for (let j = 0; j < slicedYShape.length; ++j) {\n                const targetDim = slicedYShape[j];\n                const outDim = slicedShape[j];\n                if (outDim != null && targetDim !== outDim) {\n                    throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an ` +\n                        `output of shape ${shape}, while using a loss function that ` +\n                        `expects targets to have the same shape as the output.`);\n                }\n            }\n        }\n    }\n}\n/**\n * Check inputs provided by the user.\n *\n * Porting Note: This corresponds to _standardize_input_data() in Python\n *   Keras. Because of the strong typing in TF.js, we do not need to convert\n *   the data. Specifically:\n *   1) in PyKeras, `data` can be `DataFrame` instances from pandas, for\n *      example. We don't need to worry about that here because there is no\n *      widely popular javascript/typesdcript equivalent of pandas (so far).\n *      If one becomes available in the future, we can add support.\n *   2) in PyKeras, inputs can be Python dict. But here we are stipulating\n * that the data is either a single `tf.Tensor` or an Array of `tf.Tensor`s. We\n * may add support for `Object` data inputs in the future when the need\n * arises.\n *\n * Instead, we perform basic checks for number of parameters and shapes.\n *\n * @param data: The input data.\n * @param names: Name for the inputs, from the model.\n * @param shapes: Expected shapes for the input data, from the model.\n * @param checkBatchAxis: Whether the size along the batch axis (i.e., the\n *   first dimension) will be checked for matching.\n * @param exceptionPrefix: Execption prefix message, used in generating error\n *   messages.\n * @throws ValueError: on incorrect number of inputs or mismatches in shapes.\n */\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = '') {\n    let arrays;\n    if (Array.isArray(data)) {\n        if (data.length !== names.length) {\n            throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of ` +\n                `Tensors that you are passing to your model is not the size the ` +\n                `the model expected. Expected to see ${names.length} Tensor(s),` +\n                ` but instead got ${data.length} Tensors(s).`);\n        }\n        arrays = data;\n    }\n    else {\n        if (names.length > 1) {\n            throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, ` +\n                `but only received one Tensor. Found: array with shape ` +\n                `${JSON.stringify(data.shape)}.`);\n        }\n        arrays = [data];\n    }\n    if (shapes != null) {\n        for (let i = 0; i < names.length; ++i) {\n            if (shapes[i] == null) {\n                continue;\n            }\n            const array = arrays[i];\n            if (array.shape.length !== shapes[i].length) {\n                throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} ` +\n                    `to have ${shapes[i].length} dimension(s), but got array with ` +\n                    `shape ${JSON.stringify(array.shape)}`);\n            }\n            for (let j = 0; j < shapes[i].length; ++j) {\n                if (j === 0 && !checkBatchAxis) {\n                    continue;\n                }\n                const dim = array.shape[j];\n                const refDim = shapes[i][j];\n                if (refDim != null) {\n                    if (refDim !== dim) {\n                        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ` +\n                            `${names[i]} to have shape ${JSON.stringify(shapes[i])} but ` +\n                            `got array with shape ${JSON.stringify(array.shape)}.`);\n                    }\n                }\n            }\n        }\n    }\n}\n/**\n * Maps metric functions to model outputs.\n * @param metrics An shortcut strings name, metric function, `Array` or dict\n *   (`Object`) of metric functions.\n * @param outputNames An `Array` of the names of model outputs.\n * @returns An `Array` (one entry per model output) of `Array` of metric\n *   functions. For instance, if the model has 2 outputs, and for the first\n *   output we want to compute `binaryAccuracy` and `binaryCrossentropy`,\n *   and just `binaryAccuracy` for the second output, the `Array` would look\n *   like:\n *     `[[binaryAccuracy, binaryCrossentropy],  [binaryAccuracy]]`\n * @throws TypeError: incompatible metrics format.\n */\nexport function collectMetrics(metrics, outputNames) {\n    if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n        return outputNames.map(name => []);\n    }\n    let wrappedMetrics;\n    if (typeof metrics === 'string' || typeof metrics === 'function') {\n        wrappedMetrics = [metrics];\n    }\n    else if (Array.isArray(metrics) || typeof metrics === 'object') {\n        wrappedMetrics = metrics;\n    }\n    else {\n        throw new TypeError('Type of metrics argument not understood. Expected an string,' +\n            `function, Array, or Object, found: ${metrics}`);\n    }\n    if (Array.isArray(wrappedMetrics)) {\n        // We then apply all metrics to all outputs.\n        return outputNames.map(name => wrappedMetrics);\n    }\n    else {\n        // In this case, metrics is a dict.\n        const nestedMetrics = [];\n        for (const name of outputNames) {\n            let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n            if (!Array.isArray(outputMetrics)) {\n                outputMetrics = [outputMetrics];\n            }\n            nestedMetrics.push(outputMetrics);\n        }\n        return nestedMetrics;\n    }\n}\nconst LAYERS_MODEL_FORMAT_NAME = 'layers-model';\n/**\n * A `tf.LayersModel` is a directed, acyclic graph of `tf.Layer`s plus methods\n * for training, evaluation, prediction and saving.\n *\n * `tf.LayersModel` is the basic unit of training, inference and evaluation in\n * TensorFlow.js. To create a `tf.LayersModel`, use `tf.LayersModel`.\n *\n * See also:\n *   `tf.Sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class LayersModel extends Container {\n    constructor(args) {\n        super(args);\n        this.isTraining = false;\n    }\n    /**\n     * Print a text summary of the model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - If the model has non-sequential-like topology, the inputs each layer\n     *   receives\n     * - The total number of trainable and non-trainable parameters of the model.\n     *\n     * ```js\n     * const input1 = tf.input({shape: [10]});\n     * const input2 = tf.input({shape: [20]});\n     * const dense1 = tf.layers.dense({units: 4}).apply(input1);\n     * const dense2 = tf.layers.dense({units: 8}).apply(input2);\n     * const concat = tf.layers.concatenate().apply([dense1, dense2]);\n     * const output =\n     *     tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);\n     *\n     * const model = tf.model({inputs: [input1, input2], outputs: output});\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            throw new ValueError(`This model has never been called, thus its weights have not been ` +\n                `created yet. So no summary can be displayed. Build the model ` +\n                `first (e.g., by calling it on some test data).`);\n        }\n        printSummary(this, lineLength, positions, printFn);\n    }\n    /**\n     * Configures and prepares the model for training and evaluation.  Compiling\n     * outfits the model with an optimizer, loss, and/or metrics.  Calling `fit`\n     * or `evaluate` on an un-compiled model will throw an error.\n     *\n     * @param args a `ModelCompileArgs` specifying the loss, optimizer, and\n     * metrics to be used for fitting and evaluating this model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    compile(args) {\n        if (args.loss == null) {\n            args.loss = [];\n        }\n        this.loss = args.loss;\n        if (typeof args.optimizer === 'string') {\n            this.optimizer_ = optimizers.getOptimizer(args.optimizer);\n            this.isOptimizerOwned = true;\n        }\n        else {\n            if (!(args.optimizer instanceof Optimizer)) {\n                throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n            }\n            this.optimizer_ = args.optimizer;\n            this.isOptimizerOwned = false;\n        }\n        // TODO(cais): Add lossWeights.\n        // TODO(cais): Add sampleWeightMode.\n        // Prepare loss functions.\n        let lossFunctions = [];\n        if (!Array.isArray(args.loss) && typeof args.loss !== 'string' &&\n            typeof args.loss !== 'function') {\n            args.loss = args.loss;\n            for (const name in args.loss) {\n                if (this.outputNames.indexOf(name) === -1) {\n                    throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". ` +\n                        `Only expected the following keys: ${this.outputNames}`);\n                }\n            }\n            for (const name of this.outputNames) {\n                if (args.loss[name] == null) {\n                    console.warn(`Output \"${name}\" is missing from loss dictionary. We assume ` +\n                        `this was done on purpose, and we will not be expecting data ` +\n                        `to be passed to ${name} during training`);\n                }\n                lossFunctions.push(losses.get(args.loss[name]));\n            }\n        }\n        else if (Array.isArray(args.loss)) {\n            if (args.loss.length !== this.outputs.length) {\n                throw new ValueError(`When passing an Array as loss, it should have one entry per ` +\n                    `model output. The model has ${this.outputs.length} output(s), ` +\n                    `but you passed loss=${args.loss}.`);\n            }\n            const theLosses = args.loss;\n            lossFunctions = theLosses.map(l => losses.get(l));\n        }\n        else {\n            const lossFunction = losses.get(args.loss);\n            this.outputs.forEach(_ => {\n                lossFunctions.push(lossFunction);\n            });\n        }\n        this.lossFunctions = lossFunctions;\n        this.feedOutputNames = [];\n        this.feedOutputShapes = [];\n        this.feedLossFns = [];\n        for (let i = 0; i < this.outputs.length; ++i) {\n            // TODO(cais): Logic for skipping target(s).\n            const shape = this.internalOutputShapes[i];\n            const name = this.outputNames[i];\n            this.feedOutputNames.push(name);\n            this.feedOutputShapes.push(shape);\n            this.feedLossFns.push(this.lossFunctions[i]);\n        }\n        // TODO(cais): Add logic for output masks.\n        // TODO(cais): Add logic for sample weights.\n        const skipTargetIndices = [];\n        // Prepare metrics.\n        this.metrics = args.metrics;\n        // TODO(cais): Add weightedMetrics.\n        this.metricsNames = ['loss'];\n        this.metricsTensors = [];\n        // Compute total loss.\n        // Porting Note: In PyKeras, metrics_tensors are symbolic tensor objects.\n        //   Here, metricsTensors are TypeScript functions. This difference is due\n        //   to the difference in symbolic/imperative property of the backends.\n        nameScope('loss', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                // TODO(cais): Add weightedLoss, sampleWeight and mask.\n                //   The following line should be weightedLoss\n                const weightedLoss = this.lossFunctions[i];\n                if (this.outputs.length > 1) {\n                    this.metricsTensors.push([weightedLoss, i]);\n                    this.metricsNames.push(this.outputNames[i] + '_loss');\n                }\n            }\n            // Porting Note: Due to the imperative nature of the backend, we calculate\n            //   the regularizer penalties in the totalLossFunction, instead of here.\n        });\n        const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n        // TODO(cais): Add nestedWeightedMetrics.\n        /**\n         * Helper function used in loop below.\n         */\n        const appendMetric = (outputIndex, metricName, metricTensor) => {\n            if (this.outputNames.length > 1) {\n                metricName = this.outputNames[outputIndex] + '_' + metricName;\n            }\n            this.metricsNames.push(metricName);\n            this.metricsTensors.push([metricTensor, outputIndex]);\n        };\n        nameScope('metric', () => {\n            for (let i = 0; i < this.outputs.length; ++i) {\n                if (skipTargetIndices.indexOf(i) !== -1) {\n                    continue;\n                }\n                const outputMetrics = nestedMetrics[i];\n                // TODO(cais): Add weights and outputWeightedMetrics.\n                // TODO(cais): Add optional arg `weights` to the following function.\n                const handleMetrics = (metrics) => {\n                    const metricNamePrefix = '';\n                    let metricName;\n                    let accFn;\n                    let weightedMetricFn;\n                    //  TODO(cais): Use 'weights_' for weighted metrics.\n                    for (const metric of metrics) {\n                        if (typeof metric === 'string' &&\n                            ['accuracy', 'acc', 'crossentropy', 'ce'].indexOf(metric) !==\n                                -1) {\n                            const outputShape = this.internalOutputShapes[i];\n                            if (outputShape[outputShape.length - 1] === 1 ||\n                                this.lossFunctions[i] === losses.binaryCrossentropy) {\n                                // case: binary accuracy/crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.binaryCrossentropy;\n                                }\n                            }\n                            else if (this.lossFunctions[i] ===\n                                losses.sparseCategoricalCrossentropy) {\n                                // case: categorical accuracy / crossentropy with sparse\n                                // targets.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.sparseCategoricalCrossentropy;\n                                }\n                            }\n                            else {\n                                // case: categorical accuracy / crossentropy.\n                                if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalAccuracy;\n                                }\n                                else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                    accFn = Metrics.categoricalCrossentropy;\n                                }\n                            }\n                            let suffix;\n                            if (['accuracy', 'acc'].indexOf(metric) !== -1) {\n                                suffix = 'acc';\n                            }\n                            else if (['crossentropy', 'ce'].indexOf(metric) !== -1) {\n                                suffix = 'ce';\n                            }\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = accFn;\n                            metricName = metricNamePrefix + suffix;\n                        }\n                        else {\n                            const metricFn = Metrics.get(metric);\n                            // TODO(cais): Add weighting actually.\n                            weightedMetricFn = metricFn;\n                            metricName =\n                                metricNamePrefix + Metrics.getLossOrMetricName(metric);\n                        }\n                        // TODO(cais): Add weighting and masking to metricResult.\n                        let metricResult;\n                        nameScope(metricName, () => {\n                            metricResult = weightedMetricFn;\n                        });\n                        appendMetric(i, metricName, metricResult);\n                    }\n                };\n                handleMetrics(outputMetrics);\n                // TODO(cais): Call handleMetrics with weights.\n            }\n        });\n        // Porting Notes: Given the imperative backend of tfjs-core,\n        //   there is no need for constructing the symbolic graph and placeholders.\n        this.collectedTrainableWeights = this.trainableWeights;\n    }\n    /**\n     * Check trainable weights count consistency.\n     *\n     * This will raise a warning if `this.trainableWeights` and\n     * `this.collectedTrainableWeights` are inconsistent (i.e., have different\n     * numbers of parameters).\n     * Inconsistency will typically arise when one modifies `model.trainable`\n     * without calling `model.compile()` again.\n     */\n    checkTrainableWeightsConsistency() {\n        if (this.collectedTrainableWeights == null) {\n            return;\n        }\n        if (this.trainableWeights.length !==\n            this.collectedTrainableWeights.length) {\n            console.warn('Discrepancy between trainableweights and collected trainable ' +\n                'weights. Did you set `model.trainable` without calling ' +\n                '`model.compile()` afterwards?');\n        }\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(\n     *     tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateArgs`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        const batchSize = args.batchSize == null ? 32 : args.batchSize;\n        checkBatchSize(batchSize);\n        // TODO(cais): Standardize `config.sampleWeights` as well.\n        // Validate user data.\n        const checkBatchAxis = true;\n        const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        try {\n            // TODO(cais): If uses `useLearningPhase`, set the corresponding element\n            // of the input to 0.\n            const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n            this.makeTestFunction();\n            const f = this.testFunction;\n            const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n            return singletonOrArray(testOuts);\n        }\n        finally {\n            disposeNewTensors(standardizedOuts[0], x);\n            disposeNewTensors(standardizedOuts[1], y);\n        }\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        this.makeTestFunction();\n        return evaluateDataset(this, dataset, args);\n    }\n    /**\n     * Get number of samples provided for training, evaluation or prediction.\n     *\n     * @param ins Input `tf.Tensor`.\n     * @param batchSize Integer batch size, optional.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring loop finished. Optional.\n     * @param stepsName The public API's parameter name for `steps`.\n     * @returns Number of samples provided.\n     */\n    checkNumSamples(ins, batchSize, steps, stepsName = 'steps') {\n        let numSamples;\n        if (steps != null) {\n            numSamples = null;\n            if (batchSize != null) {\n                throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.` +\n                    `Got batchSize = ${batchSize}`);\n            }\n        }\n        else if (ins != null) {\n            if (Array.isArray(ins)) {\n                numSamples = ins[0].shape[0];\n            }\n            else {\n                numSamples = ins.shape[0];\n            }\n        }\n        else {\n            throw new ValueError(`Either the input data should have a defined shape, or ` +\n                `${stepsName} shoud be specified.`);\n        }\n        return numSamples;\n    }\n    /**\n     * Execute internal tensors of the model with input data feed.\n     * @param inputs Input data feed. Must match the inputs of the model.\n     * @param outputs Names of the output tensors to be fetched. Must match\n     *   names of the SymbolicTensors that belong to the graph.\n     * @returns Fetched values for `outputs`.\n     */\n    execute(inputs, outputs) {\n        if (Array.isArray(outputs) && outputs.length === 0) {\n            throw new ValueError('`outputs` is an empty Array, which is not allowed.');\n        }\n        const outputsIsArray = Array.isArray(outputs);\n        const outputNames = (outputsIsArray ? outputs : [outputs]);\n        const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n        // Format the input into a FeedDict.\n        const feedDict = new FeedDict();\n        if (inputs instanceof Tensor) {\n            inputs = [inputs];\n        }\n        if (Array.isArray(inputs)) {\n            if (inputs.length !== this.inputs.length) {\n                throw new ValueError(`The number of inputs provided (${inputs.length}) ` +\n                    `does not match the number of inputs of this model ` +\n                    `(${this.inputs.length}).`);\n            }\n            for (let i = 0; i < this.inputs.length; ++i) {\n                feedDict.add(this.inputs[i], inputs[i]);\n            }\n        }\n        else {\n            for (const input of this.inputs) {\n                const tensorValue = inputs[input.name];\n                if (tensorValue == null) {\n                    throw new ValueError(`No value is provided for the model's input ${input.name}`);\n                }\n                feedDict.add(input, tensorValue);\n            }\n        }\n        // Run execution.\n        const executeOutputs = execute(outputSymbolicTensors, feedDict);\n        return outputsIsArray ? executeOutputs : executeOutputs[0];\n    }\n    /**\n     * Retrieve the model's internal symbolic tensors from symbolic-tensor names.\n     */\n    retrieveSymbolicTensors(symbolicTensorNames) {\n        const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n        let outputsRemaining = symbolicTensorNames.length;\n        for (const layer of this.layers) {\n            const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n            const layerOutputNames = layerOutputs.map(output => output.name);\n            for (let i = 0; i < symbolicTensorNames.length; ++i) {\n                const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n                if (index !== -1) {\n                    outputSymbolicTensors[i] = layerOutputs[index];\n                    outputsRemaining--;\n                }\n                if (outputsRemaining === 0) {\n                    break;\n                }\n            }\n            if (outputsRemaining === 0) {\n                break;\n            }\n        }\n        if (outputsRemaining > 0) {\n            const remainingNames = [];\n            outputSymbolicTensors.forEach((tensor, i) => {\n                if (tensor == null) {\n                    remainingNames.push(symbolicTensorNames[i]);\n                }\n            });\n            throw new ValueError(`Cannot find SymbolicTensors for output name(s): ` +\n                `${JSON.stringify(remainingNames)}`);\n        }\n        return outputSymbolicTensors;\n    }\n    /**\n     * Helper method to loop over some data in batches.\n     *\n     * Porting Note: Not using the functional approach in the Python equivalent\n     *   due to the imperative backend.\n     * Porting Note: Does not support step mode currently.\n     *\n     * @param ins: input data\n     * @param batchSize: integer batch size.\n     * @param verbose: verbosity model\n     * @returns: Predictions as `tf.Tensor` (if a single output) or an `Array` of\n     *   `tf.Tensor` (if multipe outputs).\n     */\n    predictLoop(ins, batchSize = 32, verbose = false) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins);\n            if (verbose) {\n                throw new NotImplementedError('Verbose predictLoop() is not implemented yet.');\n            }\n            // Sample-based predictions.\n            // Porting Note: Tensor currently does not support sliced assignments as\n            //   in numpy, e.g., x[1:3] = y. Therefore we use concatenation while\n            //   iterating over the batches.\n            const batches = makeBatches(numSamples, batchSize);\n            const outsBatches = this.outputs.map(output => []);\n            // TODO(cais): Can the scope() be pushed down inside the for loop?\n            for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                const batchOuts = tfc.tidy(() => {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    // TODO(cais): Take care of the case of the last element is a flag for\n                    //   training/test.\n                    const insBatch = sliceArrays(ins, batchStart, batchEnd);\n                    // Construct the feeds for execute();\n                    const feeds = [];\n                    if (Array.isArray(insBatch)) {\n                        for (let i = 0; i < insBatch.length; ++i) {\n                            feeds.push({ key: this.inputs[i], value: insBatch[i] });\n                        }\n                    }\n                    else {\n                        feeds.push({ key: this.inputs[0], value: insBatch });\n                    }\n                    const feedDict = new FeedDict(feeds);\n                    return execute(this.outputs, feedDict);\n                });\n                batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n            }\n            return singletonOrArray(outsBatches.map(batches => tfc.concat(batches, 0)));\n        });\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFlow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([8, 10]), {batchSize: 4}).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param args A `ModelPredictArgs` object containing optional fields.\n     *\n     * @return Prediction results as a `tf.Tensor`(s).\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n        checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n        try {\n            // TODO(cais): Take care of stateful models.\n            //   if (this.stateful) ...\n            // TODO(cais): Take care of the learning_phase boolean flag.\n            //   if (this.useLearningPhase) ...\n            const batchSize = args.batchSize == null ? 32 : args.batchSize;\n            checkBatchSize(batchSize);\n            return this.predictLoop(xsRank2OrHigher, batchSize);\n        }\n        finally {\n            disposeNewTensors(xsRank2OrHigher, x);\n        }\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predictOnBatch(tf.ones([8, 10])).print();\n     * ```\n     * @param x: Input samples, as a Tensor (for models with exactly one\n     *   input) or an array of Tensors (for models with more than one input).\n     * @return Tensor(s) of predictions\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predictOnBatch(x) {\n        checkInputData(x, this.inputNames, this.feedInputShapes, true);\n        // TODO(cais): Take care of the learning_phase boolean flag.\n        //   if (this.useLearningPhase) ...\n        const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n        return this.predictLoop(x, batchSize);\n    }\n    standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n        // TODO(cais): Add sampleWeight, classWeight\n        if (this.optimizer_ == null) {\n            throw new RuntimeError('You must compile a model before training/testing. Use ' +\n                'LayersModel.compile(modelCompileArgs).');\n        }\n        const outputShapes = [];\n        for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n            const outputShape = this.feedOutputShapes[i];\n            const lossFn = this.feedLossFns[i];\n            if (lossFn === losses.sparseCategoricalCrossentropy) {\n                outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n            }\n            else {\n                // Porting Note: Because of strong typing `lossFn` must be a function.\n                outputShapes.push(outputShape);\n            }\n        }\n        x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, 'input');\n        y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, 'target');\n        // TODO(cais): Standardize sampleWeights & classWeights.\n        checkArrayLengths(x, y, null);\n        // TODO(cais): Check sampleWeights as well.\n        checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n        if (this.stateful && batchSize != null && batchSize > 0) {\n            if (x[0].shape[0] % batchSize !== 0) {\n                throw new ValueError(`In a stateful network, you should only pass inputs with a ` +\n                    `number of samples that is divisible by the batch size ` +\n                    `${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n            }\n        }\n        return [x, y];\n    }\n    async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n        const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n        // TODO(cais): Handle sampleWeights.\n        if (sampleWeight != null) {\n            throw new Error('sample weight is not supported yet.');\n        }\n        let standardSampleWeights = null;\n        if (classWeight != null) {\n            const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n            standardSampleWeights = [];\n            for (let i = 0; i < classWeights.length; ++i) {\n                standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n            }\n        }\n        // TODO(cais): Deal with the case of model.stateful == true.\n        return [standardXs, standardYs, standardSampleWeights];\n    }\n    /**\n     * Loop over some test data in batches.\n     * @param f A Function returning a list of tensors.\n     * @param ins Array of tensors to be fed to `f`.\n     * @param batchSize Integer batch size or `null` / `undefined`.\n     * @param verbose verbosity mode.\n     * @param steps Total number of steps (batches of samples) before\n     * declaring test finished. Ignored with the default value of `null` /\n     * `undefined`.\n     * @returns Array of Scalars.\n     */\n    testLoop(f, ins, batchSize, verbose = 0, steps) {\n        return tfc.tidy(() => {\n            const numSamples = this.checkNumSamples(ins, batchSize, steps, 'steps');\n            const outs = [];\n            if (verbose > 0) {\n                throw new NotImplementedError('Verbose mode is not implemented yet.');\n            }\n            // TODO(cais): Use `indicesForConversionToDense' to prevent slow down.\n            if (steps != null) {\n                throw new NotImplementedError('steps mode in testLoop() is not implemented yet');\n            }\n            else {\n                const batches = makeBatches(numSamples, batchSize);\n                const indexArray = tensor1d(range(0, numSamples));\n                for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n                    const batchStart = batches[batchIndex][0];\n                    const batchEnd = batches[batchIndex][1];\n                    const batchIds = K.sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n                    // TODO(cais): In ins, train flag can be a number, instead of an\n                    //   Tensor? Do we need to handle this in tfjs-layers?\n                    const insBatch = sliceArraysByIndices(ins, batchIds);\n                    const batchOuts = f(insBatch);\n                    if (batchIndex === 0) {\n                        for (let i = 0; i < batchOuts.length; ++i) {\n                            outs.push(scalar(0));\n                        }\n                    }\n                    for (let i = 0; i < batchOuts.length; ++i) {\n                        const batchOut = batchOuts[i];\n                        outs[i] =\n                            tfc.add(outs[i], tfc.mul(batchEnd - batchStart, batchOut));\n                    }\n                }\n                for (let i = 0; i < outs.length; ++i) {\n                    outs[i] = tfc.div(outs[i], numSamples);\n                }\n            }\n            return outs;\n        });\n    }\n    getDedupedMetricsNames() {\n        const outLabels = this.metricsNames;\n        // Rename duplicated metrics names (can happen with an output layer\n        // shared among multiple dataflows).\n        const dedupedOutLabels = [];\n        for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            let newLabel = label;\n            if (count(outLabels, label) > 1) {\n                const dupIndex = count(outLabels.slice(0, i), label);\n                newLabel += `_${dupIndex}`;\n            }\n            dedupedOutLabels.push(newLabel);\n        }\n        return dedupedOutLabels;\n    }\n    /**\n     * Creates a function that performs the following actions:\n     *\n     * 1. computes the losses\n     * 2. sums them to get the total loss\n     * 3. call the optimizer computes the gradients of the LayersModel's\n     *    trainable weights w.r.t. the total loss and update the variables\n     * 4. calculates the metrics\n     * 5. returns the values of the losses and metrics.\n     */\n    makeTrainFunction() {\n        return (data) => {\n            const lossValues = [];\n            const inputs = data.slice(0, this.inputs.length);\n            const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n            const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n            const metricsValues = [];\n            // Create a function that computes the total loss based on the\n            // inputs. This function is used for obtaining gradients through\n            // backprop.\n            const totalLossFunction = () => {\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict, { 'training': true });\n                // TODO(cais): Take care of the case of multiple outputs from a\n                //   single layer?\n                let totalLoss;\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    let loss = lossFunction(targets[i], outputs[i]);\n                    if (sampleWeights[i] != null) {\n                        loss = computeWeightedLoss(loss, sampleWeights[i]);\n                    }\n                    // TODO(cais): push Scalar instead.\n                    const meanLoss = tfc.mean(loss);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    lossValues.push(meanLoss);\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                }\n                // Compute the metrics.\n                // TODO(cais): These should probably be calculated outside\n                //   totalLossFunction to benefit speed?\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    let weightedMetric;\n                    if (this.outputs.length > 1 && i < this.outputs.length) {\n                        weightedMetric = lossValues[i];\n                    }\n                    else {\n                        const metric = this.metricsTensors[i][0];\n                        const outputIndex = this.metricsTensors[i][1];\n                        weightedMetric =\n                            tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    }\n                    tfc.keep(weightedMetric);\n                    // TODO(cais): Use a scope() instead, to avoid ownership.\n                    metricsValues.push(weightedMetric);\n                }\n                totalLoss = tfc.mean(totalLoss);\n                // Add regularizer penalties.\n                this.calculateLosses().forEach(regularizerLoss => {\n                    totalLoss = tfc.add(totalLoss, regularizerLoss);\n                });\n                return totalLoss;\n            };\n            const variables = this.collectedTrainableWeights.map(param => param.read());\n            const returnCost = true;\n            const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n            return [totalLossValue].concat(metricsValues);\n        };\n    }\n    /**\n     * Create a function which, when invoked with an array of `tf.Tensor`s as a\n     * batch of inputs, returns the prespecified loss and metrics of the model\n     * under the batch of input data.\n     */\n    makeTestFunction() {\n        this.testFunction = (data) => {\n            return tfc.tidy(() => {\n                const valOutputs = [];\n                let totalLoss;\n                const inputs = data.slice(0, this.inputs.length);\n                const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n                const feeds = [];\n                for (let i = 0; i < this.inputs.length; ++i) {\n                    feeds.push({ key: this.inputs[i], value: inputs[i] });\n                }\n                const feedDict = new FeedDict(feeds);\n                const outputs = execute(this.outputs, feedDict);\n                // Compute total loss.\n                for (let i = 0; i < this.lossFunctions.length; ++i) {\n                    const lossFunction = this.lossFunctions[i];\n                    // TODO(cais): Add sample weighting and replace the simple\n                    // averaging.\n                    const loss = tfc.mean(lossFunction(targets[i], outputs[i]));\n                    if (i === 0) {\n                        totalLoss = loss;\n                    }\n                    else {\n                        totalLoss = tfc.add(totalLoss, loss);\n                    }\n                    valOutputs.push(totalLoss);\n                }\n                // Compute the metrics.\n                for (let i = 0; i < this.metricsTensors.length; ++i) {\n                    const metric = this.metricsTensors[i][0];\n                    const outputIndex = this.metricsTensors[i][1];\n                    // TODO(cais): Replace K.mean() with a proper weighting function.\n                    const meanMetric = tfc.mean(metric(targets[outputIndex], outputs[outputIndex]));\n                    valOutputs.push(meanMetric);\n                }\n                return valOutputs;\n            });\n        };\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a\n     * dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *     layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * for (let i = 1; i < 5 ; ++i) {\n     *   const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *       batchSize: 4,\n     *       epochs: 3\n     *   });\n     *   console.log(\"Loss after Epoch \" + i + \" : \" + h.history.loss[0]);\n     * }\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you\n     * can also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named,\n     * you can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args A `ModelFitArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input\n     * data and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        return fitTensors(this, x, y, args);\n    }\n    // TODO(cais): Add code snippet below when it's possible to instantiate\n    //   actual dataset objects.\n    /**\n     * Trains the model using a dataset object.\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for training. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs.\n     *   Of the two items in the array, the first is the input feature(s) and\n     *   the second is the output target(s).\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fitDataset(dataset, args) {\n        return fitDataset(this, dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        // TODO(cais): Support sampleWeight and classWeight.\n        // TODO(cais): Support Dataset objects.\n        const standardizeOut = await this.standardizeUserData(x, y);\n        const inputs = standardizeOut[0];\n        const targets = standardizeOut[1];\n        const trainFunction = this.makeTrainFunction();\n        const losses = trainFunction(inputs.concat(targets));\n        const lossValues = [];\n        for (const loss of losses) {\n            const v = await loss.data();\n            lossValues.push(v[0]);\n        }\n        tfc.dispose(losses);\n        return singletonOrArray(lossValues);\n    }\n    /**\n     * Extract weight values of the model.\n     *\n     * @param config: An instance of `io.SaveConfig`, which specifies\n     * model-saving options such as whether only trainable weights are to be\n     * saved.\n     * @returns A `NamedTensorMap` mapping original weight names (i.e.,\n     *   non-uniqueified weight names) to their values.\n     */\n    getNamedWeights(config) {\n        const namedWeights = [];\n        const trainableOnly = config != null && config.trainableOnly;\n        const weights = trainableOnly ? this.trainableWeights : this.weights;\n        const weightValues = this.getWeights(trainableOnly);\n        for (let i = 0; i < weights.length; ++i) {\n            if (trainableOnly && !weights[i].trainable) {\n                // Optionally skip non-trainable weights.\n                continue;\n            }\n            namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n        }\n        return namedWeights;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const input = tf.input({shape: [10]});\n     * const output = tf.layers.dense({units: 1}).apply(input);\n     * const model = tf.model({inputs: [input], outputs: [output]});\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10\n     * values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        this.stopTraining_ = stop;\n    }\n    get stopTraining() {\n        return this.stopTraining_;\n    }\n    get optimizer() {\n        return this.optimizer_;\n    }\n    set optimizer(optimizer) {\n        if (this.optimizer_ !== optimizer) {\n            this.optimizer_ = optimizer;\n            this.isOptimizerOwned = false;\n        }\n    }\n    dispose() {\n        const result = super.dispose();\n        if (result.refCountAfterDispose === 0 && this.optimizer != null &&\n            this.isOptimizerOwned) {\n            const numTensorsBeforeOptmizerDisposal = tfc.memory().numTensors;\n            this.optimizer_.dispose();\n            result.numDisposedVariables +=\n                numTensorsBeforeOptmizerDisposal - tfc.memory().numTensors;\n        }\n        return result;\n    }\n    getLossIdentifiers() {\n        let lossNames;\n        if (typeof this.loss === 'string') {\n            lossNames = toSnakeCase(this.loss);\n        }\n        else if (Array.isArray(this.loss)) {\n            for (const loss of this.loss) {\n                if (typeof loss !== 'string') {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n            lossNames = this.loss.map(name => toSnakeCase(name));\n        }\n        else {\n            const outputNames = Object.keys(this.loss);\n            lossNames = {};\n            const losses = this.loss;\n            for (const outputName of outputNames) {\n                if (typeof losses[outputName] === 'string') {\n                    lossNames[outputName] =\n                        toSnakeCase(losses[outputName]);\n                }\n                else {\n                    throw new Error('Serialization of non-string loss is not supported.');\n                }\n            }\n        }\n        return lossNames;\n    }\n    getMetricIdentifiers() {\n        if (typeof this.metrics === 'string' ||\n            typeof this.metrics === 'function') {\n            return [toSnakeCase(Metrics.getLossOrMetricName(this.metrics))];\n        }\n        else if (Array.isArray(this.metrics)) {\n            return this.metrics.map(metric => toSnakeCase(Metrics.getLossOrMetricName(metric)));\n        }\n        else {\n            const metricsIdentifiers = {};\n            for (const key in this.metrics) {\n                metricsIdentifiers[key] =\n                    toSnakeCase(Metrics.getLossOrMetricName(this.metrics[key]));\n            }\n            return metricsIdentifiers;\n        }\n    }\n    getTrainingConfig() {\n        return {\n            loss: this.getLossIdentifiers(),\n            metrics: this.getMetricIdentifiers(),\n            optimizer_config: {\n                class_name: this.optimizer.getClassName(),\n                config: this.optimizer.getConfig()\n            }\n        };\n        // TODO(cais): Add weight_metrics when they are supported.\n        // TODO(cais): Add sample_weight_mode when it's supported.\n        // TODO(cais): Add loss_weights when it's supported.\n    }\n    loadTrainingConfig(trainingConfig) {\n        if (trainingConfig.weighted_metrics != null) {\n            throw new Error('Loading weight_metrics is not supported yet.');\n        }\n        if (trainingConfig.loss_weights != null) {\n            throw new Error('Loading loss_weights is not supported yet.');\n        }\n        if (trainingConfig.sample_weight_mode != null) {\n            throw new Error('Loading sample_weight_mode is not supported yet.');\n        }\n        const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n        const optimizer = deserialize(tsConfig);\n        let loss;\n        if (typeof trainingConfig.loss === 'string') {\n            loss = toCamelCase(trainingConfig.loss);\n        }\n        else if (Array.isArray(trainingConfig.loss)) {\n            loss = trainingConfig.loss.map(lossEntry => toCamelCase(lossEntry));\n        }\n        else if (trainingConfig.loss != null) {\n            loss = {};\n            for (const key in trainingConfig.loss) {\n                loss[key] = toCamelCase(trainingConfig.loss[key]);\n            }\n        }\n        let metrics;\n        if (Array.isArray(trainingConfig.metrics)) {\n            metrics = trainingConfig.metrics.map(metric => toCamelCase(metric));\n        }\n        else if (trainingConfig.metrics != null) {\n            metrics = {};\n            for (const key in trainingConfig.metrics) {\n                metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n            }\n        }\n        this.compile({ loss, metrics, optimizer });\n    }\n    /**\n     * Save the configuration and/or weights of the LayersModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 2. Saving `model`'s topology and weights to browser\n     * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n     * then load it back.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * console.log('Prediction from original model:');\n     * model.predict(tf.ones([1, 3])).print();\n     *\n     * const saveResults = await model.save('indexeddb://my-model-1');\n     *\n     * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * loadedModel.predict(tf.ones([1, 3])).print();\n     * ```\n     *\n     * Example 3. Saving `model`'s topology and weights as two files\n     * (`my-model-1.json` and `my-model-1.weights.bin`) downloaded from\n     * browser.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('downloads://my-model-1');\n     * ```\n     *\n     * Example 4. Send  `model`'s topology and weights to an HTTP server.\n     * See the documentation of `tf.io.http` for more details\n     * including specifying request parameters and implementation of the\n     * server.\n     *\n     * ```js\n     * const model = tf.sequential(\n     *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n     * const saveResults = await model.save('http://my-server/model/upload');\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new ValueError(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new ValueError('LayersModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        const weightDataAndSpecs = await io.encodeWeights(this.getNamedWeights(config));\n        const returnString = false;\n        const unusedArg = null;\n        const modelConfig = this.toJSON(unusedArg, returnString);\n        const modelArtifacts = {\n            modelTopology: modelConfig,\n            format: LAYERS_MODEL_FORMAT_NAME,\n            generatedBy: `TensorFlow.js tfjs-layers v${version}`,\n            convertedBy: null,\n        };\n        const includeOptimizer = config == null ? false : config.includeOptimizer;\n        if (includeOptimizer && this.optimizer != null) {\n            modelArtifacts.trainingConfig = this.getTrainingConfig();\n            const weightType = 'optimizer';\n            const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io.encodeWeights(await this.optimizer.getWeights(), weightType);\n            weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n            weightDataAndSpecs.data = io.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n        }\n        if (this.userDefinedMetadata != null) {\n            // Check serialized size of user-defined metadata.\n            const checkSize = true;\n            checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n            modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n        }\n        modelArtifacts.weightData = weightDataAndSpecs.data;\n        modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n        return handlerOrURL.save(modelArtifacts);\n    }\n    /**\n     * Set user-defined metadata.\n     *\n     * The set metadata will be serialized together with the topology\n     * and weights of the model during `save()` calls.\n     *\n     * @param setUserDefinedMetadata\n     */\n    setUserDefinedMetadata(userDefinedMetadata) {\n        checkUserDefinedMetadata(userDefinedMetadata, this.name);\n        this.userDefinedMetadata = userDefinedMetadata;\n    }\n    /**\n     * Get user-defined metadata.\n     *\n     * The metadata is supplied via one of the two routes:\n     *   1. By calling `setUserDefinedMetadata()`.\n     *   2. Loaded during model loading (if the model is constructed\n     *      via `tf.loadLayersModel()`.)\n     *\n     * If no user-defined metadata is available from either of the\n     * two routes, this function will return `undefined`.\n     */\n    getUserDefinedMetadata() {\n        return this.userDefinedMetadata;\n    }\n}\n// The class name is 'Model' rather than 'LayersModel' for backwards\n// compatibility since this class name shows up in the serialization format.\n/** @nocollapse */\nLayersModel.className = 'Model';\nserialization.registerClass(LayersModel);\n/**\n * A `tf.Functional` is an alias to `tf.LayersModel`.\n *\n * See also:\n *   `tf.LayersModel`, `tf.Sequential`, `tf.loadLayersModel`.\n */\n/** @doc {heading: 'Models', subheading: 'Classes'} */\nexport class Functional extends LayersModel {\n}\nFunctional.className = 'Functional';\nserialization.registerClass(Functional);\n//# sourceMappingURL=training.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source keras/models.py */\nimport { dispose, io, serialization, util } from '@tensorflow/tfjs-core';\nimport { getUid } from './backend/state';\nimport { Input } from './engine/input_layer';\nimport { getSourceInputs, Node } from './engine/topology';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError, RuntimeError, ValueError } from './errors';\nimport { deserialize } from './layers/serialization';\nimport * as generic_utils from './utils/generic_utils';\nimport { convertPythonicToTs } from './utils/serialization_utils';\nimport { getExactlyOneShape } from './utils/types_utils';\n/**\n * Parses a JSON model configuration file and returns a model instance.\n *\n * ```js\n * // This example shows how to serialize a model using `toJSON()` and\n * // deserialize it as another model using `tf.models.modelFromJSON()`.\n * // Note: this example serializes and deserializes only the topology\n * // of the model; the weights of the loaded model will be different\n * // from those of the the original model, due to random weight\n * // initialization.\n * // To load the topology and weights of a model, use `tf.loadLayersModel()`.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.repeatVector({inputShape: [2], n: 4}));\n * // Serialize `model1` as a JSON object.\n * const model1JSON = model1.toJSON(null, false);\n * model1.summary();\n *\n * const model2 = await tf.models.modelFromJSON(model1JSON);\n * model2.summary();\n * ```\n *\n *  @param modelAndWeightsConfig JSON object or string encoding a model and\n *       weights configuration. It can also be only the topology JSON of the\n *       model, in which case the weights will not be loaded.\n *  @param custom_objects Optional dictionary mapping names\n *       (strings) to custom classes or functions to be\n *       considered during deserialization.\n * @returns A TensorFlow.js Layers `tf.LayersModel` instance (uncompiled).\n */\nexport async function modelFromJSON(modelAndWeightsConfig, customObjects) {\n    if (!('modelTopology' in modelAndWeightsConfig)) {\n        modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n    }\n    modelAndWeightsConfig = modelAndWeightsConfig;\n    let modelTopology = modelAndWeightsConfig.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        // If the model-topology JSON contains a 'model_config' field, then it is\n        // a full model JSON (e.g., from `keras.Model.save()`), which contains\n        // not only the model's architecture in its 'model_config' field, but\n        // additional information such as the model's optimizer. We use only the\n        // 'model_config' field currently.\n        modelTopology = modelTopology['model_config'];\n    }\n    const tsConfig = convertPythonicToTs(modelTopology);\n    const model = deserialize(tsConfig, customObjects);\n    if (modelAndWeightsConfig.weightsManifest != null) {\n        // Load the weight values keyed by the original tensor names in the model\n        // file that was loaded.  These should match the keys of the weight\n        // manifest.\n        const weightValues = await io.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model.weights.map(weight => weight.originalName));\n        // Map the weights to the unique tensor names generated during model loading\n        const uniqueWeightValues = {};\n        for (const weight of model.weights) {\n            uniqueWeightValues[weight.originalName] =\n                weightValues[weight.originalName];\n        }\n        model.loadWeights(uniqueWeightValues);\n        // Dispose temporary weight values.\n        dispose(weightValues);\n    }\n    return model;\n}\n/**\n * Load a model, including its topology and optionally weights.  See the\n * Tutorial named \"How to import a Keras Model\" for usage examples.\n *\n * Example 1: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 2. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * Example 4. Load a model from an HTTP server.\n *\n * ```js\n * const model = await\n *     tf.loadLayersModel('https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. This path will be\n *      interpreted as a relative HTTP path, to which `fetch` will be used to\n *      request the model topology and weight manifest JSON.\n *      The content of the JSON file is assumed to be a JSON object with the\n *      following fields and values:\n *      - 'modelTopology': A JSON object that can be either of:\n *        1. a model architecture JSON consistent with the format of the return\n *            value of `keras.Model.to_json()`\n *        2. a full model JSON in the format of `keras.models.save_model()`.\n *      - 'weightsManifest': A TensorFlow.js weights manifest.\n *      See the Python converter function `save_model()` for more details.\n *      It is also assumed that model weights can be accessed from relative\n *      paths described by the `paths` fields in weights manifest.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A progress callback of the form:\n *     `(fraction: number) => void`. This callback can be used to monitor the\n *     model-loading process.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n */\nexport async function loadLayersModelInternal(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (typeof pathOrIOHandler === 'string') {\n        const handlers = io.getLoadHandlers(pathOrIOHandler, options);\n        if (handlers.length === 0) {\n            // For backward compatibility: if no load handler can be found,\n            // assume it is a relative http path.\n            // TODO(cais): Reformat the args into a single `LoadOptions` once the core\n            // is refactored.\n            handlers.push(io.browserHTTPRequest(pathOrIOHandler, options));\n        }\n        else if (handlers.length > 1) {\n            throw new ValueError(`Found more than one (${handlers.length}) load handlers for ` +\n                `URL '${pathOrIOHandler}'`);\n        }\n        pathOrIOHandler = handlers[0];\n    }\n    return loadLayersModelFromIOHandler(pathOrIOHandler, undefined, options);\n}\n/**\n * Load a model and optionally its weights, using an IOHandler object.\n *\n * @param handler The instance of `IOHandler` to be used during the model\n *   loading.\n * @param customObjects Any optional custom objects to be used during model\n *   loading.\n * @param strict Whether the weight loading will be done in strict mode.\n *   Default: `true`.\n */\nexport async function loadLayersModelFromIOHandler(handler, customObjects, options) {\n    if (options == null) {\n        options = {};\n    }\n    if (handler.load == null) {\n        throw new ValueError('Cannot proceed with model loading because the IOHandler provided ' +\n            'does not have the `load` method implemented.');\n    }\n    const artifacts = await handler.load();\n    let modelTopology = artifacts.modelTopology;\n    if (modelTopology['model_config'] != null) {\n        modelTopology = modelTopology['model_config'];\n    }\n    const strict = options.strict == null ? true : options.strict;\n    // If weights are provided and the weight-loading mode is strict, use\n    // fast weight initialization. This skips costly initializers such as\n    // 'orthogonal' and saves unnecessary computation in cases where\n    // the initialized weight values will immediately be overwritten by\n    // loaded weight values.\n    const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n    const model = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n    const trainingConfig = artifacts.trainingConfig;\n    if (trainingConfig != null) {\n        model.loadTrainingConfig(trainingConfig);\n    }\n    if (artifacts.userDefinedMetadata != null) {\n        model.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n    }\n    // If weightData is present, load the weights into the model.\n    if (artifacts.weightData != null) {\n        // Loading weights requires weightSpecs.\n        if (artifacts.weightSpecs == null) {\n            throw new ValueError('LayersModel artifacts contains weight data, but not weight specs. ' +\n                'Therefore loading of weights cannot proceed.');\n        }\n        const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n        model.loadWeights(modelWeights, strict);\n        if (model.optimizer != null && optimizerWeights.length > 0) {\n            await model.optimizer.setWeights(optimizerWeights);\n        }\n        // Dispose temporary weight values.\n        dispose(modelWeights);\n        dispose(optimizerWeights.map(w => w.tensor));\n    }\n    return model;\n}\nfunction decodeModelAndOptimizerWeights(buffer, specs) {\n    const name2Tensor = io.decodeWeights(buffer, specs);\n    const modelWeights = {};\n    const optimizerWeights = [];\n    specs.forEach(spec => {\n        if (spec.group === 'optimizer') {\n            optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n        }\n        else {\n            modelWeights[spec.name] = name2Tensor[spec.name];\n        }\n    });\n    return { modelWeights, optimizerWeights };\n}\n/**\n * A model with a stack of layers, feeding linearly from one to the next.\n *\n * `tf.sequential` is a factory function that creates an instance of\n * `tf.Sequential`.\n *\n * ```js\n *  // Define a model for linear regression.\n *  const model = tf.sequential();\n *  model.add(tf.layers.dense({units: 1, inputShape: [1]}));\n *\n *  // Prepare the model for training: Specify the loss and the optimizer.\n *  model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n *\n *  // Generate some synthetic data for training.\n *  const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);\n *  const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);\n *\n *  // Train the model using the data then do inference on a data point the\n *  // model hasn't seen:\n *  await model.fit(xs, ys);\n *  model.predict(tf.tensor2d([5], [1, 1])).print();\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class Sequential extends LayersModel {\n    constructor(args) {\n        super({ inputs: [], outputs: [] });\n        args = args || {};\n        this.trainable = true;\n        this.built = false;\n        // Set model name.\n        this.name = (args.name != null) ? args.name : getUid('sequential_');\n        // Add to the model any layers passed to the constructor.\n        if (args.layers != null) {\n            for (const layer of args.layers) {\n                this.add(layer);\n            }\n        }\n    }\n    // Helper function to Sequential.add  Throws if the new output shape will be\n    // invalid.\n    checkShape(layer) {\n        const shape = layer.inboundNodes[0].outputTensors[0].shape;\n        if (shape.some(x => x < 0)) {\n            throw new ValueError('Negative dimension size caused by adding layer ' +\n                `${layer.name} with input shape [` +\n                `${layer.inboundNodes[0].inputTensors[0].shape}]`);\n        }\n    }\n    /**\n     * Adds a layer instance on top of the layer stack.\n     *\n     * ```js\n     *  const model = tf.sequential();\n     *  model.add(tf.layers.dense({units: 8, inputShape: [1]}));\n     *  model.add(tf.layers.dense({units: 4, activation: 'relu6'}));\n     *  model.add(tf.layers.dense({units: 1, activation: 'relu6'}));\n     *  // Note that the untrained model is random at this point.\n     *  model.predict(tf.randomNormal([10, 1])).print();\n     * ```\n     * @param layer Layer instance.\n     *\n     * @exception ValueError In case the `layer` argument does not know its\n     * input shape.\n     * @exception ValueError In case the `layer` argument has multiple output\n     *   tensors, or is already connected somewhere else (forbidden in\n     *   `Sequential` models).\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    add(layer) {\n        const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n        let modelLayer;\n        if (isLayerModelInstance) {\n            modelLayer = layer;\n            if (modelLayer.outputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            if (modelLayer.inputs.length !== 1) {\n                throw new ValueError('All layers in a Sequential model ' +\n                    'should have a single input tensor. ' +\n                    'For multi-input layers, ' +\n                    'use the functional API.');\n            }\n        }\n        if (this.outputs.length === 0) {\n            // first layer in model: check that it is an input layer\n            if (layer.inboundNodes.length === 0) {\n                // create an input layer\n                if (layer.batchInputShape == null) {\n                    throw new ValueError('The first layer in a Sequential model must ' +\n                        'get an `inputShape` or `batchInputShape` argument.');\n                }\n                // Instantiate the input layer.\n                const x = Input({\n                    batchShape: layer.batchInputShape,\n                    dtype: layer.dtype,\n                    name: layer.name + '_input'\n                });\n                // This will build the current layer and create the node connecting\n                // the current layer to the input layer we just created.\n                layer.apply(x);\n            }\n            if (isLayerModelInstance) {\n                this.outputs = modelLayer.outputs;\n                this.inputs = modelLayer.inputs;\n            }\n            else {\n                if (layer.inboundNodes.length !== 1) {\n                    throw new ValueError('A layer added to a Sequential model must not already be ' +\n                        `connected somewhere else. LayersModel received layer ${layer.name} ` +\n                        `which has ${layer.inboundNodes.length} pre-existing inbound ` +\n                        'connections.');\n                }\n                if (layer.inboundNodes[0].outputTensors.length !== 1) {\n                    throw new ValueError('All layers in a Sequential model ' +\n                        'should have a single output tensor. ' +\n                        'For multi-output layers, ' +\n                        'use the functional API.');\n                }\n                this.checkShape(layer);\n                this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n                this.inputs = getSourceInputs(this.outputs[0]);\n            }\n            this.inboundNodes = [];\n            // We create an input node, which we will keep updated\n            // as we add more layers.\n            // (This call has side effects.)\n            // tslint:disable-next-line:no-unused-expression\n            new Node({\n                outboundLayer: this,\n                inboundLayers: [],\n                nodeIndices: [],\n                tensorIndices: [],\n                inputTensors: this.inputs,\n                outputTensors: this.outputs,\n                // no model-level masking for now\n                inputMasks: generic_utils.pyListRepeat(null, this.inputs.length),\n                outputMasks: [null],\n                inputShapes: this.inputs.map(x => x.shape),\n                outputShapes: this.outputs[0].shape\n            });\n        }\n        else {\n            const outputTensor = layer.apply(this.outputs[0]);\n            if (Array.isArray(outputTensor)) {\n                throw new TypeError('All layers in a Sequential model ' +\n                    'should have a single output tensor. ' +\n                    'For multi-output layers, ' +\n                    'use the functional API.');\n            }\n            this.checkShape(layer);\n            this.outputs = [outputTensor];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n        this.layers.push(layer);\n        this.built = false;\n    }\n    /**\n     * Removes the last layer in the model.\n     *\n     * @exception TypeError if there are no layers in the model.\n     */\n    pop() {\n        if (this.layers.length === 0) {\n            throw new TypeError('There are no layers in the model.');\n        }\n        this.layers.pop();\n        if (this.layers.length === 0) {\n            this.outputs = [];\n            this.inboundNodes = [];\n            this.outboundNodes = [];\n        }\n        else {\n            const lastLayerIndex = this.layers.length - 1;\n            this.layers[lastLayerIndex].outboundNodes = [];\n            this.outputs = [this.layers[lastLayerIndex].output];\n            // update self.inbound_nodes\n            this.inboundNodes[0].outputTensors = this.outputs;\n            this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n        }\n    }\n    call(inputs, kwargs) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.call(inputs, kwargs);\n    }\n    build(inputShape) {\n        // Call `getExactlyOneShape` without using its return value,\n        // to verify that exactly one input shape is provided.\n        getExactlyOneShape(inputShape);\n        if (this.inputs.length === 0 || this.outputs.length === 0) {\n            throw new TypeError('Sequential model cannot be built: model is empty.' +\n                ' Add some layers first.');\n        }\n        // actually create the model\n        this.model = new LayersModel({\n            inputs: this.inputs,\n            outputs: this.outputs[0],\n            name: this.name + '_model'\n        });\n        this.model.trainable = this.trainable;\n        // mirror model attributes\n        this.supportsMasking = this.model.supportsMasking;\n        // TODO(michaelterry): Add caches\n        this.inputLayers = this.model.inputLayers;\n        this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n        this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n        this.outputLayers = this.model.outputLayers;\n        this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n        this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n        this.nodesByDepth = this.model.nodesByDepth;\n        this.containerNodes = this.model.containerNodes;\n        this.outputNames = this.model.outputNames;\n        this.inputNames = this.model.inputNames;\n        // TODO(michaelterry): Add feedInputNames, feedInputs, if needed.\n        // TODO(michaelterry): Add callbackModel if needed.\n        this.built = true;\n    }\n    countParams() {\n        if (!this.built) {\n            this.build();\n        }\n        return super.countParams();\n    }\n    /**\n     * Print a text summary of the Sequential model's layers.\n     *\n     * The summary includes\n     * - Name and type of all layers that comprise the model.\n     * - Output shape(s) of the layers\n     * - Number of weight parameters of each layer\n     * - The total number of trainable and non-trainable parameters of the\n     * model.\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(\n     *     tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));\n     * model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n     *\n     * model.summary();\n     * ```\n     *\n     * @param lineLength Custom line length, in number of characters.\n     * @param positions Custom widths of each of the columns, as either\n     *   fractions of `lineLength` (e.g., `[0.5, 0.75, 1]`) or absolute number\n     *   of characters (e.g., `[30, 50, 65]`). Each number corresponds to\n     *   right-most (i.e., ending) position of a column.\n     * @param printFn Custom print function. Can be used to replace the default\n     *   `console.log`. For example, you can use `x => {}` to mute the printed\n     *   messages in the console.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    summary(lineLength, positions, printFn = console.log) {\n        if (!this.built) {\n            this.build();\n        }\n        super.summary(lineLength, positions, printFn);\n    }\n    /**\n     * Sets the weights of the model.\n     *\n     * @param weights Should be a list of Tensors with shapes and types matching\n     *   the output of `model.getWeights()`.\n     */\n    setWeights(weights) {\n        if (this.model == null) {\n            this.build();\n        }\n        this.model.setWeights(weights);\n    }\n    /**\n     * Returns the loss value & metrics values for the model in test mode.\n     *\n     * Loss and metrics are specified during `compile()`, which needs to happen\n     * before calls to `evaluate()`.\n     *\n     * Computation is done in batches.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     * });\n     * result.print();\n     * ```\n     *\n     * @param x `tf.Tensor` of test data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple inputs.\n     * @param y `tf.Tensor` of target data, or an `Array` of `tf.Tensor`s if the\n     * model has multiple outputs.\n     * @param args A `ModelEvaluateConfig`, containing optional fields.\n     *\n     * @return `Scalar` test loss (if the model has a single output and no\n     *   metrics) or `Array` of `Scalar`s (if the model has multiple outputs\n     *   and/or metrics). The attribute `model.metricsNames`\n     *   will give you the display labels for the scalar outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    evaluate(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluate(x, y, args);\n    }\n    // TODO(cais): Add code snippet below once real dataset objects are\n    //   available.\n    /**\n     * Evaluate model using a dataset object.\n     *\n     * Note: Unlike `evaluate()`, this method is asynchronous (`async`);\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected\n     *   to generate a dataset iterator object, the `next()` method of which\n     *   is expected to produce data batches for evaluation. The return value\n     *   of the `next()` call ought to contain a boolean `done` field and a\n     *   `value` field. The `value` field is expected to be an array of two\n     *   `tf.Tensor`s or an array of two nested `tf.Tensor` structures. The former\n     *   case is for models with exactly one input and one output (e.g..\n     *   a sequential model). The latter case is for models with multiple\n     *   inputs and/or multiple outputs. Of the two items in the array, the\n     *   first is the input feature(s) and the second is the output target(s).\n     * @param args A configuration object for the dataset-based evaluation.\n     * @returns Loss and metric values as an Array of `Scalar` objects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async evaluateDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before being used.');\n        }\n        return this.model.evaluateDataset(dataset, args);\n    }\n    /**\n     * Generates output predictions for the input samples.\n     *\n     * Computation is done in batches.\n     *\n     * Note: the \"step\" mode of predict() is currently not supported.\n     *   This is because the TensorFow.js core backend is imperative only.\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.predict(tf.ones([2, 10])).print();\n     * ```\n     *\n     * @param x The input data, as a Tensor, or an `Array` of `tf.Tensor`s if\n     *   the model has multiple inputs.\n     * @param conifg A `ModelPredictConfig` object containing optional fields.\n     *\n     * @return `tf.Tensor`(s) of predictions.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and the model's expectations, or in case a stateful model receives a\n     *   number of samples that is not a multiple of the batch size.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(x, args = {}) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predict(x, args);\n    }\n    /**\n     * Returns predictions for a single batch of samples.\n     *\n     * @param x: Input samples, as a Tensor, or list of Tensors (if the model\n     *   has multiple inputs).\n     * @return Tensor(s) of predictions\n     */\n    predictOnBatch(x) {\n        if (this.model == null) {\n            this.build();\n        }\n        return this.model.predictOnBatch(x);\n    }\n    /**\n     * See `LayersModel.compile`.\n     *\n     * @param args\n     */\n    compile(args) {\n        this.build();\n        this.model.compile(args);\n        this.optimizer_ = this.model.optimizer;\n        // tslint:disable-next-line:no-any\n        this.isOptimizerOwned = this.model.isOptimizerOwned;\n        this.loss = this.model.loss;\n        this.metrics = this.model.metrics;\n        // TODO(cais): Add this.lossWeights, this.sampleWeightMode,\n        //   this.weightedMetrics, this.targets.\n        this.metricsTensors = this.model.metricsTensors;\n        this.metricsNames = this.model.metricsNames;\n        // TODO(cais): Add sampleWeights.\n    }\n    get optimizer() {\n        return this.model == null ? undefined : this.model.optimizer;\n    }\n    set optimizer(optimizer) {\n        this.model.optimizer = optimizer;\n    }\n    /**\n     * Trains the model for a fixed number of epochs (iterations on a dataset).\n     *\n     * ```js\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [10]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {\n     *   batchSize: 4,\n     *   epochs: 3\n     * });\n     * console.log(history.history.loss[0]);\n     * ```\n     *\n     * @param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the\n     * model has multiple inputs. If all inputs in the model are named, you can\n     * also pass a dictionary mapping input names to `tf.Tensor`s.\n     * @param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if\n     * the model has multiple outputs. If all outputs in the model are named, you\n     *  can also pass a dictionary mapping output names to `tf.Tensor`s.\n     * @param args  A `ModelFitConfig`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @exception ValueError In case of mismatch between the provided input data\n     *   and what the model expects.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async fit(x, y, args = {}) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fit(x, y, args);\n    }\n    /**\n     * Trains the model using a dataset object.\n     *\n     * ```js\n     * const xArray = [\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     *   [1, 1, 1, 1, 1, 1, 1, 1, 1],\n     * ];\n     * const yArray = [1, 1, 1, 1];\n     * // Create a dataset from the JavaScript array.\n     * const xDataset = tf.data.array(xArray);\n     * const yDataset = tf.data.array(yArray);\n     * // Zip combines the `x` and `y` Datasets into a single Dataset, the\n     * // iterator of which will return an object containing of two tensors,\n     * // corresponding to `x` and `y`.  The call to `batch(4)` will bundle\n     * // four such samples into a single object, with the same keys now pointing\n     * // to tensors that hold 4 examples, organized along the batch dimension.\n     * // The call to `shuffle(4)` causes each iteration through the dataset to\n     * // happen in a different order.  The size of the shuffle window is 4.\n     * const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})\n     *     .batch(4)\n     *     .shuffle(4);\n     * const model = tf.sequential({\n     *   layers: [tf.layers.dense({units: 1, inputShape: [9]})]\n     * });\n     * model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});\n     * const history = await model.fitDataset(xyDataset, {\n     *   epochs: 4,\n     *   callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}\n     * });\n     * ```\n     *\n     * @param dataset A dataset object. Its `iterator()` method is expected to\n     *   generate a dataset iterator object, the `next()` method of which is\n     *   expected to produce data batches for evaluation. The return value of the\n     *   `next()` call ought to contain a boolean `done` field and a `value`\n     *   field.\n     *\n     *   The `value` field is expected to be an object of with fields\n     *   `xs` and `ys`, which point to the feature tensor and the target tensor,\n     *   respectively. This case is for models with exactly one input and one\n     *   output (e.g.. a sequential model). For example:\n     *   ```js\n     *   {value: {xs: xsTensor, ys: ysTensor}, done: false}\n     *   ```\n     *\n     *   If the model has multiple inputs, the `xs` field of `value` should\n     *   be an object mapping input names to their respective feature tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: {\n     *         input_1: xsTensor1,\n     *         input_2: xsTensor2\n     *       },\n     *       ys: ysTensor\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     *   If the model has multiple outputs, the `ys` field of `value` should\n     *   be an object mapping output names to their respective target tensors.\n     *   For example:\n     *   ```js\n     *   {\n     *     value: {\n     *       xs: xsTensor,\n     *       ys: {\n     *         output_1: ysTensor1,\n     *         output_2: ysTensor2\n     *       },\n     *     },\n     *     done: false\n     *   }\n     *   ```\n     * @param args A `ModelFitDatasetArgs`, containing optional fields.\n     *\n     * @return A `History` instance. Its `history` attribute contains all\n     *   information collected during training.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async fitDataset(dataset, args) {\n        if (!this.built) {\n            throw new RuntimeError('The model needs to be compiled before ' +\n                'being used.');\n        }\n        return this.model.fitDataset(dataset, args);\n    }\n    /**\n     * Runs a single gradient update on a single batch of data.\n     *\n     * This method differs from `fit()` and `fitDataset()` in the following\n     * regards:\n     *   - It operates on exactly one batch of data.\n     *   - It returns only the loss and matric values, instead of\n     *     returning the batch-by-batch loss and metric values.\n     *   - It doesn't support fine-grained options such as verbosity and\n     *     callbacks.\n     *\n     * @param x Input data. It could be one of the following:\n     *   - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has\n     *     multiple inputs).\n     *   - An Object mapping input names to corresponding `tf.Tensor` (if the\n     *     model has named inputs).\n     * @param y Target darta. It could be either a `tf.Tensor` a multiple\n     *   `tf.Tensor`s. It should be consistent with `x`.\n     * @returns Training loss or losses (in case the model has\n     *   multiple outputs), along with metrics (if any), as numbers.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async trainOnBatch(x, y) {\n        return this.model.trainOnBatch(x, y);\n    }\n    /* See parent class for JsDoc */\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n        let configArray;\n        let extraModelConfig = {};\n        if (config instanceof Array) {\n            if (!(config[0].className != null) ||\n                config[0]['className'] === 'Merge') {\n                throw new ValueError('Legacy serialization format not supported yet.');\n            }\n            configArray = config;\n        }\n        else {\n            util.assert(config['layers'] != null, () => `When the config data for a Sequential model is not an Array, ` +\n                `it must be an Object that contains the 'layers' field.`);\n            configArray = config['layers'];\n            delete config['layers'];\n            extraModelConfig = config;\n        }\n        const model = new cls(extraModelConfig);\n        if (!(model instanceof Sequential)) {\n            throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model}`);\n        }\n        for (const conf of configArray) {\n            const customObjects = undefined;\n            const layer = deserialize(conf, customObjects, fastWeightInit);\n            if (fastWeightInit) {\n                layer.setFastWeightInitDuringBuild(true);\n            }\n            model.add(layer);\n        }\n        return model;\n    }\n    /**\n     * Setter used for force stopping of LayersModel.fit() (i.e., training).\n     *\n     * Example:\n     *\n     * ```js\n     * const model = tf.sequential();\n     * model.add(tf.layers.dense({units: 1, inputShape: [10]}));\n     * model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});\n     * const xs = tf.ones([8, 10]);\n     * const ys = tf.zeros([8, 1]);\n     *\n     * const history = await model.fit(xs, ys, {\n     *   epochs: 10,\n     *   callbacks: {\n     *     onEpochEnd: async (epoch, logs) => {\n     *       if (epoch === 2) {\n     *         model.stopTraining = true;\n     *       }\n     *     }\n     *   }\n     * });\n     *\n     * // There should be only 3 values in the loss array, instead of 10 values,\n     * // due to the stopping after 3 epochs.\n     * console.log(history.history.loss);\n     * ```\n     */\n    set stopTraining(stop) {\n        // TODO(cais): When refactoring to remove the composition pattern happens,\n        // remove this method overriding.\n        if (this.model == null) {\n            throw new ValueError('Cannot set the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        this.model.stopTraining = stop;\n    }\n    get stopTraining() {\n        if (this.model == null) {\n            throw new ValueError('Cannot get the stopTraining property of a sequential model before ' +\n                'it is compiled.');\n        }\n        return this.model.stopTraining;\n    }\n    // TODO(cais): Override get trainableWeights() here\n    // tslint:disable-next-line:no-any\n    getConfig() {\n        // NOTE(cais): We override the return type of getConfig() to `any` here,\n        //   because the `Sequential` class is a special case among `Container`\n        //   subtypes in that its getConfig() method returns an Array (not a\n        //   dict).\n        const layers = [];\n        for (const layer of this.layers) {\n            const dict = {};\n            dict['className'] = layer.getClassName();\n            dict['config'] = layer.getConfig();\n            layers.push(dict);\n        }\n        return { name: this.name, layers };\n    }\n}\n/** @nocollapse */\nSequential.className = 'Sequential';\nserialization.registerClass(Sequential);\n//# sourceMappingURL=models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { CallbackConstructorRegistry } from './base_callbacks';\nimport { Input, } from './engine/input_layer';\nimport { LayersModel } from './engine/training';\nimport { loadLayersModelInternal, Sequential } from './models';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// LayersModel and related factory methods.\n/**\n * A model is a data structure that consists of `Layers` and defines inputs\n * and outputs.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.model` is more generic, supporting an arbitrary graph (without\n * cycles) of layers. `tf.sequential` is less generic and supports only a linear\n * stack of layers.\n *\n * When creating a `tf.LayersModel`, specify its input(s) and output(s). Layers\n * are used to wire input(s) to output(s).\n *\n * For example, the following code snippet defines a model consisting of\n * two `dense` layers, with 10 and 4 units, respectively.\n *\n * ```js\n * // Define input, which has a size of 5 (not including batch dimension).\n * const input = tf.input({shape: [5]});\n *\n * // First dense layer uses relu activation.\n * const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});\n * // Second dense layer uses softmax activation.\n * const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});\n *\n * // Obtain the output symbolic tensor by applying the layers on the input.\n * const output = denseLayer2.apply(denseLayer1.apply(input));\n *\n * // Create the model based on the inputs.\n * const model = tf.model({inputs: input, outputs: output});\n *\n * // The model can be used for training, evaluation and prediction.\n * // For example, the following line runs prediction with the model on\n * // some fake data.\n * model.predict(tf.ones([2, 5])).print();\n * ```\n * See also:\n *   `tf.sequential`, `tf.loadLayersModel`.\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function model(args) {\n    return new LayersModel(args);\n}\n/**\n * Creates a `tf.Sequential` model.  A sequential model is any model where the\n * outputs of one layer are the inputs to the next layer, i.e. the model\n * topology is a simple 'stack' of layers, with no branching or skipping.\n *\n * This means that the first layer passed to a `tf.Sequential` model should have\n * a defined input shape. What that means is that it should have received an\n * `inputShape` or `batchInputShape` argument, or for some type of layers\n * (recurrent, Dense...) an `inputDim` argument.\n *\n * The key difference between `tf.model` and `tf.sequential` is that\n * `tf.sequential` is less generic, supporting only a linear stack of layers.\n * `tf.model` is more generic and supports an arbitrary graph (without\n * cycles) of layers.\n *\n * Examples:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have an input shape defined.\n * model.add(tf.layers.dense({units: 32, inputShape: [50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output, which equals\n * // `[null, 4]`. The 1st dimension is the undetermined batch dimension; the\n * // 2nd is the output size of the model's last layer.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * It is also possible to specify a batch size (with potentially undetermined\n * batch dimension, denoted by \"null\") for the first layer using the\n * `batchInputShape` key. The following example is equivalent to the above:\n *\n * ```js\n * const model = tf.sequential();\n *\n * // First layer must have a defined input shape\n * model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));\n * // Afterwards, TF.js does automatic shape inference.\n * model.add(tf.layers.dense({units: 4}));\n *\n * // Inspect the inferred shape of the model's output.\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * You can also use an `Array` of already-constructed `Layer`s to create\n * a `tf.Sequential` model:\n *\n * ```js\n * const model = tf.sequential({\n *   layers: [tf.layers.dense({units: 32, inputShape: [50]}),\n *            tf.layers.dense({units: 4})]\n * });\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Models', subheading: 'Creation'}\n */\nexport function sequential(config) {\n    return new Sequential(config);\n}\n/**\n * Load a model composed of Layer objects, including its topology and optionally\n * weights. See the Tutorial named \"How to import a Keras Model\" for usage\n * examples.\n *\n * This method is applicable to:\n *\n * 1. Models created with the `tf.layers.*`, `tf.sequential`, and\n * `tf.model` APIs of TensorFlow.js and later saved with the\n * `tf.LayersModel.save` method.\n * 2. Models converted from Keras or TensorFlow tf.keras using the\n * [tensorflowjs_converter](https://github.com/tensorflow/tfjs/tree/master/tfjs-converter).\n *\n * This mode is *not* applicable to TensorFlow `SavedModel`s or their converted\n * forms. For those models, use `tf.loadGraphModel`.\n *\n * Example 1. Load a model from an HTTP server.\n *\n * ```js\n * const model = await tf.loadLayersModel(\n *     'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');\n * model.summary();\n * ```\n *\n * Example 2: Save `model`'s topology and weights to browser [local\n * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('localstorage://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 3. Saving `model`'s topology and weights to browser\n * [IndexedDB](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API);\n * then load it back.\n *\n * ```js\n * const model = tf.sequential(\n *     {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});\n * console.log('Prediction from original model:');\n * model.predict(tf.ones([1, 3])).print();\n *\n * const saveResults = await model.save('indexeddb://my-model-1');\n *\n * const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');\n * console.log('Prediction from loaded model:');\n * loadedModel.predict(tf.ones([1, 3])).print();\n * ```\n *\n * Example 4. Load a model from user-selected files from HTML\n * [file input\n * elements](https://developer.mozilla.org/en-US/docs/Web/HTML/Element/input/file).\n *\n * ```js\n * // Note: this code snippet will not work without the HTML elements in the\n * //   page\n * const jsonUpload = document.getElementById('json-upload');\n * const weightsUpload = document.getElementById('weights-upload');\n *\n * const model = await tf.loadLayersModel(\n *     tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));\n * ```\n *\n * @param pathOrIOHandler Can be either of the two formats\n *   1. A string path to the `ModelAndWeightsConfig` JSON describing\n *      the model in the canonical TensorFlow.js format. For file://\n *      (tfjs-node-only), http:// and https:// schemas, the path can be\n *      either absolute or relative.\n *   2. An `tf.io.IOHandler` object that loads model artifacts with its `load`\n *      method.\n * @param options Optional configuration arguments for the model loading,\n *   including:\n *   - `strict`: Require that the provided weights exactly match those required\n *     by the layers.  Default true.  Passing false means that both extra\n *     weights and missing weights will be silently ignored.\n *   - `onProgress`: A function of the signature `(fraction: number) => void',\n *     that can be used as the progress callback for the model loading.\n * @returns A `Promise` of `tf.LayersModel`, with the topology and weights\n *     loaded.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport function loadLayersModel(pathOrIOHandler, options) {\n    if (options == null) {\n        options = {};\n    }\n    return loadLayersModelInternal(pathOrIOHandler, options);\n}\n/**\n * Used to instantiate an input to a model as a `tf.SymbolicTensor`.\n *\n * Users should call the `input` factory function for\n * consistency with other generator functions.\n *\n * Example:\n *\n * ```js\n * // Defines a simple logistic regression model with 32 dimensional input\n * // and 3 dimensional output.\n * const x = tf.input({shape: [32]});\n * const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);\n * const model = tf.model({inputs: x, outputs: y});\n * model.predict(tf.ones([2, 32])).print();\n * ```\n *\n * Note: `input` is only necessary when using `model`. When using\n * `sequential`, specify `inputShape` for the first layer or use `inputLayer`\n * as the first layer.\n *\n * @doc {heading: 'Models', subheading: 'Inputs'}\n */\nexport function input(config) {\n    return Input(config);\n}\nexport function registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n//# sourceMappingURL=exports.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// Layer activation functions\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject } from './utils/generic_utils';\n/**\n * Base class for Activations.\n *\n * Special note: due to cross-language compatibility reasons, the\n * static readonly className field in this family of classes must be set to\n * the initialLowerCamelCase name of the activation.\n */\nexport class Activation extends serialization.Serializable {\n    getConfig() {\n        return {};\n    }\n}\n/**\n * Exponential linear unit (ELU).\n * Reference: https://arxiv.org/abs/1511.07289\n */\nexport class Elu extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x: Input.\n     * @param alpha: Scaling factor the negative section.\n     * @return Output of the ELU activation.\n     */\n    apply(x, alpha = 1) {\n        return K.elu(x, alpha);\n    }\n}\n/** @nocollapse */\nElu.className = 'elu';\nserialization.registerClass(Elu);\n/**\n * Scaled Exponential Linear Unit. (Klambauer et al., 2017).\n * Reference: Self-Normalizing Neural Networks, https://arxiv.org/abs/1706.02515\n * Notes:\n *   - To be used together with the initialization \"lecunNormal\".\n *   - To be used together with the dropout variant \"AlphaDropout\".\n */\nexport class Selu extends Activation {\n    apply(x) {\n        return tfc.selu(x);\n    }\n}\n/** @nocollapse */\nSelu.className = 'selu';\nserialization.registerClass(Selu);\n/**\n *  Rectified linear unit\n */\nexport class Relu extends Activation {\n    apply(x) {\n        return tfc.relu(x);\n    }\n}\n/** @nocollapse */\nRelu.className = 'relu';\nserialization.registerClass(Relu);\n/**\n * Rectified linear unit activation maxing out at 6.0.\n */\nexport class Relu6 extends Activation {\n    apply(x) {\n        return tidy(() => tfc.minimum(6.0, tfc.relu(x)));\n    }\n}\n/** @nocollapse */\nRelu6.className = 'relu6';\nserialization.registerClass(Relu6);\n//* Linear activation (no-op) */\nexport class Linear extends Activation {\n    apply(x) {\n        return x;\n    }\n}\n/** @nocollapse */\nLinear.className = 'linear';\nserialization.registerClass(Linear);\n/**\n * Sigmoid activation function.\n */\nexport class Sigmoid extends Activation {\n    apply(x) {\n        return tfc.sigmoid(x);\n    }\n}\n/** @nocollapse */\nSigmoid.className = 'sigmoid';\nserialization.registerClass(Sigmoid);\n/**\n * Segment-wise linear approximation of sigmoid.\n */\nexport class HardSigmoid extends Activation {\n    apply(x) {\n        return K.hardSigmoid(x);\n    }\n}\n/** @nocollapse */\nHardSigmoid.className = 'hardSigmoid';\nserialization.registerClass(HardSigmoid);\n/**\n * Softplus activation function.\n */\nexport class Softplus extends Activation {\n    apply(x) {\n        return tfc.softplus(x);\n    }\n}\n/** @nocollapse */\nSoftplus.className = 'softplus';\nserialization.registerClass(Softplus);\n/**\n * Softsign activation function.\n */\nexport class Softsign extends Activation {\n    apply(x) {\n        return K.softsign(x);\n    }\n}\n/** @nocollapse */\nSoftsign.className = 'softsign';\nserialization.registerClass(Softsign);\n/**\n * Hyperbolic tangent function.\n */\nexport class Tanh extends Activation {\n    apply(x) {\n        return tfc.tanh(x);\n    }\n}\n/** @nocollapse */\nTanh.className = 'tanh';\nserialization.registerClass(Tanh);\n/**\n * Softmax activation function\n */\nexport class Softmax extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.softmax(x, axis);\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'softmax';\nserialization.registerClass(Softmax);\n/**\n * Log softmax activation function\n */\nexport class LogSoftmax extends Activation {\n    /**\n     * Calculate the activation function of log softmax:\n     * log( exp(x_i) / sum(exp(x)) )\n     *\n     * @param x Tensor.\n     * @param axis Integer, axis along which the softmax normalization is applied.\n     * Invalid if < 2, as softmax across 1 (the batch dimension) is assumed to be\n     * an error.\n     *\n     * @returns a Tensor of the same shape as x\n     *\n     * @throws ValueError: In case `dim(x) < 2`.\n     */\n    apply(x, axis = (-1)) {\n        return tfc.logSoftmax(x, axis);\n    }\n}\n/** @nocollapse */\nLogSoftmax.className = 'logSoftmax';\nserialization.registerClass(LogSoftmax);\n/**\n * Swish activation function\n */\nexport class Swish extends Activation {\n    /**\n     * Calculate the activation function.\n     *\n     * @param x Tensor.\n     * @param alpha Scaling factor for the sigmoid function.\n     * @returns a Tensor of the same shape as x\n     */\n    apply(x, alpha = 1) {\n        return tidy(() => tfc.sigmoid(x.mul(alpha)).mul(x));\n    }\n}\n/** @nocollapse */\nSwish.className = 'swish';\nserialization.registerClass(Swish);\nexport function serializeActivation(activation) {\n    return activation.getClassName();\n}\nexport function deserializeActivation(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'activation');\n}\nexport function getActivation(identifier) {\n    if (identifier == null) {\n        const config = {};\n        config['className'] = 'linear';\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    if (typeof identifier === 'string') {\n        const config = {};\n        config['className'] = identifier;\n        config['config'] = {};\n        return deserializeActivation(config);\n    }\n    else if (identifier instanceof Activation) {\n        return identifier;\n    }\n    else {\n        return deserializeActivation(identifier);\n    }\n}\n//# sourceMappingURL=activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* original source: keras/regularizers.py */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { abs, add, serialization, sum, tidy, zeros } from '@tensorflow/tfjs-core';\nimport * as K from './backend/tfjs_backend';\nimport { deserializeKerasObject, serializeKerasObject } from './utils/generic_utils';\nfunction assertObjectArgs(args) {\n    if (args != null && typeof args !== 'object') {\n        throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an ` +\n            `object, but received: ${args}`);\n    }\n}\n/**\n * Regularizer base class.\n */\nexport class Regularizer extends serialization.Serializable {\n}\nexport class L1L2 extends Regularizer {\n    constructor(args) {\n        super();\n        assertObjectArgs(args);\n        this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n        this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n        this.hasL1 = this.l1 !== 0;\n        this.hasL2 = this.l2 !== 0;\n    }\n    /**\n     * Porting note: Renamed from __call__.\n     * @param x Variable of which to calculate the regularization score.\n     */\n    apply(x) {\n        return tidy(() => {\n            let regularization = zeros([1]);\n            if (this.hasL1) {\n                regularization = add(regularization, sum(tfc.mul(this.l1, abs(x))));\n            }\n            if (this.hasL2) {\n                regularization =\n                    add(regularization, sum(tfc.mul(this.l2, K.square(x))));\n            }\n            return regularization.asScalar();\n        });\n    }\n    getConfig() {\n        return { 'l1': this.l1, 'l2': this.l2 };\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls({ l1: config['l1'], l2: config['l2'] });\n    }\n}\n/** @nocollapse */\nL1L2.className = 'L1L2';\nserialization.registerClass(L1L2);\nexport function l1(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nexport function l2(args) {\n    assertObjectArgs(args);\n    return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\n// Maps the JavaScript-like identifier keys to the corresponding keras symbols.\nexport const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n    'l1l2': 'L1L2'\n};\nexport function serializeRegularizer(constraint) {\n    return serializeKerasObject(constraint);\n}\nexport function deserializeRegularizer(config, customObjects = {}) {\n    return deserializeKerasObject(config, serialization.SerializationMap.getMap().classNameMap, customObjects, 'regularizer');\n}\nexport function getRegularizer(identifier) {\n    if (identifier == null) {\n        return null;\n    }\n    if (typeof identifier === 'string') {\n        const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ?\n            REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] :\n            identifier;\n        const config = { className, config: {} };\n        return deserializeRegularizer(config);\n    }\n    else if (identifier instanceof Regularizer) {\n        return identifier;\n    }\n    else {\n        return deserializeRegularizer(identifier);\n    }\n}\n//# sourceMappingURL=regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n *  Advanced activation layers.\n */\nimport { clipByValue, elu, leakyRelu, prelu, relu, serialization } from '@tensorflow/tfjs-core';\nimport { Softmax as softmaxActivation } from '../activations';\nimport { cast } from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class ReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maxValue = args.maxValue;\n        }\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        let output = relu(inputs);\n        if (this.maxValue != null) {\n            output = clipByValue(output, 0, this.maxValue);\n        }\n        return output;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { maxValue: this.maxValue };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReLU.className = 'ReLU';\nserialization.registerClass(ReLU);\nexport class LeakyReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 0.3;\n        if (args == null) {\n            args = {};\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return leakyRelu(x, this.alpha);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLeakyReLU.className = 'LeakyReLU';\nserialization.registerClass(LeakyReLU);\nexport class PReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA_INITIALIZER = 'zeros';\n        if (args == null) {\n            args = {};\n        }\n        this.supportsMasking = true;\n        this.alphaInitializer =\n            getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n        this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n        this.alphaConstraint = getConstraint(args.alphaConstraint);\n        if (args.sharedAxes == null) {\n            this.sharedAxes = null;\n        }\n        else if (Array.isArray(args.sharedAxes)) {\n            this.sharedAxes = args.sharedAxes;\n        }\n        else if (typeof args.sharedAxes === 'number') {\n            this.sharedAxes = [args.sharedAxes];\n        }\n        else {\n            throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, ` +\n                `but got ${args.sharedAxes}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const paramShape = inputShape.slice(1);\n        if (this.sharedAxes != null) {\n            for (const i of this.sharedAxes) {\n                paramShape[i - 1] = 1;\n            }\n        }\n        this.alpha = this.addWeight('alpha', paramShape, 'float32', this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n        // Set input spec.\n        const axes = {};\n        if (this.sharedAxes != null) {\n            for (let i = 1; i < inputShape.length; ++i) {\n                axes[i] = inputShape[i];\n            }\n        }\n        this.inputSpec = [new InputSpec({\n                ndim: inputShape.length,\n                axes,\n            })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        inputs = getExactlyOneTensor(inputs);\n        return prelu(inputs, this.alpha.read());\n    }\n    getConfig() {\n        const config = {\n            alphaInitializer: serializeInitializer(this.alphaInitializer),\n            alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n            alphaConstraint: serializeConstraint(this.alphaConstraint),\n            sharedAxes: this.sharedAxes\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPReLU.className = 'PReLU';\nserialization.registerClass(PReLU);\nexport class ELU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_ALPHA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n            throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ` +\n                `ELU layer yet.`);\n        }\n        this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return elu(x);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { alpha: this.alpha };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nELU.className = 'ELU';\nserialization.registerClass(ELU);\nexport class ThresholdedReLU extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_THETA = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return x.mul(cast(x.greater(this.theta), 'float32'));\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { theta: this.theta };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nThresholdedReLU.className = 'ThresholdedReLU';\nserialization.registerClass(ThresholdedReLU);\nexport class Softmax extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.DEFAULT_AXIS = 1.0;\n        if (args == null) {\n            args = {};\n        }\n        this.softmax = new softmaxActivation().apply;\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    }\n    call(inputs, kwargs) {\n        const x = getExactlyOneTensor(inputs);\n        return this.softmax(x, this.axis);\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const config = { axis: this.axis };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nSoftmax.className = 'Softmax';\nserialization.registerClass(Softmax);\n//# sourceMappingURL=advanced_activations.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { ValueError } from '../errors';\nimport { pyListRepeat } from './generic_utils';\nimport { isInteger, max } from './math_utils';\n/**\n * Transforms a single number of array of numbers into an array of numbers.\n * @param value\n * @param n: The size of the tuple to be returned.\n * @param name: Name of the parameter, used for generating error messages.\n * @returns An array of numbers.\n */\nexport function normalizeArray(value, n, name) {\n    if (typeof value === 'number') {\n        return pyListRepeat(value, n);\n    }\n    else {\n        if (value.length !== n) {\n            throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers.` +\n                ` Received: ${value.length} elements.`);\n        }\n        for (let i = 0; i < n; ++i) {\n            const singleValue = value[i];\n            if (!isInteger(singleValue)) {\n                throw new ValueError(`The ${name} argument must be an integer or tuple of ${n}` +\n                    ` integers. Received: ${JSON.stringify(value)} including a` +\n                    ` non-integer number ${singleValue}`);\n            }\n        }\n        return value;\n    }\n}\n/**\n * Determines output length of a convolution given input length.\n * @param inputLength\n * @param filterSize\n * @param padding\n * @param stride\n * @param dilation: dilation rate.\n */\nexport function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n    if (inputLength == null) {\n        return inputLength;\n    }\n    const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n    let outputLength;\n    if (padding === 'same') {\n        outputLength = inputLength;\n    }\n    else { // VALID\n        outputLength = inputLength - dilatedFilterSize + 1;\n    }\n    return Math.floor((outputLength + stride - 1) / stride);\n}\nexport function deconvLength(dimSize, strideSize, kernelSize, padding) {\n    if (dimSize == null) {\n        return null;\n    }\n    if (padding === 'valid') {\n        dimSize = dimSize * strideSize + max([kernelSize - strideSize, 0]);\n    }\n    else if (padding === 'same') {\n        dimSize = dimSize * strideSize;\n    }\n    else {\n        throw new ValueError(`Unsupport padding mode: ${padding}.`);\n    }\n    return dimSize;\n}\n//# sourceMappingURL=conv_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength, deconvLength, normalizeArray } from '../utils/conv_utils';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Transpose and cast the input before the conv2d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv2DInput(x, dataFormat) {\n    // TODO(cais): Cast type to float32 if not.\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 1]); // NCHW -> NHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * Transpose and cast the input before the conv3d.\n * @param x Input image tensor.\n * @param dataFormat\n */\nexport function preprocessConv3DInput(x, dataFormat) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        if (dataFormat === 'channelsFirst') {\n            return tfc.transpose(x, [0, 2, 3, 4, 1]); // NCDHW -> NDHWC.\n        }\n        else {\n            return x;\n        }\n    });\n}\n/**\n * 1D-convolution with bias added.\n *\n * Porting Note: This function does not exist in the Python Keras backend.\n *   It is exactly the same as `conv2d`, except the added `bias`.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.\n * @param bias Bias, rank-3, of shape `[outDepth]`.\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1dWithBias(x, kernel, bias, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        // Check the ranks of x, kernel and bias.\n        if (x.shape.length !== 3) {\n            throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ` +\n                `${x.shape.length} instead.`);\n        }\n        if (kernel.shape.length !== 3) {\n            throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        if (bias != null && bias.shape.length !== 1) {\n            throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ` +\n                `${kernel.shape.length} instead`);\n        }\n        // TODO(cais): Support CAUSAL padding mode.\n        if (dataFormat === 'channelsFirst') {\n            x = tfc.transpose(x, [0, 2, 1]); // NCW -> NWC.\n        }\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        let y = tfc.conv1d(x, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        return y;\n    });\n}\n/**\n * 1D-convolution.\n *\n * @param x Input tensor, rank-3, of shape `[batchSize, width, inChannels]`.\n * @param kernel Kernel, rank-3, of shape `[filterWidth, inDepth, outDepth]`.s\n * @param strides\n * @param padding Padding mode.\n * @param dataFormat Data format.\n * @param dilationRate\n * @returns The result of the 1D convolution.\n * @throws ValueError, if `x`, `kernel` or `bias` is not of the correct rank.\n */\nexport function conv1d(x, kernel, strides = 1, padding = 'valid', dataFormat, dilationRate = 1) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv1dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 2D pooling.\n */\nexport function conv2d(x, kernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv2dWithBiasActivation(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 2D Convolution with an added bias and optional activation.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv2d`, except the added `bias`.\n */\nexport function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = 'valid', dataFormat, dilationRate, activation = null) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 3 && x.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        if (kernel.rank !== 3 && kernel.rank !== 4) {\n            throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, ` +\n                `but received ${x.rank}.`);\n        }\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv1dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.fused.conv2d({\n            x: y,\n            filter: kernel,\n            strides: strides,\n            pad: padding === 'same' ? 'same' : 'valid',\n            dilations: dilationRate,\n            dataFormat: 'NHWC',\n            bias,\n            activation\n        });\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\n/**\n * 3D Convolution.\n * @param x\n * @param kernel kernel of the convolution.\n * @param strides strides array.\n * @param padding padding mode. Default to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param dilationRate dilation rate array.\n * @returns Result of the 3D convolution.\n */\nexport function conv3d(x, kernel, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        return conv3dWithBias(x, kernel, null, strides, padding, dataFormat, dilationRate);\n    });\n}\n/**\n * 3D Convolution with an added bias.\n * Note: This function does not exist in the Python Keras Backend. This function\n * is exactly the same as `conv3d`, except the added `bias`.\n */\nexport function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        if (x.rank !== 4 && x.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        if (kernel.rank !== 4 && kernel.rank !== 5) {\n            throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ` +\n                `${x.rank}.`);\n        }\n        let y = preprocessConv3DInput(x, dataFormat);\n        if (padding === 'causal') {\n            throw new NotImplementedError('The support for CAUSAL padding mode in conv3dWithBias is not ' +\n                'implemented yet.');\n        }\n        y = tfc.conv3d(y, kernel, strides, padding === 'same' ? 'same' : 'valid', 'NDHWC', dilationRate);\n        if (bias != null) {\n            y = K.biasAdd(y, bias);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]);\n        }\n        return y;\n    });\n}\n/**\n * Abstract convolution layer.\n */\nexport class BaseConv extends Layer {\n    constructor(rank, args) {\n        super(args);\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        BaseConv.verifyArgs(args);\n        this.rank = rank;\n        generic_utils.assertPositiveInteger(this.rank, 'rank');\n        if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n            throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is ` +\n                `not implemented yet.`);\n        }\n        this.kernelSize = normalizeArray(args.kernelSize, rank, 'kernelSize');\n        this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.activation = getActivation(args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, 'dilationRate');\n        if (this.rank === 1 &&\n            (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n            throw new ValueError(`dilationRate must be a number or an array of a single number ` +\n                `for 1D convolution, but received ` +\n                `${JSON.stringify(this.dilationRate)}`);\n        }\n        else if (this.rank === 2) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate = [this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 2) {\n                throw new ValueError(`dilationRate must be a number or array of two numbers for 2D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n        else if (this.rank === 3) {\n            if (typeof this.dilationRate === 'number') {\n                this.dilationRate =\n                    [this.dilationRate, this.dilationRate, this.dilationRate];\n            }\n            else if (this.dilationRate.length !== 3) {\n                throw new ValueError(`dilationRate must be a number or array of three numbers for 3D ` +\n                    `convolution, but received ${JSON.stringify(this.dilationRate)}`);\n            }\n        }\n    }\n    static verifyArgs(args) {\n        // Check config.kernelSize type and shape.\n        generic_utils.assert('kernelSize' in args, `required key 'kernelSize' not in config`);\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 3)) {\n            throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with ` +\n                `length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n    getConfig() {\n        const config = {\n            kernelSize: this.kernelSize,\n            strides: this.strides,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/**\n * Abstract nD convolution layer.  Ancestor of convolution layers which reduce\n * across channels, i.e., Conv1D and Conv2D, but not DepthwiseConv2D.\n */\nexport class Conv extends BaseConv {\n    constructor(rank, args) {\n        super(rank, args);\n        this.kernel = null;\n        Conv.verifyArgs(args);\n        this.filters = args.filters;\n        generic_utils.assertPositiveInteger(this.filters, 'filters');\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs;\n            const biasValue = this.bias == null ? null : this.bias.read();\n            const fusedActivationName = generic_utils.mapActivationToFusedKernel(this.activation.getClassName());\n            if (fusedActivationName != null && this.rank === 2) {\n                outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n            }\n            else {\n                if (this.rank === 1) {\n                    outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n                }\n                else if (this.rank === 2) {\n                    // TODO(cais): Move up to constructor.\n                    outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else if (this.rank === 3) {\n                    outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n                }\n                else {\n                    throw new NotImplementedError('convolutions greater than 3D are not implemented yet.');\n                }\n                if (this.activation != null) {\n                    outputs = this.activation.apply(outputs);\n                }\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const newSpace = [];\n        const space = (this.dataFormat === 'channelsLast') ?\n            inputShape.slice(1, inputShape.length - 1) :\n            inputShape.slice(2);\n        for (let i = 0; i < space.length; ++i) {\n            const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === 'number' ? this.dilationRate :\n                this.dilationRate[i]);\n            newSpace.push(newDim);\n        }\n        let outputShape = [inputShape[0]];\n        if (this.dataFormat === 'channelsLast') {\n            outputShape = outputShape.concat(newSpace);\n            outputShape.push(this.filters);\n        }\n        else {\n            outputShape.push(this.filters);\n            outputShape = outputShape.concat(newSpace);\n        }\n        return outputShape;\n    }\n    getConfig() {\n        const config = {\n            filters: this.filters,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    static verifyArgs(args) {\n        // Check config.filters type, shape, and value.\n        if (!('filters' in args) || typeof args.filters !== 'number' ||\n            args.filters < 1) {\n            throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 ` +\n                `but got ${JSON.stringify(args.filters)}`);\n        }\n    }\n}\nexport class Conv2D extends Conv {\n    constructor(args) {\n        super(2, args);\n        Conv2D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if ((typeof args.kernelSize !== 'number') &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 2)) {\n            throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with ` +\n                `length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv2D.className = 'Conv2D';\nserialization.registerClass(Conv2D);\nexport class Conv3D extends Conv {\n    constructor(args) {\n        super(3, args);\n        Conv3D.verifyArgs(args);\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number') {\n            if (!(Array.isArray(args.kernelSize) &&\n                (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n                throw new ValueError(`Conv3D expects config.kernelSize to be number or` +\n                    ` [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n            }\n        }\n    }\n}\n/** @nocollapse */\nConv3D.className = 'Conv3D';\nserialization.registerClass(Conv3D);\nexport class Conv2DTranspose extends Conv2D {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n        if (this.padding !== 'same' && this.padding !== 'valid') {\n            throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' ` +\n                `and 'valid', but received padding mode ${this.padding}`);\n        }\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length !== 4) {\n            throw new ValueError('Input should have rank 4; Received input shape: ' +\n                JSON.stringify(inputShape));\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError('The channel dimension of the inputs should be defined. ' +\n                'Found `None`.');\n        }\n        const inputDim = inputShape[channelAxis];\n        const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n        this.kernel = this.addWeight('kernel', kernelShape, 'float32', this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        // Set input spec.\n        this.inputSpec =\n            [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            if (input.shape.length !== 4) {\n                throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but ` +\n                    `received a tensor of rank-${input.shape.length}`);\n            }\n            const inputShape = input.shape;\n            const batchSize = inputShape[0];\n            let hAxis;\n            let wAxis;\n            if (this.dataFormat === 'channelsFirst') {\n                hAxis = 2;\n                wAxis = 3;\n            }\n            else {\n                hAxis = 1;\n                wAxis = 2;\n            }\n            const height = inputShape[hAxis];\n            const width = inputShape[wAxis];\n            const kernelH = this.kernelSize[0];\n            const kernelW = this.kernelSize[1];\n            const strideH = this.strides[0];\n            const strideW = this.strides[1];\n            // Infer the dynamic output shape.\n            const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n            const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n            // Porting Note: We don't branch based on `this.dataFormat` here,\n            // because\n            //   the tjfs-core function `conv2dTranspose` called below always\n            //   assumes channelsLast.\n            const outputShape = [batchSize, outHeight, outWidth, this.filters];\n            if (this.dataFormat !== 'channelsLast') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n            }\n            let outputs = tfc.conv2dTranspose(input, this.kernel.read(), outputShape, this.strides, this.padding);\n            if (this.dataFormat !== 'channelsLast') {\n                outputs = tfc.transpose(outputs, [0, 3, 1, 2]);\n            }\n            if (this.bias != null) {\n                outputs =\n                    K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        let channelAxis;\n        let heightAxis;\n        let widthAxis;\n        if (this.dataFormat === 'channelsFirst') {\n            channelAxis = 1;\n            heightAxis = 2;\n            widthAxis = 3;\n        }\n        else {\n            channelAxis = 3;\n            heightAxis = 1;\n            widthAxis = 2;\n        }\n        const kernelH = this.kernelSize[0];\n        const kernelW = this.kernelSize[1];\n        const strideH = this.strides[0];\n        const strideW = this.strides[1];\n        outputShape[channelAxis] = this.filters;\n        outputShape[heightAxis] =\n            deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n        outputShape[widthAxis] =\n            deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n        return outputShape;\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['dilationRate'];\n        return config;\n    }\n}\n/** @nocollapse */\nConv2DTranspose.className = 'Conv2DTranspose';\nserialization.registerClass(Conv2DTranspose);\nexport class SeparableConv extends Conv {\n    constructor(rank, config) {\n        super(rank, config);\n        this.DEFAULT_DEPTHWISE_INITIALIZER = 'glorotUniform';\n        this.DEFAULT_POINTWISE_INITIALIZER = 'glorotUniform';\n        this.depthwiseKernel = null;\n        this.pointwiseKernel = null;\n        if (config.filters == null) {\n            throw new ValueError('The `filters` configuration field is required by SeparableConv, ' +\n                'but is unspecified.');\n        }\n        if (config.kernelInitializer != null || config.kernelRegularizer != null ||\n            config.kernelConstraint != null) {\n            throw new ValueError('Fields kernelInitializer, kernelRegularizer and kernelConstraint ' +\n                'are invalid for SeparableConv2D. Use depthwiseInitializer, ' +\n                'depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, ' +\n                'pointwiseRegularizer and pointwiseConstraint instead.');\n        }\n        if (config.padding != null && config.padding !== 'same' &&\n            config.padding !== 'valid') {\n            throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: ` +\n                `'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n        }\n        this.depthMultiplier =\n            config.depthMultiplier == null ? 1 : config.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n        this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n        this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n        this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n        this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n        this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < this.rank + 2) {\n            throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ` +\n                `${this.rank + 2}, but received input shape: ` +\n                `${JSON.stringify(inputShape)}`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError(`The channel dimension of the inputs should be defined, ` +\n                `but found ${JSON.stringify(inputShape[channelAxis])}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n        const pointwiseKernelShape = [];\n        for (let i = 0; i < this.rank; ++i) {\n            pointwiseKernelShape.push(1);\n        }\n        pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n        const trainable = true;\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, 'float32', this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n        this.pointwiseKernel = this.addWeight('pointwise_kernel', pointwiseKernelShape, 'float32', this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.filters], 'float32', this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let output;\n            if (this.rank === 1) {\n                throw new NotImplementedError('1D separable convolution is not implemented yet.');\n            }\n            else if (this.rank === 2) {\n                if (this.dataFormat === 'channelsFirst') {\n                    inputs = tfc.transpose(inputs, [0, 2, 3, 1]); // NCHW -> NHWC.\n                }\n                output = tfc.separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, 'NHWC');\n            }\n            if (this.useBias) {\n                output = K.biasAdd(output, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            if (this.dataFormat === 'channelsFirst') {\n                output = tfc.transpose(output, [0, 3, 1, 2]); // NHWC -> NCHW.\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['kernelInitializer'];\n        delete config['kernelRegularizer'];\n        delete config['kernelConstraint'];\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['pointwiseInitializer'] =\n            serializeInitializer(this.pointwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['pointwiseRegularizer'] =\n            serializeRegularizer(this.pointwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseConstraint);\n        config['pointwiseConstraint'] =\n            serializeConstraint(this.pointwiseConstraint);\n        return config;\n    }\n}\n/** @nocollapse */\nSeparableConv.className = 'SeparableConv';\nexport class SeparableConv2D extends SeparableConv {\n    constructor(args) {\n        super(2, args);\n    }\n}\n/** @nocollapse */\nSeparableConv2D.className = 'SeparableConv2D';\nserialization.registerClass(SeparableConv2D);\nexport class Conv1D extends Conv {\n    constructor(args) {\n        super(1, args);\n        Conv1D.verifyArgs(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getConfig() {\n        const config = super.getConfig();\n        delete config['rank'];\n        delete config['dataFormat'];\n        return config;\n    }\n    static verifyArgs(args) {\n        // config.kernelSize must be a number or array of numbers.\n        if (typeof args.kernelSize !== 'number' &&\n            !generic_utils.checkArrayTypeAndLength(args.kernelSize, 'number', 1, 1)) {\n            throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with ` +\n                `length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n        }\n    }\n}\n/** @nocollapse */\nConv1D.className = 'Conv1D';\nserialization.registerClass(Conv1D);\nexport class Cropping2D extends Layer {\n    constructor(args) {\n        super(args);\n        if (typeof args.cropping === 'number') {\n            this.cropping =\n                [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n        }\n        else if (typeof args.cropping[0] === 'number') {\n            this.cropping = [\n                [args.cropping[0], args.cropping[0]],\n                [args.cropping[1], args.cropping[1]]\n            ];\n        }\n        else {\n            this.cropping = args.cropping;\n        }\n        this.dataFormat =\n            args.dataFormat === undefined ? 'channelsLast' : args.dataFormat;\n        this.inputSpec = [{ ndim: 4 }];\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            return [\n                inputShape[0], inputShape[1],\n                inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n            ];\n        }\n        else {\n            return [\n                inputShape[0],\n                inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n                inputShape[2] - this.cropping[1][0] - this.cropping[1][1], inputShape[3]\n            ];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n            }\n            else {\n                const hSliced = K.sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n                return K.sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n            }\n        });\n    }\n    getConfig() {\n        const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nCropping2D.className = 'Cropping2D';\nserialization.registerClass(Cropping2D);\nexport class UpSampling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_SIZE = [2, 2];\n        this.inputSpec = [{ ndim: 4 }];\n        this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        if (this.dataFormat === 'channelsFirst') {\n            const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n            const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n            return [inputShape[0], inputShape[1], height, width];\n        }\n        else {\n            const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n            const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n            return [inputShape[0], height, width, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            let input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            if (this.dataFormat === 'channelsFirst') {\n                input = tfc.transpose(input, [0, 2, 3, 1]);\n                const height = this.size[0] * inputShape[2];\n                const width = this.size[1] * inputShape[3];\n                const resized = input.resizeNearestNeighbor([height, width]);\n                return tfc.transpose(resized, [0, 3, 1, 2]);\n            }\n            else {\n                const height = this.size[0] * inputShape[1];\n                const width = this.size[1] * inputShape[2];\n                return input.resizeNearestNeighbor([height, width]);\n            }\n        });\n    }\n    getConfig() {\n        const config = { size: this.size, dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nUpSampling2D.className = 'UpSampling2D';\nserialization.registerClass(UpSampling2D);\n//# sourceMappingURL=convolutional.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Depthwise Convolutional Layers\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { BaseConv, preprocessConv2DInput } from './convolutional';\n/**\n * 2D convolution with separable filters.\n * @param x Input tensor.\n * @param depthwiseKernel Convolution kernel for depthwise convolution.\n * @param strides Strides (Array of two integers).\n * @param padding Padding model.\n * @param dataFormat Data format.\n * @param dilationRate Array of two integers, dilation rates for the separable\n *   convolution.\n * @returns Output tensor.\n * @throws ValueError If depthwiseKernel is not a 4D array.\n */\nexport function depthwiseConv2d(x, depthwiseKernel, strides = [1, 1], padding = 'valid', dataFormat, dilationRate) {\n    return tidy(() => {\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        checkDataFormat(dataFormat);\n        let y = preprocessConv2DInput(x, dataFormat);\n        if (x.rank !== 4) {\n            throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ` +\n                `${x.rank}-D`);\n        }\n        if (depthwiseKernel.rank !== 4) {\n            throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ` +\n                `${depthwiseKernel.rank}-D`);\n        }\n        y = tfc.depthwiseConv2d(y, depthwiseKernel, strides, padding === 'same' ? 'same' : 'valid', 'NHWC', dilationRate);\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]);\n        }\n        return y;\n    });\n}\nexport class DepthwiseConv2D extends BaseConv {\n    constructor(args) {\n        super(2, args);\n        this.depthwiseKernel = null;\n        this.depthMultiplier =\n            args.depthMultiplier == null ? 1 : args.depthMultiplier;\n        this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n        this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 4) {\n            throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. ` +\n                `Received input shape: ${JSON.stringify(inputShape)}.`);\n        }\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : 3;\n        if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n            throw new ValueError('The channel dimension of the inputs to DepthwiseConv2D should ' +\n                `be defined, but is not (${inputShape[channelAxis]}).`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const depthwiseKernelShape = [\n            this.kernelSize[0], this.kernelSize[1], inputDim, this.depthMultiplier\n        ];\n        this.depthwiseKernel = this.addWeight('depthwise_kernel', depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            let outputs = depthwiseConv2d(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n            // TODO(cais): Add support for dilation.\n            if (this.useBias) {\n                outputs = K.biasAdd(outputs, this.bias.read(), this.dataFormat);\n            }\n            if (this.activation != null) {\n                outputs = this.activation.apply(outputs);\n            }\n            return outputs;\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        const cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        const outFilters = this.dataFormat === 'channelsFirst' ?\n            inputShape[1] * this.depthMultiplier :\n            inputShape[3] * this.depthMultiplier;\n        const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n        const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], outFilters, outRows, outCols];\n        }\n        else {\n            // In this case, assume 'channelsLast'.\n            return [inputShape[0], outRows, outCols, outFilters];\n        }\n    }\n    getConfig() {\n        const config = super.getConfig();\n        config['depthMultiplier'] = this.depthMultiplier;\n        config['depthwiseInitializer'] =\n            serializeInitializer(this.depthwiseInitializer);\n        config['depthwiseRegularizer'] =\n            serializeRegularizer(this.depthwiseRegularizer);\n        config['depthwiseConstraint'] =\n            serializeConstraint(this.depthwiseRegularizer);\n        return config;\n    }\n}\n/** @nocollapse */\nDepthwiseConv2D.className = 'DepthwiseConv2D';\nserialization.registerClass(DepthwiseConv2D);\n//# sourceMappingURL=convolutional_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, SymbolicTensor } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';\nimport { batchGetValue, batchSetValue } from '../variables';\nimport { deserialize } from './serialization';\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(inputs, initialState, constants, numConstants) {\n    if (Array.isArray(inputs)) {\n        if (initialState != null || constants != null) {\n            throw new ValueError('When inputs is an array, neither initialState or constants ' +\n                'should be provided');\n        }\n        if (numConstants != null) {\n            constants = inputs.slice(inputs.length - numConstants, inputs.length);\n            inputs = inputs.slice(0, inputs.length - numConstants);\n        }\n        if (inputs.length > 1) {\n            initialState = inputs.slice(1, inputs.length);\n        }\n        inputs = inputs[0];\n    }\n    function toListOrNull(x) {\n        if (x == null || Array.isArray(x)) {\n            return x;\n        }\n        else {\n            return [x];\n        }\n    }\n    initialState = toListOrNull(initialState);\n    constants = toListOrNull(constants);\n    return { inputs, initialState, constants };\n}\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n    return tfc.tidy(() => {\n        const ndim = inputs.shape.length;\n        if (ndim < 3) {\n            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n        }\n        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n        // ...].\n        const axes = [1, 0].concat(math_utils.range(2, ndim));\n        inputs = tfc.transpose(inputs, axes);\n        if (constants != null) {\n            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +\n                'constants yet.');\n        }\n        // Porting Note: the unroll option is ignored by the imperative backend.\n        if (unroll) {\n            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +\n                'imperative deeplearn.js backend.');\n        }\n        if (mask != null) {\n            mask = mask.asType('bool').asType('float32');\n            if (mask.rank === ndim - 1) {\n                mask = tfc.expandDims(mask, -1);\n            }\n            mask = tfc.transpose(mask, axes);\n        }\n        if (goBackwards) {\n            inputs = tfc.reverse(inputs, 0);\n            if (mask != null) {\n                mask = tfc.reverse(mask, 0);\n            }\n        }\n        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n        //   use the usual TypeScript control flow to iterate over the time steps in\n        //   the inputs.\n        // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n        // outputs.\n        //   This is not idiomatic in TypeScript. The info regarding whether we are\n        //   in a learning (i.e., training) phase for RNN is passed in a different\n        //   way.\n        const perStepOutputs = [];\n        let lastOutput;\n        let states = initialStates;\n        const timeSteps = inputs.shape[0];\n        const perStepInputs = tfc.unstack(inputs);\n        let perStepMasks;\n        if (mask != null) {\n            perStepMasks = tfc.unstack(mask);\n        }\n        for (let t = 0; t < timeSteps; ++t) {\n            const currentInput = perStepInputs[t];\n            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n            if (mask == null) {\n                lastOutput = stepOutputs[0];\n                states = stepOutputs[1];\n            }\n            else {\n                const maskedOutputs = tfc.tidy(() => {\n                    const stepMask = perStepMasks[t];\n                    const negStepMask = tfc.onesLike(stepMask).sub(stepMask);\n                    // TODO(cais): Would tfc.where() be better for performance?\n                    const output = stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask));\n                    const newStates = states.map((state, i) => {\n                        return stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask));\n                    });\n                    return { output, newStates };\n                });\n                lastOutput = maskedOutputs.output;\n                states = maskedOutputs.newStates;\n            }\n            if (needPerStepOutputs) {\n                perStepOutputs.push(lastOutput);\n            }\n        }\n        let outputs;\n        if (needPerStepOutputs) {\n            const axis = 1;\n            outputs = tfc.stack(perStepOutputs, axis);\n        }\n        return [lastOutput, outputs, states];\n    });\n}\nexport class RNN extends Layer {\n    constructor(args) {\n        super(args);\n        let cell;\n        if (args.cell == null) {\n            throw new ValueError('cell property is missing for the constructor of RNN.');\n        }\n        else if (Array.isArray(args.cell)) {\n            cell = new StackedRNNCells({ cells: args.cell });\n        }\n        else {\n            cell = args.cell;\n        }\n        if (cell.stateSize == null) {\n            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +\n                'integers, one integer per RNN state).');\n        }\n        this.cell = cell;\n        this.returnSequences =\n            args.returnSequences == null ? false : args.returnSequences;\n        this.returnState = args.returnState == null ? false : args.returnState;\n        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n        this._stateful = args.stateful == null ? false : args.stateful;\n        this.unroll = args.unroll == null ? false : args.unroll;\n        this.supportsMasking = true;\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n        this.stateSpec = null;\n        this.states_ = null;\n        // TODO(cais): Add constantsSpec and numConstants.\n        this.numConstants = null;\n        // TODO(cais): Look into the use of initial_state in the kwargs of the\n        //   constructor.\n        this.keptStates = [];\n    }\n    // Porting Note: This is the equivalent of `RNN.states` property getter in\n    //   PyKeras.\n    getStates() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            return math_utils.range(0, numStates).map(x => null);\n        }\n        else {\n            return this.states_;\n        }\n    }\n    // Porting Note: This is the equivalent of the `RNN.states` property setter in\n    //   PyKeras.\n    setStates(states) {\n        this.states_ = states;\n    }\n    computeOutputShape(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        // TODO(cais): Remove the casting once stacked RNN cells become supported.\n        let stateSize = this.cell.stateSize;\n        if (!Array.isArray(stateSize)) {\n            stateSize = [stateSize];\n        }\n        const outputDim = stateSize[0];\n        let outputShape;\n        if (this.returnSequences) {\n            outputShape = [inputShape[0], inputShape[1], outputDim];\n        }\n        else {\n            outputShape = [inputShape[0], outputDim];\n        }\n        if (this.returnState) {\n            const stateShape = [];\n            for (const dim of stateSize) {\n                stateShape.push([inputShape[0], dim]);\n            }\n            return [outputShape].concat(stateShape);\n        }\n        else {\n            return outputShape;\n        }\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (Array.isArray(mask)) {\n                mask = mask[0];\n            }\n            const outputMask = this.returnSequences ? mask : null;\n            if (this.returnState) {\n                const stateMask = this.states.map(s => null);\n                return [outputMask].concat(stateMask);\n            }\n            else {\n                return outputMask;\n            }\n        });\n    }\n    /**\n     * Get the current state tensors of the RNN.\n     *\n     * If the state hasn't been set, return an array of `null`s of the correct\n     * length.\n     */\n    get states() {\n        if (this.states_ == null) {\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            const output = [];\n            for (let i = 0; i < numStates; ++i) {\n                output.push(null);\n            }\n            return output;\n        }\n        else {\n            return this.states_;\n        }\n    }\n    set states(s) {\n        this.states_ = s;\n    }\n    build(inputShape) {\n        // Note inputShape will be an Array of Shapes of initial states and\n        // constants if these are passed in apply().\n        const constantShape = null;\n        if (this.numConstants != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        if (isArrayOfShapes(inputShape)) {\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        const batchSize = this.stateful ? inputShape[0] : null;\n        const inputDim = inputShape.slice(2);\n        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n        // Allow cell (if RNNCell Layer) to build before we set or validate\n        // stateSpec.\n        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (constantShape != null) {\n            throw new NotImplementedError('Constants support is not implemented in RNN yet.');\n        }\n        else {\n            this.cell.build(stepInputShape);\n        }\n        // Set or validate stateSpec.\n        let stateSize;\n        if (Array.isArray(this.cell.stateSize)) {\n            stateSize = this.cell.stateSize;\n        }\n        else {\n            stateSize = [this.cell.stateSize];\n        }\n        if (this.stateSpec != null) {\n            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {\n                throw new ValueError(`An initialState was passed that is not compatible with ` +\n                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n                    `However cell.stateSize is ${this.cell.stateSize}`);\n            }\n        }\n        else {\n            this.stateSpec =\n                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));\n        }\n        if (this.stateful) {\n            this.resetStates();\n        }\n    }\n    /**\n     * Reset the state tensors of the RNN.\n     *\n     * If the `states` argument is `undefined` or `null`, will set the\n     * state tensor(s) of the RNN to all-zero tensors of the appropriate\n     * shape(s).\n     *\n     * If `states` is provided, will set the state tensors of the RNN to its\n     * value.\n     *\n     * @param states Optional externally-provided initial states.\n     * @param training Whether this call is done during training. For stateful\n     *   RNNs, this affects whether the old states are kept or discarded. In\n     *   particular, if `training` is `true`, the old states will be kept so\n     *   that subsequent backpropgataion through time (BPTT) may work properly.\n     *   Else, the old states will be discarded.\n     */\n    resetStates(states, training = false) {\n        tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const batchSize = this.inputSpec[0].shape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.states_ == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ =\n                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n                }\n                else {\n                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training === true) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const dim = Array.isArray(this.cell.stateSize) ?\n                        this.cell.stateSize[index] :\n                        this.cell.stateSize;\n                    const expectedShape = [batchSize, dim];\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    apply(inputs, kwargs) {\n        // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        // If any of `initial_state` or `constants` are specified and are\n        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n        // the input_spec to include them.\n        let additionalInputs = [];\n        let additionalSpecs = [];\n        if (initialState != null) {\n            kwargs['initialState'] = initialState;\n            additionalInputs = additionalInputs.concat(initialState);\n            this.stateSpec = [];\n            for (const state of initialState) {\n                this.stateSpec.push(new InputSpec({ shape: state.shape }));\n            }\n            // TODO(cais): Use the following instead.\n            // this.stateSpec = initialState.map(state => new InputSpec({shape:\n            // state.shape}));\n            additionalSpecs = additionalSpecs.concat(this.stateSpec);\n        }\n        if (constants != null) {\n            kwargs['constants'] = constants;\n            additionalInputs = additionalInputs.concat(constants);\n            // TODO(cais): Add this.constantsSpec.\n            this.numConstants = constants.length;\n        }\n        const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n        if (isTensor) {\n            // Compute full input spec, including state and constants.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call with temporarily replaced inputSpec.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    // tslint:disable-next-line:no-any\n    call(inputs, kwargs) {\n        // Input shape: `[samples, time (padded with zeros), input_dim]`.\n        // Note that the .build() method of subclasses **must** define\n        // this.inputSpec and this.stateSpec owith complete input shapes.\n        return tidy(() => {\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            let initialState = kwargs == null ? null : kwargs['initialState'];\n            inputs = getExactlyOneTensor(inputs);\n            if (initialState == null) {\n                if (this.stateful) {\n                    initialState = this.states_;\n                }\n                else {\n                    initialState = this.getInitialState(inputs);\n                }\n            }\n            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n            if (initialState.length !== numStates) {\n                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +\n                    `${initialState.length} initial state(s).`);\n            }\n            if (this.unroll) {\n                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');\n            }\n            const cellCallKwargs = { training };\n            // TODO(cais): Add support for constants.\n            const step = (inputs, states) => {\n                // `inputs` and `states` are concatenated to form a single `Array` of\n                // `tf.Tensor`s as the input to `cell.call()`.\n                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);\n                // Marshall the return value into output and new states.\n                return [outputs[0], outputs.slice(1)];\n            };\n            // TODO(cais): Add support for constants.\n            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n            const lastOutput = rnnOutputs[0];\n            const outputs = rnnOutputs[1];\n            const states = rnnOutputs[2];\n            if (this.stateful) {\n                this.resetStates(states, training);\n            }\n            const output = this.returnSequences ? outputs : lastOutput;\n            // TODO(cais): Porperty set learning phase flag.\n            if (this.returnState) {\n                return [output].concat(states);\n            }\n            else {\n                return output;\n            }\n        });\n    }\n    getInitialState(inputs) {\n        return tidy(() => {\n            // Build an all-zero tensor of shape [samples, outputDim].\n            // [Samples, timeSteps, inputDim].\n            let initialState = tfc.zeros(inputs.shape);\n            // [Samples].\n            initialState = tfc.sum(initialState, [1, 2]);\n            initialState = K.expandDims(initialState); // [Samples, 1].\n            if (Array.isArray(this.cell.stateSize)) {\n                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n            }\n            else {\n                return this.cell.stateSize > 1 ?\n                    [K.tile(initialState, [1, this.cell.stateSize])] :\n                    [initialState];\n            }\n        });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        return this.cell.trainableWeights;\n    }\n    get nonTrainableWeights() {\n        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n        if (!this.trainable) {\n            return this.cell.weights;\n        }\n        return this.cell.nonTrainableWeights;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.cell != null) {\n            this.cell.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            returnSequences: this.returnSequences,\n            returnState: this.returnState,\n            goBackwards: this.goBackwards,\n            stateful: this.stateful,\n            unroll: this.unroll,\n        };\n        if (this.numConstants != null) {\n            config['numConstants'] = this.numConstants;\n        }\n        const cellConfig = this.cell.getConfig();\n        if (this.getClassName() === RNN.className) {\n            config['cell'] = {\n                'className': this.cell.getClassName(),\n                'config': cellConfig,\n            };\n        }\n        // this order is necessary, to prevent cell name from replacing layer name\n        return Object.assign({}, cellConfig, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cellConfig = config['cell'];\n        const cell = deserialize(cellConfig, customObjects);\n        return new cls(Object.assign(config, { cell }));\n    }\n}\n/** @nocollapse */\nRNN.className = 'RNN';\nserialization.registerClass(RNN);\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport class RNNCell extends Layer {\n}\nexport class SimpleRNNCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, `units`);\n        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        // TODO(cais): Use regularizer.\n        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        this.built = true;\n    }\n    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n    //   `inputs` and `states`. Here, the two tensors are combined into an\n    //   `Tensor[]` Array as the first input argument.\n    //   Similarly, PyKeras' equivalent of this method returns two values:\n    //    `output` and `[output]`. Here the two are combined into one length-2\n    //    `Tensor[]`, consisting of `output` repeated.\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n            }\n            let prevOutput = inputs[1];\n            inputs = inputs[0];\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(prevOutput),\n                    rate: this.recurrentDropout,\n                    training\n                });\n            }\n            let h;\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            if (dpMask != null) {\n                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n            }\n            else {\n                h = K.dot(inputs, this.kernel.read());\n            }\n            if (this.bias != null) {\n                h = K.biasAdd(h, this.bias.read());\n            }\n            if (recDpMask != null) {\n                prevOutput = tfc.mul(prevOutput, recDpMask);\n            }\n            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n            if (this.activation != null) {\n                output = this.activation.apply(output);\n            }\n            // TODO(cais): Properly set learning phase on output tensor?\n            return [output, output];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nSimpleRNNCell.className = 'SimpleRNNCell';\nserialization.registerClass(SimpleRNNCell);\nexport class SimpleRNN extends RNN {\n    constructor(args) {\n        args.cell = new SimpleRNNCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nSimpleRNN.className = 'SimpleRNN';\nserialization.registerClass(SimpleRNN);\nexport class GRUCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.resetAfter) {\n            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = this.units;\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (inputs.length !== 2) {\n                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            inputs = inputs[0];\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2, regardless of the actual value of\n            // config.implementation.\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 3\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 3\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            let z;\n            let r;\n            let hh;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let matrixX = K.dot(inputs, this.kernel.read());\n            if (this.useBias) {\n                matrixX = K.biasAdd(matrixX, this.bias.read());\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            const recurrentKernelValue = this.recurrentKernel.read();\n            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n            const matrixInner = K.dot(hTMinus1, rk1);\n            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);\n            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n            hh = this.activation.apply(tfc.add(xH, recurrentH));\n            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n            resetAfter: false\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nGRUCell.className = 'GRUCell';\nserialization.registerClass(GRUCell);\nexport class GRU extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new GRUCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nGRU.className = 'GRU';\nserialization.registerClass(GRU);\nexport class LSTMCell extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_ACTIVATION = 'tanh';\n        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :\n            args.activation);\n        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n        this.useBias = args.useBias == null ? true : args.useBias;\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.unitForgetBias = args.unitForgetBias;\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n        this.recurrentDropout = math_utils.min([\n            1,\n            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n        ]);\n        this.implementation = args.implementation;\n        this.stateSize = [this.units, this.units];\n        this.dropoutMask = null;\n        this.recurrentDropoutMask = null;\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const inputDim = inputShape[inputShape.length - 1];\n        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        let biasInitializer;\n        if (this.useBias) {\n            if (this.unitForgetBias) {\n                const capturedBiasInit = this.biasInitializer;\n                const capturedUnits = this.units;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            // TODO(cais): More informative variable names?\n                            const bI = capturedBiasInit.apply([capturedUnits]);\n                            const bF = (new Ones()).apply([capturedUnits]);\n                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        else {\n            this.bias = null;\n        }\n        // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n        //   of the weights and bias in the call() method, at execution time.\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            inputs = inputs;\n            if (inputs.length !== 3) {\n                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            let hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            inputs = inputs[0];\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(inputs),\n                    rate: this.dropout,\n                    training,\n                    count: 4\n                });\n            }\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: 4\n                });\n            }\n            const dpMask = this.dropoutMask;\n            const recDpMask = this.recurrentDropoutMask;\n            // Note: For superior performance, TensorFlow.js always uses\n            // implementation 2 regardless of the actual value of\n            // config.implementation.\n            let i;\n            let f;\n            let c;\n            let o;\n            if (0 < this.dropout && this.dropout < 1) {\n                inputs = tfc.mul(inputs, dpMask[0]);\n            }\n            let z = K.dot(inputs, this.kernel.read());\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n            }\n            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n            if (this.useBias) {\n                z = K.biasAdd(z, this.bias.read());\n            }\n            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n            i = this.recurrentActivation.apply(z0);\n            f = this.recurrentActivation.apply(z1);\n            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n            o = this.recurrentActivation.apply(z3);\n            const h = tfc.mul(o, this.activation.apply(c));\n            // TODO(cais): Add use_learning_phase flag properly.\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            recurrentActivation: serializeActivation(this.recurrentActivation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            unitForgetBias: this.unitForgetBias,\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint),\n            dropout: this.dropout,\n            recurrentDropout: this.recurrentDropout,\n            implementation: this.implementation,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n}\n/** @nocollapse */\nLSTMCell.className = 'LSTMCell';\nserialization.registerClass(LSTMCell);\nexport class LSTM extends RNN {\n    constructor(args) {\n        if (args.implementation === 0) {\n            console.warn('`implementation=0` has been deprecated, and now defaults to ' +\n                '`implementation=1`. Please update your layer call.');\n        }\n        args.cell = new LSTMCell(args);\n        super(args);\n        // TODO(cais): Add activityRegularizer.\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        if (config['implmentation'] === 0) {\n            config['implementation'] = 1;\n        }\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nLSTM.className = 'LSTM';\nserialization.registerClass(LSTM);\nexport class StackedRNNCells extends RNNCell {\n    constructor(args) {\n        super(args);\n        this.cells = args.cells;\n    }\n    get stateSize() {\n        // States are a flat list in reverse order of the cell stack.\n        // This allows perserving the requirement `stack.statesize[0] ===\n        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n        // assuming one LSTM has states `[h, c]`.\n        const stateSize = [];\n        for (const cell of this.cells.slice().reverse()) {\n            if (Array.isArray(cell.stateSize)) {\n                stateSize.push(...cell.stateSize);\n            }\n            else {\n                stateSize.push(cell.stateSize);\n            }\n        }\n        return stateSize;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            let states = inputs.slice(1);\n            // Recover per-cell states.\n            const nestedStates = [];\n            for (const cell of this.cells.slice().reverse()) {\n                if (Array.isArray(cell.stateSize)) {\n                    nestedStates.push(states.splice(0, cell.stateSize.length));\n                }\n                else {\n                    nestedStates.push(states.splice(0, 1));\n                }\n            }\n            nestedStates.reverse();\n            // Call the cells in order and store the returned states.\n            const newNestedStates = [];\n            let callInputs;\n            for (let i = 0; i < this.cells.length; ++i) {\n                const cell = this.cells[i];\n                states = nestedStates[i];\n                // TODO(cais): Take care of constants.\n                if (i === 0) {\n                    callInputs = [inputs[0]].concat(states);\n                }\n                else {\n                    callInputs = [callInputs[0]].concat(states);\n                }\n                callInputs = cell.call(callInputs, kwargs);\n                newNestedStates.push(callInputs.slice(1));\n            }\n            // Format the new states as a flat list in reverse cell order.\n            states = [];\n            for (const cellStates of newNestedStates.slice().reverse()) {\n                states.push(...cellStates);\n            }\n            return [callInputs[0]].concat(states);\n        });\n    }\n    build(inputShape) {\n        if (isArrayOfShapes(inputShape)) {\n            // TODO(cais): Take care of input constants.\n            // const constantShape = inputShape.slice(1);\n            inputShape = inputShape[0];\n        }\n        inputShape = inputShape;\n        let outputDim;\n        this.cells.forEach((cell, i) => {\n            nameScope(`RNNCell_${i}`, () => {\n                // TODO(cais): Take care of input constants.\n                cell.build(inputShape);\n                if (Array.isArray(cell.stateSize)) {\n                    outputDim = cell.stateSize[0];\n                }\n                else {\n                    outputDim = cell.stateSize;\n                }\n                inputShape = [inputShape[0], outputDim];\n            });\n        });\n        this.built = true;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const getCellConfig = (cell) => {\n            return {\n                'className': cell.getClassName(),\n                'config': cell.getConfig(),\n            };\n        };\n        const cellConfigs = this.cells.map(getCellConfig);\n        const config = { 'cells': cellConfigs };\n        return Object.assign({}, baseConfig, config);\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const cells = [];\n        for (const cellConfig of config['cells']) {\n            cells.push(deserialize(cellConfig, customObjects));\n        }\n        return new cls({ cells });\n    }\n    get trainableWeights() {\n        if (!this.trainable) {\n            return [];\n        }\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.trainableWeights);\n        }\n        return weights;\n    }\n    get nonTrainableWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.nonTrainableWeights);\n        }\n        if (!this.trainable) {\n            const trainableWeights = [];\n            for (const cell of this.cells) {\n                trainableWeights.push(...cell.trainableWeights);\n            }\n            return trainableWeights.concat(weights);\n        }\n        return weights;\n    }\n    /**\n     * Retrieve the weights of a the model.\n     *\n     * @returns A flat `Array` of `tf.Tensor`s.\n     */\n    getWeights() {\n        const weights = [];\n        for (const cell of this.cells) {\n            weights.push(...cell.weights);\n        }\n        return batchGetValue(weights);\n    }\n    /**\n     * Set the weights of the model.\n     *\n     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n     *     the output of `getWeights()`.\n     */\n    setWeights(weights) {\n        const tuples = [];\n        for (const cell of this.cells) {\n            const numParams = cell.weights.length;\n            const inputWeights = weights.splice(numParams);\n            for (let i = 0; i < cell.weights.length; ++i) {\n                tuples.push([cell.weights[i], inputWeights[i]]);\n            }\n        }\n        batchSetValue(tuples);\n    }\n}\n/** @nocollapse */\nStackedRNNCells.className = 'StackedRNNCells';\nserialization.registerClass(StackedRNNCells);\nexport function generateDropoutMask(args) {\n    const { ones, rate, training = false, count = 1 } = args;\n    const droppedInputs = () => K.dropout(ones(), rate);\n    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n    // just in case count is provided with null or undefined\n    if (!count || count <= 1) {\n        return tfc.keep(createMask().clone());\n    }\n    const masks = Array(count).fill(undefined).map(createMask);\n    return masks.map(m => tfc.keep(m.clone()));\n}\n//# sourceMappingURL=recurrent.js.map", "/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nvar __rest = (this && this.__rest) || function (s, e) {\n    var t = {};\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n        t[p] = s[p];\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n                t[p[i]] = s[p[i]];\n        }\n    return t;\n};\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { AttributeError, NotImplementedError, ValueError } from '../errors';\nimport { Initializer } from '../initializers';\nimport { convOutputLength, normalizeArray } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\nimport { generateDropoutMask, LSTMCell, RNN, RNNCell } from './recurrent';\nclass ConvRNN2DCell extends RNNCell {\n}\n/**\n * Base class for convolutional-recurrent layers.\n */\nclass ConvRNN2D extends RNN {\n    constructor(args) {\n        if (args.unroll) {\n            throw new NotImplementedError('Unrolling is not possible with convolutional RNNs.');\n        }\n        if (Array.isArray(args.cell)) {\n            throw new NotImplementedError('It is not possible at the moment to stack convolutional cells.');\n        }\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (this.cell.dropoutMask != null) {\n                tfc.dispose(this.cell.dropoutMask);\n                this.cell.dropoutMask = null;\n            }\n            if (this.cell.recurrentDropoutMask != null) {\n                tfc.dispose(this.cell.recurrentDropoutMask);\n                this.cell.recurrentDropoutMask = null;\n            }\n            if (kwargs && kwargs['constants']) {\n                throw new ValueError('ConvRNN2D cell does not support constants');\n            }\n            const mask = kwargs == null ? null : kwargs['mask'];\n            const training = kwargs == null ? null : kwargs['training'];\n            const initialState = kwargs == null ? null : kwargs['initialState'];\n            return super.call(inputs, { mask, training, initialState });\n        });\n    }\n    computeOutputShape(inputShape) {\n        let outShape = this.computeSingleOutputShape(inputShape);\n        if (!this.returnSequences) {\n            outShape = [outShape[0], ...outShape.slice(2)];\n        }\n        if (this.returnState) {\n            outShape =\n                [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n        }\n        return outShape;\n    }\n    getInitialState(inputs) {\n        return tfc.tidy(() => {\n            const { stateSize } = this.cell;\n            const inputShape = inputs.shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const initialState = tfc.zeros(stateShape);\n            if (Array.isArray(stateSize)) {\n                return Array(stateSize.length).fill(initialState);\n            }\n            return [initialState];\n        });\n    }\n    resetStates(states, training = false) {\n        tfc.tidy(() => {\n            if (!this.stateful) {\n                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');\n            }\n            const inputShape = this.inputSpec[0].shape;\n            const outputShape = this.computeSingleOutputShape(inputShape);\n            const stateShape = [outputShape[0], ...outputShape.slice(2)];\n            const batchSize = inputShape[0];\n            if (batchSize == null) {\n                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +\n                    'the batch size of your input tensors: \\n' +\n                    '- If using a Sequential model, specify the batch size by ' +\n                    'passing a `batchInputShape` option to your first layer.\\n' +\n                    '- If using the functional API, specify the batch size by ' +\n                    'passing a `batchShape` option to your Input layer.');\n            }\n            // Initialize state if null.\n            if (this.getStates() == null) {\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_ = [tfc.zeros(stateShape)];\n                }\n            }\n            else if (states == null) {\n                // Dispose old state tensors.\n                tfc.dispose(this.states_);\n                // For stateful RNNs, fully dispose kept old states.\n                if (this.keptStates != null) {\n                    tfc.dispose(this.keptStates);\n                    this.keptStates = [];\n                }\n                if (Array.isArray(this.cell.stateSize)) {\n                    this.states_ = this.cell.stateSize.map(() => tfc.zeros(stateShape));\n                }\n                else {\n                    this.states_[0] = tfc.zeros(stateShape);\n                }\n            }\n            else {\n                if (!Array.isArray(states)) {\n                    states = [states];\n                }\n                if (states.length !== this.states_.length) {\n                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +\n                        `but it received ${states.length} state value(s). Input ` +\n                        `received: ${states}`);\n                }\n                if (training) {\n                    // Store old state tensors for complete disposal later, i.e., during\n                    // the next no-arg call to this method. We do not dispose the old\n                    // states immediately because that BPTT (among other things) require\n                    // them.\n                    this.keptStates.push(this.states_.slice());\n                }\n                else {\n                    tfc.dispose(this.states_);\n                }\n                for (let index = 0; index < this.states_.length; ++index) {\n                    const value = states[index];\n                    const expectedShape = stateShape;\n                    if (!util.arraysEqual(value.shape, expectedShape)) {\n                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +\n                            `expected shape=${expectedShape}, received shape=${value.shape}`);\n                    }\n                    this.states_[index] = value;\n                }\n            }\n            this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n        });\n    }\n    computeSingleOutputShape(inputShape) {\n        const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n        const isChannelsFirst = dataFormat === 'channelsFirst';\n        const h = inputShape[isChannelsFirst ? 3 : 2];\n        const w = inputShape[isChannelsFirst ? 4 : 3];\n        const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n        const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n        const outShape = [\n            ...inputShape.slice(0, 2),\n            ...(isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters])\n        ];\n        return outShape;\n    }\n}\n/** @nocollapse */\nConvRNN2D.className = 'ConvRNN2D';\nexport class ConvLSTM2DCell extends LSTMCell {\n    constructor(args) {\n        const { filters, kernelSize, strides, padding, dataFormat, dilationRate, } = args;\n        super(Object.assign({}, args, { units: filters }));\n        this.filters = filters;\n        assertPositiveInteger(this.filters, 'filters');\n        this.kernelSize = normalizeArray(kernelSize, 2, 'kernelSize');\n        this.kernelSize.forEach(size => assertPositiveInteger(size, 'kernelSize'));\n        this.strides = normalizeArray(strides || 1, 2, 'strides');\n        this.strides.forEach(stride => assertPositiveInteger(stride, 'strides'));\n        this.padding = padding || 'valid';\n        checkPaddingMode(this.padding);\n        this.dataFormat = dataFormat || 'channelsLast';\n        checkDataFormat(this.dataFormat);\n        this.dilationRate = normalizeArray(dilationRate || 1, 2, 'dilationRate');\n        this.dilationRate.forEach(rate => assertPositiveInteger(rate, 'dilationRate'));\n    }\n    build(inputShape) {\n        var _a;\n        inputShape = getExactlyOneShape(inputShape);\n        const channelAxis = this.dataFormat === 'channelsFirst' ? 1 : inputShape.length - 1;\n        if (inputShape[channelAxis] == null) {\n            throw new ValueError(`The channel dimension of the input should be defined. ` +\n                `Found ${inputShape[channelAxis]}`);\n        }\n        const inputDim = inputShape[channelAxis];\n        const numOfKernels = 4;\n        const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n        this.kernel = this.addWeight('kernel', kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n        const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n        this.recurrentKernel = this.addWeight('recurrent_kernel', recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n        if (this.useBias) {\n            let biasInitializer;\n            if (this.unitForgetBias) {\n                const init = this.biasInitializer;\n                const filters = this.filters;\n                biasInitializer = new (_a = class CustomInit extends Initializer {\n                        apply(shape, dtype) {\n                            const biasI = init.apply([filters]);\n                            const biasF = tfc.ones([filters]);\n                            const biasCAndO = init.apply([filters * 2]);\n                            return K.concatenate([biasI, biasF, biasCAndO]);\n                        }\n                    },\n                    /** @nocollapse */\n                    _a.className = 'CustomInit',\n                    _a)();\n            }\n            else {\n                biasInitializer = this.biasInitializer;\n            }\n            this.bias = this.addWeight('bias', [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tfc.tidy(() => {\n            if (inputs.length !== 3) {\n                throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ` +\n                    `${inputs.length}.`);\n            }\n            const training = kwargs['training'] || false;\n            const x = inputs[0]; // Current input\n            const hTMinus1 = inputs[1]; // Previous memory state.\n            const cTMinus1 = inputs[2]; // Previous carry state.\n            const numOfKernels = 4;\n            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n                this.dropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(x),\n                    rate: this.dropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const dropoutMask = this.dropoutMask;\n            const applyDropout = (x, mask, index) => {\n                if (!mask || !mask[index]) {\n                    return x;\n                }\n                return tfc.mul(mask[index], x);\n            };\n            let xI = applyDropout(x, dropoutMask, 0);\n            let xF = applyDropout(x, dropoutMask, 1);\n            let xC = applyDropout(x, dropoutMask, 2);\n            let xO = applyDropout(x, dropoutMask, 3);\n            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n                this.recurrentDropoutMask == null) {\n                this.recurrentDropoutMask = generateDropoutMask({\n                    ones: () => tfc.onesLike(hTMinus1),\n                    rate: this.recurrentDropout,\n                    training,\n                    count: numOfKernels\n                });\n            }\n            const recDropoutMask = this.recurrentDropoutMask;\n            let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n            let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n            let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n            let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n            const kernelChannelAxis = 3;\n            const [kernelI, kernelF, kernelC, kernelO] = tfc.split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n            const [biasI, biasF, biasC, biasO] = this.useBias ?\n                tfc.split(this.bias.read(), numOfKernels) :\n                [null, null, null, null];\n            xI = this.inputConv(xI, kernelI, biasI, this.padding);\n            xF = this.inputConv(xF, kernelF, biasF, this.padding);\n            xC = this.inputConv(xC, kernelC, biasC, this.padding);\n            xO = this.inputConv(xO, kernelO, biasO, this.padding);\n            const [recKernelI, recKernelF, recKernelC, recKernelO] = tfc.split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n            hI = this.recurrentConv(hI, recKernelI);\n            hF = this.recurrentConv(hF, recKernelF);\n            hC = this.recurrentConv(hC, recKernelC);\n            hO = this.recurrentConv(hO, recKernelO);\n            const i = this.recurrentActivation.apply(tfc.add(xI, hI));\n            const f = this.recurrentActivation.apply(tfc.add(xF, hF));\n            const c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(tfc.add(xC, hC))));\n            const h = tfc.mul(this.recurrentActivation.apply(tfc.add(xO, hO)), this.activation.apply(c));\n            return [h, h, c];\n        });\n    }\n    getConfig() {\n        const _a = super.getConfig(), { 'units': _ } = _a, baseConfig = __rest(_a, ['units']);\n        const config = {\n            filters: this.filters,\n            kernelSize: this.kernelSize,\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n            dilationRate: this.dilationRate,\n            strides: this.strides,\n        };\n        return Object.assign({}, baseConfig, config);\n    }\n    inputConv(x, w, b, padding) {\n        const out = tfc.conv2d(x, w, this.strides, (padding || 'valid'), this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC', this.dilationRate);\n        if (b) {\n            return K.biasAdd(out, b, this.dataFormat);\n        }\n        return out;\n    }\n    recurrentConv(x, w) {\n        const strides = 1;\n        return tfc.conv2d(x, w, strides, 'same', this.dataFormat === 'channelsFirst' ? 'NCHW' : 'NHWC');\n    }\n}\n/** @nocollapse */\nConvLSTM2DCell.className = 'ConvLSTM2DCell';\ntfc.serialization.registerClass(ConvLSTM2DCell);\nexport class ConvLSTM2D extends ConvRNN2D {\n    constructor(args) {\n        const cell = new ConvLSTM2DCell(args);\n        super(Object.assign({}, args, { cell }));\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        return new cls(config);\n    }\n}\n/** @nocollapse */\nConvLSTM2D.className = 'ConvLSTM2D';\ntfc.serialization.registerClass(ConvLSTM2D);\n//# sourceMappingURL=convolutional_recurrent.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Basic Layers.\n */\nimport { any, notEqual, serialization, tidy, transpose, util } from '@tensorflow/tfjs-core';\nimport { getActivation, serializeActivation } from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport { assertPositiveInteger, mapActivationToFusedKernel } from '../utils/generic_utils';\nimport { arrayProd, range } from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Dropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.rate = Math.max(Math.min(args.rate, 1), 0);\n        // So that the scalar doesn't get tidied up between executions.\n        this.noiseShape = args.noiseShape;\n        this.seed = args.seed;\n        this.supportsMasking = true;\n    }\n    getNoiseShape(input) {\n        if (this.noiseShape == null) {\n            return this.noiseShape;\n        }\n        const inputShape = input.shape;\n        const noiseShape = [];\n        for (let i = 0; i < this.noiseShape.length; ++i) {\n            noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n        }\n        return noiseShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (0 < this.rate && this.rate < 1) {\n                const training = kwargs['training'] == null ? false : kwargs['training'];\n                const noiseShape = this.getNoiseShape(input);\n                const output = K.inTrainPhase(() => K.dropout(input, this.rate, noiseShape, this.seed), () => input, training);\n                return output;\n            }\n            return inputs;\n        });\n    }\n    getConfig() {\n        const config = {\n            rate: this.rate,\n            noiseShape: this.noiseShape,\n            seed: this.seed,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    dispose() {\n        return super.dispose();\n    }\n}\n/** @nocollapse */\nDropout.className = 'Dropout';\nserialization.registerClass(Dropout);\nexport class SpatialDropout1D extends Dropout {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [{ ndim: 3 }];\n    }\n    getNoiseShape(input) {\n        const inputShape = input.shape;\n        return [inputShape[0], 1, inputShape[2]];\n    }\n}\n/** @nocollapse */\nSpatialDropout1D.className = 'SpatialDropout1D';\nserialization.registerClass(SpatialDropout1D);\nexport class Dense extends Layer {\n    constructor(args) {\n        super(args);\n        // Default activation: Linear (none).\n        this.activation = null;\n        this.useBias = true;\n        this.kernel = null;\n        this.bias = null;\n        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n        this.DEFAULT_BIAS_INITIALIZER = 'zeros';\n        if (args.batchInputShape == null && args.inputShape == null &&\n            args.inputDim != null) {\n            // This logic is copied from Layer's constructor, since we can't\n            // do exactly what the Python constructor does for Dense().\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            this.batchInputShape = [batchSize, args.inputDim];\n        }\n        this.units = args.units;\n        assertPositiveInteger(this.units, 'units');\n        this.activation = getActivation(args.activation);\n        if (args.useBias != null) {\n            this.useBias = args.useBias;\n        }\n        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n        this.biasInitializer =\n            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n        this.kernelConstraint = getConstraint(args.kernelConstraint);\n        this.biasConstraint = getConstraint(args.biasConstraint);\n        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n        this.biasRegularizer = getRegularizer(args.biasRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.supportsMasking = true;\n        this.inputSpec = [{ minNDim: 2 }];\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const inputLastDim = inputShape[inputShape.length - 1];\n        if (this.kernel == null) {\n            this.kernel = this.addWeight('kernel', [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n            if (this.useBias) {\n                this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n            }\n        }\n        this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n        this.built = true;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        outputShape[outputShape.length - 1] = this.units;\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Dense layer accepts only a single input.\n            const input = getExactlyOneTensor(inputs);\n            const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n            let output;\n            if (fusedActivationName != null) {\n                output = K.dot(input, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n            }\n            else {\n                output = K.dot(input, this.kernel.read());\n                if (this.bias != null) {\n                    output = K.biasAdd(output, this.bias.read());\n                }\n                if (this.activation != null) {\n                    output = this.activation.apply(output);\n                }\n            }\n            return output;\n        });\n    }\n    getConfig() {\n        const config = {\n            units: this.units,\n            activation: serializeActivation(this.activation),\n            useBias: this.useBias,\n            kernelInitializer: serializeInitializer(this.kernelInitializer),\n            biasInitializer: serializeInitializer(this.biasInitializer),\n            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n            biasRegularizer: serializeRegularizer(this.biasRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            kernelConstraint: serializeConstraint(this.kernelConstraint),\n            biasConstraint: serializeConstraint(this.biasConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDense.className = 'Dense';\nserialization.registerClass(Dense);\nexport class Flatten extends Layer {\n    constructor(args) {\n        args = args || {};\n        super(args);\n        this.inputSpec = [{ minNDim: 3 }];\n        this.dataFormat = args.dataFormat;\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        for (const dim of inputShape.slice(1)) {\n            if (dim == null) {\n                throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined ` +\n                    `(got ${inputShape.slice(1)}). Make sure to pass a complete ` +\n                    `\"input_shape\" or \"batch_input_shape\" argument to the first ` +\n                    `layer in your model.`);\n            }\n        }\n        return [inputShape[0], arrayProd(inputShape, 1)];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            let input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsFirst' && input.rank > 1) {\n                const permutation = [0];\n                for (let i = 2; i < input.rank; ++i) {\n                    permutation.push(i);\n                }\n                permutation.push(1);\n                input = input.transpose(permutation);\n            }\n            return K.batchFlatten(input);\n        });\n    }\n    getConfig() {\n        const config = {};\n        if (this.dataFormat != null) {\n            config['dataFormat'] = this.dataFormat;\n        }\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nFlatten.className = 'Flatten';\nserialization.registerClass(Flatten);\nexport class Activation extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.activation = getActivation(args.activation);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            return this.activation.apply(input);\n        });\n    }\n    getConfig() {\n        const config = { activation: serializeActivation(this.activation) };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nActivation.className = 'Activation';\nserialization.registerClass(Activation);\nexport class RepeatVector extends Layer {\n    constructor(args) {\n        super(args);\n        this.n = args.n;\n        this.inputSpec = [{ ndim: 2 }];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], this.n, inputShape[1]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = getExactlyOneTensor(inputs);\n            return K.repeat(inputs, this.n);\n        });\n    }\n    getConfig() {\n        const config = {\n            n: this.n,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nRepeatVector.className = 'RepeatVector';\nserialization.registerClass(RepeatVector);\nexport class Reshape extends Layer {\n    constructor(args) {\n        super(args);\n        this.targetShape = args.targetShape;\n        // Make sure that all unknown dimensions are represented as `null`.\n        for (let i = 0; i < this.targetShape.length; ++i) {\n            if (this.isUnknown(this.targetShape[i])) {\n                this.targetShape[i] = null;\n            }\n        }\n    }\n    isUnknown(dim) {\n        return dim < 0 || dim == null;\n    }\n    /**\n     * Finds and replaces a missing dimension in output shape.\n     *\n     * This is a near direct port of the internal Numpy function\n     * `_fix_unknown_dimension` in `numpy/core/src/multiarray/shape.c`.\n     *\n     * @param inputShape: Original shape of array begin reshape.\n     * @param outputShape: Target shape of the array, with at most a single\n     * `null` or negative number, which indicates an underdetermined dimension\n     * that should be derived from `inputShape` and the known dimensions of\n     *   `outputShape`.\n     * @returns: The output shape with `null` replaced with its computed value.\n     * @throws: ValueError: If `inputShape` and `outputShape` do not match.\n     */\n    fixUnknownDimension(inputShape, outputShape) {\n        const errorMsg = 'Total size of new array must be unchanged.';\n        const finalShape = outputShape.slice();\n        let known = 1;\n        let unknown = null;\n        for (let i = 0; i < finalShape.length; ++i) {\n            const dim = finalShape[i];\n            if (this.isUnknown(dim)) {\n                if (unknown === null) {\n                    unknown = i;\n                }\n                else {\n                    throw new ValueError('Can only specifiy one unknown dimension.');\n                }\n            }\n            else {\n                known *= dim;\n            }\n        }\n        const originalSize = arrayProd(inputShape);\n        if (unknown !== null) {\n            if (known === 0 || originalSize % known !== 0) {\n                throw new ValueError(errorMsg);\n            }\n            finalShape[unknown] = originalSize / known;\n        }\n        else if (originalSize !== known) {\n            throw new ValueError(errorMsg);\n        }\n        return finalShape;\n    }\n    computeOutputShape(inputShape) {\n        let anyUnknownDims = false;\n        for (let i = 0; i < inputShape.length; ++i) {\n            if (this.isUnknown(inputShape[i])) {\n                anyUnknownDims = true;\n                break;\n            }\n        }\n        if (anyUnknownDims) {\n            return inputShape.slice(0, 1).concat(this.targetShape);\n        }\n        else {\n            return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n            return input.reshape(outputShape);\n        });\n    }\n    getConfig() {\n        const config = {\n            targetShape: this.targetShape,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nReshape.className = 'Reshape';\nserialization.registerClass(Reshape);\nexport class Permute extends Layer {\n    constructor(args) {\n        super(args);\n        if (args.dims == null) {\n            throw new Error('Required configuration field `dims` is missing during Permute ' +\n                'constructor call.');\n        }\n        if (!Array.isArray(args.dims)) {\n            throw new Error('Permute constructor requires `dims` to be an Array, but received ' +\n                `${args.dims} instead.`);\n        }\n        // Check the validity of the permutation indices.\n        const expectedSortedIndices = range(1, args.dims.length + 1);\n        if (!util.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n            throw new Error('Invalid permutation `dims`: ' + JSON.stringify(args.dims) +\n                ' `dims` must contain consecutive integers starting from 1.');\n        }\n        this.dims = args.dims;\n        this.dimsIncludingBatch = [0].concat(this.dims);\n        this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const outputShape = inputShape.slice();\n        this.dims.forEach((dim, i) => {\n            outputShape[i + 1] = inputShape[dim];\n        });\n        return outputShape;\n    }\n    call(inputs, kwargs) {\n        return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n    }\n    getConfig() {\n        const config = {\n            dims: this.dims,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nPermute.className = 'Permute';\nserialization.registerClass(Permute);\nexport class Masking extends Layer {\n    constructor(args) {\n        super(args == null ? {} : args);\n        this.supportsMasking = true;\n        if (args != null) {\n            this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n        }\n        else {\n            this.maskValue = 0;\n        }\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { maskValue: this.maskValue };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    computeMask(inputs, mask) {\n        const input = getExactlyOneTensor(inputs);\n        const axis = -1;\n        return any(notEqual(input, this.maskValue), axis);\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const axis = -1;\n            const keepDims = true;\n            const booleanMask = any(notEqual(input, this.maskValue), axis, keepDims);\n            const output = input.mul(booleanMask.asType(input.dtype));\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMasking.className = 'Masking';\nserialization.registerClass(Masking);\n//# sourceMappingURL=core.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Embedding Layer.\n *\n * Original source: keras/constraints.py\n */\nimport { notEqual, serialization, tidy, zerosLike } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nexport class Embedding extends Layer {\n    constructor(args) {\n        super(args);\n        this.embeddings = null;\n        this.DEFAULT_EMBEDDINGS_INITIALIZER = 'randomUniform';\n        if (args.batchInputShape == null && args.inputShape == null) {\n            // Porting Note: This logic is copied from Layer's constructor, since we\n            // can't do exactly what the Python constructor does for Embedding().\n            // Specifically, the super constructor can not be called after the\n            // mutation of the `config` argument.\n            let batchSize = null;\n            if (args.batchSize != null) {\n                batchSize = args.batchSize;\n            }\n            if (args.inputLength == null) {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (None, )\n                this.batchInputShape = [batchSize, null];\n            }\n            else {\n                // Fix super-constructor to what it would have done if\n                // 'config.inputShape' were (config.inputLength, )\n                this.batchInputShape =\n                    [batchSize].concat(generic_utils.toList(args.inputLength));\n            }\n        }\n        this.inputDim = args.inputDim;\n        generic_utils.assertPositiveInteger(this.inputDim, 'inputDim');\n        this.outputDim = args.outputDim;\n        generic_utils.assertPositiveInteger(this.outputDim, 'outputDim');\n        this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n        this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n        this.activityRegularizer = getRegularizer(args.activityRegularizer);\n        this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n        this.maskZero = args.maskZero;\n        this.supportsMasking = args.maskZero;\n        this.inputLength = args.inputLength;\n    }\n    build(inputShape) {\n        this.embeddings = this.addWeight('embeddings', [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n        this.built = true;\n    }\n    // Override warnOnIncompatibleInputShape because an embedding layer allows\n    // the input to have varying ranks.\n    warnOnIncompatibleInputShape(inputShape) { }\n    computeMask(inputs, mask) {\n        return tidy(() => {\n            if (!this.maskZero) {\n                return null;\n            }\n            else {\n                inputs = getExactlyOneTensor(inputs);\n                return notEqual(inputs, zerosLike(inputs));\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (this.inputLength == null) {\n            return [...inputShape, this.outputDim];\n        }\n        // inputLength can be an array if input is 3D or higher.\n        const inLens = generic_utils.toList(this.inputLength);\n        if (inLens.length !== inputShape.length - 1) {\n            throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                `input shape has shape ${inputShape}`);\n        }\n        else {\n            let i = 0;\n            for (let k = 0; k < inLens.length; ++k) {\n                const s1 = inLens[k];\n                const s2 = inputShape[k + 1];\n                if ((s1 != null) && (s2 != null) && (s1 !== s2)) {\n                    throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received ` +\n                        `input shape has shape ${inputShape}`);\n                }\n                else if (s1 == null) {\n                    inLens[i] = s2;\n                }\n                i++;\n            }\n        }\n        return [inputShape[0], ...inLens, this.outputDim];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Embedding layer accepts only a single input.\n            let input = getExactlyOneTensor(inputs);\n            if (input.dtype !== 'int32') {\n                input = K.cast(input, 'int32');\n            }\n            const output = K.gather(this.embeddings.read(), input.as1D());\n            return output.reshape(getExactlyOneShape(this.computeOutputShape(input.shape)));\n        });\n    }\n    getConfig() {\n        const config = {\n            inputDim: this.inputDim,\n            outputDim: this.outputDim,\n            embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n            embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n            activityRegularizer: serializeRegularizer(this.activityRegularizer),\n            embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n            maskZero: this.maskZero,\n            inputLength: this.inputLength\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nEmbedding.className = 'Embedding';\nserialization.registerClass(Embedding);\n//# sourceMappingURL=embeddings.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Merge Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { l2Normalize } from '../losses';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as mathUtils from '../utils/math_utils';\nimport { getExactlyOneShape } from '../utils/types_utils';\n/**\n * Generic Merge layer for element-wise merge functions.\n *\n * Used to implement `Sum`, `Average`, `Concatenate`, etc.\n */\nexport class Merge extends Layer {\n    constructor(args) {\n        super(args || {});\n        this.supportsMasking = true;\n    }\n    /**\n     * Logic for merging multiple tensors, to be overridden by subclasses.\n     * @param inputs\n     */\n    mergeFunction(inputs) {\n        throw new NotImplementedError();\n    }\n    /**\n     * Computes the shape of the result of an elementwise operation.\n     *\n     * @param shape1: Shape of the first tensor.\n     * @param shape2: Shape of the second tensor.\n     * @returns Expected output shape when an elementwise operation is carried\n     *   out on 2 tensors with shapes `shape1` and `shape2`.\n     * @throws ValueError: If `shape1` and `shape2` are not compatible for\n     *   element-wise operations.\n     */\n    computeElementwiseOpOutputShape(shape1, shape2) {\n        if (shape1 == null || shape2 == null) {\n            return null;\n        }\n        else if (shape1.length < shape2.length) {\n            return this.computeElementwiseOpOutputShape(shape2, shape1);\n        }\n        else if (shape2.length === 0) {\n            return shape1;\n        }\n        const outputShape = shape1.slice(0, shape1.length - shape2.length);\n        for (let k = 0; k < shape2.length; ++k) {\n            const i = shape1[shape1.length - shape2.length + k];\n            const j = shape2[k];\n            if (i == null || j == null || i < 0 || j < 0) {\n                outputShape.push(null);\n            }\n            else if (i === 1) {\n                outputShape.push(j);\n            }\n            else if (j === 1) {\n                outputShape.push(i);\n            }\n            else {\n                if (i !== j) {\n                    throw new ValueError('Operands could not be broadcast together with shapes ' +\n                        JSON.stringify(shape1) + ' ' + JSON.stringify(shape2));\n                }\n                outputShape.push(i);\n            }\n        }\n        return outputShape;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.\n        if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n            // Make sure that inputShape is an Array of shape.\n            inputShape = [getExactlyOneShape(inputShape)];\n        }\n        inputShape = inputShape;\n        if (inputShape.length < 2) {\n            throw new ValueError('A merge layer should be called on an Array of at least 2 inputs.' +\n                ` Got ${inputShape.length} input(s).`);\n        }\n        // Make sure that there is at most one unique batch size among the input\n        // shapes.\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length > 1) {\n            throw new ValueError(`Can not merge tensors with different batch sizes. ` +\n                `Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n        }\n        let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        // If the inputs have different ranks, we have to reshape them to make them\n        // broadcastable.\n        const allRanks = inputShape.map(shape => shape.length);\n        if (inputShape.indexOf(null) === -1 &&\n            generic_utils.unique(allRanks).length === 1) {\n            this.reshapeRequired = false;\n        }\n        else {\n            this.reshapeRequired = true;\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            inputs = inputs;\n            if (this.reshapeRequired) {\n                const reshapedInputs = [];\n                const inputDims = inputs.map(input => input.rank);\n                if (inputDims.indexOf(null) === -1) {\n                    // If ranks of all inputs are available, we simply expand each of them\n                    // at axis=1 until all of them have the same rank.\n                    const maxNDim = mathUtils.max(inputDims);\n                    for (let x of inputs) {\n                        const xNDim = x.rank;\n                        for (let k = 0; k < maxNDim - xNDim; ++k) {\n                            x = K.expandDims(x, 1);\n                        }\n                        reshapedInputs.push(x);\n                    }\n                    return this.mergeFunction(reshapedInputs);\n                }\n                else {\n                    // Transpose all inputs so that batch size is the last dimension.\n                    // [batchSize, dim1, dim2, ...] -> [dim1, dim2, ..., batchSize]\n                    let transposed = false;\n                    for (const x of inputs) {\n                        const xNDim = x.rank;\n                        if (xNDim == null) {\n                            const xShape = x.shape;\n                            const batchSize = xShape[0];\n                            const newShape = xShape.slice(1).concat([batchSize]);\n                            let xTransposed = x.reshape([batchSize].concat(mathUtils.arrayProd(xShape.slice(1))));\n                            xTransposed = tfc.transpose(xTransposed, [1, 0]);\n                            xTransposed = xTransposed.reshape(newShape);\n                            reshapedInputs.push(xTransposed);\n                            transposed = true;\n                        }\n                        else if (xNDim > 1) {\n                            const dims = mathUtils.range(1, xNDim).concat([0]);\n                            reshapedInputs.push(tfc.transpose(x, dims));\n                            transposed = true;\n                        }\n                        else {\n                            // We don't transpose inputs if they are 1D vectors or scalars.\n                            reshapedInputs.push(x);\n                        }\n                    }\n                    let y = this.mergeFunction(reshapedInputs);\n                    const yNDim = y.rank;\n                    if (transposed) {\n                        // If inputs have been transposed, we have to transpose the output\n                        // too.\n                        if (yNDim == null) {\n                            const yShape = y.shape;\n                            const yNDim = yShape.length;\n                            const batchSize = yShape[yNDim - 1];\n                            const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n                            y = tfc.transpose(y.reshape([-1, batchSize]), [1, 0])\n                                .reshape(newShape);\n                        }\n                        else if (yNDim > 1) {\n                            const dims = [yNDim - 1].concat(mathUtils.range(0, yNDim - 1));\n                            y = tfc.transpose(y, dims);\n                        }\n                    }\n                    return y;\n                }\n            }\n            else {\n                return this.mergeFunction(inputs);\n            }\n        });\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        let outputShape;\n        if (inputShape[0] == null) {\n            outputShape = null;\n        }\n        else {\n            outputShape = inputShape[0].slice(1);\n        }\n        for (let i = 1; i < inputShape.length; ++i) {\n            const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n            outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n        }\n        let batchSizes = [];\n        for (const shape of inputShape) {\n            if (shape != null && shape[0] !== null) {\n                batchSizes.push(shape[0]);\n            }\n        }\n        batchSizes = generic_utils.unique(batchSizes);\n        if (batchSizes.length === 1) {\n            outputShape = batchSizes.concat(outputShape);\n        }\n        else {\n            outputShape = [null].concat(outputShape);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return tfc.tidy(() => {\n            if (mask == null) {\n                return null;\n            }\n            if (!Array.isArray(mask)) {\n                throw new ValueError('`mask` should be an Array');\n            }\n            if (!Array.isArray(inputs)) {\n                throw new ValueError('`inputs` should be an Array');\n            }\n            if (mask.length !== inputs.length) {\n                throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same ` +\n                    `length, but have different lengths ` +\n                    `(${inputs.length} vs ${mask.length})`);\n            }\n            if (mask.every(m => m == null)) {\n                return null;\n            }\n            mask = mask.map(m => m == null ? m : tfc.expandDims(m, 0));\n            let output = mask[0];\n            for (let i = 1; i < mask.length - 1; ++i) {\n                output = tfc.logicalAnd(output, mask[i]);\n            }\n            return output;\n        });\n    }\n}\nexport class Add extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nAdd.className = 'Add';\nserialization.registerClass(Add);\n/**\n * Calculate the element-wise sum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Add` layer, by using no input argument\n *    or a single configuration argument. The resultant `Add` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const addLayer = tf.layers.add();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = addLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.add([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.add([input1, input2]).print();\n * // Gives [[11, 22], [33, 44]].\n *\n */\nexport function add(config) {\n    if (Array.isArray(config)) {\n        const layer = new Add({});\n        return layer.apply(config);\n    }\n    else {\n        return new Add(config);\n    }\n}\nexport class Multiply extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.mul(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMultiply.className = 'Multiply';\nserialization.registerClass(Multiply);\n/**\n * Calculate the element-wise product of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Multiply` layer, by using no input argument\n *    or a single configuration argument. The resultant `Multiply` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const multiplyLayer = tf.layers.multiply();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = multiplyLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.multiply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.multiply([input1, input2]).print();\n * // Gives [[10, 40], [90, 160]].\n *\n */\nexport function multiply(config) {\n    if (Array.isArray(config)) {\n        const layer = new Multiply({});\n        return layer.apply(config);\n    }\n    else {\n        return new Multiply(config);\n    }\n}\nexport class Average extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0].clone();\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.add(output, inputs[i]);\n            }\n            return tfc.mul(1 / inputs.length, output);\n        });\n    }\n}\n/** @nocollapse */\nAverage.className = 'Average';\nserialization.registerClass(Average);\n/**\n * Calculate the element-wise arithmetic mean of inputs, which all have the same\n * shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Average` layer, by using no input argument\n *    or a single configuration argument. The resultant `Average` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const averageLayer = tf.layers.average();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = averageLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.average([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const input2 = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n * tf.layers.average([input1, input2]).print();\n * // Gives [[5.5, 11], [16.5, 22]].\n *\n */\nexport function average(config) {\n    if (Array.isArray(config)) {\n        const layer = new Average({});\n        return layer.apply(config);\n    }\n    else {\n        return new Average(config);\n    }\n}\nexport class Maximum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.maximum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMaximum.className = 'Maximum';\nserialization.registerClass(Maximum);\n/**\n * Calculate the element-wise maximum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Maximum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Maximum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const maximumLayer = tf.layers.maximum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = maximumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.maximum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.maximum([input1, input2]).print();\n * // Gives [[10, 20], [30, 40]].\n *\n */\nexport function maximum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Maximum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Maximum(config);\n    }\n}\nexport class Minimum extends Merge {\n    constructor(args) {\n        super(args);\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            let output = inputs[0];\n            for (let i = 1; i < inputs.length; ++i) {\n                output = tfc.minimum(output, inputs[i]);\n            }\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nMinimum.className = 'Minimum';\nserialization.registerClass(Minimum);\n/**\n * Calculate the element-wise minimum of inputs, which all have the same shape.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Minimum` layer, by using no input argument\n *    or a single configuration argument. The resultant `Minimum` layer can then\n *    be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const minimumLayer = tf.layers.minimum();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = minimumLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const output = tf.layers.minimum([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([1, 20, 3, 40], [2, 2]);\n * const input2 = tf.tensor2d([10, 2, 30, 4], [2, 2]);\n * tf.layers.minimum([input1, input2]).print();\n * // Gives [[1, 2], [3, 4]].\n *\n */\nexport function minimum(config) {\n    if (Array.isArray(config)) {\n        const layer = new Minimum({});\n        return layer.apply(config);\n    }\n    else {\n        return new Minimum(config);\n    }\n}\nexport class Concatenate extends Merge {\n    constructor(args) {\n        super(args);\n        this.DEFAULT_AXIS = -1;\n        if (args == null) {\n            args = {};\n        }\n        this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        // Used purely for shape validation.]\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) ||\n            inputShape.length === 1) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of at least 2 ' +\n                'inputs');\n        }\n        inputShape = inputShape;\n        let allNoneShape = true;\n        for (const shape of inputShape) {\n            if (shape != null) {\n                allNoneShape = false;\n                break;\n            }\n        }\n        if (allNoneShape) {\n            return;\n        }\n        const shapeSet = [];\n        for (let i = 0; i < inputShape.length; ++i) {\n            const shapeWithoutConcatAxis = inputShape[i].slice();\n            shapeWithoutConcatAxis.splice(this.axis, 1);\n            let exists = false;\n            for (const shape of shapeSet) {\n                if (util.arraysEqual(shape, shapeWithoutConcatAxis)) {\n                    exists = true;\n                    break;\n                }\n            }\n            if (!exists) {\n                shapeSet.push(shapeWithoutConcatAxis);\n            }\n        }\n        if (shapeSet.length > 1) {\n            throw new ValueError('A `Concatenate` layer requires inputs with matching shapes ' +\n                'except for the concat axis. Got input shapes: ' +\n                JSON.stringify(inputShape));\n        }\n    }\n    mergeFunction(inputs) {\n        return tidy(() => {\n            return K.concatenate(inputs, this.axis);\n        });\n    }\n    computeOutputShape(inputShape) {\n        if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n            throw new ValueError('A `Concatenate` layer should be called on a list of inputs.');\n        }\n        const inputShapes = inputShape;\n        const outputShape = inputShapes[0].slice();\n        const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n        // Porting Note: the line above is because TypeScript doesn't support\n        //   negative indices.\n        for (const shape of inputShapes.slice(1)) {\n            if (outputShape[axis] == null || shape[axis] == null) {\n                outputShape[axis] = null;\n                break;\n            }\n            outputShape[axis] += shape[axis];\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        if (mask == null) {\n            return null;\n        }\n        if (!Array.isArray(mask)) {\n            throw new ValueError('`mask` should be an array for Concatenate');\n        }\n        if (!Array.isArray(inputs)) {\n            throw new ValueError('`inputs` should be an array for Concatenate');\n        }\n        if (mask.length !== inputs.length) {\n            throw new ValueError(`Mismatch in the length of mask (${mask.length}) ` +\n                `and the legnth of inputs (${inputs.length})`);\n        }\n        return tfc.tidy(() => {\n            let allNullMasks = true;\n            mask.forEach(m => {\n                if (m != null) {\n                    allNullMasks = false;\n                    return;\n                }\n            });\n            if (allNullMasks) {\n                return null;\n            }\n            const outputMasks = [];\n            for (let i = 0; i < inputs.length; ++i) {\n                if (mask[i] == null) {\n                    // Input is unmasked. Append all 1's to masks.\n                    outputMasks.push(tfc.onesLike(inputs[i]).asType('bool'));\n                }\n                else if (mask[i].rank < inputs[i].rank) {\n                    // Mask is smaller than the input, expand it.\n                    outputMasks.push(tfc.expandDims(mask[i], -1));\n                }\n                else {\n                    outputMasks.push(mask[i]);\n                }\n            }\n            const concatenatedMasks = tfc.concat(outputMasks, this.axis);\n            return tfc.all(concatenatedMasks, -1, false);\n        });\n    }\n    getConfig() {\n        const config = {\n            'axis': this.axis,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nConcatenate.className = 'Concatenate';\nserialization.registerClass(Concatenate);\n/**\n * Concatenate an `Array` of inputs.\n *\n * This function can be invoked in three ways.\n *\n * 1. Construct an instance of `Concatenate` layer, by using no input argument\n *    or a single configuration argument. The resultant `Concatenate` layer can\n *    then be used on `tf.SymbolicTensor`s or `tf.Tensor`s. For example:\n *\n * ```js\n * const concatLayer = tf.layers.concatenate();\n *\n * // The layer can be applied to inputs.\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = concatLayer.apply([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 7], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 2. Invoke directly on an `Array` of `tf.SymbolicTensor`s. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.SymbolicTensor`. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 3]});\n * const input2 = tf.input({shape: [2, 4]});\n * const output = tf.layers.concatenate([input1, input2]);\n * console.log(output.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension and the last dimension as the result of concatenating the\n * // last dimensions of the two inputs.\n * ```\n *\n * 3. Invoke directly on `tf.Tensor`s, i.e., concrete values. This constructs\n *    an `Layer` object internally and calls its `apply` method on the inputs,\n *    generating a new `tf.Tensor` as the result of the computation. For\n * example:\n *\n * ```js\n * const input1 = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);\n * const input2 = tf.tensor2d([[10, 20], [30, 40]], [2, 2]);\n * tf.layers.concatenate([input1, input2]).print();\n * // Gives [[1, 2, 10, 20], [3, 4, 30, 40]].\n *\n */\nexport function concatenate(config) {\n    if (Array.isArray(config)) {\n        const layer = new Concatenate({});\n        return layer.apply(config);\n    }\n    else {\n        return new Concatenate(config);\n    }\n}\n/**\n * Interpretable potentially negative axis index.\n *\n * For example, given axis = -1, and dim = 3, this function will return 2.\n *\n * @param axis The axis index, may be a positive, zero or negative integer.\n * @param dim Total number of dimensions, a positive integer.\n * @returns A non-negative axis index equivalent to the input `axis`.\n */\nfunction interpretAxis(axis, dim) {\n    while (axis < 0) {\n        axis += dim;\n    }\n    return axis;\n}\nfunction batchDot(x, y, axes) {\n    if (x.shape.length > 3 || y.shape.length > 3) {\n        throw new NotImplementedError('batchDot is not implemented for tensors of 4D or higher rank yet');\n    }\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, ` +\n        `but got ${x.shape.length}`);\n    tfc.util.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, ` +\n        `but got ${y.shape.length}`);\n    if (typeof axes === 'number') {\n        axes = [axes, axes];\n    }\n    if (x.dtype === 'complex64' || y.dtype === 'complex64') {\n        throw new NotImplementedError('batchDot is not implemented for complex64-type Tensors yet.');\n    }\n    const xNDim = x.shape.length;\n    const yNDim = y.shape.length;\n    if (axes == null) {\n        // Behave like batchMatmul by default.\n        axes = [xNDim - 1, yNDim - 2];\n    }\n    const axesArray = axes;\n    return tfc.tidy(() => {\n        let diff;\n        if (xNDim > yNDim) {\n            diff = xNDim - yNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            y = y.reshape(y.shape.concat(diffShape));\n        }\n        else if (yNDim > xNDim) {\n            diff = yNDim - xNDim;\n            const diffShape = [];\n            for (let i = 0; i < diff; ++i) {\n                diffShape.push(1);\n            }\n            x = x.reshape(x.shape.concat(diffShape));\n        }\n        else {\n            diff = 0;\n        }\n        let out;\n        if (x.shape.length === 2 && y.shape.length === 2) {\n            if (axesArray[0] === axesArray[1]) {\n                out = x.mul(y).sum(axesArray[0]);\n            }\n            else {\n                out = x.transpose([1, 0]).mul(y).sum(axesArray[1]);\n            }\n        }\n        else {\n            const adjX = axesArray[0] !== x.shape.length - 1;\n            const adjY = axesArray[1] === y.shape.length - 1;\n            out = x.matMul(y, adjX, adjY);\n        }\n        if (diff > 0) {\n            let idx;\n            if (xNDim > yNDim) {\n                idx = xNDim + yNDim - 3;\n            }\n            else {\n                idx = xNDim - 1;\n            }\n            const squeezeAxes = [];\n            for (let i = idx; i < idx + diff; ++i) {\n                squeezeAxes.push(i);\n            }\n            out = out.squeeze(squeezeAxes);\n        }\n        if (out.shape.length === 1) {\n            out = out.expandDims(1);\n        }\n        return out;\n    });\n}\nexport class Dot extends Merge {\n    constructor(args) {\n        super(args);\n        this.axes = args.axes;\n        this.normalize = args.normalize == null ? false : args.normalize;\n        this.supportsMasking = true;\n        this.reshapeRequired = false;\n    }\n    build(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0];\n        const shape2 = inputShape[1];\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        if (shape1[axes[0]] !== shape2[axes[1]]) {\n            throw new ValueError(`Dimension incompatibility: ` +\n                `${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n        }\n    }\n    mergeFunction(inputs) {\n        if (inputs.length !== 2) {\n            throw new ValueError('A `Dot` layer must be called on exactly 2 inputs, ' +\n                `but received ${inputs.length} input(s).`);\n        }\n        let x1 = inputs[0];\n        let x2 = inputs[1];\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            axes = [\n                interpretAxis(this.axes, x1.shape.length),\n                interpretAxis(this.axes, x2.shape.length)\n            ];\n        }\n        else {\n            axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n        }\n        if (this.normalize) {\n            x1 = l2Normalize(x1, axes[0]);\n            x2 = l2Normalize(x2, axes[1]);\n        }\n        return batchDot(x1, x2, axes);\n    }\n    interpretAxes(shape1, shape2) {\n        let axes;\n        if (!Array.isArray(this.axes)) {\n            // `this.axes` is a single integer.\n            axes = [\n                interpretAxis(this.axes, shape1.length),\n                interpretAxis(this.axes, shape2.length)\n            ];\n        }\n        else {\n            // `this.axes` is an Array of integers.\n            axes = this.axes;\n        }\n        return axes;\n    }\n    computeOutputShape(inputShape) {\n        tfc.util.assert(Array.isArray(inputShape) && inputShape.length === 2 &&\n            Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => 'A `Dot` layer should be called on a list of exactly 2 inputs.');\n        const shape1 = inputShape[0].slice();\n        const shape2 = inputShape[1].slice();\n        if (shape1.length > 3 || shape2.length > 3) {\n            throw new NotImplementedError('Dot layer does not support tensors of 4D or higher rank yet.');\n        }\n        const axes = this.interpretAxes(shape1, shape2);\n        shape1.splice(axes[0], 1);\n        shape2.splice(axes[1], 1);\n        shape2.splice(0, 1);\n        const outputShape = shape1.concat(shape2);\n        if (outputShape.length === 1) {\n            outputShape.push(1);\n        }\n        return outputShape;\n    }\n    computeMask(inputs, mask) {\n        return null;\n    }\n    getConfig() {\n        const config = {\n            'axes': this.axes,\n            'normalize': this.normalize\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nDot.className = 'Dot';\nserialization.registerClass(Dot);\n// TODO(cais): Add functional interfaces for the merge layers.\n//# sourceMappingURL=merge.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Noise Layers.\n */\nimport { greaterEqual, randomUniform, serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { Layer } from '../engine/topology';\nimport { getExactlyOneTensor } from '../utils/types_utils';\nexport class GaussianNoise extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.stddev = args.stddev;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { stddev: this.stddev };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            const noised = () => K.randomNormal(input.shape, 0, this.stddev).add(input);\n            const output = K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            return output;\n        });\n    }\n}\n/** @nocollapse */\nGaussianNoise.className = 'GaussianNoise';\nserialization.registerClass(GaussianNoise);\nexport class GaussianDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            const input = getExactlyOneTensor(inputs);\n            if (this.rate > 0 && this.rate < 1) {\n                const noised = () => {\n                    const stddev = Math.sqrt(this.rate / (1 - this.rate));\n                    return input.mul(K.randomNormal(input.shape, 1, stddev));\n                };\n                return K.inTrainPhase(noised, () => input, kwargs['training'] || false);\n            }\n            return input;\n        });\n    }\n}\n/** @nocollapse */\nGaussianDropout.className = 'GaussianDropout';\nserialization.registerClass(GaussianDropout);\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n */\nexport class AlphaDropout extends Layer {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n        this.rate = args.rate;\n        this.noiseShape = args.noiseShape;\n    }\n    _getNoiseShape(inputs) {\n        return this.noiseShape || getExactlyOneTensor(inputs).shape;\n    }\n    computeOutputShape(inputShape) {\n        return inputShape;\n    }\n    getConfig() {\n        const baseConfig = super.getConfig();\n        const config = { rate: this.rate };\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            if (this.rate < 1 && this.rate > 0) {\n                const noiseShape = this._getNoiseShape(inputs);\n                const droppedInputs = () => {\n                    const input = getExactlyOneTensor(inputs);\n                    const alpha = 1.6732632423543772848170429916717;\n                    const scale = 1.0507009873554804934193349852946;\n                    const alphaP = -alpha * scale;\n                    let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n                    keptIdx = K.cast(keptIdx, 'float32'); // get default dtype.\n                    // Get affine transformation params.\n                    const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n                    const b = -a * alphaP * this.rate;\n                    // Apply mask.\n                    const x = input.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));\n                    return x.mul(a).add(b);\n                };\n                return K.inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs['training'] || false);\n            }\n            return inputs;\n        });\n    }\n}\n/** @nocollapse */\nAlphaDropout.className = 'AlphaDropout';\nserialization.registerClass(AlphaDropout);\n//# sourceMappingURL=noise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Normalization layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { moments, serialization, tidy, util } from '@tensorflow/tfjs-core';\nimport { getConstraint, serializeConstraint } from '../constraints';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { getInitializer, serializeInitializer } from '../initializers';\nimport { getRegularizer, serializeRegularizer } from '../regularizers';\nimport * as generic_utils from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Applies batch normalization on x given mean, var, beta and gamma.\n *\n * I.e. returns:\n *   `output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`\n *\n * @param x Input tensor.\n * @param mean Mean of batch.\n * @param variance Variance of batch.\n * @param beta Tensor with which to center the input.\n * @param gamma Tensor by which to scale the input.\n * @param epsilon Fuzz factor.\n * @returns The result of the batch normalization.\n */\nexport function batchNormalization(x, mean, variance, beta, gamma, epsilon = 1e-3) {\n    let out;\n    if (x.rank === 2) {\n        out = tfc.batchNorm2d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 3) {\n        // TODO(cais): Check rank; give proper error message.\n        out = tfc.batchNorm3d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else if (x.rank === 4) {\n        out = tfc.batchNorm4d(x, mean, variance, beta, gamma, epsilon);\n    }\n    else {\n        throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} ` +\n            `yet`);\n    }\n    return out;\n}\n/**\n * Non-broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const normed = batchNormalization(x, mean, variance, beta, gamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Broadcasting batch normalization for use in training (not inference).\n *\n * The input is normalized to zero mean and unit variance along the\n * `reductionAxes`, followed by scaling with `gamma` and shifted by `beta`.\n * The result of that is returned as the first element\n * of the returned `Array`. The other two elements are the mean and variance,\n * respectively.\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    return tidy(() => {\n        const meanAndVariance = tfc.moments(x, reductionAxes);\n        const mean = meanAndVariance.mean;\n        const variance = meanAndVariance.variance;\n        const targetShape = [];\n        for (const axis of math_utils.range(0, x.rank)) {\n            if (reductionAxes.indexOf(axis) !== -1) {\n                targetShape.push(1);\n            }\n            else {\n                targetShape.push(x.shape[axis]);\n            }\n        }\n        const broadcastMean = mean.reshape(targetShape);\n        const broadcastVariance = variance.reshape(targetShape);\n        const broadcastGamma = gamma == null ? null : gamma.reshape(targetShape);\n        const broadcastBeta = beta == null ? null : beta.reshape(targetShape);\n        const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon);\n        return [normed, mean, variance];\n    });\n}\n/**\n * Batch normalization for use in training (not inference).\n *\n * @param x Input tensor to be normalized.\n * @param gamma Tensor by which to scale the input.\n * @param beta Tensor by which to center the input.\n * @param reductionAxes Axes over which to normalize.\n * @param epsilon Fuzz factor.\n * @returns An `Array` of three `Tensors`:\n *   [normalized tensor, mean of input, variance of input].\n */\nexport function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon = 1e-3) {\n    if (util.arraysEqual(reductionAxes.slice().sort(), math_utils.range(0, x.rank - 1))) {\n        return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n    else {\n        return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon);\n    }\n}\nexport class BatchNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.supportsMasking = true;\n        this.axis = args.axis == null ? -1 : args.axis;\n        this.momentum = args.momentum == null ? 0.99 : args.momentum;\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.movingMeanInitializer =\n            getInitializer(args.movingMeanInitializer || 'zeros');\n        this.movingVarianceInitializer =\n            getInitializer(args.movingVarianceInitializer || 'ones');\n        this.betaConstraint = getConstraint(args.betaConstraint);\n        this.gammaConstraint = getConstraint(args.gammaConstraint);\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const axis = this.axis >= 0 ? this.axis : (this.axis + inputShape.length);\n        const dim = inputShape[axis];\n        if (dim == null) {\n            throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but ` +\n                `the layer received an input with shape ` +\n                `${JSON.stringify(inputShape)}.`);\n        }\n        this.inputSpec =\n            [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n        const shape = [dim];\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n        }\n        this.movingMean = this.addWeight('moving_mean', shape, null, this.movingMeanInitializer, null, false);\n        this.movingVariance = this.addWeight('moving_variance', shape, null, this.movingVarianceInitializer, null, false);\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const training = kwargs['training'] == null ? false : kwargs['training'];\n            const input = getExactlyOneTensor(inputs);\n            const inputShape = input.shape;\n            const ndim = inputShape.length;\n            const reductionAxes = math_utils.range(0, ndim);\n            const axis = this.axis >= 0 ? this.axis : (this.axis + ndim);\n            reductionAxes.splice(axis, 1);\n            const broadcastShape = generic_utils.pyListRepeat(1, ndim);\n            broadcastShape[axis] = inputShape[axis];\n            const sortedReductionAxes = reductionAxes.slice();\n            sortedReductionAxes.sort();\n            const needsBroadcasting = !util.arraysEqual(sortedReductionAxes, math_utils.range(0, ndim).slice(0, ndim - 1));\n            const normalizeInference = () => {\n                if (needsBroadcasting) {\n                    const broadcastMovingMean = this.movingMean.read().reshape(broadcastShape);\n                    const broadcastMovingVariance = this.movingVariance.read().reshape(broadcastShape);\n                    const broadcastBeta = this.center ? this.beta.read().reshape(broadcastShape) : null;\n                    const broadcastGamma = this.scale ? this.gamma.read().reshape(broadcastShape) : null;\n                    return batchNormalization(input, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n                }\n                else {\n                    return batchNormalization(input, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n                }\n            };\n            if (!training) {\n                return normalizeInference();\n            }\n            const [normedTraining, mean, variance] = normalizeBatchInTraining(input, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n            const doMovingAverage = (variable, value, momentum) => {\n                tfc.tidy(() => {\n                    const decay = 1 - momentum;\n                    const origValue = variable.read();\n                    const updateDelta = origValue.sub(value).mul(decay);\n                    variable.write(origValue.sub(updateDelta));\n                });\n            };\n            // Perform updates to moving mean and moving variance for training.\n            // Porting Note: In PyKeras, these updates to `movingMean` and\n            //   `movingAverage` are done as a deferred Graph, added to the `Layer`'s\n            //   `update`s using the `add_update()` method. Here we do it imperatively\n            //   and encapsulate the updates in a function that is invoked\n            //   immediately.\n            const updateMovingMeanAndVariance = () => {\n                doMovingAverage(this.movingMean, mean, this.momentum);\n                doMovingAverage(this.movingVariance, variance, this.momentum);\n            };\n            updateMovingMeanAndVariance();\n            return normedTraining;\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            momentum: this.momentum,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n            movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n            betaConstraint: serializeConstraint(this.betaConstraint),\n            gammaConstraint: serializeConstraint(this.gammaConstraint)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nBatchNormalization.className = 'BatchNormalization';\nserialization.registerClass(BatchNormalization);\nexport class LayerNormalization extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.axis = args.axis == null ? -1 : args.axis;\n        if (typeof this.axis === 'number') {\n            if (!Number.isInteger(this.axis)) {\n                throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n            }\n        }\n        else if (Array.isArray(this.axis)) {\n            for (const axis of this.axis) {\n                if (!Number.isInteger(axis)) {\n                    throw new Error(`Expected axis to be an array of integers, ` +\n                        `but received ${JSON.stringify(this.axis)}`);\n                }\n            }\n        }\n        else {\n            throw new Error(`Expected axis to be an integer or an array of integers, ` +\n                `but received ${JSON.stringify(this.axis)}`);\n        }\n        this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n        this.center = args.center == null ? true : args.center;\n        this.scale = args.scale == null ? true : args.scale;\n        this.betaInitializer = getInitializer(args.betaInitializer || 'zeros');\n        this.gammaInitializer = getInitializer(args.gammaInitializer || 'ones');\n        this.betaRegularizer = getRegularizer(args.betaRegularizer);\n        this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const nDims = inputShape.length;\n        // Convert axis to array and resolve negatives.\n        if (typeof this.axis === 'number') {\n            this.axis = [this.axis];\n        }\n        for (let i = 0; i < this.axis.length; ++i) {\n            if (this.axis[i] < 0) {\n                this.axis[i] += nDims;\n            }\n        }\n        // Further validate axes.\n        for (const axis of this.axis) {\n            if (axis < 0 || axis >= nDims) {\n                throw new Error(`Invalid axis: ${axis}`);\n            }\n        }\n        if (this.axis.length !== generic_utils.unique(this.axis).length) {\n            throw new Error(`Found duplicate axes in: ${this.axis}`);\n        }\n        const paramShape = this.axis.map(axis => inputShape[axis]);\n        const trainable = true;\n        if (this.scale) {\n            this.gamma = this.addWeight('gamma', paramShape, 'float32', this.gammaInitializer, this.gammaRegularizer, trainable);\n        }\n        else {\n            this.gamma = null;\n        }\n        if (this.center) {\n            this.beta = this.addWeight('beta', paramShape, 'float32', this.betaInitializer, this.betaRegularizer, trainable);\n        }\n        else {\n            this.beta = null;\n        }\n        this.built = true;\n    }\n    call(inputs, kwargs) {\n        const input = getExactlyOneTensor(inputs);\n        const inputShape = input.shape;\n        const nDims = inputShape.length;\n        return tidy(() => {\n            const keepDims = true;\n            let { mean, variance } = moments(input, this.axis, keepDims);\n            const broadcastShape = generic_utils.pyListRepeat(1, nDims);\n            for (const dim of this.axis) {\n                broadcastShape[dim] = inputShape[dim];\n            }\n            const broadcast = (v) => {\n                if (v != null && v.shape.length !== nDims &&\n                    this.axis !== [nDims - 1]) {\n                    return v.reshape(broadcastShape);\n                }\n                else {\n                    return v;\n                }\n            };\n            let scale = broadcast(this.gamma.read());\n            let offset = broadcast(this.beta.read());\n            // TODO(https://github.com/tensorflow/tfjs/issues/2120): The tiling below\n            // is a workaround for the limitation of core's batchNormalization?d don't\n            // support broadcasting in their gradients. In addition, the tiling is\n            // necessary to ensure correctness on the browser CPU backend regardless\n            // of forward or backward computation. Remove this workaround once the\n            // limitation is addressed. See .\n            const momentsTiling = [];\n            const scaleOffsetTiling = [];\n            for (let i = 0; i < nDims; ++i) {\n                if (this.axis.indexOf(i) !== -1) {\n                    momentsTiling.push(inputShape[i]);\n                    scaleOffsetTiling.push(1);\n                }\n                else {\n                    momentsTiling.push(1);\n                    scaleOffsetTiling.push(inputShape[i]);\n                }\n            }\n            mean = mean.tile(momentsTiling);\n            variance = variance.tile(momentsTiling);\n            scale = scale.tile(scaleOffsetTiling);\n            offset = offset.tile(scaleOffsetTiling);\n            return batchNormalization(input, mean, variance, offset, scale, this.epsilon);\n        });\n    }\n    getConfig() {\n        const config = {\n            axis: this.axis,\n            epsilon: this.epsilon,\n            center: this.center,\n            scale: this.scale,\n            betaInitializer: serializeInitializer(this.betaInitializer),\n            gammaInitializer: serializeInitializer(this.gammaInitializer),\n            betaRegularizer: serializeRegularizer(this.betaRegularizer),\n            gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nLayerNormalization.className = 'LayerNormalization';\nserialization.registerClass(LayerNormalization);\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Padding Layers.\n */\n// Porting Note: In Python Keras, the padding layers are in convolutional.py,\n//   but we decided to put them in a separate file (padding.ts) for clarity.\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport { InputSpec, Layer } from '../engine/topology';\nimport { ValueError } from '../errors';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\n/**\n * Pads the middle dimension of a 3D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of 2 integers, how many zeros to add at the start and\n *   end of the middle dimension (i.e., dimension 1).\n * @return A padded 3D `tf.Tensor`.\n */\nexport function temporalPadding(x, padding) {\n    return tidy(() => {\n        if (x.rank !== 3) {\n            throw new ValueError(`temporalPadding expects input tensor to be 3-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [1, 1];\n        }\n        if (padding.length !== 2) {\n            throw new ValueError(`temporalPadding expects input padding pattern to be a length-2 ` +\n                `array, but received a length-${padding.length} array.`);\n        }\n        const pattern = [[0, 0], padding, [0, 0]];\n        return tfc.pad(x, pattern);\n    });\n}\n/**\n * Pads the 2nd and 3rd dimensions of a 4D tensor.\n *\n * @param x Input `tf.Tensor` to be padded.\n * @param padding `Array` of two `Array`s, each of which is an `Array` of two\n *   integers. The amount of padding at the beginning and end of the 2nd and 3rd\n *   dimensions, respectively.\n * @param dataFormat 'channelsLast' (default) or 'channelsFirst'.\n * @return Padded 4D `tf.Tensor`.\n */\nexport function spatial2dPadding(x, padding, dataFormat) {\n    return tidy(() => {\n        if (x.rank !== 4) {\n            throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ` +\n                `${x.rank}-D tensor.`);\n        }\n        if (padding == null) {\n            padding = [[1, 1], [1, 1]];\n        }\n        if (padding.length !== 2 || padding[0].length !== 2 ||\n            padding[1].length !== 2) {\n            throw new ValueError('spatial2dPadding expects `padding` to be an Array of two Arrays, ' +\n                'each of which is an Array of two integers.');\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (dataFormat !== 'channelsLast' && dataFormat !== 'channelsFirst') {\n            throw new ValueError(`Unknown data format: ${dataFormat}. ` +\n                `Supported data formats are 'channelsLast' and 'channelsFirst.`);\n        }\n        let pattern;\n        if (dataFormat === 'channelsFirst') {\n            pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n        }\n        else {\n            pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n        }\n        return tfc.pad(x, pattern);\n    });\n}\nexport class ZeroPadding2D extends Layer {\n    constructor(args) {\n        if (args == null) {\n            args = {};\n        }\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n        // TODO(cais): Maybe refactor the following logic surrounding `padding`\n        //   into a helper method.\n        if (args.padding == null) {\n            this.padding = [[1, 1], [1, 1]];\n        }\n        else if (typeof args.padding === 'number') {\n            this.padding =\n                [[args.padding, args.padding], [args.padding, args.padding]];\n        }\n        else {\n            args.padding = args.padding;\n            if (args.padding.length !== 2) {\n                throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but ` +\n                    `received a length-${args.padding.length} array.`);\n            }\n            let heightPadding;\n            let widthPadding;\n            if (typeof args.padding[0] === 'number') {\n                heightPadding = [args.padding[0], args.padding[0]];\n                widthPadding = [args.padding[1], args.padding[1]];\n            }\n            else {\n                args.padding = args.padding;\n                if (args.padding[0].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[0].length} array.`);\n                }\n                heightPadding = args.padding[0];\n                if (args.padding[1].length !== 2) {\n                    throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, ` +\n                        `but received a length-${args.padding[1].length} array.`);\n                }\n                widthPadding = args.padding[1];\n            }\n            this.padding = [heightPadding, widthPadding];\n        }\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows;\n        let cols;\n        if (this.dataFormat === 'channelsFirst') {\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[3] != null && inputShape[3] >= 0) {\n                cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            if (inputShape[1] != null && inputShape[1] >= 0) {\n                rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n            }\n            else {\n                rows = null;\n            }\n            if (inputShape[2] != null && inputShape[2] >= 0) {\n                cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n            }\n            else {\n                cols = null;\n            }\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n    }\n    getConfig() {\n        const config = {\n            padding: this.padding,\n            dataFormat: this.dataFormat,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\n/** @nocollapse */\nZeroPadding2D.className = 'ZeroPadding2D';\nserialization.registerClass(ZeroPadding2D);\n//# sourceMappingURL=padding.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * TensorFlow.js Layers: Pooling Layers.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport { imageDataFormat } from '../backend/common';\nimport * as K from '../backend/tfjs_backend';\nimport { checkDataFormat, checkPaddingMode, checkPoolMode } from '../common';\nimport { InputSpec } from '../engine/topology';\nimport { Layer } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { convOutputLength } from '../utils/conv_utils';\nimport { assertPositiveInteger } from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { preprocessConv2DInput, preprocessConv3DInput } from './convolutional';\n/**\n * 2D pooling.\n * @param x\n * @param poolSize\n * @param stridesdes strides. Defaults to [1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 2D pooling.\n */\nexport function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // TODO(cais): Remove the preprocessing step once deeplearn.js supports\n        // dataFormat as an input argument.\n        x = preprocessConv2DInput(x, dataFormat); // x is NHWC after preprocessing.\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            // TODO(cais): Rank check?\n            y = tfc.maxPool(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            // TODO(cais): Check the dtype and rank of x and give clear error message\n            //   if those are incorrect.\n            y = tfc.avgPool(\n            // TODO(cais): Rank check?\n            x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 3, 1, 2]); // NHWC -> NCHW.\n        }\n        return y;\n    });\n}\n/**\n * 3D pooling.\n * @param x\n * @param poolSize. Default to [1, 1, 1].\n * @param strides strides. Defaults to [1, 1, 1].\n * @param padding padding. Defaults to 'valid'.\n * @param dataFormat data format. Defaults to 'channelsLast'.\n * @param poolMode Mode of pooling. Defaults to 'max'.\n * @returns Result of the 3D pooling.\n */\nexport function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n    return tidy(() => {\n        checkDataFormat(dataFormat);\n        checkPoolMode(poolMode);\n        checkPaddingMode(padding);\n        if (strides == null) {\n            strides = [1, 1, 1];\n        }\n        if (padding == null) {\n            padding = 'valid';\n        }\n        if (dataFormat == null) {\n            dataFormat = imageDataFormat();\n        }\n        if (poolMode == null) {\n            poolMode = 'max';\n        }\n        // x is NDHWC after preprocessing.\n        x = preprocessConv3DInput(x, dataFormat);\n        let y;\n        const paddingString = (padding === 'same') ? 'same' : 'valid';\n        if (poolMode === 'max') {\n            y = tfc.maxPool3d(x, poolSize, strides, paddingString);\n        }\n        else { // 'avg'\n            y = tfc.avgPool3d(x, poolSize, strides, paddingString);\n        }\n        if (dataFormat === 'channelsFirst') {\n            y = tfc.transpose(y, [0, 4, 1, 2, 3]); // NDHWC -> NCDHW.\n        }\n        return y;\n    });\n}\n/**\n * Abstract class for different pooling 1D layers.\n */\nexport class Pooling1D extends Layer {\n    /**\n     *\n     * @param args Parameters for the Pooling layer.\n     *\n     * config.poolSize defaults to 2.\n     */\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = 2;\n        }\n        super(args);\n        if (typeof args.poolSize === 'number') {\n            this.poolSize = [args.poolSize];\n        }\n        else if (Array.isArray(args.poolSize) &&\n            args.poolSize.length === 1 &&\n            typeof args.poolSize[0] === 'number') {\n            this.poolSize = args.poolSize;\n        }\n        else {\n            throw new ValueError(`poolSize for 1D convolutional layer must be a number or an ` +\n                `Array of a single number, but received ` +\n                `${JSON.stringify(args.poolSize)}`);\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else {\n            if (typeof args.strides === 'number') {\n                this.strides = [args.strides];\n            }\n            else if (Array.isArray(args.strides) &&\n                args.strides.length === 1 &&\n                typeof args.strides[0] === 'number') {\n                this.strides = args.strides;\n            }\n            else {\n                throw new ValueError(`strides for 1D convolutional layer must be a number or an ` +\n                    `Array of a single number, but received ` +\n                    `${JSON.stringify(args.strides)}`);\n            }\n        }\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n        return [inputShape[0], length, inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            // Add dummy last dimension.\n            inputs = K.expandDims(getExactlyOneTensor(inputs), 2);\n            const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, 'channelsLast');\n            // Remove dummy last dimension.\n            return tfc.squeeze(output, [2]);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling1D.className = 'MaxPooling1D';\nserialization.registerClass(MaxPooling1D);\nexport class AveragePooling1D extends Pooling1D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling1D.className = 'AveragePooling1D';\nserialization.registerClass(AveragePooling1D);\n/**\n * Abstract class for different pooling 2D layers.\n */\nexport class Pooling2D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 2) {\n                throw new ValueError(`If the strides property of a 2D pooling layer is an Array, ` +\n                    `it is expected to have a length of 2, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        rows =\n            convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n        cols =\n            convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], rows, cols];\n        }\n        else {\n            return [inputShape[0], rows, cols, inputShape[3]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling2D.className = 'MaxPooling2D';\nserialization.registerClass(MaxPooling2D);\nexport class AveragePooling2D extends Pooling2D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool2d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling2D.className = 'AveragePooling2D';\nserialization.registerClass(AveragePooling2D);\n/**\n * Abstract class for different pooling 3D layers.\n */\nexport class Pooling3D extends Layer {\n    constructor(args) {\n        if (args.poolSize == null) {\n            args.poolSize = [2, 2, 2];\n        }\n        super(args);\n        this.poolSize = Array.isArray(args.poolSize) ?\n            args.poolSize :\n            [args.poolSize, args.poolSize, args.poolSize];\n        if (args.strides == null) {\n            this.strides = this.poolSize;\n        }\n        else if (Array.isArray(args.strides)) {\n            if (args.strides.length !== 3) {\n                throw new ValueError(`If the strides property of a 3D pooling layer is an Array, ` +\n                    `it is expected to have a length of 3, but received length ` +\n                    `${args.strides.length}.`);\n            }\n            this.strides = args.strides;\n        }\n        else {\n            // `config.strides` is a number.\n            this.strides = [args.strides, args.strides, args.strides];\n        }\n        assertPositiveInteger(this.poolSize, 'poolSize');\n        assertPositiveInteger(this.strides, 'strides');\n        this.padding = args.padding == null ? 'valid' : args.padding;\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        checkPaddingMode(this.padding);\n        this.inputSpec = [new InputSpec({ ndim: 5 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        let depths = this.dataFormat === 'channelsFirst' ? inputShape[2] : inputShape[1];\n        let rows = this.dataFormat === 'channelsFirst' ? inputShape[3] : inputShape[2];\n        let cols = this.dataFormat === 'channelsFirst' ? inputShape[4] : inputShape[3];\n        depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n        rows =\n            convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n        cols =\n            convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n        if (this.dataFormat === 'channelsFirst') {\n            return [inputShape[0], inputShape[1], depths, rows, cols];\n        }\n        else {\n            return [inputShape[0], depths, rows, cols, inputShape[4]];\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            this.invokeCallHook(inputs, kwargs);\n            return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n        });\n    }\n    getConfig() {\n        const config = {\n            poolSize: this.poolSize,\n            padding: this.padding,\n            strides: this.strides,\n            dataFormat: this.dataFormat\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class MaxPooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'max');\n    }\n}\n/** @nocollapse */\nMaxPooling3D.className = 'MaxPooling3D';\nserialization.registerClass(MaxPooling3D);\nexport class AveragePooling3D extends Pooling3D {\n    constructor(args) {\n        super(args);\n    }\n    poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n        checkDataFormat(dataFormat);\n        checkPaddingMode(padding);\n        return pool3d(inputs, poolSize, strides, padding, dataFormat, 'avg');\n    }\n}\n/** @nocollapse */\nAveragePooling3D.className = 'AveragePooling3D';\nserialization.registerClass(AveragePooling3D);\n/**\n * Abstract class for different global pooling 1D layers.\n */\nexport class GlobalPooling1D extends Layer {\n    constructor(args) {\n        super(args);\n        this.inputSpec = [new InputSpec({ ndim: 3 })];\n    }\n    computeOutputShape(inputShape) {\n        return [inputShape[0], inputShape[2]];\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n}\nexport class GlobalAveragePooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.mean(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling1D.className = 'GlobalAveragePooling1D';\nserialization.registerClass(GlobalAveragePooling1D);\nexport class GlobalMaxPooling1D extends GlobalPooling1D {\n    constructor(args) {\n        super(args || {});\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            return tfc.max(input, 1);\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling1D.className = 'GlobalMaxPooling1D';\nserialization.registerClass(GlobalMaxPooling1D);\n/**\n * Abstract class for different global pooling 2D layers.\n */\nexport class GlobalPooling2D extends Layer {\n    constructor(args) {\n        super(args);\n        this.dataFormat =\n            args.dataFormat == null ? 'channelsLast' : args.dataFormat;\n        checkDataFormat(this.dataFormat);\n        this.inputSpec = [new InputSpec({ ndim: 4 })];\n    }\n    computeOutputShape(inputShape) {\n        inputShape = inputShape;\n        if (this.dataFormat === 'channelsLast') {\n            return [inputShape[0], inputShape[3]];\n        }\n        else {\n            return [inputShape[0], inputShape[1]];\n        }\n    }\n    call(inputs, kwargs) {\n        throw new NotImplementedError();\n    }\n    getConfig() {\n        const config = { dataFormat: this.dataFormat };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n}\nexport class GlobalAveragePooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.mean(input, [1, 2]);\n            }\n            else {\n                return tfc.mean(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalAveragePooling2D.className = 'GlobalAveragePooling2D';\nserialization.registerClass(GlobalAveragePooling2D);\nexport class GlobalMaxPooling2D extends GlobalPooling2D {\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const input = getExactlyOneTensor(inputs);\n            if (this.dataFormat === 'channelsLast') {\n                return tfc.max(input, [1, 2]);\n            }\n            else {\n                return tfc.max(input, [2, 3]);\n            }\n        });\n    }\n}\n/** @nocollapse */\nGlobalMaxPooling2D.className = 'GlobalMaxPooling2D';\nserialization.registerClass(GlobalMaxPooling2D);\n//# sourceMappingURL=pooling.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * Layers that augment the functionality of a base layer.\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { serialization, tidy } from '@tensorflow/tfjs-core';\nimport * as K from '../backend/tfjs_backend';\nimport { nameScope } from '../common';\nimport { InputSpec, Layer, SymbolicTensor } from '../engine/topology';\nimport { NotImplementedError, ValueError } from '../errors';\nimport { VALID_BIDIRECTIONAL_MERGE_MODES } from '../keras_format/common';\nimport * as generic_utils from '../utils/generic_utils';\nimport { getExactlyOneShape, getExactlyOneTensor } from '../utils/types_utils';\nimport { rnn, standardizeArgs } from './recurrent';\nimport { deserialize } from './serialization';\n/**\n * Abstract wrapper base class.\n *\n * Wrappers take another layer and augment it in various ways.\n * Do not use this class as a layer, it is only an abstract base class.\n * Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.\n */\nexport class Wrapper extends Layer {\n    constructor(args) {\n        // Porting Note: In PyKeras, `self.layer` is set prior to the calling\n        //   `super()`. But we can't do that here due to TypeScript's restriction.\n        //   See: https://github.com/Microsoft/TypeScript/issues/8277\n        //   As a result, we have to add checks in `get trainable()` and\n        //   `set trainable()` below in order to prevent using `this.layer` when\n        //   its value is `undefined`. The super constructor does use the getter\n        //   and the setter of `this.layer`.\n        super(args);\n        this.layer = args.layer;\n    }\n    build(inputShape) {\n        this.built = true;\n    }\n    // TODO(cais): Implement activityRegularizer getter.\n    get trainable() {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            return this.layer.trainable;\n        }\n        else {\n            return false;\n        }\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        if (this.layer != null) {\n            this.layer.trainable = value;\n        }\n    }\n    get trainableWeights() {\n        return this.layer.trainableWeights;\n    }\n    // TODO(cais): Implement setter for trainableWeights.\n    get nonTrainableWeights() {\n        return this.layer.nonTrainableWeights;\n    }\n    // TODO(cais): Implement setter for nonTrainableWeights.\n    get updates() {\n        // tslint:disable-next-line:no-any\n        return this.layer._updates;\n    }\n    // TODO(cais): Implement getUpdatesFor().\n    get losses() {\n        return this.layer.losses;\n    }\n    // TODO(cais): Implement getLossesFor().\n    getWeights() {\n        return this.layer.getWeights();\n    }\n    setWeights(weights) {\n        this.layer.setWeights(weights);\n    }\n    getConfig() {\n        const config = {\n            'layer': {\n                'className': this.layer.getClassName(),\n                'config': this.layer.getConfig(),\n            }\n        };\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.layer != null) {\n            this.layer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config, customObjects = {}) {\n        const layerConfig = config['layer'];\n        const layer = deserialize(layerConfig, customObjects);\n        delete config['layer'];\n        const newConfig = { layer };\n        Object.assign(newConfig, config);\n        return new cls(newConfig);\n    }\n}\nexport class TimeDistributed extends Wrapper {\n    constructor(args) {\n        super(args);\n        this.supportsMasking = true;\n    }\n    build(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        if (inputShape.length < 3) {\n            throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received ` +\n                `input shape ${JSON.stringify(inputShape)}`);\n        }\n        this.inputSpec = [{ shape: inputShape }];\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        if (!this.layer.built) {\n            this.layer.build(childInputShape);\n            this.layer.built = true;\n        }\n        super.build(inputShape);\n    }\n    computeOutputShape(inputShape) {\n        inputShape = getExactlyOneShape(inputShape);\n        const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n        const childOutputShape = this.layer.computeOutputShape(childInputShape);\n        const timesteps = inputShape[1];\n        return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            // TODO(cais): Add 'training' and 'useLearningPhase' to kwargs.\n            inputs = getExactlyOneTensor(inputs);\n            // Porting Note: In tfjs-layers, `inputs` are always concrete tensor\n            // values. Hence the inputs can't have an undetermined first (batch)\n            // dimension, which is why we always use the K.rnn approach here.\n            const step = (inputs, states) => {\n                // TODO(cais): Add useLearningPhase.\n                // NOTE(cais): `layer.call` may return a length-1 array of Tensor in\n                //   some cases (e.g., `layer` is a `Sequential` instance), which is\n                //   why `getExactlyOneTensor` is used below.\n                const output = getExactlyOneTensor(this.layer.call(inputs, kwargs));\n                return [output, []];\n            };\n            const rnnOutputs = rnn(step, inputs, [], false /* goBackwards */, null /* mask */, null /* constants */, false /* unroll */, true /* needPerStepOutputs */);\n            const y = rnnOutputs[1];\n            // TODO(cais): Add activity regularization.\n            // TODO(cais): Add useLearningPhase.\n            return y;\n        });\n    }\n}\n/** @nocollapse */\nTimeDistributed.className = 'TimeDistributed';\nserialization.registerClass(TimeDistributed);\nexport function checkBidirectionalMergeMode(value) {\n    generic_utils.checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, 'BidirectionalMergeMode', value);\n}\nconst DEFAULT_BIDIRECTIONAL_MERGE_MODE = 'concat';\nexport class Bidirectional extends Wrapper {\n    constructor(args) {\n        super(args);\n        // Note: When creating `this.forwardLayer`, the original Layer object\n        //   (`config.layer`) ought to be cloned. This is why we call\n        //   `getConfig()` followed by `deserialize()`. Without this cloning,\n        //   the layer names saved during serialization will incorrectly contain\n        //   the 'forward_' prefix. In Python Keras, this is done using\n        //   `copy.copy` (shallow copy), which does not have a simple equivalent\n        //   in JavaScript. JavaScript's `Object.assign()` does not copy\n        //   methods.\n        const layerConfig = args.layer.getConfig();\n        const forwDict = {};\n        forwDict['className'] = args.layer.getClassName();\n        forwDict['config'] = layerConfig;\n        this.forwardLayer = deserialize(forwDict);\n        layerConfig['goBackwards'] =\n            layerConfig['goBackwards'] === true ? false : true;\n        const backDict = {};\n        backDict['className'] = args.layer.getClassName();\n        backDict['config'] = layerConfig;\n        this.backwardLayer = deserialize(backDict);\n        this.forwardLayer.name = 'forward_' + this.forwardLayer.name;\n        this.backwardLayer.name = 'backward_' + this.backwardLayer.name;\n        this.mergeMode = args.mergeMode === undefined ?\n            DEFAULT_BIDIRECTIONAL_MERGE_MODE :\n            args.mergeMode;\n        checkBidirectionalMergeMode(this.mergeMode);\n        if (args.weights) {\n            throw new NotImplementedError('weights support is not implemented for Bidirectional layer yet.');\n        }\n        this._stateful = args.layer.stateful;\n        this.returnSequences = args.layer.returnSequences;\n        this.returnState = args.layer.returnState;\n        this.supportsMasking = true;\n        this._trainable = true;\n        this.inputSpec = args.layer.inputSpec;\n        this.numConstants = null;\n    }\n    get trainable() {\n        return this._trainable;\n    }\n    set trainable(value) {\n        // Porting Note: the check of `this.layer` here is necessary due to the\n        //   way the `constructor` of this class is written (see Porting Note\n        //   above).\n        this._trainable = value;\n        if (this.forwardLayer != null) {\n            this.forwardLayer.trainable = value;\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.trainable = value;\n        }\n    }\n    getWeights() {\n        return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n    }\n    setWeights(weights) {\n        const numWeights = weights.length;\n        const numeightsOver2 = Math.floor(numWeights / 2);\n        this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n        this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n    }\n    computeOutputShape(inputShape) {\n        let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n        if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n            layerShapes = [layerShapes];\n        }\n        layerShapes = layerShapes;\n        let outputShape;\n        let outputShapes;\n        let stateShape;\n        if (this.returnState) {\n            stateShape = layerShapes.slice(1);\n            outputShape = layerShapes[0];\n        }\n        else {\n            outputShape = layerShapes[0];\n        }\n        outputShape = outputShape;\n        if (this.mergeMode === 'concat') {\n            outputShape[outputShape.length - 1] *= 2;\n            outputShapes = [outputShape];\n        }\n        else if (this.mergeMode == null) {\n            outputShapes = [outputShape, outputShape.slice()];\n        }\n        else {\n            outputShapes = [outputShape];\n        }\n        if (this.returnState) {\n            if (this.mergeMode == null) {\n                return outputShapes.concat(stateShape).concat(stateShape.slice());\n            }\n            return [outputShape].concat(stateShape).concat(stateShape.slice());\n        }\n        return generic_utils.singletonOrArray(outputShapes);\n    }\n    apply(inputs, kwargs) {\n        let initialState = kwargs == null ? null : kwargs['initialState'];\n        let constants = kwargs == null ? null : kwargs['constants'];\n        if (kwargs == null) {\n            kwargs = {};\n        }\n        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n        inputs = standardized.inputs;\n        initialState = standardized.initialState;\n        constants = standardized.constants;\n        if (Array.isArray(inputs)) {\n            initialState = inputs.slice(1);\n            inputs = inputs[0];\n        }\n        if ((initialState == null || initialState.length === 0) &&\n            constants == null) {\n            return super.apply(inputs, kwargs);\n        }\n        const additionalInputs = [];\n        const additionalSpecs = [];\n        if (initialState != null) {\n            const numStates = initialState.length;\n            if (numStates % 2 > 0) {\n                throw new ValueError('When passing `initialState` to a Bidrectional RNN, ' +\n                    'the state should be an Array containing the states of ' +\n                    'the underlying RNNs.');\n            }\n            kwargs['initialState'] = initialState;\n            additionalInputs.push(...initialState);\n            const stateSpecs = initialState\n                .map(state => new InputSpec({ shape: state.shape }));\n            this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n            this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n            additionalSpecs.push(...stateSpecs);\n        }\n        if (constants != null) {\n            throw new NotImplementedError('Support for constants in Bidirectional layers is not ' +\n                'implemented yet.');\n        }\n        const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n        for (const tensor of additionalInputs) {\n            if (tensor instanceof SymbolicTensor !== isSymbolicTensor) {\n                throw new ValueError('The initial state of a Bidirectional layer cannot be ' +\n                    'specified as a mix of symbolic and non-symbolic tensors');\n            }\n        }\n        if (isSymbolicTensor) {\n            // Compute the full input and specs, including the states.\n            const fullInput = [inputs].concat(additionalInputs);\n            const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n            // Perform the call temporarily and replace inputSpec.\n            // Note: with initial states symbolic calls and non-symbolic calls to\n            // this method differ in how the initial states are passed. For\n            // symbolic calls, the initial states are passed in the first arg, as\n            // an Array of SymbolicTensors; for non-symbolic calls, they are\n            // passed in the second arg as a part of the kwargs. Hence the need to\n            // temporarily modify inputSpec here.\n            // TODO(cais): Make refactoring so that this hacky code below is no\n            // longer needed.\n            const originalInputSpec = this.inputSpec;\n            this.inputSpec = fullInputSpec;\n            const output = super.apply(fullInput, kwargs);\n            this.inputSpec = originalInputSpec;\n            return output;\n        }\n        else {\n            return super.apply(inputs, kwargs);\n        }\n    }\n    call(inputs, kwargs) {\n        return tidy(() => {\n            const initialState = kwargs['initialState'];\n            let y;\n            let yRev;\n            if (initialState == null) {\n                y = this.forwardLayer.call(inputs, kwargs);\n                yRev = this.backwardLayer.call(inputs, kwargs);\n            }\n            else {\n                const forwardState = initialState.slice(0, initialState.length / 2);\n                const backwardState = initialState.slice(initialState.length / 2);\n                y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n                yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n            }\n            let states;\n            if (this.returnState) {\n                if (Array.isArray(y)) {\n                    states = y.slice(1).concat(yRev.slice(1));\n                }\n                else {\n                }\n                y = y[0];\n                yRev = yRev[0];\n            }\n            if (this.returnSequences) {\n                yRev = tfc.reverse(yRev, 1);\n            }\n            let output;\n            if (this.mergeMode === 'concat') {\n                output = K.concatenate([y, yRev]);\n            }\n            else if (this.mergeMode === 'sum') {\n                output = tfc.add(y, yRev);\n            }\n            else if (this.mergeMode === 'ave') {\n                output = tfc.mul(.5, tfc.add(y, yRev));\n            }\n            else if (this.mergeMode === 'mul') {\n                output = tfc.mul(y, yRev);\n            }\n            else if (this.mergeMode == null) {\n                output = [y, yRev];\n            }\n            // TODO(cais): Properly set learning phase.\n            if (this.returnState) {\n                if (this.mergeMode == null) {\n                    return output.concat(states);\n                }\n                return [output].concat(states);\n            }\n            return output;\n        });\n    }\n    resetStates(states) {\n        this.forwardLayer.resetStates();\n        this.backwardLayer.resetStates();\n    }\n    build(inputShape) {\n        nameScope(this.forwardLayer.name, () => {\n            this.forwardLayer.build(inputShape);\n        });\n        nameScope(this.backwardLayer.name, () => {\n            this.backwardLayer.build(inputShape);\n        });\n        this.built = true;\n    }\n    computeMask(inputs, mask) {\n        if (Array.isArray(mask)) {\n            mask = mask[0];\n        }\n        let outputMask;\n        if (this.returnSequences) {\n            if (this.mergeMode == null) {\n                outputMask = [mask, mask];\n            }\n            else {\n                outputMask = mask;\n            }\n        }\n        else {\n            if (this.mergeMode == null) {\n                outputMask = [null, null];\n            }\n            else {\n                outputMask = null;\n            }\n        }\n        if (this.returnState) {\n            const states = this.forwardLayer.states;\n            const stateMask = states.map(state => null);\n            if (Array.isArray(outputMask)) {\n                return outputMask.concat(stateMask).concat(stateMask);\n            }\n            else {\n                return [outputMask].concat(stateMask).concat(stateMask);\n            }\n        }\n        else {\n            return outputMask;\n        }\n    }\n    get trainableWeights() {\n        return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n    }\n    get nonTrainableWeights() {\n        return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n    }\n    // TODO(cais): Implement constraints().\n    setFastWeightInitDuringBuild(value) {\n        super.setFastWeightInitDuringBuild(value);\n        if (this.forwardLayer != null) {\n            this.forwardLayer.setFastWeightInitDuringBuild(value);\n        }\n        if (this.backwardLayer != null) {\n            this.backwardLayer.setFastWeightInitDuringBuild(value);\n        }\n    }\n    getConfig() {\n        const config = {\n            'mergeMode': this.mergeMode,\n        };\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        const baseConfig = super.getConfig();\n        Object.assign(config, baseConfig);\n        return config;\n    }\n    /** @nocollapse */\n    static fromConfig(cls, config) {\n        const rnnLayer = deserialize(config['layer']);\n        delete config['layer'];\n        // TODO(cais): Add logic for `numConstants` once the property is added.\n        if (config['numConstants'] != null) {\n            throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants ` +\n                `present is not supported yet.`);\n        }\n        // tslint:disable-next-line:no-any\n        const newConfig = config;\n        newConfig['layer'] = rnnLayer;\n        return new cls(newConfig);\n    }\n}\n/** @nocollapse */\nBidirectional.className = 'Bidirectional';\nserialization.registerClass(Bidirectional);\n//# sourceMappingURL=wrappers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport { InputLayer } from './engine/input_layer';\nimport { Layer } from './engine/topology';\nimport { input } from './exports';\nimport { ELU, LeakyReLU, PReLU, ReLU, Softmax, ThresholdedReLU } from './layers/advanced_activations';\nimport { Conv1D, Conv2D, Conv2DTranspose, Conv3D, Cropping2D, SeparableConv2D, UpSampling2D } from './layers/convolutional';\nimport { DepthwiseConv2D } from './layers/convolutional_depthwise';\nimport { ConvLSTM2D, ConvLSTM2DCell } from './layers/convolutional_recurrent';\nimport { Activation, Dense, Dropout, Flatten, Masking, Permute, RepeatVector, Reshape, SpatialDropout1D } from './layers/core';\nimport { Embedding } from './layers/embeddings';\nimport { Add, Average, Concatenate, Dot, Maximum, Minimum, Multiply } from './layers/merge';\nimport { AlphaDropout, GaussianDropout, GaussianNoise } from './layers/noise';\nimport { BatchNormalization, LayerNormalization } from './layers/normalization';\nimport { ZeroPadding2D } from './layers/padding';\nimport { AveragePooling1D, AveragePooling2D, AveragePooling3D, GlobalAveragePooling1D, GlobalAveragePooling2D, GlobalMaxPooling1D, GlobalMaxPooling2D, MaxPooling1D, MaxPooling2D, MaxPooling3D } from './layers/pooling';\nimport { GRU, GRUCell, LSTM, LSTMCell, RNN, RNNCell, SimpleRNN, SimpleRNNCell, StackedRNNCells } from './layers/recurrent';\nimport { Bidirectional, TimeDistributed } from './layers/wrappers';\n// TODO(cais): Add doc string to all the public static functions in this\n//   class; include exectuable JavaScript code snippets where applicable\n//   (b/74074458).\n// Input Layer.\n/**\n * An input layer is an entry point into a `tf.LayersModel`.\n *\n * `InputLayer` is generated automatically for `tf.Sequential`` models by\n * specifying the `inputshape` or `batchInputShape` for the first layer.  It\n * should not be specified explicitly. However, it can be useful sometimes,\n * e.g., when constructing a sequential model from a subset of another\n * sequential model's layers. Like the code snippet below shows.\n *\n * ```js\n * // Define a model which simply adds two inputs.\n * const model1 = tf.sequential();\n * model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));\n * model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));\n * model1.summary();\n * model1.predict(tf.zeros([1, 4])).print();\n *\n * // Construct another model, reusing the second layer of `model1` while\n * // not using the first layer of `model1`. Note that you cannot add the second\n * // layer of `model` directly as the first layer of the new sequential model,\n * // because doing so will lead to an error related to the fact that the layer\n * // is not an input layer. Instead, you need to create an `inputLayer` and add\n * // it to the new sequential model before adding the reused layer.\n * const model2 = tf.sequential();\n * // Use an inputShape that matches the input shape of `model1`'s second\n * // layer.\n * model2.add(tf.layers.inputLayer({inputShape: [3]}));\n * model2.add(model1.layers[1]);\n * model2.summary();\n * model2.predict(tf.zeros([1, 3])).print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Inputs', namespace: 'layers'}\n */\nexport function inputLayer(args) {\n    return new InputLayer(args);\n}\n// Advanced Activation Layers.\n/**\n * Exponetial Linear Unit (ELU).\n *\n * It follows:\n * `f(x) =  alpha * (exp(x) - 1.) for x < 0`,\n * `f(x) = x for x >= 0`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Fast and Accurate Deep Network Learning by Exponential Linear Units\n * (ELUs)](https://arxiv.org/abs/1511.07289v1)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function elu(args) {\n    return new ELU(args);\n}\n/**\n * Rectified Linear Unit activation function.\n *\n * Input shape:\n *   Arbitrary. Use the config field `inputShape` (Array of integers, does\n *   not include the sample axis) when using this layer as the first layer\n *   in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function reLU(args) {\n    return new ReLU(args);\n}\n/**\n * Leaky version of a rectified linear unit.\n *\n * It allows a small gradient when the unit is not active:\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function leakyReLU(args) {\n    return new LeakyReLU(args);\n}\n/**\n * Parameterized version of a leaky rectified linear unit.\n *\n * It follows\n * `f(x) = alpha * x for x < 0.`\n * `f(x) = x for x >= 0.`\n * wherein `alpha` is a trainable weight.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function prelu(args) {\n    return new PReLU(args);\n}\n/**\n * Softmax activation layer.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function softmax(args) {\n    return new Softmax(args);\n}\n/**\n * Thresholded Rectified Linear Unit.\n *\n * It follows:\n * `f(x) = x for x > theta`,\n * `f(x) = 0 otherwise`.\n *\n * Input shape:\n *   Arbitrary. Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n * Output shape:\n *   Same shape as the input.\n *\n * References:\n *   - [Zero-Bias Autoencoders and the Benefits of Co-Adapting\n * Features](http://arxiv.org/abs/1402.3337)\n *\n * @doc {\n *   heading: 'Layers',\n *   subheading: 'Advanced Activation',\n *   namespace: 'layers'\n * }\n */\nexport function thresholdedReLU(args) {\n    return new ThresholdedReLU(args);\n}\n// Convolutional Layers.\n/**\n * 1D convolution layer (e.g., temporal convolution).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input over a single spatial (or temporal) dimension\n * to produce a tensor of outputs.\n *\n * If `use_bias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model, provide an\n * `inputShape` argument `Array` or `null`.\n *\n * For example, `inputShape` would be:\n * - `[10, 128]` for sequences of 10 vectors of 128-dimensional vectors\n * - `[null, 128]` for variable-length sequences of 128-dimensional vectors.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional',  namespace: 'layers'}\n */\nexport function conv1d(args) {\n    return new Conv1D(args);\n}\n/**\n * 2D convolution layer (e.g. spatial convolution over images).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 3]` for 128x128 RGB pictures\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2d(args) {\n    return new Conv2D(args);\n}\n/**\n * Transposed convolutional layer (sometimes called Deconvolution).\n *\n * The need for transposed convolutions generally arises\n * from the desire to use a transformation going in the opposite direction of\n * a normal convolution, i.e., from something that has the shape of the output\n * of some convolution to something that has the shape of its input while\n * maintaining a connectivity pattern that is compatible with said\n * convolution.\n *\n * When using this layer as the first layer in a model, provide the\n * configuration `inputShape` (`Array` of integers, does not include the\n * sample axis), e.g., `inputShape: [128, 128, 3]` for 128x128 RGB pictures in\n * `dataFormat: 'channelsLast'`.\n *\n * Input shape:\n *   4D tensor with shape:\n *   `[batch, channels, rows, cols]` if `dataFormat` is `'channelsFirst'`.\n *   or 4D tensor with shape\n *   `[batch, rows, cols, channels]` if `dataFormat` is `'channelsLast`.\n *\n * Output shape:\n *   4D tensor with shape:\n *   `[batch, filters, newRows, newCols]` if `dataFormat` is\n * `'channelsFirst'`. or 4D tensor with shape:\n *   `[batch, newRows, newCols, filters]` if `dataFormat` is `'channelsLast'`.\n *\n * References:\n *   - [A guide to convolution arithmetic for deep\n * learning](https://arxiv.org/abs/1603.07285v1)\n *   - [Deconvolutional\n * Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv2dTranspose(args) {\n    return new Conv2DTranspose(args);\n}\n/**\n * 3D convolution layer (e.g. spatial convolution over volumes).\n *\n * This layer creates a convolution kernel that is convolved\n * with the layer input to produce a tensor of outputs.\n *\n * If `useBias` is True, a bias vector is created and added to the outputs.\n *\n * If `activation` is not `null`, it is applied to the outputs as well.\n *\n * When using this layer as the first layer in a model,\n * provide the keyword argument `inputShape`\n * (Array of integers, does not include the sample axis),\n * e.g. `inputShape=[128, 128, 128, 1]` for 128x128x128 grayscale volumes\n * in `dataFormat='channelsLast'`.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function conv3d(args) {\n    return new Conv3D(args);\n}\n/**\n * Depthwise separable 2D convolution.\n *\n * Separable convolution consists of first performing\n * a depthwise spatial convolution\n * (which acts on each input channel separately)\n * followed by a pointwise convolution which mixes together the resulting\n * output channels. The `depthMultiplier` argument controls how many\n * output channels are generated per input channel in the depthwise step.\n *\n * Intuitively, separable convolutions can be understood as\n * a way to factorize a convolution kernel into two smaller kernels,\n * or as an extreme version of an Inception block.\n *\n * Input shape:\n *   4D tensor with shape:\n *     `[batch, channels, rows, cols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, rows, cols, channels]` if data_format='channelsLast'.\n *\n * Output shape:\n *   4D tensor with shape:\n *     `[batch, filters, newRows, newCols]` if data_format='channelsFirst'\n *   or 4D tensor with shape:\n *     `[batch, newRows, newCols, filters]` if data_format='channelsLast'.\n *     `rows` and `cols` values might have changed due to padding.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function separableConv2d(args) {\n    return new SeparableConv2D(args);\n}\n/**\n * Cropping layer for 2D input (e.g., image).\n *\n * This layer can crop an input\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, croppedRows, croppedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, croppedRows, croppedCols]`.\n *\n * Examples\n * ```js\n *\n * const model = tf.sequential();\n * model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],\n *                                inputShape: [128, 128, 3]}));\n * //now output shape is [batch, 124, 124, 3]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function cropping2D(args) {\n    return new Cropping2D(args);\n}\n/**\n * Upsampling layer for 2D inputs.\n *\n * Repeats the rows and columns of the data\n * by size[0] and size[1] respectively.\n *\n *\n * Input shape:\n *    4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *         `[batch, rows, cols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *        `[batch, channels, rows, cols]`\n *\n * Output shape:\n *     4D tensor with shape:\n *     - If `dataFormat` is `\"channelsLast\"`:\n *        `[batch, upsampledRows, upsampledCols, channels]`\n *     - If `dataFormat` is `\"channelsFirst\"`:\n *         `[batch, channels, upsampledRows, upsampledCols]`\n *\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function upSampling2d(args) {\n    return new UpSampling2D(args);\n}\n// Convolutional(depthwise) Layers.\n/**\n * Depthwise separable 2D convolution.\n *\n * Depthwise Separable convolutions consists in performing just the first step\n * in a depthwise spatial convolution (which acts on each input channel\n * separately). The `depthMultplier` argument controls how many output channels\n * are generated per input channel in the depthwise step.\n *\n * @doc {heading: 'Layers', subheading: 'Convolutional', namespace: 'layers'}\n */\nexport function depthwiseConv2d(args) {\n    return new DepthwiseConv2D(args);\n}\n// Basic Layers.\n/**\n * Applies an activation function to an output.\n *\n * This layer applies element-wise activation function.  Other layers, notably\n * `dense` can also apply activation functions.  Use this isolated activation\n * function to extract the values before and after the\n * activation. For instance:\n *\n * ```js\n * const input = tf.input({shape: [5]});\n * const denseLayer = tf.layers.dense({units: 1});\n * const activationLayer = tf.layers.activation({activation: 'relu6'});\n *\n * // Obtain the output symbolic tensors by applying the layers in order.\n * const denseOutput = denseLayer.apply(input);\n * const activationOutput = activationLayer.apply(denseOutput);\n *\n * // Create the model based on the inputs.\n * const model = tf.model({\n *     inputs: input,\n *     outputs: [denseOutput, activationOutput]\n * });\n *\n * // Collect both outputs and print separately.\n * const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));\n * denseOut.print();\n * activationOut.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function activation(args) {\n    return new Activation(args);\n}\n/**\n * Creates a dense (fully connected) layer.\n *\n * This layer implements the operation:\n *   `output = activation(dot(input, kernel) + bias)`\n *\n * `activation` is the element-wise activation function\n *   passed as the `activation` argument.\n *\n * `kernel` is a weights matrix created by the layer.\n *\n * `bias` is a bias vector created by the layer (only applicable if `useBias`\n * is `true`).\n *\n * **Input shape:**\n *\n *   nD `tf.Tensor` with shape: `(batchSize, ..., inputDim)`.\n *\n *   The most common situation would be\n *   a 2D input with shape `(batchSize, inputDim)`.\n *\n * **Output shape:**\n *\n *   nD tensor with shape: `(batchSize, ..., units)`.\n *\n *   For instance, for a 2D input with shape `(batchSize, inputDim)`,\n *   the output would have shape `(batchSize, units)`.\n *\n * Note: if the input to the layer has a rank greater than 2, then it is\n * flattened prior to the initial dot product with the kernel.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dense(args) {\n    return new Dense(args);\n}\n/**\n * Applies\n * [dropout](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf) to\n * the input.\n *\n * Dropout consists in randomly setting a fraction `rate` of input units to 0 at\n * each update during training time, which helps prevent overfitting.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function dropout(args) {\n    return new Dropout(args);\n}\n/**\n * Spatial 1D version of Dropout.\n *\n * This Layer type performs the same function as the Dropout layer, but it drops\n * entire 1D feature maps instead of individual elements. For example, if an\n * input example consists of 3 timesteps and the feature map for each timestep\n * has a size of 4, a `spatialDropout1d` layer may zero out the feature maps\n * of the 1st timesteps and 2nd timesteps completely while sparing all feature\n * elements of the 3rd timestep.\n *\n * If adjacent frames (timesteps) are strongly correlated (as is normally the\n * case in early convolution layers), regular dropout will not regularize the\n * activation and will otherwise just result in merely an effective learning\n * rate decrease. In this case, `spatialDropout1d` will help promote\n * independence among feature maps and should be used instead.\n *\n * **Arguments:**\n *   rate: A floating-point number >=0 and <=1. Fraction of the input elements\n *     to drop.\n *\n * **Input shape:**\n *   3D tensor with shape `(samples, timesteps, channels)`.\n *\n * **Output shape:**\n *   Same as the input shape.\n *\n * References:\n *   - [Efficient Object Localization Using Convolutional\n *      Networks](https://arxiv.org/abs/1411.4280)\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function spatialDropout1d(args) {\n    return new SpatialDropout1D(args);\n}\n/**\n * Flattens the input. Does not affect the batch size.\n *\n * A `Flatten` layer flattens each batch in its inputs to 1D (making the output\n * 2D).\n *\n * For example:\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const flattenLayer = tf.layers.flatten();\n * // Inspect the inferred output shape of the flatten layer, which\n * // equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the\n * // flattening. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(flattenLayer.apply(input).shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function flatten(args) {\n    return new Flatten(args);\n}\n/**\n * Repeats the input n times in a new dimension.\n *\n * ```js\n *  const model = tf.sequential();\n *  model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));\n *  const x = tf.tensor2d([[10, 20]]);\n *  // Use the model to do inference on a data point the model hasn't see\n *  model.predict(x).print();\n *  // output shape is now [batch, 2, 4]\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function repeatVector(args) {\n    return new RepeatVector(args);\n}\n/**\n * Reshapes an input to a certain shape.\n *\n * ```js\n * const input = tf.input({shape: [4, 3]});\n * const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});\n * // Inspect the inferred output shape of the Reshape layer, which\n * // equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)\n * console.log(JSON.stringify(reshapeLayer.apply(input).shape));\n * ```\n *\n * Input shape:\n *   Arbitrary, although all dimensions in the input shape must be fixed.\n *   Use the configuration `inputShape` when using this layer as the\n *   first layer in a model.\n *\n *\n * Output shape:\n *   [batchSize, targetShape[0], targetShape[1], ...,\n *    targetShape[targetShape.length - 1]].\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function reshape(args) {\n    return new Reshape(args);\n}\n/**\n * Permutes the dimensions of the input according to a given pattern.\n *\n * Useful for, e.g., connecting RNNs and convnets together.\n *\n * Example:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.permute({\n *   dims: [2, 1],\n *   inputShape: [10, 64]\n * }));\n * console.log(model.outputShape);\n * // Now model's output shape is [null, 64, 10], where null is the\n * // unpermuted sample (batch) dimension.\n * ```\n *\n * Input shape:\n *   Arbitrary. Use the configuration field `inputShape` when using this\n *   layer as the first layer in a model.\n *\n * Output shape:\n *   Same rank as the input shape, but with the dimensions re-ordered (i.e.,\n *   permuted) according to the `dims` configuration of this layer.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function permute(args) {\n    return new Permute(args);\n}\n/**\n * Maps positive integers (indices) into dense vectors of fixed size.\n * eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]\n *\n * **Input shape:** 2D tensor with shape: `[batchSize, sequenceLength]`.\n *\n * **Output shape:** 3D tensor with shape: `[batchSize, sequenceLength,\n * outputDim]`.\n *\n * @doc {heading: 'Layers', subheading: 'Basic', namespace: 'layers'}\n */\nexport function embedding(args) {\n    return new Embedding(args);\n}\n// Merge Layers.\n/**\n * Layer that performs element-wise addition on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). The inputs are specified as an\n * `Array` when the `apply` method of the `Add` layer instance is called. For\n * example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const addLayer = tf.layers.add();\n * const sum = addLayer.apply([input1, input2]);\n * console.log(JSON.stringify(sum.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function add(args) {\n    return new Add(args);\n}\n/**\n * Layer that performs element-wise averaging on an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape, and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const averageLayer = tf.layers.average();\n * const average = averageLayer.apply([input1, input2]);\n * console.log(JSON.stringify(average.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function average(args) {\n    return new Average(args);\n}\n/**\n * Layer that concatenates an `Array` of inputs.\n *\n * It takes a list of tensors, all of the same shape except for the\n * concatenation axis, and returns a single tensor, the concatenation\n * of all inputs. For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 3]});\n * const concatLayer = tf.layers.concatenate();\n * const output = concatLayer.apply([input1, input2]);\n * console.log(JSON.stringify(output.shape));\n * // You get [null, 2, 5], with the first dimension as the undetermined batch\n * // dimension. The last dimension (5) is the result of concatenating the\n * // last dimensions of the inputs (2 and 3).\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function concatenate(args) {\n    return new Concatenate(args);\n}\n/**\n * Layer that computes the element-wise maximum an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const maxLayer = tf.layers.maximum();\n * const max = maxLayer.apply([input1, input2]);\n * console.log(JSON.stringify(max.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function maximum(args) {\n    return new Maximum(args);\n}\n/**\n * Layer that computes the element-wise minimum of an `Array` of inputs.\n *\n * It takes as input a list of tensors, all of the same shape and returns a\n * single tensor (also of the same shape). For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const minLayer = tf.layers.minimum();\n * const min = minLayer.apply([input1, input2]);\n * console.log(JSON.stringify(min.shape));\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function minimum(args) {\n    return new Minimum(args);\n}\n/**\n * Layer that multiplies (element-wise) an `Array` of inputs.\n *\n * It takes as input an Array of tensors, all of the same\n * shape, and returns a single tensor (also of the same shape).\n * For example:\n *\n * ```js\n * const input1 = tf.input({shape: [2, 2]});\n * const input2 = tf.input({shape: [2, 2]});\n * const input3 = tf.input({shape: [2, 2]});\n * const multiplyLayer = tf.layers.multiply();\n * const product = multiplyLayer.apply([input1, input2, input3]);\n * console.log(product.shape);\n * // You get [null, 2, 2], with the first dimension as the undetermined batch\n * // dimension.\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function multiply(args) {\n    return new Multiply(args);\n}\n/**\n * Layer that computes a dot product between samples in two tensors.\n *\n * E.g., if applied to a list of two tensors `a` and `b` both of shape\n * `[batchSize, n]`, the output will be a tensor of shape `[batchSize, 1]`,\n * where each entry at index `[i, 0]` will be the dot product between\n * `a[i, :]` and `b[i, :]`.\n *\n * Example:\n *\n * ```js\n * const dotLayer = tf.layers.dot({axes: -1});\n * const x1 = tf.tensor2d([[10, 20], [30, 40]]);\n * const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);\n *\n * // Invoke the layer's apply() method in eager (imperative) mode.\n * const y = dotLayer.apply([x1, x2]);\n * y.print();\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Merge', namespace: 'layers'}\n */\nexport function dot(args) {\n    return new Dot(args);\n}\n// Normalization Layers.\n/**\n * Batch normalization layer (Ioffe and Szegedy, 2014).\n *\n * Normalize the activations of the previous layer at each batch,\n * i.e. applies a transformation that maintains the mean activation\n * close to 0 and the activation standard deviation close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape` (Array of integers, does\n *   not include the sample axis) when calling the constructor of this class,\n *   if this layer is used as a first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Batch Normalization: Accelerating Deep Network Training by Reducing\n * Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function batchNormalization(args) {\n    return new BatchNormalization(args);\n}\n/**\n * Layer-normalization layer (Ba et al., 2016).\n *\n * Normalizes the activations of the previous layer for each given example in a\n * batch independently, instead of across a batch like in `batchNormalization`.\n * In other words, this layer applies a transformation that maintanis the mean\n * activation within each example close to0 and activation variance close to 1.\n *\n * Input shape:\n *   Arbitrary. Use the argument `inputShape` when using this layer as the first\n *   layer in a model.\n *\n * Output shape:\n *   Same as input.\n *\n * References:\n *   - [Layer Normalization](https://arxiv.org/abs/1607.06450)\n *\n * @doc {heading: 'Layers', subheading: 'Normalization', namespace: 'layers'}\n */\nexport function layerNormalization(args) {\n    return new LayerNormalization(args);\n}\n// Padding Layers.\n/**\n * Zero-padding layer for 2D input (e.g., image).\n *\n * This layer can add rows and columns of zeros\n * at the top, bottom, left and right side of an image tensor.\n *\n * Input shape:\n *   4D tensor with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, rows, cols, channels]`\n *   - If `data_format` is `\"channels_first\"`:\n *     `[batch, channels, rows, cols]`.\n *\n * Output shape:\n *   4D with shape:\n *   - If `dataFormat` is `\"channelsLast\"`:\n *     `[batch, paddedRows, paddedCols, channels]`\n *    - If `dataFormat` is `\"channelsFirst\"`:\n *     `[batch, channels, paddedRows, paddedCols]`.\n *\n * @doc {heading: 'Layers', subheading: 'Padding', namespace: 'layers'}\n */\nexport function zeroPadding2d(args) {\n    return new ZeroPadding2D(args);\n}\n// Pooling Layers.\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape: `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * `tf.avgPool1d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling1d(args) {\n    return new AveragePooling1D(args);\n}\nexport function avgPool1d(args) {\n    return averagePooling1d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling1d(args) {\n    return averagePooling1d(args);\n}\n/**\n * Average pooling operation for spatial data.\n *\n * Input shape:\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, rows, cols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *  - If `dataFormat === CHANNEL_LAST`:\n *      4D tensor with shape:\n *      `[batchSize, pooleRows, pooledCols, channels]`\n *  - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *      `[batchSize, channels, pooleRows, pooledCols]`\n *\n * `tf.avgPool2d` is an alias.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling2d(args) {\n    return new AveragePooling2D(args);\n}\nexport function avgPool2d(args) {\n    return averagePooling2d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling2d(args) {\n    return averagePooling2d(args);\n}\n/**\n * Average pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      4D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function averagePooling3d(args) {\n    return new AveragePooling3D(args);\n}\nexport function avgPool3d(args) {\n    return averagePooling3d(args);\n}\n// For backwards compatibility.\n// See https://github.com/tensorflow/tfjs/issues/152\nexport function avgPooling3d(args) {\n    return averagePooling3d(args);\n}\n/**\n * Global average pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling1d(args) {\n    return new GlobalAveragePooling1D(args);\n}\n/**\n * Global average pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalAveragePooling2d(args) {\n    return new GlobalAveragePooling2D(args);\n}\n/**\n * Global max pooling operation for temporal data.\n *\n * Input Shape: 3D tensor with shape: `[batchSize, steps, features]`.\n *\n * Output Shape:2D tensor with shape: `[batchSize, features]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling1d(args) {\n    return new GlobalMaxPooling1D(args);\n}\n/**\n * Global max pooling operation for spatial data.\n *\n * Input shape:\n *   - If `dataFormat` is `CHANNEL_LAST`:\n *       4D tensor with shape: `[batchSize, rows, cols, channels]`.\n *   - If `dataFormat` is `CHANNEL_FIRST`:\n *       4D tensor with shape: `[batchSize, channels, rows, cols]`.\n *\n * Output shape:\n *   2D tensor with shape: `[batchSize, channels]`.\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function globalMaxPooling2d(args) {\n    return new GlobalMaxPooling2D(args);\n}\n/**\n * Max pooling operation for temporal data.\n *\n * Input shape:  `[batchSize, inLength, channels]`\n *\n * Output shape: `[batchSize, pooledLength, channels]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling1d(args) {\n    return new MaxPooling1D(args);\n}\n/**\n * Max pooling operation for spatial data.\n *\n * Input shape\n *   - If `dataFormat === CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, rows, cols, channels]`\n *   - If `dataFormat === CHANNEL_FIRST`:\n *      4D tensor with shape:\n *       `[batchSize, channels, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=CHANNEL_LAST`:\n *       4D tensor with shape:\n *       `[batchSize, pooleRows, pooledCols, channels]`\n *   - If `dataFormat=CHANNEL_FIRST`:\n *       4D tensor with shape:\n *       `[batchSize, channels, pooleRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling2d(args) {\n    return new MaxPooling2D(args);\n}\n/**\n * Max pooling operation for 3D data.\n *\n * Input shape\n *   - If `dataFormat === channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, depths, rows, cols, channels]`\n *   - If `dataFormat === channelsFirst`:\n *      5D tensor with shape:\n *       `[batchSize, channels, depths, rows, cols]`\n *\n * Output shape\n *   - If `dataFormat=channelsLast`:\n *       5D tensor with shape:\n *       `[batchSize, pooledDepths, pooledRows, pooledCols, channels]`\n *   - If `dataFormat=channelsFirst`:\n *       5D tensor with shape:\n *       `[batchSize, channels, pooledDepths, pooledRows, pooledCols]`\n *\n * @doc {heading: 'Layers', subheading: 'Pooling', namespace: 'layers'}\n */\nexport function maxPooling3d(args) {\n    return new MaxPooling3D(args);\n}\n// Recurrent Layers.\n/**\n * Gated Recurrent Unit - Cho et al. 2014.\n *\n * This is an `RNN` layer consisting of one `GRUCell`. However, unlike\n * the underlying `GRUCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.gru({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `GRUCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gru(args) {\n    return new GRU(args);\n}\n/**\n * Cell class for `GRU`.\n *\n * `GRUCell` is distinct from the `RNN` subclass `GRU` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `GRU` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.gruCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `GRUCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.gruCell({units: 4}),\n *   tf.layers.gruCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `gruCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `GRUCell`, use the\n * `tf.layers.gru`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function gruCell(args) {\n    return new GRUCell(args);\n}\n/**\n * Long-Short Term Memory layer - Hochreiter 1997.\n *\n * This is an `RNN` layer consisting of one `LSTMCell`. However, unlike\n * the underlying `LSTMCell`, the `apply` method of `LSTM` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const lstm = tf.layers.lstm({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = lstm.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `LSTMCell`'s number of units.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstm(args) {\n    return new LSTM(args);\n}\n/**\n * Cell class for `LSTM`.\n *\n * `LSTMCell` is distinct from the `RNN` subclass `LSTM` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `LSTM` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.lstmCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `LSTMCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.lstmCell({units: 4}),\n *   tf.layers.lstmCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `lstmCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `LSTMCell`, use the\n * `tf.layers.lstm`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function lstmCell(args) {\n    return new LSTMCell(args);\n}\n/**\n * Fully-connected RNN where the output is to be fed back to input.\n *\n * This is an `RNN` layer consisting of one `SimpleRNNCell`. However, unlike\n * the underlying `SimpleRNNCell`, the `apply` method of `SimpleRNN` operates\n * on a sequence of inputs. The shape of the input (not including the first,\n * batch dimension) needs to be at least 2-D, with the first dimension being\n * time steps. For example:\n *\n * ```js\n * const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});\n *\n * // Create an input with 10 time steps.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the `SimpleRNNCell`'s number of units.\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNN(args) {\n    return new SimpleRNN(args);\n}\n/**\n * Cell class for `SimpleRNN`.\n *\n * `SimpleRNNCell` is distinct from the `RNN` subclass `SimpleRNN` in that its\n * `apply` method takes the input data of only a single time step and returns\n * the cell's output at the time step, while `SimpleRNN` takes the input data\n * over a number of time steps. For example:\n *\n * ```js\n * const cell = tf.layers.simpleRNNCell({units: 2});\n * const input = tf.input({shape: [10]});\n * const output = cell.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10]: This is the cell's output at a single time step. The 1st\n * // dimension is the unknown batch size.\n * ```\n *\n * Instance(s) of `SimpleRNNCell` can be used to construct `RNN` layers. The\n * most typical use of this workflow is to combine a number of cells into a\n * stacked RNN cell (i.e., `StackedRNNCell` internally) and use it to create an\n * RNN. For example:\n *\n * ```js\n * const cells = [\n *   tf.layers.simpleRNNCell({units: 4}),\n *   tf.layers.simpleRNNCell({units: 8}),\n * ];\n * const rnn = tf.layers.rnn({cell: cells, returnSequences: true});\n *\n * // Create an input with 10 time steps and a length-20 vector at each step.\n * const input = tf.input({shape: [10, 20]});\n * const output = rnn.apply(input);\n *\n * console.log(JSON.stringify(output.shape));\n * // [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the\n * // same as the sequence length of `input`, due to `returnSequences`: `true`;\n * // 3rd dimension is the last `SimpleRNNCell`'s number of units.\n * ```\n *\n * To create an `RNN` consisting of only *one* `SimpleRNNCell`, use the\n * `tf.layers.simpleRNN`.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function simpleRNNCell(args) {\n    return new SimpleRNNCell(args);\n}\n/**\n * Convolutional LSTM layer - Xingjian Shi 2015.\n *\n * This is an `ConvRNN2D` layer consisting of one `ConvLSTM2DCell`. However,\n * unlike the underlying `ConvLSTM2DCell`, the `apply` method of `ConvLSTM2D`\n * operates on a sequence of inputs. The shape of the input (not including the\n * first, batch dimension) needs to be 4-D, with the first dimension being time\n * steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const batchSize = 4;\n * const sequenceLength = 2;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [batchSize, sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const layer = tf.layers.convLstm2d({filters, kernelSize});\n *\n * const output = layer.apply(input);\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2d(args) {\n    return new ConvLSTM2D(args);\n}\n/**\n * Cell class for `ConvLSTM2D`.\n *\n * `ConvLSTM2DCell` is distinct from the `ConvRNN2D` subclass `ConvLSTM2D` in\n * that its `call` method takes the input data of only a single time step and\n * returns the cell's output at the time step, while `ConvLSTM2D` takes the\n * input data over a number of time steps. For example:\n *\n * ```js\n * const filters = 3;\n * const kernelSize = 3;\n *\n * const sequenceLength = 1;\n * const size = 5;\n * const channels = 3;\n *\n * const inputShape = [sequenceLength, size, size, channels];\n * const input = tf.ones(inputShape);\n *\n * const cell = tf.layers.convLstm2dCell({filters, kernelSize});\n *\n * cell.build(input.shape);\n *\n * const outputSize = size - kernelSize + 1;\n * const outShape = [sequenceLength, outputSize, outputSize, filters];\n *\n * const initialH = tf.zeros(outShape);\n * const initialC = tf.zeros(outShape);\n *\n * const [o, h, c] = cell.call([input, initialH, initialC], {});\n * ```\n */\n/** @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'} */\nexport function convLstm2dCell(args) {\n    return new ConvLSTM2DCell(args);\n}\n/**\n * Base class for recurrent layers.\n *\n * Input shape:\n *   3D tensor with shape `[batchSize, timeSteps, inputDim]`.\n *\n * Output shape:\n *   - if `returnState`, an Array of tensors (i.e., `tf.Tensor`s). The first\n *     tensor is the output. The remaining tensors are the states at the\n *     last time step, each with shape `[batchSize, units]`.\n *   - if `returnSequences`, the output will have shape\n *     `[batchSize, timeSteps, units]`.\n *   - else, the output will have shape `[batchSize, units]`.\n *\n * Masking:\n *   This layer supports masking for input data with a variable number\n *   of timesteps. To introduce masks to your data,\n *   use an embedding layer with the `mask_zero` parameter\n *   set to `True`.\n *\n * Notes on using statefulness in RNNs:\n *   You can set RNN layers to be 'stateful', which means that the states\n *   computed for the samples in one batch will be reused as initial states\n *   for the samples in the next batch. This assumes a one-to-one mapping\n *   between samples in different successive batches.\n *\n *   To enable statefulness:\n *     - specify `stateful: true` in the layer constructor.\n *     - specify a fixed batch size for your model, by passing\n *       if sequential model:\n *         `batchInputShape=[...]` to the first layer in your model.\n *       else for functional model with 1 or more Input layers:\n *         `batchShape=[...]` to all the first layers in your model.\n *       This is the expected shape of your inputs *including the batch size*.\n *       It should be a tuple of integers, e.g. `(32, 10, 100)`.\n *     - specify `shuffle=False` when calling fit().\n *\n *   To reset the states of your model, call `.resetStates()` on either\n *   a specific layer, or on your entire model.\n *\n * Note on specifying the initial state of RNNs\n *   You can specify the initial state of RNN layers symbolically by\n *   calling them with the option `initialState`. The value of\n *   `initialState` should be a tensor or list of tensors representing\n *   the initial state of the RNN layer.\n *\n *   You can specify the initial state of RNN layers numerically by\n *   calling `resetStates` with the keyword argument `states`. The value of\n *   `states` should be a numpy array or list of numpy arrays representing\n *   the initial state of the RNN layer.\n *\n * Note on passing external constants to RNNs\n *   You can pass \"external\" constants to the cell using the `constants`\n *   keyword argument of `RNN.call` method. This requires that the `cell.call`\n *   method accepts the same keyword argument `constants`. Such constants\n *   can be used to conditon the cell transformation on additional static inputs\n *   (not changing over time), a.k.a an attention mechanism.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function rnn(args) {\n    return new RNN(args);\n}\n/**\n * Wrapper allowing a stack of RNN cells to behave as a single cell.\n *\n * Used to implement efficient stacked RNNs.\n *\n * @doc {heading: 'Layers', subheading: 'Recurrent', namespace: 'layers'}\n */\nexport function stackedRNNCells(args) {\n    return new StackedRNNCells(args);\n}\n// Wrapper Layers.\n/** @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'} */\nexport function bidirectional(args) {\n    return new Bidirectional(args);\n}\n/**\n * This wrapper applies a layer to every temporal slice of an input.\n *\n * The input should be at least 3D,  and the dimension of the index `1` will be\n * considered to be the temporal dimension.\n *\n * Consider a batch of 32 samples, where each sample is a sequence of 10 vectors\n * of 16 dimensions. The batch input shape of the layer is then `[32,  10,\n * 16]`, and the `inputShape`, not including the sample dimension, is\n * `[10, 16]`.\n *\n * You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10\n * timesteps, independently:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.dense({units: 8}),\n *   inputShape: [10, 16],\n * }));\n *\n * // Now model.outputShape = [null, 10, 8].\n * // The output will then have shape `[32, 10, 8]`.\n *\n * // In subsequent layers, there is no need for `inputShape`:\n * model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * // Now model.outputShape = [null, 10, 32].\n * ```\n *\n * The output will then have shape `[32, 10, 32]`.\n *\n * `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for\n * instance a `Conv2D` layer.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.timeDistributed({\n *   layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),\n *   inputShape: [10, 299, 299, 3],\n * }));\n * console.log(JSON.stringify(model.outputs[0].shape));\n * ```\n *\n * @doc {heading: 'Layers', subheading: 'Wrapper', namespace: 'layers'}\n */\nexport function timeDistributed(args) {\n    return new TimeDistributed(args);\n}\n// Aliases for pooling.\nexport const globalMaxPool1d = globalMaxPooling1d;\nexport const globalMaxPool2d = globalMaxPooling2d;\nexport const maxPool1d = maxPooling1d;\nexport const maxPool2d = maxPooling2d;\nexport { Layer, RNN, RNNCell, input /* alias for tf.input */ };\n/**\n * Apply additive zero-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * This is useful to mitigate overfitting\n * (you could see it as a form of random data augmentation).\n * Gaussian Noise (GS) is a natural choice as corruption process\n * for real valued inputs.\n *\n * # Arguments\n *     stddev: float, standard deviation of the noise distribution.\n *\n * # Input shape\n *         Arbitrary. Use the keyword argument `input_shape`\n *         (tuple of integers, does not include the samples axis)\n *         when using this layer as the first layer in a model.\n *\n * # Output shape\n *         Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianNoise(args) {\n    return new GaussianNoise(args);\n}\n/**\n * Apply multiplicative 1-centered Gaussian noise.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](\n *      http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function gaussianDropout(args) {\n    return new GaussianDropout(args);\n}\n/**\n * Applies Alpha Dropout to the input.\n *\n * As it is a regularization layer, it is only active at training time.\n *\n * Alpha Dropout is a `Dropout` that keeps mean and variance of inputs\n * to their original values, in order to ensure the self-normalizing property\n * even after this dropout.\n * Alpha Dropout fits well to Scaled Exponential Linear Units\n * by randomly setting activations to the negative saturation value.\n *\n * Arguments:\n *   - `rate`: float, drop probability (as with `Dropout`).\n *     The multiplicative noise will have\n *     standard deviation `sqrt(rate / (1 - rate))`.\n *   - `noise_shape`: A 1-D `Tensor` of type `int32`, representing the\n *     shape for randomly generated keep/drop flags.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * References:\n *   - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *\n * @doc {heading: 'Layers', subheading: 'Noise', namespace: 'layers'}\n */\nexport function alphaDropout(args) {\n    return new AlphaDropout(args);\n}\n/**\n * Masks a sequence by using a mask value to skip timesteps.\n *\n * If all features for a given sample timestep are equal to `mask_value`,\n * then the sample timestep will be masked (skipped) in all downstream layers\n * (as long as they support masking).\n *\n * If any downstream layer does not support masking yet receives such\n * an input mask, an exception will be raised.\n *\n * Arguments:\n *   - `maskValue`: Either None or mask value to skip.\n *\n * Input shape:\n *   Arbitrary. Use the keyword argument `inputShape`\n *   (tuple of integers, does not include the samples axis)\n *   when using this layer as the first layer in a model.\n *\n * Output shape:\n *   Same shape as input.\n *\n * @doc {heading: 'Layers', subheading: 'Mask', namespace: 'layers'}\n */\nexport function masking(args) {\n    return new Masking(args);\n}\n//# sourceMappingURL=exports_layers.js.map", "import * as losses from './losses';\nimport * as metrics from './metrics';\n/**\n * Binary accuracy metric function.\n *\n * `yTrue` and `yPred` can have 0-1 values. Example:\n * ```js\n * const x = tf.tensor2d([[1, 1, 1, 1], [0, 0, 0, 0]], [2, 4]);\n * const y = tf.tensor2d([[1, 0, 1, 0], [0, 0, 0, 1]], [2, 4]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * `yTrue` and `yPred` can also have floating-number values between 0 and 1, in\n * which case the values will be thresholded at 0.5 to yield 0-1 values (i.e.,\n * a value >= 0.5 and <= 1.0 is interpreted as 1.\n * )\n * Example:\n * ```js\n * const x = tf.tensor1d([1, 1, 1, 1, 0, 0, 0, 0]);\n * const y = tf.tensor1d([0.2, 0.4, 0.6, 0.8, 0.2, 0.3, 0.4, 0.7]);\n * const accuracy = tf.metrics.binaryAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryAccuracy(yTrue, yPred) {\n    return metrics.binaryAccuracy(yTrue, yPred);\n}\n/**\n * Binary crossentropy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0], [1], [1], [1]]);\n * const y = tf.tensor2d([[0], [0], [0.5], [1]]);\n * const crossentropy = tf.metrics.binaryCrossentropy(x, y);\n * crossentropy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth.\n * @param yPred Binary Tensor of prediction, probabilities for the `1` case.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function binaryCrossentropy(yTrue, yPred) {\n    return metrics.binaryCrossentropy(yTrue, yPred);\n}\n/**\n * Sparse categorical accuracy metric function.\n *\n * Example:\n * ```js\n *\n * const yTrue = tf.tensor1d([1, 1, 2, 2, 0]);\n * const yPred = tf.tensor2d(\n *      [[0, 1, 0], [1, 0, 0], [0, 0.4, 0.6], [0, 0.6, 0.4], [0.7, 0.3, 0]]);\n * const crossentropy = tf.metrics.sparseCategoricalAccuracy(yTrue, yPred);\n * crossentropy.print();\n * ```\n *\n * @param yTrue True labels: indices.\n * @param yPred Predicted probabilities or logits.\n * @returns Accuracy tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function sparseCategoricalAccuracy(yTrue, yPred) {\n    return metrics.sparseCategoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical accuracy metric function.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d([[0, 0, 0, 1], [0, 0, 0, 1]]);\n * const y = tf.tensor2d([[0.1, 0.8, 0.05, 0.05], [0.1, 0.05, 0.05, 0.8]]);\n * const accuracy = tf.metrics.categoricalAccuracy(x, y);\n * accuracy.print();\n * ```\n *\n * @param yTrue Binary Tensor of truth: one-hot encoding of categories.\n * @param yPred Binary Tensor of prediction: probabilities or logits for the\n *   same categories as in `yTrue`.\n * @return Accuracy Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalAccuracy(yTrue, yPred) {\n    return metrics.categoricalAccuracy(yTrue, yPred);\n}\n/**\n * Categorical crossentropy between an output tensor and a target tensor.\n *\n * @param target A tensor of the same shape as `output`.\n * @param output A tensor resulting from a softmax (unless `fromLogits` is\n *  `true`, in which case `output` is expected to be the logits).\n * @param fromLogits Boolean, whether `output` is the result of a softmax, or is\n *   a tensor of logits.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function categoricalCrossentropy(yTrue, yPred) {\n    return metrics.categoricalCrossentropy(yTrue, yPred);\n}\n/**\n * Computes the precision of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const precision = tf.metrics.precision(x, y);\n * precision.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Precision Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function precision(yTrue, yPred) {\n    return metrics.precision(yTrue, yPred);\n}\n/**\n * Computes the recall of the predictions with respect to the labels.\n *\n * Example:\n * ```js\n * const x = tf.tensor2d(\n *    [\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [1, 0, 0, 0],\n *      [0, 0, 1, 0]\n *    ]\n * );\n *\n * const y = tf.tensor2d(\n *    [\n *      [0, 0, 1, 0],\n *      [0, 1, 0, 0],\n *      [0, 0, 0, 1],\n *      [0, 1, 0, 0],\n *      [0, 1, 0, 0]\n *    ]\n * );\n *\n * const recall = tf.metrics.recall(x, y);\n * recall.print();\n * ```\n *\n * @param yTrue The ground truth values. Expected to be contain only 0-1 values.\n * @param yPred The predicted values. Expected to be contain only 0-1 values.\n * @return Recall Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function recall(yTrue, yPred) {\n    return metrics.recall(yTrue, yPred);\n}\n/**\n * Loss or metric function: Cosine proximity.\n *\n * Mathematically, cosine proximity is defined as:\n *   `-sum(l2Normalize(yTrue) * l2Normalize(yPred))`,\n * wherein `l2Normalize()` normalizes the L2 norm of the input to 1 and `*`\n * represents element-wise multiplication.\n *\n * ```js\n * const yTrue = tf.tensor2d([[1, 0], [1, 0]]);\n * const yPred = tf.tensor2d([[1 / Math.sqrt(2), 1 / Math.sqrt(2)], [0, 1]]);\n * const proximity = tf.metrics.cosineProximity(yTrue, yPred);\n * proximity.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Cosine proximity Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function cosineProximity(yTrue, yPred) {\n    return losses.cosineProximity(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute error.\n *\n * Mathematically, mean absolute error is defined as:\n *   `mean(abs(yPred - yTrue))`,\n * wherein the `mean` is applied over feature dimensions.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [0, 0], [2, 3]]);\n * const yPred = tf.tensor2d([[0, 1], [0, 1], [-2, -3]]);\n * const mse = tf.metrics.meanAbsoluteError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsoluteError(yTrue, yPred) {\n    return losses.meanAbsoluteError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean absolute percentage error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [10, 20]]);\n * const yPred = tf.tensor2d([[0, 1], [11, 24]]);\n * const mse = tf.metrics.meanAbsolutePercentageError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MAPE`, `tf.metrics.mape`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean absolute percentage error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanAbsolutePercentageError(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function MAPE(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\nexport function mape(yTrue, yPred) {\n    return losses.meanAbsolutePercentageError(yTrue, yPred);\n}\n/**\n * Loss or metric function: Mean squared error.\n *\n * ```js\n * const yTrue = tf.tensor2d([[0, 1], [3, 4]]);\n * const yPred = tf.tensor2d([[0, 1], [-3, -4]]);\n * const mse = tf.metrics.meanSquaredError(yTrue, yPred);\n * mse.print();\n * ```\n *\n * Aliases: `tf.metrics.MSE`, `tf.metrics.mse`.\n *\n * @param yTrue Truth Tensor.\n * @param yPred Prediction Tensor.\n * @return Mean squared error Tensor.\n *\n * @doc {heading: 'Metrics', namespace: 'metrics'}\n */\nexport function meanSquaredError(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function MSE(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\nexport function mse(yTrue, yPred) {\n    return losses.meanSquaredError(yTrue, yPred);\n}\n//# sourceMappingURL=exports_metrics.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nexport { modelFromJSON } from './models';\n//# sourceMappingURL=exports_models.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\nimport * as regularizers from './regularizers';\n// tslint:disable-next-line:max-line-length\nimport { L1L2 } from './regularizers';\n/**\n * Regularizer for L1 and L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x)) + sum(l2 * x^2)\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1l2(config) {\n    return new L1L2(config);\n}\n/**\n * Regularizer for L1 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l1 * abs(x))\n * @param args l1 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l1(config) {\n    return regularizers.l1(config);\n}\n/**\n * Regularizer for L2 regularization.\n *\n * Adds a term to the loss to penalize large weights:\n * loss += sum(l2 * x^2)\n * @param args l2 config.\n *\n * @doc {heading: 'Regularizers', namespace: 'regularizers'}\n */\nexport function l2(config) {\n    return regularizers.l2(config);\n}\n//# sourceMappingURL=exports_regularizers.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/* Original source: keras/callbacks.py */\nimport { BaseCallback } from './base_callbacks';\nimport { LayersModel } from './engine/training';\nimport { NotImplementedError } from './errors';\nimport { resolveScalarsInLogs } from './logs';\nexport class Callback extends BaseCallback {\n    constructor() {\n        super(...arguments);\n        /** Instance of `keras.models.Model`. Reference of the model being trained. */\n        this.model = null;\n    }\n    setModel(model) {\n        if (!(model instanceof LayersModel)) {\n            throw new Error('model must be a LayersModel, not some other Container');\n        }\n        this.model = model;\n    }\n}\nfunction less(currVal, prevVal) {\n    return currVal < prevVal;\n}\nfunction greater(currVal, prevVal) {\n    return currVal > prevVal;\n}\n/**\n * A Callback that stops training when a monitored quantity has stopped\n * improving.\n */\nexport class EarlyStopping extends Callback {\n    constructor(args) {\n        super();\n        if (args == null) {\n            args = {};\n        }\n        if (args.restoreBestWeights) {\n            throw new NotImplementedError('restoreBestWeights = True is not implemented in EarlyStopping yet.');\n        }\n        this.monitor = args.monitor || 'val_loss';\n        this.minDelta = Math.abs(args.minDelta || 0);\n        this.patience = args.patience || 0;\n        this.verbose = args.verbose || 0;\n        this.mode = args.mode || 'auto';\n        this.baseline = args.baseline;\n        if (['auto', 'min', 'max'].indexOf(this.mode) === -1) {\n            console.warn(`EarlyStopping mode '${this.mode}' is invalid. ` +\n                `Falling back to mode 'auto'.`);\n            this.mode = 'auto';\n        }\n        if (this.mode === 'min') {\n            this.monitorFunc = less;\n        }\n        else if (this.mode === 'max') {\n            this.monitorFunc = greater;\n        }\n        else {\n            // For mode === 'auto'.\n            if (this.monitor.indexOf('acc') !== -1) {\n                this.monitorFunc = greater;\n            }\n            else {\n                this.monitorFunc = less;\n            }\n        }\n        if (this.monitorFunc === less) {\n            this.minDelta *= -1;\n        }\n    }\n    async onTrainBegin(logs) {\n        this.wait = 0;\n        this.stoppedEpoch = 0;\n        if (this.baseline != null) {\n            this.best = this.baseline;\n        }\n        else {\n            this.best = this.monitorFunc === less ? Infinity : -Infinity;\n        }\n    }\n    async onEpochEnd(epoch, logs) {\n        await resolveScalarsInLogs(logs);\n        const current = this.getMonitorValue(logs);\n        if (current == null) {\n            return;\n        }\n        if (this.monitorFunc(current - this.minDelta, this.best)) {\n            this.best = current;\n            this.wait = 0;\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n        else {\n            this.wait++;\n            if (this.wait >= this.patience) {\n                this.stoppedEpoch = epoch;\n                this.model.stopTraining = true;\n            }\n            // TODO(cais): Logic for restoreBestWeights.\n        }\n    }\n    async onTrainEnd(logs) {\n        if (this.stoppedEpoch > 0 && this.verbose) {\n            console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n        }\n    }\n    getMonitorValue(logs) {\n        if (logs == null) {\n            logs = {};\n        }\n        const monitorValue = logs[this.monitor];\n        if (monitorValue == null) {\n            console.warn(`Metric for EarlyStopping ${this.monitor} is not available. ` +\n                `Available metrics are: ${Object.keys(logs)}`);\n        }\n        return monitorValue;\n    }\n}\n/**\n * Factory function for a Callback that stops training when a monitored\n * quantity has stopped improving.\n *\n * Early stopping is a type of regularization, and protects model against\n * overfitting.\n *\n * The following example based on fake data illustrates how this callback\n * can be used during `tf.LayersModel.fit()`:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense({\n *   units: 3,\n *   activation: 'softmax',\n *   kernelInitializer: 'ones',\n *   inputShape: [2]\n * }));\n * const xs = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const ys = tf.tensor2d([[1, 0, 0], [0, 1, 0]], [2, 3]);\n * const xsVal = tf.tensor2d([4, 3, 2, 1], [2, 2]);\n * const ysVal = tf.tensor2d([[0, 0, 1], [0, 1, 0]], [2, 3]);\n * model.compile(\n *     {loss: 'categoricalCrossentropy', optimizer: 'sgd', metrics: ['acc']});\n *\n * // Without the EarlyStopping callback, the val_acc value would be:\n * //   0.5, 0.5, 0.5, 0.5, ...\n * // With val_acc being monitored, training should stop after the 2nd epoch.\n * const history = await model.fit(xs, ys, {\n *   epochs: 10,\n *   validationData: [xsVal, ysVal],\n *   callbacks: tf.callbacks.earlyStopping({monitor: 'val_acc'})\n * });\n *\n * // Expect to see a length-2 array.\n * console.log(history.history.val_acc);\n * ```\n *\n * @doc {\n *   heading: 'Callbacks',\n *   namespace: 'callbacks'\n * }\n */\nexport function earlyStopping(args) {\n    return new EarlyStopping(args);\n}\nexport const callbacks = { earlyStopping };\n//# sourceMappingURL=callbacks.js.map", "/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n// This file lists all exports of TensorFlow.js Layers\nimport * as constraints from './exports_constraints';\nimport * as initializers from './exports_initializers';\nimport * as layers from './exports_layers';\nimport * as metrics from './exports_metrics';\nimport * as models from './exports_models';\nimport * as regularizers from './exports_regularizers';\nexport { CallbackList, CustomCallback, History } from './base_callbacks';\nexport { Callback, callbacks, EarlyStopping } from './callbacks';\nexport { InputSpec, SymbolicTensor } from './engine/topology';\nexport { LayersModel } from './engine/training';\nexport { input, loadLayersModel, model, registerCallbackConstructor, sequential } from './exports';\nexport { RNN } from './layers/recurrent';\nexport { Sequential } from './models';\nexport { LayerVariable } from './variables';\nexport { version as version_layers } from './version';\nexport { constraints, initializers, layers, metrics, models, regularizers };\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/** DataType enum. */\nexport var DataType;\n(function (DataType) {\n    DataType[DataType[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n    DataType[DataType[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n    DataType[DataType[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n    DataType[DataType[\"DT_INT32\"] = 3] = \"DT_INT32\";\n    DataType[DataType[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n    DataType[DataType[\"DT_INT16\"] = 5] = \"DT_INT16\";\n    DataType[DataType[\"DT_INT8\"] = 6] = \"DT_INT8\";\n    DataType[DataType[\"DT_STRING\"] = 7] = \"DT_STRING\";\n    DataType[DataType[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n    DataType[DataType[\"DT_INT64\"] = 9] = \"DT_INT64\";\n    DataType[DataType[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n    DataType[DataType[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n    DataType[DataType[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n    DataType[DataType[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n    DataType[DataType[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n    DataType[DataType[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n    DataType[DataType[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n    DataType[DataType[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n    DataType[DataType[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n    DataType[DataType[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n    DataType[DataType[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n    DataType[DataType[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n    DataType[DataType[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n    DataType[DataType[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n    DataType[DataType[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n    DataType[DataType[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n    DataType[DataType[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n    DataType[DataType[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n    DataType[DataType[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n})(DataType || (DataType = {}));\nexport var SaverDef;\n(function (SaverDef) {\n    /** CheckpointFormatVersion enum. */\n    let CheckpointFormatVersion;\n    (function (CheckpointFormatVersion) {\n        CheckpointFormatVersion[CheckpointFormatVersion[\"LEGACY\"] = 0] = \"LEGACY\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V1\"] = 1] = \"V1\";\n        CheckpointFormatVersion[CheckpointFormatVersion[\"V2\"] = 2] = \"V2\";\n    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n//# sourceMappingURL=compiled_api.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst CUSTOM_OPS = {};\n/**\n * Register an Op for graph model executor. This allow you to register\n * TensorFlow custom op or override existing op.\n *\n * Here is an example of registering a new MatMul Op.\n * ```js\n * const customMatmul = (node) =>\n *    tf.matMul(\n *        node.inputs[0], node.inputs[1],\n *        node.attrs['transpose_a'], node.attrs['transpose_b']);\n *\n * tf.registerOp('MatMul', customMatmul);\n * ```\n * The inputs and attrs of the node object is based on the TensorFlow op\n * registry.\n *\n * @param name The Tensorflow Op name.\n * @param opFunc An op function which is called with the current graph node\n * during execution and needs to return a tensor or a list of tensors. The node\n * has the following attributes:\n *    - attr: A map from attribute name to its value\n *    - inputs: A list of input tensors\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function registerOp(name, opFunc) {\n    const opMapper = {\n        tfOpName: name,\n        category: 'custom',\n        inputs: [],\n        attrs: [],\n        customExecutor: opFunc\n    };\n    CUSTOM_OPS[name] = opMapper;\n}\n/**\n * Retrieve the OpMapper object for the registered op.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function getRegisteredOp(name) {\n    return CUSTOM_OPS[name];\n}\n/**\n * Deregister the Op for graph model executor.\n *\n * @param name The Tensorflow Op name.\n *\n * @doc {heading: 'Models', subheading: 'Op Registry'}\n */\nexport function deregisterOp(name) {\n    delete CUSTOM_OPS[name];\n}\n//# sourceMappingURL=register.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { clone, util } from '@tensorflow/tfjs-core';\nexport function getParamValue(paramName, node, tensorMap, context, resourceManager) {\n    const inputParam = node.inputParams[paramName];\n    if (inputParam && inputParam.inputIndexStart !== undefined) {\n        const start = inputParam.inputIndexStart;\n        const end = inputParam.inputIndexEnd === 0 ?\n            undefined :\n            (inputParam.inputIndexEnd === undefined ? start + 1 :\n                inputParam.inputIndexEnd);\n        if (inputParam.type === 'tensor') {\n            return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);\n        }\n        if (inputParam.type === 'tensors') {\n            const inputs = node.inputNames.slice(start, end);\n            return inputs.map(name => getTensor(name, tensorMap, context, resourceManager));\n        }\n        const tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);\n        const data = tensor.dataSync();\n        return inputParam.type === 'number' ?\n            data[0] :\n            util.toNestedArray(tensor.shape, data);\n    }\n    const attrParam = node.attrParams[paramName];\n    return attrParam && attrParam.value;\n}\n/**\n * Retrieve the tensor from tensorsMap based on input name.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function getTensor(name, tensorsMap, context, resourceManager) {\n    const [nodeName, index] = parseNodeName(name);\n    if (resourceManager != null) {\n        const tensor = resourceManager.getHashTableHandleByName(nodeName);\n        if (tensor != null) {\n            return tensor;\n        }\n    }\n    const contextId = context.currentContextIds.find(contextId => {\n        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];\n    });\n    return contextId !== undefined ?\n        tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] :\n        undefined;\n}\n/**\n * Retrieve the tensors based on input name for current context.\n * @param name Node input name\n * @param tensorsMap Tensors map keyed by the node\n */\nexport function getTensorsForCurrentContenxt(name, tensorsMap, context) {\n    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\n/**\n * Returns the node name and index from the Node input name.\n * @param inputName The input name of the node, in format of\n * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is\n * default to 0.\n */\nexport function getNodeNameAndIndex(inputName, context) {\n    const [nodeName, index] = parseNodeName(inputName);\n    return [\n        getNodeNameWithContextId(nodeName, context && context.currentContextId),\n        index\n    ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n    return !!contextId ? `${name}-${contextId}` : name;\n}\nexport function parseNodeName(name) {\n    const parts = name.split(':');\n    if (parts.length === 1) {\n        return [name, 0];\n    }\n    const nodeName = parts[0];\n    return [nodeName, Number(parts[parts.length - 1])];\n}\nexport function split(arr, size) {\n    const res = [];\n    for (let i = 0; i < arr.length; i += size) {\n        res.push(arr.slice(i, i + size));\n    }\n    return res;\n}\nexport function getPadding(node, tensorMap, context) {\n    let pad = getParamValue('pad', node, tensorMap, context);\n    if (pad === 'explicit') {\n        // This is 1d array, we need to convert it to 2d array\n        pad = getParamValue('explicitPaddings', node, tensorMap, context);\n        const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n        for (let i = 0; i < 4; i++) {\n            explicitPadding[i][0] = pad[i * 2];\n            explicitPadding[i][1] = pad[i * 2 + 1];\n        }\n        return explicitPadding;\n    }\n    return pad;\n}\n/**\n *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to\n *  avoid disposal. This is important for TensorArray and TensorList ops, since\n *  internally they use a tensor as the id for TensorArray and TensorList, and\n * to simplify lookup, they also use Tensor.id as the key to the internal map.\n * These id tensors have been marked as kept in the backend, we need avoid clone\n * them in order to create new Tensor.id.\n * @param tensor\n */\nexport function cloneTensor(tensor) {\n    return tensor.kept ? tensor : clone(tensor);\n}\n//# sourceMappingURL=utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Add',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddV2',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AddN',\n        'category': 'arithmetic',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'BiasAdd',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sub',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'RealDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Div',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'DivNoNan',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorDiv',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mul',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Maximum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Minimum',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Pow',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SquaredDifference',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Mod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'FloorMod',\n        'category': 'arithmetic',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    }\n];\n//# sourceMappingURL=arithmetic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Abs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atan2',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ceil',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ClipByValue',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'clip_value_min', 'name': 'clipValueMin', 'type': 'number' },\n            { 'tfName': 'clip_value_max', 'name': 'clipValueMax', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Complex',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'real', 'type': 'tensor' },\n            { 'start': 1, 'name': 'imag', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ComplexAbs',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cos',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Cosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Elu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Exp',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Floor',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Imag',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Neg',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Real',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'outputType',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Prelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'alpha', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Relu6',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'clipValueMin',\n                'name': 'clipValueMin',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'clipValueMax',\n                'name': 'clipValueMax',\n                'type': 'number',\n                'defaultValue': 6\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Selu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sigmoid',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sin',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Rsqrt',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Square',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tan',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Tanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Sign',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Round',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Expm1',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Log1p',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Reciprocal',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Softplus',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Asinh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Acosh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Atanh',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Erf',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axes', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'keep_dims',\n                'name': 'keepDims',\n                'type': 'bool',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LeakyRelu',\n        'category': 'basic_math',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 0.2\n            },\n            {\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=basic_math.js.map", "export const json = [\n    {\n        'tfOpName': 'LoopCond',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'pred', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Switch',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'data', 'type': 'tensor' },\n            { 'start': 1, 'name': 'pred', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'Merge',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Enter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'frame_name', 'name': 'frameName', 'type': 'string' },\n            { 'tfName': 'is_constant', 'name': 'isConstant', 'type': 'bool' }\n        ]\n    },\n    {\n        'tfOpName': 'Exit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NextIteration',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'size', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'dynamic_size', 'name': 'dynamicSize', 'type': 'bool' },\n            { 'tfName': 'clear_after_read', 'name': 'clearAfterRead', 'type': 'bool' },\n            {\n                'tfName': 'identical_element_shapes',\n                'name': 'identicalElementShapes',\n                'type': 'bool'\n            },\n            { 'tfName': 'tensor_array_name', 'name': 'name', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayWriteV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayReadV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{\n                'tfName': 'dtype',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    },\n    {\n        'tfOpName': 'TensorArrayGatherV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayScatterV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArrayConcatV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }, {\n                'tfName': 'element_shape_except0',\n                'name': 'elementShapeExcept0',\n                'type': 'shape',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArraySplitV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n            { 'start': 3, 'name': 'flowIn', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorArraySizeV3',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'flowIn', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorArrayCloseV3',\n        'category': 'control',\n        'inputs': [{ 'start': 0, 'name': 'tensorArrayId', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'StatelessIf',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'If',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'cond', 'type': 'tensor' },\n            { 'start': 1, 'end': 0, 'name': 'args', 'type': 'tensors' }\n        ],\n        'attrs': [\n            { 'tfName': 'then_branch', 'name': 'thenBranch', 'type': 'func' },\n            { 'tfName': 'else_branch', 'name': 'elseBranch', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'StatelessWhile',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'While',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'cond', 'name': 'cond', 'type': 'func' },\n            { 'tfName': 'body', 'name': 'body', 'type': 'func' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListScatter',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListScatterV2',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 3, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGather',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'number[]' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListGetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListSetItem',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'index', 'type': 'number' },\n            { 'start': 2, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListReserve',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 1, 'name': 'numElements', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListFromTensor',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' }\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListStack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' },\n            { 'tfName': 'num_elements', 'name': 'numElements', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListSplit',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n            { 'start': 2, 'name': 'lengths', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListConcat',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_shape', 'name': 'elementShape', 'type': 'shape' },\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'TensorListPopBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'elementShape', 'type': 'shape' },\n        ],\n        'attrs': [{ 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TensorListPushBack',\n        'category': 'control',\n        'inputs': [\n            { 'start': 0, 'name': 'tensorListId', 'type': 'tensor' },\n            { 'start': 1, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'element_dtype', 'name': 'elementDType', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=control.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'AvgPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPoolWithArgmax',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' }, {\n                'tfName': 'include_batch_in_index',\n                'name': 'includeBatchInIndex',\n                'type': 'bool'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'AvgPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MaxPool3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            { 'tfName': 'ksize', 'name': 'kernelSize', 'type': 'number[]' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Conv1D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'stride', 'name': 'stride', 'type': 'number' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NWC'\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'dilation',\n                'name': 'dilation',\n                'type': 'number',\n                'defaultValue': 1\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            { 'tfName': 'useCudnnOnGpu', 'name': 'useCudnnOnGpu', 'type': 'bool' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': '_FusedConv2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'use_cudnn_on_gpu',\n                'name': 'useCudnnOnGpu',\n                'type': 'bool',\n                'defaultValue': true\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n        ]\n    },\n    {\n        'tfOpName': 'Conv2DBackpropInput',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 2, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 0, 'name': 'outputShape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2d',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'input', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'explicit_paddings',\n                'name': 'explicitPaddings',\n                'type': 'number[]',\n                'defaultValue': []\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'FusedDepthwiseConv2dNative',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true },\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            {\n                'tfName': 'dilations',\n                'name': 'dilations',\n                'type': 'number[]',\n                'defaultValue': [1, 1, 1, 1]\n            },\n            {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Conv3D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }, {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'defaultValue': 'NHWC'\n            },\n            { 'tfName': 'dilations', 'name': 'dilations', 'type': 'number[]' }\n        ],\n    },\n    {\n        'tfOpName': 'Dilation2D',\n        'category': 'convolution',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'filter', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'strides', 'name': 'strides', 'type': 'number[]' },\n            { 'tfName': 'rates', 'name': 'dilations', 'type': 'number[]' },\n            { 'tfName': 'padding', 'name': 'pad', 'type': 'string' }\n        ]\n    }\n];\n//# sourceMappingURL=convolution.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Fill',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n            { 'start': 1, 'name': 'value', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'LinSpace',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'num', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'OneHot',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'depth', 'type': 'number' },\n            { 'start': 2, 'name': 'onValue', 'type': 'number', 'defaultValue': 1 },\n            { 'start': 3, 'name': 'offValue', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [\n            {\n                'tfName': 'axis',\n                'name': 'axis',\n                'type': 'number',\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Ones',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'OnesLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'RandomUniform',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'minval',\n                'name': 'minval',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'maxval',\n                'name': 'maxval',\n                'type': 'number',\n                'defaultValue': 1\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Range',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'start', 'type': 'number' },\n            { 'start': 1, 'name': 'stop', 'type': 'number' },\n            { 'start': 2, 'name': 'step', 'type': 'number', 'defaultValue': 0 },\n        ],\n        'attrs': [{ 'tfName': 'Tidx', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'TruncatedNormal',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'means',\n                'name': 'mean',\n                'type': 'number',\n                'defaultValue': 0.0\n            },\n            {\n                'tfName': 'stddev',\n                'name': 'stdDev',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' }, {\n                'tfName': 'seed2',\n                'name': 'seed2',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'T', 'name': 'T', 'type': 'number', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Zeros',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'ZerosLike',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{ 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' }]\n    },\n    {\n        'tfOpName': 'Multinomial',\n        'category': 'creation',\n        'inputs': [\n            { 'start': 0, 'name': 'logits', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numSamples', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'seed', 'name': 'seed', 'type': 'number' },\n            { 'tfName': 'seed2', 'name': 'seed2', 'type': 'number' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype' },\n            { 'tfName': 'output_dtype', 'name': 'output_dtype', 'type': 'dtype' }\n        ]\n    }\n];\n//# sourceMappingURL=creation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'NonMaxSuppressionV2',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV3',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV4',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' }\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'T_threshold',\n                'name': 'threshold',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            {\n                'tfName': 'pad_to_max_output_size',\n                'name': 'padToMaxOutputSize',\n                'type': 'bool'\n            }\n        ]\n    },\n    {\n        'tfOpName': 'NonMaxSuppressionV5',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scores', 'type': 'tensor' },\n            { 'start': 2, 'name': 'maxOutputSize', 'type': 'number' },\n            { 'start': 3, 'name': 'iouThreshold', 'type': 'number' },\n            { 'start': 4, 'name': 'scoreThreshold', 'type': 'number' },\n            { 'start': 5, 'name': 'softNmsSigma', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Where',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ListDiff',\n        'category': 'dynamic',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'y', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=dynamic.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'TopKV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'k', 'type': 'number' },\n        ],\n        'attrs': [{ 'tfName': 'sorted', 'name': 'sorted', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Unique',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n    },\n    {\n        'tfOpName': 'UniqueV2',\n        'category': 'evaluation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n    },\n];\n//# sourceMappingURL=evaluation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'PlaceholderWithDefault',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'default', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'Placeholder',\n        'category': 'graph',\n        'attrs': [\n            { 'tfName': 'shape', 'name': 'shape', 'type': 'shape' },\n            { 'tfName': 'dtype', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    { 'tfOpName': 'Const', 'category': 'graph' }, {\n        'tfOpName': 'Identity',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IdentityN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Snapshot',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Rank',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Size',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'Shape',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'ShapeN',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'end': 0, 'name': 'x', 'type': 'tensors' }]\n    },\n    {\n        'tfOpName': 'Print',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'data', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'message', 'name': 'message', 'type': 'string' }, {\n                'tfName': 'first_n',\n                'name': 'firstN',\n                'type': 'number',\n                'notSupported': true\n            },\n            {\n                'tfName': 'summarize',\n                'name': 'summarize',\n                'type': 'number',\n                'defaultValue': 3\n            }\n        ]\n    },\n    { 'tfOpName': 'NoOp', 'category': 'graph', 'inputs': [] }, {\n        'tfOpName': 'StopGradient',\n        'category': 'graph',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'FakeQuantWithMinMaxVars',\n        'category': 'graph',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'min', 'name': 'min', 'type': 'number' },\n            { 'tfName': 'max', 'name': 'max', 'type': 'number' }\n        ]\n    }\n];\n//# sourceMappingURL=graph.js.map", "export const json = [\n    {\n        'tfOpName': 'HashTable',\n        'category': 'hash_table',\n        'inputs': [],\n        'attrs': [\n            { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' },\n            {\n                'tfName': 'use_node_name_sharing',\n                'name': 'useNodeNameSharing',\n                'type': 'bool'\n            },\n            { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' },\n            { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' },\n        ]\n    },\n    {\n        'tfOpName': 'HashTableV2',\n        'category': 'hash_table',\n        'inputs': [],\n        'attrs': [\n            { 'tfName': 'shared_name', 'name': 'sharedName', 'type': 'string' },\n            {\n                'tfName': 'use_node_name_sharing',\n                'name': 'useNodeNameSharing',\n                'type': 'bool'\n            },\n            { 'tfName': 'key_dtype', 'name': 'keyDType', 'type': 'dtype' },\n            { 'tfName': 'value_dtype', 'name': 'valueDType', 'type': 'dtype' },\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableImport',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'values', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableImportV2',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'values', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableFind',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LookupTableFindV2',\n        'category': 'hash_table',\n        'inputs': [\n            { 'start': 0, 'name': 'tableHandle', 'type': 'tensor' },\n            { 'start': 1, 'name': 'keys', 'type': 'tensor' },\n            { 'start': 2, 'name': 'defaultValue', 'type': 'tensor' }\n        ],\n        'attrs': [\n            { 'tfName': 'Tin', 'name': 'tIn', 'type': 'dtype', 'notSupported': true }, {\n                'tfName': 'Tout',\n                'name': 'tOut',\n                'type': 'dtype',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=hash_table.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ResizeBilinear',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ResizeNearestNeighbor',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'images', 'type': 'tensor' },\n            { 'start': 1, 'name': 'size', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'align_corners', 'name': 'alignCorners', 'type': 'bool' },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'CropAndResize',\n        'category': 'image',\n        'inputs': [\n            { 'start': 0, 'name': 'image', 'type': 'tensor' },\n            { 'start': 1, 'name': 'boxes', 'type': 'tensor' },\n            { 'start': 2, 'name': 'boxInd', 'type': 'tensor' },\n            { 'start': 3, 'name': 'cropSize', 'type': 'number[]' },\n        ],\n        'attrs': [\n            { 'tfName': 'method', 'name': 'method', 'type': 'string' }, {\n                'tfName': 'extrapolation_value',\n                'name': 'extrapolationValue',\n                'type': 'number'\n            }\n        ]\n    }\n];\n//# sourceMappingURL=image.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Equal',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'NotEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Greater',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'GreaterEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Less',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LessEqual',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalAnd',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalNot',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'LogicalOr',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Select',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'SelectV2',\n        'category': 'logical',\n        'inputs': [\n            { 'start': 0, 'name': 'condition', 'type': 'tensor' },\n            { 'start': 1, 'name': 'a', 'type': 'tensor' },\n            { 'start': 2, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=logical.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': '_FusedMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n            { 'start': 2, end: 0, 'name': 'args', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'num_args', 'name': 'numArgs', 'type': 'number' }, {\n                'tfName': 'fused_ops',\n                'name': 'fusedOps',\n                'type': 'string[]',\n                'defaultValue': []\n            },\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.0001\n            },\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'MatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'transpose_a',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'transpose_b',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMul',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'BatchMatMulV2',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'a', 'type': 'tensor' },\n            { 'start': 1, 'name': 'b', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'adj_x',\n                'name': 'transposeA',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            {\n                'tfName': 'adj_y',\n                'name': 'transposeB',\n                'type': 'bool',\n                'defaultValue': false\n            },\n            { 'tfName': 'T', 'name': 'dtype', 'type': 'dtype', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'Transpose',\n        'category': 'matrices',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'perm', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'T',\n                'name': 'dtype',\n                'type': 'dtype',\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=matrices.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FusedBatchNorm',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV2',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'FusedBatchNormV3',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'scale', 'type': 'tensor' },\n            { 'start': 2, 'name': 'offset', 'type': 'tensor' },\n            { 'start': 3, 'name': 'mean', 'type': 'tensor' },\n            { 'start': 4, 'name': 'variance', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'epsilon',\n                'name': 'epsilon',\n                'type': 'number',\n                'defaultValue': 0.001\n            },\n            {\n                'tfName': 'data_format',\n                'name': 'dataFormat',\n                'type': 'string',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'LRN',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'depth_radius',\n                'name': 'radius',\n                'type': 'number',\n                'defaultValue': 5\n            },\n            { 'tfName': 'bias', 'name': 'bias', 'type': 'number', 'defaultValue': 1.0 },\n            {\n                'tfName': 'alpha',\n                'name': 'alpha',\n                'type': 'number',\n                'defaultValue': 1.0\n            },\n            {\n                'tfName': 'beta',\n                'name': 'beta',\n                'type': 'number',\n                'defaultValue': 0.5\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Softmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'LogSoftmax',\n        'category': 'normalization',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'normalization',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': true,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=normalization.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Max',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Mean',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Min',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Sum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'All',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Any',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'ArgMax',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'ArgMin',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'Prod',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'keep_dims', 'name': 'keepDims', 'type': 'bool' }]\n    },\n    {\n        'tfOpName': 'Cumsum',\n        'category': 'reduction',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' },\n        ],\n        'attrs': [\n            { 'tfName': 'exclusive', 'name': 'exclusive', 'type': 'bool' },\n            { 'tfName': 'reverse', 'name': 'reverse', 'type': 'bool' }\n        ]\n    }\n];\n//# sourceMappingURL=reduction.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'ConcatV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': -1, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': -1, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'Concat',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 1, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n            { 'start': 0, 'name': 'axis', 'type': 'number' }\n        ],\n        'attrs': [{ 'tfName': 'N', 'name': 'n', 'type': 'number', 'defaultValue': 2 }]\n    },\n    {\n        'tfOpName': 'GatherV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Gather',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reverse',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'dims', 'type': 'bool', 'notSupported': true }\n        ]\n    },\n    {\n        'tfOpName': 'ReverseV2',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Slice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'size', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'StridedSlice',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'begin', 'type': 'number[]' },\n            { 'start': 2, 'name': 'end', 'type': 'number[]' },\n            { 'start': 3, 'name': 'strides', 'type': 'number[]' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'begin_mask',\n                'name': 'beginMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'end_mask',\n                'name': 'endMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'new_axis_mask',\n                'name': 'newAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'ellipsis_mask',\n                'name': 'ellipsisMask',\n                'type': 'number',\n                'defaultValue': 0\n            },\n            {\n                'tfName': 'shrink_axis_mask',\n                'name': 'shrinkAxisMask',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Pack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'end': 0, 'name': 'tensors', 'type': 'tensors' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'Unpack',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'tensor', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'axis', 'name': 'axis', 'type': 'number', 'defaultValue': 0 }, {\n                'tfName': 'num',\n                'name': 'num',\n                'type': 'number',\n                'defaultValue': 0,\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Tile',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'reps', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Split',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'axis', 'type': 'number', 'defaultValue': 0 },\n            { 'start': 1, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'num_split',\n                'name': 'numOrSizeSplits',\n                'type': 'number',\n                'defaultValue': 1\n            }]\n    },\n    {\n        'tfOpName': 'SplitV',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'numOrSizeSplits', 'type': 'number[]' },\n            { 'start': 2, 'name': 'axis', 'type': 'number', 'defaultValue': 0 }\n        ]\n    },\n    {\n        'tfOpName': 'ScatterNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'indices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'values', 'type': 'tensor' },\n            { 'start': 2, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'GatherNd',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'indices', 'type': 'tensor' }\n        ]\n    },\n    {\n        'tfOpName': 'SparseToDense',\n        'category': 'slice_join',\n        'inputs': [\n            { 'start': 0, 'name': 'sparseIndices', 'type': 'tensor' },\n            { 'start': 1, 'name': 'outputShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'sparseValues', 'type': 'tensor' },\n            { 'start': 3, 'name': 'defaultValue', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'validate_indices',\n                'name': 'validateIndices',\n                'type': 'bool',\n                'defaultValue': false,\n                'notSupported': true\n            }]\n    }\n];\n//# sourceMappingURL=slice_join.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'FFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'IFFT',\n        'category': 'spectral',\n        'inputs': [{ 'start': 0, 'name': 'x', 'type': 'tensor' }]\n    },\n    {\n        'tfOpName': 'RFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    },\n    {\n        'tfOpName': 'IRFFT',\n        'category': 'spectral',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' }, {\n                'start': 1,\n                'name': 'fft_length',\n                'type': 'number',\n                'notSupported': true\n            }\n        ]\n    }\n];\n//# sourceMappingURL=spectral.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const json = [\n    {\n        'tfOpName': 'Cast',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            {\n                'tfName': 'SrcT',\n                'name': 'sdtype',\n                'type': 'dtype',\n                'notSupported': true\n            },\n            { 'tfName': 'DstT', 'name': 'dtype', 'type': 'dtype' }\n        ]\n    },\n    {\n        'tfOpName': 'ExpandDims',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'axis', 'type': 'number' }\n        ]\n    },\n    {\n        'tfOpName': 'MirrorPad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{ 'tfName': 'mode', 'name': 'mode', 'type': 'string' }]\n    },\n    {\n        'tfOpName': 'Pad',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' },\n        ],\n        'attrs': [{\n                'tfName': 'constant_value',\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }]\n    },\n    {\n        'tfOpName': 'PadV2',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'padding', 'type': 'number[]' }, {\n                'start': 2,\n                'name': 'constantValue',\n                'type': 'number',\n                'defaultValue': 0\n            }\n        ]\n    },\n    {\n        'tfOpName': 'Reshape',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'Squeeze',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [{\n                'tfName': 'axis',\n                'tfDeprecatedName': 'squeeze_dims',\n                'name': 'axis',\n                'type': 'number[]'\n            }]\n    },\n    {\n        'tfOpName': 'SpaceToBatchND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'paddings', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'BatchToSpaceND',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'blockShape', 'type': 'number[]' },\n            { 'start': 2, 'name': 'crops', 'type': 'number[]' }\n        ]\n    },\n    {\n        'tfOpName': 'DepthToSpace',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n        ],\n        'attrs': [\n            { 'tfName': 'block_size', 'name': 'blockSize', 'type': 'number' },\n            { 'tfName': 'data_format', 'name': 'dataFormat', 'type': 'string' }\n        ]\n    },\n    {\n        'tfOpName': 'BroadcastTo',\n        'category': 'transformation',\n        'inputs': [\n            { 'start': 0, 'name': 'x', 'type': 'tensor' },\n            { 'start': 1, 'name': 'shape', 'type': 'number[]' },\n        ],\n        'attrs': []\n    }\n];\n//# sourceMappingURL=transformation.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as tensorflow from '../data/compiled_api';\nimport { getRegisteredOp } from './custom_op/register';\nimport { getNodeNameAndIndex } from './executors/utils';\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as hashTable from './op_list/hash_table';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as spectral from './op_list/spectral';\nimport * as transformation from './op_list/transformation';\nexport class OperationMapper {\n    // Singleton instance for the mapper\n    static get Instance() {\n        return this._instance || (this._instance = new this());\n    }\n    // Loads the op mapping from the JSON file.\n    constructor() {\n        const ops = [\n            arithmetic, basicMath, control, convolution, creation, dynamic,\n            evaluation, logical, image, graph, matrices, normalization, reduction,\n            sliceJoin, spectral, transformation, hashTable\n        ];\n        const mappersJson = [].concat(...ops.map(op => op.json));\n        this.opMappers = mappersJson.reduce((map, mapper) => {\n            map[mapper.tfOpName] = mapper;\n            return map;\n        }, {});\n    }\n    // Converts the model inference graph from Tensorflow GraphDef to local\n    // representation for TensorFlow.js API\n    transformGraph(graph, signature = {}) {\n        const tfNodes = graph.node;\n        const placeholders = [];\n        const weights = [];\n        const initNodes = [];\n        const nodes = tfNodes.reduce((map, node) => {\n            map[node.name] = this.mapNode(node);\n            if (node.op.startsWith('Placeholder')) {\n                placeholders.push(map[node.name]);\n            }\n            else if (node.op === 'Const') {\n                weights.push(map[node.name]);\n            }\n            else if (node.input == null || node.input.length === 0) {\n                initNodes.push(map[node.name]);\n            }\n            return map;\n        }, {});\n        let inputs = [];\n        const outputs = [];\n        let inputNodeNameToKey = {};\n        let outputNodeNameToKey = {};\n        if (signature != null) {\n            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n        }\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        // if signature has not outputs set, add any node that does not have\n        // outputs.\n        if (Object.keys(outputNodeNameToKey).length === 0) {\n            allNodes.forEach(key => {\n                const node = nodes[key];\n                if (node.children.length === 0) {\n                    outputs.push(node);\n                }\n            });\n        }\n        else {\n            Object.keys(outputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node != null) {\n                    node.signatureKey = outputNodeNameToKey[name];\n                    outputs.push(node);\n                }\n            });\n        }\n        if (Object.keys(inputNodeNameToKey).length > 0) {\n            Object.keys(inputNodeNameToKey).forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                const node = nodes[nodeName];\n                if (node) {\n                    node.signatureKey = inputNodeNameToKey[name];\n                    inputs.push(node);\n                }\n            });\n        }\n        else {\n            inputs = placeholders;\n        }\n        let functions = {};\n        if (graph.library != null && graph.library.function != null) {\n            functions = graph.library.function.reduce((functions, func) => {\n                functions[func.signature.name] = this.mapFunction(func);\n                return functions;\n            }, {});\n        }\n        const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n        if (initNodes.length > 0) {\n            result.initNodes = initNodes;\n        }\n        return result;\n    }\n    mapSignatureEntries(entries) {\n        return Object.keys(entries || {})\n            .reduce((prev, curr) => {\n            prev[entries[curr].name] = curr;\n            return prev;\n        }, {});\n    }\n    mapNode(node) {\n        // Unsupported ops will cause an error at run-time (not parse time), since\n        // they may not be used by the actual execution subgraph.\n        const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n        if (node.attr == null) {\n            node.attr = {};\n        }\n        const newNode = {\n            name: node.name,\n            op: node.op,\n            category: mapper.category,\n            inputNames: (node.input ||\n                []).map(input => input.startsWith('^') ? input.substr(1) : input),\n            inputs: [],\n            children: [],\n            inputParams: {},\n            attrParams: {},\n            rawAttrs: node.attr\n        };\n        if (mapper.inputs != null) {\n            newNode.inputParams =\n                mapper.inputs.reduce((map, param) => {\n                    map[param.name] = {\n                        type: param.type,\n                        inputIndexStart: param.start,\n                        inputIndexEnd: param.end\n                    };\n                    return map;\n                }, {});\n        }\n        if (mapper.attrs != null) {\n            newNode.attrParams =\n                mapper.attrs.reduce((map, param) => {\n                    const type = param.type;\n                    let value = undefined;\n                    switch (param.type) {\n                        case 'string':\n                            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'string[]':\n                            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number':\n                            value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0));\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'number[]':\n                            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool':\n                            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'bool[]':\n                            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape':\n                            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'shape[]':\n                            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype':\n                            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'dtype[]':\n                            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'func':\n                            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n                            if (value === undefined && !!param.tfDeprecatedName) {\n                                value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n                            }\n                            break;\n                        case 'tensor':\n                        case 'tensors':\n                            break;\n                        default:\n                            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n                    }\n                    map[param.name] = { value, type };\n                    return map;\n                }, {});\n        }\n        return newNode;\n    }\n    // map the TFunctionDef to TFJS graph object\n    mapFunction(functionDef) {\n        const tfNodes = functionDef.nodeDef;\n        const placeholders = [];\n        const weights = [];\n        let nodes = {};\n        if (tfNodes != null) {\n            nodes = tfNodes.reduce((map, node) => {\n                map[node.name] = this.mapNode(node);\n                if (node.op === 'Const') {\n                    weights.push(map[node.name]);\n                }\n                return map;\n            }, {});\n        }\n        const inputs = [];\n        const outputs = [];\n        functionDef.signature.inputArg.forEach(arg => {\n            const [nodeName,] = getNodeNameAndIndex(arg.name);\n            const node = {\n                name: nodeName,\n                op: 'Placeholder',\n                inputs: [],\n                inputNames: [],\n                category: 'graph',\n                inputParams: {},\n                attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } },\n                children: []\n            };\n            node.signatureKey = arg.name;\n            inputs.push(node);\n            nodes[nodeName] = node;\n        });\n        const allNodes = Object.keys(nodes);\n        allNodes.forEach(key => {\n            const node = nodes[key];\n            node.inputNames.forEach(name => {\n                const [nodeName,] = getNodeNameAndIndex(name);\n                node.inputs.push(nodes[nodeName]);\n                nodes[nodeName].children.push(node);\n            });\n        });\n        const returnNodeMap = functionDef.ret;\n        functionDef.signature.outputArg.forEach(output => {\n            const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n            const node = nodes[nodeName];\n            if (node != null) {\n                node.defaultOutput = index;\n                outputs.push(node);\n            }\n        });\n        const signature = this.mapArgsToSignature(functionDef);\n        return { nodes, inputs, outputs, weights, placeholders, signature };\n    }\n    mapArgsToSignature(functionDef) {\n        return {\n            methodName: functionDef.signature.name,\n            inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg);\n                return map;\n            }, {}),\n            outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n                return map;\n            }, {}),\n        };\n    }\n    mapArgToTensorInfo(arg, nameMap) {\n        let name = arg.name;\n        if (nameMap != null) {\n            name = nameMap[name];\n        }\n        return { name, dtype: arg.type };\n    }\n}\nexport function decodeBase64(text) {\n    const global = env().global;\n    if (typeof global.atob !== 'undefined') {\n        return global.atob(text);\n    }\n    else if (typeof Buffer !== 'undefined') {\n        return new Buffer(text, 'base64').toString();\n    }\n    else {\n        throw new Error('Unable to decode base64 in this environment. ' +\n            'Missing built-in atob() or Buffer()');\n    }\n}\nexport function parseStringParam(s, keepCase) {\n    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n    return keepCase ? value : value.toLowerCase();\n}\nexport function getStringParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param != null) {\n        return parseStringParam(param.s, keepCase);\n    }\n    return def;\n}\nexport function getBoolParam(attrs, name, def) {\n    const param = attrs[name];\n    return param ? param.b : def;\n}\nexport function getNumberParam(attrs, name, def) {\n    const param = attrs[name] || {};\n    const value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def);\n    return (typeof value === 'number') ? value : parseInt(value, 10);\n}\nexport function parseDtypeParam(value) {\n    if (typeof (value) === 'string') {\n        // tslint:disable-next-line:no-any\n        value = tensorflow.DataType[value];\n    }\n    switch (value) {\n        case tensorflow.DataType.DT_FLOAT:\n            return 'float32';\n        case tensorflow.DataType.DT_INT32:\n        case tensorflow.DataType.DT_INT64:\n        case tensorflow.DataType.DT_INT8:\n        case tensorflow.DataType.DT_UINT8:\n            return 'int32';\n        case tensorflow.DataType.DT_BOOL:\n            return 'bool';\n        case tensorflow.DataType.DT_DOUBLE:\n            return 'float32';\n        case tensorflow.DataType.DT_STRING:\n            return 'string';\n        default:\n            // Unknown dtype error will happen at runtime (instead of parse time),\n            // since these nodes might not be used by the actual subgraph execution.\n            return null;\n    }\n}\nexport function getFuncParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.func) {\n        return param.func.name;\n    }\n    return def;\n}\nexport function getDtypeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.type) {\n        return parseDtypeParam(param.type);\n    }\n    return def;\n}\nexport function getDtypeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.type) {\n        return param.list.type.map(v => parseDtypeParam(v));\n    }\n    return def;\n}\nexport function parseTensorShapeParam(shape) {\n    if (shape.unknownRank) {\n        return undefined;\n    }\n    if (shape.dim != null) {\n        return shape.dim.map(dim => (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10));\n    }\n    return [];\n}\nexport function getTensorShapeParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.shape) {\n        return parseTensorShapeParam(param.shape);\n    }\n    return def;\n}\nexport function getNumericArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param) {\n        return ((param.list.f && param.list.f.length ? param.list.f :\n            param.list.i) ||\n            [])\n            .map(v => (typeof v === 'number') ? v : parseInt(v, 10));\n    }\n    return def;\n}\nexport function getStringArrayParam(attrs, name, def, keepCase = false) {\n    const param = attrs[name];\n    if (param && param.list && param.list.s) {\n        return param.list.s.map((v) => {\n            return parseStringParam(v, keepCase);\n        });\n    }\n    return def;\n}\nexport function getTensorShapeArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.shape) {\n        return param.list.shape.map((v) => {\n            return parseTensorShapeParam(v);\n        });\n    }\n    return def;\n}\nexport function getBoolArrayParam(attrs, name, def) {\n    const param = attrs[name];\n    if (param && param.list && param.list.b) {\n        return param.list.b;\n    }\n    return def;\n}\n//# sourceMappingURL=operation_mapper.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getTensor } from '../executors/utils';\nimport { getBoolArrayParam, getBoolParam, getDtypeArrayParam, getDtypeParam, getNumberParam, getNumericArrayParam, getStringArrayParam, getStringParam, getTensorShapeArrayParam, getTensorShapeParam } from '../operation_mapper';\n/**\n * Helper class for lookup inputs and params for nodes in the model graph.\n */\nexport class NodeValueImpl {\n    constructor(node, tensorMap, context) {\n        this.node = node;\n        this.tensorMap = tensorMap;\n        this.context = context;\n        this.inputs = [];\n        this.attrs = {};\n        this.inputs = node.inputNames.map(name => this.getInput(name));\n        if (node.rawAttrs != null) {\n            this.attrs = Object.keys(node.rawAttrs)\n                .reduce((attrs, key) => {\n                attrs[key] = this.getAttr(key);\n                return attrs;\n            }, {});\n        }\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getInput(name) {\n        return getTensor(name, this.tensorMap, this.context);\n    }\n    /**\n     * Return the value of the attribute or input param.\n     * @param name String: name of attribute or input param.\n     */\n    getAttr(name, defaultValue) {\n        const value = this.node.rawAttrs[name];\n        if (value.tensor != null) {\n            return getTensor(name, this.tensorMap, this.context);\n        }\n        if (value.i != null || value.f != null) {\n            return getNumberParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.s != null) {\n            return getStringParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.b != null) {\n            return getBoolParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.shape != null) {\n            return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.type != null) {\n            return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n        }\n        if (value.list != null) {\n            if (value.list.i != null || value.list.f != null) {\n                return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.s != null) {\n                return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.shape != null) {\n                return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.b != null) {\n                return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n            if (value.list.type != null) {\n                return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n            }\n        }\n        return defaultValue;\n    }\n}\n//# sourceMappingURL=node_value_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This file exports ops used by the converters executors. By default it\n * re-exports all ops. In a custom build this is aliased to a file that will\n * only exports ops for a given model.json.\n */\nexport * from './ops';\n//# sourceMappingURL=ops_for_converter.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BiasAdd':\n        case 'AddV2':\n        case 'Add': {\n            return [tfOps.add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'AddN': {\n            return [tfOps.addN(getParamValue('tensors', node, tensorMap, context))];\n        }\n        case 'FloorMod':\n        case 'Mod':\n            return [tfOps.mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'Mul':\n            return [tfOps.mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        case 'RealDiv':\n        case 'Div': {\n            return [tfOps.div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'DivNoNan': {\n            return [tfOps.divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'FloorDiv': {\n            return [tfOps.floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Sub': {\n            return [tfOps.sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Minimum': {\n            return [tfOps.minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Maximum': {\n            return [tfOps.maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Pow': {\n            return [tfOps.pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'SquaredDifference': {\n            return [tfOps.squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'arithmetic';\n//# sourceMappingURL=arithmetic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Abs':\n        case 'ComplexAbs':\n            return [tfOps.abs(getParamValue('x', node, tensorMap, context))];\n        case 'Acos':\n            return [tfOps.acos(getParamValue('x', node, tensorMap, context))];\n        case 'Acosh':\n            return [tfOps.acosh(getParamValue('x', node, tensorMap, context))];\n        case 'Asin':\n            return [tfOps.asin(getParamValue('x', node, tensorMap, context))];\n        case 'Asinh':\n            return [tfOps.asinh(getParamValue('x', node, tensorMap, context))];\n        case 'Atan':\n            return [tfOps.atan(getParamValue('x', node, tensorMap, context))];\n        case 'Atan2':\n            return [tfOps.atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))];\n        case 'Atanh':\n            return [tfOps.atanh(getParamValue('x', node, tensorMap, context))];\n        case 'Ceil':\n            return [tfOps.ceil(getParamValue('x', node, tensorMap, context))];\n        case 'Complex':\n            return [tfOps.complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))];\n        case 'Cos':\n            return [tfOps.cos(getParamValue('x', node, tensorMap, context))];\n        case 'Cosh':\n            return [tfOps.cosh(getParamValue('x', node, tensorMap, context))];\n        case 'Elu':\n            return [tfOps.elu(getParamValue('x', node, tensorMap, context))];\n        case 'Erf':\n            return [tfOps.erf(getParamValue('x', node, tensorMap, context))];\n        case 'Exp':\n            return [tfOps.exp(getParamValue('x', node, tensorMap, context))];\n        case 'Expm1': {\n            return [tfOps.expm1(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Floor':\n            return [tfOps.floor(getParamValue('x', node, tensorMap, context))];\n        case 'Log':\n            return [tfOps.log(getParamValue('x', node, tensorMap, context))];\n        case 'Log1p': {\n            return [tfOps.log1p(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Imag':\n            return [tfOps.imag(getParamValue('x', node, tensorMap, context))];\n        case 'Neg':\n            return [tfOps.neg(getParamValue('x', node, tensorMap, context))];\n        case 'Reciprocal': {\n            return [tfOps.reciprocal(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Real':\n            return [tfOps.real(getParamValue('x', node, tensorMap, context))];\n        case 'Relu':\n            return [tfOps.relu(getParamValue('x', node, tensorMap, context))];\n        case 'Round': {\n            return [tfOps.round(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Selu':\n            return [tfOps.selu(getParamValue('x', node, tensorMap, context))];\n        case 'Sigmoid':\n            return [tfOps.sigmoid(getParamValue('x', node, tensorMap, context))];\n        case 'Sin':\n            return [tfOps.sin(getParamValue('x', node, tensorMap, context))];\n        case 'Sign': {\n            return [tfOps.sign(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sinh': {\n            return [tfOps.sinh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Softplus': {\n            return [tfOps.softplus(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Sqrt': {\n            return [tfOps.sqrt(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Square': {\n            return [tfOps.square(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tanh': {\n            return [tfOps.tanh(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'Tan':\n            return [tfOps.tan(getParamValue('x', node, tensorMap, context))];\n        case 'Relu6':\n        case 'ClipByValue':\n            return [tfOps.clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))];\n        case 'Rsqrt':\n            return [tfOps.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n        case 'Prod':\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))];\n        case 'LeakyRelu':\n            return [tfOps.leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        case 'Prelu':\n            return [tfOps.prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'basic_math';\n//# sourceMappingURL=basic_math_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * This differs from util.assertShapesMatch in that it allows values of\n * negative one, an undefined size of a dimensinon, in a shape to match\n * anything.\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = '') {\n    util.assert(shapesEqualAllowUndefinedSize(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function shapesEqualAllowUndefinedSize(n1, n2) {\n    if (n1.length !== n2.length) {\n        return false;\n    }\n    for (let i = 0; i < n1.length; i++) {\n        if (n1[i] !== -1 && n2[i] !== -1 && n1[i] !== n2[i]) {\n            return false;\n        }\n    }\n    return true;\n}\n//# sourceMappingURL=tensor_utils.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * The TensorArray object keeps an array of Tensors.  It\n * allows reading from the array and writing to the array.\n */\nexport class TensorArray {\n    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n        this.name = name;\n        this.dtype = dtype;\n        this.maxSize = maxSize;\n        this.elementShape = elementShape;\n        this.identicalElementShapes = identicalElementShapes;\n        this.dynamicSize = dynamicSize;\n        this.clearAfterRead = clearAfterRead;\n        this.tensors = [];\n        this.closed_ = false;\n        this.idTensor = scalar(0);\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    get closed() {\n        return this.closed_;\n    }\n    /**\n     * Dispose the tensors and idTensor and mark the TensoryArray as closed.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.tensor.id)) {\n                tensor.tensor.dispose();\n            }\n        });\n        this.tensors = [];\n        this.closed_ = true;\n        this.idTensor.dispose();\n    }\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Read the value at location index in the TensorArray.\n     * @param index Number the index to read from.\n     */\n    read(index) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || index >= this.size()) {\n            throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n        }\n        const tensorWithState = this.tensors[index];\n        if (tensorWithState.cleared) {\n            throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` +\n                `(perhaps try setting clear_after_read = false?).`);\n        }\n        if (this.clearAfterRead) {\n            tensorWithState.cleared = true;\n        }\n        tensorWithState.read = true;\n        return tensorWithState.tensor;\n    }\n    /**\n     * Helper method to read multiple tensors from the specified indices.\n     */\n    readMany(indices) {\n        return indices.map(index => this.read(index));\n    }\n    /**\n     * Write value into the index of the TensorArray.\n     * @param index number the index to write to.\n     * @param tensor\n     */\n    write(index, tensor) {\n        if (this.closed_) {\n            throw new Error(`TensorArray ${this.name} has already been closed.`);\n        }\n        if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n            throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n        }\n        const t = this.tensors[index] || {};\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);\n        }\n        // Set the shape for the first time write to unknow shape tensor array\n        if (this.size() === 0 &&\n            (this.elementShape == null || this.elementShape.length === 0)) {\n            this.elementShape = tensor.shape;\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n        if (t.read) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n        }\n        if (t.written) {\n            throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n        }\n        t.tensor = tensor;\n        keep(tensor);\n        t.written = true;\n        this.tensors[index] = t;\n    }\n    /**\n     * Helper method to write multiple tensors to the specified indices.\n     */\n    writeMany(indices, tensors) {\n        if (indices.length !== tensors.length) {\n            throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` +\n                `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n        }\n        indices.forEach((i, index) => this.write(i, tensors[index]));\n    }\n    /**\n     * Return selected values in the TensorArray as a packed Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param [indices] number[] Optional. Taking values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size(). If not specified returns\n     *    all tensors in the original order.\n     * @param [dtype]\n     */\n    gather(indices, dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n        }\n        if (!indices) {\n            indices = [];\n            for (let i = 0; i < this.size(); i++) {\n                indices.push(i);\n            }\n        }\n        else {\n            indices = indices.slice(0, this.size());\n        }\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        // Read all the PersistentTensors into a vector to keep track of\n        // their memory.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: ');\n        return stack(tensors, 0);\n    }\n    /**\n     * Return the values in the TensorArray as a concatenated Tensor.\n     */\n    concat(dtype) {\n        if (!!dtype && dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n        }\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        const indices = [];\n        for (let i = 0; i < this.size(); i++) {\n            indices.push(i);\n        }\n        // Collect all the tensors from the tensors array.\n        const tensors = this.readMany(indices);\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n        return concat(tensors, 0);\n    }\n    /**\n     * Scatter the values of a Tensor in specific indices of a TensorArray.\n     * @param indices nummber[] values in [0, max_value). If the\n     *    TensorArray is not dynamic, max_value=size().\n     * @param tensor Tensor input tensor.\n     */\n    scatter(indices, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        if (indices.length !== tensor.shape[0]) {\n            throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n        }\n        const maxIndex = Math.max(...indices);\n        if (!this.dynamicSize && maxIndex >= this.maxSize) {\n            throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n        }\n        this.writeMany(indices, unstack(tensor, 0));\n    }\n    /**\n     * Split the values of a Tensor into the TensorArray.\n     * @param length number[] with the lengths to use when splitting value along\n     *    its first dimension.\n     * @param tensor Tensor, the tensor to split.\n     */\n    split(length, tensor) {\n        if (tensor.dtype !== this.dtype) {\n            throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);\n        }\n        let totalLength = 0;\n        const cumulativeLengths = length.map(len => {\n            totalLength += len;\n            return totalLength;\n        });\n        if (totalLength !== tensor.shape[0]) {\n            throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n        }\n        if (!this.dynamicSize && length.length !== this.maxSize) {\n            throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` +\n                'and the TensorArray is not marked as dynamically resizeable');\n        }\n        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n        const tensors = [];\n        tidy(() => {\n            tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n            for (let i = 0; i < length.length; ++i) {\n                const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n                const indices = [0, previousLength, 0];\n                const sizes = [1, length[i], elementPerRow];\n                tensors[i] = reshape(slice(tensor, indices, sizes), this.elementShape);\n            }\n            return tensors;\n        });\n        const indices = [];\n        for (let i = 0; i < length.length; i++) {\n            indices[i] = i;\n        }\n        this.writeMany(indices, tensors);\n    }\n}\n//# sourceMappingURL=tensor_array.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { concat, keep, reshape, scalar, slice, stack, tensor, tidy, unstack } from '@tensorflow/tfjs-core';\nimport { assertShapesMatchAllowUndefinedSize } from './tensor_utils';\n/**\n * TensorList stores a container of `tf.Tensor` objects, which are accessible\n * via tensors field.\n *\n * In order to get a copy of the underlying list, use the copy method:\n * ```\n *    TensorList b = a.copy();\n *    b.tensors().pushBack(t);  // This does not modify a.tensors().\n * ```\n *\n * Note that this is not a deep copy: the memory locations of the underlying\n * tensors will still point to the same locations of the corresponding tensors\n * in the original.\n */\nexport class TensorList {\n    /**\n     *\n     * @param tensors list of tensors\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1\n     *   meaning that the size of `tensors` is unbounded.\n     */\n    constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n        this.tensors = tensors;\n        this.elementShape = elementShape;\n        this.elementDtype = elementDtype;\n        if (tensors != null) {\n            tensors.forEach(tensor => {\n                if (elementDtype !== tensor.dtype) {\n                    throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);\n                }\n                assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: ');\n                keep(tensor);\n            });\n        }\n        this.idTensor = scalar(0);\n        this.maxNumElements = maxNumElements;\n        keep(this.idTensor);\n    }\n    get id() {\n        return this.idTensor.id;\n    }\n    /**\n     * Get a new TensorList containing a copy of the underlying tensor container.\n     */\n    copy() {\n        return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n    }\n    /**\n     * Dispose the tensors and idTensor and clear the tensor list.\n     */\n    clearAndClose(keepIds) {\n        this.tensors.forEach(tensor => {\n            if (keepIds == null || !keepIds.has(tensor.id)) {\n                tensor.dispose();\n            }\n        });\n        this.tensors.length = 0;\n        this.idTensor.dispose();\n    }\n    /**\n     * The size of the tensors in the tensor list.\n     */\n    size() {\n        return this.tensors.length;\n    }\n    /**\n     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)\n     * tf.Tensor.\n     * @param elementShape shape of each tensor\n     * @param elementDtype data type of each tensor\n     * @param numElements the number of elements to stack\n     */\n    stack(elementShape, elementDtype, numElements = -1) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (numElements !== -1 && this.tensors.length !== numElements) {\n            throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: ');\n        return tidy(() => {\n            const reshapedTensors = this.tensors.map(tensor => reshape(tensor, elementShape));\n            return stack(reshapedTensors, 0);\n        });\n    }\n    /**\n     * Pop a tensor from the end of the list.\n     * @param elementShape shape of the tensor\n     * @param elementDtype data type of the tensor\n     */\n    popBack(elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (this.size() === 0) {\n            throw new Error('Trying to pop from an empty list.');\n        }\n        const tensor = this.tensors.pop();\n        assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: ');\n        return reshape(tensor, elementShape);\n    }\n    /**\n     * Push a tensor to the end of the list.\n     * @param tensor Tensor to be pushed.\n     */\n    pushBack(tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: ');\n        if (this.maxNumElements === this.size()) {\n            throw new Error(`Trying to push element into a full list.`);\n        }\n        keep(tensor);\n        this.tensors.push(tensor);\n    }\n    /**\n     * Update the size of the list.\n     * @param size the new size of the list.\n     */\n    resize(size) {\n        if (size < 0) {\n            throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n        }\n        if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n            throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n        }\n        this.tensors.length = size;\n    }\n    /**\n     * Retrieve the element at the provided index\n     * @param elementShape shape of the tensor\n     * @param elementDtype dtype of the tensor\n     * @param elementIndex index of the tensor\n     */\n    getItem(elementIndex, elementShape, elementDtype) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 || elementIndex > this.tensors.length) {\n            throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n        }\n        if (this.tensors[elementIndex] == null) {\n            throw new Error(`element at index ${elementIndex} is null.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: ');\n        return this.tensors[elementIndex];\n    }\n    /**\n     * Set the tensor at the index\n     * @param elementIndex index of the tensor\n     * @param tensor the tensor to be inserted into the list\n     */\n    setItem(elementIndex, tensor) {\n        if (tensor.dtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);\n        }\n        if (elementIndex < 0 ||\n            this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n            throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: ');\n        keep(tensor);\n        this.tensors[elementIndex] = tensor;\n    }\n    /**\n     * Return selected values in the TensorList as a stacked Tensor. All of\n     * selected values must have been written and their shapes must all match.\n     * @param indices indices of tensors to gather\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    gather(indices, elementDtype, elementShape) {\n        if (elementDtype !== this.elementDtype) {\n            throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        // When indices is greater than the size of the list, indices beyond the\n        // size of the list are ignored.\n        indices = indices.slice(0, this.size());\n        if (indices.length === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = indices.map(i => reshape(this.tensors[i], elementShape));\n            return stack(tensors, 0);\n        });\n    }\n    /**\n     * Return the values in the TensorList as a concatenated Tensor.\n     * @param elementDtype output tensor dtype\n     * @param elementShape output tensor element shape\n     */\n    concat(elementDtype, elementShape) {\n        if (!!elementDtype && elementDtype !== this.elementDtype) {\n            throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: ');\n        if (this.size() === 0) {\n            return tensor([], [0].concat(this.elementShape));\n        }\n        return tidy(() => {\n            const tensors = this.tensors.map(t => reshape(t, elementShape));\n            return concat(tensors, 0);\n        });\n    }\n}\n/**\n * Creates a TensorList which, when stacked, has the value of tensor.\n * @param tensor from tensor\n * @param elementShape output tensor element shape\n */\nexport function fromTensor(tensor, elementShape, elementDtype) {\n    const dtype = tensor.dtype;\n    if (tensor.shape.length < 1) {\n        throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);\n    }\n    if (tensor.dtype !== elementDtype) {\n        throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);\n    }\n    const outputShape = tensor.shape.slice(1);\n    assertShapesMatchAllowUndefinedSize(outputShape, elementShape, 'TensorList shape mismatch: ');\n    const tensorList = unstack(tensor);\n    return new TensorList(tensorList, elementShape, dtype);\n}\n/**\n * Return a TensorList of the given size with empty elements.\n * @param elementShape the shape of the future elements of the list\n * @param elementDtype the desired type of elements in the list\n * @param numElements the number of elements to reserve\n */\nexport function reserve(elementShape, elementDtype, numElements) {\n    return new TensorList([], elementShape, elementDtype, numElements);\n}\n/**\n * Put tensors at specific indices of a stacked tensor into a TensorList.\n * @param indices list of indices on how to scatter the tensor.\n * @param tensor input tensor.\n * @param elementShape the shape of the future elements of the list\n * @param numElements the number of elements to scatter\n */\nexport function scatter(tensor, indices, elementShape, numElements) {\n    if (indices.length !== tensor.shape[0]) {\n        throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n        throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n    }\n    const list = new TensorList([], elementShape, tensor.dtype, numElements);\n    const tensors = unstack(tensor, 0);\n    indices.forEach((value, index) => {\n        list.setItem(value, tensors[index]);\n    });\n    return list;\n}\n/**\n * Split the values of a Tensor into a TensorList.\n * @param length the lengths to use when splitting value along\n *    its first dimension.\n * @param tensor the tensor to split.\n * @param elementShape the shape of the future elements of the list\n */\nexport function split(tensor, length, elementShape) {\n    let totalLength = 0;\n    const cumulativeLengths = length.map(len => {\n        totalLength += len;\n        return totalLength;\n    });\n    if (totalLength !== tensor.shape[0]) {\n        throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor.shape}`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;\n    const tensors = tidy(() => {\n        const tensors = [];\n        tensor = reshape(tensor, [1, totalLength, elementPerRow]);\n        for (let i = 0; i < length.length; ++i) {\n            const previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1];\n            const indices = [0, previousLength, 0];\n            const sizes = [1, length[i], elementPerRow];\n            tensors[i] = reshape(slice(tensor, indices, sizes), elementShape);\n        }\n        tensor.dispose();\n        return tensors;\n    });\n    const list = new TensorList([], elementShape, tensor.dtype, length.length);\n    for (let i = 0; i < tensors.length; i++) {\n        list.setItem(i, tensors[i]);\n    }\n    return list;\n}\n//# sourceMappingURL=tensor_list.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { scalar } from '@tensorflow/tfjs-core';\nimport { TensorArray } from '../../executor/tensor_array';\nimport { fromTensor, reserve, scatter, split } from '../../executor/tensor_list';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'If':\n        case 'StatelessIf': {\n            const thenFunc = getParamValue('thenBranch', node, tensorMap, context);\n            const elseFunc = getParamValue('elseBranch', node, tensorMap, context);\n            const cond = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            const condValue = await cond.data();\n            if (condValue[0]) {\n                return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n            else {\n                return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n            }\n        }\n        case 'While':\n        case 'StatelessWhile': {\n            const bodyFunc = getParamValue('body', node, tensorMap, context);\n            const condFunc = getParamValue('cond', node, tensorMap, context);\n            const args = getParamValue('args', node, tensorMap, context);\n            // Calculate the condition of the loop\n            const condResult = (await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap));\n            const argIds = args.map(tensor => tensor.id);\n            let condValue = await condResult[0].data();\n            // Dispose the intermediate tensors for condition function\n            condResult.forEach(tensor => {\n                if (!tensor.kept && argIds.indexOf(tensor.id) === -1) {\n                    tensor.dispose();\n                }\n            });\n            let result = args;\n            while (condValue[0]) {\n                // Record the previous result for intermediate tensor tracking\n                const origResult = result;\n                // Execution the body of the loop\n                result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n                const resultIds = result.map(tensor => tensor.id);\n                // Dispose the intermediate tensor for body function that is not global\n                // kept, not input/output of the body function\n                origResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n                // Recalcuate the condition of the loop using the latest results.\n                const condResult = (await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap));\n                condValue = await condResult[0].data();\n                // Dispose the intermediate tensors for condition function\n                condResult.forEach(tensor => {\n                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1 &&\n                        resultIds.indexOf(tensor.id) === -1) {\n                        tensor.dispose();\n                    }\n                });\n            }\n            return result;\n        }\n        case 'LoopCond': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            return [cloneTensor(pred)];\n        }\n        case 'Switch': {\n            const pred = getParamValue('pred', node, tensorMap, context);\n            let data = getParamValue('data', node, tensorMap, context);\n            if (!data.kept) {\n                data = cloneTensor(data);\n            }\n            // Outputs nodes :0 => false, :1 => true\n            return (await pred.data())[0] ? [undefined, data] : [data, undefined];\n        }\n        case 'Merge': {\n            const inputName = node.inputNames.find(name => getTensor(name, tensorMap, context) !== undefined);\n            if (inputName) {\n                const data = getTensor(inputName, tensorMap, context);\n                return [cloneTensor(data)];\n            }\n            return undefined;\n        }\n        case 'Enter': {\n            const frameId = getParamValue('frameName', node, tensorMap, context);\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.enterFrame(frameId);\n            return [cloneTensor(data)];\n        }\n        case 'Exit': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.exitFrame();\n            return [cloneTensor(data)];\n        }\n        case 'NextIteration': {\n            const data = getParamValue('tensor', node, tensorMap, context);\n            context.nextIteration();\n            return [cloneTensor(data)];\n        }\n        case 'TensorArrayV3': {\n            const size = getParamValue('size', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const dynamicSize = getParamValue('dynamicSize', node, tensorMap, context);\n            const clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context);\n            const identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context);\n            const name = getParamValue('name', node, tensorMap, context);\n            const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n            context.addTensorArray(tensorArray);\n            return [tensorArray.idTensor, scalar(1.0)];\n        }\n        case 'TensorArrayWriteV3': {\n            const id = getParamValue('tensorArrayId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const writeTensorArray = context.getTensorArray(id.id);\n            writeTensorArray.write(index, writeTensor);\n            return [writeTensorArray.idTensor];\n        }\n        case 'TensorArrayReadV3': {\n            const readId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const readTensorArray = context.getTensorArray(readId.id);\n            return [readTensorArray.read(readIndex)];\n        }\n        case 'TensorArrayGatherV3': {\n            const gatherId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const gatherDtype = getParamValue('dtype', node, tensorMap, context);\n            const gatherTensorArray = context.getTensorArray(gatherId.id);\n            return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n        }\n        case 'TensorArrayScatterV3': {\n            const scatterId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const scatterTensorArray = context.getTensorArray(scatterId.id);\n            scatterTensorArray.scatter(scatterIndices, scatterTensor);\n            return [scatterTensorArray.idTensor];\n        }\n        case 'TensorArrayConcatV3': {\n            const concatId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const concatTensorArray = context.getTensorArray(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            return [concatTensorArray.concat(concatDtype)];\n        }\n        case 'TensorArraySplitV3': {\n            const splitId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const splitTensorArray = context.getTensorArray(splitId.id);\n            splitTensorArray.split(lengths, splitTensor);\n            return [splitTensorArray.idTensor];\n        }\n        case 'TensorArraySizeV3': {\n            const sizeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const sizeTensorArray = context.getTensorArray(sizeId.id);\n            return [scalar(sizeTensorArray.size(), 'int32')];\n        }\n        case 'TensorArrayCloseV3': {\n            const closeId = getParamValue('tensorArrayId', node, tensorMap, context);\n            const closeTensorArray = context.getTensorArray(closeId.id);\n            closeTensorArray.clearAndClose();\n            return [closeTensorArray.idTensor];\n        }\n        case 'TensorListSetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const index = getParamValue('index', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.setItem(index, writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGetItem': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const readIndex = getParamValue('index', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.getItem(readIndex, elementShape, elementDType)];\n        }\n        case 'TensorListScatterV2':\n        case 'TensorListScatter': {\n            const scatterIndices = getParamValue('indices', node, tensorMap, context);\n            const scatterTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListReserve': {\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = reserve(elementShape, elementDtype, numElements);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListGather': {\n            const gatherId = getParamValue('tensorListId', node, tensorMap, context);\n            const gatherIndices = getParamValue('indices', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(gatherId.id);\n            return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n        }\n        case 'TensorListStack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const numElements = getParamValue('numElements', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.stack(elementShape, elementDtype, numElements)];\n        }\n        case 'TensorListFromTensor': {\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDtype = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = fromTensor(tensor, elementShape, elementDtype);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListConcat': {\n            const concatId = getParamValue('tensorListId', node, tensorMap, context);\n            const tensorList = context.getTensorList(concatId.id);\n            const concatDtype = getParamValue('dtype', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            return [tensorList.concat(concatDtype, elementShape)];\n        }\n        case 'TensorListPushBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const writeTensor = getParamValue('tensor', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            tensorList.pushBack(writeTensor);\n            return [tensorList.idTensor];\n        }\n        case 'TensorListPopBack': {\n            const idTensor = getParamValue('tensorListId', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const elementDType = getParamValue('elementDType', node, tensorMap, context);\n            const tensorList = context.getTensorList(idTensor.id);\n            return [tensorList.popBack(elementShape, elementDType)];\n        }\n        case 'TensorListSplit': {\n            const splitTensor = getParamValue('tensor', node, tensorMap, context);\n            const elementShape = getParamValue('elementShape', node, tensorMap, context);\n            const lengths = getParamValue('lengths', node, tensorMap, context);\n            const tensorList = split(splitTensor, lengths, elementShape);\n            context.addTensorList(tensorList);\n            return [tensorList.idTensor];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'control';\n//# sourceMappingURL=control_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getPadding, getParamValue } from './utils';\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n    const isBiasAdd = extraOp === 'biasadd';\n    const isPrelu = activationFunc === 'prelu';\n    const isBatchNorm = extraOp === 'fusedbatchnorm';\n    const numArgs = getParamValue('numArgs', node, tensorMap, context);\n    if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n                'must have two extra arguments: bias and alpha.');\n        }\n        if (!isPrelu && numArgs !== 1) {\n            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n                'one extra argument: bias.');\n        }\n    }\n    if (isBatchNorm) {\n        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.');\n    }\n    const stride = getParamValue('strides', node, tensorMap, context);\n    const pad = getPadding(node, tensorMap, context);\n    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n        .toUpperCase();\n    const dilations = getParamValue('dilations', node, tensorMap, context);\n    const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n    return {\n        stride,\n        pad,\n        dataFormat,\n        dilations,\n        biasArg,\n        preluArg,\n        activationFunc\n    };\n}\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Conv1D': {\n            const stride = getParamValue('stride', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilation = getParamValue('dilation', node, tensorMap, context);\n            return [tfOps.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];\n        }\n        case 'Conv2D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.conv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'FusedDepthwiseConv2dNative': {\n            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n            return [tfOps.fused.depthwiseConv2d({\n                    x: getParamValue('x', node, tensorMap, context),\n                    filter: getParamValue('filter', node, tensorMap, context),\n                    strides: [stride[1], stride[2]],\n                    pad: pad,\n                    dataFormat: dataFormat,\n                    dilations: [dilations[1], dilations[2]],\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            return [tfOps.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getPadding(node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            return [tfOps.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)\n                .toUpperCase();\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            return [tfOps.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPool': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];\n        }\n        case 'MaxPoolWithArgmax': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);\n            const { result, indexes } = tfOps.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);\n            return [result, indexes];\n        }\n        case 'AvgPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'MaxPool3D': {\n            const stride = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);\n            return [tfOps.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];\n        }\n        case 'Dilation2D': {\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const pad = getParamValue('pad', node, tensorMap, context);\n            const dilations = getParamValue('dilations', node, tensorMap, context);\n            // strides: [1, stride_height, stride_width, 1].\n            const strideHeight = strides[1];\n            const strideWidth = strides[2];\n            // dilations: [1, dilation_height, dilation_width, 1].\n            const dilationHeight = dilations[1];\n            const dilationWidth = dilations[2];\n            return [tfOps.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'convolution';\n//# sourceMappingURL=convolution_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Fill': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const dtype = getParamValue('dtype', node, tensorMap, context);\n            const value = getParamValue('value', node, tensorMap, context);\n            return [tfOps.fill(shape, value, dtype)];\n        }\n        case 'LinSpace': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const num = getParamValue('num', node, tensorMap, context);\n            return [tfOps.linspace(start, stop, num)];\n        }\n        case 'Multinomial': {\n            const logits = getParamValue('logits', node, tensorMap, context);\n            const numSamples = getParamValue('numSamples', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.multinomial(logits, numSamples, seed)];\n        }\n        case 'OneHot': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const depth = getParamValue('depth', node, tensorMap, context);\n            const onValue = getParamValue('onValue', node, tensorMap, context);\n            const offValue = getParamValue('offValue', node, tensorMap, context);\n            return [tfOps.oneHot(indices, depth, onValue, offValue)];\n        }\n        case 'Ones': {\n            return [tfOps.ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'OnesLike': {\n            return [tfOps.onesLike(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RandomUniform': {\n            return [tfOps.randomUniform(\n                // tslint:disable-next-line:no-any\n                getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'Range': {\n            const start = getParamValue('start', node, tensorMap, context);\n            const stop = getParamValue('stop', node, tensorMap, context);\n            const step = getParamValue('step', node, tensorMap, context);\n            return [tfOps.range(start, stop, step, getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'TruncatedNormal': {\n            const shape = getParamValue('shape', node, tensorMap, context);\n            const mean = getParamValue('mean', node, tensorMap, context);\n            const stdDev = getParamValue('stdDev', node, tensorMap, context);\n            const seed = getParamValue('seed', node, tensorMap, context);\n            return [tfOps.truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)];\n        }\n        case 'Zeros': {\n            return [tfOps.zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ZerosLike': {\n            return [tfOps.zerosLike(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'creation';\n//# sourceMappingURL=creation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nfunction nmsParams(node, tensorMap, context) {\n    const boxes = getParamValue('boxes', node, tensorMap, context);\n    const scores = getParamValue('scores', node, tensorMap, context);\n    const maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context);\n    const iouThreshold = getParamValue('iouThreshold', node, tensorMap, context);\n    const scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context);\n    const softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context);\n    return {\n        boxes,\n        scores,\n        maxOutputSize,\n        iouThreshold,\n        scoreThreshold,\n        softNmsSigma\n    };\n}\nexport const executeOp = async (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'NonMaxSuppressionV5': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n            return [result.selectedIndices, result.selectedScores];\n        }\n        case 'NonMaxSuppressionV4': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            const padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context);\n            const result = await tfOps.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n            return [result.selectedIndices, result.validOutputs];\n        }\n        case 'NonMaxSuppressionV3':\n        case 'NonMaxSuppressionV2': {\n            const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n            return [await tfOps.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n        }\n        case 'Where': {\n            const condition = tfOps.cast(getParamValue('condition', node, tensorMap, context), 'bool');\n            const result = [await tfOps.whereAsync(condition)];\n            condition.dispose();\n            return result;\n        }\n        case 'ListDiff': {\n            return tfOps.setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context));\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'dynamic';\n//# sourceMappingURL=dynamic_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'TopKV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const k = getParamValue('k', node, tensorMap, context);\n            const sorted = getParamValue('sorted', node, tensorMap, context);\n            const result = tfOps.topk(x, k, sorted);\n            return [result.values, result.indices];\n        }\n        case 'Unique': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const result = tfOps.unique(x);\n            return [result.values, result.indices];\n        }\n        case 'UniqueV2': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const result = tfOps.unique(x, axis);\n            return [result.values, result.indices];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'evaluation';\n//# sourceMappingURL=evaluation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { cloneTensor, getParamValue, getTensor } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Const': {\n            return tensorMap[node.name];\n        }\n        case 'PlaceholderWithDefault':\n            const def = getParamValue('default', node, tensorMap, context);\n            return [getTensor(node.name, tensorMap, context) || def];\n        case 'Placeholder':\n            return [getTensor(node.name, tensorMap, context)];\n        case 'Identity':\n        case 'StopGradient':\n        case 'FakeQuantWithMinMaxVars': { // This op is currently ignored.\n            const data = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(data)];\n        }\n        case 'IdentityN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => cloneTensor(t));\n        case 'Snapshot':\n            const snapshot = getParamValue('x', node, tensorMap, context);\n            return [cloneTensor(snapshot)];\n        case 'Shape':\n            return [tfOps.tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')];\n        case 'ShapeN':\n            return getParamValue('x', node, tensorMap, context)\n                .map((t) => tfOps.tensor1d(t.shape));\n        case 'Size':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).size, 'int32')];\n        case 'Rank':\n            return [tfOps.scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')];\n        case 'NoOp':\n            return [tfOps.scalar(1)];\n        case 'Print':\n            const input = getParamValue('x', node, tensorMap, context);\n            const data = getParamValue('data', node, tensorMap, context);\n            const message = getParamValue('message', node, tensorMap, context);\n            const summarize = getParamValue('summarize', node, tensorMap, context);\n            console.warn('The graph has a tf.print() operation,' +\n                'usually used for debugging, which slows down performance.');\n            console.log(message);\n            for (let i = 0; i < data.length; i++) {\n                console.log(Array.prototype.slice.call(data[i].dataSync())\n                    .slice(0, summarize));\n            }\n            return [input];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'graph';\n//# sourceMappingURL=graph_executor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { keep, scalar, stack, tidy, unstack, util } from '@tensorflow/tfjs-core';\n/**\n * Hashtable contains a set of tensors, which can be accessed by key.\n */\nexport class HashTable {\n    /**\n     * Constructor of HashTable. Creates a hash table.\n     *\n     * @param keyDType `dtype` of the table keys.\n     * @param valueDType `dtype` of the table values.\n     */\n    constructor(keyDType, valueDType) {\n        this.keyDType = keyDType;\n        this.valueDType = valueDType;\n        this.handle = scalar(0);\n        // tslint:disable-next-line: no-any\n        this.tensorMap = new Map();\n        keep(this.handle);\n    }\n    get id() {\n        return this.handle.id;\n    }\n    /**\n     * Dispose the tensors and handle and clear the hashtable.\n     */\n    clearAndClose() {\n        this.tensorMap.forEach(value => value.dispose());\n        this.tensorMap.clear();\n        this.handle.dispose();\n    }\n    /**\n     * The number of items in the hash table.\n     */\n    size() {\n        return this.tensorMap.size;\n    }\n    /**\n     * Replaces the contents of the table with the specified keys and values.\n     * @param keys Keys to store in the hashtable.\n     * @param values Values to store in the hashtable.\n     */\n    async import(keys, values) {\n        this.checkKeyAndValueTensor(keys, values);\n        // We only store the primitive values of the keys, this allows lookup\n        // to be O(1).\n        const $keys = await keys.data();\n        // Clear the hashTable before inserting new values.\n        this.tensorMap.forEach(value => value.dispose());\n        this.tensorMap.clear();\n        return tidy(() => {\n            const $values = unstack(values);\n            const keysLength = $keys.length;\n            const valuesLength = $values.length;\n            util.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ` +\n                `${keysLength} elements, the values has ${valuesLength} ` +\n                `elements.`);\n            for (let i = 0; i < keysLength; i++) {\n                const key = $keys[i];\n                const value = $values[i];\n                keep(value);\n                this.tensorMap.set(key, value);\n            }\n            return this.handle;\n        });\n    }\n    /**\n     * Looks up keys in a hash table, outputs the corresponding values.\n     *\n     * Performs batch lookups, for every element in the key tensor, `find`\n     * stacks the corresponding value into the return tensor.\n     *\n     * If an element is not present in the table, the given `defaultValue` is\n     * used.\n     *\n     * @param keys Keys to look up. Must have the same type as the keys of the\n     *     table.\n     * @param defaultValue The scalar `defaultValue` is the value output for keys\n     *     not present in the table. It must also be of the same type as the\n     *     table values.\n     */\n    async find(keys, defaultValue) {\n        this.checkKeyAndValueTensor(keys, defaultValue);\n        const $keys = await keys.data();\n        return tidy(() => {\n            const result = [];\n            for (let i = 0; i < $keys.length; i++) {\n                const key = $keys[i];\n                const value = this.findWithDefault(key, defaultValue);\n                result.push(value);\n            }\n            return stack(result);\n        });\n    }\n    // tslint:disable-next-line: no-any\n    findWithDefault(key, defaultValue) {\n        const result = this.tensorMap.get(key);\n        return result != null ? result : defaultValue;\n    }\n    checkKeyAndValueTensor(key, value) {\n        if (key.dtype !== this.keyDType) {\n            throw new Error(`Expect key dtype ${this.keyDType}, but got ` +\n                `${key.dtype}`);\n        }\n        if (value.dtype !== this.valueDType) {\n            throw new Error(`Expect value dtype ${this.valueDType}, but got ` +\n                `${value.dtype}`);\n        }\n    }\n}\n//# sourceMappingURL=hash_table.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { HashTable } from '../../executor/hash_table';\nimport { getParamValue } from './utils';\nexport const executeOp = async (node, tensorMap, context, resourceManager) => {\n    switch (node.op) {\n        case 'HashTable':\n        case 'HashTableV2': {\n            const keyDType = getParamValue('keyDType', node, tensorMap, context);\n            const valueDType = getParamValue('valueDType', node, tensorMap, context);\n            const hashTable = new HashTable(keyDType, valueDType);\n            resourceManager.addHashTable(node.name, hashTable);\n            return [hashTable.handle];\n        }\n        case 'LookupTableImport':\n        case 'LookupTableImportV2': {\n            const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n            const keys = getParamValue('keys', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const hashTable = resourceManager.getHashTableById(handle.id);\n            return [await hashTable.import(keys, values)];\n        }\n        case 'LookupTableFind':\n        case 'LookupTableFindV2': {\n            const handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager);\n            const keys = getParamValue('keys', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            const hashTable = resourceManager.getHashTableById(handle.id);\n            return [await hashTable.find(keys, defaultValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'hash_table';\n//# sourceMappingURL=hash_table_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ResizeBilinear': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'ResizeNearestNeighbor': {\n            const images = getParamValue('images', node, tensorMap, context);\n            const size = getParamValue('size', node, tensorMap, context);\n            const alignCorners = getParamValue('alignCorners', node, tensorMap, context);\n            return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners)];\n        }\n        case 'CropAndResize': {\n            const image = getParamValue('image', node, tensorMap, context);\n            const boxes = getParamValue('boxes', node, tensorMap, context);\n            const boxInd = getParamValue('boxInd', node, tensorMap, context);\n            const cropSize = getParamValue('cropSize', node, tensorMap, context);\n            const method = getParamValue('method', node, tensorMap, context);\n            const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);\n            return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'image';\n//# sourceMappingURL=image_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Equal': {\n            return [tfOps.equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'NotEqual': {\n            return [tfOps.notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Greater': {\n            return [tfOps.greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'GreaterEqual': {\n            return [tfOps.greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Less': {\n            return [tfOps.less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LessEqual': {\n            return [tfOps.lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalAnd': {\n            return [tfOps.logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'LogicalNot': {\n            return [tfOps.logicalNot(getParamValue('a', node, tensorMap, context))];\n        }\n        case 'LogicalOr': {\n            return [tfOps.logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        case 'Select':\n        case 'SelectV2': {\n            return [tfOps.where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'logical';\n//# sourceMappingURL=logical_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n            return [tfOps.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];\n        case 'Transpose':\n            return [tfOps.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];\n        case '_FusedMatMul':\n            const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);\n            const isBiasAdd = extraOp === 'biasadd';\n            const isPrelu = activationFunc === 'prelu';\n            const numArgs = getParamValue('numArgs', node, tensorMap, context);\n            if (isBiasAdd) {\n                if (isPrelu && numArgs !== 2) {\n                    throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +\n                        'extra arguments: bias and alpha.');\n                }\n                if (!isPrelu && numArgs !== 1) {\n                    throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');\n                }\n            }\n            const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);\n            return [tfOps.fused.matMul({\n                    a: getParamValue('a', node, tensorMap, context),\n                    b: getParamValue('b', node, tensorMap, context),\n                    transposeA: getParamValue('transposeA', node, tensorMap, context),\n                    transposeB: getParamValue('transposeB', node, tensorMap, context),\n                    bias: biasArg,\n                    activation: activationFunc,\n                    preluActivationWeights: preluArg\n                })];\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'matrices';\n//# sourceMappingURL=matrices_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FusedBatchNorm':\n        case 'FusedBatchNormV2': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'FusedBatchNormV3': {\n            return [tfOps.batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))];\n        }\n        case 'LRN': {\n            return [tfOps.localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))];\n        }\n        case 'Softmax': {\n            return [tfOps.softmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'LogSoftmax': {\n            return [tfOps.logSoftmax(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'SparseToDense': {\n            return [tfOps.sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'normalization';\n//# sourceMappingURL=normalization_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Max': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.max(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Mean': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.mean(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Min': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.min(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Sum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.sum(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'All': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.all(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Any': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.any(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'ArgMax': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMax(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'ArgMin': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.argMin(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Prod': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const keepDims = getParamValue('keepDims', node, tensorMap, context);\n            return [tfOps.prod(getParamValue('x', node, tensorMap, context), axis, keepDims)];\n        }\n        case 'Cumsum': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const exclusive = getParamValue('exclusive', node, tensorMap, context);\n            const reverse = getParamValue('reverse', node, tensorMap, context);\n            return [tfOps.cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'reduction';\n//# sourceMappingURL=reduction_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'ConcatV2':\n        case 'Concat': {\n            const n = getParamValue('n', node, tensorMap, context);\n            const axis = getParamValue('axis', node, tensorMap, context);\n            let inputs = getParamValue('tensors', node, tensorMap, context);\n            inputs = inputs.slice(0, n);\n            return [tfOps.concat(inputs, axis)];\n        }\n        case 'GatherV2':\n        case 'Gather': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gather(input, tfOps.cast(indices, 'int32'), axis)];\n        }\n        case 'ReverseV2':\n        case 'Reverse': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const input = getParamValue('x', node, tensorMap, context);\n            return [tfOps.reverse(input, axis)];\n        }\n        case 'Slice': {\n            // tslint:disable-next-line:no-any\n            const begin = getParamValue('begin', node, tensorMap, context);\n            // tslint:disable-next-line:no-any\n            const size = getParamValue('size', node, tensorMap, context);\n            return [tfOps.slice(getParamValue('x', node, tensorMap, context), begin, size)];\n        }\n        case 'StridedSlice': {\n            const begin = getParamValue('begin', node, tensorMap, context);\n            const end = getParamValue('end', node, tensorMap, context);\n            const strides = getParamValue('strides', node, tensorMap, context);\n            const beginMask = getParamValue('beginMask', node, tensorMap, context);\n            const endMask = getParamValue('endMask', node, tensorMap, context);\n            const ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context);\n            const newAxisMask = getParamValue('newAxisMask', node, tensorMap, context);\n            const shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return [tfOps.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n        }\n        case 'Pack': {\n            return tidy(() => {\n                const axis = getParamValue('axis', node, tensorMap, context);\n                const tensors = getParamValue('tensors', node, tensorMap, context);\n                // Reshape the tensors to the first tensor's shape if they don't\n                // match.\n                const shape = tensors[0].shape;\n                const squeezedShape = tfOps.squeeze(tensors[0]).shape;\n                const mapped = tensors.map(tensor => {\n                    const sameShape = util.arraysEqual(tensor.shape, shape);\n                    if (!sameShape &&\n                        !util.arraysEqual(tfOps.squeeze(tensor).shape, squeezedShape)) {\n                        throw new Error('the input tensors shape does not match');\n                    }\n                    return sameShape ? tensor : tfOps.reshape(tensor, shape);\n                });\n                return [tfOps.stack(mapped, axis)];\n            });\n        }\n        case 'Unpack': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const tensor = getParamValue('tensor', node, tensorMap, context);\n            return tfOps.unstack(tensor, axis);\n        }\n        case 'Tile': {\n            const reps = getParamValue('reps', node, tensorMap, context);\n            return [tfOps.tile(getParamValue('x', node, tensorMap, context), reps)];\n        }\n        case 'Split':\n        case 'SplitV': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            const numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context);\n            const tensor = getParamValue('x', node, tensorMap, context);\n            return tfOps.split(tensor, numOrSizeSplits, axis);\n        }\n        case 'ScatterNd': {\n            const indices = getParamValue('indices', node, tensorMap, context);\n            const values = getParamValue('values', node, tensorMap, context);\n            const shape = getParamValue('shape', node, tensorMap, context);\n            return [tfOps.scatterND(indices, values, shape)];\n        }\n        case 'GatherNd': {\n            const x = getParamValue('x', node, tensorMap, context);\n            const indices = getParamValue('indices', node, tensorMap, context);\n            return [tfOps.gatherND(x, indices)];\n        }\n        case 'SparseToDense': {\n            const indices = getParamValue('sparseIndices', node, tensorMap, context);\n            const shape = getParamValue('outputShape', node, tensorMap, context);\n            const sparseValues = getParamValue('sparseValues', node, tensorMap, context);\n            const defaultValue = getParamValue('defaultValue', node, tensorMap, context);\n            return [tfOps.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ?\n                    defaultValue :\n                    tfOps.cast(defaultValue, sparseValues.dtype))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'slice_join';\n//# sourceMappingURL=slice_join_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'FFT': {\n            return [tfOps.fft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IFFT': {\n            return [tfOps.ifft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'RFFT': {\n            return [tfOps.rfft(getParamValue('x', node, tensorMap, context))];\n        }\n        case 'IRFFT': {\n            return [tfOps.irfft(getParamValue('x', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'spectral';\n//# sourceMappingURL=spectral_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\nimport { getParamValue } from './utils';\nexport const executeOp = (node, tensorMap, context) => {\n    switch (node.op) {\n        case 'Cast': {\n            return [tfOps.cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))];\n        }\n        case 'ExpandDims': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.expandDims(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Squeeze': {\n            const axis = getParamValue('axis', node, tensorMap, context);\n            return [tfOps.squeeze(getParamValue('x', node, tensorMap, context), axis)];\n        }\n        case 'Reshape': {\n            return [tfOps.reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        case 'MirrorPad': {\n            return [tfOps.mirrorPad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('mode', node, tensorMap, context))];\n        }\n        case 'PadV2':\n        case 'Pad': {\n            return [tfOps.pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))];\n        }\n        case 'SpaceToBatchND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const paddings = getParamValue('paddings', node, tensorMap, context);\n            return [tfOps.spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)];\n        }\n        case 'BatchToSpaceND': {\n            const blockShape = getParamValue('blockShape', node, tensorMap, context);\n            const crops = getParamValue('crops', node, tensorMap, context);\n            return [tfOps.batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)];\n        }\n        case 'DepthToSpace': {\n            const blockSize = getParamValue('blockSize', node, tensorMap, context);\n            const dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase();\n            return [tfOps.depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)];\n        }\n        case 'BroadcastTo': {\n            return [tfOps.broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))];\n        }\n        default:\n            throw TypeError(`Node type ${node.op} is not implemented`);\n    }\n};\nexport const CATEGORY = 'transformation';\n//# sourceMappingURL=transformation_executor.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tfc from '@tensorflow/tfjs-core';\nimport { NodeValueImpl } from './custom_op/node_value_impl';\nimport { getRegisteredOp } from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as hashTable from './executors/hash_table_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as transformation from './executors/transformation_executor';\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function executeOp(node, tensorMap, context, resourceManager) {\n    const value = ((node, tensorMap, context) => {\n        switch (node.category) {\n            case 'arithmetic':\n                return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));\n            case 'basic_math':\n                return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));\n            case 'control':\n                return control.executeOp(node, tensorMap, context);\n            case 'convolution':\n                return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));\n            case 'creation':\n                return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n            case 'dynamic':\n                return dynamic.executeOp(node, tensorMap, context);\n            case 'evaluation':\n                return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));\n            case 'image':\n                return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n            case 'graph':\n                return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n            case 'logical':\n                return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n            case 'matrices':\n                return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n            case 'normalization':\n                return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));\n            case 'reduction':\n                return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));\n            case 'slice_join':\n                return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n            case 'spectral':\n                return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n            case 'transformation':\n                return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));\n            case 'hash_table':\n                return hashTable.executeOp(node, tensorMap, context, resourceManager);\n            case 'custom':\n                const opMapper = getRegisteredOp(node.op);\n                if (opMapper && opMapper.customExecutor) {\n                    return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));\n                }\n                else {\n                    throw TypeError(`Custom op ${node.op} is not registered.`);\n                }\n            default:\n                throw TypeError(`Unknown op '${node.op}'. File an issue at ` +\n                    `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                    `, or register a custom execution with tf.registerOp()`);\n        }\n    })(node, tensorMap, context);\n    if (tfc.util.isPromise(value)) {\n        return value.then((data) => [].concat(data));\n    }\n    return [].concat(value);\n}\n//# sourceMappingURL=operation_executor.js.map", "/**\n * ExecutionContext captures the runtime environment of the node. It keeps\n * track of the current frame and iteration for the control flow ops.\n *\n * For example, typical Dynamic RNN model may contain loops, for which\n * TensorFlow will generate graphs with Enter/Exit nodes to control the\n * current execution frame, and NextIteration Nodes for iteration id increment.\n * For model with branch logic, TensorFLow will generate Switch/Merge ops.\n */\nexport class ExecutionContext {\n    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n        this.weightMap = weightMap;\n        this.tensorArrayMap = tensorArrayMap;\n        this.tensorListMap = tensorListMap;\n        this.functionMap = functionMap;\n        this.rootContext = { id: 0, frameName: '', iterationId: 0 };\n        this.contexts = [this.rootContext];\n        this.lastId = 0;\n        this.generateCurrentContextIds();\n    }\n    newFrame(id, frameName) {\n        return { id, frameName, iterationId: 0 };\n    }\n    /**\n     * Set the current context\n     * @param contexts: ExecutionContextInfo[] the current path of execution\n     * frames\n     */\n    set currentContext(contexts) {\n        if (this.contexts !== contexts) {\n            this.contexts = contexts;\n            this.generateCurrentContextIds();\n        }\n    }\n    get currentContext() {\n        return this.contexts;\n    }\n    /**\n     * Returns the current context in string format.\n     */\n    get currentContextId() {\n        return this._currentContextIds[0];\n    }\n    /**\n     * Returns the current context and all parent contexts in string format.\n     * This allow access to the nodes in the current and parent frames.\n     */\n    get currentContextIds() {\n        return this._currentContextIds;\n    }\n    generateCurrentContextIds() {\n        const names = [];\n        for (let i = 0; i < this.contexts.length - 1; i++) {\n            const contexts = this.contexts.slice(0, this.contexts.length - i);\n            names.push(this.contextIdforContexts(contexts));\n        }\n        names.push('');\n        this._currentContextIds = names;\n    }\n    contextIdforContexts(contexts) {\n        return contexts ?\n            contexts\n                .map(context => (context.id === 0 && context.iterationId === 0) ?\n                '' :\n                `${context.frameName}-${context.iterationId}`)\n                .join('/') :\n            '';\n    }\n    /**\n     * Enter a new frame, a new context is pushed on the current context list.\n     * @param frameId new frame id\n     */\n    enterFrame(frameId) {\n        if (this.contexts) {\n            this.lastId++;\n            this.contexts = this.contexts.slice();\n            this.contexts.push(this.newFrame(this.lastId, frameId));\n            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n        }\n    }\n    /**\n     * Exit the current frame, the last context is removed from the current\n     * context list.\n     */\n    exitFrame() {\n        if (this.contexts && this.contexts.length > 1) {\n            this.contexts = this.contexts.slice();\n            this.contexts.splice(-1);\n            this.currentContextIds.shift();\n        }\n        else {\n            throw new Error('Cannot exit frame, the context is empty');\n        }\n    }\n    /**\n     * Enter the next iteration of a loop, the iteration id of last context is\n     * increased.\n     */\n    nextIteration() {\n        if (this.contexts && this.contexts.length > 0) {\n            this.contexts = this.contexts.slice();\n            this.lastId++;\n            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n            context.iterationId += 1;\n            context.id = this.lastId;\n            this.contexts.splice(-1, 1, context);\n            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n        }\n        else {\n            throw new Error('Cannot increase frame iteration, the context is empty');\n        }\n    }\n    getWeight(name) {\n        return this.weightMap[name];\n    }\n    addTensorArray(tensorArray) {\n        this.tensorArrayMap[tensorArray.id] = tensorArray;\n    }\n    getTensorArray(id) {\n        return this.tensorArrayMap[id];\n    }\n    addTensorList(tensorList) {\n        this.tensorListMap[tensorList.id] = tensorList;\n    }\n    getTensorList(id) {\n        return this.tensorListMap[id];\n    }\n    dispose(keepIds) {\n        for (const key in this.tensorArrayMap) {\n            this.tensorArrayMap[key].clearAndClose(keepIds);\n        }\n        for (const key in this.tensorListMap) {\n            this.tensorListMap[key].clearAndClose(keepIds);\n        }\n    }\n}\n//# sourceMappingURL=execution_context.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { parseNodeName } from '../operations/executors/utils';\n/**\n * Given graph inputs and desired outputs, find the minimal set of nodes\n * to execute in order to compute the outputs. In addition return other useful\n * info such:\n * - Missing inputs needed to compute the output.\n * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).\n * - Alternative inputs in order to avoid async (dynamic op) execution.\n */\nexport function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n    const usedNodes = new Set();\n    const missingInputs = [];\n    let dynamicNode = null;\n    let syncInputs = null;\n    // Start with the outputs, going backwards and find all the nodes that are\n    // needed to compute those outputs.\n    const seen = new Set();\n    const inputNodeNames = Object.keys(inputs).map(name => parseNodeName(name)[0]);\n    let initNodeNames = [];\n    if (initNodes != null) {\n        initNodeNames = initNodes.map(node => parseNodeName(node.name)[0]);\n    }\n    const frontier = [...outputs];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n            if (dynamicNode == null) {\n                dynamicNode = node;\n                syncInputs = dynamicNode.children.map(child => child.name)\n                    .filter(name => usedNodes.has(name));\n            }\n        }\n        usedNodes.add(node.name);\n        // Weights are dead end since we already have their values.\n        if (weightMap[node.name] != null) {\n            continue;\n        }\n        // This node is a dead end since it's one of the user-provided inputs.\n        if (inputNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        // This node is a dead end since it doesn't have any inputs.\n        if (initNodeNames.indexOf(node.name) !== -1) {\n            continue;\n        }\n        if (node.inputs.length === 0) {\n            missingInputs.push(node.name);\n            continue;\n        }\n        node.inputs.forEach(input => {\n            // Don't add to the frontier if it is already there.\n            if (seen.has(input.name)) {\n                return;\n            }\n            seen.add(input.name);\n            frontier.push(input);\n        });\n    }\n    return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\n/**\n * Given the execution info, return a list of nodes in topological order that\n * need to be executed to compute the output.\n */\nexport function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n    const { usedNodes, inputs } = executionInfo;\n    const frontier = [];\n    const inputNodes = Object.keys(inputs)\n        .map(name => parseNodeName(name)[0])\n        .map(name => graph.nodes[name]);\n    const initNodes = graph.initNodes;\n    inputNodes.forEach(input => {\n        if (usedNodes.has(input.name)) {\n            frontier.push(input);\n        }\n    });\n    graph.weights.forEach(weight => {\n        if (usedNodes.has(weight.name)) {\n            frontier.push(weight);\n        }\n    });\n    if (initNodes != null) {\n        initNodes.forEach(node => {\n            if (usedNodes.has(node.name)) {\n                frontier.push(node);\n            }\n        });\n    }\n    const seen = new Set();\n    const orderedNodes = [];\n    while (frontier.length > 0) {\n        const node = frontier.pop();\n        seen.add(node.name);\n        if (!weightMap[node.name]) {\n            orderedNodes.push(node);\n        }\n        node.children.forEach(child => {\n            if (!seen.has(child.name) && usedNodes.has(child.name) &&\n                child.inputs.every(input => seen.has(input.name))) {\n                frontier.push(child);\n            }\n        });\n    }\n    return orderedNodes;\n}\nconst CONTROL_FLOW_OPS = [\n    'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf',\n    'StatelessWhile', 'if', 'While'\n];\nconst DYNAMIC_SHAPE_OPS = [\n    'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where'\n];\nconst HASH_TABLE_OPS = [\n    'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2',\n    'LookupTableFind', 'LookupTableFindV2'\n];\nexport function isControlFlow(node) {\n    return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nexport function isDynamicShape(node) {\n    return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\nexport function isHashTable(node) {\n    return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n//# sourceMappingURL=model_analysis.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { tidy, util } from '@tensorflow/tfjs-core';\nimport { getNodeNameAndIndex, getParamValue, getTensor, getTensorsForCurrentContenxt, parseNodeName } from '../operations/executors/utils';\nimport { executeOp } from '../operations/operation_executor';\nimport { ExecutionContext } from './execution_context';\nimport { getExecutionSubgraph, getNodesInTopologicalOrder, isControlFlow } from './model_analysis';\nexport class GraphExecutor {\n    /**\n     *\n     * @param graph Graph the model or function graph to be executed.\n     * @param parent When building function exector you need to set the parent\n     * executor. Since the weights and function executor maps are set at parant\n     * level, that function executor can access the function maps and weight maps\n     * through the parent.\n     */\n    constructor(graph, parent) {\n        this.graph = graph;\n        this.parent = parent;\n        this.compiledMap = new Map();\n        this._weightMap = {};\n        this.SEPERATOR = ',';\n        this._functions = {};\n        this._functionExecutorMap = {};\n        this._outputs = graph.outputs;\n        this._inputs = graph.inputs;\n        this._initNodes = graph.initNodes;\n        this._signature = graph.signature;\n        this._functions = graph.functions;\n        // create sub-graph executors\n        if (graph.functions != null) {\n            Object.keys(graph.functions).forEach(name => {\n                this._functionExecutorMap[name] =\n                    new GraphExecutor(graph.functions[name], this);\n            });\n        }\n    }\n    get weightIds() {\n        return this.parent ? this.parent.weightIds : this._weightIds;\n    }\n    get functionExecutorMap() {\n        return this.parent ? this.parent.functionExecutorMap :\n            this._functionExecutorMap;\n    }\n    get weightMap() {\n        return this.parent ? this.parent.weightMap : this._weightMap;\n    }\n    set weightMap(weightMap) {\n        const weightIds = Object.keys(weightMap).map(key => weightMap[key].map(tensor => tensor.id));\n        this._weightIds = [].concat(...weightIds);\n        this._weightMap = weightMap;\n    }\n    /**\n     * Set `ResourceManager` shared by executors of a model.\n     * @param resourceManager: `ResourceManager` of the `GraphModel`.\n     */\n    set resourceManager(resourceManager) {\n        this._resourceManager = resourceManager;\n    }\n    get inputs() {\n        return this._inputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get outputs() {\n        return this._outputs.map(node => {\n            return {\n                name: node.name,\n                shape: node.attrParams['shape'] ?\n                    node.attrParams['shape'].value :\n                    undefined,\n                dtype: node.attrParams['dtype'] ?\n                    node.attrParams['dtype'].value :\n                    undefined\n            };\n        });\n    }\n    get inputNodes() {\n        return this._inputs.map(node => node.signatureKey || node.name);\n    }\n    get outputNodes() {\n        return this._outputs.map((node) => {\n            const name = node.signatureKey || node.name;\n            return node.defaultOutput ? (`${name}:${node.defaultOutput}`) : name;\n        });\n    }\n    get functions() {\n        return Object.keys(this._functions).reduce((map, key) => {\n            map[key] = this._functions[key].signature;\n            return map;\n        }, {});\n    }\n    getCompilationKey(inputs, outputs) {\n        const sortedInputs = inputs.map(node => node.name).sort();\n        const sortedOutputs = outputs.map(node => node.name).sort();\n        return sortedInputs.join(this.SEPERATOR) + '--' +\n            sortedOutputs.join(this.SEPERATOR);\n    }\n    /**\n     * Compiles the inference graph and returns the minimal set of nodes that are\n     * required for execution, in the correct execution order.\n     */\n    compile(inputs, outputs) {\n        const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n        const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n        if (dynamicNode != null) {\n            throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` +\n                `the dynamic op '${dynamicNode.op}'. Please use ` +\n                `model.executeAsync() instead. Alternatively, to avoid the ` +\n                `dynamic ops, specify the inputs [${syncInputs}]`);\n        }\n        if (missingInputs.length > 0) {\n            const outNames = outputs.map(n => n.name);\n            const inNames = Object.keys(inputs);\n            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` +\n                `[${inNames}]. Missing the following inputs: [${missingInputs}]`);\n        }\n        return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n    }\n    /**\n     * Executes the inference for given input tensors.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs Optional. output node name from the Tensorflow model, if\n     * no outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     */\n    execute(inputs, outputs) {\n        inputs = this.mapInputs(inputs);\n        const names = Object.keys(inputs).sort();\n        this.checkInputs(inputs);\n        this.checkInputShapeAndType(inputs);\n        outputs = this.mapOutputs(outputs);\n        this.checkOutputs(outputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputs.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n        // Do nothing if the compiled graph cache contains the input.\n        let orderedNodes = this.compiledMap.get(compilationKey);\n        if (orderedNodes == null) {\n            orderedNodes = this.compile(inputs, outputNodes);\n            this.compiledMap.set(compilationKey, orderedNodes);\n        }\n        const tensorArrayMap = {};\n        const tensorListMap = {};\n        return tidy(() => {\n            const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n            const tensorsMap = Object.assign({}, this.weightMap);\n            Object.keys(inputs).forEach(name => {\n                const [nodeName, index] = parseNodeName(name);\n                const tensors = [];\n                tensors[index] = inputs[name];\n                tensorsMap[nodeName] = tensors;\n            });\n            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n            const intermediateTensorConsumerCount = {};\n            for (let i = 0; i < orderedNodes.length; i++) {\n                const node = orderedNodes[i];\n                if (!tensorsMap[node.name]) {\n                    const tensors = executeOp(node, tensorsMap, context, this._resourceManager);\n                    if (util.isPromise(tensors)) {\n                        throw new Error(`The execution of the op '${node.op}' returned a promise. ` +\n                            `Please use model.executeAsync() instead.`);\n                    }\n                    tensorsMap[node.name] = tensors;\n                    this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n                }\n            }\n            // dispose the context for the root executor\n            if (this.parent == null) {\n                context.dispose(tensorsToKeep);\n            }\n            return outputs.map(name => getTensor(name, tensorsMap, context));\n        });\n    }\n    getFrozenTensorIds(tensorMap) {\n        const ids = [].concat.apply([], Object.keys(tensorMap)\n            .map(key => tensorMap[key])\n            .map(tensors => tensors.map(tensor => tensor.id)));\n        return new Set(ids);\n    }\n    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n        // Skip output nodes and any control flow nodes, since its dependency is\n        // tricky to track correctly.\n        if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) {\n            return;\n        }\n        tensorMap[nodeName].forEach(tensor => {\n            if (tensor != null) {\n                intermediateTensorConsumerCount[tensor.id] =\n                    (intermediateTensorConsumerCount[tensor.id] || 0) +\n                        node.children.length;\n            }\n        });\n        node.inputs.forEach(input => {\n            // Skip any control flow nodes, since its dependency is tricky to track\n            // correctly.\n            if (input.category !== 'control') {\n                const tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context);\n                if (tensors != null) {\n                    tensors.forEach(tensor => {\n                        if (tensor && !tensorsToKeep.has(tensor.id)) {\n                            const count = intermediateTensorConsumerCount[tensor.id];\n                            if (count === 1) {\n                                tensor.dispose();\n                                delete intermediateTensorConsumerCount[tensor.id];\n                            }\n                            else if (count != null) {\n                                // only intermediate nodes has count set, inputs and weights are\n                                // not.\n                                intermediateTensorConsumerCount[tensor.id]--;\n                            }\n                        }\n                    });\n                }\n            }\n        });\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     */\n    async executeAsync(inputs, outputs) {\n        return this._executeAsync(inputs, outputs);\n    }\n    /**\n     * Executes the inference for given input tensors in Async fashion.\n     * @param inputs Tensor map for the model inputs, keyed by the input node\n     * names.\n     * @param outputs Optional. output node name from the Tensorflow model,\n     * if no outputs are specified, the default outputs of the model would be\n     * used. You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     * @param isFunctionExecution Optional. Flag for executing a function.\n     * @param tensorArrayMap Optional, global TensorArray map by id. Used for\n     * function execution.\n     * @param tensorArrayMap Optinal global TensorList map by id. Used for\n     * function execution.\n     */\n    async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n        if (!isFunctionExecution) {\n            inputs = this.mapInputs(inputs);\n            this.checkInputs(inputs);\n            this.checkInputShapeAndType(inputs);\n            outputs = this.mapOutputs(outputs);\n            this.checkOutputs(outputs);\n        }\n        const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n        // Graph with control flow op requires runtime evaluation of the execution\n        // order, while without control flow the execution order is pre-determined\n        // in the compile method.\n        const tensorMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n        const results = outputs.map(name => getTensor(name, tensorMap, context));\n        // dispose all the intermediate tensors\n        const outputIds = results.map(t => t.id);\n        const inputIds = Object.keys(inputs).map(name => inputs[name].id);\n        const keepIds = new Set([...outputIds, ...inputIds, ...this.weightIds]);\n        Object.keys(tensorMap).forEach(key => {\n            const tensorArray = tensorMap[key];\n            tensorArray.forEach(tensor => {\n                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) {\n                    tensor.dispose();\n                }\n            });\n        });\n        // dispose the context for the root executor\n        if (this.parent == null) {\n            context.dispose(keepIds);\n        }\n        return results;\n    }\n    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n        const mappedInputs = inputs.reduce((map, tensor, index) => {\n            map[this.inputs[index].name] = tensor;\n            return map;\n        }, {});\n        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n    }\n    /**\n     * When there are control flow nodes in the graph, the graph execution use\n     * ExecutionContext to keep track of the frames and loop iterators.\n     * @param inputs placeholder tensors for the graph.\n     * @param context the execution context object for current execution.\n     * @param outputNames Optional. output node name from the Tensorflow model,\n     * if no outputs are specified, the default outputs of the model would be\n     * used. You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     * @param isFunctionExecution Flag for executing a function.\n     */\n    async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n        const names = Object.keys(inputs);\n        const inputNodes = names.map(name => this.graph.nodes[parseNodeName(name)[0]]);\n        const outputNodeNames = outputNames.map(name => parseNodeName(name)[0]);\n        let outputNodes = outputNodeNames.map(name => this.graph.nodes[name]);\n        // If no outputs are specified, then use the default outputs of the model.\n        if (outputNodes.length === 0) {\n            outputNodes = this._outputs;\n        }\n        const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);\n        // First nodes to execute include inputNodes, weights, and initNodes.\n        const stack = [\n            ...inputNodes, ...this.graph.weights, ...(this._initNodes || [])\n        ].map(node => {\n            return { node, contexts: context.currentContext };\n        });\n        const tensorsMap = Object.assign({}, this.weightMap);\n        Object.keys(inputs).forEach(name => {\n            const [nodeName, index] = parseNodeName(name);\n            const tensors = [];\n            tensors[index] = inputs[name];\n            tensorsMap[nodeName] = tensors;\n        });\n        const intermediateTensorConsumerCount = {};\n        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n        const added = {};\n        while (stack.length > 0) {\n            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n            await Promise.all(promises);\n        }\n        if (dynamicNode == null && !isFunctionExecution) {\n            console.warn(`This model execution did not contain any nodes with control flow ` +\n                `or dynamic output shapes. You can use model.execute() instead.`);\n        }\n        const missingOutputs = outputNodes\n            .filter(node => !isControlFlow(node) &&\n            !getTensor(node.name, tensorsMap, context))\n            .map(node => node.name);\n        if (missingOutputs.length > 0) {\n            let alternativeMsg = '';\n            if (dynamicNode != null) {\n                alternativeMsg =\n                    `Alternatively, to avoid the dynamic ops, use model.execute() ` +\n                        `and specify the inputs [${syncInputs}]`;\n            }\n            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` +\n                `inputs [${names}]. Consider providing the following inputs: ` +\n                `[${missingInputs}]. ${alternativeMsg}`);\n        }\n        return tensorsMap;\n    }\n    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n        const promises = [];\n        while (stack.length > 0) {\n            const item = stack.pop();\n            context.currentContext = item.contexts;\n            let nodeName = '';\n            // The tensor of the Enter op with isConstant set should be set\n            // in the parent scope, so it will be available as constant for the\n            // whole loop.\n            if (item.node.op === 'Enter' &&\n                getParamValue('isConstant', item.node, tensorMap, context)) {\n                [nodeName] = getNodeNameAndIndex(item.node.name, context);\n            }\n            // only process nodes that are not in the tensorMap yet, this include\n            // inputNodes and internal initNodes.\n            if (tensorMap[item.node.name] == null) {\n                const tensors = executeOp(item.node, tensorMap, context, this._resourceManager);\n                if (!nodeName) {\n                    [nodeName] = getNodeNameAndIndex(item.node.name, context);\n                }\n                const currentContext = context.currentContext;\n                if (util.isPromise(tensors)) {\n                    promises.push(tensors.then(t => {\n                        tensorMap[nodeName] = t;\n                        context.currentContext = currentContext;\n                        this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                        this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                        return t;\n                    }));\n                }\n                else {\n                    tensorMap[nodeName] = tensors;\n                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n                }\n            }\n            else {\n                this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);\n            }\n        }\n        return promises;\n    }\n    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {\n        node.children.forEach((childNode) => {\n            const [nodeName,] = getNodeNameAndIndex(childNode.name, context);\n            if (added[nodeName] || !usedNodes.has(childNode.name)) {\n                return;\n            }\n            // Merge op can be pushed if any of its inputs has value.\n            if (childNode.op === 'Merge') {\n                if (childNode.inputNames.some(name => {\n                    return !!getTensor(name, tensorMap, context);\n                })) {\n                    added[nodeName] = true;\n                    stack.push({ contexts: context.currentContext, node: childNode });\n                }\n            }\n            else // Otherwise all inputs must to have value.\n             if (childNode.inputNames.every(name => {\n                return !!getTensor(name, tensorMap, context);\n            })) {\n                added[nodeName] = true;\n                stack.push({ contexts: context.currentContext, node: childNode });\n            }\n        });\n    }\n    /**\n     * Releases the memory used by the weight tensors.\n     */\n    dispose() {\n        Object.keys(this.weightMap)\n            .forEach(key => this.weightMap[key].forEach(tensor => tensor.dispose()));\n    }\n    checkInputShapeAndType(inputs) {\n        Object.keys(inputs).forEach(name => {\n            const input = inputs[name];\n            const [nodeName,] = parseNodeName(name);\n            const node = this.graph.nodes[nodeName];\n            if (node.attrParams['shape'] && node.attrParams['shape'].value) {\n                const shape = node.attrParams['shape'].value;\n                const match = shape.length === input.shape.length &&\n                    input.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n                util.assert(match, () => `The shape of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be [${shape}], but was ` +\n                    `[${input.shape}]`);\n            }\n            if (node.attrParams['dtype'] && node.attrParams['dtype'].value) {\n                util.assert(input.dtype === node.attrParams['dtype'].value, () => `The dtype of dict['${node.name}'] provided in ` +\n                    `model.execute(dict) must be ` +\n                    `${node.attrParams['dtype'].value}, but was ${input.dtype}`);\n            }\n        });\n    }\n    mapInputs(inputs) {\n        const result = {};\n        for (const inputName in inputs) {\n            if (this._signature != null && this._signature.inputs != null &&\n                this._signature.inputs[inputName] != null) {\n                const tensor = this._signature.inputs[inputName];\n                result[tensor.name] = inputs[inputName];\n            }\n            else {\n                result[inputName] = inputs[inputName];\n            }\n        }\n        return result;\n    }\n    checkInputs(inputs) {\n        const notInGraph = Object.keys(inputs).filter(name => {\n            const [nodeName] = parseNodeName(name);\n            return this.graph.nodes[nodeName] == null;\n        });\n        if (notInGraph.length > 0) {\n            throw new Error(`The dict provided in model.execute(dict) has ` +\n                `keys: [${notInGraph}] that are not part of graph`);\n        }\n    }\n    mapOutputs(outputs) {\n        return outputs.map(name => {\n            if (this._signature != null && this._signature.outputs != null &&\n                this._signature.outputs[name] != null) {\n                const tensor = this._signature.outputs[name];\n                return tensor.name;\n            }\n            return name;\n        }, {});\n    }\n    checkOutputs(outputs) {\n        outputs.forEach(name => {\n            const [normalizedName] = parseNodeName(name);\n            if (!this.graph.nodes[normalizedName]) {\n                throw new Error(`The output '${name}' is not found in the graph`);\n            }\n        });\n    }\n}\n//# sourceMappingURL=graph_executor.js.map", "/**\n * Contains global resources of a model.\n */\nexport class ResourceManager {\n    constructor(hashTableNameToHandle = {}, hashTableMap = {}) {\n        this.hashTableNameToHandle = hashTableNameToHandle;\n        this.hashTableMap = hashTableMap;\n    }\n    /**\n     * Register a `HashTable` in the resource manager.\n     *\n     * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,\n     * where id is the table handle tensor's id.\n     *\n     * @param name Op node name that creates the `HashTable`.\n     * @param hashTable The `HashTable` to be added to resource manager.\n     */\n    addHashTable(name, hashTable) {\n        this.hashTableNameToHandle[name] = hashTable.handle;\n        this.hashTableMap[hashTable.id] = hashTable;\n    }\n    /**\n     * Get the table handle by node name.\n     * @param name Op node name that creates the `HashTable`. This name is also\n     *     used in the inputs list of lookup and import `HashTable` ops.\n     */\n    getHashTableHandleByName(name) {\n        return this.hashTableNameToHandle[name];\n    }\n    /**\n     * Get the actual `HashTable` by its handle tensor's id.\n     * @param id The id of the handle tensor.\n     */\n    getHashTableById(id) {\n        return this.hashTableMap[id];\n    }\n    /**\n     * Dispose `ResourceManager`, including its hashTables and tensors in them.\n     */\n    dispose() {\n        for (const key in this.hashTableMap) {\n            this.hashTableMap[key].clearAndClose();\n            delete this.hashTableMap[key];\n        }\n        for (const name in this.hashTableNameToHandle) {\n            this.hashTableNameToHandle[name].dispose();\n            delete this.hashTableNameToHandle[name];\n        }\n    }\n}\n//# sourceMappingURL=resource_manager.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { io, Tensor } from '@tensorflow/tfjs-core';\nimport { OperationMapper } from '../operations/operation_mapper';\nimport { GraphExecutor } from './graph_executor';\nimport { ResourceManager } from './resource_manager';\nexport const TFHUB_SEARCH_PARAM = '?tfjs-format=file';\nexport const DEFAULT_MODEL_NAME = 'model.json';\n/**\n * A `tf.GraphModel` is a directed, acyclic graph built from a\n * SavedModel GraphDef and allows inference execution.\n *\n * A `tf.GraphModel` can only be created by loading from a model converted from\n * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using\n * the command line converter tool and loaded via `tf.loadGraphModel`.\n *\n * @doc {heading: 'Models', subheading: 'Classes'}\n */\nexport class GraphModel {\n    /**\n     * @param modelUrl url for the model, or an `io.IOHandler`.\n     * @param weightManifestUrl url for the weight file generated by\n     * scripts/convert.py script.\n     * @param requestOption options for Request, which allows to send credentials\n     * and custom headers.\n     * @param onProgress Optional, progress callback function, fired periodically\n     * before the load is completed.\n     */\n    constructor(modelUrl, loadOptions = {}) {\n        this.modelUrl = modelUrl;\n        this.loadOptions = loadOptions;\n        this.version = 'n/a';\n        if (loadOptions == null) {\n            this.loadOptions = {};\n        }\n        this.resourceManager = new ResourceManager();\n    }\n    // Returns the version information for the tensorflow model GraphDef.\n    get modelVersion() {\n        return this.version;\n    }\n    get inputNodes() {\n        return this.executor.inputNodes;\n    }\n    get outputNodes() {\n        return this.executor.outputNodes;\n    }\n    get inputs() {\n        return this.executor.inputs;\n    }\n    get outputs() {\n        return this.executor.outputs;\n    }\n    get weights() {\n        return this.executor.weightMap;\n    }\n    findIOHandler() {\n        const path = this.modelUrl;\n        if (path.load != null) {\n            // Path is an IO Handler.\n            this.handler = path;\n        }\n        else if (this.loadOptions.requestInit != null) {\n            this.handler = io.browserHTTPRequest(path, this.loadOptions);\n        }\n        else {\n            const handlers = io.getLoadHandlers(path, this.loadOptions);\n            if (handlers.length === 0) {\n                // For backward compatibility: if no load handler can be found,\n                // assume it is a relative http path.\n                handlers.push(io.browserHTTPRequest(path, this.loadOptions));\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) load handlers for ` +\n                    `URL '${[path]}'`);\n            }\n            this.handler = handlers[0];\n        }\n    }\n    /**\n     * Loads the model and weight files, construct the in memory weight map and\n     * compile the inference graph.\n     */\n    async load() {\n        this.findIOHandler();\n        if (this.handler.load == null) {\n            throw new Error('Cannot proceed with model loading because the IOHandler provided ' +\n                'does not have the `load` method implemented.');\n        }\n        const artifacts = await this.handler.load();\n        return this.loadSync(artifacts);\n    }\n    /**\n     * Synchronously construct the in memory weight map and\n     * compile the inference graph. Also initialize hashtable if any.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    loadSync(artifacts) {\n        this.artifacts = artifacts;\n        const graph = this.artifacts.modelTopology;\n        let signature = {};\n        if (this.artifacts.userDefinedMetadata != null) {\n            signature = // tslint:disable-next-line:no-any\n                this.artifacts.userDefinedMetadata.signature;\n        }\n        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n        const weightMap = io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n        this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, signature));\n        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n        // Attach a model-level resourceManager to each executor to share resources,\n        // such as `HashTable`.\n        this.executor.resourceManager = this.resourceManager;\n        if (artifacts.modelInitializer != null) {\n            const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n            this.initializer = new GraphExecutor(initializer);\n            this.initializer.weightMap = this.executor.weightMap;\n            // Attach a model-level resourceManager to the initializer, the\n            // hashTables created from when executing the initializer will be stored\n            // in the resourceManager.\n            this.initializer.resourceManager = this.resourceManager;\n            this.initializer.executeAsync({}, []);\n        }\n        return true;\n    }\n    /**\n     * Save the configuration and/or weights of the GraphModel.\n     *\n     * An `IOHandler` is an object that has a `save` method of the proper\n     * signature defined. The `save` method manages the storing or\n     * transmission of serialized data (\"artifacts\") that represent the\n     * model's topology and weights onto or via a specific medium, such as\n     * file downloads, local storage, IndexedDB in the web browser and HTTP\n     * requests to a server. TensorFlow.js provides `IOHandler`\n     * implementations for a number of frequently used saving mediums, such as\n     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`\n     * for more details.\n     *\n     * This method also allows you to refer to certain types of `IOHandler`s\n     * as URL-like string shortcuts, such as 'localstorage://' and\n     * 'indexeddb://'.\n     *\n     * Example 1: Save `model`'s topology and weights to browser [local\n     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);\n     * then load it back.\n     *\n     * ```js\n     * const modelUrl =\n     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n     * const model = await tf.loadGraphModel(modelUrl);\n     * const zeros = tf.zeros([1, 224, 224, 3]);\n     * model.predict(zeros).print();\n     *\n     * const saveResults = await model.save('localstorage://my-model-1');\n     *\n     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');\n     * console.log('Prediction from loaded model:');\n     * model.predict(zeros).print();\n     * ```\n     *\n     * @param handlerOrURL An instance of `IOHandler` or a URL-like,\n     * scheme-based string shortcut for `IOHandler`.\n     * @param config Options for saving the model.\n     * @returns A `Promise` of `SaveResult`, which summarizes the result of\n     * the saving, such as byte sizes of the saved artifacts for the model's\n     *   topology and weight values.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}\n     */\n    async save(handlerOrURL, config) {\n        if (typeof handlerOrURL === 'string') {\n            const handlers = io.getSaveHandlers(handlerOrURL);\n            if (handlers.length === 0) {\n                throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n            }\n            else if (handlers.length > 1) {\n                throw new Error(`Found more than one (${handlers.length}) save handlers for ` +\n                    `URL '${handlerOrURL}'`);\n            }\n            handlerOrURL = handlers[0];\n        }\n        if (handlerOrURL.save == null) {\n            throw new Error('GraphModel.save() cannot proceed because the IOHandler ' +\n                'provided does not have the `save` attribute defined.');\n        }\n        return handlerOrURL.save(this.artifacts);\n    }\n    /**\n     * Execute the inference for the input tensors.\n     *\n     * @param input The input tensors, when there is single input for the model,\n     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,\n     * inputs params should be in either `tf.Tensor`[] if the input order is\n     * fixed, or otherwise NamedTensorMap format.\n     *\n     * For model with multiple inputs, we recommend you use NamedTensorMap as the\n     * input type, if you use `tf.Tensor`[], the order of the array needs to\n     * follow the\n     * order of inputNodes array. @see {@link GraphModel.inputNodes}\n     *\n     * You can also feed any intermediate nodes using the NamedTensorMap as the\n     * input type. For example, given the graph\n     *    InputNode => Intermediate => OutputNode,\n     * you can execute the subgraph Intermediate => OutputNode by calling\n     *    model.execute('IntermediateNode' : tf.tensor(...));\n     *\n     * This is useful for models that uses tf.dynamic_rnn, where the intermediate\n     * state needs to be fed manually.\n     *\n     * For batch inference execution, the tensors for each input need to be\n     * concatenated together. For example with mobilenet, the required input shape\n     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].\n     * If we are provide a batched data of 100 images, the input tensor should be\n     * in the shape of [100, 244, 244, 3].\n     *\n     * @param config Prediction configuration for specifying the batch size and\n     * output node names. Currently the batch size option is ignored for graph\n     * model.\n     *\n     * @returns Inference result tensors. The output would be single `tf.Tensor`\n     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]\n     * will be returned for model with multiple outputs.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    predict(inputs, config) {\n        return this.execute(inputs, this.outputNodes);\n    }\n    normalizeInputs(inputs) {\n        if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n            // The input is already a NamedTensorMap.\n            return inputs;\n        }\n        inputs = Array.isArray(inputs) ? inputs : [inputs];\n        if (inputs.length !== this.inputNodes.length) {\n            throw new Error('Input tensor count mismatch,' +\n                `the graph model has ${this.inputNodes.length} placeholders, ` +\n                `while there are ${inputs.length} input tensors.`);\n        }\n        return this.inputNodes.reduce((map, inputName, i) => {\n            map[inputName] = inputs[i];\n            return map;\n        }, {});\n    }\n    normalizeOutputs(outputs) {\n        outputs = outputs || this.outputNodes;\n        return !Array.isArray(outputs) ? [outputs] : outputs;\n    }\n    /**\n     * Executes inference for the model for given input tensors.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no\n     * outputs are specified, the default outputs of the model would be used.\n     * You can inspect intermediate nodes of the model by adding them to the\n     * outputs array.\n     *\n     * @returns A single tensor if provided with a single output or no outputs\n     * are provided and there is only one default output, otherwise return a\n     * tensor array. The order of the tensor array is the same as the outputs\n     * if provided, otherwise the order of outputNodes attribute of the model.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    execute(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = this.executor.execute(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    /**\n     * Executes inference for the model for given input tensors in async\n     * fashion, use this method when your model contains control flow ops.\n     * @param inputs tensor, tensor array or tensor map of the inputs for the\n     * model, keyed by the input node names.\n     * @param outputs output node name from the Tensorflow model, if no outputs\n     * are specified, the default outputs of the model would be used. You can\n     * inspect intermediate nodes of the model by adding them to the outputs\n     * array.\n     *\n     * @returns A Promise of single tensor if provided with a single output or\n     * no outputs are provided and there is only one default output, otherwise\n     * return a tensor map.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    async executeAsync(inputs, outputs) {\n        inputs = this.normalizeInputs(inputs);\n        outputs = this.normalizeOutputs(outputs);\n        const result = await this.executor.executeAsync(inputs, outputs);\n        return result.length > 1 ? result : result[0];\n    }\n    convertTensorMapToTensorsMap(map) {\n        return Object.keys(map).reduce((newMap, key) => {\n            newMap[key] = [map[key]];\n            return newMap;\n        }, {});\n    }\n    /**\n     * Releases the memory used by the weight tensors and resourceManager.\n     *\n     * @doc {heading: 'Models', subheading: 'Classes'}\n     */\n    dispose() {\n        this.executor.dispose();\n        if (this.initializer) {\n            this.initializer.dispose();\n        }\n        this.resourceManager.dispose();\n    }\n}\n/**\n * Load a graph model given a URL to the model definition.\n *\n * Example of loading MobileNetV2 from a URL and making a prediction with a\n * zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';\n * const model = await tf.loadGraphModel(modelUrl);\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n *\n * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with\n * a zeros input:\n *\n * ```js\n * const modelUrl =\n *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';\n * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});\n * const zeros = tf.zeros([1, 224, 224, 3]);\n * model.predict(zeros).print();\n * ```\n * @param modelUrl The url or an `io.IOHandler` that loads the model.\n * @param options Options for the HTTP request, which allows to send credentials\n *    and custom headers.\n *\n * @doc {heading: 'Models', subheading: 'Loading'}\n */\nexport async function loadGraphModel(modelUrl, options = {}) {\n    if (modelUrl == null) {\n        throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' +\n            'or an IOHandler that loads the model');\n    }\n    if (options == null) {\n        options = {};\n    }\n    if (options.fromTFHub) {\n        if (modelUrl.load == null) {\n            if (!modelUrl.endsWith('/')) {\n                modelUrl = modelUrl + '/';\n            }\n            modelUrl = `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n        }\n    }\n    const model = new GraphModel(modelUrl, options);\n    await model.load();\n    return model;\n}\n//# sourceMappingURL=graph_model.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { GraphModel, loadGraphModel } from './executor/graph_model';\nexport { deregisterOp, registerOp } from './operations/custom_op/register';\nexport { version as version_converter } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\n/**\n * Apply a mapping function to a nested structure in a recursive manner.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapResult`.  The `DeepMapResult` either provides a\n *   replacement value for that node (i.e., replacing the subtree), or indicates\n *   that the node should be processed recursively.\n */\nexport function deepMap(input, mapFn) {\n    return deepMapInternal(input, mapFn);\n}\n/**\n * @param seen: A Map of known object mappings (i.e., memoized results of\n *   `mapFn()`)\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepMapInternal(input, mapFn, seen = new Map(), containedIn = new Set()) {\n    if (input == null) {\n        return null;\n    }\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    if (seen.has(input)) {\n        return seen.get(input);\n    }\n    const result = mapFn(input);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep map function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        seen.set(input, result.value);\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const child = input[k];\n            const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// TODO(soergel, kangyizhang) Reconsider naming of deepZip() to avoid confusion\n// with zip()\n/**\n * Zip nested structures together in a recursive manner.\n *\n * This has the effect of transposing or pivoting data, e.g. converting it from\n * a row-major representation to a column-major representation.\n *\n * For example, `deepZip([{a: 1, b: 2}, {a: 3, b: 4}])` returns\n * `{a: [1, 3], b: [2, 4]}`.\n *\n * The inputs should all have the same nested structure (i.e., of arrays and\n * dicts).  The result is a single object with the same nested structure, where\n * the leaves are arrays collecting the values of the inputs at that location\n * (or, optionally, the result of a custom function applied to those arrays).\n *\n * @param inputs: An array of the objects to zip together.\n * @param zipFn: (optional) A function that expects an array of elements at a\n *   single node of the object tree, and returns a `DeepMapResult`.  The\n *   `DeepMapResult` either provides a result value for that node (i.e.,\n *   representing the subtree), or indicates that the node should be processed\n *   recursively.  The default zipFn recurses as far as possible and places\n *   arrays at the leaves.\n */\nexport function deepZip(inputs, zipFn = zipToList) {\n    return deepZipInternal(inputs, zipFn);\n}\n/**\n * @param containedIn: An set containing objects on the reference path currently\n *   being processed (used to detect cycles).\n */\nfunction deepZipInternal(inputs, zipFn, containedIn = new Set()) {\n    // The recursion follows the structure of input 0; it's assumed that all the\n    // other inputs have the same structure.\n    const input = inputs[0];\n    if (containedIn.has(input)) {\n        throw new Error('Circular references are not supported.');\n    }\n    const result = zipFn(inputs);\n    if (result.recurse && result.value !== null) {\n        throw new Error('A deep zip function may not return both a value and recurse=true.');\n    }\n    if (!result.recurse) {\n        return result.value;\n    }\n    else if (isIterable(input)) {\n        // tslint:disable-next-line:no-any\n        const mappedIterable = Array.isArray(input) ? [] : {};\n        containedIn.add(input);\n        for (const k in input) {\n            const children = inputs.map(x => x[k]);\n            const childResult = deepZipInternal(children, zipFn, containedIn);\n            mappedIterable[k] = childResult;\n        }\n        containedIn.delete(input);\n        return mappedIterable;\n    }\n    else {\n        throw new Error(`Can't recurse into non-iterable type: ${input}`);\n    }\n}\n// tslint:disable-next-line:no-any\nexport function zipToList(x) {\n    if (x === null) {\n        return null;\n    }\n    // TODO(soergel): validate array type?\n    if (isIterable(x[0])) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: x, recurse: false };\n    }\n}\n/**\n * Apply an async mapping function to a nested structure in a recursive manner.\n *\n * This first creates a nested structure of Promises, and then awaits all of\n * those, resulting in a single Promise for a resolved nested structure.\n *\n * The result of the mapping is an object with the same nested structure (i.e.,\n * of arrays and dicts) as the input, except that some subtrees are replaced,\n * according to the results of the mapping function.\n *\n * Mappings are memoized.  Thus, if the nested structure contains the same\n * object in multiple positions, the output will contain the same mapped object\n * in those positions.  Cycles are not supported, however.\n *\n * @param input: The object to which to apply the mapping function.\n * @param mapFn: A function that expects a single node of the object tree, and\n *   returns a `DeepMapAsyncResult`.  The `DeepMapAsyncResult` either provides\n *   a `Promise` for a replacement value for that node (i.e., replacing the\n *   subtree), or indicates that the node should be processed recursively.  Note\n *   that the decision whether or not to recurse must be made immediately; only\n *   the mapped value may be promised.\n */\nexport async function deepMapAndAwaitAll(input, mapFn) {\n    const seen = new Map();\n    // First do a normal deepMap, collecting Promises in 'seen' as a side effect.\n    deepMapInternal(input, mapFn, seen);\n    // Replace the Promises in 'seen' in place.\n    // Note TypeScript provides no async map iteration, and regular map iteration\n    // is broken too, so sadly we have to do Array.from() to make it work.\n    // (There's no advantage to Promise.all(), and that would be tricky anyway.)\n    for (const key of Array.from(seen.keys())) {\n        const value = seen.get(key);\n        if (tf.util.isPromise(value)) {\n            const mappedValue = await value;\n            seen.set(key, mappedValue);\n        }\n    }\n    // Normal deepMap again, this time filling in the resolved values.\n    // It's unfortunate that we have to do two passes.\n    // TODO(soergel): test performance and think harder about a fast solution.\n    const result = deepMapInternal(input, mapFn, seen);\n    return result;\n}\n/**\n * Determine whether the argument is iterable.\n *\n * @returns true if the argument is an array or any non-Tensor object.\n */\n// tslint:disable-next-line:no-any\nexport function isIterable(obj) {\n    return obj != null && (!ArrayBuffer.isView(obj)) &&\n        (Array.isArray(obj) ||\n            (typeof obj === 'object' && !(obj instanceof tf.Tensor)));\n}\n/**\n * Determine whether the argument can be converted to Tensor.\n *\n * Tensors, primitives, arrays, and TypedArrays all qualify; anything else does\n * not.\n *\n * @returns true if the argument can be converted to Tensor.\n */\n// tslint:disable-next-line:no-any\nexport function canTensorify(obj) {\n    return obj == null || isPrimitive(obj) || Array.isArray(obj) ||\n        (typeof obj === 'object' && (obj instanceof tf.Tensor)) ||\n        tf.util.isTypedArray(obj);\n}\n/**\n * Returns true if the given `value` is a primitive type. Otherwise returns\n * false. This is equivalant to node util.isPrimitive\n */\nfunction isPrimitive(value) {\n    return (value === null ||\n        (typeof value !== 'object' && typeof value !== 'function'));\n}\n//# sourceMappingURL=deep_map.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { deepMap, isIterable } from './deep_map';\nexport function deepClone(container) {\n    return deepMap(container, cloneIfTensor);\n}\n// tslint:disable-next-line: no-any\nfunction cloneIfTensor(item) {\n    if (item instanceof tf.Tensor) {\n        return ({ value: item.clone(), recurse: false });\n    }\n    else if (isIterable(item)) {\n        return { value: null, recurse: true };\n    }\n    else {\n        return { value: item, recurse: false };\n    }\n}\n//# sourceMappingURL=deep_clone.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * A ring buffer, providing O(1) FIFO, LIFO, and related operations.\n */\nexport class RingBuffer {\n    /**\n     * Constructs a `RingBuffer`.\n     * @param capacity The number of items that the buffer can accomodate.\n     */\n    constructor(capacity) {\n        this.capacity = capacity;\n        // Note we store the indices in the range 0 <= index < 2*capacity.\n        // This allows us to distinguish the full from the empty case.\n        // See https://www.snellman.net/blog/archive/2016-12-13-ring-buffers/\n        this.begin = 0; // inclusive\n        this.end = 0; // exclusive\n        if (capacity == null) {\n            throw new RangeError('Can\\'t create a ring buffer of unknown capacity.');\n        }\n        if (capacity < 1) {\n            throw new RangeError('Can\\'t create ring buffer of capacity < 1.');\n        }\n        this.data = new Array(capacity);\n        this.doubledCapacity = 2 * capacity;\n    }\n    /**\n     * Map any index into the range 0 <= index < 2*capacity.\n     */\n    wrap(index) {\n        // don't trust % on negative numbers\n        while (index < 0) {\n            index += this.doubledCapacity;\n        }\n        return index % this.doubledCapacity;\n    }\n    get(index) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t get item at a negative index.');\n        }\n        return this.data[index % this.capacity];\n    }\n    set(index, value) {\n        if (index < 0) {\n            throw new RangeError('Can\\'t set item at a negative index.');\n        }\n        this.data[index % this.capacity] = value;\n    }\n    /**\n     * Returns the current number of items in the buffer.\n     */\n    length() {\n        let length = this.end - this.begin;\n        if (length < 0) {\n            length = this.doubledCapacity + length;\n        }\n        return length;\n    }\n    /**\n     * Reports whether the buffer is full.\n     * @returns true if the number of items in the buffer equals its capacity, and\n     *   false otherwise.\n     */\n    isFull() {\n        return this.length() === this.capacity;\n    }\n    /**\n     * Reports whether the buffer is empty.\n     * @returns true if the number of items in the buffer equals zero, and\n     *   false otherwise.\n     */\n    isEmpty() {\n        return this.length() === 0;\n    }\n    /**\n     * Adds an item to the end of the buffer.\n     */\n    push(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.set(this.end, value);\n        this.end = this.wrap(this.end + 1);\n    }\n    /**\n     * Adds many items to the end of the buffer, in order.\n     */\n    pushAll(values) {\n        for (const value of values) {\n            this.push(value);\n        }\n    }\n    /**\n     * Removes and returns the last item in the buffer.\n     */\n    pop() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        this.end = this.wrap(this.end - 1);\n        const result = this.get(this.end);\n        this.set(this.end, undefined);\n        return result;\n    }\n    /**\n     * Adds an item to the beginning of the buffer.\n     */\n    unshift(value) {\n        if (this.isFull()) {\n            throw new RangeError('Ring buffer is full.');\n        }\n        this.begin = this.wrap(this.begin - 1);\n        this.set(this.begin, value);\n    }\n    /**\n     * Removes and returns the first item in the buffer.\n     */\n    shift() {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const result = this.get(this.begin);\n        this.set(this.begin, undefined);\n        this.begin = this.wrap(this.begin + 1);\n        return result;\n    }\n    /**\n     * Removes and returns a specific item in the buffer, and moves the last item\n     * to the vacated slot.  This is useful for implementing a shuffling stream.\n     * Note that this operation necessarily scrambles the original order.\n     *\n     * @param relativeIndex: the index of the item to remove, relative to the\n     *   first item in the buffer (e.g., hiding the ring nature of the underlying\n     *   storage).\n     */\n    shuffleExcise(relativeIndex) {\n        if (this.isEmpty()) {\n            throw new RangeError('Ring buffer is empty.');\n        }\n        const index = this.wrap(this.begin + relativeIndex);\n        const result = this.get(index);\n        this.set(index, this.pop());\n        return result;\n    }\n}\n//# sourceMappingURL=ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { RingBuffer } from './ring_buffer';\nexport class GrowingRingBuffer extends RingBuffer {\n    /**\n     * Constructs a `GrowingRingBuffer`.\n     */\n    constructor() {\n        super(GrowingRingBuffer.INITIAL_CAPACITY);\n    }\n    isFull() {\n        return false;\n    }\n    push(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.push(value);\n    }\n    unshift(value) {\n        if (super.isFull()) {\n            this.expand();\n        }\n        super.unshift(value);\n    }\n    /**\n     * Doubles the capacity of the buffer.\n     */\n    expand() {\n        const newCapacity = this.capacity * 2;\n        const newData = new Array(newCapacity);\n        const len = this.length();\n        // Rotate the buffer to start at index 0 again, since we can't just\n        // allocate more space at the end.\n        for (let i = 0; i < len; i++) {\n            newData[i] = this.get(this.wrap(this.begin + i));\n        }\n        this.data = newData;\n        this.capacity = newCapacity;\n        this.doubledCapacity = 2 * this.capacity;\n        this.begin = 0;\n        this.end = len;\n    }\n}\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n//# sourceMappingURL=growing_ring_buffer.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { deepClone } from '../util/deep_clone';\nimport { deepMapAndAwaitAll, deepZip, zipToList } from '../util/deep_map';\nimport { GrowingRingBuffer } from '../util/growing_ring_buffer';\nimport { RingBuffer } from '../util/ring_buffer';\n// Here we implement a simple asynchronous iterator.\n// This lets us avoid using either third-party stream libraries or\n// recent TypeScript language support requiring polyfills.\n/**\n * Create a `LazyIterator` from an array of items.\n */\nexport function iteratorFromItems(items) {\n    return new ArrayIterator(items);\n}\n/**\n * Create a `LazyIterator` of incrementing integers.\n */\nexport function iteratorFromIncrementing(start) {\n    let i = start;\n    return iteratorFromFunction(() => ({ value: i++, done: false }));\n}\n/**\n * Create a `LazyIterator` from a function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * await iter.forEachAsync(e => console.log(e));\n * ```\n *\n * @param func A function that produces data on each call.\n */\nexport function iteratorFromFunction(func) {\n    return new FunctionCallIterator(func);\n}\n/**\n * Create a `LazyIterator` by concatenating underlying streams, which are\n * themselves provided as a stream.\n *\n * This can also be thought of as a \"stream flatten\" operation.\n *\n * @param baseIterators A stream of streams to be concatenated.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n    return new ChainedIterator(baseIterators, baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by concatenating streams produced by calling a\n * stream-generating function a given number of times.\n *\n * Since a `LazyIterator` is read-once, it cannot be repeated, but this\n * function can be used to achieve a similar effect:\n *\n *   LazyIterator.ofConcatenatedFunction(() => new MyIterator(), 6);\n *\n * @param iteratorFunc: A function that produces a new stream on each call.\n * @param count: The number of times to call the function.\n * @param baseErrorHandler An optional function that can intercept `Error`s\n *   raised during a `next()` call on the base stream.  This function can decide\n *   whether the error should be propagated, whether the error should be\n *   ignored, or whether the base stream should be terminated.\n */\nexport function iteratorFromConcatenatedFunction(iteratorFunc, count, baseErrorHandler) {\n    return iteratorFromConcatenated(iteratorFromFunction(iteratorFunc).take(count), baseErrorHandler);\n}\n/**\n * Create a `LazyIterator` by zipping together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nexport function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    return new ZipIterator(iterators, mismatchMode);\n}\n/**\n * An asynchronous iterator, providing lazy access to a potentially\n * unbounded stream of elements.\n *\n * Iterator can be obtained from a dataset:\n * `const iter = await dataset.iterator();`\n */\nexport class LazyIterator {\n    /**\n     * Collect all remaining elements of a bounded stream into an array.\n     * Obviously this will succeed only for small streams that fit in memory.\n     * Useful for testing.\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArray() {\n        const result = [];\n        let x = await this.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await this.next();\n        }\n        return result;\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of stream elements, which will resolve\n     *   when the stream is exhausted.\n     */\n    async toArrayForTest() {\n        const stream = this.prefetch(100);\n        const result = [];\n        let x = await stream.next();\n        while (!x.done) {\n            result.push(x.value);\n            x = await stream.next();\n        }\n        return result;\n    }\n    /**\n     * Draw items from the stream until it is exhausted.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveFully() {\n        let x = await this.next();\n        while (!x.done) {\n            x = await this.next();\n        }\n    }\n    /**\n     * Draw items from the stream until it is exhausted, or a predicate fails.\n     *\n     * This can be useful when the stream has side effects but no output.  In\n     * that case, calling this function guarantees that the stream will be\n     * fully processed.\n     */\n    async resolveWhile(predicate) {\n        let x = await this.next();\n        let shouldContinue = predicate(x.value);\n        while ((!x.done) && shouldContinue) {\n            x = await this.next();\n            shouldContinue = predicate(x.value);\n        }\n    }\n    /**\n     * Handles errors thrown on this stream using a provided handler function.\n     *\n     * @param handler A function that handles any `Error` thrown during a `next()`\n     *   call and returns true if the stream should continue (dropping the failed\n     *   call) or false if the stream should quietly terminate.  If the handler\n     *   itself throws (or rethrows) an `Error`, that will be propagated.\n     *\n     * @returns A `LazyIterator` of elements passed through from upstream,\n     *   possibly filtering or terminating on upstream `next()` calls that\n     *   throw an `Error`.\n     */\n    handleErrors(handler) {\n        return new ErrorHandlingLazyIterator(this, handler);\n    }\n    // TODO(soergel): Implement reduce() etc.\n    /**\n     * Filters this stream according to `predicate`.\n     *\n     * @param predicate A function mapping a stream element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `LazyIterator` of elements for which the predicate was true.\n     */\n    filter(predicate) {\n        return new FilterIterator(this, predicate);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    map(transform) {\n        return new MapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through an async 1-to-1 transform.\n     *\n     * @param transform A function mapping a stream element to a `Promise` for a\n     *   transformed stream element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    mapAsync(transform) {\n        return new AsyncMapIterator(this, transform);\n    }\n    /**\n     * Maps this stream through a 1-to-1 transform, forcing serial execution.\n     *\n     * @param transform A function mapping a stream element to a transformed\n     *   element.\n     *\n     * @returns A `LazyIterator` of transformed elements.\n     */\n    serialMapAsync(transform) {\n        return new AsyncMapIterator(this, transform).serial();\n    }\n    /**\n     * Maps this stream through a 1-to-many transform.\n     *\n     * @param transform A function mapping a stream element to an array of\n     *   transformed elements.\n     *\n     * @returns A `DataStream` of transformed elements.\n     */\n    flatmap(transform) {\n        return new FlatmapIterator(this, transform);\n    }\n    /**\n     * Apply a function to every element of the stream.\n     *\n     * @param f A function to apply to each stream element.\n     */\n    async forEachAsync(f) {\n        return this.map(f).resolveFully();\n    }\n    /**\n     * Apply a function to every element of the stream, forcing serial execution.\n     *\n     * @param f A function to apply to each stream element.  Should return 'true'\n     *   to indicate that the stream should continue, or 'false' to cause it to\n     *   terminate.\n     */\n    async serialForEach(f) {\n        return this.serialMapAsync(f).resolveWhile(x => (x === true));\n    }\n    /**\n     * Groups elements into batches, represented as arrays of elements.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"Row-major\" means that the resulting batch is simply a collection of\n     * rows: `[row1, row2, row3, ...]`.  This is contrast to the column-major\n     * form, which is needed for vectorized computation.\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `LazyIterator` of batches of elements, represented as arrays\n     *   of the original element type.\n     */\n    rowMajorBatch(batchSize, smallLastBatch = true) {\n        return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n    }\n    /**\n     * Groups elements into batches, represented in column-major form.\n     *\n     * We can think of the elements of this iterator as 'rows' (even if they are\n     * nested structures).  By the same token, consecutive values for a given\n     * key within the elements form a 'column'.  This matches the usual sense of\n     * 'row' and 'column' when processing tabular data (e.g., parsing a CSV).\n     *\n     * Thus, \"column-major\" means that the resulting batch is a (potentially\n     * nested) structure representing the columns.  Each column entry, then,\n     * contains a collection of the values found in that column for a range of\n     * input elements.  This representation allows for vectorized computation, in\n     * contrast to the row-major form.\n     *\n     * The inputs should all have the same nested structure (i.e., of arrays and\n     * dicts).  The result is a single object with the same nested structure,\n     * where the leaves are arrays collecting the values of the inputs at that\n     * location (or, optionally, the result of a custom function applied to those\n     * arrays).\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @param zipFn: (optional) A function that expects an array of elements at a\n     *   single node of the object tree, and returns a `DeepMapResult`.  The\n     *   `DeepMapResult` either provides a result value for that node (i.e.,\n     *   representing the subtree), or indicates that the node should be processed\n     *   recursively.  The default zipFn recurses as far as possible and places\n     *   arrays at the leaves.\n     * @returns A `LazyIterator` of batches of elements, represented as an object\n     *   with collections at the leaves.\n     */\n    columnMajorBatch(batchSize, smallLastBatch = true, \n    // tslint:disable-next-line:no-any\n    zipFn = zipToList) {\n        // First collect the desired number of input elements as a row-major batch.\n        const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n        // Now 'rotate' or 'pivot' the data, collecting all values from each column\n        // in the batch (i.e., for each key within the elements) into an array.\n        return rowBatches.map(x => deepZip(x, zipFn));\n    }\n    /**\n     * Concatenate this `LazyIterator` with another.\n     *\n     * @param iterator A `LazyIterator` to be concatenated onto this one.\n     * @param baseErrorHandler An optional function that can intercept `Error`s\n     *   raised during a `next()` call on the base stream.  This function can\n     *   decide whether the error should be propagated, whether the error should\n     *   be ignored, or whether the base stream should be terminated.\n     * @returns A `LazyIterator`.\n     */\n    concatenate(iterator, baseErrorHandler) {\n        return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n    }\n    /**\n     * Limits this stream to return at most `count` items.\n     *\n     * @param count The maximum number of items to provide from the stream. If\n     * a negative or undefined value is given, the entire stream is returned\n     *   unaltered.\n     */\n    take(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new TakeIterator(this, count);\n    }\n    /**\n     * Skips the first `count` items in this stream.\n     *\n     * @param count The number of items to skip.  If a negative or undefined\n     * value is given, the entire stream is returned unaltered.\n     */\n    skip(count) {\n        if (count < 0 || count == null) {\n            return this;\n        }\n        return new SkipIterator(this, count);\n    }\n    /**\n     * Prefetch the first `bufferSize` items in this stream.\n     *\n     * Note this prefetches Promises, but makes no guarantees about when those\n     * Promises resolve.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     */\n    prefetch(bufferSize) {\n        return new PrefetchIterator(this, bufferSize);\n    }\n    // TODO(soergel): deep sharded shuffle, where supported\n    /**\n     * Randomly shuffles the elements of this stream.\n     *\n     * @param bufferSize: An integer specifying the number of elements from\n     * this stream from which the new stream will sample.\n     * @param seed: (Optional.) An integer specifying the random seed that\n     * will be used to create the distribution.\n     */\n    shuffle(windowSize, seed) {\n        return new ShuffleIterator(this, windowSize, seed);\n    }\n    /**\n     * Force an iterator to execute serially: each next() call will await the\n     * prior one, so that they cannot execute concurrently.\n     */\n    serial() {\n        return new SerialIterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on LazyIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// Iterators that just extend LazyIterator directly\n// ============================================================================\nclass ArrayIterator extends LazyIterator {\n    constructor(items) {\n        super();\n        this.items = items;\n        this.trav = 0;\n    }\n    summary() {\n        return `Array of ${this.items.length} items`;\n    }\n    async next() {\n        if (this.trav >= this.items.length) {\n            return { value: null, done: true };\n        }\n        const item = this.items[this.trav];\n        this.trav++;\n        return { value: deepClone(item), done: false };\n    }\n}\nclass FunctionCallIterator extends LazyIterator {\n    constructor(nextFn) {\n        super();\n        this.nextFn = nextFn;\n    }\n    summary() {\n        return `Function call`;\n    }\n    async next() {\n        try {\n            return this.nextFn();\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message =\n                `Error thrown while iterating through a dataset: ${e.message}`;\n            throw e;\n        }\n    }\n}\nclass SerialIterator extends LazyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Serial`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        return this.upstream.next();\n    }\n}\nclass SkipIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Skip`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // TODO(soergel): consider tradeoffs of reading in parallel, eg.\n        // collecting next() promises in an Array and then waiting for\n        // Promise.all() of those. Benefit: pseudo-parallel execution.  Drawback:\n        // maybe delayed GC.\n        while (this.count++ < this.maxCount) {\n            const skipped = await this.upstream.next();\n            // short-circuit if upstream is already empty\n            if (skipped.done) {\n                return skipped;\n            }\n            tf.dispose(skipped.value);\n        }\n        return this.upstream.next();\n    }\n}\nclass TakeIterator extends LazyIterator {\n    constructor(upstream, maxCount) {\n        super();\n        this.upstream = upstream;\n        this.maxCount = maxCount;\n        this.count = 0;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Take`;\n    }\n    async next() {\n        if (this.count++ >= this.maxCount) {\n            return { value: null, done: true };\n        }\n        return this.upstream.next();\n    }\n}\n// Note this batch just groups items into row-wise element arrays.\n// Rotating these to a column-wise representation happens only at the dataset\n// level.\nclass RowMajorBatchIterator extends LazyIterator {\n    constructor(upstream, batchSize, enableSmallLastBatch = true) {\n        super();\n        this.upstream = upstream;\n        this.batchSize = batchSize;\n        this.enableSmallLastBatch = enableSmallLastBatch;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> RowMajorBatch`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        const batch = [];\n        while (batch.length < this.batchSize) {\n            const item = await this.upstream.next();\n            if (item.done) {\n                if (this.enableSmallLastBatch && batch.length > 0) {\n                    return { value: batch, done: false };\n                }\n                return { value: null, done: true };\n            }\n            batch.push(item.value);\n        }\n        return { value: batch, done: false };\n    }\n}\nclass FilterIterator extends LazyIterator {\n    constructor(upstream, predicate) {\n        super();\n        this.upstream = upstream;\n        this.predicate = predicate;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Filter`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            const item = await this.upstream.next();\n            if (item.done || this.predicate(item.value)) {\n                return item;\n            }\n            tf.dispose(item.value);\n        }\n    }\n}\nclass MapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Map`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\nclass ErrorHandlingLazyIterator extends LazyIterator {\n    constructor(upstream, handler) {\n        super();\n        this.upstream = upstream;\n        this.handler = handler;\n        this.count = 0;\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    summary() {\n        return `${this.upstream.summary()} -> handleErrors`;\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        while (true) {\n            try {\n                return await this.upstream.next();\n            }\n            catch (e) {\n                if (!this.handler(e)) {\n                    return { value: null, done: true };\n                }\n                // If the handler returns true, loop and fetch the next upstream item.\n                // If the upstream iterator throws an endless stream of errors, and if\n                // the handler says to ignore them, then we loop forever here.  That is\n                // the correct behavior-- it's up to the handler to decide when to stop.\n            }\n        }\n    }\n}\nclass AsyncMapIterator extends LazyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> AsyncMap`;\n    }\n    async next() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return { value: null, done: true };\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // That's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying\n        // any intermediate Tensors.  Here we are concerned only about the\n        // inputs.\n        const mapped = await this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mapped);\n        // TODO(soergel) faster intersection\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return { value: mapped, done: false };\n    }\n}\n// Iterators that maintain a queue of pending items\n// ============================================================================\n/**\n * A base class for transforming streams that operate by maintaining an\n * output queue of elements that are ready to return via next().  This is\n * commonly required when the transformation is 1-to-many:  A call to next()\n * may trigger a call to the underlying stream, which will produce many\n * mapped elements of this stream-- of which we need to return only one, so\n * we have to queue the rest.\n */\nexport class OneToManyIterator extends LazyIterator {\n    constructor() {\n        super();\n        this.outputQueue = new GrowingRingBuffer();\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    async serialNext() {\n        // Fetch so that the queue contains at least one item if possible.\n        // If the upstream source is exhausted, AND there are no items left in\n        // the output queue, then this stream is also exhausted.\n        while (this.outputQueue.length() === 0) {\n            // TODO(soergel): consider parallel reads.\n            if (!await this.pump()) {\n                return { value: null, done: true };\n            }\n        }\n        return { value: this.outputQueue.shift(), done: false };\n    }\n}\nclass FlatmapIterator extends OneToManyIterator {\n    constructor(upstream, transform) {\n        super();\n        this.upstream = upstream;\n        this.transform = transform;\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Flatmap`;\n    }\n    async pump() {\n        const item = await this.upstream.next();\n        if (item.done) {\n            return false;\n        }\n        const inputTensors = tf.tensor_util.getTensorsInContainer(item.value);\n        // Careful: the transform may mutate the item in place.\n        // that's why we have to remember the input Tensors above, and then\n        // below dispose only those that were not passed through to the output.\n        // Note too that the transform function is responsible for tidying any\n        // intermediate Tensors.  Here we are concerned only about the inputs.\n        const mappedArray = this.transform(item.value);\n        const outputTensors = tf.tensor_util.getTensorsInContainer(mappedArray);\n        this.outputQueue.pushAll(mappedArray);\n        // TODO(soergel) faster intersection, and deduplicate outputTensors\n        // TODO(soergel) move to tf.disposeExcept(in, out)?\n        for (const t of inputTensors) {\n            if (!tf.tensor_util.isTensorInList(t, outputTensors)) {\n                t.dispose();\n            }\n        }\n        return true;\n    }\n}\n/**\n * Provides a `LazyIterator` that concatenates a stream of underlying\n * streams.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n */\nexport class ChainedIterator extends LazyIterator {\n    constructor(iterators, baseErrorHandler) {\n        super();\n        this.baseErrorHandler = baseErrorHandler;\n        // Strict Promise execution order:\n        // a next() call may not even begin until the previous one completes.\n        this.lastRead = null;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.iterator = null;\n        this.moreIterators = iterators;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of chained summaries';\n        return `${upstreamSummaries} -> Chained`;\n    }\n    async next() {\n        this.lastRead = this.readFromChain(this.lastRead);\n        return this.lastRead;\n    }\n    async readFromChain(lastRead) {\n        // Must await on the previous read since the previous read may have advanced\n        // the stream of streams, from which we need to read.\n        // This is unfortunate since we can't parallelize reads. Which means\n        // prefetching of chained streams is a no-op.\n        // One solution is to prefetch immediately upstream of this.\n        await lastRead;\n        if (this.iterator == null) {\n            const iteratorResult = await this.moreIterators.next();\n            if (iteratorResult.done) {\n                // No more streams to stream from.\n                return { value: null, done: true };\n            }\n            this.iterator = iteratorResult.value;\n            if (this.baseErrorHandler != null) {\n                this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n            }\n        }\n        const itemResult = await this.iterator.next();\n        if (itemResult.done) {\n            this.iterator = null;\n            return this.readFromChain(lastRead);\n        }\n        return itemResult;\n    }\n}\nexport var ZipMismatchMode;\n(function (ZipMismatchMode) {\n    ZipMismatchMode[ZipMismatchMode[\"FAIL\"] = 0] = \"FAIL\";\n    ZipMismatchMode[ZipMismatchMode[\"SHORTEST\"] = 1] = \"SHORTEST\";\n    ZipMismatchMode[ZipMismatchMode[\"LONGEST\"] = 2] = \"LONGEST\"; // use nulls for exhausted streams; use up the longest stream.\n})(ZipMismatchMode || (ZipMismatchMode = {}));\n/**\n * Provides a `LazyIterator` that zips together an array, dict, or nested\n * structure of `LazyIterator`s (and perhaps additional constants).\n *\n * The underlying streams must provide elements in a consistent order such\n * that they correspond.\n *\n * Typically, the underlying streams should have the same number of\n * elements. If they do not, the behavior is determined by the\n * `mismatchMode` argument.\n *\n * The nested structure of the `iterators` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Doing this in a concurrency-safe way requires some trickery.  In\n * particular, we want this stream to return the elements from the\n * underlying streams in the correct order according to when next() was\n * called, even if the resulting Promises resolve in a different order.\n *\n * @param iterators: An array or object containing LazyIterators at the\n * leaves.\n * @param mismatchMode: Determines what to do when one underlying iterator\n * is exhausted before the others.  `ZipMismatchMode.FAIL` (the default)\n * causes an error to be thrown in this case.  `ZipMismatchMode.SHORTEST`\n * causes the zipped iterator to terminate with the furst underlying\n * streams, so elements remaining on the longer streams are ignored.\n * `ZipMismatchMode.LONGEST` causes the zipped stream to continue, filling\n * in nulls for the exhausted streams, until all streams are exhausted.\n */\nclass ZipIterator extends LazyIterator {\n    constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n        super();\n        this.iterators = iterators;\n        this.mismatchMode = mismatchMode;\n        this.count = 0;\n        this.currentPromise = null;\n    }\n    summary() {\n        const upstreamSummaries = 'TODO: fill in upstream of zip summaries';\n        return `{${upstreamSummaries}} -> Zip`;\n    }\n    async nextState(afterState) {\n        // This chaining ensures that the underlying next() are not even called\n        // before the previous ones have resolved.\n        await afterState;\n        // Collect underlying iterator \"done\" signals as a side effect in\n        // getNext()\n        let numIterators = 0;\n        let iteratorsDone = 0;\n        function getNext(container) {\n            if (container instanceof LazyIterator) {\n                const result = container.next();\n                return {\n                    value: result.then(x => {\n                        numIterators++;\n                        if (x.done) {\n                            iteratorsDone++;\n                        }\n                        return x.value;\n                    }),\n                    recurse: false\n                };\n            }\n            else {\n                return { value: null, recurse: true };\n            }\n        }\n        const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n        if (numIterators === iteratorsDone) {\n            // The streams have all ended.\n            return { value: null, done: true };\n        }\n        if (iteratorsDone > 0) {\n            switch (this.mismatchMode) {\n                case ZipMismatchMode.FAIL:\n                    throw new Error('Zipped streams should have the same length. ' +\n                        `Mismatched at element ${this.count}.`);\n                case ZipMismatchMode.SHORTEST:\n                    return { value: null, done: true };\n                case ZipMismatchMode.LONGEST:\n                default:\n                // Continue.  The exhausted streams already produced value: null.\n            }\n        }\n        this.count++;\n        return { value: mapped, done: false };\n    }\n    async next() {\n        this.currentPromise = this.nextState(this.currentPromise);\n        return this.currentPromise;\n    }\n}\n// Iterators that maintain a ring buffer of pending promises\n// ============================================================================\n/**\n * A stream that prefetches a given number of items from an upstream source,\n * returning them in FIFO order.\n *\n * Note this prefetches Promises, but makes no guarantees about when those\n * Promises resolve.\n */\nexport class PrefetchIterator extends LazyIterator {\n    constructor(upstream, bufferSize) {\n        super();\n        this.upstream = upstream;\n        this.bufferSize = bufferSize;\n        this.buffer = new RingBuffer(bufferSize);\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Prefetch`;\n    }\n    /**\n     * Refill the prefetch buffer.  Returns only after the buffer is full, or\n     * the upstream source is exhausted.\n     */\n    refill() {\n        while (!this.buffer.isFull()) {\n            const v = this.upstream.next();\n            this.buffer.push(v);\n        }\n    }\n    next() {\n        this.refill();\n        // This shift will never throw an error because the buffer is always\n        // full after a refill. If the stream is exhausted, the buffer will be\n        // full of Promises that will resolve to the end-of-stream signal.\n        return this.buffer.shift();\n    }\n}\n/**\n * A stream that performs a sliding-window random shuffle on an upstream\n * source. This is like a `PrefetchIterator` except that the items are\n * returned in randomized order.  Mixing naturally improves as the buffer\n * size increases.\n */\nexport class ShuffleIterator extends PrefetchIterator {\n    constructor(upstream, windowSize, seed) {\n        super(upstream, windowSize);\n        this.upstream = upstream;\n        this.windowSize = windowSize;\n        // Local state that should not be clobbered by out-of-order execution.\n        this.upstreamExhausted = false;\n        this.random = seedrandom.alea(seed || tf.util.now().toString());\n        this.lastRead = Promise.resolve({ value: null, done: false });\n    }\n    async next() {\n        // This sets this.lastRead to a new Promise right away, as opposed to\n        // saying `await this.lastRead; this.lastRead = this.serialNext();` which\n        // would not work because this.nextRead would be updated only after the\n        // promise resolves.\n        this.lastRead = this.lastRead.then(() => this.serialNext());\n        return this.lastRead;\n    }\n    randomInt(max) {\n        return Math.floor(this.random() * max);\n    }\n    chooseIndex() {\n        return this.randomInt(this.buffer.length());\n    }\n    async serialNext() {\n        // TODO(soergel): consider performance\n        if (!this.upstreamExhausted) {\n            this.refill();\n        }\n        while (!this.buffer.isEmpty()) {\n            const chosenIndex = this.chooseIndex();\n            const result = await this.buffer.shuffleExcise(chosenIndex);\n            if (result.done) {\n                this.upstreamExhausted = true;\n            }\n            else {\n                this.refill();\n                return result;\n            }\n        }\n        return { value: null, done: true };\n    }\n}\n//# sourceMappingURL=lazy_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport * as seedrandom from 'seedrandom';\nimport { iteratorFromConcatenated, iteratorFromFunction, iteratorFromItems, iteratorFromZipped, ZipMismatchMode } from './iterators/lazy_iterator';\nimport { canTensorify, deepMapAndAwaitAll, isIterable } from './util/deep_map';\n// TODO(soergel): consider vectorized operations within the pipeline.\n/**\n * Represents a potentially large list of independent data elements (typically\n * 'samples' or 'examples').\n *\n * A 'data example' may be a primitive, an array, a map from string keys to\n * values, or any nested structure of these.\n *\n * A `Dataset` represents an ordered collection of elements, together with a\n * chain of transformations to be performed on those elements. Each\n * transformation is a method of `Dataset` that returns another `Dataset`, so\n * these may be chained, e.g.\n * `const processedDataset = rawDataset.filter(...).map(...).batch(...)`.\n *\n * Data loading and transformation is done in a lazy, streaming fashion.  The\n * dataset may be iterated over multiple times; each iteration starts the data\n * loading anew and recapitulates the transformations.\n *\n * A `Dataset` is typically processed as a stream of unbatched examples --i.e.,\n * its transformations are applied one example at a time. Batching produces a\n * new `Dataset` where each element is a batch. Batching should usually come\n * last in a pipeline, because data transformations are easier to express on a\n * per-example basis than on a per-batch basis.\n *\n * The following code examples are calling `await dataset.forEachAsync(...)` to\n * iterate once over the entire dataset in order to print out the data.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class Dataset {\n    constructor() {\n        this.size = null;\n    }\n    // TODO(soergel): Make Datasets report whether repeated iterator() calls\n    // produce the same result (e.g., reading from a file) or different results\n    // (e.g., from the webcam).  Currently we don't make this distinction but it\n    // could be important for the user to know.\n    // abstract isDeterministic(): boolean;\n    /**\n     * Groups elements into batches.\n     *\n     * It is assumed that each of the incoming dataset elements has the same\n     * structure-- i.e. the same set of keys at each location in an object\n     * hierarchy.  For each key, the resulting `Dataset` provides a batched\n     * element collecting all of the incoming values for that key.\n     *\n     *  * Incoming primitives are grouped into a 1-D Tensor.\n     *  * Incoming Tensors are grouped into a new Tensor where the 0'th axis is\n     *    the batch dimension.\n     *  * Incoming arrays are converted to Tensor and then batched.\n     *  * A nested array is interpreted as an n-D Tensor, so the batched result\n     *    has n+1 dimensions.\n     *  * An array that cannot be converted to Tensor produces an error.\n     *\n     * If an array should not be batched as a unit, it should first be converted\n     * to an object with integer keys.\n     *\n     * Here are a few examples:\n     *\n     * Batch a dataset of numbers:\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8]).batch(4);\n     * await a.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of arrays:\n     * ```js\n     * const b = tf.data.array([[1], [2], [3], [4], [5], [6], [7], [8]]).batch(4);\n     * await b.forEachAsync(e => e.print());\n     * ```\n     *\n     * Batch a dataset of objects:\n     * ```js\n     * const c = tf.data.array([{a: 1, b: 11}, {a: 2, b: 12}, {a: 3, b: 13},\n     *   {a: 4, b: 14}, {a: 5, b: 15}, {a: 6, b: 16}, {a: 7, b: 17},\n     *   {a: 8, b: 18}]).batch(4);\n     * await c.forEachAsync(e => {\n     *   console.log('{');\n     *   for(var key in e) {\n     *     console.log(key+':');\n     *     e[key].print();\n     *   }\n     *   console.log('}');\n     * })\n     * ```\n     *\n     * @param batchSize The number of elements desired per batch.\n     * @param smallLastBatch Whether to emit the final batch when it has fewer\n     *   than batchSize elements. Default true.\n     * @returns A `Dataset`, from which a stream of batches can be obtained.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    batch(batchSize, smallLastBatch = true) {\n        const base = this;\n        tf.util.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n        let size;\n        if (this.size === Infinity || this.size == null) {\n            // If the size of this dataset is infinity or null, the new size keeps the\n            // same.\n            size = this.size;\n        }\n        else if (smallLastBatch) {\n            // If the size of this dataset is known and include small last batch, the\n            // new size is full batch count plus last batch.\n            size = Math.ceil(this.size / batchSize);\n        }\n        else {\n            // If the size of this dataset is known and not include small last batch,\n            // the new size is full batch count.\n            size = Math.floor(this.size / batchSize);\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator())\n                .columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n        }, size);\n    }\n    /**\n     * Concatenates this `Dataset` with another.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * const b = tf.data.array([4, 5, 6]);\n     * const c = a.concatenate(b);\n     * await c.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param dataset A `Dataset` to be concatenated onto this one.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    concatenate(dataset) {\n        const base = this;\n        let size;\n        if (this.size === Infinity || dataset.size === Infinity) {\n            // If the size of any of these two dataset is infinity, new size is\n            // infinity.\n            size = Infinity;\n        }\n        else if (this.size != null && dataset.size != null) {\n            // If the size of both datasets are known and not infinity, new size is\n            // sum the size of these two datasets.\n            size = this.size + dataset.size;\n        }\n        else {\n            // If neither of these two datasets has infinite size and any of these two\n            // datasets' size is null, the new size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n    }\n    /**\n     * Filters this dataset according to `predicate`.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n     *   .filter(x => x%2 === 0);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param predicate A function mapping a dataset element to a boolean or a\n     * `Promise` for one.\n     *\n     * @returns A `Dataset` of elements for which the predicate was true.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    filter(predicate) {\n        const base = this;\n        let size;\n        if (this.size === Infinity) {\n            // If the size of this dataset is infinity, new size is infinity\n            size = Infinity;\n        }\n        else {\n            // If this dataset has limited elements, new size is null because it might\n            // exhausted randomly.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).filter(x => tf.tidy(() => predicate(x)));\n        }, size);\n    }\n    /**\n     * Apply a function to every element of the dataset.\n     *\n     * After the function is applied to a dataset element, any Tensors contained\n     * within that element are disposed.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param f A function to apply to each dataset element.\n     * @returns A `Promise` that resolves after all elements have been processed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async forEachAsync(f) {\n        return (await this.iterator()).forEachAsync(f);\n    }\n    /**\n     * Maps this dataset through a 1-to-1 transform.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).map(x => x*x);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a transformed\n     *   dataset element.\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    map(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).map(x => tf.tidy(() => transform(x)));\n        }, this.size);\n    }\n    /**\n     * Maps this dataset through an async 1-to-1 transform.\n     *\n     * ```js\n     * const a =\n     *  tf.data.array([1, 2, 3]).mapAsync(x => new Promise(function(resolve){\n     *    setTimeout(() => {\n     *      resolve(x * x);\n     *    }, Math.random()*1000 + 500);\n     *  }));\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @param transform A function mapping a dataset element to a `Promise` for a\n     *   transformed dataset element.  This transform is responsible for disposing\n     *   any intermediate `Tensor`s, i.e. by wrapping its computation in\n     *   `tf.tidy()`; that cannot be automated here (as it is in the synchronous\n     *   `map()` case).\n     *\n     * @returns A `Dataset` of transformed elements.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    mapAsync(transform) {\n        const base = this;\n        return datasetFromIteratorFn(async () => {\n            return (await base.iterator()).mapAsync(transform);\n        }, this.size);\n    }\n    /**\n     *  Creates a `Dataset` that prefetches elements from this dataset.\n     *\n     * @param bufferSize: An integer specifying the number of elements to be\n     *   prefetched.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    prefetch(bufferSize) {\n        if (bufferSize == null) {\n            throw new RangeError('`Dataset.prefetch()` requires bufferSize to be specified.');\n        }\n        const base = this;\n        return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n    }\n    /**\n     * Repeats this dataset `count` times.\n     *\n     * NOTE: If this dataset is a function of global state (e.g. a random number\n     * generator), then different repetitions may produce different elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3]).repeat(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: (Optional) An integer, representing the number of times\n     *   the dataset should be repeated. The default behavior (if `count` is\n     *   `undefined` or negative) is for the dataset be repeated indefinitely.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    repeat(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count > 0) {\n            // If this dataset has size and count is positive, new size is current\n            // size multiply count. This also covers the case that current size is\n            // infinity.\n            size = this.size * count;\n        }\n        else if (count === 0) {\n            // If count is 0, new size is 0.\n            size = 0;\n        }\n        else if (this.size != null && (count === undefined || count < 0)) {\n            // If this dataset has size and count is undefined or negative, the\n            // dataset will be repeated indefinitely and new size is infinity.\n            size = Infinity;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => {\n            const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n            return iteratorFromConcatenated(iteratorIterator.take(count));\n        }, size);\n    }\n    /**\n     * Creates a `Dataset` that skips `count` initial elements from this dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).skip(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be skipped\n     *   to form the new dataset.  If `count` is greater than the size of this\n     *   dataset, the new dataset will contain no elements.  If `count`\n     *   is `undefined` or negative, skips the entire dataset.\n     *\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    skip(count) {\n        const base = this;\n        let size;\n        if (this.size != null && count >= 0 && this.size >= count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is current size minus skipped size.This also covers the case that\n            // current size is infinity.\n            size = this.size - count;\n        }\n        else if (this.size != null &&\n            (this.size < count || count === undefined || count < 0)) {\n            // If the size of this dataset is smaller than count, or count is\n            // undefined or negative, skips the entire dataset and the new size is 0.\n            size = 0;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).skip(count), size);\n    }\n    /**\n     * Pseudorandomly shuffles the elements of this dataset. This is done in a\n     * streaming manner, by sampling from a given number of prefetched elements.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).shuffle(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param bufferSize: An integer specifying the number of elements from this\n     *   dataset from which the new dataset will sample.\n     * @param seed: (Optional) An integer specifying the random seed that will\n     *   be used to create the distribution.\n     * @param reshuffleEachIteration: (Optional) A boolean, which if true\n     *   indicates that the dataset should be pseudorandomly reshuffled each time\n     *   it is iterated over. If false, elements will be returned in the same\n     *   shuffled order on each iteration. (Defaults to `true`.)\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n        if (bufferSize == null || bufferSize < 0) {\n            if (this.size == null) {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.');\n            }\n            else {\n                throw new RangeError('`Dataset.shuffle()` requires bufferSize to be specified.  ' +\n                    'If your data fits in main memory (for regular JS objects), ' +\n                    'and/or GPU memory (for `tf.Tensor`s), consider setting ' +\n                    `bufferSize to the dataset size (${this.size} elements)`);\n            }\n        }\n        const base = this;\n        const random = seedrandom.alea(seed || tf.util.now().toString());\n        return datasetFromIteratorFn(async () => {\n            let seed2 = random.int32();\n            if (reshuffleEachIteration) {\n                seed2 += random.int32();\n            }\n            return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n        }, this.size);\n    }\n    /**\n     * Creates a `Dataset` with at most `count` initial elements from this\n     * dataset.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]).take(3);\n     * await a.forEachAsync(e => console.log(e));\n     * ```\n     *\n     * @param count: The number of elements of this dataset that should be taken\n     *   to form the new dataset.  If `count` is `undefined` or negative, or if\n     *   `count` is greater than the size of this dataset, the new dataset will\n     *   contain all elements of this dataset.\n     * @returns A `Dataset`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    take(count) {\n        const base = this;\n        let size;\n        if (this.size != null && this.size > count) {\n            // If the size of this dataset is greater than count, the new dataset's\n            // size is count.\n            size = count;\n        }\n        else if (this.size != null && this.size <= count) {\n            // If the size of this dataset is equal or smaller than count, the new\n            // dataset's size is the size of this dataset.\n            size = this.size;\n        }\n        else {\n            // If the size of this dataset is null, the new dataset's size is null.\n            size = null;\n        }\n        return datasetFromIteratorFn(async () => (await base.iterator()).take(count), size);\n    }\n    /**\n     * Collect all elements of this dataset into an array.\n     *\n     * Obviously this will succeed only for small datasets that fit in memory.\n     * Useful for testing and generally should be avoided if possible.\n     *\n     * ```js\n     * const a = tf.data.array([1, 2, 3, 4, 5, 6]);\n     * console.log(await a.toArray());\n     * ```\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async toArray() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArray();\n    }\n    /**\n     * Collect all elements of this dataset into an array with prefetching 100\n     * elements. This is useful for testing, because the prefetch changes the\n     * order in which the Promises are resolved along the processing pipeline.\n     * This may help expose bugs where results are dependent on the order of\n     * Promise resolution rather than on the logical order of the stream (i.e.,\n     * due to hidden mutable state).\n     *\n     * @returns A Promise for an array of elements, which will resolve\n     *   when a new stream has been obtained and fully consumed.\n     */\n    async toArrayForTest() {\n        if (this.size === Infinity) {\n            throw new Error('Can not convert infinite data stream to array.');\n        }\n        return (await this.iterator()).toArrayForTest();\n    }\n}\n// TODO(soergel): deep sharded shuffle, where supported\nDataset.MAX_BUFFER_SIZE = 10000;\n/**\n * Create a `Dataset` defined by a provided iterator() function.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const iter = tf.data.iteratorFromFunction(func);\n * const ds = tf.data.datasetFromIteratorFn(iter);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n */\nexport function datasetFromIteratorFn(iteratorFn, size = null) {\n    return new class extends Dataset {\n        constructor() {\n            super(...arguments);\n            this.size = size;\n        }\n        /*\n         * Provide a new stream of elements.  Note this will also start new streams\n         * from any underlying `Dataset`s.\n         */\n        async iterator() {\n            return iteratorFn();\n        }\n    }();\n}\n/**\n * Create a `Dataset` from an array of elements.\n *\n * Create a Dataset from an array of objects:\n * ```js\n * const a = tf.data.array([{'item': 1}, {'item': 2}, {'item': 3}]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n *\n * Create a Dataset from an array of numbers:\n * ```js\n * const a = tf.data.array([4, 5, 6]);\n * await a.forEachAsync(e => console.log(e));\n * ```\n * @param items An array of elements that will be parsed as items in a dataset.\n *\n * @doc {heading: 'Data', subheading: 'Creation', namespace: 'data'}\n */\nexport function array(items) {\n    return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\n/**\n * Create a `Dataset` by zipping together an array, dict, or nested\n * structure of `Dataset`s (and perhaps additional constants).\n * The underlying datasets must provide elements in a consistent order such that\n * they correspond.\n *\n * The number of elements in the resulting dataset is the same as the size of\n * the smallest dataset in datasets.\n *\n * The nested structure of the `datasets` argument determines the\n * structure of elements in the resulting iterator.\n *\n * Note this means that, given an array of two datasets that produce dict\n * elements, the result is a dataset that produces elements that are arrays\n * of two dicts:\n *\n * Zip an array of datasets:\n * ```js\n * console.log('Zip two datasets of objects:');\n * const ds1 = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const ds2 = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const ds3 = tf.data.zip([ds1, ds2]);\n * await ds3.forEachAsync(e => console.log(JSON.stringify(e)));\n *\n * // If the goal is to merge the dicts in order to produce elements like\n * // {a: ..., b: ...}, this requires a second step such as:\n * console.log('Merge the objects:');\n * const ds4 = ds3.map(x => {return {a: x[0].a, b: x[1].b}});\n * await ds4.forEachAsync(e => console.log(e));\n * ```\n *\n * Zip a dict of datasets:\n * ```js\n * const a = tf.data.array([{a: 1}, {a: 2}, {a: 3}]);\n * const b = tf.data.array([{b: 4}, {b: 5}, {b: 6}]);\n * const c = tf.data.zip({c: a, d: b});\n * await c.forEachAsync(e => console.log(JSON.stringify(e)));\n * ```\n *\n * @doc {heading: 'Data', subheading: 'Operations', namespace: 'data'}\n */\nexport function zip(datasets) {\n    // manually type-check the argument for JS users\n    if (!isIterable(datasets)) {\n        throw new Error('The argument to zip() must be an object or array.');\n    }\n    let size;\n    if (Array.isArray(datasets)) {\n        for (let i = 0; i < datasets.length; i++) {\n            size = size == null ? datasets[i].size :\n                Math.min(size, datasets[i].size);\n        }\n    }\n    else if (datasets instanceof Object) {\n        for (const ds in datasets) {\n            size = size == null ? datasets[ds].size :\n                Math.min(size, datasets[ds].size);\n        }\n    }\n    return datasetFromIteratorFn(async () => {\n        const streams = await deepMapAndAwaitAll(datasets, d => {\n            if (d instanceof Dataset) {\n                return { value: d.iterator(), recurse: false };\n            }\n            else if (isIterable(d)) {\n                return { value: null, recurse: true };\n            }\n            else {\n                throw new Error('Leaves of the structure passed to zip() must be Datasets, ' +\n                    'not primitives.');\n            }\n        });\n        return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n    }, size);\n}\n/**\n * A zip function for use with deepZip, passed via the columnMajorBatch call.\n *\n * Accepts an array of identically-structured nested elements and either batches\n * them (if they are primitives, numeric arrays, or Tensors) or requests\n * recursion (if not).\n */\n// tslint:disable-next-line:no-any\nfunction deepBatchConcat(rows) {\n    if (rows === null) {\n        return null;\n    }\n    // use the first item to decide whether to recurse or batch here.\n    const exampleRow = rows[0];\n    if (canTensorify(exampleRow)) {\n        // rows is an array of primitives, Tensors, or arrays.  Batch them.\n        const value = batchConcat(rows);\n        return { value, recurse: false };\n    }\n    // the example row is an object, so recurse into it.\n    return { value: null, recurse: true };\n}\n/**\n * Assembles a list of same-shaped numbers, number arrays, or Tensors\n * into a single new Tensor where axis 0 is the batch dimension.\n */\nfunction batchConcat(arrays) {\n    if (arrays.length === 0) {\n        // We can't return an empty Tensor because we don't know the element shape.\n        throw new Error('Can\\'t make a batch of zero elements.');\n    }\n    if (arrays[0] instanceof tf.Tensor) {\n        // Input is an array of Tensors\n        return tf.stack(arrays);\n    }\n    else {\n        // Input is a possibly-nested array of numbers.\n        return tf.tensor(arrays);\n    }\n}\n//# sourceMappingURL=dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { Dataset } from '../dataset';\n/**\n * Represents a potentially large collection of text lines.\n *\n * The results are not batched.\n */\nexport class TextLineDataset extends Dataset {\n    /**\n     * Create a `TextLineDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     */\n    constructor(input) {\n        super();\n        this.input = input;\n    }\n    async iterator() {\n        const inputIterator = await this.input.iterator();\n        const utf8Iterator = inputIterator.decodeUTF8();\n        const lineIterator = utf8Iterator.split('\\n').map(line => {\n            // Windows/DOS format text file has extra line breaker at the end of line.\n            if (line.endsWith('\\r')) {\n                line = line.slice(0, -1);\n            }\n            return line;\n        });\n        return lineIterator;\n    }\n}\n//# sourceMappingURL=text_line_dataset.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { Dataset } from '../dataset';\nimport { TextLineDataset } from './text_line_dataset';\nconst CODE_QUOTE = '\"';\nconst STATE_OUT = Symbol('out');\nconst STATE_FIELD = Symbol('field');\nconst STATE_QUOTE = Symbol('quote');\nconst STATE_QUOTE_AFTER_QUOTE = Symbol('quoteafterquote');\nconst STATE_WITHIN_QUOTE_IN_QUOTE = Symbol('quoteinquote');\n/**\n * Represents a potentially large collection of delimited text records.\n *\n * The produced `TensorContainer`s each contain one key-value pair for\n * every column of the table.  When a field is empty in the incoming data, the\n * resulting value is `undefined`, or throw error if it is required.  Values\n * that can be parsed as numbers are emitted as type `number`, other values\n * are parsed as `string`.\n *\n * The results are not batched.\n *\n * @doc {heading: 'Data', subheading: 'Classes', namespace: 'data'}\n */\nexport class CSVDataset extends Dataset {\n    /**\n     * Create a `CSVDataset`.\n     *\n     * @param input A `DataSource` providing a chunked, UTF8-encoded byte stream.\n     * @param csvConfig (Optional) A CSVConfig object that contains configurations\n     *     of reading and decoding from CSV file(s).\n     *\n     *     hasHeader: (Optional) A boolean value that indicates whether the first\n     *     row of provided CSV file is a header line with column names, and should\n     *     not be included in the data. Defaults to `true`.\n     *\n     *     columnNames: (Optional) A list of strings that corresponds to\n     *     the CSV column names, in order. If provided, it ignores the column\n     *     names inferred from the header row. If not provided, infers the column\n     *     names from the first row of the records. If hasHeader is false and\n     *     columnNames is not provided, this method throws an error.\n     *\n     *     columnConfigs: (Optional) A dictionary whose key is column names, value\n     *     is an object stating if this column is required, column's data type,\n     *     default value, and if this column is label. If provided, keys must\n     *     correspond to names provided in columnNames or inferred from the file\n     *     header lines. If isLabel is true any column, returns an array of two\n     *     items: the first item is a dict of features key/value pairs, the second\n     *     item is a dict of labels key/value pairs. If no feature is marked as\n     *     label, returns a dict of features only.\n     *\n     *     configuredColumnsOnly (Optional) If true, only columns provided in\n     *     columnConfigs will be parsed and provided during iteration.\n     *\n     *     delimiter (Optional) The string used to parse each line of the input\n     *     file. Defaults to `,`.\n     */\n    constructor(input, csvConfig) {\n        super();\n        this.input = input;\n        this.hasHeader = true;\n        this.fullColumnNames = null;\n        this.columnNamesValidated = false;\n        this.columnConfigs = null;\n        this.configuredColumnsOnly = false;\n        this.delimiter = ',';\n        this.delimWhitespace = false;\n        this.base = new TextLineDataset(input);\n        if (!csvConfig) {\n            csvConfig = {};\n        }\n        this.hasHeader = csvConfig.hasHeader === false ? false : true;\n        this.fullColumnNames = csvConfig.columnNames;\n        this.columnConfigs = csvConfig.columnConfigs;\n        this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n        if (csvConfig.delimWhitespace) {\n            util.assert(csvConfig.delimiter == null, () => 'Delimiter should not be provided when delimWhitespace is true.');\n            this.delimWhitespace = true;\n            this.delimiter = ' ';\n        }\n        else {\n            this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ',';\n        }\n    }\n    /**\n     * Returns column names of the csv dataset. If `configuredColumnsOnly` is\n     * true, return column names in `columnConfigs`. If `configuredColumnsOnly` is\n     * false and `columnNames` is provided, `columnNames`. If\n     * `configuredColumnsOnly` is false and `columnNames` is not provided, return\n     * all column names parsed from the csv file. For example usage please go to\n     * `tf.data.csv`.\n     *\n     * @doc {heading: 'Data', subheading: 'Classes'}\n     */\n    async columnNames() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) :\n            this.fullColumnNames;\n    }\n    /* 1) If `columnNames` is provided as string[], use this string[] as output\n     * keys in corresponding order. The length must match the number of inferred\n     * columns if `hasHeader` is true .\n     * 2) If `columnNames` is not provided, parse header line as `columnNames` if\n     * hasHeader is true. If `hasHeader` is false, throw an error.\n     * 3) If `columnConfigs` is provided, all the keys in `columnConfigs` must\n     * exist in parsed `columnNames`.\n     */\n    async setColumnNames() {\n        const columnNamesFromFile = await this.maybeReadHeaderLine();\n        if (!this.fullColumnNames && !columnNamesFromFile) {\n            // Throw an error if columnNames is not provided and no header line.\n            throw new Error('Column names must be provided if there is no header line.');\n        }\n        else if (this.fullColumnNames && columnNamesFromFile) {\n            // Check provided columnNames match header line.\n            util.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => 'The length of provided columnNames (' +\n                this.fullColumnNames.length.toString() +\n                ') does not match the length of the header line read from ' +\n                'file (' + columnNamesFromFile.length.toString() + ').');\n        }\n        if (!this.fullColumnNames) {\n            this.fullColumnNames = columnNamesFromFile;\n        }\n        // Check if there are duplicate column names.\n        const counts = this.fullColumnNames.reduce((countAcc, name) => {\n            countAcc[name] = (countAcc[name] + 1) || 1;\n            return countAcc;\n        }, {});\n        const duplicateNames = Object.keys(counts).filter((name) => (counts[name] > 1));\n        util.assert(duplicateNames.length === 0, () => 'Duplicate column names found: ' + duplicateNames.toString());\n        // Check if keys in columnConfigs match columnNames.\n        if (this.columnConfigs) {\n            for (const key of Object.keys(this.columnConfigs)) {\n                const index = this.fullColumnNames.indexOf(key);\n                if (index === -1) {\n                    throw new Error('The key \"' + key +\n                        '\" provided in columnConfigs does not match any of the column ' +\n                        'names (' + this.fullColumnNames.toString() + ').');\n                }\n            }\n        }\n        this.columnNamesValidated = true;\n    }\n    async maybeReadHeaderLine() {\n        if (this.hasHeader) {\n            const iter = await this.base.iterator();\n            const firstElement = await iter.next();\n            if (firstElement.done) {\n                throw new Error('No data was found for CSV parsing.');\n            }\n            const firstLine = firstElement.value;\n            const headers = this.parseRow(firstLine, false);\n            return headers;\n        }\n        else {\n            return null;\n        }\n    }\n    async iterator() {\n        if (!this.columnNamesValidated) {\n            await this.setColumnNames();\n        }\n        let lines = await this.base.iterator();\n        if (this.hasHeader) {\n            // We previously read the first line to get the columnNames.\n            // Now that we're providing data, skip it.\n            lines = lines.skip(1);\n        }\n        return lines.map(x => this.makeDataElement(x));\n    }\n    makeDataElement(line) {\n        const values = this.parseRow(line);\n        const features = {};\n        const labels = {};\n        for (let i = 0; i < this.fullColumnNames.length; i++) {\n            const key = this.fullColumnNames[i];\n            const config = this.columnConfigs ? this.columnConfigs[key] : null;\n            if (this.configuredColumnsOnly && !config) {\n                // This column is not selected.\n                continue;\n            }\n            else {\n                const value = values[i];\n                let parsedValue = null;\n                if (value === '') {\n                    // If default value is provided, use it. If default value is not\n                    // provided, set as undefined.\n                    if (config && config.default !== undefined) {\n                        parsedValue = config.default;\n                    }\n                    else if (config && (config.required || config.isLabel)) {\n                        throw new Error(`Required column ${key} is empty in this line: ${line}`);\n                    }\n                    else {\n                        parsedValue = undefined;\n                    }\n                }\n                else {\n                    // A value is present, so parse it based on type\n                    const valueAsNum = Number(value);\n                    if (isNaN(valueAsNum)) {\n                        // The value is a string and this column is declared as boolean\n                        // in config, parse it as boolean.\n                        if (config && config.dtype === 'bool') {\n                            parsedValue = this.getBoolean(value);\n                        }\n                        else {\n                            // Set value as string\n                            parsedValue = value;\n                        }\n                    }\n                    else if (!config || !config.dtype) {\n                        // If this value is a number and no type config is provided, return\n                        // it as number.\n                        parsedValue = valueAsNum;\n                    }\n                    else {\n                        // If this value is a number and data type is provided, parse it\n                        // according to provided data type.\n                        switch (config.dtype) {\n                            case 'float32':\n                                parsedValue = valueAsNum;\n                                break;\n                            case 'int32':\n                                parsedValue = Math.floor(valueAsNum);\n                                break;\n                            case 'bool':\n                                parsedValue = this.getBoolean(value);\n                                break;\n                            default:\n                                parsedValue = valueAsNum;\n                        }\n                    }\n                }\n                // Check if this column is label.\n                (config && config.isLabel) ? labels[key] = parsedValue :\n                    features[key] = parsedValue;\n            }\n        }\n        // If label exists, return an object of features and labels as {xs:features,\n        // ys:labels}, otherwise return features only.\n        if (Object.keys(labels).length === 0) {\n            return features;\n        }\n        else {\n            return { xs: features, ys: labels };\n        }\n    }\n    getBoolean(value) {\n        if (value === '1' || value.toLowerCase() === 'true') {\n            return 1;\n        }\n        else {\n            return 0;\n        }\n    }\n    // adapted from https://beta.observablehq.com/@mbostock/streaming-csv\n    parseRow(line, validateElementCount = true) {\n        const result = [];\n        let readOffset = 0;\n        const readLength = line.length;\n        let currentState = STATE_OUT;\n        // Goes through the line to parse quote.\n        for (let i = 0; i < readLength; i++) {\n            switch (currentState) {\n                // Before enter a new field\n                case STATE_OUT:\n                    switch (line.charAt(i)) {\n                        // Enter a quoted field\n                        case CODE_QUOTE:\n                            readOffset = i + 1;\n                            currentState = STATE_QUOTE;\n                            break;\n                        // Read an empty field\n                        case this.delimiter:\n                            readOffset = i + 1;\n                            // If delimiter is white space and configured to collapse\n                            // multiple white spaces, ignore this white space.\n                            if (this.delimiter === ' ' && this.delimWhitespace) {\n                                break;\n                            }\n                            result.push('');\n                            currentState = STATE_OUT;\n                            break;\n                        // Enter an unquoted field\n                        default:\n                            currentState = STATE_FIELD;\n                            readOffset = i;\n                            break;\n                    }\n                    break;\n                // In an unquoted field\n                case STATE_FIELD:\n                    switch (line.charAt(i)) {\n                        // Exit an unquoted field, add it to result\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        default:\n                    }\n                    break;\n                // In a quoted field\n                case STATE_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Read a quote after a quote\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE_AFTER_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                // This state means it's right after a second quote in a field\n                case STATE_QUOTE_AFTER_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Finished a quoted field\n                        case this.delimiter:\n                            result.push(line.substring(readOffset, i - 1));\n                            currentState = STATE_OUT;\n                            readOffset = i + 1;\n                            break;\n                        // Finished a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        // In a quoted part in a quoted field\n                        default:\n                            currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n                            break;\n                    }\n                    break;\n                case STATE_WITHIN_QUOTE_IN_QUOTE:\n                    switch (line.charAt(i)) {\n                        // Exit a quoted part in a quoted field\n                        case CODE_QUOTE:\n                            currentState = STATE_QUOTE;\n                            break;\n                        default:\n                    }\n                    break;\n                default:\n            }\n        }\n        // Adds last item based on if it is quoted.\n        if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n            result.push(line.substring(readOffset, readLength - 1));\n        }\n        else {\n            result.push(line.substring(readOffset));\n        }\n        // Check if each row has the same number of elements as column names.\n        if (validateElementCount && result.length !== this.fullColumnNames.length) {\n            throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n        }\n        return result;\n    }\n}\n// TODO(soergel): add more basic datasets for parity with tf.data\n// tf.data.FixedLengthRecordDataset()\n// tf.data.TFRecordDataset()\n//# sourceMappingURL=csv_dataset.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env, tensor, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of tensors from microphone audio stream. The tensors are\n * representing audio data as frequency-domain spectrogram generated with\n * browser's native FFT. Tensors representing time-domain waveform is available\n * based on configuration. Only works in browser environment.\n */\nexport class MicrophoneIterator extends LazyIterator {\n    constructor(microphoneConfig) {\n        super();\n        this.microphoneConfig = microphoneConfig;\n        this.isClosed = false;\n        this.fftSize = microphoneConfig.fftSize || 1024;\n        const fftSizeLog2 = Math.log2(this.fftSize);\n        if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 ||\n            !Number.isInteger(fftSizeLog2)) {\n            throw new Error(`Invalid fftSize: it must be a power of 2 between ` +\n                `2 to 4 and 2 to 14, but got ${this.fftSize}`);\n        }\n        this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n        this.sampleRateHz = microphoneConfig.sampleRateHz;\n        this.columnTruncateLength =\n            microphoneConfig.columnTruncateLength || this.fftSize;\n        this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n        this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n        this.includeSpectrogram =\n            microphoneConfig.includeSpectrogram === false ? false : true;\n        this.includeWaveform =\n            microphoneConfig.includeWaveform === true ? true : false;\n        if (!this.includeSpectrogram && !this.includeWaveform) {\n            throw new Error('Both includeSpectrogram and includeWaveform are false. ' +\n                'At least one type of data should be returned.');\n        }\n    }\n    summary() {\n        return `microphone`;\n    }\n    // Construct a MicrophoneIterator and start the audio stream.\n    static async create(microphoneConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('microphone API is only supported in browser environment.');\n        }\n        const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n        // Call async function start() to initialize the audio stream.\n        await microphoneIterator.start();\n        return microphoneIterator;\n    }\n    // Start the audio stream and FFT.\n    async start() {\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                audio: this.audioTrackConstraints == null ? true :\n                    this.audioTrackConstraints,\n                video: false\n            });\n        }\n        catch (e) {\n            throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain audio from microphone.');\n        }\n        const ctxConstructor = \n        // tslint:disable-next-line:no-any\n        window.AudioContext || window.webkitAudioContext;\n        this.audioContext = new ctxConstructor();\n        if (!this.sampleRateHz) {\n            // If sample rate is not provided, use the available sample rate on\n            // device.\n            this.sampleRateHz = this.audioContext.sampleRate;\n        }\n        else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n            throw new Error(`Mismatch in sampling rate: ` +\n                `Expected: ${this.sampleRateHz}; ` +\n                `Actual: ${this.audioContext.sampleRate}`);\n        }\n        const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n        this.analyser = this.audioContext.createAnalyser();\n        this.analyser.fftSize = this.fftSize * 2;\n        this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n        streamSource.connect(this.analyser);\n        this.freqData = new Float32Array(this.fftSize);\n        this.timeData = new Float32Array(this.fftSize);\n        return;\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let spectrogramTensor;\n        let waveformTensor;\n        const audioDataQueue = await this.getAudioData();\n        if (this.includeSpectrogram) {\n            const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n            spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n        }\n        if (this.includeWaveform) {\n            const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n            waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n        }\n        return {\n            value: { 'spectrogram': spectrogramTensor, 'waveform': waveformTensor },\n            done: false\n        };\n    }\n    // Capture one result from the audio stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    async getAudioData() {\n        const freqDataQueue = [];\n        const timeDataQueue = [];\n        let currentFrames = 0;\n        return new Promise(resolve => {\n            const intervalID = setInterval(() => {\n                if (this.includeSpectrogram) {\n                    this.analyser.getFloatFrequencyData(this.freqData);\n                    // If the audio stream is initializing, return empty queue.\n                    if (this.freqData[0] === -Infinity) {\n                        resolve({ freqDataQueue, timeDataQueue });\n                    }\n                    freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n                }\n                if (this.includeWaveform) {\n                    this.analyser.getFloatTimeDomainData(this.timeData);\n                    timeDataQueue.push(this.timeData.slice());\n                }\n                // Clean interval and return when all frames have been collected\n                if (++currentFrames === this.numFrames) {\n                    clearInterval(intervalID);\n                    resolve({ freqDataQueue, timeDataQueue });\n                }\n            }, this.fftSize / this.sampleRateHz * 1e3);\n        });\n    }\n    // Stop the audio stream and pause the iterator.\n    stop() {\n        if (!this.isClosed) {\n            this.isClosed = true;\n            this.analyser.disconnect();\n            this.audioContext.close();\n            if (this.stream != null && this.stream.getTracks().length > 0) {\n                this.stream.getTracks()[0].stop();\n            }\n        }\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite audio stream to array.');\n    }\n    // Return audio sampling rate in Hz\n    getSampleRate() {\n        return this.sampleRateHz;\n    }\n    flattenQueue(queue) {\n        const frameSize = queue[0].length;\n        const freqData = new Float32Array(queue.length * frameSize);\n        queue.forEach((data, i) => freqData.set(data, i * frameSize));\n        return freqData;\n    }\n    getTensorFromAudioDataArray(freqData, shape) {\n        const vals = new Float32Array(util.sizeFromShape(shape));\n        // If the data is less than the output shape, the rest is padded with zeros.\n        vals.set(freqData, vals.length - freqData.length);\n        return tensor(vals, shape);\n    }\n}\n//# sourceMappingURL=microphone_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { browser, env, image, tensor1d, tensor2d, tidy, util } from '@tensorflow/tfjs-core';\nimport { LazyIterator } from './lazy_iterator';\n/**\n * Provide a stream of image tensors from webcam video stream. Only works in\n * browser environment.\n */\nexport class WebcamIterator extends LazyIterator {\n    constructor(webcamVideoElement, webcamConfig) {\n        super();\n        this.webcamVideoElement = webcamVideoElement;\n        this.webcamConfig = webcamConfig;\n        this.isClosed = true;\n        this.resize = false;\n        if (this.needToResize()) {\n            this.resize = true;\n            this.cropSize =\n                [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n            this.cropBoxInd = tensor1d([0], 'int32');\n            if (this.webcamConfig.centerCrop) {\n                // Calculate the box based on resizing shape.\n                const widthCroppingRatio = this.webcamConfig.resizeWidth * 1.0 / this.webcamVideoElement.width;\n                const heightCroppingRatio = this.webcamConfig.resizeHeight * 1.0 /\n                    this.webcamVideoElement.height;\n                const widthCropStart = (1 - widthCroppingRatio) / 2;\n                const heightCropStart = (1 - heightCroppingRatio) / 2;\n                const widthCropEnd = widthCropStart + widthCroppingRatio;\n                const heightCropEnd = heightCroppingRatio + heightCropStart;\n                this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n            }\n            else {\n                this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n            }\n        }\n    }\n    summary() {\n        return `webcam`;\n    }\n    // Construct a WebcamIterator and start it's video stream.\n    static async create(webcamVideoElement, webcamConfig = {}) {\n        if (env().get('IS_NODE')) {\n            throw new Error('tf.data.webcam is only supported in browser environment.');\n        }\n        if (!webcamVideoElement) {\n            // If webcam video element is not provided, create a hidden video element\n            // with provided width and height.\n            webcamVideoElement = document.createElement('video');\n            if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n                throw new Error('Please provide webcam video element, or resizeWidth and ' +\n                    'resizeHeight to create a hidden video element.');\n            }\n            webcamVideoElement.width = webcamConfig.resizeWidth;\n            webcamVideoElement.height = webcamConfig.resizeHeight;\n        }\n        const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n        // Call async function to initialize the video stream.\n        await webcamIterator.start();\n        return webcamIterator;\n    }\n    // Async function to start video stream.\n    async start() {\n        if (this.webcamConfig.facingMode) {\n            util.assert((this.webcamConfig.facingMode === 'user') ||\n                (this.webcamConfig.facingMode === 'environment'), () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. ` +\n                `Please provide 'user' or 'environment'`);\n        }\n        try {\n            this.stream = await navigator.mediaDevices.getUserMedia({\n                video: {\n                    deviceId: this.webcamConfig.deviceId,\n                    facingMode: this.webcamConfig.facingMode ?\n                        this.webcamConfig.facingMode :\n                        'user',\n                    width: this.webcamVideoElement.width,\n                    height: this.webcamVideoElement.height\n                }\n            });\n        }\n        catch (e) {\n            // Modify the error message but leave the stack trace intact\n            e.message = `Error thrown while initializing video stream: ${e.message}`;\n            throw e;\n        }\n        if (!this.stream) {\n            throw new Error('Could not obtain video from webcam.');\n        }\n        // Older browsers may not have srcObject\n        try {\n            this.webcamVideoElement.srcObject = this.stream;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n        }\n        // Start the webcam video stream\n        this.webcamVideoElement.play();\n        this.isClosed = false;\n        return new Promise(resolve => {\n            // Add event listener to make sure the webcam has been fully initialized.\n            this.webcamVideoElement.onloadedmetadata = () => {\n                resolve();\n            };\n        });\n    }\n    async next() {\n        if (this.isClosed) {\n            return { value: null, done: true };\n        }\n        let img;\n        try {\n            img = browser.fromPixels(this.webcamVideoElement);\n        }\n        catch (e) {\n            throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n        }\n        if (this.resize) {\n            try {\n                return { value: this.cropAndResizeFrame(img), done: false };\n            }\n            catch (e) {\n                throw new Error(`Error thrown cropping the video: ${e.message}`);\n            }\n            finally {\n                img.dispose();\n            }\n        }\n        else {\n            return { value: img, done: false };\n        }\n    }\n    needToResize() {\n        // If resizeWidth and resizeHeight are provided, and different from the\n        // width and height of original HTMLVideoElement, then resizing and cropping\n        // is required.\n        if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight &&\n            (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth ||\n                this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n            return true;\n        }\n        return false;\n    }\n    // Cropping and resizing each frame based on config\n    cropAndResizeFrame(img) {\n        return tidy(() => {\n            const expandedImage = img.toFloat().expandDims(0);\n            let resizedImage;\n            resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, 'bilinear');\n            // Extract image from batch cropping.\n            const shape = resizedImage.shape;\n            return resizedImage.reshape(shape.slice(1));\n        });\n    }\n    // Capture one frame from the video stream, and extract the value from\n    // iterator.next() result.\n    async capture() {\n        return (await this.next()).value;\n    }\n    // Stop the video stream and pause webcam iterator.\n    stop() {\n        const tracks = this.stream.getTracks();\n        tracks.forEach(track => track.stop());\n        try {\n            this.webcamVideoElement.srcObject = null;\n        }\n        catch (error) {\n            console.log(error);\n            this.webcamVideoElement.src = null;\n        }\n        this.isClosed = true;\n    }\n    // Override toArray() function to prevent collecting.\n    toArray() {\n        throw new Error('Can not convert infinite video stream to array.');\n    }\n}\n//# sourceMappingURL=webcam_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * Represents a data source readable as a stream of binary data chunks.\n *\n * Because `Dataset`s can be read repeatedly (via `Dataset.iterator()`), this\n * provides a means to repeatedly create streams from the underlying data\n * sources.\n */\nexport class DataSource {\n}\n// TODO(soergel): consider convenience factory functions here\n// in combination with chainable source->dataset above, e.g.:\n// tf.data.url(...).asCsvDataset().shuffle().batch()\n//# sourceMappingURL=datasource.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nexport class StringIterator extends LazyIterator {\n    /**\n     * Splits a string stream on a given separator.\n     *\n     * It is assumed that the incoming chunk boundaries have no semantic meaning,\n     * so conceptually the incoming stream is treated simply as the concatenation\n     * of its elements.\n     *\n     * The outgoing stream provides chunks corresponding to the results of the\n     * standard string split() operation (even if such a chunk spanned incoming\n     * chunks).  The separators are not included.\n     *\n     * A typical usage is to split a text file (represented as a stream with\n     * arbitrary chunk boundaries) into lines.\n     *\n     * @param upstream A readable stream of strings that can be treated as\n     *   concatenated.\n     * @param separator A character to split on.\n     */\n    split(separator) {\n        return new SplitIterator(this, separator);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on StringIterator.  Unfortunately they can't be placed in separate files, due\n// to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class SplitIterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass SplitIterator extends StringIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.impl = new SplitIteratorImpl(upstream, separator);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\nclass SplitIteratorImpl extends OneToManyIterator {\n    constructor(upstream, separator) {\n        super();\n        this.upstream = upstream;\n        this.separator = separator;\n        // A partial string at the end of an upstream chunk\n        this.carryover = '';\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Split('${this.separator}')`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        if (chunkResult.done) {\n            if (this.carryover === '') {\n                return false;\n            }\n            // Pretend that the pump succeeded in order to emit the small last batch.\n            // The next pump() call will actually fail.\n            this.outputQueue.push(this.carryover);\n            this.carryover = '';\n            return true;\n        }\n        const lines = chunkResult.value.split(this.separator);\n        // Note the behavior: \" ab \".split(' ') === ['', 'ab', '']\n        // Thus the carryover may be '' if the separator falls on a chunk\n        // boundary; this produces the correct result.\n        lines[0] = this.carryover + lines[0];\n        for (const line of lines.slice(0, -1)) {\n            this.outputQueue.push(line);\n        }\n        this.carryover = lines[lines.length - 1];\n        return true;\n    }\n}\n//# sourceMappingURL=string_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { LazyIterator, OneToManyIterator } from './lazy_iterator';\nimport { StringIterator } from './string_iterator';\nexport class ByteChunkIterator extends LazyIterator {\n    /**\n     * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n     *\n     * The byte arrays producetd from the ByteChunkIterator on which this is\n     * called will be interpreted as concatenated.  No assumptions are made about\n     * the boundaries of the incoming chunks, so a multi-byte UTF8 encoding of a\n     * character may span the boundary between chunks.  This naturally happens,\n     * for instance, when reading fixed-size byte arrays from a file.\n     */\n    decodeUTF8() {\n        return new Utf8Iterator(this);\n    }\n}\n// ============================================================================\n// The following private classes serve to implement the chainable methods\n// on ByteChunkIterator.  Unfortunately they can't be placed in separate files,\n// due to resulting trouble with circular imports.\n// ============================================================================\n// We wanted multiple inheritance, e.g.\n//   class Utf8Iterator extends QueueIterator<string>, StringIterator\n// but the TypeScript mixin approach is a bit hacky, so we take this adapter\n// approach instead.\nclass Utf8Iterator extends StringIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        this.impl = new Utf8IteratorImpl(upstream);\n    }\n    summary() {\n        return this.impl.summary();\n    }\n    async next() {\n        return this.impl.next();\n    }\n}\n/**\n * Decode a stream of UTF8-encoded byte arrays to a stream of strings.\n *\n * This is tricky because the incoming byte array boundaries may disrupt a\n * multi-byte UTF8 character. Thus any incomplete character data at the end of\n * a chunk must be carried over and prepended to the next chunk before\n * decoding. Luckily with native decoder, TextDecoder in browser and\n * string_decoder in node, byte array boundaries are handled automatically.\n *\n * In the context of an input pipeline for machine learning, UTF8 decoding is\n * needed to parse text files containing training examples or prediction\n * requests (e.g., formatted as CSV or JSON). We cannot use the built-in\n * decoding provided by FileReader.readAsText() because here we are in a\n * streaming context, which FileReader does not support.\n *\n * @param upstream A `LazyIterator` of `Uint8Arrays` containing UTF8-encoded\n *   text, which should be interpreted as concatenated.  No assumptions are\n *   made about the boundaries of the incoming chunks, so a multi-byte UTF8\n *   encoding of a character may span the boundary between chunks.  This\n *   naturally happens, for instance, when reading fixed-size byte arrays from a\n *   file.\n */\nclass Utf8IteratorImpl extends OneToManyIterator {\n    constructor(upstream) {\n        super();\n        this.upstream = upstream;\n        if (env().get('IS_BROWSER')) {\n            this.decoder = new TextDecoder('utf-8');\n        }\n        else {\n            // tslint:disable-next-line:no-require-imports\n            const { StringDecoder } = require('string_decoder');\n            this.decoder = new StringDecoder('utf8');\n        }\n    }\n    summary() {\n        return `${this.upstream.summary()} -> Utf8`;\n    }\n    async pump() {\n        const chunkResult = await this.upstream.next();\n        let chunk;\n        if (chunkResult.done) {\n            return false;\n        }\n        else {\n            chunk = chunkResult.value;\n        }\n        let text;\n        if (env().get('IS_BROWSER')) {\n            text = this.decoder.decode(chunk, { stream: true });\n        }\n        else {\n            text = this.decoder.write(Buffer.from(chunk.buffer));\n        }\n        this.outputQueue.push(text);\n        return true;\n    }\n}\n//# sourceMappingURL=byte_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// inspired by https://github.com/maxogden/filereader-stream\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { ByteChunkIterator } from './byte_chunk_iterator';\n/**\n * Provide a stream of chunks from a File, Blob, or Uint8Array.\n * @param file The source File, Blob or Uint8Array.\n * @param options Optional settings controlling file reading.\n * @returns a lazy Iterator of Uint8Arrays containing sequential chunks of the\n *   input File, Blob or Uint8Array.\n */\nexport class FileChunkIterator extends ByteChunkIterator {\n    constructor(file, options = {}) {\n        super();\n        this.file = file;\n        this.options = options;\n        util.assert((file instanceof Uint8Array) ||\n            (env().get('IS_BROWSER') ?\n                (file instanceof File || file instanceof Blob) :\n                false), () => 'FileChunkIterator only supports File, Blob and Uint8Array ' +\n            'right now.');\n        this.offset = options.offset || 0;\n        // default 1MB chunk has tolerable perf on large files\n        this.chunkSize = options.chunkSize || 1024 * 1024;\n    }\n    summary() {\n        return `FileChunks ${this.file}`;\n    }\n    async next() {\n        if (this.offset >= ((this.file instanceof Uint8Array) ?\n            this.file.byteLength :\n            this.file.size)) {\n            return { value: null, done: true };\n        }\n        const chunk = new Promise((resolve, reject) => {\n            const end = this.offset + this.chunkSize;\n            if (this.file instanceof Uint8Array) {\n                // Note if end > this.uint8Array.byteLength, we just get a small last\n                // chunk.\n                resolve(new Uint8Array(this.file.slice(this.offset, end)));\n            }\n            else {\n                // This branch assumes that this.file type is File or Blob, which\n                // means it is in the browser environment.\n                // TODO(soergel): is this a performance issue?\n                const fileReader = new FileReader();\n                fileReader.onload = (event) => {\n                    let data = fileReader.result;\n                    // Not sure we can trust the return type of\n                    // FileReader.readAsArrayBuffer See e.g.\n                    // https://github.com/node-file-api/FileReader/issues/2\n                    if (data instanceof ArrayBuffer) {\n                        data = new Uint8Array(data);\n                    }\n                    if (!(data instanceof Uint8Array)) {\n                        return reject(new TypeError('FileReader returned unknown type.'));\n                    }\n                    resolve(data);\n                };\n                fileReader.onabort = (event) => {\n                    return reject(new Error('Aborted'));\n                };\n                fileReader.onerror = (event) => {\n                    return reject(new Error(event.type));\n                };\n                // TODO(soergel): better handle onabort, onerror\n                // Note if end > this.file.size, we just get a small last chunk.\n                const slice = this.file.slice(this.offset, end);\n                // We can't use readAsText here (even if we know the file is text)\n                // because the slice boundary may fall within a multi-byte character.\n                fileReader.readAsArrayBuffer(slice);\n            }\n            this.offset = end;\n        });\n        return { value: (await chunk), done: false };\n    }\n}\n//# sourceMappingURL=file_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FileChunkIterator } from './file_chunk_iterator';\n/**\n * Provide a stream of chunks from a URL.\n *\n * Note this class first downloads the entire file into memory before providing\n * the first element from the stream.  This is because the Fetch API does not\n * yet reliably provide a reader stream for the response body.\n */\nexport async function urlChunkIterator(url, options = {}) {\n    let urlString;\n    let requestInit;\n    if ((typeof url) === 'string') {\n        urlString = url;\n    }\n    else {\n        urlString = url.url;\n        requestInit = getRequestInitFromRequest(url);\n    }\n    const response = await util.fetch(urlString, requestInit);\n    if (response.ok) {\n        const uint8Array = new Uint8Array(await response.arrayBuffer());\n        return new FileChunkIterator(uint8Array, options);\n    }\n    else {\n        throw new Error(response.statusText);\n    }\n}\n// Generate RequestInit from Request to match tf.util.fetch signature.\nconst getRequestInitFromRequest = (request) => {\n    const init = {\n        method: request.method,\n        headers: request.headers,\n        body: request.body,\n        mode: request.mode,\n        credentials: request.credentials,\n        cache: request.cache,\n        redirect: request.redirect,\n        referrer: request.referrer,\n        integrity: request.integrity,\n    };\n    return init;\n};\n//# sourceMappingURL=url_chunk_iterator.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n// Skip tslint any type check cause this method is aiming to check type of\n// input.\n// tslint:disable-next-line:no-any\nexport function isLocalPath(source) {\n    return (typeof source === 'string') && source.substr(0, 7) === 'file://';\n}\n//# sourceMappingURL=source_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { DataSource } from '../datasource';\nimport { FileChunkIterator } from '../iterators/file_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\n/**\n * Represents a file, blob, or Uint8Array readable as a stream of binary data\n * chunks.\n */\nexport class FileDataSource extends DataSource {\n    /**\n     * Create a `FileDataSource`.\n     *\n     * @param input Local file path, or `File`/`Blob`/`Uint8Array` object to\n     *     read. Local file only works in node environment.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(input, options = {}) {\n        super();\n        this.input = input;\n        this.options = options;\n    }\n    async iterator() {\n        if (isLocalPath(this.input) && env().get('IS_NODE')) {\n            // tslint:disable-next-line:no-require-imports\n            const fs = require('fs');\n            this.input = fs.readFileSync(this.input.substr(7));\n        }\n        // TODO(kangyizhang): Add LocalFileChunkIterator to split local streaming\n        // with file in browser.\n        return new FileChunkIterator(this.input, this.options);\n    }\n}\n//# sourceMappingURL=file_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { DataSource } from '../datasource';\nimport { urlChunkIterator } from '../iterators/url_chunk_iterator';\nimport { isLocalPath } from '../util/source_util';\nimport { FileDataSource } from './file_data_source';\n/*\n * Represents a URL readable as a stream of binary data chunks.\n */\nexport class URLDataSource extends DataSource {\n    /**\n     * Create a `URLDataSource`.\n     *\n     * @param url A source URL string, or a `Request` object.\n     * @param options Options passed to the underlying `FileChunkIterator`s,\n     *   such as {chunksize: 1024}.\n     */\n    constructor(url, fileOptions = {}) {\n        super();\n        this.url = url;\n        this.fileOptions = fileOptions;\n    }\n    // TODO(soergel): provide appropriate caching options.  Currently this\n    // will download the URL anew for each call to iterator().  Since we have\n    // to treat the downloaded file as a blob/buffer anyway, we may as well retain\n    // it-- but that raises GC issues.  Also we may want a persistent disk cache.\n    async iterator() {\n        if (isLocalPath(this.url)) {\n            return (new FileDataSource(this.url, this.fileOptions))\n                .iterator();\n        }\n        else {\n            return urlChunkIterator(this.url, this.fileOptions);\n        }\n    }\n}\n//# sourceMappingURL=url_data_source.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\nimport { datasetFromIteratorFn } from './dataset';\nimport { CSVDataset } from './datasets/csv_dataset';\nimport { iteratorFromFunction } from './iterators/lazy_iterator';\nimport { MicrophoneIterator } from './iterators/microphone_iterator';\nimport { WebcamIterator } from './iterators/webcam_iterator';\nimport { URLDataSource } from './sources/url_data_source';\n/**\n * Create a `CSVDataset` by reading and decoding CSV file(s) from provided URL\n * or local path if it's in Node environment.\n *\n * Note: If isLabel in columnConfigs is `true` for at least one column, the\n * element in returned `CSVDataset` will be an object of\n * `{xs:features, ys:labels}`: xs is a dict of features key/value pairs, ys\n * is a dict of labels key/value pairs. If no column is marked as label,\n * returns a dict of features only.\n *\n * ```js\n * const csvUrl =\n * 'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';\n *\n * async function run() {\n *   // We want to predict the column \"medv\", which represents a median value of\n *   // a home (in $1000s), so we mark it as a label.\n *   const csvDataset = tf.data.csv(\n *     csvUrl, {\n *       columnConfigs: {\n *         medv: {\n *           isLabel: true\n *         }\n *       }\n *     });\n *\n *   // Number of features is the number of column names minus one for the label\n *   // column.\n *   const numOfFeatures = (await csvDataset.columnNames()).length - 1;\n *\n *   // Prepare the Dataset for training.\n *   const flattenedDataset =\n *     csvDataset\n *     .map(({xs, ys}) =>\n *       {\n *         // Convert xs(features) and ys(labels) from object form (keyed by\n *         // column name) to array form.\n *         return {xs:Object.values(xs), ys:Object.values(ys)};\n *       })\n *     .batch(10);\n *\n *   // Define the model.\n *   const model = tf.sequential();\n *   model.add(tf.layers.dense({\n *     inputShape: [numOfFeatures],\n *     units: 1\n *   }));\n *   model.compile({\n *     optimizer: tf.train.sgd(0.000001),\n *     loss: 'meanSquaredError'\n *   });\n *\n *   // Fit the model using the prepared Dataset\n *   return model.fitDataset(flattenedDataset, {\n *     epochs: 10,\n *     callbacks: {\n *       onEpochEnd: async (epoch, logs) => {\n *         console.log(epoch + ':' + logs.loss);\n *       }\n *     }\n *   });\n * }\n *\n * await run();\n * ```\n *\n * @param source URL or local path to get CSV file. If it's a local path, it\n * must have prefix `file://` and it only works in node environment.\n * @param csvConfig (Optional) A CSVConfig object that contains configurations\n *     of reading and decoding from CSV file(s).\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function csv(source, csvConfig = {}) {\n    return new CSVDataset(new URLDataSource(source), csvConfig);\n}\n/**\n * Create a `Dataset` that produces each element by calling a provided function.\n *\n * Note that repeated iterations over this `Dataset` may produce different\n * results, because the function will be called anew for each element of each\n * iteration.\n *\n * Also, beware that the sequence of calls to this function may be out of order\n * in time with respect to the logical order of the Dataset. This is due to the\n * asynchronous lazy nature of stream processing, and depends on downstream\n * transformations (e.g. .shuffle()). If the provided function is pure, this is\n * no problem, but if it is a closure over a mutable state (e.g., a traversal\n * pointer), then the order of the produced elements may be scrambled.\n *\n * ```js\n * let i = -1;\n * const func = () =>\n *    ++i < 5 ? {value: i, done: false} : {value: null, done: true};\n * const ds = tf.data.func(func);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param f A function that produces one data element on each call.\n */\nexport function func(f) {\n    const iter = iteratorFromFunction(f);\n    return datasetFromIteratorFn(async () => iter);\n}\n/**\n * Create a `Dataset` that produces each element from provided JavaScript\n * generator, which is a function*\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions),\n * or a function that returns an\n * iterator\n * (https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Iterators_and_Generators#Generator_functions).\n *\n * The returned iterator should have `.next()` function that returns element in\n * format of `{value: TensorContainer, done:boolean}`.\n *\n * Example of creating a dataset from an iterator factory:\n * ```js\n * function makeIterator() {\n *   const numElements = 10;\n *   let index = 0;\n *\n *   const iterator = {\n *     next: () => {\n *       let result;\n *       if (index < numElements) {\n *         result = {value: index, done: false};\n *         index++;\n *         return result;\n *       }\n *       return {value: index, done: true};\n *     }\n *   };\n *   return iterator;\n * }\n * const ds = tf.data.generator(makeIterator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * Example of creating a dataset from a generator:\n * ```js\n * function* dataGenerator() {\n *   const numElements = 10;\n *   let index = 0;\n *   while (index < numElements) {\n *     const x = index;\n *     index++;\n *     yield x;\n *   }\n * }\n *\n * const ds = tf.data.generator(dataGenerator);\n * await ds.forEachAsync(e => console.log(e));\n * ```\n *\n * @param generator A Javascript generator function that returns a JavaScript\n *     iterator.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   configParamIndices: [1]\n *  }\n */\nexport function generator(generator) {\n    return datasetFromIteratorFn(async () => {\n        const gen = await generator();\n        return iteratorFromFunction(() => gen.next());\n    });\n}\n/**\n * Create an iterator that generate `Tensor`s from webcam video stream. This API\n * only works in Browser environment when the device has webcam.\n *\n * Note: this code snippet only works when the device has a webcam. It will\n * request permission to open the webcam when running.\n * ```js\n * const videoElement = document.createElement('video');\n * videoElement.width = 100;\n * videoElement.height = 100;\n * const cam = await tf.data.webcam(videoElement);\n * const img = await cam.capture();\n * img.print();\n * cam.stop();\n * ```\n *\n * @param webcamVideoElement A `HTMLVideoElement` used to play video from\n *     webcam. If this element is not provided, a hidden `HTMLVideoElement` will\n *     be created. In that case, `resizeWidth` and `resizeHeight` must be\n *     provided to set the generated tensor shape.\n * @param webcamConfig A `WebcamConfig` object that contains configurations of\n *     reading and manipulating data from webcam video stream.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function webcam(webcamVideoElement, webcamConfig) {\n    return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\n/**\n * Create an iterator that generate frequency-domain spectrogram `Tensor`s from\n * microphone audio stream with browser's native FFT. This API only works in\n * browser environment when the device has microphone.\n *\n * Note: this code snippet only works when the device has a microphone. It will\n * request permission to open the microphone when running.\n * ```js\n * const mic = await tf.data.microphone({\n *   fftSize: 1024,\n *   columnTruncateLength: 232,\n *   numFramesPerSpectrogram: 43,\n *   sampleRateHz:44100,\n *   includeSpectrogram: true,\n *   includeWaveform: true\n * });\n * const audioData = await mic.capture();\n * const spectrogramTensor = audioData.spectrogram;\n * spectrogramTensor.print();\n * const waveformTensor = audioData.waveform;\n * waveformTensor.print();\n * mic.stop();\n * ```\n *\n * @param microphoneConfig A `MicrophoneConfig` object that contains\n *     configurations of reading audio data from microphone.\n *\n * @doc {\n *   heading: 'Data',\n *   subheading: 'Creation',\n *   namespace: 'data',\n *   ignoreCI: true\n *  }\n */\nexport async function microphone(microphoneConfig) {\n    return MicrophoneIterator.create(microphoneConfig);\n}\n//# sourceMappingURL=readers.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport { array, Dataset, zip } from './dataset';\nexport { CSVDataset } from './datasets/csv_dataset';\nexport { TextLineDataset } from './datasets/text_line_dataset';\nexport { csv, func, generator, microphone, webcam } from './readers';\nexport { FileDataSource } from './sources/file_data_source';\nexport { URLDataSource } from './sources/url_data_source';\nexport { version as version_data } from './version';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors in the CPU backend.`);\n        }\n    });\n}\n//# sourceMappingURL=cpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { backend_util, DataStorage, engine, env, kernel_impls, KernelBackend, max, slice_util, TensorBuffer, upcastType, util } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV3Impl = kernel_impls.nonMaxSuppressionV3Impl;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport * as seedrandom from 'seedrandom';\nimport { assertNotComplex } from './cpu_util';\nexport class MathBackendCPU extends KernelBackend {\n    constructor() {\n        super();\n        this.blockSize = 48;\n        this.firstUse = true;\n        this.data = new DataStorage(this, engine());\n    }\n    write(values, shape, dtype) {\n        if (this.firstUse) {\n            this.firstUse = false;\n            if (env().get('IS_NODE')) {\n                backend_util.warn('\\n============================\\n' +\n                    'Hi there \uD83D\uDC4B. Looks like you are running TensorFlow.js in ' +\n                    'Node.js. To speed things up dramatically, install our node ' +\n                    'backend, which binds to TensorFlow C++, by running ' +\n                    'npm i @tensorflow/tfjs-node, ' +\n                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n                    'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n                    'suffix for CUDA) at the start of your program. ' +\n                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n                    '\\n============================');\n            }\n        }\n        const dataId = {};\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n        return dataId;\n    }\n    /**\n     * Create a data bucket in cpu backend.\n     * @param shape Shape of the `TensorInfo`.\n     * @param dtype DType of the `TensorInfo`.\n     * @param values The value of the `TensorInfo` stored as a flattened array.\n     */\n    makeTensorInfo(shape, dtype, values) {\n        let outId;\n        if (dtype === 'string' && values != null && values.length > 0 &&\n            util.isString(values[0])) {\n            const encodedValues = values.map(d => util.encodeString(d));\n            outId = this.write(encodedValues, shape, dtype);\n        }\n        else {\n            outId = this.write(values, shape, dtype);\n        }\n        return { dataId: outId, shape, dtype };\n    }\n    /** Increase refCount of a `TensorData`. */\n    incRef(dataId) {\n        const tensorData = this.data.get(dataId);\n        tensorData.refCount++;\n    }\n    /** Decrease refCount of a `TensorData`. */\n    decRef(dataId) {\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        this.data.set(dataId, { values, dtype, refCount: 1 });\n    }\n    numDataIds() {\n        return this.data.numDataIds();\n    }\n    async read(dataId) {\n        return this.readSync(dataId);\n    }\n    readSync(dataId) {\n        const { dtype, complexTensorInfos } = this.data.get(dataId);\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            return backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        return this.data.get(dataId).values;\n    }\n    bufferSync(t) {\n        const data = this.readSync(t.dataId);\n        let decodedData = data;\n        if (t.dtype === 'string') {\n            try {\n                // Decode the bytes into string.\n                decodedData = data.map(d => util.decodeString(d));\n            }\n            catch (_a) {\n                throw new Error('Failed to decode encoded string bytes into utf-8');\n            }\n        }\n        return tf.buffer(t.shape, t.dtype, decodedData);\n    }\n    makeOutput(values, shape, dtype) {\n        const dataId = this.write(values, shape, dtype);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    disposeData(dataId) {\n        if (this.data.has(dataId)) {\n            const { complexTensorInfos } = this.data.get(dataId);\n            if (complexTensorInfos != null) {\n                this.disposeData(complexTensorInfos.real.dataId);\n                this.disposeData(complexTensorInfos.imag.dataId);\n            }\n            this.data.delete(dataId);\n        }\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.data.has(dataId)) {\n            const tensorData = this.data.get(dataId);\n            tensorData.refCount--;\n            if (tensorData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    async time(f) {\n        const start = util.now();\n        f();\n        const kernelMs = util.now() - start;\n        return { kernelMs };\n    }\n    memory() {\n        return {\n            // Unreliable due to automatic gc. The numbers above are cumulative.\n            unreliable: true,\n            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +\n                    'collection, the true allocated memory may be less.']\n        };\n    }\n    stridedSlice(x, begin, end, strides) {\n        assertNotComplex(x, 'stridedSlice');\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tf.tensor([], outShape);\n        }\n        const buffer = tf.buffer(outShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const loc = buffer.indexToLoc(i);\n            const newLoc = new Array(loc.length);\n            for (let j = 0; j < newLoc.length; j++) {\n                newLoc[j] = loc[j] * strides[j] + begin[j];\n            }\n            buffer.set(xBuf.get(...newLoc), ...loc);\n        }\n        return buffer.toTensor();\n    }\n    diag(x) {\n        const xVals = this.readSync(x.dataId);\n        const buffer = tf.buffer([x.size, x.size], x.dtype);\n        const vals = buffer.values;\n        for (let i = 0; i < xVals.length; i++) {\n            vals[i * x.size + i] = xVals[i];\n        }\n        return buffer.toTensor();\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = tf.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    reverse(x, axis) {\n        assertNotComplex(x, 'reverse');\n        const buffer = tf.buffer(x.shape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < buffer.size; i++) {\n            const outLoc = buffer.indexToLoc(i);\n            const inLoc = outLoc.slice();\n            axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n            buffer.set(xBuf.get(...inLoc), ...outLoc);\n        }\n        return buffer.toTensor();\n    }\n    neg(x) {\n        assertNotComplex(x, 'neg');\n        // TODO(lina128): Use mul directly once neg is modularized.\n        return tf.mul(tf.scalar(-1), x);\n    }\n    addN(tensors) {\n        assertNotComplex(tensors, 'addN');\n        const vals = tensors.map(t => this.readSync(t.dataId));\n        const result = tf.buffer(tensors[0].shape, tensors[0].dtype);\n        const resultVals = result.values;\n        for (let i = 0; i < tensors.length; i++) {\n            const currVals = vals[i];\n            for (let j = 0; j < resultVals.length; j++) {\n                resultVals[j] += currVals[j];\n            }\n        }\n        return result.toTensor();\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(lina128): Use sub directly once softmax is modularized.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = tf.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax\n        // kernel modularization.\n        return tf.div(b, sumExp);\n    }\n    pow(a, b) {\n        assertNotComplex([a, b], 'pow');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue));\n    }\n    floorDiv(a, b) {\n        assertNotComplex([a, b], 'floorDiv');\n        const op = (a, b) => Math.floor(a / b);\n        const outputDtype = 'int32';\n        return this.broadcastedBinaryOp(a, b, outputDtype, op);\n    }\n    sum(x, axes) {\n        assertNotComplex(x, 'sum');\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let sum = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                sum += aVals[offset + j];\n            }\n            vals[i] = sum;\n        }\n        return result;\n    }\n    prod(x, axes) {\n        assertNotComplex(x, 'sum');\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(outShape, resultDtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let prod = 1;\n            for (let j = 0; j < reduceSize; ++j) {\n                prod *= aVals[offset + j];\n            }\n            vals[i] = prod;\n        }\n        return result;\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        assertNotComplex(x, 'unsortedSegmentSum');\n        const res = [];\n        // Reshape the segment id's so that they can be broadcast with\n        // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n        const numIters = x.rank - segmentIds.rank;\n        for (let i = 0; i < numIters; ++i) {\n            segmentIds = segmentIds.expandDims(i + 1);\n        }\n        for (let i = 0; i < numSegments; ++i) {\n            const segmentId = tf.scalar(i, 'int32');\n            const mask = tf.equal(segmentId, segmentIds).asType('float32');\n            const sum = mask.mul(x).sum(0);\n            res.push(sum);\n        }\n        return tf.stack(res);\n    }\n    argMin(x, axis) {\n        assertNotComplex(x, 'argMin');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            let minIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                    minIndex = j;\n                }\n            }\n            vals[i] = minIndex;\n        }\n        return result;\n    }\n    argMax(x, axis) {\n        assertNotComplex(x, 'argMax');\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, 'int32');\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let max = aVals[offset];\n            let maxIndex = 0;\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value > max) {\n                    max = value;\n                    maxIndex = j;\n                }\n            }\n            vals[i] = maxIndex;\n        }\n        return result;\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        assertNotComplex(x, 'cumsum');\n        if (axis !== x.rank - 1) {\n            throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const resultDtype = upcastType(x.dtype, 'int32');\n        const result = tf.zeros(x.shape, resultDtype);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        const finalDim = x.shape[x.rank - 1];\n        const indexAdjuster = reverse ?\n            (i, j) => i + finalDim - j - 1 :\n            (i, j) => i + j;\n        for (let i = 0; i < aVals.length; i += finalDim) {\n            for (let j = 0; j < finalDim; j++) {\n                const idx = indexAdjuster(i, j);\n                if (j === 0) {\n                    vals[idx] = exclusive ? 0 : aVals[idx];\n                }\n                else {\n                    const prevIdx = indexAdjuster(i, j - 1);\n                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                        aVals[idx] + vals[prevIdx];\n                }\n            }\n        }\n        return result;\n    }\n    equal(a, b) {\n        assertNotComplex([a, b], 'equal');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal === bVal) ? 1 : 0;\n        });\n    }\n    notEqual(a, b) {\n        assertNotComplex([a, b], 'notEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal !== bVal) ? 1 : 0;\n        });\n    }\n    less(a, b) {\n        assertNotComplex([a, b], 'less');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal < bVal) ? 1 : 0;\n        });\n    }\n    lessEqual(a, b) {\n        assertNotComplex([a, b], 'lessEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal <= bVal) ? 1 : 0;\n        });\n    }\n    greater(a, b) {\n        assertNotComplex([a, b], 'greater');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal > bVal) ? 1 : 0;\n        });\n    }\n    greaterEqual(a, b) {\n        assertNotComplex([a, b], 'greaterEqual');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return (aVal >= bVal) ? 1 : 0;\n        });\n    }\n    logicalAnd(a, b) {\n        assertNotComplex([a, b], 'logicalAnd');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal && bVal;\n        });\n    }\n    logicalOr(a, b) {\n        assertNotComplex([a, b], 'logicalOr');\n        return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n            return aVal || bVal;\n        });\n    }\n    select(condition, a, b) {\n        assertNotComplex([condition, a, b], 'select');\n        const values = this.readSync(condition.dataId);\n        const aValues = this.readSync(a.dataId);\n        const bValues = this.readSync(b.dataId);\n        const result = tf.zeros(a.shape, upcastType(a.dtype, b.dtype));\n        const newValues = this.readSync(result.dataId);\n        let index = 0;\n        const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n            1 :\n            util.sizeFromShape(a.shape.slice(1));\n        for (let i = 0; i < values.length; i++) {\n            for (let j = 0; j < offset; j++) {\n                if (values[i] === 1) {\n                    newValues[index++] = aValues[i];\n                }\n                else {\n                    newValues[index++] = bValues[i];\n                }\n            }\n        }\n        return result;\n    }\n    where(condition) {\n        assertNotComplex([condition], 'where');\n        const condVals = this.readSync(condition.dataId);\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        assertNotComplex(x, 'topk');\n        const xVals = this.readSync(x.dataId);\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        assertNotComplex(x, 'min');\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let min = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                if (value < min) {\n                    min = value;\n                }\n            }\n            vals[i] = min;\n        }\n        return result;\n    }\n    minimum(a, b) {\n        assertNotComplex([a, b], 'minimum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n    }\n    mod(a, b) {\n        assertNotComplex([a, b], 'mod');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const rem = aVal % bVal;\n            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n                return rem;\n            }\n            else {\n                return (rem + bVal) % bVal;\n            }\n        });\n    }\n    maximum(a, b) {\n        assertNotComplex([a, b], 'maximum');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n    }\n    all(x, axes) {\n        assertNotComplex(x, 'all');\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let all = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                all = all && value;\n            }\n            vals[i] = all;\n        }\n        return result;\n    }\n    any(x, axes) {\n        assertNotComplex(x, 'any');\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const result = tf.zeros(outShape, x.dtype);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const vals = this.readSync(result.dataId);\n        const aVals = this.readSync(x.dataId);\n        for (let i = 0; i < vals.length; ++i) {\n            const offset = i * reduceSize;\n            let anyVal = aVals[offset];\n            for (let j = 0; j < reduceSize; ++j) {\n                const value = aVals[offset + j];\n                anyVal = anyVal || value;\n            }\n            vals[i] = anyVal;\n        }\n        return result;\n    }\n    squaredDifference(a, b) {\n        assertNotComplex([a, b], 'squaredDifference');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n            const diff = aVal - bVal;\n            return diff * diff;\n        });\n    }\n    eluDer(dy, y) {\n        assertNotComplex([dy, y], 'eluDer');\n        const resultValues = new Float32Array(y.size);\n        const values = this.readSync(y.dataId);\n        const dyValues = this.readSync(dy.dataId);\n        for (let i = 0; i < values.length; ++i) {\n            const v = values[i];\n            if (v >= 1) {\n                resultValues[i] = dyValues[i];\n            }\n            else {\n                resultValues[i] = dyValues[i] * (v + 1);\n            }\n        }\n        return this.makeOutput(resultValues, y.shape, 'float32');\n    }\n    atan2(a, b) {\n        assertNotComplex([a, b], 'atan2');\n        return this.broadcastedBinaryOp(a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue));\n    }\n    tile(x, reps) {\n        assertNotComplex(x, 'tile');\n        return tile(this.bufferSync(x), reps);\n    }\n    gather(x, indices, axis) {\n        assertNotComplex([x, indices], 'gather');\n        const newShape = x.shape.slice();\n        const indicesValues = this.readSync(indices.dataId);\n        newShape[axis] = indicesValues.length;\n        const result = tf.buffer(newShape, x.dtype);\n        const xBuf = this.bufferSync(x);\n        for (let i = 0; i < result.size; ++i) {\n            const newLoc = result.indexToLoc(i);\n            const originalLoc = newLoc.slice();\n            originalLoc[axis] = indicesValues[newLoc[axis]];\n            const originalIndex = xBuf.locToIndex(originalLoc);\n            result.values[i] = xBuf.values[originalIndex];\n        }\n        return result.toTensor();\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        assertNotComplex([x], 'batchToSpaceND');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return tf.transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    pool3d(x, convInfo, poolType) {\n        assertNotComplex(x, 'pool3d');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n            Number.POSITIVE_INFINITY);\n        const xValues = this.readSync(x.dataId);\n        const output = tf.buffer(convInfo.outShape, x.dtype);\n        const outputVals = output.values;\n        const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] *\n            convInfo.outShape[3] * convInfo.outShape[4];\n        const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n        const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n        const outputColStrides = convInfo.outShape[4];\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            const outputBatchOffset = batch * outputBatchStrides;\n            const inputBatchOffset = batch * x.strides[0];\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n                            let minMaxValue = initialValue;\n                            let avgValue = 0;\n                            let count = 0;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const xDepthOffset = inputBatchOffset + xDepth * x.strides[1];\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const xRowOffset = xDepthOffset + xRow * x.strides[2];\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const xColOffset = xRowOffset + xCol * x.strides[3];\n                                        const pixel = xValues[xColOffset + channel];\n                                        if ((poolType === 'max' && pixel > minMaxValue)) {\n                                            minMaxValue = pixel;\n                                        }\n                                        else if (poolType === 'avg') {\n                                            avgValue += pixel;\n                                            count++;\n                                        }\n                                        if (isNaN(minMaxValue)) {\n                                            break;\n                                        }\n                                    }\n                                    if (isNaN(minMaxValue)) {\n                                        break;\n                                    }\n                                }\n                                if (isNaN(minMaxValue)) {\n                                    break;\n                                }\n                            }\n                            const outputOffset = outputColOffset + channel;\n                            outputVals[outputOffset] =\n                                poolType === 'avg' ? avgValue / count : minMaxValue;\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    avgPool3d(x, convInfo) {\n        assertNotComplex(x, 'avgPool3d');\n        return this.pool3d(x, convInfo, 'avg').toFloat();\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        assertNotComplex([dy, x], 'avgPool3dBackprop');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins.\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    maxPool3d(x, convInfo) {\n        assertNotComplex(x, 'maxPool3d');\n        return this.pool3d(x, convInfo, 'max').toFloat();\n    }\n    maxPool3dPositions(x, convInfo) {\n        const maxPositions = tf.buffer(convInfo.outShape, 'int32');\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const xBuf = this.bufferSync(x);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n                    const xDepthCorner = yDepth * strideDepth - padFront;\n                    let xDepthMin = xDepthCorner;\n                    while (xDepthMin < 0) {\n                        xDepthMin += dilationDepth;\n                    }\n                    const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n                    for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n                        const xRowCorner = yRow * strideHeight - padTop;\n                        let xRowMin = xRowCorner;\n                        while (xRowMin < 0) {\n                            xRowMin += dilationHeight;\n                        }\n                        const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n                        for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n                            const xColCorner = yCol * strideWidth - padLeft;\n                            let xColMin = xColCorner;\n                            while (xColMin < 0) {\n                                xColMin += dilationWidth;\n                            }\n                            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n                            // Shader code begins\n                            let maxValue = Number.NEGATIVE_INFINITY;\n                            let maxPosition = -1;\n                            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n                                const wDepth = xDepth - xDepthCorner;\n                                for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                                    const wRow = xRow - xRowCorner;\n                                    for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                                        const wCol = xCol - xColCorner;\n                                        const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                                        if (pixel >= maxValue) {\n                                            maxValue = pixel;\n                                            maxPosition = wDepth * effectiveFilterHeight *\n                                                effectiveFilterWidth +\n                                                wRow * effectiveFilterHeight + wCol;\n                                        }\n                                    }\n                                }\n                            }\n                            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return maxPositions.toTensor();\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        assertNotComplex([x, y], 'maxPool3dBackprop');\n        const maxPositions = this.maxPool3dPositions(x, convInfo);\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const dx = tf.buffer(x.shape, 'float32');\n        const maxPosBuf = this.bufferSync(maxPositions);\n        const dyBuf = this.bufferSync(dy);\n        for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n            for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n                for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n                    for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n                        for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n                            // Shader code begins\n                            const dyDepthCorner = dxDepth - padFront;\n                            const dyRowCorner = dxRow - padTop;\n                            const dyColCorner = dxCol - padLeft;\n                            let dotProd = 0;\n                            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n                                const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n                                if (dyDepth < 0 || dyDepth >= convInfo.outDepth ||\n                                    Math.floor(dyDepth) !== dyDepth) {\n                                    continue;\n                                }\n                                for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                                    const dyRow = (dyRowCorner + wRow) / strideHeight;\n                                    if (dyRow < 0 || dyRow >= convInfo.outHeight ||\n                                        Math.floor(dyRow) !== dyRow) {\n                                        continue;\n                                    }\n                                    for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                                        const dyCol = (dyColCorner + wCol) / strideWidth;\n                                        if (dyCol < 0 || dyCol >= convInfo.outWidth ||\n                                            Math.floor(dyCol) !== dyCol) {\n                                            continue;\n                                        }\n                                        const maxPos = effectiveFilterDepth *\n                                            effectiveFilterHeight * effectiveFilterWidth -\n                                            1 -\n                                            maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth +\n                                            wRow * effectiveFilterWidth + wCol;\n                                        const mask = maxPos === curPos ? 1 : 0;\n                                        if (mask === 0) {\n                                            continue;\n                                        }\n                                        const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                                        dotProd += pixel * mask;\n                                    }\n                                }\n                            }\n                            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n                        }\n                    }\n                }\n            }\n        }\n        return dx.toTensor();\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeBilinear');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        let outputIdx = 0;\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        for (let b = 0; b < batch; b++) {\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceRowFloor = Math.floor(sourceFracRow);\n                const rowFrac = sourceFracRow - sourceRowFloor;\n                const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n                const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n                const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceColFloor = Math.floor(sourceFracCol);\n                    const colFrac = sourceFracCol - sourceColFloor;\n                    const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n                    const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n                    const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n                    const topRightOffset = topRowOffset + sourceColCeil * x.strides[2];\n                    const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const topLeft = xValues[topLeftOffest + d];\n                        const bottomLeft = xValues[botLeftOffset + d];\n                        const topRight = xValues[topRightOffset + d];\n                        const bottomRight = xValues[botRightOffest + d];\n                        const top = topLeft + (topRight - topLeft) * colFrac;\n                        const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n                        const newValue = top + (bottom - top) * rowFrac;\n                        result[outputIdx++] = newValue;\n                    }\n                }\n            }\n        }\n        return tf.tensor(result, [batch, newHeight, newWidth, numChannels]);\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeBilinearBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass and add the\n        // corresponding coefficient from dy to the gradient (with some\n        // interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n        const dyValues = this.readSync(dy.dataId);\n        let offset = 0;\n        for (let b = 0; b < batch; b++) {\n            const bOffset = b * x.strides[0];\n            for (let r = 0; r < yHeight; r++) {\n                const dxR = r * heightScale;\n                const topDxRIndex = Math.floor(dxR);\n                const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n                const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n                const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n                const dxRLerp = dxR - topDxRIndex;\n                const inverseDxRLerp = 1.0 - dxRLerp;\n                for (let c = 0; c < yWidth; c++) {\n                    const dxC = c * widthScale;\n                    const leftDxCIndex = Math.floor(dxC);\n                    const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n                    const dxCLerp = dxC - leftDxCIndex;\n                    const inverseDxCLerp = 1.0 - dxCLerp;\n                    const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n                    const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n                    const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];\n                    const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];\n                    const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n                    const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n                    const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n                    const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n                    for (let d = 0; d < depth; d++) {\n                        const dyVal = dyValues[offset++];\n                        output[topLeftRCOffset + d] +=\n                            dyVal * inverseDxRLerpTimesInverseDxCLerp;\n                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n                        output[bottomLeftRCOffset + d] +=\n                            dyVal * dxRLerpTimesInverseDxCLerp;\n                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        assertNotComplex(x, 'resizeNearestNeighbor');\n        const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n        const xValues = this.readSync(x.dataId);\n        const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n        const effectiveInputSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutputSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n        const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n        let outputOffset = 0;\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < newHeight; r++) {\n                const sourceFracRow = effectiveRowSizeRatio * r;\n                const sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                    Math.floor(sourceFracRow));\n                const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n                for (let c = 0; c < newWidth; c++) {\n                    const sourceFracCol = effectiveColSizeRatio * c;\n                    const sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                        Math.floor(sourceFracCol));\n                    const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n                    for (let d = 0; d < numChannels; d++) {\n                        // Begin shader.\n                        // Compute the fractional index of the source.\n                        const newVal = xValues[colOffset + d];\n                        output[outputOffset++] = newVal;\n                    }\n                }\n            }\n        }\n        return tf.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n        const [batch, xHeight, xWidth, depth] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        const output = new Float32Array(batch * xHeight * xWidth * depth);\n        const dyValues = this.readSync(dy.dataId);\n        // In the backwards pass, we want to find the pixels that were generated\n        // for each pixel in the input image the forward pass\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        // Loop over the output space.\n        for (let b = 0; b < batch; b++) {\n            const batchOffset = b * x.strides[0];\n            for (let r = 0; r < xHeight; r++) {\n                const rowOffset = batchOffset + r * x.strides[1];\n                // Compute bounds for where in dy we will look\n                const startRLerp = Math.floor(r * invHeightScale);\n                const startDyR = Math.floor(startRLerp - (winHeight / 2));\n                for (let c = 0; c < xWidth; c++) {\n                    const colOffset = rowOffset + c * x.strides[2];\n                    // Compute bounds for where in dy we will look\n                    const startCLerp = Math.floor(c * invWidthScale);\n                    const startDyC = Math.floor(startCLerp - (winWidth / 2));\n                    for (let d = 0; d < depth; d++) {\n                        let accum = 0;\n                        // loop over dy\n                        for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n                            const dyR = dyRIndex + startDyR;\n                            // Guard against the window exceeding the bounds of dy\n                            if (dyR < 0 || dyR >= yHeight) {\n                                continue;\n                            }\n                            const dyROffset = batchOffset + dyR * dy.strides[1];\n                            const sourceFracRow = dyR * heightScale;\n                            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :\n                                Math.floor(sourceFracRow));\n                            if (r !== sourceNearestRow) {\n                                continue;\n                            }\n                            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                                const dyC = dyCIndex + startDyC;\n                                // Guard against the window exceeding the bounds of dy\n                                if (dyC < 0 || dyC >= yWidth) {\n                                    continue;\n                                }\n                                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                                const sourceFracCol = dyC * widthScale;\n                                const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :\n                                    Math.floor(sourceFracCol));\n                                if (c === sourceNearestCol) {\n                                    accum += dyValues[dyCOffset + d];\n                                }\n                            }\n                        }\n                        output[colOffset + d] = accum;\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(output, x.shape, x.dtype);\n    }\n    localResponseNormalization4D(x, depthRadius, bias, alpha, beta) {\n        assertNotComplex(x, 'localResponseNormalization4D');\n        const channels = x.shape[3];\n        const maxD = channels - 1;\n        const xValues = this.readSync(x.dataId);\n        const size = x.size;\n        const result = new Float32Array(size);\n        function sumAcrossChannels(offset) {\n            const currentChannel = offset % channels;\n            let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n            const endSumOffset = offset - currentChannel +\n                Math.min(currentChannel + depthRadius, maxD);\n            let sum = 0.0;\n            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n                const z = xValues[beginSumOffset];\n                sum += z * z;\n            }\n            return sum;\n        }\n        for (let offset = 0; offset < size; offset++) {\n            const sum = sumAcrossChannels(offset);\n            const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n            result[offset] = val;\n        }\n        return tf.tensor4d(result, x.shape);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        assertNotComplex(dy, 'LRNGrad');\n        const channels = dy.shape[3];\n        const dyValues = this.readSync(dy.dataId);\n        const inputImageValues = this.readSync(inputImage.dataId);\n        const outputImageValues = this.readSync(outputImage.dataId);\n        const result = new Float32Array(dy.size);\n        const size = dy.size;\n        for (let offset = 0; offset < size; offset++) {\n            const currentChannel = offset % channels;\n            const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n            const depthEnd = (offset - currentChannel) +\n                Math.min(channels, currentChannel + depthRadius + 1);\n            let norm = 0;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                norm += Math.pow(inputImageValues[k], 2);\n            }\n            norm = alpha * norm + bias;\n            for (let k = depthBegin; k < depthEnd; k++) {\n                let dyi = -2 * alpha * beta * inputImageValues[k] *\n                    outputImageValues[offset] / norm;\n                if (offset === k) {\n                    dyi += Math.pow(norm, -beta);\n                }\n                dyi *= dyValues[offset];\n                result[k] += dyi;\n            }\n        }\n        return tf.tensor4d(result, dy.shape);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        assertNotComplex(logits, 'multinomial');\n        const probabilities = normalized ? logits : tf.softmax(logits);\n        const batchSize = probabilities.shape[0];\n        const numEvents = probabilities.shape[1];\n        const res = tf.zeros([batchSize, numSamples], 'int32');\n        const resVals = this.readSync(res.dataId);\n        const probVals = this.readSync(probabilities.dataId);\n        for (let b = 0; b < batchSize; ++b) {\n            const offset = b * numEvents;\n            // The cdf won't include the last event. It will be implicit if no other\n            // event happened.\n            const cdf = new Float32Array(numEvents - 1);\n            cdf[0] = probVals[offset];\n            for (let event = 1; event < cdf.length; ++event) {\n                cdf[event] = cdf[event - 1] + probVals[offset + event];\n            }\n            const random = seedrandom.alea(seed.toString());\n            const outOffset = b * numSamples;\n            for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n                const r = random();\n                // Assume last event happened by default.\n                resVals[outOffset + sampleId] = cdf.length;\n                for (let event = 0; event < cdf.length; event++) {\n                    if (r < cdf[event]) {\n                        resVals[outOffset + sampleId] = event;\n                        break;\n                    }\n                }\n            }\n        }\n        return res;\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        assertNotComplex(indices, 'oneHot');\n        const res = new Float32Array(indices.size * depth);\n        res.fill(offValue);\n        const indicesVal = this.readSync(indices.dataId);\n        for (let event = 0; event < indices.size; ++event) {\n            if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n                res[event * depth + indicesVal[event]] = onValue;\n            }\n        }\n        return tf.tensor2d(res, [indices.size, depth], 'int32');\n    }\n    nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n        assertNotComplex(boxes, 'nonMaxSuppression');\n        const boxesVals = this.readSync(boxes.dataId);\n        const scoresVals = this.readSync(scores.dataId);\n        return nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(dataFormat === 'NHWC', () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = x.shape[1];\n        const inputWidth = x.shape[2];\n        const inputDepth = x.shape[3];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const xValues = this.readSync(x.dataId);\n        const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n        let outputIdx = 0;\n        for (let b = 0; b < batchSize; ++b) {\n            for (let h = 0; h < outputHeight; ++h) {\n                const inH = Math.floor(h / blockSize);\n                const offsetH = (h % blockSize);\n                for (let w = 0; w < outputWidth; ++w) {\n                    const inW = Math.floor(w / blockSize);\n                    const offsetW = (w % blockSize);\n                    const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n                    for (let d = 0; d < outputDepth; ++d) {\n                        const inD = d + offsetD;\n                        const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n                        result[outputIdx++] = xValues[inputIdx];\n                    }\n                }\n            }\n        }\n        return tf.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);\n    }\n    broadcastedBinaryOp(a, b, dtype, op) {\n        const newShape = backend_util.assertAndGetBroadcastShape(a.shape, b.shape);\n        const result = tf.buffer(newShape, dtype);\n        const aVals = this.readSync(a.dataId);\n        const bVals = this.readSync(b.dataId);\n        const aBroadcastDims = backend_util.getBroadcastDims(a.shape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(b.shape, newShape);\n        const resVals = result.values;\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resVals.length; ++i) {\n                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            const aBuf = this.bufferSync(a);\n            const bBuf = this.bufferSync(b);\n            for (let i = 0; i < resVals.length; ++i) {\n                const loc = result.indexToLoc(i);\n                const aLoc = loc.slice(-a.rank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = aBuf.locToIndex(aLoc);\n                const bLoc = loc.slice(-b.rank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = bBuf.locToIndex(bLoc);\n                resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return result.toTensor();\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    dispose() { }\n    floatPrecision() {\n        return 32;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return super.epsilon();\n    }\n    cropAndResize(images, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n        const numBoxes = boxes.shape[0];\n        const [cropHeight, cropWidth] = cropSize;\n        const output = tf.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32');\n        const boxVals = this.readSync(boxes.dataId);\n        const boxIndVals = this.readSync(boxIndex.dataId);\n        const imageVals = this.readSync(images.dataId);\n        const inStride = images.strides; // to calculate flat indexes into image\n        const outStride = output.strides; // to calculate flat indexes into output\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n        for (let b = 0; b < numBoxes; b++) {\n            const startInd = b * 4;\n            const y1 = boxVals[startInd];\n            const x1 = boxVals[startInd + 1];\n            const y2 = boxVals[startInd + 2];\n            const x2 = boxVals[startInd + 3];\n            const bInd = boxIndVals[b];\n            if (bInd >= batch) {\n                continue;\n            }\n            const heightScale = (cropHeight > 1) ?\n                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n                0;\n            const widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n            for (let y = 0; y < cropHeight; y++) {\n                const yInd = (cropHeight > 1) ?\n                    y1 * (imageHeight - 1) + y * (heightScale) :\n                    0.5 * (y1 + y2) * (imageHeight - 1);\n                if (yInd < 0 || yInd > imageHeight - 1) {\n                    for (let x = 0; x < cropWidth; x++) {\n                        for (let c = 0; c < numChannels; c++) {\n                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = extrapolationValue;\n                        }\n                    }\n                    continue;\n                }\n                if (method === 'bilinear') {\n                    const topInd = Math.floor(yInd);\n                    const bottomInd = Math.ceil(yInd);\n                    const yLerp = yInd - topInd;\n                    for (let x = 0; x < cropWidth; x++) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const leftInd = Math.floor(xInd);\n                        const rightInd = Math.ceil(xInd);\n                        const xLerp = xInd - leftInd;\n                        for (let c = 0; c < numChannels; c++) {\n                            let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                                bInd * inStride[0];\n                            const topRight = imageVals[ind];\n                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomLeft = imageVals[ind];\n                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                                bInd * inStride[0];\n                            const bottomRight = imageVals[ind];\n                            const top = topLeft + (topRight - topLeft) * xLerp;\n                            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[ind] = top + ((bottom - top) * yLerp);\n                        }\n                    }\n                }\n                else { // method == \"nearest\"\n                    for (let x = 0; x < cropWidth; ++x) {\n                        const xInd = (cropWidth > 1) ?\n                            x1 * (imageWidth - 1) + x * widthScale :\n                            0.5 * (x1 + x2) * (imageWidth - 1);\n                        if (xInd < 0 || xInd > imageWidth - 1) {\n                            for (let c = 0; c < numChannels; c++) {\n                                const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                                output.values[ind] = extrapolationValue;\n                            }\n                            continue;\n                        }\n                        const closestX = Math.round(xInd);\n                        const closestY = Math.round(yInd);\n                        for (let c = 0; c < numChannels; c++) {\n                            const inInd = c + closestX * inStride[2] +\n                                closestY * inStride[1] + bInd * inStride[0];\n                            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                            output.values[outInd] = imageVals[inInd];\n                        }\n                    }\n                }\n            }\n        }\n        return output.toTensor();\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        if (numSlices === 0) {\n            return tf.tensor([], resultShape, x.dtype);\n        }\n        const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n        const indicesData = this.readSync(indices.dataId);\n        const xData = this.readSync(x.dataId);\n        for (let i = 0; i < numSlices; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                flattenIndex += dim * strides[j];\n                index.push(dim);\n            }\n            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n            }\n        }\n        return buffer.toTensor().reshape(resultShape);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const defaultValue = tf.scalar(0);\n        const sumDupeIndices = true;\n        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported for string tensors');\n        }\n        else {\n            // TODO(lina128): Use fill kernel directly once this kernel is\n            // modularized.\n            return tf.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        const values = util.getArrayFromDType(x.dtype, util.sizeFromShape(x.shape));\n        return this.makeOutput(values, x.shape, x.dtype);\n    }\n    linspace(start, stop, num) {\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const indicesData = this.readSync(indices.dataId);\n        const updatesData = this.readSync(updates.dataId);\n        if (outputSize === 0) {\n            return tf.tensor([], shape, updates.dtype);\n        }\n        const buffer = new TensorBuffer(flattenShape, updates.dtype);\n        buffer.values.fill(this.readSync(defaultValue.dataId)[0]);\n        for (let i = 0; i < numUpdates; i++) {\n            const index = [];\n            let flattenIndex = 0;\n            for (let j = 0; j < sliceRank; j++) {\n                const dim = indicesData[i * sliceRank + j];\n                index.push(dim);\n                flattenIndex += dim * strides[j];\n            }\n            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n                throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n            }\n            for (let k = 0; k < sliceSize; k++) {\n                if (sumDupeIndices) {\n                    buffer.values[flattenIndex * sliceSize + k] +=\n                        updatesData[i * sliceSize + k];\n                }\n                else {\n                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n                        updatesData[0] :\n                        updatesData[i * sliceSize + k];\n                }\n            }\n        }\n        return buffer.toTensor().reshape(shape);\n    }\n}\n//# sourceMappingURL=backend_cpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Abs, util } from '@tensorflow/tfjs-core';\nexport function simpleAbsImpl(vals) {\n    const resultValues = new Float32Array(vals.length);\n    for (let i = 0; i < vals.length; ++i) {\n        resultValues[i] = Math.abs(vals[i]);\n    }\n    return resultValues;\n}\nexport const abs = (args) => {\n    const { x } = args.inputs;\n    const cpuBackend = args.backend;\n    let resultValues = new Float32Array(util.sizeFromShape(x.shape));\n    if (x.dtype !== 'complex64') {\n        const values = cpuBackend.data.get(x.dataId).values;\n        resultValues = simpleAbsImpl(values);\n    }\n    else {\n        const complexVals = cpuBackend.data.get(x.dataId);\n        const real = complexVals.complexTensorInfos.real;\n        const imag = complexVals.complexTensorInfos.imag;\n        const realVals = cpuBackend.data.get(real.dataId).values;\n        const imagVals = cpuBackend.data.get(imag.dataId).values;\n        for (let i = 0; i < realVals.length; i++) {\n            const real = realVals[i];\n            const imag = imagVals[i];\n            resultValues[i] = Math.hypot(real, imag);\n        }\n    }\n    return cpuBackend.makeOutput(resultValues, x.shape, 'float32');\n};\nexport const absConfig = {\n    kernelName: Abs,\n    backendName: 'cpu',\n    kernelFunc: abs,\n};\n//# sourceMappingURL=Abs.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for binary ops. Supports broadcast.\n */\nexport function createSimpleBinaryKernelImpl(op) {\n    return (aShape, bShape, aVals, bVals, dtype) => {\n        const newShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultRank = newShape.length;\n        const resultStrides = util.computeStrides(newShape);\n        const resultSize = util.sizeFromShape(newShape);\n        const result = util.getTypedArrayFromDType(dtype, resultSize);\n        const aRank = aShape.length;\n        const bRank = bShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bStrides = util.computeStrides(bShape);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, newShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, newShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < result.length; ++i) {\n                result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n            }\n        }\n        else {\n            for (let i = 0; i < result.length; ++i) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                result[i] = op(aVals[aIndex], bVals[bIndex]);\n            }\n        }\n        return [result, newShape];\n    };\n}\n//# sourceMappingURL=binary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const realVals = backend.data.get(real.dataId).values;\n    const imagVals = backend.data.get(imag.dataId).values;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.data.get(complexInfo.dataId);\n    // The complex tensor owns the underlying real and imag tensorInfos, only the\n    // complex tensor tracks refCount, when complexData is disposed the\n    // underlying tensorData will be disposed.\n    complex.complexTensorInfos = {\n        real: backend.makeTensorInfo(real.shape, 'float32', realVals),\n        imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals)\n    };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'cpu',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'cpu',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const real = backend.data.get(input.dataId).complexTensorInfos.real;\n    const realVal = backend.data.get(real.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the real value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(real.shape, real.dtype, realVal);\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'cpu',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { real } from './Real';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(lina128): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        const values = backend.data.get(x.dataId).values;\n        const resultValues = Int32Array.from(values);\n        return backend.makeTensorInfo(x.shape, 'int32', resultValues);\n    }\n    if (dtype === 'bool') {\n        // This is essentially the result of notEqual(x, 0). We avoid using\n        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->\n        // cast -> notEqual -> binary_utils.\n        const xVals = backend.data.get(x.dataId).values;\n        const zero = util.toTypedArray([0], x.dtype);\n        const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => (a !== b) ? 1 : 0)(x.shape, [], xVals, zero, 'bool');\n        return backend.makeTensorInfo(resultShape, 'bool', resultData);\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'cpu',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { cast } from '../kernels/Cast';\nimport { complex } from '../kernels/Complex';\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param name Kernel name.\n * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.\n * @param binaryKernelComplexImpl Optional. If exists, represents a\n *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype\n *     is `complex64`.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n    if (complexImpl == null) {\n        return ({ inputs, backend }) => {\n            const { a, b } = inputs;\n            const cpuBackend = backend;\n            assertNotComplex([a, b], name);\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        };\n    }\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const cpuBackend = backend;\n        if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n            const $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n            const aReal = $aComplexVals.complexTensorInfos.real;\n            const aImag = $aComplexVals.complexTensorInfos.imag;\n            const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n            const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n            const $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } });\n            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n            const bReal = $bComplexVals.complexTensorInfos.real;\n            const bImag = $bComplexVals.complexTensorInfos.imag;\n            const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n            const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n            const resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData);\n            const resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData);\n            const result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n            cpuBackend.disposeIntermediateTensorInfo($aComplex);\n            cpuBackend.disposeIntermediateTensorInfo($bComplex);\n            cpuBackend.disposeIntermediateTensorInfo(resultReal);\n            cpuBackend.disposeIntermediateTensorInfo(resultImag);\n            return result;\n        }\n        else {\n            const aVals = cpuBackend.data.get(a.dataId).values;\n            const bVals = cpuBackend.data.get(b.dataId).values;\n            const $dtype = dtype || a.dtype;\n            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n        }\n    };\n}\n/**\n * Template that creates the complex type implementation for binary ops.\n * Supports broadcast.\n */\nexport function createComplexBinaryKernelImpl(op) {\n    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n        const resultShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const resultSize = util.sizeFromShape(resultShape);\n        const resultRank = resultShape.length;\n        const resultStrides = util.computeStrides(resultShape);\n        const resultRealVals = util.getTypedArrayFromDType('float32', resultSize);\n        const resultImagVals = util.getTypedArrayFromDType('float32', resultSize);\n        const aBroadcastDims = backend_util.getBroadcastDims(aShape, resultShape);\n        const bBroadcastDims = backend_util.getBroadcastDims(bShape, resultShape);\n        const aVals = backend_util.mergeRealAndImagArrays(aRealVals, aImagVals);\n        const bVals = backend_util.mergeRealAndImagArrays(bRealVals, bImagVals);\n        const aRank = aShape.length;\n        const aStrides = util.computeStrides(aShape);\n        const bRank = bShape.length;\n        const bStrides = util.computeStrides(bShape);\n        if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const aIdx = i % aVals.length;\n                const bIdx = i % bVals.length;\n                const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n                resultRealVals[i] = result.real;\n                resultImagVals[i] = result.imag;\n            }\n        }\n        else {\n            for (let i = 0; i < resultRealVals.length; i++) {\n                const loc = util.indexToLoc(i, resultRank, resultStrides);\n                const aLoc = loc.slice(-aRank);\n                aBroadcastDims.forEach(d => aLoc[d] = 0);\n                const aIndex = util.locToIndex(aLoc, aRank, aStrides);\n                const bLoc = loc.slice(-bRank);\n                bBroadcastDims.forEach(d => bLoc[d] = 0);\n                const bIndex = util.locToIndex(bLoc, bRank, bStrides);\n                const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n                resultRealVals[i] = opResult.real;\n                resultImagVals[i] = opResult.imag;\n            }\n        }\n        return [resultRealVals, resultImagVals, resultShape];\n    };\n}\n//# sourceMappingURL=kernel_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const addImpl = createSimpleBinaryKernelImpl(((a, b) => a + b));\nexport const addComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal + bReal, imag: aImag + bImag };\n}));\nexport const add = binaryKernelFunc(Add, addImpl, addComplexImpl);\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'cpu',\n    kernelFunc: add\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Template that creates implementation for unary op.\n */\nexport function createSimpleUnaryImpl(op) {\n    return (values, dtype, attrs) => {\n        const newValues = util.getTypedArrayFromDType(dtype, values.length);\n        for (let i = 0; i < values.length; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return newValues;\n    };\n}\n//# sourceMappingURL=unary_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param name Kernel name.\n * @param op A `SimpleUnaryOperation` for the kernel.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFunc(name, op, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const xSize = util.sizeFromShape(x.shape);\n        const $dtype = dtype || x.dtype;\n        const newValues = util.getArrayFromDType($dtype, xSize);\n        for (let i = 0; i < xSize; ++i) {\n            newValues[i] = op(values[i], attrs);\n        }\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for unary ops from the given\n * `SimpleUnaryImpl`..\n * @param name Kernel name.\n * @param unaryImpl A `SimpleUnaryImpl` that implements the op.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the input. This is mainly used in certain\n *     kernels that return bool type, such as isFinite, isInf, etc.\n */\nexport function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n    return ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        assertNotComplex(x, name);\n        if (x.dtype === 'string' || dtype === 'string') {\n            throw new Error('unaryKernelFunc does not support string input/output');\n        }\n        const cpuBackend = backend;\n        const values = cpuBackend.data.get(x.dataId).values;\n        const $dtype = dtype || x.dtype;\n        const newValues = unaryImpl(values, $dtype, attrs);\n        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n    };\n}\n//# sourceMappingURL=unary_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Ceil } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nexport const ceil = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nexport const ceilConfig = {\n    kernelName: Ceil,\n    backendName: 'cpu',\n    kernelFunc: ceil,\n};\n//# sourceMappingURL=Ceil.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Exp } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nexport const exp = unaryKernelFuncFromImpl(Exp, expImpl);\nexport const expConfig = {\n    kernelName: Exp,\n    backendName: 'cpu',\n    kernelFunc: exp,\n};\n//# sourceMappingURL=Exp.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Expm1 } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nexport const expm1 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nexport const expm1Config = {\n    kernelName: Expm1,\n    backendName: 'cpu',\n    kernelFunc: expm1,\n};\n//# sourceMappingURL=Expm1.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Floor } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nexport const floor = unaryKernelFuncFromImpl(Floor, floorImpl);\nexport const floorConfig = {\n    kernelName: Floor,\n    backendName: 'cpu',\n    kernelFunc: floor,\n};\n//# sourceMappingURL=Floor.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nexport const log = unaryKernelFuncFromImpl(Log, logImpl);\nexport const logConfig = {\n    kernelName: Log,\n    backendName: 'cpu',\n    kernelFunc: log,\n};\n//# sourceMappingURL=Log.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function maxImpl(aVals, reduceSize, outShape, dtype) {\n    const vals = util.getTypedArrayFromDType(dtype, util.sizeFromShape(outShape));\n    for (let i = 0; i < vals.length; ++i) {\n        const offset = i * reduceSize;\n        let max = aVals[offset];\n        for (let j = 0; j < reduceSize; ++j) {\n            const value = aVals[offset + j];\n            if (value > max) {\n                max = value;\n            }\n        }\n        vals[i] = max;\n    }\n    return vals;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Multiply } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const multiplyImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue * bValue));\nexport const multiplyComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return {\n        real: aReal * bReal - aImag * bImag,\n        imag: aReal * bImag + aImag * bReal\n    };\n}));\nexport const multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'cpu',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const notEqualImpl = createSimpleBinaryKernelImpl(((a, b) => (a !== b) ? 1 : 0));\nexport const notEqual = binaryKernelFunc(NotEqual, notEqualImpl, null /* complexOp */, 'bool');\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'cpu',\n    kernelFunc: notEqual\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Rsqrt } from '@tensorflow/tfjs-core';\nimport { createSimpleUnaryImpl } from '../utils/unary_impl';\nimport { unaryKernelFuncFromImpl } from '../utils/unary_utils';\nexport const rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nexport const rsqrt = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nexport const rsqrtConfig = {\n    kernelName: Rsqrt,\n    backendName: 'cpu',\n    kernelFunc: rsqrt,\n};\n//# sourceMappingURL=Rsqrt.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Slice, slice_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function sliceImpl(vals, begin, size, shape, dtype) {\n    const isContinous = slice_util.isSliceContinous(shape, begin, size);\n    const length = util.sizeFromShape(size);\n    const xStrides = util.computeStrides(shape);\n    if (isContinous) {\n        const flatOffset = slice_util.computeFlatOffset(begin, xStrides);\n        return vals.subarray(flatOffset, flatOffset + length);\n    }\n    const outVals = util.getTypedArrayFromDType(dtype, length);\n    for (let i = 0; i < length; ++i) {\n        const rank = size.length;\n        const strides = util.computeStrides(size);\n        const loc = util.indexToLoc(i, rank, strides);\n        const xLoc = loc.map((idx, j) => idx + begin[j]);\n        const xIndex = util.locToIndex(xLoc, shape.length, xStrides);\n        outVals[i] = vals[xIndex];\n    }\n    return outVals;\n}\nexport function slice(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { begin, size } = attrs;\n    assertNotComplex(x, 'slice');\n    const [$begin, $size] = slice_util.parseSliceParams(x, begin, size);\n    slice_util.assertParamsValid(x, $begin, $size);\n    const vals = backend.data.get(x.dataId).values;\n    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n    return backend.makeTensorInfo($size, x.dtype, outVals);\n}\nexport const sliceConfig = {\n    kernelName: Slice,\n    backendName: 'cpu',\n    kernelFunc: slice\n};\n//# sourceMappingURL=Slice.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const squaredDifferenceImpl = createSimpleBinaryKernelImpl(((a, b) => {\n    const diff = a - b;\n    return diff * diff;\n}));\nexport const squaredDifference = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'cpu',\n    kernelFunc: squaredDifference\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc, createComplexBinaryKernelImpl } from '../utils/kernel_utils';\nexport const subImpl = createSimpleBinaryKernelImpl(((aValue, bValue) => aValue - bValue));\nexport const subComplexImpl = createComplexBinaryKernelImpl(((aReal, aImag, bReal, bImag) => {\n    return { real: aReal - bReal, imag: aImag - bImag };\n}));\nexport const sub = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'cpu',\n    kernelFunc: sub\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport function transposeImpl(xVals, xShape, dtype, perm, newShape) {\n    const xRank = xShape.length;\n    const xSize = util.sizeFromShape(xShape);\n    const xStrides = util.computeStrides(xShape);\n    const newStrides = util.computeStrides(newShape);\n    const result = util.getTypedArrayFromDType(dtype, util.sizeFromShape(newShape));\n    for (let i = 0; i < xSize; ++i) {\n        const loc = util.indexToLoc(i, xRank, xStrides);\n        // Permute location.\n        const newLoc = new Array(loc.length);\n        for (let i = 0; i < newLoc.length; i++) {\n            newLoc[i] = loc[perm[i]];\n        }\n        const newIndex = util.locToIndex(newLoc, xRank, newStrides);\n        result[newIndex] = xVals[i];\n    }\n    return result;\n}\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer, util } from '@tensorflow/tfjs-core';\nexport function uniqueImpl(values, axis, shape, dtype) {\n    // Normalize and validate axis.\n    const $axis = util.parseAxisParam(axis, shape)[0];\n    // Calculate the new shape that is suitable for extracting data along the\n    // given axis.\n    //\n    // The rank is 3.\n    // The size of the 1st dimension is the size of all the axes < the given axis.\n    // The size of the 2nd dimension is the same as the size of the given axis.\n    // The size of the 3rd dimension is the size of all the axes > the given axis.\n    //\n    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n    // newShape would be: [2*3, 5, 4].\n    //\n    // Note that this is not the final output shape. This will be the shape for an\n    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n    // values along the given axis. To demonstrate how it works, consider the\n    // following example:\n    //\n    // Input: a 3D tensor, with shape [1, 2, 3]\n    // [\n    //   [\n    //      [1,2,3],\n    //      [4,5,6]\n    //   ]\n    // ]\n    // Axis: 2 (the last axis).\n    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n    //\n    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n    // 1*2. The re-shaped data would look like:\n    //\n    // [\n    //   [\n    //     [1], [2], [3]\n    //   ],\n    //   [\n    //     [4], [5], [6]\n    //   ]\n    // ]\n    //\n    // Then, we can construct a 3-level nested loop by the following dimension\n    // order to extract the values along the axis (dimension1):\n    // i: dimension1       // 0,1,2 (newShape[1])\n    //   m: dimension0     // 0,1   (newShape[0])\n    //     n: dimension2   // 0     (newShape[2])\n    //\n    //                       m, i, n\n    //                      ---------\n    // Iteration 0: data at [0, 0, 0] => \"1\"\n    // Iteration 1: data at [1, 0, 0] => \"4\"\n    // We got [1,4].\n    // Iteration 2: data at [0, 1, 0] => \"2\"\n    // Iteration 3: data at [1, 1, 0] => \"5\"\n    // We got [2,5].\n    // Iteration 4: data at [0, 2, 0] => \"3\"\n    // Iteration 5: data at [1, 2, 0] => \"6\"\n    // We got [3,6].\n    const newShape = [1, shape[0], 1];\n    for (let i = 0; i < $axis; i++) {\n        newShape[0] *= shape[i];\n    }\n    newShape[1] = shape[$axis];\n    for (let i = $axis + 1; i < shape.length; i++) {\n        newShape[2] *= shape[i];\n    }\n    // A map from unique elements (their string representations) to their values\n    // in \"indices\" (below).\n    const uniqueElements = {};\n    // The indices of each unique element in the original tensor along the given\n    // axis. It is 1D and has the same size as the given axis.\n    const indices = new Int32Array(shape[$axis]);\n    // Create a buffer so we can easily extract value at a given location.\n    const inputBuffer = new TensorBuffer(newShape, dtype, values);\n    // The indices along the given axis that have unique elements. This is a\n    // de-duped version of \"indices\" above.\n    const uniqueIndices = [];\n    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n    for (let i = 0; i < shape[$axis]; i++) {\n        // Extract values along the axis.\n        let element;\n        if (is1DTensor) {\n            // Fast path for 1D tensor input.\n            element = values[i].toString();\n        }\n        else {\n            const axisValues = [];\n            for (let m = 0; m < newShape[0]; m++) {\n                for (let n = 0; n < newShape[2]; n++) {\n                    axisValues.push(inputBuffer.get(m, i, n));\n                }\n            }\n            element = axisValues.join(',');\n        }\n        // Dedup and update various indices.\n        if (uniqueElements[element] !== undefined) {\n            indices[i] = uniqueElements[element];\n        }\n        else {\n            const uniqueIndex = Object.keys(uniqueElements).length;\n            uniqueElements[element] = uniqueIndex;\n            indices[i] = uniqueIndex;\n            uniqueIndices.push(i);\n        }\n    }\n    // Now we know where each of the unique elements are located along the axis\n    // (uniqueIndices). Extract them from input buffer and store them in the\n    // output buffer.\n    const outputTmpShape = newShape.slice();\n    outputTmpShape[1] = Object.keys(uniqueElements).length;\n    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n    uniqueIndices.forEach((uniqueElementIndex, i) => {\n        for (let m = 0; m < newShape[0]; m++) {\n            for (let n = 0; n < newShape[2]; n++) {\n                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n            }\n        }\n    });\n    // The output shape can be calculated from the input shape with the size of\n    // the given axis replaced by the number of unique elements along that axis.\n    const outputShape = shape.slice();\n    outputShape[$axis] = outputTmpShape[1];\n    return {\n        outputValues: outputBuffer.values,\n        outputShape,\n        indices,\n    };\n}\n//# sourceMappingURL=Unique_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Shared functionality among backends.\nexport { simpleAbsImpl } from './kernels/Abs';\nexport { addImpl } from './kernels/Add';\nexport { ceilImpl } from './kernels/Ceil';\nexport { expImpl } from './kernels/Exp';\nexport { expm1Impl } from './kernels/Expm1';\nexport { floorImpl } from './kernels/Floor';\nexport { logImpl } from './kernels/Log';\nexport { maxImpl } from './kernels/Max_impl';\nexport { multiplyImpl } from './kernels/Multiply';\nexport { notEqualImpl } from './kernels/NotEqual';\nexport { rsqrtImpl } from './kernels/Rsqrt';\nexport { sliceImpl } from './kernels/Slice';\nexport { squaredDifferenceImpl } from './kernels/SquaredDifference';\nexport { subImpl } from './kernels/Sub';\nexport { transposeImpl } from './kernels/Transpose_impl';\nexport { uniqueImpl } from './kernels/Unique_impl';\n//# sourceMappingURL=shared.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/*\n * base.ts contains all the exports from tfjs-backend-cpu\n * without auto-kernel registration\n */\nimport { registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendCPU } from './backend_cpu';\nimport * as shared from './shared';\nexport { MathBackendCPU } from './backend_cpu';\nexport { version as version_cpu } from './version';\nexport { shared };\n// Side effects for default initialization of MathBackendCPU\nregisterBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Elu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const elu = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : (Math.exp(xi) - 1));\nexport const eluConfig = {\n    kernelName: Elu,\n    backendName: 'cpu',\n    kernelFunc: elu,\n};\n//# sourceMappingURL=Elu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Prelu } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nconst preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\nexport function prelu(args) {\n    const { inputs, backend } = args;\n    const { x, alpha } = inputs;\n    assertNotComplex([x, alpha], 'prelu');\n    const aVals = backend.data.get(x.dataId).values;\n    const bVals = backend.data.get(alpha.dataId).values;\n    const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, x.dtype);\n    return backend.makeTensorInfo(resultShape, x.dtype, resultData);\n}\nexport const preluConfig = {\n    kernelName: Prelu,\n    backendName: 'cpu',\n    kernelFunc: prelu,\n};\n//# sourceMappingURL=Prelu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const relu = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));\nexport const reluConfig = {\n    kernelName: Relu,\n    backendName: 'cpu',\n    kernelFunc: relu,\n};\n//# sourceMappingURL=Relu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Relu6 } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const relu6 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));\nexport const relu6Config = {\n    kernelName: Relu6,\n    backendName: 'cpu',\n    kernelFunc: relu6,\n};\n//# sourceMappingURL=Relu6.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { elu } from '../kernels/Elu';\nimport { identity } from '../kernels/Identity';\nimport { prelu } from '../kernels/Prelu';\nimport { relu } from '../kernels/Relu';\nimport { relu6 } from '../kernels/Relu6';\nexport function applyActivation(backend, x, activation, preluActivationWeights) {\n    if (activation === 'linear') {\n        return identity({ inputs: { x }, backend });\n    }\n    else if (activation === 'relu') {\n        return relu({ inputs: { x }, backend });\n    }\n    else if (activation === 'elu') {\n        return elu({ inputs: { x }, backend });\n    }\n    else if (activation === 'relu6') {\n        return relu6({ inputs: { x }, backend });\n    }\n    else if (activation === 'prelu') {\n        return prelu({ inputs: { x, alpha: preluActivationWeights }, backend });\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);\n}\n//# sourceMappingURL=fused_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    backend.incRef(x.dataId);\n    const xData = backend.data.get(x.dataId);\n    if (xData.complexTensorInfos != null) {\n        const real = xData.complexTensorInfos.real;\n        const imag = xData.complexTensorInfos.imag;\n        real.shape = $shape;\n        imag.shape = $shape;\n    }\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'cpu',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { BatchMatMul, buffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { reshape } from './Reshape';\nexport function batchMatMul(args) {\n    const { inputs, backend, attrs } = args;\n    const { a, b } = inputs;\n    const { transposeA, transposeB } = attrs;\n    assertNotComplex([a, b], 'matMul');\n    const aRank = a.shape.length;\n    const bRank = b.shape.length;\n    const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n    const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n    const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n    const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n    const outerDimsA = a.shape.slice(0, -2);\n    const outerDimsB = b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n    const batchDimsCompatible = batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n    util.assert(aRank >= 2 && bRank >= 2 && batchDimsCompatible, () => `Error in matMul: the input batch dimensions must either be the ` +\n        `same or at least one input batch dimension must be 1. Got input ` +\n        `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n    const outShapeOuterDims = batchDimA > batchDimB ? a.shape.slice(0, -2) : b.shape.slice(0, -2);\n    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n    util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n        `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +\n        `${b.shape} and transposeA=${transposeA}` +\n        ` and transposeB=${transposeB} must match.`);\n    const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] :\n        [batchDimA, outerShapeA, innerShapeA];\n    const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] :\n        [batchDimB, innerShapeB, outerShapeB];\n    // The rest of the implementation is designed to operate on rank-3 tensors\n    const a3d = reshape({ inputs: { x: a }, backend, attrs: { shape: a3dShape } });\n    const b3d = reshape({ inputs: { x: b }, backend, attrs: { shape: b3dShape } });\n    const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n    const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n    const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n    const batchDim = Math.max(batchDimA, batchDimB);\n    const a3dValues = backend.data.get(a3d.dataId).values;\n    const b3dValues = backend.data.get(b3d.dataId).values;\n    const a3dStrides = util.computeStrides(a3d.shape);\n    const b3dStrides = util.computeStrides(b3d.shape);\n    const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n        [a3dStrides[0], 1, a3dStrides[1]] :\n        [a3dStrides[0], a3dStrides[1], 1];\n    const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n        [1, b3dStrides[1], b3dStrides[0]] :\n        [b3dStrides[1], 1, b3dStrides[0]];\n    const size = leftDim * rightDim;\n    const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n    const resVals = result.values;\n    const blockSize = backend.blockSize;\n    for (let bi = 0; bi < batchDim; bi++) {\n        for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n            for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n                for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n                    // for when blockSize doesn't evenly divide the input\n                    const iBlock = Math.min(i0 + blockSize, leftDim);\n                    const jBlock = Math.min(j0 + blockSize, rightDim);\n                    const kBlock = Math.min(k0 + blockSize, sharedDim);\n                    for (let i = i0; i < iBlock; i++) {\n                        for (let j = j0; j < jBlock; j++) {\n                            let sum = 0.0;\n                            for (let k = k0; k < kBlock; k++) {\n                                const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;\n                                const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;\n                                const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];\n                                const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];\n                                sum += aVal * bVal;\n                            }\n                            resVals[bi * size + (i * rightDim + j)] += sum;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    backend.disposeIntermediateTensorInfo(a3d);\n    backend.disposeIntermediateTensorInfo(b3d);\n    // set correct shape on output.\n    return backend.makeTensorInfo(outShape, result.dtype, result.values);\n}\nexport const batchMatMulConfig = {\n    kernelName: BatchMatMul,\n    backendName: 'cpu',\n    kernelFunc: batchMatMul,\n};\n//# sourceMappingURL=BatchMatMul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { _FusedMatMul } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { batchMatMul } from './BatchMatMul';\nexport function _fusedMatMul(args) {\n    const { inputs, backend, attrs } = args;\n    const { a, b, bias, preluActivationWeights } = inputs;\n    const { transposeA, transposeB, activation } = attrs;\n    let current;\n    let addRes;\n    let activationRes;\n    const intermediates = [];\n    const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend });\n    current = matMulRes;\n    if (bias) {\n        addRes = add({ inputs: { a: current, b: bias }, backend });\n        intermediates.push(current);\n        current = addRes;\n    }\n    if (activation) {\n        activationRes =\n            applyActivation(backend, current, activation, preluActivationWeights);\n        intermediates.push(current);\n        current = activationRes;\n    }\n    for (const i of intermediates) {\n        backend.disposeIntermediateTensorInfo(i);\n    }\n    return current;\n}\nexport const _fusedMatMulConfig = {\n    kernelName: _FusedMatMul,\n    backendName: 'cpu',\n    kernelFunc: _fusedMatMul,\n};\n//# sourceMappingURL=_FusedMatMul.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acos = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nexport const acosConfig = {\n    kernelName: Acos,\n    backendName: 'cpu',\n    kernelFunc: acos,\n};\n//# sourceMappingURL=Acos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Acosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const acosh = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nexport const acoshConfig = {\n    kernelName: Acosh,\n    backendName: 'cpu',\n    kernelFunc: acosh,\n};\n//# sourceMappingURL=Acosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asin = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nexport const asinConfig = {\n    kernelName: Asin,\n    backendName: 'cpu',\n    kernelFunc: asin,\n};\n//# sourceMappingURL=Asin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Asinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const asinh = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nexport const asinhConfig = {\n    kernelName: Asinh,\n    backendName: 'cpu',\n    kernelFunc: asinh,\n};\n//# sourceMappingURL=Asinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atan = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nexport const atanConfig = {\n    kernelName: Atan,\n    backendName: 'cpu',\n    kernelFunc: atan,\n};\n//# sourceMappingURL=Atan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const atanh = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nexport const atanhConfig = {\n    kernelName: Atanh,\n    backendName: 'cpu',\n    kernelFunc: atanh,\n};\n//# sourceMappingURL=Atanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { buffer } from '@tensorflow/tfjs-core';\nexport function pool(xValues, xShape, dtype, strides, convInfo, poolType) {\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n        Number.POSITIVE_INFINITY);\n    const output = buffer(convInfo.outShape, dtype);\n    const outputVals = output.values;\n    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const outputBatchOffset = b * outputBatchStrides;\n        const inputBatchOffset = b * strides[0];\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    const xCMin = Math.max(0, xCCorner);\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let minMaxValue = initialValue;\n                    let avgValue = 0;\n                    let count = 0;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const xROffset = inputBatchOffset + xR * strides[1];\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const xCOffset = xROffset + xC * strides[2];\n                            const pixel = xValues[xCOffset + d];\n                            if ((poolType === 'max' && pixel > minMaxValue)) {\n                                minMaxValue = pixel;\n                            }\n                            else if (poolType === 'avg') {\n                                avgValue += pixel;\n                                count++;\n                            }\n                        }\n                        if (isNaN(minMaxValue)) {\n                            break;\n                        }\n                    }\n                    const outputOffset = outputRowOffset + yC * outputColStrides + d;\n                    outputVals[outputOffset] =\n                        poolType === 'avg' ? avgValue / count : minMaxValue;\n                }\n            }\n        }\n    }\n    return output;\n}\nexport function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n    const maxPositions = buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const xBuf = buffer(xShape, dtype, xValues);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                const xRCorner = yR * strideHeight - padTop;\n                let xRMin = xRCorner;\n                while (xRMin < 0) {\n                    xRMin += dilationHeight;\n                }\n                // const xRMin = Math.max(0, xRCorner);\n                const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const xCCorner = yC * strideWidth - padLeft;\n                    let xCMin = xCCorner;\n                    while (xCMin < 0) {\n                        xCMin += dilationWidth;\n                    }\n                    const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n                    let maxValue = Number.NEGATIVE_INFINITY;\n                    let maxPosition = -1;\n                    for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n                        const wR = xR - xRCorner;\n                        for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                            const wC = xC - xCCorner;\n                            const pixel = xBuf.get(b, xR, xC, d);\n                            if (pixel > maxValue) {\n                                maxValue = pixel;\n                                if (flattenPositions) {\n                                    maxPosition = includeBatchInIndex ?\n                                        ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) *\n                                            convInfo.inChannels +\n                                            d :\n                                        (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                                }\n                                else {\n                                    maxPosition = wR * effectiveFilterWidth + wC;\n                                }\n                            }\n                        }\n                    }\n                    maxPositions.set(maxPosition, b, yR, yC, d);\n                }\n            }\n        }\n    }\n    return maxPositions;\n}\n//# sourceMappingURL=pool_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'avg');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'cpu',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util, buffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel;\n                        }\n                    }\n                    dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function batchNorm(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, scale, offset, mean, variance } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    assertNotComplex([x, mean, variance, scale, offset], 'batchNorm');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const xVals = backend.data.get(x.dataId).values;\n    const mVals = backend.data.get(mean.dataId).values;\n    const varVals = backend.data.get(variance.dataId).values;\n    const sVals = scale ? backend.data.get(scale.dataId).values :\n        new Float32Array([1]);\n    const offVals = offset ?\n        backend.data.get(offset.dataId).values :\n        new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n        outVals[i] = offVals[offi++] +\n            (xVals[i] - mVals[mi++]) * sVals[si++] /\n                Math.sqrt(varVals[vi++] + varianceEpsilon);\n        if (offi >= offValsLength) {\n            offi = 0;\n        }\n        if (mi >= mValsLength) {\n            mi = 0;\n        }\n        if (si >= sValsLength) {\n            si = 0;\n        }\n        if (vi >= varValsLength) {\n            vi = 0;\n        }\n    }\n    return backend.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'cpu',\n    kernelFunc: batchNorm,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ClipByValue } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const clip = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n    const clipAttrs = attrs;\n    if (xi > clipAttrs.clipValueMax) {\n        return clipAttrs.clipValueMax;\n    }\n    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nexport const clipConfig = {\n    kernelName: ClipByValue,\n    backendName: 'cpu',\n    kernelFunc: clip,\n};\n//# sourceMappingURL=Clip.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;\n    const imagVal = backend.data.get(imag.dataId).values;\n    // When complex tensor is disposed, its underlying parts will be disposed too.\n    // Make new tensor out of the imag value of the complex. This makes sure the\n    // value is still accessible even if complex tensor is disposed.\n    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'cpu',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    let outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    if ($inputs[0].dtype === 'complex64') {\n        const reals = $inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = $inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concat({ inputs: reals, backend, attrs: { axis: $axis } });\n        const imagConcated = concat({ inputs: imags, backend, attrs: { axis: $axis } });\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const inputs2D = $inputs.map(t => {\n        const innerSize = util.sizeFromShape(t.shape.slice($axis));\n        const shape = [-1, innerSize];\n        return reshape({ inputs: { x: t }, backend, attrs: { shape } });\n    });\n    // Concats 2d tensors along axis=1.\n    outShape =\n        backend_util.computeOutShape(inputs2D.map(t => t.shape), 1 /* axis */);\n    const outVals = util.getTypedArrayFromDType($inputs[0].dtype, util.sizeFromShape(outShape));\n    if (inputs2D[0].shape[0] === 1) {\n        // Use built-in TypedArray.set() method for speed.\n        let offset = 0;\n        inputs2D.forEach(t => {\n            const val = backend.data.get(t.dataId).values;\n            const size = util.sizeFromShape(t.shape);\n            outVals.set(val, offset);\n            offset += size;\n        });\n    }\n    else {\n        let colOffset = 0;\n        inputs2D.forEach(t => {\n            const tVals = backend.data.get(t.dataId).values;\n            let tIdx = 0;\n            for (let row = 0; row < t.shape[0]; ++row) {\n                const resIdx = row * outShape[1] + colOffset;\n                for (let col = 0; col < t.shape[1]; ++col) {\n                    outVals[resIdx + col] = tVals[tIdx++];\n                }\n            }\n            colOffset += t.shape[1];\n        });\n    }\n    const finalOutShape = backend_util.computeOutShape($inputs.map(t => t.shape), $axis);\n    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n    inputs2D.forEach(t => backend.disposeIntermediateTensorInfo(t));\n    return outInfo;\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'cpu',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2D, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode } = attrs;\n    assertNotComplex([x, filter], 'conv2d');\n    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat);\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const xBatchStride = xStrides[0];\n    const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];\n    const xColStride = isChannelsLast ? xStrides[2] : 1;\n    const xChannelStride = isChannelsLast ? 1 : xStrides[1];\n    const yBatchStride = y.strides[0];\n    const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n    const yColStride = isChannelsLast ? y.strides[2] : 1;\n    const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xBatchStride;\n        const yOffset1 = b * yBatchStride;\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset2 = yOffset1 + yR * yRowStride;\n            const xRCorner = yR * convInfo.strideHeight - padTop;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const xR = xRCorner + wR * dilationHeight;\n                if (xR < 0 || xR >= convInfo.inHeight) {\n                    continue;\n                }\n                const wOffset1 = wR * filterStrides[0];\n                const xOffset2 = xOffset1 + xR * xRowStride;\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const yOffset3 = yOffset2 + yC * yColStride;\n                    const xCCorner = yC * convInfo.strideWidth - padLeft;\n                    for (let wC = 0; wC < filterWidth; ++wC) {\n                        const xC = xCCorner + wC * dilationWidth;\n                        if (xC < 0 || xC >= convInfo.inWidth) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wC * filterStrides[1];\n                        const xOffset3 = xOffset2 + xC * xColStride;\n                        let wOffset3 = wOffset2;\n                        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                            const xVal = xVals[xOffset3 + d1 * xChannelStride];\n                            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                yVals[yOffset3 + d2 * yChannelStride] +=\n                                    xVal * wVals[wOffset3 + d2];\n                            }\n                            wOffset3 += convInfo.outChannels;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, yVals);\n}\nexport const conv2DConfig = {\n    kernelName: Conv2D,\n    backendName: 'cpu',\n    kernelFunc: conv2D\n};\n//# sourceMappingURL=Conv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2DBackpropFilter, TensorBuffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2DBackpropFilter(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, pad, dataFormat, dimRoundingMode, filterShape } = attrs;\n    assertNotComplex([x, dy], 'conv2dBackpropFilter');\n    const $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad, dimRoundingMode, false /* depthwise */, $dataFormat);\n    const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n    const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n    const dW = new TensorBuffer(convInfo.filterShape, 'float32');\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const xVals = backend.data.get(x.dataId).values;\n    const dyVals = backend.data.get(dy.dataId).values;\n    const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n    const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n    for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n        for (let wC = 0; wC < filterWidth; ++wC) {\n            const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n            const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    let dotProd = 0;\n                    for (let b = 0; b < convInfo.batchSize; ++b) {\n                        for (let yR = yRMin; yR < yRMax; ++yR) {\n                            const xR = wR + yR * strideHeight - topPad;\n                            for (let yC = yCMin; yC < yCMax; ++yC) {\n                                const xC = wC + yC * strideWidth - leftPad;\n                                if (isChannelsLast) {\n                                    dotProd += xBuf.get(b, xR, xC, d1) *\n                                        dyBuf.get(b, yR, yC, d2);\n                                }\n                                else {\n                                    dotProd += xBuf.get(b, d1, xR, xC) *\n                                        dyBuf.get(b, d2, yR, yC);\n                                }\n                            }\n                        }\n                    }\n                    dW.set(dotProd, wR, wC, d1, d2);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nexport const conv2DBackpropFilterConfig = {\n    kernelName: Conv2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: conv2DBackpropFilter\n};\n//# sourceMappingURL=Conv2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv2DBackpropInput, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv2DBackpropInput(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { inputShape, strides, pad, dataFormat, dimRoundingMode } = attrs;\n    assertNotComplex([dy, filter], 'conv2dBackpropInput');\n    const filterStrides = util.computeStrides(filter.shape);\n    const dyStrides = util.computeStrides(dy.shape);\n    let $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = backend_util.computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2] = filterStrides;\n    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n    $dataFormat = convInfo.dataFormat;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const isChannelsLast = $dataFormat === 'channelsLast';\n    const xBatchStride = dx.strides[0];\n    const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n    const xColStride = isChannelsLast ? dx.strides[2] : 1;\n    const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n    const yBatchStride = dyStrides[0];\n    const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n    const yColStride = isChannelsLast ? dyStrides[2] : 1;\n    const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            for (let xR = 0; xR < inHeight; ++xR) {\n                const xRCorner = xR - topPad;\n                const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                for (let xC = 0; xC < inWidth; ++xC) {\n                    const xCCorner = xC - leftPad;\n                    const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                    const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                    let dotProd = 0;\n                    for (let yR = xRMin; yR < yRMax; ++yR) {\n                        const wR = yR * strideHeight - xRCorner;\n                        for (let yC = xCMin; yC < yCMax; ++yC) {\n                            const wC = yC * strideWidth - xCCorner;\n                            const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n                            const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                            for (let d2 = 0; d2 < outChannels; ++d2) {\n                                const pixel = dyValues[dyOffset + yChannelStride * d2];\n                                const weight = fltValues[fltOffset + d2];\n                                dotProd += pixel * weight;\n                            }\n                        }\n                    }\n                    const dxOffset = xBatchStride * b + xRowStride * xR +\n                        xColStride * xC + xChannelStride * d1;\n                    dxValues[dxOffset] = dotProd;\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const conv2DBackpropInputConfig = {\n    kernelName: Conv2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: conv2DBackpropInput\n};\n//# sourceMappingURL=Conv2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3D, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dilations } = attrs;\n    assertNotComplex([x, filter], 'conv3d');\n    const convInfo = backend_util.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad);\n    const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;\n    const padFront = padInfo.front;\n    const padLeft = padInfo.left;\n    const padTop = padInfo.top;\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xStrides[0];\n        const yOffset1 = b * y.strides[0];\n        for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n            const yOffset2 = yOffset1 + yF * y.strides[1];\n            const xFCorner = yF * convInfo.strideDepth - padFront;\n            for (let wF = 0; wF < filterDepth; ++wF) {\n                const xF = xFCorner + wF * dilationDepth;\n                if (xF < 0 || xF >= convInfo.inDepth) {\n                    continue;\n                }\n                const wOffset1 = wF * filterStrides[0];\n                const xOffset2 = xOffset1 + xF * xStrides[1];\n                for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n                    const yOffset3 = yOffset2 + yR * y.strides[2];\n                    const xRCorner = yR * convInfo.strideHeight - padTop;\n                    for (let wR = 0; wR < filterHeight; ++wR) {\n                        const xR = xRCorner + wR * dilationHeight;\n                        if (xR < 0 || xR >= convInfo.inHeight) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wR * filterStrides[1];\n                        const xOffset3 = xOffset2 + xR * xStrides[2];\n                        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                            const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                            const xCCorner = yC * convInfo.strideWidth - padLeft;\n                            for (let wC = 0; wC < filterWidth; ++wC) {\n                                const xC = xCCorner + wC * dilationWidth;\n                                if (xC < 0 || xC >= convInfo.inWidth) {\n                                    continue;\n                                }\n                                const wOffset3 = wOffset2 + wC * filterStrides[2];\n                                const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                                let wOffset4 = wOffset3;\n                                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                                    const xVal = xVals[xOffset4 + d1];\n                                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                                        yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                                    }\n                                    wOffset4 += convInfo.outChannels;\n                                }\n                            }\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nexport const conv3DConfig = {\n    kernelName: Conv3D,\n    backendName: 'cpu',\n    kernelFunc: conv3D\n};\n//# sourceMappingURL=Conv3D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3DBackpropFilterV2, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3DBackpropFilterV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, pad, filterShape } = attrs;\n    assertNotComplex([x, dy], 'conv3dBackpropFilterV2');\n    const xStrides = util.computeStrides(x.shape);\n    const dyStrides = util.computeStrides(dy.shape);\n    const convInfo = backend_util.computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad);\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dw = new TensorBuffer(convInfo.filterShape, 'float32');\n    const dwValues = dw.values;\n    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n    const xValues = backend.data.get(x.dataId).values;\n    const [xS0, xS1, xS2, xS3] = xStrides;\n    const frontPad = convInfo.padInfo.front;\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    for (let wF = 0; wF < filterDepth; ++wF) {\n        const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n        const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n        const wOffset1 = wF * dwS0;\n        for (let wR = 0; wR < filterHeight; ++wR) {\n            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n            const wOffset2 = wR * dwS1 + wOffset1;\n            for (let wC = 0; wC < filterWidth; ++wC) {\n                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n                const wOffset3 = wC * dwS2 + wOffset2;\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    const wOffset4 = d1 * dwS3 + wOffset3;\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                        let dotProd = 0;\n                        for (let b = 0; b < convInfo.batchSize; ++b) {\n                            const xOffset1 = b * xS0;\n                            const yOffset1 = b * dyS0;\n                            for (let yF = yFMin; yF < yFMax; ++yF) {\n                                const xF = wF + yF * strideDepth - frontPad;\n                                const xOffset2 = xF * xS1 + xOffset1;\n                                const yOffset2 = yF * dyS1 + yOffset1;\n                                for (let yR = yRMin; yR < yRMax; ++yR) {\n                                    const xR = wR + yR * strideHeight - topPad;\n                                    const xOffset3 = xR * xS2 + xOffset2;\n                                    const yOffset3 = yR * dyS2 + yOffset2;\n                                    for (let yC = yCMin; yC < yCMax; ++yC) {\n                                        const xC = wC + yC * strideWidth - leftPad;\n                                        const xOffset4 = xC * xS3 + xOffset3;\n                                        const yOffset4 = yC * dyS3 + yOffset3;\n                                        dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                                    }\n                                }\n                            }\n                        }\n                        dwValues[wOffset4 + d2] = dotProd;\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\nexport const conv3DBackpropFilterV2Config = {\n    kernelName: Conv3DBackpropFilterV2,\n    backendName: 'cpu',\n    kernelFunc: conv3DBackpropFilterV2\n};\n//# sourceMappingURL=Conv3DBackpropFilterV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Conv3DBackpropInputV2, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function conv3DBackpropInputV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { pad, strides, inputShape } = attrs;\n    assertNotComplex([dy], 'conv3dBackpropInputV2');\n    const dyStrides = util.computeStrides(dy.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const convInfo = backend_util.computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n    const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n    const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            // Frames of depth\n            for (let xF = 0; xF < inDepth; ++xF) {\n                const xFCorner = xF - frontPad;\n                const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n                const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n                // Rows as per standard 2d matrix notation\n                for (let xR = 0; xR < inHeight; ++xR) {\n                    const xRCorner = xR - topPad;\n                    const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                    const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                    // Columns as per standard 2d matrix notation\n                    for (let xC = 0; xC < inWidth; ++xC) {\n                        const xCCorner = xC - leftPad;\n                        const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                        const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                        let dotProd = 0;\n                        for (let yF = xFMin; yF < yFMax; ++yF) {\n                            const wF = yF * strideDepth - xFCorner;\n                            for (let yR = xRMin; yR < yRMax; ++yR) {\n                                const wR = yR * strideHeight - xRCorner;\n                                for (let yC = xCMin; yC < yCMax; ++yC) {\n                                    const wC = yC * strideWidth - xCCorner;\n                                    const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                                    const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                                        fltS1 * (filterHeight - 1 - wR) +\n                                        fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                                    for (let d2 = 0; d2 < outChannels; ++d2) {\n                                        const pixel = dyValues[dyOffset + d2];\n                                        const weight = fltValues[fltOffset + d2];\n                                        dotProd += pixel * weight;\n                                    }\n                                }\n                            }\n                        }\n                        dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                            dotProd;\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const conv3DBackpropInputV2Config = {\n    kernelName: Conv3DBackpropInputV2,\n    backendName: 'cpu',\n    kernelFunc: conv3DBackpropInputV2\n};\n//# sourceMappingURL=Conv3DBackpropInputV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cos = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'cpu',\n    kernelFunc: cos,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cosh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const cosh = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nexport const coshConfig = {\n    kernelName: Cosh,\n    backendName: 'cpu',\n    kernelFunc: cosh,\n};\n//# sourceMappingURL=Cosh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNative, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNative(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter } = inputs;\n    const { strides, pad, dilations, dimRoundingMode } = attrs;\n    assertNotComplex([x, filter], 'depthwiseConv2DNative');\n    const xStrides = util.computeStrides(x.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    let $dilations = dilations;\n    if ($dilations == null) {\n        $dilations = [1, 1];\n    }\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n        `1. Got strides ${strides} and dilations '${$dilations}'`);\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */);\n    const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;\n    const padLeft = padInfo.left;\n    const padTop = padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const y = new TensorBuffer(convInfo.outShape, x.dtype);\n    const xVals = backend.data.get(x.dataId).values;\n    const wVals = backend.data.get(filter.dataId).values;\n    const yVals = y.values;\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        const xOffset1 = b * xStrides[0];\n        const yOffset1 = b * y.strides[0];\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset2 = yOffset1 + yR * y.strides[1];\n            const xRCorner = yR * convInfo.strideHeight - padLeft;\n            for (let wR = 0; wR < filterHeight; ++wR) {\n                const xR = xRCorner + wR * dilationHeight;\n                if (xR < 0 || xR >= convInfo.inHeight) {\n                    continue;\n                }\n                const wOffset1 = wR * filterStrides[0];\n                const xOffset2 = xOffset1 + xR * xStrides[1];\n                for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                    const yOffset3 = yOffset2 + yC * y.strides[2];\n                    const xCCorner = yC * convInfo.strideWidth - padTop;\n                    for (let wC = 0; wC < filterWidth; ++wC) {\n                        const xC = xCCorner + wC * dilationWidth;\n                        if (xC < 0 || xC >= convInfo.inWidth) {\n                            continue;\n                        }\n                        const wOffset2 = wOffset1 + wC * filterStrides[1];\n                        const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n                        let yOffset4 = yOffset3;\n                        let wOffset3 = wOffset2;\n                        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                            const xVal = xVals[xOffset3 + d1];\n                            for (let q = 0; q < chMul; ++q) {\n                                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                            }\n                            yOffset4 += chMul;\n                            wOffset3 += chMul;\n                        }\n                    }\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nexport const depthwiseConv2dNativeConfig = {\n    kernelName: DepthwiseConv2dNative,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNative\n};\n//# sourceMappingURL=DepthwiseConv2dNative.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNativeBackpropFilter, TensorBuffer } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNativeBackpropFilter(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, dy } = inputs;\n    const { strides, dilations, pad, dimRoundingMode, filterShape } = attrs;\n    assertNotComplex([x, dy], 'depthwiseConv2dNativeBackpropFilter');\n    const convInfo = backend_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n    const dW = new TensorBuffer(convInfo.filterShape, 'float32');\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const xVals = backend.data.get(x.dataId).values;\n    const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n    const dyVals = backend.data.get(dy.dataId).values;\n    const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n    for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n        for (let wC = 0; wC < filterWidth; ++wC) {\n            const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n            const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                const d1 = Math.trunc(d2 / chMul);\n                const dm = d2 % chMul;\n                let dotProd = 0;\n                for (let b = 0; b < convInfo.batchSize; ++b) {\n                    for (let yR = yRMin; yR < yRMax; ++yR) {\n                        const xR = wR + yR * strideHeight - topPad;\n                        for (let yC = yCMin; yC < yCMax; ++yC) {\n                            const xC = wC + yC * strideWidth - leftPad;\n                            dotProd += xBuf.get(b, xR, xC, d1) *\n                                dyBuf.get(b, yR, yC, d2);\n                        }\n                    }\n                }\n                dW.set(dotProd, wR, wC, d1, dm);\n            }\n        }\n    }\n    return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nexport const depthwiseConv2dNativeBackpropFilterConfig = {\n    kernelName: DepthwiseConv2dNativeBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNativeBackpropFilter\n};\n//# sourceMappingURL=DepthwiseConv2dNativeBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, DepthwiseConv2dNativeBackpropInput, TensorBuffer, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function depthwiseConv2dNativeBackpropInput(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, filter } = inputs;\n    const { strides, dilations, pad, dimRoundingMode, inputShape } = attrs;\n    assertNotComplex([dy, filter], 'depthwiseConv2DNativeBackpropInput');\n    const dyStrides = util.computeStrides(dy.shape);\n    const filterStrides = util.computeStrides(filter.shape);\n    const convInfo = backend_util.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);\n    const dx = new TensorBuffer(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = backend.data.get(dy.dataId).values;\n    const [dyS0, dyS1, dyS2] = dyStrides;\n    const fltValues = backend.data.get(filter.dataId).values;\n    const [fltS0, fltS1, fltS2] = filterStrides;\n    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const chMul = outChannels / inChannels;\n    for (let b = 0; b < batchSize; ++b) {\n        for (let d1 = 0; d1 < inChannels; ++d1) {\n            for (let xR = 0; xR < inHeight; ++xR) {\n                const xRCorner = xR - topPad;\n                const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n                const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n                for (let xC = 0; xC < inWidth; ++xC) {\n                    const xCCorner = xC - leftPad;\n                    const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n                    const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n                    let dotProd = 0;\n                    for (let yR = xRMin; yR < yRMax; ++yR) {\n                        const wR = yR * strideHeight - xRCorner;\n                        for (let yC = xCMin; yC < yCMax; ++yC) {\n                            const wC = yC * strideWidth - xCCorner;\n                            const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                            const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                                fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n                            for (let dm = 0; dm < chMul; ++dm) {\n                                const d2 = d1 * chMul + dm;\n                                const pixel = dyValues[dyOffset + d2];\n                                const weight = fltValues[fltOffset + dm];\n                                dotProd += pixel * weight;\n                            }\n                        }\n                    }\n                    dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const depthwiseConv2dNativeBackpropInputConfig = {\n    kernelName: DepthwiseConv2dNativeBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: depthwiseConv2dNativeBackpropInput\n};\n//# sourceMappingURL=DepthwiseConv2dNativeBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2D, util } from '@tensorflow/tfjs-core';\nexport const dilation2dConfig = {\n    kernelName: Dilation2D,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const xVals = cpuBackend.data.get(x.dataId).values;\n        const xRank = x.shape.length;\n        const filterVals = cpuBackend.data.get(filter.dataId).values;\n        const filterRank = filter.shape.length;\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        const outSize = util.sizeFromShape(outShape);\n        const outRank = outShape.length;\n        const outputVals = util.getArrayFromDType(x.dtype, outSize);\n        // Upsampling the input by fill in `dilation size - 1` values between each\n        // input value.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));\n                                        const val = xVals[xIndex] + filterVals[filterIndex];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n                        outputVals[outputIndex] = curVal;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropFilter, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropFilterConfig = {\n    kernelName: Dilation2DBackpropFilter,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed filter gradients has the same dimensions as the filter:\n        // [filterHeight, filterWidth, depth]\n        const gradients = util.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hMax = 0;\n                        let wMax = 0;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hMax = h;\n                                            wMax = w;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n        return { dataId, shape: filter.shape, dtype: filter.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropFilter.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';\nexport const dilation2dBackpropInputConfig = {\n    kernelName: Dilation2DBackpropInput,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { x, filter, dy } = inputs;\n        const { strides, pad, dilations } = attrs;\n        const cpuBackend = backend;\n        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);\n        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n        // The computed gradients has the same dimensions as the input:\n        // [batch, inputHeight, inputCols, inChannel]\n        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);\n        // In the case of multiple argmax branches, we only back-propagate along the\n        // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n        // similarly to the max-pooling backward routines.\n        // This implementation follows the TF c++ implementation:\n        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n        for (let b = 0; b < batchSize; ++b) {\n            for (let hOut = 0; hOut < outHeight; ++hOut) {\n                const hBeg = hOut * strideHeight - padInfo.top;\n                for (let wOut = 0; wOut < outWidth; ++wOut) {\n                    const wBeg = wOut * strideWidth - padInfo.left;\n                    for (let d = 0; d < inChannels; ++d) {\n                        let curVal = Number.MIN_SAFE_INTEGER;\n                        let hInMax = (hBeg < 0) ? 0 : hBeg;\n                        let wInMax = (wBeg < 0) ? 0 : wBeg;\n                        for (let h = 0; h < filterHeight; ++h) {\n                            const hIn = hBeg + h * dilationHeight;\n                            if (hIn >= 0 && hIn < inHeight) {\n                                for (let w = 0; w < filterWidth; ++w) {\n                                    const wIn = wBeg + w * dilationWidth;\n                                    if (wIn >= 0 && wIn < inWidth) {\n                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                                        if (val > curVal) {\n                                            curVal = val;\n                                            hInMax = hIn;\n                                            wInMax = wIn;\n                                        }\n                                    }\n                                }\n                            }\n                        }\n                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Dilation2DBackpropInput.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { createSimpleBinaryKernelImpl } from '../utils/binary_impl';\nimport { binaryKernelFunc } from '../utils/kernel_utils';\nexport const divImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nexport const div = binaryKernelFunc(Div, divImpl);\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'cpu',\n    kernelFunc: div\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Erf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst p = backend_util.ERF_P;\nconst a1 = backend_util.ERF_A1;\nconst a2 = backend_util.ERF_A2;\nconst a3 = backend_util.ERF_A3;\nconst a4 = backend_util.ERF_A4;\nconst a5 = backend_util.ERF_A5;\nexport const erf = unaryKernelFunc(Erf, (xi) => {\n    const sign = Math.sign(xi);\n    const v = Math.abs(xi);\n    const t = 1.0 / (1.0 + p * v);\n    return sign *\n        (1.0 -\n            (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n                Math.exp(-v * v));\n});\nexport const erfConfig = {\n    kernelName: Erf,\n    backendName: 'cpu',\n    kernelFunc: erf,\n};\n//# sourceMappingURL=Erf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { add } from '../kernels/Add';\nimport { complex } from '../kernels/Complex';\nimport { concat } from '../kernels/Concat';\nimport { divConfig } from '../kernels/Div';\nimport { identity } from '../kernels/Identity';\nimport { imag } from '../kernels/Imag';\nimport { multiply } from '../kernels/Multiply';\nimport { real } from '../kernels/Real';\nimport { slice } from '../kernels/Slice';\nimport { sub } from '../kernels/Sub';\n/**\n * Calculate FFT of inner most elements of batch tensor.\n */\nexport function fftBatch(input, inverse, cpuBackend) {\n    const inputShape = input.shape;\n    const batch = inputShape[0];\n    const innerDim = inputShape[1];\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const real2D = inputVals.complexTensorInfos.real;\n    const imag2D = inputVals.complexTensorInfos.imag;\n    // Collects real and imaginary values separately.\n    const resultShape = [batch, innerDim];\n    const resultSize = util.sizeFromShape(resultShape);\n    const resultReal = util.getTypedArrayFromDType('float32', resultSize);\n    const resultImag = util.getTypedArrayFromDType('float32', resultSize);\n    for (let b = 0; b < batch; b++) {\n        // TODO: Support slice ops for complex type.\n        const r = slice({\n            inputs: { x: real2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const i = slice({\n            inputs: { x: imag2D },\n            backend: cpuBackend,\n            attrs: { begin: [b, 0], size: [1, innerDim] }\n        });\n        const input = complex({ inputs: { real: r, imag: i }, backend: cpuBackend });\n        // Run FFT by batch element.\n        const { real, imag } = fftImpl(input, inverse, cpuBackend);\n        const res = backend_util.mergeRealAndImagArrays(real, imag);\n        for (let d = 0; d < innerDim; d++) {\n            const c = backend_util.getComplexWithIndex(res, d);\n            resultReal[b * innerDim + d] = c.real;\n            resultImag[b * innerDim + d] = c.imag;\n        }\n        cpuBackend.disposeIntermediateTensorInfo(r);\n        cpuBackend.disposeIntermediateTensorInfo(i);\n        cpuBackend.disposeIntermediateTensorInfo(input);\n    }\n    const $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal);\n    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag);\n    const result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n    cpuBackend.disposeIntermediateTensorInfo($realInfo);\n    cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n    return result;\n}\nexport function fftImpl(input, inverse, cpuBackend) {\n    const inputSize = util.sizeFromShape(input.shape);\n    const inputVals = cpuBackend.data.get(input.dataId);\n    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n    if (isExponentOf2(inputSize)) {\n        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n        const resultShape = [input.shape[0], input.shape[1]];\n        if (inverse) {\n            const realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real);\n            const imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag);\n            const sizeInfo = cpuBackend.makeTensorInfo([], 'float32', util.createScalarValue(inputSize, 'float32'));\n            const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });\n            const divRealInfo = divConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n            const divImagInfo = divConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n            cpuBackend.disposeIntermediateTensorInfo(realInfo);\n            cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n            return { real: divRealVals, imag: divImagVals };\n        }\n        return result;\n    }\n    else {\n        const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n        return backend_util.splitRealAndImagArrays(rawOutput);\n    }\n}\nfunction isExponentOf2(size) {\n    return (size & size - 1) === 0;\n}\n// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n    if (size === 1) {\n        return { real: realVals, imag: imagVals };\n    }\n    const data = backend_util.mergeRealAndImagArrays(realVals, imagVals);\n    const half = size / 2;\n    const evenComplex = backend_util.complexWithEvenIndex(data);\n    const evenRealVals = evenComplex.real;\n    const evenImagVals = evenComplex.imag;\n    const evenShape = [evenRealVals.length];\n    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals);\n    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals);\n    const evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n    const oddComplex = backend_util.complexWithOddIndex(data);\n    const oddRealVals = oddComplex.real;\n    const oddImagVals = oddComplex.imag;\n    const oddShape = [oddRealVals.length];\n    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals);\n    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals);\n    const oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n    // Recursive call for half part of original input.\n    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n    const $evenRealVals = $evenComplex.real;\n    const $evenImagVals = $evenComplex.imag;\n    const $evenShape = [$evenRealVals.length];\n    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals);\n    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals);\n    const $evenTensorInfo = complex({\n        inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n        backend: cpuBackend\n    });\n    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n    const $oddRealVals = $oddComplex.real;\n    const $oddImagVals = $oddComplex.imag;\n    const $oddShape = [$oddRealVals.length];\n    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals);\n    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals);\n    const $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n    const e = backend_util.exponents(size, inverse);\n    const eShape = [e.real.length];\n    const eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real);\n    const eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag);\n    const complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n    const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n    const addPart = add({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const subPart = sub({\n        inputs: { a: $evenTensorInfo, b: exponentInfo },\n        backend: cpuBackend\n    });\n    const addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend });\n    const addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend });\n    const subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend });\n    const $real = concat({\n        inputs: [addPartReal, subPartReal],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $imag = concat({\n        inputs: [addPartImag, subPartImag],\n        backend: cpuBackend,\n        attrs: { axis: 0 }\n    });\n    const $realVals = cpuBackend.data.get($real.dataId).values;\n    const $imagVals = cpuBackend.data.get($imag.dataId).values;\n    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n    cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n    cpuBackend.disposeIntermediateTensorInfo(addPart);\n    cpuBackend.disposeIntermediateTensorInfo(subPart);\n    cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n    cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n    cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n    cpuBackend.disposeIntermediateTensorInfo($real);\n    cpuBackend.disposeIntermediateTensorInfo($imag);\n    return { real: $realVals, imag: $imagVals };\n}\n// Calculate fourier transform by multplying sinusoid matrix.\nfunction fourierTransformByMatmul(data, size, inverse) {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n        let real = 0.0;\n        let imag = 0.0;\n        for (let c = 0; c < size; c++) {\n            const e = backend_util.exponent(r * c, size, inverse);\n            const term = backend_util.getComplexWithIndex(data, c);\n            real += term.real * e.real - term.imag * e.imag;\n            imag += term.real * e.imag + term.imag * e.real;\n        }\n        if (inverse) {\n            real /= size;\n            imag /= size;\n        }\n        backend_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n}\n//# sourceMappingURL=fft_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, false, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'cpu',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Fill, util } from '@tensorflow/tfjs-core';\nexport function fill(args) {\n    const { backend, attrs } = args;\n    const { shape, value, dtype } = attrs;\n    const $dtype = dtype || util.inferDtype(value);\n    const values = util.getArrayFromDType($dtype, util.sizeFromShape(shape));\n    fillValues(values, value, $dtype);\n    return backend.makeTensorInfo(shape, $dtype, values);\n}\nexport const fillConfig = {\n    kernelName: Fill,\n    backendName: 'cpu',\n    kernelFunc: fill\n};\nfunction fillValues(values, value, dtype) {\n    if (dtype === 'string') {\n        values.fill(value);\n    }\n    else {\n        values.fill(value);\n    }\n}\n//# sourceMappingURL=Fill.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight, util } from '@tensorflow/tfjs-core';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const coordX = Math.round(imageWidth - x);\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        let outputValue = imageVals[outIdx];\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedConv2D } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { conv2D } from './Conv2D';\nexport function fusedConv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter, bias, preluActivationWeights } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation } = attrs;\n    let result = conv2D({\n        inputs: { x, filter },\n        backend,\n        attrs: { strides, pad, dataFormat, dilations, dimRoundingMode }\n    });\n    if (bias) {\n        const resultOld = result;\n        result = add({ inputs: { a: result, b: bias }, backend });\n        backend.disposeIntermediateTensorInfo(resultOld);\n    }\n    if (activation) {\n        const resultOld = result;\n        result =\n            applyActivation(backend, result, activation, preluActivationWeights);\n        backend.disposeIntermediateTensorInfo(resultOld);\n    }\n    return result;\n}\nexport const fusedConv2DConfig = {\n    kernelName: FusedConv2D,\n    backendName: 'cpu',\n    kernelFunc: fusedConv2D\n};\n//# sourceMappingURL=FusedConv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FusedDepthwiseConv2D } from '@tensorflow/tfjs-core';\nimport { applyActivation } from '../utils/fused_utils';\nimport { add } from './Add';\nimport { depthwiseConv2dNative } from './DepthwiseConv2dNative';\nexport function fusedDepthwiseConv2D(args) {\n    const { inputs, backend, attrs } = args;\n    const { x, filter, bias, preluActivationWeights } = inputs;\n    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation } = attrs;\n    let result = depthwiseConv2dNative({\n        inputs: { x, filter },\n        backend,\n        attrs: { strides, pad, dataFormat, dilations, dimRoundingMode }\n    });\n    if (bias) {\n        const oldResult = result;\n        result = add({ inputs: { a: result, b: bias }, backend });\n        backend.disposeIntermediateTensorInfo(oldResult);\n    }\n    if (activation) {\n        const oldResult = result;\n        result =\n            applyActivation(backend, result, activation, preluActivationWeights);\n        backend.disposeIntermediateTensorInfo(oldResult);\n    }\n    return result;\n}\nexport const fusedDepthwiseConv2DConfig = {\n    kernelName: FusedDepthwiseConv2D,\n    backendName: 'cpu',\n    kernelFunc: fusedDepthwiseConv2D\n};\n//# sourceMappingURL=FusedDepthwiseConv2D.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT, util } from '@tensorflow/tfjs-core';\nimport { fftBatch } from '../utils/fft_utils';\nimport { reshape } from './Reshape';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputSize = util.sizeFromShape(input.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({\n        inputs: { x: input },\n        backend,\n        attrs: { shape: [batch, innerDimensionSize] }\n    });\n    const result = fftBatch(input2D, true, backend);\n    const resultReshaped = reshape({ inputs: { x: result }, backend, attrs: { shape: input.shape } });\n    backend.disposeIntermediateTensorInfo(input2D);\n    backend.disposeIntermediateTensorInfo(result);\n    return resultReshaped;\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'cpu',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsFinite } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isFinite = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, 'bool');\nexport const isFiniteConfig = {\n    kernelName: IsFinite,\n    backendName: 'cpu',\n    kernelFunc: isFinite,\n};\n//# sourceMappingURL=IsFinite.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsInf } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isInf = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, 'bool');\nexport const isInfConfig = {\n    kernelName: IsInf,\n    backendName: 'cpu',\n    kernelFunc: isInf,\n};\n//# sourceMappingURL=IsInf.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IsNan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const isNaN = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, 'bool');\nexport const isNaNConfig = {\n    kernelName: IsNan,\n    backendName: 'cpu',\n    kernelFunc: isNaN,\n};\n//# sourceMappingURL=IsNaN.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Log1p } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const log1p = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nexport const log1pConfig = {\n    kernelName: Log1p,\n    backendName: 'cpu',\n    kernelFunc: log1p,\n};\n//# sourceMappingURL=Log1p.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { LogicalNot } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const logicalNot = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, 'bool');\nexport const logicalNotConfig = {\n    kernelName: LogicalNot,\n    backendName: 'cpu',\n    kernelFunc: logicalNot,\n};\n//# sourceMappingURL=LogicalNot.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const cpuBackend = backend;\n        let xShape = x.shape;\n        const xRank = xShape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, xShape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        let xVals = cpuBackend.data.get(x.dataId).values;\n        if (permutedAxes != null) {\n            const newShape = new Array(xRank);\n            for (let i = 0; i < newShape.length; i++) {\n                newShape[i] = xShape[permutedAxes[i]];\n            }\n            xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n            xShape = newShape;\n        }\n        assertNotComplex(x, 'max');\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(xShape, axes);\n        const reduceSize = util.sizeFromShape(reduceShape);\n        const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n        const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // reshape\n            const newShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n            outShape = newShape;\n        }\n        return { dataId, shape: outShape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { pool } from '../utils/pool_utils';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    let res;\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        res = identity({ inputs: { x }, backend });\n    }\n    else {\n        const xValues = backend.data.get(x.dataId).values;\n        const strides = util.computeStrides(x.shape);\n        const buffer = pool(xValues, x.shape, x.dtype, strides, convInfo, 'max');\n        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);\n    }\n    return res;\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'cpu',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, buffer, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolPositions } from '../utils/pool_utils';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const xValues = backend.data.get(x.dataId).values;\n    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = buffer(x.shape, 'float32');\n    const dyData = backend.data.get(dy.dataId).values;\n    const dyBuf = buffer(dy.shape, 'float32', dyData);\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n        for (let d = 0; d < convInfo.inChannels; ++d) {\n            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n                    // Shader code begins.\n                    const dyRCorner = dxR - padTop;\n                    const dyCCorner = dxC - padLeft;\n                    let dotProd = 0;\n                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n                        const dyR = (dyRCorner + wR) / strideHeight;\n                        if (dyR < 0 || dyR >= convInfo.outHeight ||\n                            Math.floor(dyR) !== dyR) {\n                            continue;\n                        }\n                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                            const dyC = (dyCCorner + wC) / strideWidth;\n                            if (dyC < 0 || dyC >= convInfo.outWidth ||\n                                Math.floor(dyC) !== dyC) {\n                                continue;\n                            }\n                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                                maxPosBuf.get(b, dyR, dyC, d);\n                            const curPos = wR * effectiveFilterWidth + wC;\n                            const mask = maxPos === curPos ? 1 : 0;\n                            if (mask === 0) {\n                                continue;\n                            }\n                            const pixel = dyBuf.get(b, dyR, dyC, d);\n                            dotProd += pixel * mask;\n                        }\n                    }\n                    dx.set(dotProd, b, dxR, dxC, d);\n                }\n            }\n        }\n    }\n    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'cpu',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { maxPoolPositions, pool } from '../utils/pool_utils';\nexport function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n    const strides = util.computeStrides(xShape);\n    const maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max');\n    const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n    return [maxPools.values, maxPositions.values];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'MaxPoolWithArgmax');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad);\n        const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n        return [\n            { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n            { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' }\n        ];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MirrorPad, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function mirrorPad(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    assertNotComplex(x, 'mirrorPad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const end = paddings.map((p, i) => p[0] + x.shape[i]);\n    const offset = mode === 'reflect' ? 0 : 1;\n    const xVals = backend.data.get(x.dataId).values;\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    for (let i = 0; i < resultSize; i++) {\n        let coords = util.indexToLoc(i, resultRank, resultStrides);\n        for (let i = 0; i < resultRank; i++) {\n            if (coords[i] < start[i]) {\n                coords[i] = start[i] * 2 - coords[i] - offset;\n            }\n            else if (coords[i] >= end[i]) {\n                coords[i] = (end[i] - 1) * 2 - coords[i] + offset;\n            }\n        }\n        coords = coords.map((c, i) => c - start[i]);\n        const inIndex = util.locToIndex(coords, xRank, xStrides);\n        resVals[i] = xVals[inIndex];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const mirrorPadConfig = {\n    kernelName: MirrorPad,\n    backendName: 'cpu',\n    kernelFunc: mirrorPad\n};\n//# sourceMappingURL=MirrorPad.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionPadded');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nimport { kernel_impls } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nimport { assertNotComplex } from '../cpu_util';\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const cpuBackend = backend;\n        assertNotComplex(boxes, 'NonMaxSuppressionWithScore');\n        const boxesVals = cpuBackend.data.get(boxes.dataId).values;\n        const scoresVals = cpuBackend.data.get(scores.dataId).values;\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { PadV2, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport function padV2(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { paddings, constantValue } = attrs;\n    assertNotComplex(x, 'pad');\n    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xVals = backend.data.get(x.dataId).values;\n    const xSize = util.sizeFromShape(x.shape);\n    const xRank = x.shape.length;\n    const xStrides = util.computeStrides(x.shape);\n    const resultSize = util.sizeFromShape(outShape);\n    const resultRank = outShape.length;\n    const resultStrides = util.computeStrides(outShape);\n    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);\n    if (constantValue !== 0) {\n        resVals.fill(constantValue);\n    }\n    for (let i = 0; i < xSize; i++) {\n        const coords = util.indexToLoc(i, xRank, xStrides);\n        const outCoords = coords.map((c, i) => c + start[i]);\n        const outIndex = util.locToIndex(outCoords, resultRank, resultStrides);\n        resVals[outIndex] = xVals[i];\n    }\n    const outId = backend.write(resVals, outShape, x.dtype);\n    return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nexport const padV2Config = {\n    kernelName: PadV2,\n    backendName: 'cpu',\n    kernelFunc: padV2\n};\n//# sourceMappingURL=PadV2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reciprocal } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const reciprocal = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nexport const reciprocalConfig = {\n    kernelName: Reciprocal,\n    backendName: 'cpu',\n    kernelFunc: reciprocal,\n};\n//# sourceMappingURL=Reciprocal.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, RotateWithOffset, util } from '@tensorflow/tfjs-core';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const cpuBackend = backend;\n        const output = util.getTypedArrayFromDType(image.dtype, util.sizeFromShape(image.shape));\n        const [batch, imageHeight, imageWidth, numChannels] = image.shape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const fullOpacityValue = 255;\n        const sinFactor = Math.sin(radians);\n        const cosFactor = Math.cos(radians);\n        const imageVals = cpuBackend.data.get(image.dataId).values;\n        for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n            for (let row = 0; row < imageHeight; row++) {\n                const rowOffset = row * (imageWidth * numChannels);\n                for (let col = 0; col < imageWidth; col++) {\n                    const colOffset = col * numChannels;\n                    for (let channel = 0; channel < numChannels; channel++) {\n                        const coords = [batch, row, col, channel];\n                        const x = coords[2];\n                        const y = coords[1];\n                        // coordX/coordY are the result of rotating and translating x/y.\n                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n                        coordX = Math.round(coordX + centerX);\n                        coordY = Math.round(coordY + centerY);\n                        let outputValue = fillValue;\n                        if (typeof fillValue !== 'number') {\n                            if (channel === 3) {\n                                outputValue = fullOpacityValue;\n                            }\n                            else {\n                                outputValue = fillValue[channel];\n                            }\n                        }\n                        // If the coordinate position falls within the image boundaries...\n                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 &&\n                            coordY < imageHeight) {\n                            // set the output to the image value at the coordinate position.\n                            const rotatedRowOffset = coordY * (imageWidth * numChannels);\n                            const rotatedColOffset = coordX * numChannels;\n                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n                            outputValue = imageVals[imageIdx];\n                        }\n                        const outIdx = batchOffset + rowOffset + colOffset + channel;\n                        output[outIdx] = outputValue;\n                    }\n                }\n            }\n        }\n        const dataId = cpuBackend.write(output, image.shape, image.dtype);\n        return { dataId, shape: image.shape, dtype: image.dtype };\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Round } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const round = unaryKernelFunc(Round, (xi) => {\n    // The algorithm is based on banker's rounding.\n    const base = Math.floor(xi);\n    if (xi - base < 0.5) {\n        return Math.floor(xi);\n    }\n    else if (xi - base > 0.5) {\n        return Math.ceil(xi);\n    }\n    else {\n        if (base % 2.0 === 0.0) {\n            return base;\n        }\n        else {\n            return base + 1.0;\n        }\n    }\n});\nexport const roundConfig = {\n    kernelName: Round,\n    backendName: 'cpu',\n    kernelFunc: round,\n};\n//# sourceMappingURL=Round.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Selu } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nconst scaleAlpha = backend_util.SELU_SCALEALPHA;\nconst scale = backend_util.SELU_SCALE;\nexport const selu = unaryKernelFunc(Selu, (xi) => {\n    if (xi >= 0) {\n        return scale * xi;\n    }\n    else {\n        return scaleAlpha * (Math.exp(xi) - 1);\n    }\n});\nexport const seluConfig = {\n    kernelName: Selu,\n    backendName: 'cpu',\n    kernelFunc: selu,\n};\n//# sourceMappingURL=Selu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sigmoid } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sigmoid = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nexport const sigmoidConfig = {\n    kernelName: Sigmoid,\n    backendName: 'cpu',\n    kernelFunc: sigmoid,\n};\n//# sourceMappingURL=Sigmoid.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sign } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sign = unaryKernelFunc(Sign, (xi) => {\n    if (xi < 0) {\n        return -1;\n    }\n    else if (xi > 0) {\n        return 1;\n    }\n    else {\n        return 0;\n    }\n});\nexport const signConfig = {\n    kernelName: Sign,\n    backendName: 'cpu',\n    kernelFunc: sign,\n};\n//# sourceMappingURL=Sign.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sin = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'cpu',\n    kernelFunc: sin,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sinh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sinh = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nexport const sinhConfig = {\n    kernelName: Sinh,\n    backendName: 'cpu',\n    kernelFunc: sinh,\n};\n//# sourceMappingURL=Sinh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Softplus } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\n// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n// epsilon is the difference between 1.0 and the next representable float.\n// For a single precision 32 bit float this should be 2^-23, see:\n// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\nconst epsilon = 1.1920928955078125e-7;\nconst threshold = Math.log(epsilon) + 2.0;\nexport const softplus = unaryKernelFunc(Softplus, (xi) => {\n    // Value above which exp(x) may overflow, but softplus(x) == x\n    // is within machine epsilon.\n    const tooLarge = xi > -threshold;\n    // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n    // is within machine epsilon.\n    const tooSmall = xi < threshold;\n    const expX = Math.exp(xi);\n    let result;\n    if (tooSmall) {\n        result = expX;\n    }\n    else if (tooLarge) {\n        result = xi;\n    }\n    else {\n        result = Math.log(1.0 + expX);\n    }\n    return result;\n});\nexport const softplusConfig = {\n    kernelName: Softplus,\n    backendName: 'cpu',\n    kernelFunc: softplus,\n};\n//# sourceMappingURL=Softplus.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { transposeImpl } from './Transpose_impl';\nexport function transpose(args) {\n    const { inputs, attrs, backend } = args;\n    const { x } = inputs;\n    const { perm } = attrs;\n    assertNotComplex(x, 'transpose');\n    const xRank = x.shape.length;\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[perm[i]];\n    }\n    const values = backend.data.get(x.dataId).values;\n    const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n    const dataId = backend.write(result, newShape, x.dtype);\n    return { dataId, shape: newShape, dtype: x.dtype };\n}\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'cpu',\n    kernelFunc: transpose\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, SpaceToBatchND, util } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { padV2Config } from './PadV2';\nimport { reshape } from './Reshape';\nimport { transpose } from './Transpose';\nexport function spaceToBatchND(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { blockShape, paddings } = attrs;\n    assertNotComplex([x], 'spaceToBatchND');\n    const prod = util.sizeFromShape(blockShape);\n    const completePaddings = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n        completePaddings.push([0, 0]);\n    }\n    const paddedX = padV2Config.kernelFunc({\n        inputs: { x },\n        backend,\n        attrs: { paddings: completePaddings, constantValue: 0 }\n    });\n    const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n    const reshapeInputs = { x: paddedX };\n    const reshapeAttrs = { shape: reshapedPaddedShape };\n    const paddedXReshaped = reshape({ inputs: reshapeInputs, backend, attrs: reshapeAttrs });\n    const transposeInputs = { x: paddedXReshaped };\n    const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n    const paddedXT = transpose({ inputs: transposeInputs, backend, attrs: transposeAttrs });\n    const resultReshapeInputs = { x: paddedXT };\n    const resultReshapeAttrs = { shape: flattenShape };\n    const result = reshape({ inputs: resultReshapeInputs, backend, attrs: resultReshapeAttrs });\n    backend.disposeIntermediateTensorInfo(paddedX);\n    backend.disposeIntermediateTensorInfo(paddedXReshaped);\n    backend.disposeIntermediateTensorInfo(paddedXT);\n    return result;\n}\nexport const spaceToBatchNDConfig = {\n    kernelName: SpaceToBatchND,\n    backendName: 'cpu',\n    kernelFunc: spaceToBatchND\n};\n//# sourceMappingURL=SpaceToBatchND.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sqrt } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const sqrt = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nexport const sqrtConfig = {\n    kernelName: Sqrt,\n    backendName: 'cpu',\n    kernelFunc: sqrt,\n};\n//# sourceMappingURL=Sqrt.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'cpu',\n    kernelFunc: ({ inputs, backend }) => {\n        const { x } = inputs;\n        const cpuBackend = backend;\n        assertNotComplex(x, 'square');\n        const values = cpuBackend.data.get(x.dataId).values;\n        const newValues = new Float32Array(values.length);\n        for (let i = 0; i < values.length; ++i) {\n            const value = values[i];\n            newValues[i] = value * value;\n        }\n        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n        return { dataId, shape: x.shape, dtype: x.dtype };\n    }\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Step } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const step = unaryKernelFunc(Step, (xi, attrs) => {\n    const stepAttrs = attrs;\n    if (isNaN(xi)) {\n        return NaN;\n    }\n    else {\n        return xi > 0 ? 1 : stepAttrs.alpha;\n    }\n});\nexport const stepConfig = {\n    kernelName: Step,\n    backendName: 'cpu',\n    kernelFunc: step,\n};\n//# sourceMappingURL=Step.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tan = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'cpu',\n    kernelFunc: tan,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tanh } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../utils/unary_utils';\nexport const tanh = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nexport const tanhConfig = {\n    kernelName: Tanh,\n    backendName: 'cpu',\n    kernelFunc: tanh,\n};\n//# sourceMappingURL=Tanh.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { assertNotComplex } from '../cpu_util';\nimport { uniqueImpl } from './Unique_impl';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    const values = backend.data.get(x.dataId).values;\n    const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'cpu',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// We explicitly import the modular kernels so they get registered in the\n// global registry when we compile the library. A modular build would replace\n// the contents of this file and import only the kernels that are needed.\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { _fusedMatMulConfig } from './kernels/_FusedMatMul';\nimport { absConfig } from './kernels/Abs';\nimport { acosConfig } from './kernels/Acos';\nimport { acoshConfig } from './kernels/Acosh';\nimport { addConfig } from './kernels/Add';\nimport { asinConfig } from './kernels/Asin';\nimport { asinhConfig } from './kernels/Asinh';\nimport { atanConfig } from './kernels/Atan';\nimport { atanhConfig } from './kernels/Atanh';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchMatMulConfig } from './kernels/BatchMatMul';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { ceilConfig } from './kernels/Ceil';\nimport { clipConfig } from './kernels/Clip';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { conv2DConfig } from './kernels/Conv2D';\nimport { conv2DBackpropFilterConfig } from './kernels/Conv2DBackpropFilter';\nimport { conv2DBackpropInputConfig } from './kernels/Conv2DBackpropInput';\nimport { conv3DConfig } from './kernels/Conv3D';\nimport { conv3DBackpropFilterV2Config } from './kernels/Conv3DBackpropFilterV2';\nimport { conv3DBackpropInputV2Config } from './kernels/Conv3DBackpropInputV2';\nimport { cosConfig } from './kernels/Cos';\nimport { coshConfig } from './kernels/Cosh';\nimport { depthwiseConv2dNativeConfig } from './kernels/DepthwiseConv2dNative';\nimport { depthwiseConv2dNativeBackpropFilterConfig } from './kernels/DepthwiseConv2dNativeBackpropFilter';\nimport { depthwiseConv2dNativeBackpropInputConfig } from './kernels/DepthwiseConv2dNativeBackpropInput';\nimport { dilation2dConfig } from './kernels/Dilation2D';\nimport { dilation2dBackpropFilterConfig } from './kernels/Dilation2DBackpropFilter';\nimport { dilation2dBackpropInputConfig } from './kernels/Dilation2DBackpropInput';\nimport { divConfig } from './kernels/Div';\nimport { eluConfig } from './kernels/Elu';\nimport { erfConfig } from './kernels/Erf';\nimport { expConfig } from './kernels/Exp';\nimport { expm1Config } from './kernels/Expm1';\nimport { fftConfig } from './kernels/FFT';\nimport { fillConfig } from './kernels/Fill';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { floorConfig } from './kernels/Floor';\nimport { fusedConv2DConfig } from './kernels/FusedConv2D';\nimport { fusedDepthwiseConv2DConfig } from './kernels/FusedDepthwiseConv2D';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { isFiniteConfig } from './kernels/IsFinite';\nimport { isInfConfig } from './kernels/IsInf';\nimport { isNaNConfig } from './kernels/IsNaN';\nimport { logConfig } from './kernels/Log';\nimport { log1pConfig } from './kernels/Log1p';\nimport { logicalNotConfig } from './kernels/LogicalNot';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { mirrorPadConfig } from './kernels/MirrorPad';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { padV2Config } from './kernels/PadV2';\nimport { preluConfig } from './kernels/Prelu';\nimport { realConfig } from './kernels/Real';\nimport { reciprocalConfig } from './kernels/Reciprocal';\nimport { reluConfig } from './kernels/Relu';\nimport { relu6Config } from './kernels/Relu6';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { roundConfig } from './kernels/Round';\nimport { rsqrtConfig } from './kernels/Rsqrt';\nimport { seluConfig } from './kernels/Selu';\nimport { sigmoidConfig } from './kernels/Sigmoid';\nimport { signConfig } from './kernels/Sign';\nimport { sinConfig } from './kernels/Sin';\nimport { sinhConfig } from './kernels/Sinh';\nimport { sliceConfig } from './kernels/Slice';\nimport { softplusConfig } from './kernels/Softplus';\nimport { spaceToBatchNDConfig } from './kernels/SpaceToBatchND';\nimport { sqrtConfig } from './kernels/Sqrt';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { stepConfig } from './kernels/Step';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { tanhConfig } from './kernels/Tanh';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    _fusedMatMulConfig,\n    absConfig,\n    acosConfig,\n    acoshConfig,\n    addConfig,\n    asinConfig,\n    asinhConfig,\n    atanConfig,\n    atanhConfig,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchMatMulConfig,\n    batchNormConfig,\n    castConfig,\n    ceilConfig,\n    clipConfig,\n    complexConfig,\n    concatConfig,\n    conv2DBackpropFilterConfig,\n    conv2DBackpropInputConfig,\n    conv2DConfig,\n    conv3DBackpropFilterV2Config,\n    conv3DBackpropInputV2Config,\n    conv3DConfig,\n    cosConfig,\n    coshConfig,\n    depthwiseConv2dNativeConfig,\n    depthwiseConv2dNativeBackpropFilterConfig,\n    depthwiseConv2dNativeBackpropInputConfig,\n    dilation2dConfig,\n    dilation2dBackpropInputConfig,\n    dilation2dBackpropFilterConfig,\n    divConfig,\n    eluConfig,\n    erfConfig,\n    expConfig,\n    expm1Config,\n    fftConfig,\n    fillConfig,\n    flipLeftRightConfig,\n    floorConfig,\n    fusedConv2DConfig,\n    fusedDepthwiseConv2DConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    isFiniteConfig,\n    isInfConfig,\n    isNaNConfig,\n    logConfig,\n    log1pConfig,\n    logicalNotConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    maxConfig,\n    mirrorPadConfig,\n    multiplyConfig,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    padV2Config,\n    preluConfig,\n    realConfig,\n    reciprocalConfig,\n    reluConfig,\n    relu6Config,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    roundConfig,\n    rsqrtConfig,\n    seluConfig,\n    sigmoidConfig,\n    signConfig,\n    sinConfig,\n    sinhConfig,\n    sliceConfig,\n    softplusConfig,\n    spaceToBatchNDConfig,\n    sqrtConfig,\n    squareConfig,\n    squaredDifferenceConfig,\n    stepConfig,\n    subConfig,\n    tanConfig,\n    tanhConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nconst contexts = {};\nconst WEBGL_ATTRIBUTES = {\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: true\n};\nexport function clearWebGLContext(webGLVersion) {\n    delete contexts[webGLVersion];\n}\nexport function setWebGLContext(webGLVersion, gl) {\n    contexts[webGLVersion] = gl;\n}\nexport function getWebGLContext(webGLVersion) {\n    if (!(webGLVersion in contexts)) {\n        const newCtx = getWebGLRenderingContext(webGLVersion);\n        if (newCtx !== null) {\n            contexts[webGLVersion] = newCtx;\n        }\n        else {\n            console.log('Could not get context for WebGL version', webGLVersion);\n            return null;\n        }\n    }\n    const gl = contexts[webGLVersion];\n    if (gl.isContextLost()) {\n        delete contexts[webGLVersion];\n        return getWebGLContext(webGLVersion);\n    }\n    gl.disable(gl.DEPTH_TEST);\n    gl.disable(gl.STENCIL_TEST);\n    gl.disable(gl.BLEND);\n    gl.disable(gl.DITHER);\n    gl.disable(gl.POLYGON_OFFSET_FILL);\n    gl.disable(gl.SAMPLE_COVERAGE);\n    gl.enable(gl.SCISSOR_TEST);\n    gl.enable(gl.CULL_FACE);\n    gl.cullFace(gl.BACK);\n    return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n    if (typeof OffscreenCanvas !== 'undefined' && webGLVersion === 2) {\n        return new OffscreenCanvas(300, 150);\n    }\n    else if (typeof document !== 'undefined') {\n        return document.createElement('canvas');\n    }\n    else {\n        throw new Error('Cannot create a canvas in this context');\n    }\n}\nfunction getWebGLRenderingContext(webGLVersion) {\n    if (webGLVersion !== 1 && webGLVersion !== 2) {\n        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n    }\n    const canvas = createCanvas(webGLVersion);\n    canvas.addEventListener('webglcontextlost', (ev) => {\n        ev.preventDefault();\n        delete contexts[webGLVersion];\n    }, false);\n    if (webGLVersion === 1) {\n        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));\n    }\n    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);\n}\n//# sourceMappingURL=canvas_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nexport var PackingScheme;\n(function (PackingScheme) {\n    /**\n     * All values in a single texel are densely packed without any constraints.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 4]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   010|011   020|021\n     * -------   -------   -------\n     * 002|003   012|013   022|023\n     *\n     * 100|101   110|111   120|121\n     * -------   -------   -------\n     * 102|103   112|113   122|123\n     *\n     */\n    PackingScheme[PackingScheme[\"DENSE\"] = 0] = \"DENSE\";\n    /**\n     * Single texels contain only values from the same batch, and from adjacent\n     * rows and columns.\n     *\n     * This is how the shader encodes a tensor with shape = [2, 3, 5]\n     * (indices are [batch, row, col]).\n     *\n     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx\n     * -------   -------   -------   -------   -------   -------\n     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n     *\n     */\n    PackingScheme[PackingScheme[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nexport var TextureUsage;\n(function (TextureUsage) {\n    TextureUsage[TextureUsage[\"RENDER\"] = 0] = \"RENDER\";\n    TextureUsage[TextureUsage[\"UPLOAD\"] = 1] = \"UPLOAD\";\n    TextureUsage[TextureUsage[\"PIXELS\"] = 2] = \"PIXELS\";\n    TextureUsage[TextureUsage[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nexport var PhysicalTextureType;\n(function (PhysicalTextureType) {\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n    PhysicalTextureType[PhysicalTextureType[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n    PhysicalTextureType[PhysicalTextureType[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nexport function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns, rows];\n}\nexport function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n    return matrixSize * channelsPerTexture;\n}\nexport function getColorMatrixTextureShapeWidthHeight(rows, columns) {\n    return [columns * 4, rows];\n}\n/**\n * Get shape for densely packed RGBA texture.\n */\nexport function getDenseTexShape(shape) {\n    const size = util.sizeFromShape(shape);\n    const texelsNeeded = Math.ceil(size / 4);\n    return util.sizeToSquarishShape(texelsNeeded);\n}\nexport function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {\n    if (unpackedSize % channelsPerTexture !== 0) {\n        throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` +\n            `${channelsPerTexture}`);\n    }\n    return unpackedSize / channelsPerTexture;\n}\nexport function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {\n    const requiredSize = unpackedArray.length * channels / 4;\n    if (matrix.length < requiredSize) {\n        throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);\n    }\n    let dst = 0;\n    for (let src = 0; src < unpackedArray.length; src += 4) {\n        for (let c = 0; c < channels; c++) {\n            matrix[dst++] = unpackedArray[src + c];\n        }\n    }\n}\nexport function getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n    return [\n        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n    ];\n}\nexport function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return w * h * 4;\n}\nexport function getTextureConfig(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    let internalFormatFloat;\n    let internalFormatHalfFloat;\n    let internalFormatPackedHalfFloat;\n    let internalFormatPackedFloat;\n    let textureFormatFloat;\n    let downloadTextureFormat;\n    let downloadUnpackNumChannels;\n    let defaultNumChannels;\n    let textureTypeHalfFloat;\n    let textureTypeFloat;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        internalFormatFloat = glany.R32F;\n        internalFormatHalfFloat = glany.R16F;\n        internalFormatPackedHalfFloat = glany.RGBA16F;\n        internalFormatPackedFloat = glany.RGBA32F;\n        textureFormatFloat = glany.RED;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 1;\n        textureTypeHalfFloat = glany.HALF_FLOAT;\n        textureTypeFloat = glany.FLOAT;\n    }\n    else {\n        internalFormatFloat = gl.RGBA;\n        internalFormatHalfFloat = gl.RGBA;\n        internalFormatPackedHalfFloat = gl.RGBA;\n        internalFormatPackedFloat = glany.RGBA;\n        textureFormatFloat = gl.RGBA;\n        downloadUnpackNumChannels = 4;\n        defaultNumChannels = 4;\n        textureTypeHalfFloat = textureHalfFloatExtension != null ?\n            textureHalfFloatExtension.HALF_FLOAT_OES :\n            null;\n        textureTypeFloat = gl.FLOAT;\n    }\n    downloadTextureFormat = gl.RGBA;\n    return {\n        internalFormatFloat,\n        internalFormatHalfFloat,\n        internalFormatPackedHalfFloat,\n        internalFormatPackedFloat,\n        textureFormatFloat,\n        downloadTextureFormat,\n        downloadUnpackNumChannels,\n        defaultNumChannels,\n        textureTypeHalfFloat,\n        textureTypeFloat\n    };\n}\n//# sourceMappingURL=tex_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext } from './canvas_util';\nimport { getTextureConfig } from './tex_util';\nexport function callAndCheck(gl, func) {\n    const returnValue = func();\n    if (env().getBool('DEBUG')) {\n        checkWebGLError(gl);\n    }\n    return returnValue;\n}\nfunction checkWebGLError(gl) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n        throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n}\n// https://en.wikipedia.org/wiki/Half-precision_floating-point_format\nconst MIN_FLOAT16 = 5.96e-8;\nconst MAX_FLOAT16 = 65504;\nexport function canBeRepresented(num) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED') || num === 0 ||\n        (MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16)) {\n        return true;\n    }\n    return false;\n}\nexport function getWebGLErrorMessage(gl, status) {\n    switch (status) {\n        case gl.NO_ERROR:\n            return 'NO_ERROR';\n        case gl.INVALID_ENUM:\n            return 'INVALID_ENUM';\n        case gl.INVALID_VALUE:\n            return 'INVALID_VALUE';\n        case gl.INVALID_OPERATION:\n            return 'INVALID_OPERATION';\n        case gl.INVALID_FRAMEBUFFER_OPERATION:\n            return 'INVALID_FRAMEBUFFER_OPERATION';\n        case gl.OUT_OF_MEMORY:\n            return 'OUT_OF_MEMORY';\n        case gl.CONTEXT_LOST_WEBGL:\n            return 'CONTEXT_LOST_WEBGL';\n        default:\n            return `Unknown error code ${status}`;\n    }\n}\nexport function getExtensionOrThrow(gl, extensionName) {\n    return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nexport function createVertexShader(gl, vertexShaderSource) {\n    const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), 'Unable to create vertex WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n    callAndCheck(gl, () => gl.compileShader(vertexShader));\n    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n        console.log(gl.getShaderInfoLog(vertexShader));\n        throw new Error('Failed to compile vertex shader.');\n    }\n    return vertexShader;\n}\nexport function createFragmentShader(gl, fragmentShaderSource) {\n    const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), 'Unable to create fragment WebGLShader.');\n    callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n    callAndCheck(gl, () => gl.compileShader(fragmentShader));\n    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n        throw new Error('Failed to compile fragment shader.');\n    }\n    return fragmentShader;\n}\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n    if (lineNumberRegexResult == null) {\n        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n        console.log(shaderSource);\n        return;\n    }\n    const lineNumber = +lineNumberRegexResult[1];\n    const shaderLines = shaderSource.split('\\n');\n    const pad = shaderLines.length.toString().length + 2;\n    const linesWithLineNumbers = shaderLines.map((line, lineNumber) => util.rightPad((lineNumber + 1).toString(), pad) + line);\n    let maxLineLength = 0;\n    for (let i = 0; i < linesWithLineNumbers.length; i++) {\n        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n    }\n    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n    console.log(beforeErrorLines.join('\\n'));\n    console.log(shaderInfoLog.split('\\n')[0]);\n    console.log(`%c ${util.rightPad(errorLine[0], maxLineLength)}`, 'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n    console.log(afterErrorLines.join('\\n'));\n}\nexport function createProgram(gl) {\n    return throwIfNull(gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\nexport function linkProgram(gl, program) {\n    callAndCheck(gl, () => gl.linkProgram(program));\n    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Failed to link vertex and fragment shaders.');\n    }\n}\nexport function validateProgram(gl, program) {\n    callAndCheck(gl, () => gl.validateProgram(program));\n    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n        console.log(gl.getProgramInfoLog(program));\n        throw new Error('Shader program validation failed.');\n    }\n}\nexport function createStaticVertexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function createStaticIndexBuffer(gl, data) {\n    const buffer = throwIfNull(gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n    return buffer;\n}\nexport function getNumChannels() {\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        return 1;\n    }\n    return 4;\n}\nexport function createTexture(gl) {\n    return throwIfNull(gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\nexport function validateTextureSize(width, height) {\n    const maxTextureSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if ((width <= 0) || (height <= 0)) {\n        const requested = `[${width}x${height}]`;\n        throw new Error('Requested texture size ' + requested + ' is invalid.');\n    }\n    if ((width > maxTextureSize) || (height > maxTextureSize)) {\n        const requested = `[${width}x${height}]`;\n        const max = `[${maxTextureSize}x${maxTextureSize}]`;\n        throw new Error('Requested texture size ' + requested +\n            ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n    }\n}\nexport function createFramebuffer(gl) {\n    return throwIfNull(gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\nexport function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n    const loc = gl.getAttribLocation(program, attribute);\n    if (loc === -1) {\n        // The GPU compiler decided to strip out this attribute because it's unused,\n        // thus no need to bind.\n        return false;\n    }\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n    callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n    callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n    return true;\n}\nexport function bindTextureUnit(gl, texture, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nexport function unbindTextureUnit(gl, textureUnit) {\n    validateTextureUnit(gl, textureUnit);\n    callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n    callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function getProgramUniformLocationOrThrow(gl, program, uniformName) {\n    return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nexport function getProgramUniformLocation(gl, program, uniformName) {\n    return gl.getUniformLocation(program, uniformName);\n}\nexport function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n    callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n    callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nexport function bindCanvasToFramebuffer(gl) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n    callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nexport function bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nexport function unbindColorTextureFromFramebuffer(gl, framebuffer) {\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n    callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nexport function validateFramebuffer(gl) {\n    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n    if (status !== gl.FRAMEBUFFER_COMPLETE) {\n        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n    }\n}\nexport function getFramebufferErrorMessage(gl, status) {\n    switch (status) {\n        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n        case gl.FRAMEBUFFER_UNSUPPORTED:\n            return 'FRAMEBUFFER_UNSUPPORTED';\n        default:\n            return `unknown error ${status}`;\n    }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n    const tOrNull = callAndCheck(gl, () => returnTOrNull());\n    if (tOrNull == null) {\n        throw new Error(failureMessage);\n    }\n    return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n    const glTextureUnit = textureUnit + gl.TEXTURE0;\n    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n        throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n    }\n}\nexport function getBatchDim(shape, dimsToSkip = 2) {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nexport function getRowsCols(shape) {\n    if (shape.length === 0) {\n        throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n    return [\n        shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n}\nexport function getShapeAs3D(shape) {\n    let shapeAs3D = [1, 1, 1];\n    const isScalar = shape.length === 0 || (shape.length === 1 && shape[0] === 1);\n    if (!isScalar) {\n        shapeAs3D =\n            [getBatchDim(shape), ...getRowsCols(shape)];\n    }\n    return shapeAs3D;\n}\nexport function getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n    let maxTexSize = env().getNumber('WEBGL_MAX_TEXTURE_SIZE');\n    if (isPacked) {\n        maxTexSize = maxTexSize * 2;\n        // This logic ensures we accurately count the number of packed texels needed\n        // to accommodate the tensor. We can only pack values in the same texel if\n        // they are from adjacent pairs of rows/cols within the same batch. So if a\n        // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n        // fact that the texels containing the third row are half empty.\n        logShape = logShape.map((d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n        // Packed texture height is at least 2 (the channel height of a single\n        // texel).\n        if (logShape.length === 1) {\n            logShape = [2, logShape[0]];\n        }\n    }\n    // If logical shape is 2, we don't squeeze, since we want to match physical.\n    if (logShape.length !== 2) {\n        const squeezeResult = util.squeezeShape(logShape);\n        logShape = squeezeResult.newShape;\n    }\n    let size = util.sizeFromShape(logShape);\n    if (logShape.length <= 1 && size <= maxTexSize) {\n        return [1, size];\n    }\n    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&\n        logShape[1] <= maxTexSize) {\n        return logShape;\n    }\n    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n        logShape[2] <= maxTexSize) {\n        return [logShape[0] * logShape[1], logShape[2]];\n    }\n    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2]];\n    }\n    else if (logShape.length === 4 &&\n        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n        logShape[3] <= maxTexSize) {\n        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n    }\n    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&\n        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n    }\n    else {\n        if (isPacked) {\n            // For packed textures size equals the number of channels required to\n            // accommodate the texture data. However in order to squarify such that\n            // inner dimensions stay even, we rewrite size to equal the number of\n            // texels. Then in the return statement we rehydrate the squarified\n            // dimensions to channel units.\n            const batchDim = getBatchDim(logShape);\n            let rows = 2, cols = 2;\n            if (logShape.length) {\n                [rows, cols] = getRowsCols(logShape);\n            }\n            size = batchDim * (rows / 2) * (cols / 2);\n            return util.sizeToSquarishShape(size).map(d => d * 2);\n        }\n        return util.sizeToSquarishShape(size);\n    }\n}\nfunction isEven(n) {\n    return n % 2 === 0;\n}\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1, shape2) {\n    shape1 = shape1.slice(-2);\n    shape2 = shape2.slice(-2);\n    if (util.arraysEqual(shape1, shape2)) {\n        return true;\n    }\n    if (!shape1.length || !shape2.length) { // One of the shapes is a scalar.\n        return true;\n    }\n    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n        shape2[1] === 0) {\n        return true;\n    }\n    if (shape1.length !== shape2.length) { // One of the shapes is a vector.\n        const shape1Cols = shape1.slice(-1)[0];\n        const shape2Cols = shape2.slice(-1)[0];\n        if (shape1Cols === shape2Cols) {\n            return true;\n        }\n        if (isEven(shape1Cols) && isEven(shape2Cols) &&\n            (shape1[0] === 1 || shape2[0] === 1)) {\n            return true;\n        }\n    }\n    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\n// We cache webgl params because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nlet MAX_TEXTURE_SIZE;\nlet MAX_TEXTURES_IN_SHADER;\nexport function getWebGLMaxTextureSize(webGLVersion) {\n    if (MAX_TEXTURE_SIZE == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n    }\n    return MAX_TEXTURE_SIZE;\n}\nexport function resetMaxTextureSize() {\n    MAX_TEXTURE_SIZE = null;\n}\nexport function resetMaxTexturesInShader() {\n    MAX_TEXTURES_IN_SHADER = null;\n}\nexport function getMaxTexturesInShader(webGLVersion) {\n    if (MAX_TEXTURES_IN_SHADER == null) {\n        const gl = getWebGLContext(webGLVersion);\n        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n    }\n    // We cap at 16 to avoid spurious runtime \"memory exhausted\" error.\n    return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion) {\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    let queryTimerVersion;\n    const gl = getWebGLContext(webGLVersion);\n    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n        webGLVersion === 2) {\n        queryTimerVersion = 2;\n    }\n    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n        queryTimerVersion = 1;\n    }\n    else {\n        queryTimerVersion = 0;\n    }\n    return queryTimerVersion;\n}\nexport function hasExtension(gl, extensionName) {\n    const ext = gl.getExtension(extensionName);\n    return ext != null;\n}\nexport function isWebGLVersionEnabled(webGLVersion) {\n    try {\n        const gl = getWebGLContext(webGLVersion);\n        if (gl != null) {\n            return true;\n        }\n    }\n    catch (e) {\n        console.log('Error when getting WebGL context: ', e);\n        return false;\n    }\n    return false;\n}\nexport function isCapableOfRenderingToFloatTexture(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n    }\n    else {\n        if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n            return false;\n        }\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\n/**\n * Check if we can download values from a float/half-float texture.\n *\n * Note that for performance reasons we use binding a texture to a framebuffer\n * as a proxy for ability to download float values later using readPixels. The\n * texture params of this texture will not match those in readPixels exactly\n * but if we are unable to bind some kind of float texture to the frameBuffer\n * then we definitely will not be able to read float values from it.\n */\nexport function isDownloadFloatTextureEnabled(webGLVersion) {\n    if (webGLVersion === 0) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    if (webGLVersion === 1) {\n        if (!hasExtension(gl, 'OES_texture_float')) {\n            return false;\n        }\n        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n            return false;\n        }\n    }\n    else {\n        if (hasExtension(gl, 'EXT_color_buffer_float')) {\n            return createFloatTextureAndBindToFramebuffer(gl);\n        }\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n        }\n        return false;\n    }\n    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n    return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n    const texConfig = getTextureConfig(gl);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(\n// tslint:disable-next-line:no-any\ngl, textureHalfFloatExtension) {\n    const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    const width = 1;\n    const height = 1;\n    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n    const frameBuffer = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    gl.deleteTexture(texture);\n    gl.deleteFramebuffer(frameBuffer);\n    return isFrameBufferComplete;\n}\nexport function isWebGLFenceEnabled(webGLVersion) {\n    if (webGLVersion !== 2) {\n        return false;\n    }\n    const gl = getWebGLContext(webGLVersion);\n    // tslint:disable-next-line:no-any\n    const isEnabled = gl.fenceSync != null;\n    return isEnabled;\n}\nexport function assertNotComplex(tensor, opName) {\n    if (!Array.isArray(tensor)) {\n        tensor = [tensor];\n    }\n    tensor.forEach(t => {\n        if (t != null) {\n            util.assert(t.dtype !== 'complex64', () => `${opName} does not support complex64 tensors ` +\n                'in the WebGL backend.');\n        }\n    });\n}\n//# sourceMappingURL=webgl_util.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { device_util, env } from '@tensorflow/tfjs-core';\nimport { getMaxTexturesInShader, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isCapableOfRenderingToFloatTexture, isDownloadFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled } from './webgl_util';\nconst ENV = env();\n/**\n * This file contains WebGL-specific flag registrations.\n */\n/**\n * True if WebGL is supported.\n */\nENV.registerFlag('HAS_WEBGL', () => ENV.getNumber('WEBGL_VERSION') > 0);\n/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */\nENV.registerFlag('WEBGL_VERSION', () => {\n    if (isWebGLVersionEnabled(2)) {\n        return 2;\n    }\n    else if (isWebGLVersionEnabled(1)) {\n        return 1;\n    }\n    return 0;\n});\n/** Whether to check for numerical representation problems. */\nENV.registerFlag('WEBGL_CHECK_NUMERICAL_PROBLEMS', () => false);\nENV.registerFlag('WEBGL_BUFFER_SUPPORTED', () => ENV.get('WEBGL_VERSION') === 2);\n/** Whether the WebGL backend will sometimes forward ops to the CPU. */\nENV.registerFlag('WEBGL_CPU_FORWARD', () => true);\n/** Whether the WebGL backend will always use f16 textures for rendering. */\nENV.registerFlag('WEBGL_FORCE_F16_TEXTURES', () => false);\n/** Whether to turn all packing related flags on. */\nENV.registerFlag('WEBGL_PACK', () => ENV.getBool('HAS_WEBGL'));\n/** Whether we will pack the batchnormalization op. */\nENV.registerFlag('WEBGL_PACK_NORMALIZATION', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the clip op. */\nENV.registerFlag('WEBGL_PACK_CLIP', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack the depthwise conv op. */\n// TODO: https://github.com/tensorflow/tfjs/issues/1679\nENV.registerFlag('WEBGL_PACK_DEPTHWISECONV', () => false);\n/** Whether we will pack binary ops. */\nENV.registerFlag('WEBGL_PACK_BINARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack unary ops. */\nENV.registerFlag('WEBGL_PACK_UNARY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack array ops. */\nENV.registerFlag('WEBGL_PACK_ARRAY_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack image ops. */\nENV.registerFlag('WEBGL_PACK_IMAGE_OPERATIONS', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will pack reduce ops. */\nENV.registerFlag('WEBGL_PACK_REDUCE', () => ENV.getBool('WEBGL_PACK'));\n/** Whether packed WebGL kernels lazily unpack their outputs. */\nENV.registerFlag('WEBGL_LAZILY_UNPACK', () => ENV.getBool('WEBGL_PACK'));\n/** Whether we will use the im2col algorithm to speed up convolutions. */\nENV.registerFlag('WEBGL_CONV_IM2COL', () => ENV.getBool('WEBGL_PACK'));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURE_SIZE', () => getWebGLMaxTextureSize(ENV.getNumber('WEBGL_VERSION')));\n/** The maximum texture dimension. */\nENV.registerFlag('WEBGL_MAX_TEXTURES_IN_SHADER', () => getMaxTexturesInShader(ENV.getNumber('WEBGL_VERSION')));\n/**\n * The disjoint_query_timer extension version.\n * 0: disabled, 1: EXT_disjoint_timer_query, 2:\n * EXT_disjoint_timer_query_webgl2.\n * In Firefox with WebGL 2.0,\n * EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n * WebGL 1.0 extension.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', () => {\n    const webGLVersion = ENV.getNumber('WEBGL_VERSION');\n    if (webGLVersion === 0) {\n        return 0;\n    }\n    return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\n/**\n * Whether the timer object from the disjoint_query_timer extension gives\n * timing information that is reliable.\n */\nENV.registerFlag('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', () => ENV.getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n    !device_util.isMobile());\n/**\n * Whether the device is physically capable of rendering to float32 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_CAPABLE', () => isCapableOfRenderingToFloatTexture(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Whether rendering to float32 textures is enabled. If disabled, renders to\n * float16 textures.\n */\nENV.registerFlag('WEBGL_RENDER_FLOAT32_ENABLED', () => {\n    return ENV.getBool('WEBGL_FORCE_F16_TEXTURES') ?\n        false :\n        ENV.getBool('WEBGL_RENDER_FLOAT32_CAPABLE');\n});\n/**\n * Whether downloading float textures is enabled (16 or 32 bit). If disabled,\n * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.\n */\nENV.registerFlag('WEBGL_DOWNLOAD_FLOAT_ENABLED', () => isDownloadFloatTextureEnabled(ENV.getNumber('WEBGL_VERSION')));\n/** Whether the fence API is available. */\nENV.registerFlag('WEBGL_FENCE_API_ENABLED', () => isWebGLFenceEnabled(ENV.getNumber('WEBGL_VERSION')));\n/**\n * Tensors with size <= than this will be uploaded as uniforms, not textures.\n */\nENV.registerFlag('WEBGL_SIZE_UPLOAD_UNIFORM', () => {\n    // Use uniform uploads only when 32bit floats are supported. In\n    // 16bit\n    // environments there are problems with comparing a 16bit texture value\n    // with a 32bit uniform value.\n    const useUniforms = ENV.getBool('WEBGL_RENDER_FLOAT32_ENABLED');\n    return useUniforms ? 4 : 0;\n});\n/**\n * If the total number of bytes allocated on the GPU is greater than this\n * number, we will aggressively delete textures upon disposal with\n * gl.deleteMatrixTexture, rather than making them available for reuse.\n *\n * Default value -1 indicates that we will never aggressively delete textures.\n */\nENV.registerFlag('WEBGL_DELETE_TEXTURE_THRESHOLD', () => {\n    return -1;\n}, threshold => {\n    if (threshold < 0 && threshold !== -1) {\n        throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` +\n            `delete) or at least 0, but got ${threshold}.`);\n    }\n});\n//# sourceMappingURL=flags_webgl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import shared functionality from tfjs-backend-cpu without triggering\n// side effects.\n// tslint:disable-next-line: no-imports-from-dist\nimport * as shared from '@tensorflow/tfjs-backend-cpu/dist/shared';\nconst { simpleAbsImpl: simpleAbsImplCPU, addImpl: addImplCPU, ceilImpl: ceilImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, multiplyImpl: multiplyImplCPU, rsqrtImpl: rsqrtImplCPU, sliceImpl: sliceImplCPU, subImpl: subImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU, } = shared;\nexport { simpleAbsImplCPU, addImplCPU, ceilImplCPU, expImplCPU, expm1ImplCPU, logImplCPU, multiplyImplCPU, sliceImplCPU, subImplCPU, floorImplCPU, maxImplCPU, rsqrtImplCPU, transposeImplCPU, uniqueImplCPU, };\n//# sourceMappingURL=shared.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AddNPackedProgram {\n    constructor(outputShape, shapes) {\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const snippets = [];\n        // Get target elements from every input tensor.\n        this.variableNames.forEach(variable => {\n            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);\n        });\n        // Calculate the sum of all elements.\n        const operation = this.variableNames\n            .map(variable => {\n            return `v${variable}`;\n        })\n            .join(' + ');\n        this.userCode = `\n      void main() {\n        ${snippets.join('\\n        ')}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=addn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ArgMinMaxProgram {\n    constructor(reduceInfo, op, firstPass) {\n        this.variableNames = ['A'];\n        const { windowSize, batchSize, outSize } = reduceInfo;\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        this.outputShape = [batchSize, outSize];\n        const compOp = (op === 'max') ? '>' : '<';\n        const indexSnippet = firstPass ?\n            'inOffset + i;' :\n            'round(getBestIndicesA(batch, inOffset + i));';\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport function getVecChannels(name, rank) {\n    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\nexport function getChannels(name, rank) {\n    if (rank === 1) {\n        return [name];\n    }\n    return getVecChannels(name, rank);\n}\nexport function getSourceCoords(rank, dims) {\n    if (rank === 1) {\n        return 'rc';\n    }\n    let coords = '';\n    for (let i = 0; i < rank; i++) {\n        coords += dims[i];\n        if (i < rank - 1) {\n            coords += ',';\n        }\n    }\n    return coords;\n}\n//# sourceMappingURL=packing_util.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nexport function getGlslDifferences() {\n    let version;\n    let attribute;\n    let varyingVs;\n    let varyingFs;\n    let texture2D;\n    let output;\n    let defineOutput;\n    let defineSpecialNaN;\n    let defineSpecialInf;\n    let defineRound;\n    if (env().getNumber('WEBGL_VERSION') === 2) {\n        version = '#version 300 es';\n        attribute = 'in';\n        varyingVs = 'out';\n        varyingFs = 'in';\n        texture2D = 'texture';\n        output = 'outputColor';\n        defineOutput = 'out vec4 outputColor;';\n        // Use custom isnan definition to work across differences between\n        // implementations on various platforms. While this should happen in ANGLE\n        // we still see differences between android and windows (on chrome) when\n        // using isnan directly.\n        defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        return (val > 0.0 || val < 0.0) ? false : val != 0.0;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n        // In webgl 2 we do not need to specify a custom isinf so there is no\n        // need for a special INFINITY constant.\n        defineSpecialInf = ``;\n        defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    else {\n        version = '';\n        attribute = 'attribute';\n        varyingVs = 'varying';\n        varyingFs = 'varying';\n        texture2D = 'texture2D';\n        output = 'gl_FragColor';\n        defineOutput = '';\n        // WebGL1 has no built in isnan so we define one here.\n        defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n        defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n        defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n    }\n    return {\n        version,\n        attribute,\n        varyingVs,\n        varyingFs,\n        texture2D,\n        output,\n        defineOutput,\n        defineSpecialNaN,\n        defineSpecialInf,\n        defineRound\n    };\n}\n//# sourceMappingURL=glsl_version.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(coords, shape, index = 'index') {\n    const strides = util.computeStrides(shape);\n    return strides\n        .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n    })\n        .join('');\n}\nfunction buildVec(x) {\n    if (x.length === 1) {\n        return `${x[0]}`;\n    }\n    return `vec${x.length}(${x.join(',')})`;\n}\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x, y) {\n    if (x.length !== y.length) {\n        throw new Error(`Vectors to be dotted must be of the same length -` +\n            `got ${x.length} and ${y.length}`);\n    }\n    const slices = [];\n    const nearestVec4 = Math.floor(x.length / 4);\n    const nearestVec4Remainder = x.length % 4;\n    for (let i = 0; i < nearestVec4; i++) {\n        const xSlice = x.slice(i * 4, i * 4 + 4);\n        const ySlice = y.slice(i * 4, i * 4 + 4);\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    if (nearestVec4Remainder !== 0) {\n        let xSlice = x.slice(nearestVec4 * 4);\n        let ySlice = y.slice(nearestVec4 * 4);\n        if (xSlice.length === 1) {\n            xSlice = xSlice.map(d => `float(${d})`);\n            ySlice = ySlice.map(d => `float(${d})`);\n        }\n        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n    }\n    return slices.map((d, i) => `dot(${d})`).join('+');\n}\n/**\n * Produces GLSL that computes the flat index from 3D coordinates.\n */\nexport function getFlatIndexFrom3D(shape) {\n    const strides = util.computeStrides(shape).map(d => d.toString());\n    return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nexport const ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n//# sourceMappingURL=shader_compiler_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nconst { getBroadcastDims } = backend_util;\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport function makeShader(inputsInfo, outputShape, userCode, usesPackedTextures) {\n    const prefixSnippets = [];\n    inputsInfo.forEach(x => {\n        const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n        // Snippet when we decided to upload the values as uniform.\n        if (x.shapeInfo.isUniform) {\n            prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`);\n        }\n        else {\n            prefixSnippets.push(`uniform sampler2D ${x.name};`);\n            prefixSnippets.push(`uniform int offset${x.name};`);\n        }\n    });\n    const inputPrefixSnippet = prefixSnippets.join('\\n');\n    const inputSamplingSnippet = inputsInfo\n        .map(x => getInputSamplingSnippet(x, outputShape, usesPackedTextures))\n        .join('\\n');\n    const outTexShape = outputShape.texShape;\n    const glsl = getGlslDifferences();\n    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n    let outputSamplingSnippet;\n    let floatTextureSetOutputSnippet;\n    let shaderPrefix = getShaderPrefix(glsl);\n    if (outputShape.isPacked) {\n        outputSamplingSnippet =\n            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n    }\n    else {\n        outputSamplingSnippet =\n            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n    }\n    if (usesPackedTextures) {\n        shaderPrefix += SHADER_PACKED_PREFIX;\n    }\n    const source = [\n        shaderPrefix, floatTextureSampleSnippet, floatTextureSetOutputSnippet,\n        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n    ].join('\\n');\n    return source;\n}\nfunction getSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getSamplerScalar(inInfo);\n        case 1:\n            return getSampler1D(inInfo);\n        case 2:\n            return getSampler2D(inInfo);\n        case 3:\n            return getSampler3D(inInfo);\n        case 4:\n            return getSampler4D(inInfo);\n        case 5:\n            return getSampler5D(inInfo);\n        case 6:\n            return getSampler6D(inInfo);\n        default:\n            throw new Error(`${shape.length}-D input sampling` +\n                ` is not yet supported`);\n    }\n}\nfunction getPackedSamplerFromInInfo(inInfo) {\n    const shape = inInfo.shapeInfo.logicalShape;\n    switch (shape.length) {\n        case 0:\n            return getPackedSamplerScalar(inInfo);\n        case 1:\n            return getPackedSampler1D(inInfo);\n        case 2:\n            return getPackedSampler2D(inInfo);\n        case 3:\n            return getPackedSampler3D(inInfo);\n        default:\n            return getPackedSamplerND(inInfo);\n    }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false) {\n    let res = '';\n    if (usesPackedTextures) {\n        res += getPackedSamplerFromInInfo(inInfo);\n    }\n    else {\n        res += getSamplerFromInInfo(inInfo);\n    }\n    const inShape = inInfo.shapeInfo.logicalShape;\n    const outShape = outShapeInfo.logicalShape;\n    if (inShape.length <= outShape.length) {\n        if (usesPackedTextures) {\n            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n        else {\n            res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n        }\n    }\n    return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutputPacked1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutputPacked2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutputPacked3DCoords(outShape, outTexShape);\n        default:\n            return getOutputPackedNDCoords(outShape, outTexShape);\n    }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape) {\n    switch (outShape.length) {\n        case 0:\n            return getOutputScalarCoords();\n        case 1:\n            return getOutput1DCoords(outShape, outTexShape);\n        case 2:\n            return getOutput2DCoords(outShape, outTexShape);\n        case 3:\n            return getOutput3DCoords(outShape, outTexShape);\n        case 4:\n            return getOutput4DCoords(outShape, outTexShape);\n        case 5:\n            return getOutput5DCoords(outShape, outTexShape);\n        case 6:\n            return getOutput6DCoords(outShape, outTexShape);\n        default:\n            throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n    }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n    return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n    return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n    return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n    const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n    return SHADER_PREFIX;\n}\nconst SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n    return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (packedTexShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n    }\n    if (packedTexShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape) {\n    if (texShape[0] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n    }\n    if (texShape[1] === 1) {\n        return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n    }\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n    let texelsInBatchN = texelsInBatch;\n    let batches = ``;\n    let coords = 'b, r, c';\n    for (let b = 2; b < shape.length - 1; b++) {\n        texelsInBatchN *= shape[shape.length - b - 1];\n        batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n        coords = `b${b}, ` + coords;\n    }\n    return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);\n    return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n    return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape) {\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n    }\n    // texels needed to accommodate a logical row\n    const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n    /**\n     * getOutputCoords\n     *\n     * resTexRC: The rows and columns of the texels. If you move over one\n     * texel to the right in the packed texture, you are moving over one column\n     * (not two).\n     *\n     * index: The texel index\n     */\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape) {\n    if (util.arraysEqual(shape, texShape)) {\n        return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n    }\n    if (shape[1] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    if (shape[0] === 1) {\n        return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n    return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        return `float ${funcName}() {return ${texName};}`;\n    }\n    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n    if (texNumR === 1 && texNumC === 1) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo) {\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const tNumR = texShape[0];\n    const tNumC = texShape[1];\n    if (tNumC === 1 && tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    if (tNumC === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (tNumR === 1) {\n        return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const glsl = getGlslDifferences();\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const valuesPerRow = Math.ceil(shape[1] / 2);\n    return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    if (texShape != null && util.arraysEqual(shape, texShape)) {\n        const texNumR = texShape[0];\n        const texNumC = texShape[1];\n        return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const offset = getFlatOffsetUniformName(texName);\n    if (texNumC === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    if (texNumR === 1) {\n        // index is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    if (shape[0] === 1) {\n        const squeezedShape = shape.slice(1);\n        const keptDims = [1, 2];\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['b', 'row', 'col'];\n        return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[2] / 2);\n    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride0 = shape[1] * shape[2];\n    const stride1 = shape[2];\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    const squeezedShape = newShape;\n    if (squeezedShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n        const params = ['row', 'col', 'depth'];\n        return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n    }\n    if (texNumC === stride1 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const rank = shape.length;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const texShape = inputInfo.shapeInfo.texShape;\n    const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n    const texNumR = packedTexShape[0];\n    const texNumC = packedTexShape[1];\n    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n    let params = `int b, int row, int col`;\n    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n    for (let b = 2; b < rank - 1; b++) {\n        params = `int b${b}, ` + params;\n        texelsInBatch *= shape[rank - b - 1];\n        index = `b${b} * ${texelsInBatch} + ` + index;\n    }\n    const glsl = getGlslDifferences();\n    return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride2 = shape[3];\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride2 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const stride3 = shape[4];\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride3 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n    const shape = inputInfo.shapeInfo.logicalShape;\n    const texName = inputInfo.name;\n    const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n    const { newShape, keptDims } = util.squeezeShape(shape);\n    if (newShape.length < shape.length) {\n        const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n        const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n        return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n    }\n    const stride4 = shape[5];\n    const stride3 = shape[4] * stride4;\n    const stride2 = shape[3] * stride3;\n    const stride1 = shape[2] * stride2;\n    const stride0 = shape[1] * stride1;\n    if (inputInfo.shapeInfo.isUniform) {\n        // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n    }\n    const flatOffset = inputInfo.shapeInfo.flatOffset;\n    const texShape = inputInfo.shapeInfo.texShape;\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    if (texNumC === stride0 && flatOffset == null) {\n        // texC is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    if (texNumC === stride4 && flatOffset == null) {\n        // texR is used directly as physical (no risk of float16 overflow).\n        return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const offset = getFlatOffsetUniformName(texName);\n    return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n    const texName = inputInfo.name;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    if (inSize < 2) {\n        return `return ${texName};`;\n    }\n    return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const type = getCoordsDataType(outRank);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    let output = `return outputValue;`;\n    const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n    const isInputScalar = inSize === 1;\n    const outSize = util.sizeFromShape(outShapeInfo.logicalShape);\n    const isOutputScalar = outSize === 1;\n    if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n        output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n    }\n    else if (isInputScalar && !isOutputScalar) {\n        if (outRank === 1) {\n            output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n        }\n        else {\n            output = `\n        return vec4(outputValue.x);\n      `;\n        }\n    }\n    else if (broadcastDims.length) {\n        const rows = inRank - 2;\n        const cols = inRank - 1;\n        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.x);`;\n        }\n        else if (broadcastDims.indexOf(rows) > -1) {\n            output = `return vec4(outputValue.x, outputValue.y, ` +\n                `outputValue.x, outputValue.y);`;\n        }\n        else if (broadcastDims.indexOf(cols) > -1) {\n            output = `return vec4(outputValue.xx, outputValue.zz);`;\n        }\n    }\n    return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n    const texName = inputInfo.name;\n    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n    const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n    const outTexShape = outShapeInfo.texShape;\n    const inTexShape = inputInfo.shapeInfo.texShape;\n    const inRank = inputInfo.shapeInfo.logicalShape.length;\n    const outRank = outShapeInfo.logicalShape.length;\n    if (!inputInfo.shapeInfo.isUniform && inRank === outRank &&\n        inputInfo.shapeInfo.flatOffset == null &&\n        util.arraysEqual(inTexShape, outTexShape)) {\n        return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n    }\n    const type = getCoordsDataType(outRank);\n    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n    const rankDiff = outRank - inRank;\n    let coordsSnippet;\n    const fields = ['x', 'y', 'z', 'w', 'u', 'v'];\n    if (inRank === 0) {\n        coordsSnippet = '';\n    }\n    else if (outRank < 2 && broadcastDims.length >= 1) {\n        coordsSnippet = 'coords = 0;';\n    }\n    else {\n        coordsSnippet =\n            broadcastDims.map(d => `coords.${fields[d + rankDiff]} = 0;`)\n                .join('\\n');\n    }\n    let unpackedCoordsSnippet = '';\n    if (outRank < 2 && inRank > 0) {\n        unpackedCoordsSnippet = 'coords';\n    }\n    else {\n        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n            .map((s, i) => `coords.${fields[i + rankDiff]}`)\n            .join(', ');\n    }\n    return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nexport function getCoordsDataType(rank) {\n    if (rank <= 1) {\n        return 'int';\n    }\n    else if (rank === 2) {\n        return 'ivec2';\n    }\n    else if (rank === 3) {\n        return 'ivec3';\n    }\n    else if (rank === 4) {\n        return 'ivec4';\n    }\n    else if (rank === 5) {\n        return 'ivec5';\n    }\n    else if (rank === 6) {\n        return 'ivec6';\n    }\n    else {\n        throw Error(`GPU for rank ${rank} is not yet supported`);\n    }\n}\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n    // Deep copy.\n    const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n    newInputInfo.shapeInfo.logicalShape = squeezedShape;\n    return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n    return keptDims.map(d => params[d]).join(', ');\n}\n//# sourceMappingURL=shader_compiler.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ArgMinMaxPackedProgram {\n    constructor(shape, windowSize, op, firstPass) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        util.assert(shape.length > 2, () => `Packed arg${op.charAt(0).toUpperCase() +\n            op.slice(1)} supports only inputs with rank above 2.`);\n        const inSize = shape[shape.length - 1];\n        const outSize = Math.ceil(inSize / windowSize);\n        this.outputShape = shape.slice(0, -1);\n        if (outSize > 1) {\n            this.outputShape.push(outSize);\n        }\n        if (!firstPass) {\n            this.variableNames.push('bestIndicesA');\n        }\n        const outShape = this.outputShape;\n        const rank = outShape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        let sourceLocSetup;\n        let sourceRank;\n        if (outSize === 1) {\n            sourceRank = rank + 1;\n            const sourceLocDType = getCoordsDataType(sourceRank);\n            sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);\n        ++${coords[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);\n        --${coords[rank - 2]};`;\n        }\n        else {\n            sourceRank = rank;\n            sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords[rank - 2]};`;\n        }\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, sourceRank);\n        const inChannel = '.' + channels[sourceRank - 1]; // e.g. \".b\" for rank 3.\n        const intChannels = channels.map(x => 'int ' + x);\n        const srcRCoords = getChannels('sourceLocR', sourceRank - 1).concat('inIdx.r');\n        const srcGCoords = getChannels('sourceLocG', sourceRank - 1).concat('inIdx.g');\n        const srcBCoords = getChannels('sourceLocB', sourceRank - 1).concat('inIdx.b');\n        const srcACoords = getChannels('sourceLocA', sourceRank - 1).concat('inIdx.a');\n        const compOp = (op === 'max') ? 'greaterThan' : 'lessThan';\n        const fetchCandidateIdx = firstPass ? '' : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n        const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n        const getBestIndicesAChannelSnippet = firstPass ? '' : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n        this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=argminmax_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class AvgPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterHeight * filterWidth);\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class AvgPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=avg_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nconst CHECK_NAN_SNIPPET = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nif (b == 0.0) {\n  return 1.0;\n}\nreturn (round(mod(b, 2.0)) != 1) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const EQUAL = `return float(a == b);`;\nexport const LESS = `return float(a < b);`;\nexport const LESS_EQUAL = `return float(a <= b);`;\nexport const GREATER = `return float(a > b);`;\nexport const GREATER_EQUAL = `return float(a >= b);`;\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nexport const PRELU = `return (a < 0.) ? b * a : a;`;\nexport class BinaryOpProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['A', 'B'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nconst CHECK_NAN_SNIPPET = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nexport const INT_DIV = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nexport const POW = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const PRELU = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nexport const ELU_DER = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nexport const EQUAL = `\n  return vec4(equal(a, b));\n`;\nexport const NOT_EQUAL = `\n  return vec4(notEqual(a, b));\n`;\nexport const LESS = `\n  return vec4(lessThan(a, b));\n`;\nexport const LESS_EQUAL = `\n  return vec4(lessThanEqual(a, b));\n`;\nexport const GREATER = `\n  return vec4(greaterThan(a, b));\n`;\nexport const GREATER_EQUAL = `\n  return vec4(greaterThanEqual(a, b));\n`;\nexport const LOGICAL_AND = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nexport const LOGICAL_OR = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nexport const MAX = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MIN = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport const MOD = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` +\n    CHECK_NAN_SNIPPET + `\n  return result;\n`;\nexport class BinaryOpPackedProgram {\n    constructor(op, aShape, bShape, checkOutOfBounds = false) {\n        this.variableNames = ['A', 'B'];\n        this.supportsBroadcasting = true;\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        const rank = this.outputShape.length;\n        let checkOutOfBoundsString = '';\n        if (checkOutOfBounds) {\n            if (rank === 0 || util.sizeFromShape(this.outputShape) === 1) {\n                checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n            }\n            else {\n                const dtype = getCoordsDataType(rank);\n                checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n                if (rank === 1) {\n                    checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n                }\n                else {\n                    const channels = getChannels('coords', rank);\n                    checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n                }\n            }\n        }\n        this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ClipPackedProgram {\n    constructor(aShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      uniform float minVal;\n      uniform float maxVal;\n\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n    }\n    getCustomSetupFunc(min, max) {\n        return (gpgpu, webGLProgram) => {\n            if (this.minLoc == null) {\n                this.minLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'minVal');\n                this.maxLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'maxVal');\n            }\n            gpgpu.gl.uniform1f(this.minLoc, min);\n            gpgpu.gl.uniform1f(this.maxLoc, max);\n        };\n    }\n}\n//# sourceMappingURL=clip_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ComplexAbsProgram {\n    constructor(shape) {\n        this.variableNames = ['real', 'imag'];\n        this.outputShape = shape;\n        this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n    }\n}\n//# sourceMappingURL=complex_abs_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class Conv3DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padFront = filterDepth - 1 - convInfo.padInfo.front;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DDerFilterProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'dy'];\n        this.outputShape = convInfo.filterShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class DepthwiseConv2DDerInputProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'W'];\n        this.outputShape = convInfo.inShape;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const padTop = filterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Conv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 1 : 2;\n        const colDim = isChannelsLast ? 2 : 3;\n        const channelDim = isChannelsLast ? 3 : 1;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivationWeights) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivationWeights) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\nexport class Conv3DProgram {\n    constructor(convInfo) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterDepth = convInfo.filterDepth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n        const inputDepthVec4Remainder = convInfo.inChannels % 4;\n        this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthwiseConv2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const channelMul = convInfo.outChannels / convInfo.inChannels;\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `\n          float activation(float x) {\n            ${activation}\n          }\n        `;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class DepthwiseConvPacked2DProgram {\n    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['x', 'W'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = convInfo.outShape;\n        const xNumRows = convInfo.inHeight;\n        const xNumCols = convInfo.inWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const filterHeight = convInfo.filterHeight;\n        const filterWidth = convInfo.filterWidth;\n        const texelsAcross = filterWidth;\n        let mainLoop = `int xR; int xC; int xCOffset;`;\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `\n          vec4 xTexelR${r}C${c * 2} = vec4(0.);\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n            }\n        }\n        /**\n         * This vectorized implementation works by gathering the values needed for\n         * each output channel's dot product into vec4's and then multiplying them\n         * all together (this happens in the final double for-loop below). Most of\n         * the main loop consists of constructing these vec4's with the minimum\n         * number of texture2D calls, which means making use of all four returned\n         * values from a texture2D call at once.\n         */\n        for (let r = 0; r < filterHeight; r++) {\n            for (let texelC = 0; texelC < texelsAcross; texelC++) {\n                const c = texelC * 2;\n                mainLoop += `\n          xR = xRCorner + ${r * dilationHeight};\n          xC = xCCorner + ${c * dilationWidth};\n        `;\n                if (strideWidth === 1) {\n                    if (c < filterWidth) {\n                        // If padding is odd, the outer texels have to be composed.\n                        if (padLeft % 2 === 1) {\n                            // TODO: Ensure vec4 previous does not result in redundant sample,\n                            // and avoid setting xTexelRC's that exceed the boundary in the\n                            // first place rather than resetting them to vec4(0)).\n                            // To compute xCOffset:\n                            // - If padding is odd, we must add 1 to ensure we ask for an\n                            // even-numbered row.\n                            // - We subtract 2 to access the previous texel.\n                            mainLoop += `\n                xCOffset = xC + 1;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    xTexelR${r}C${c}.zw = vec2(0.);\n                  }\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + 1 - 2;\n                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  vec4 previous = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if(xCOffset + 1 >= ${xNumCols}) {\n                    previous.zw = vec2(0.);\n                  }\n\n                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);\n                } else {\n                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);\n                }\n              `;\n                        }\n                        else {\n                            // Padding is even, so xRC corresponds to a single texel.\n                            mainLoop += `\n                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xR${r}C${c} = xTexelR${r}C${c};\n              `;\n                        }\n                        if (c + 1 < filterWidth) {\n                            // If dilation is even, the second entry should match the first\n                            // (either both are composed or both are single samples). But if\n                            // dilation is odd, then the second entry should be the opposite\n                            // of the first (if the first is composed, the second is a single\n                            // sample, and vice versa.)\n                            const nextTexelOffset = padLeft % 2 === 0 ?\n                                util.nearestLargerEven(dilationWidth) :\n                                dilationWidth;\n                            if ((dilationWidth % 2 === 0 && padLeft % 2 === 1) ||\n                                (dilationWidth % 2 !== 0 && padLeft % 2 !== 1)) {\n                                mainLoop += `\n                  xCOffset = xC + ${padLeft % 2} + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n                `;\n                                // If dilation > 1 then the xRC's will not be able to share any\n                                // values, so each xRC will require two unique calls to getX.\n                                if (dilationWidth > 1) {\n                                    mainLoop += `\n                    xCOffset -= 2;\n                    if(xR >= 0 && xR < ${xNumRows} &&\n                      xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                    } else {\n                      xTexelR${r}C${c} = vec4(0.);\n                    }\n                  `;\n                                }\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.xy);\n                `;\n                            }\n                            else {\n                                mainLoop += `\n                  xCOffset = xC + ${nextTexelOffset};\n\n                  if(xR >= 0 && xR < ${xNumRows} &&\n                    xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                  }\n\n                  xR${r}C${c + 1} = xTexelR${r}C${c + 2};\n                `;\n                            }\n                        }\n                    }\n                }\n                else { // stride > 1\n                    if (c < filterWidth) {\n                        mainLoop += `\n              if(xR >= 0 && xR < ${xNumRows}) {\n            `;\n                        // Depending on whether padLeft is even or odd, we want either the\n                        // xy or zw channels from X texels for xR${r}C${c}. If padLeft is\n                        // even, xR${r}C${c + 1} is simply the zw channels of texels we've\n                        // already sampled. But if padLeft is odd, xR${r}C{$c + 1}.zw will\n                        // need to come from the xy channels of a new texel, hence the `vec4\n                        // final` initialized below.\n                        if (padLeft % 2 === 1) {\n                            mainLoop += `\n                xCOffset = xC + 1 - ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xC + 1, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  vec4 final = vec4(0.);\n                  xCOffset = xC + 1 + ${strideWidth};\n                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xR${r}C${c + 1} = vec4(xTexelR${r}C${c + 2}.xy, final.xy);\n                `;\n                            }\n                        }\n                        else {\n                            mainLoop += `\n                if(xC >= 0 && xC < ${xNumCols}) {\n                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);\n                } else {\n                  xTexelR${r}C${c} = vec4(0.);\n                }\n\n                xCOffset = xC + ${strideWidth};\n                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {\n                  xTexelR${r}C${c + 2} = getX(batch, xR, xCOffset, d1);\n                } else {\n                  xTexelR${r}C${c + 2} = vec4(0.);\n                }\n\n                xR${r}C${c} = vec4(\n                  xTexelR${r}C${c}.xy, xTexelR${r}C${c + 2}.xy);\n              `;\n                            if (c + 1 < filterWidth) {\n                                mainLoop += `\n                  xR${r}C${c + 1} = vec4(\n                    xTexelR${r}C${c}.zw, xTexelR${r}C${c + 2}.zw);\n                `;\n                            }\n                        }\n                        mainLoop += `}`;\n                    }\n                }\n                if (c < filterWidth) {\n                    mainLoop += `\n            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);\n            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);\n          `;\n                    if (c + 1 < filterWidth) {\n                        mainLoop += `\n              vec4 wTexelR${r}C${c + 1} = getW(${r}, ${c + 1}, d1, q);\n              wR${r}C${c + 1} =\n                vec4(wTexelR${r}C${c + 1}.xz, wTexelR${r}C${c + 1}.xz);`;\n                    }\n                }\n            }\n        }\n        for (let r = 0; r < filterHeight; r++) {\n            for (let c = 0; c < filterWidth; c++) {\n                mainLoop += `dotProd += xR${r}C${c} * wR${r}C${c};`;\n            }\n        }\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 dotProd = vec4(0.);\n\n        ${mainLoop}\n\n        vec4 result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=conv_packed_gpu_depthwise.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class CropAndResizeProgram {\n    constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n        this.variableNames = ['Image', 'Boxes', 'BoxInd'];\n        this.outputShape = [];\n        const [batch, imageHeight, imageWidth, depth] = imageShape;\n        const [numBoxes,] = boxShape;\n        const [cropHeight, cropWidth] = cropSize;\n        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n        const methodId = method === 'bilinear' ? 1 : 0;\n        const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n        const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n            [\n                `${(imageHeight - 1) / (cropHeight - 1)}`,\n                '(y2-y1) * height_ratio',\n                `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (y1+y2) * ${inputHeightFloat}`,\n            ];\n        const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n            [\n                `${(imageWidth - 1) / (cropWidth - 1)}`,\n                '(x2-x1) * width_ratio',\n                `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n            ] :\n            [\n                '0.0',\n                '0.0',\n                `0.5 * (x1+x2) * ${inputWidthFloat}`,\n            ];\n        // Reference implementation\n        // tslint:disable-next-line:max-line-length\n        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n        this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=crop_and_resize_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class CumSumProgram {\n    constructor(shape, exclusive, reverse) {\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        const rank = shape.length;\n        const val = exclusive ? '0.0' : `getX(${getCoords(rank, 'coords')})`;\n        const length = shape[shape.length - 1];\n        let condition = '';\n        let idxString = '';\n        // When exclusive is set, the cumsum op becomes roll op that copies the\n        // value from the previous index based on the direction specified by the\n        // reverse flag.\n        if (exclusive) {\n            condition = reverse ? `end != ${length - 1}` : 'end != 0';\n            idxString = reverse ? 'end + 1' : 'end - 1';\n        }\n        else {\n            condition = reverse ? `end + pow2 < ${length}` : 'end >= pow2';\n            idxString = (reverse ? 'end + pow2' : 'end - pow2');\n        }\n        this.userCode = `\n      uniform float index;\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n    }\n    getCustomSetupFunc(index) {\n        return (gpgpu, webGLProgram) => {\n            if (this.index == null) {\n                this.index = gpgpu.getUniformLocation(webGLProgram, 'index');\n            }\n            gpgpu.gl.uniform1f(this.index, index);\n        };\n    }\n}\nfunction getCoords(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.x, ${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.x, ${name}.y, ${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\nfunction getFinalCoord(rank, name) {\n    if (rank === 1) {\n        return `${name}`;\n    }\n    else if (rank === 2) {\n        return `${name}.y`;\n    }\n    else if (rank === 3) {\n        return `${name}.z`;\n    }\n    else if (rank === 4) {\n        return `${name}.w`;\n    }\n    else {\n        throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=cumsum_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nimport { getDenseTexShape, PackingScheme } from './tex_util';\nexport class DecodeMatrixPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outPackingScheme = PackingScheme.DENSE;\n        const texShape = getDenseTexShape(outputShape);\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n          vec2(${texShape[0]}, ${texShape[1]}));\n        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=decode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DepthToSpaceProgram {\n    constructor(outputShape, blockSize, dataFormat) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.outputShape = outputShape;\n        this.blockSize = blockSize;\n        this.dataFormat = dataFormat;\n        this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n    }\n    getHeightCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[1]`;\n        }\n        else {\n            return `coords[2]`;\n        }\n    }\n    getWidthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[2]`;\n        }\n        else {\n            return `coords[3]`;\n        }\n    }\n    getDepthCoordString() {\n        if (this.dataFormat === 'NHWC') {\n            return `coords[3]`;\n        }\n        else {\n            return `coords[1]`;\n        }\n    }\n    getOutputDepthSize() {\n        if (this.dataFormat === 'NHWC') {\n            return this.outputShape[3];\n        }\n        else {\n            return this.outputShape[1];\n        }\n    }\n    getInputSamplingString() {\n        if (this.dataFormat === 'NHWC') {\n            return `getX(b, in_h, in_w, in_d)`;\n        }\n        else {\n            return `getX(b, in_d, in_h, in_w)`;\n        }\n    }\n}\n//# sourceMappingURL=depth_to_space_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class DiagProgram {\n    constructor(size) {\n        this.variableNames = ['X'];\n        this.outputShape = [size, size];\n        this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=diag_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport { ENCODE_FLOAT_SNIPPET } from './shader_compiler_util';\nimport { TextureUsage } from './tex_util';\nexport class EncodeFloatPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outTexUsage = TextureUsage.DOWNLOAD;\n        const glsl = getGlslDifferences();\n        this.outputShape = outputShape;\n        this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_float_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\nexport class EncodeMatrixProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let output = `result`;\n        if (inputIsUnsignedByte) {\n            output = `floor(result * 255. + 0.5)`;\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / ${width};\n        int c = imod(flatIndex, ${width});\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as shader_util from './shader_compiler_util';\n/*\nThis is how the shader encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n */\nexport class EncodeMatrixPackedProgram {\n    constructor(outputShape, texShape, inputIsUnsignedByte = false) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width] = texShape;\n        this.outputShape = outputShape;\n        let mainLoop = '';\n        let output = 'result';\n        if (inputIsUnsignedByte) {\n            output = 'floor(result * 255. + 0.5)';\n        }\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                const channel = row * 2 + col;\n                mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${outputShape[2]}) {\n            localCoords[2] += ${col};\n            if(localCoords[1] + ${row} < ${outputShape[1]}) {\n              localCoords[1] += ${row};\n\n              flatIndex = getFlatIndex(localCoords);\n              offset = imod(flatIndex, 4);\n\n              flatIndex = idiv(flatIndex, 4, 1.);\n\n              r = flatIndex / ${width};\n              c = imod(flatIndex, ${width});\n              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);\n              values = ${glsl.texture2D}(A, uv);\n\n              if(offset == 0) {\n                result[${channel}] = values[0];\n              } else if(offset == 1) {\n                result[${channel}] = values[1];\n              } else if(offset == 2) {\n                result[${channel}] = values[2];\n              } else {\n                result[${channel}] = values[3];\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        vec4 result = vec4(0.);\n        int flatIndex, r, c, offset;\n        ivec3 localCoords;\n        vec2 uv;\n        vec4 values;\n\n        ${mainLoop}\n\n        ${glsl.output} = ${output};\n      }\n    `;\n    }\n}\n//# sourceMappingURL=encode_matrix_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FillProgram {\n    constructor(shape, value) {\n        this.outputShape = [];\n        this.variableNames = ['x'];\n        this.outputShape = shape;\n        this.userCode = `\n      uniform float value;\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n    }\n    getCustomSetupFunc(value) {\n        return (gpgpu, webGLProgram) => {\n            if (this.valueLoc == null) {\n                this.valueLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'value');\n            }\n            gpgpu.gl.uniform1f(this.valueLoc, value);\n        };\n    }\n}\n//# sourceMappingURL=fill_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class GatherProgram {\n    constructor(aShape, indicesLength, axis) {\n        this.variableNames = ['A', 'indices'];\n        const outputShape = aShape.slice();\n        outputShape[axis] = indicesLength;\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape, axis);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape, axis) {\n    const rank = aShape.length;\n    if (rank > 4) {\n        throw Error(`Gather for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `int(getIndices(resRC))`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        if (i === axis) {\n            sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n        }\n        else {\n            sourceCoords.push(`${currentCoords[i]}`);\n        }\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=gather_gpu.js.map", "import { getCoordsDataType } from './shader_compiler';\nexport class GatherNDProgram {\n    constructor(sliceDim, strides, shape) {\n        this.sliceDim = sliceDim;\n        this.strides = strides;\n        this.variableNames = ['x', 'indices'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=gather_nd_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport function createVertexShader(gl) {\n    const glsl = getGlslDifferences();\n    const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n    return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\nexport function createVertexBuffer(gl) {\n    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n    const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n    return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\nexport function createIndexBuffer(gl) {\n    // OpenGL (and WebGL) have \"CCW == front\" winding\n    const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n    webgl_util.validateTextureSize(width, height);\n    const texture = webgl_util.createTexture(gl);\n    const tex2d = gl.TEXTURE_2D;\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n    return texture;\n}\nexport function getInternalFormatForFloat32MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatFloat;\n}\nexport function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16MatrixTexture(textureConfig) {\n    return textureConfig.internalFormatHalfFloat;\n}\nexport function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nexport function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n    return textureConfig.downloadTextureFormat;\n}\nexport function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nexport function getInternalFormatForPackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedFloat;\n}\nexport function createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nexport function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n    return textureConfig.internalFormatPackedHalfFloat;\n}\nexport function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n    const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nexport function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n    const posOffset = 0; // x is the first buffer element\n    const uvOffset = 3 * 4; // uv comes after [x y z]\n    const stride = (3 * 4) + (2 * 4); // xyz + uv, each entry is 4-byte float.\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n    const success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n    return success &&\n        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\nexport function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    let dataForUpload, texelDataType, internalFormat;\n    if (data instanceof Uint8Array) {\n        dataForUpload = new Uint8Array(width * height * 4);\n        texelDataType = gl.UNSIGNED_BYTE;\n        internalFormat = gl.RGBA;\n    }\n    else {\n        dataForUpload = new Float32Array(width * height * 4);\n        texelDataType = gl.FLOAT;\n        internalFormat = textureConfig.internalFormatPackedFloat;\n    }\n    dataForUpload.set(data);\n    webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function uploadPixelDataToTexture(gl, texture, pixels) {\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n    if (pixels.data instanceof Uint8Array) {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n    else {\n        webgl_util.callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n    webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nexport function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const valuesPerTexel = 4;\n    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n    webgl_util.callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n    webgl_util.callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n    return buffer;\n}\nexport function downloadFloat32MatrixFromBuffer(gl, buffer, size) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(size);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n    const [w, h] = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n    const numChannels = 4;\n    const downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n    // decoding of the 4 bytes that back each 32 bit float.\n    return new Float32Array(downloadTarget.buffer);\n}\nexport function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n    const gl2 = gl;\n    const downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);\n    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n    return downloadTarget;\n}\nexport function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n    webgl_util.callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n    return packedRGBA;\n}\n//# sourceMappingURL=gpgpu_util.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport { getWebGLContext, setWebGLContext } from './canvas_util';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\nexport class GPGPUContext {\n    constructor(gl) {\n        this.outputTexture = null;\n        this.program = null;\n        this.disposed = false;\n        this.vertexAttrsAreBound = false;\n        this.itemsToPoll = [];\n        const glVersion = env().getNumber('WEBGL_VERSION');\n        if (gl != null) {\n            this.gl = gl;\n            setWebGLContext(glVersion, gl);\n        }\n        else {\n            this.gl = getWebGLContext(glVersion);\n        }\n        // WebGL 2.0 enables texture floats without an extension.\n        let COLOR_BUFFER_FLOAT = 'WEBGL_color_buffer_float';\n        const COLOR_BUFFER_HALF_FLOAT = 'EXT_color_buffer_half_float';\n        if (env().getNumber('WEBGL_VERSION') === 1) {\n            const TEXTURE_FLOAT = 'OES_texture_float';\n            const TEXTURE_HALF_FLOAT = 'OES_texture_half_float';\n            this.textureFloatExtension =\n                webgl_util.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n            if (webgl_util.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n                this.textureHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support half float textures, yet the ' +\n                    'environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    webgl_util.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n            }\n            else if (env().get('WEBGL_FORCE_F16_TEXTURES')) {\n                throw new Error('GL context does not support color renderable half floats, yet ' +\n                    'the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.');\n            }\n        }\n        else {\n            COLOR_BUFFER_FLOAT = 'EXT_color_buffer_float';\n            if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n                this.colorBufferFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_FLOAT);\n            }\n            else if (webgl_util.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n                this.colorBufferHalfFloatExtension =\n                    this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n            }\n            else {\n                throw new Error('GL context does not support color renderable floats');\n            }\n        }\n        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n        this.framebuffer = webgl_util.createFramebuffer(this.gl);\n        this.textureConfig =\n            tex_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n    }\n    get debug() {\n        return env().getBool('DEBUG');\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        if (this.program != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n                ' This is probably a resource leak, delete the program with ' +\n                'GPGPUContext.deleteProgram before disposing.');\n        }\n        if (this.outputTexture != null) {\n            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +\n                'texture.  This is probably a resource leak, delete the output ' +\n                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n                'disposing.');\n        }\n        const gl = this.gl;\n        webgl_util.callAndCheck(gl, () => gl.finish());\n        webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n        webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n        this.disposed = true;\n    }\n    createFloat32MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createFloat16MatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createUnsignedBytesMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    uploadPixelDataToTexture(texture, pixels) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n    }\n    uploadDenseMatrixToTexture(texture, width, height, data) {\n        this.throwIfDisposed();\n        gpgpu_util.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n    }\n    createFloat16PackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    createPackedMatrixTexture(rows, columns) {\n        this.throwIfDisposed();\n        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n    }\n    deleteMatrixTexture(texture) {\n        this.throwIfDisposed();\n        if (this.outputTexture === texture) {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n            this.outputTexture = null;\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n    }\n    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n    }\n    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {\n        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n    }\n    downloadFloat32MatrixFromBuffer(buffer, size) {\n        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);\n    }\n    createBufferFromTexture(texture, rows, columns) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = gpgpu_util.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    createAndWaitForFence() {\n        const fenceContext = this.createFence(this.gl);\n        return this.pollFence(fenceContext);\n    }\n    createFence(gl) {\n        let query;\n        let isFencePassed;\n        if (env().getBool('WEBGL_FENCE_API_ENABLED')) {\n            const gl2 = gl;\n            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n            gl.flush();\n            isFencePassed = () => {\n                const status = gl2.clientWaitSync(sync, 0, 0);\n                return status === gl2.ALREADY_SIGNALED ||\n                    status === gl2.CONDITION_SATISFIED;\n            };\n            query = sync;\n        }\n        else if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n            query = this.beginQuery();\n            this.endQuery();\n            isFencePassed = () => this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n        }\n        else {\n            // If we have no way to fence, return true immediately. This will fire in\n            // WebGL 1.0 when there is no disjoint query timer. In this case, because\n            // the fence passes immediately, we'll immediately ask for a download of\n            // the texture, which will cause the UI thread to hang.\n            isFencePassed = () => true;\n        }\n        return { query, isFencePassed };\n    }\n    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n        return this.downloadMatrixDriver(texture, () => gpgpu_util.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n    }\n    createProgram(fragmentShaderSource) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        const fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);\n        const vertexShader = gpgpu_util.createVertexShader(gl);\n        const program = webgl_util.createProgram(gl);\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n        webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n        webgl_util.linkProgram(gl, program);\n        if (this.debug) {\n            webgl_util.validateProgram(gl, program);\n        }\n        if (!this.vertexAttrsAreBound) {\n            this.setProgram(program);\n            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n        }\n        return program;\n    }\n    deleteProgram(program) {\n        this.throwIfDisposed();\n        if (program === this.program) {\n            this.program = null;\n        }\n        if (program != null) {\n            webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n        }\n    }\n    setProgram(program) {\n        this.throwIfDisposed();\n        this.program = program;\n        if ((this.program != null) && this.debug) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n    }\n    getUniformLocation(program, uniformName, shouldThrow = true) {\n        this.throwIfDisposed();\n        if (shouldThrow) {\n            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n        }\n        else {\n            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);\n        }\n    }\n    getAttributeLocation(program, attribute) {\n        this.throwIfDisposed();\n        return webgl_util.callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n    }\n    getUniformLocationNoThrow(program, uniformName) {\n        this.throwIfDisposed();\n        return this.gl.getUniformLocation(program, uniformName);\n    }\n    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        webgl_util.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n    }\n    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n    }\n    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n        this.throwIfDisposed();\n        const [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n    }\n    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n    }\n    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n    }\n    debugValidate() {\n        if (this.program != null) {\n            webgl_util.validateProgram(this.gl, this.program);\n        }\n        webgl_util.validateFramebuffer(this.gl);\n    }\n    executeProgram() {\n        this.throwIfDisposed();\n        this.throwIfNoProgram();\n        const gl = this.gl;\n        if (this.debug) {\n            this.debugValidate();\n        }\n        webgl_util.callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n    }\n    blockUntilAllProgramsCompleted() {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n    }\n    getQueryTimerExtension() {\n        if (this.disjointQueryTimerExtension == null) {\n            this.disjointQueryTimerExtension =\n                webgl_util.getExtensionOrThrow(this.gl, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                    'EXT_disjoint_timer_query_webgl2' :\n                    'EXT_disjoint_timer_query');\n        }\n        return this.disjointQueryTimerExtension;\n    }\n    getQueryTimerExtensionWebGL2() {\n        return this.getQueryTimerExtension();\n    }\n    getQueryTimerExtensionWebGL1() {\n        return this.getQueryTimerExtension();\n    }\n    beginQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const query = gl2.createQuery();\n            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n            return query;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        const query = ext.createQueryEXT();\n        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n        return query;\n    }\n    endQuery() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            gl2.endQuery(ext.TIME_ELAPSED_EXT);\n            return;\n        }\n        const ext = this.getQueryTimerExtensionWebGL1();\n        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n    }\n    async waitForQueryAndGetTime(query) {\n        await util.repeatedTry(() => this.disposed || // while testing contexts are created / disposed\n            // in rapid succession, so without this check we\n            // may poll for the query timer indefinitely\n            this.isQueryAvailable(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n        return this.getQueryTime(query, env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    }\n    getQueryTime(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return null;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n            // Return milliseconds.\n            return timeElapsedNanos / 1000000;\n        }\n    }\n    isQueryAvailable(query, queryTimerVersion) {\n        if (queryTimerVersion === 0) {\n            return true;\n        }\n        if (queryTimerVersion === 2) {\n            const gl2 = this.gl;\n            const ext = this.getQueryTimerExtensionWebGL2();\n            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n        else {\n            const ext = this.getQueryTimerExtensionWebGL1();\n            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n            if (this.disjoint == null) {\n                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n            }\n            return available && !this.disjoint;\n        }\n    }\n    pollFence(fenceContext) {\n        return new Promise(resolve => {\n            this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n        });\n    }\n    pollItems() {\n        // Find the last query that has finished.\n        const index = linearSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n        for (let i = 0; i <= index; ++i) {\n            const { resolveFn } = this.itemsToPoll[i];\n            resolveFn();\n        }\n        this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n    }\n    addItemToPoll(isDoneFn, resolveFn) {\n        this.itemsToPoll.push({ isDoneFn, resolveFn });\n        if (this.itemsToPoll.length > 1) {\n            // We already have a running loop that polls.\n            return;\n        }\n        // Start a new loop that polls.\n        util.repeatedTry(() => {\n            this.pollItems();\n            // End the loop if no more items to poll.\n            return this.itemsToPoll.length === 0;\n        });\n    }\n    bindTextureToFrameBuffer(texture) {\n        this.throwIfDisposed();\n        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(this.gl);\n        }\n    }\n    unbindTextureToFrameBuffer() {\n        if (this.outputTexture != null) {\n            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n            if (this.debug) {\n                webgl_util.validateFramebuffer(this.gl);\n            }\n        }\n        else {\n            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n        }\n    }\n    downloadMatrixDriver(texture, downloadAndDecode) {\n        this.bindTextureToFrameBuffer(texture);\n        const result = downloadAndDecode();\n        this.unbindTextureToFrameBuffer();\n        return result;\n    }\n    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n        this.throwIfDisposed();\n        const gl = this.gl;\n        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n        if (this.debug) {\n            webgl_util.validateFramebuffer(gl);\n        }\n        this.outputTexture = outputMatrixTextureMaybePacked;\n        webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n        webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n    }\n    setOutputMatrixWriteRegionDriver(x, y, width, height) {\n        this.throwIfDisposed();\n        webgl_util.callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n    }\n    throwIfDisposed() {\n        if (this.disposed) {\n            throw new Error('Attempted to use disposed GPGPUContext.');\n        }\n    }\n    throwIfNoProgram() {\n        if (this.program == null) {\n            throw new Error('No GPU program is currently set.');\n        }\n    }\n}\n/**\n * Finds the index of the last true element using linear search.\n * Note: We can't do binary search because Chrome expects us to explicitly\n * test all fences before download:\n * https://github.com/tensorflow/tfjs/issues/1145\n */\nexport function linearSearchLastTrue(arr) {\n    let i = 0;\n    for (; i < arr.length; ++i) {\n        const isDone = arr[i]();\n        if (!isDone) {\n            break;\n        }\n    }\n    return i - 1;\n}\n//# sourceMappingURL=gpgpu_context.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, util } from '@tensorflow/tfjs-core';\nimport * as shader_compiler from './shader_compiler';\nexport function compileProgram(gpgpu, program, inputs, output) {\n    const userCode = program.userCode;\n    const inputInfos = inputs.map((input, i) => {\n        const shapeInfo = {\n            logicalShape: input.shape,\n            texShape: input.isUniform ? null : input.texData.texShape,\n            isUniform: input.isUniform,\n            isPacked: input.isUniform ? false : input.texData.isPacked,\n            flatOffset: null\n        };\n        if (input.texData != null && input.texData.slice != null &&\n            input.texData.slice.flatOffset > 0) {\n            shapeInfo.flatOffset = input.texData.slice.flatOffset;\n        }\n        return { name: program.variableNames[i], shapeInfo };\n    });\n    const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n    const outShapeInfo = {\n        logicalShape: output.shape,\n        texShape: output.texData.texShape,\n        isUniform: false,\n        isPacked: output.texData.isPacked,\n        flatOffset: null\n    };\n    const source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.packedInputs);\n    const webGLProgram = gpgpu.createProgram(source);\n    // Add special uniforms (NAN, INFINITY)\n    let infLoc = null;\n    const nanLoc = gpgpu.getUniformLocation(webGLProgram, 'NAN', false);\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        infLoc = gpgpu.getUniformLocation(webGLProgram, 'INFINITY', false);\n    }\n    // Add user-defined uniforms\n    const uniformLocations = {};\n    for (let i = 0; i < program.variableNames.length; i++) {\n        const varName = program.variableNames[i];\n        const shouldThrow = false;\n        uniformLocations[varName] =\n            gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n        uniformLocations[`offset${varName}`] =\n            gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    }\n    return {\n        program,\n        source,\n        webGLProgram,\n        uniformLocations,\n        inShapeInfos,\n        outShapeInfo,\n        infLoc,\n        nanLoc,\n    };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n    if (shapeInfos.length !== inputs.length) {\n        throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` +\n            `was executed with ${inputs.length} inputs`);\n    }\n    shapeInfos.forEach((s, i) => {\n        const shapeA = s.logicalShape;\n        const input = inputs[i];\n        const shapeB = input.shape;\n        if (!util.arraysEqual(shapeA, shapeB)) {\n            throw Error(`Binary was compiled with different shapes than ` +\n                `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n        }\n        // The input is uploaded as uniform.\n        if (s.isUniform && input.isUniform) {\n            return;\n        }\n        const texShapeA = s.texShape;\n        const texShapeB = input.isUniform ? null : input.texData.texShape;\n        if (!util.arraysEqual(texShapeA, texShapeB)) {\n            throw Error(`Binary was compiled with different texture shapes than the` +\n                ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n        }\n    });\n}\nexport function runProgram(gpgpu, binary, inputs, output, customSetup) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n    const outTex = output.texData.texture;\n    const outTexShape = output.texData.texShape;\n    if (output.texData.isPacked) {\n        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    else {\n        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n    }\n    gpgpu.setProgram(binary.webGLProgram);\n    // Set special uniforms (NAN, INFINITY)\n    if (env().getNumber('WEBGL_VERSION') === 1) {\n        if (binary.infLoc !== null) {\n            gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n        }\n    }\n    if (binary.nanLoc !== null) {\n        gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n    }\n    // Set user-defined inputs\n    inputs.forEach((input, i) => {\n        const varName = binary.program.variableNames[i];\n        const varLoc = binary.uniformLocations[varName];\n        const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n        if (varLoc == null) {\n            // The compiler inferred that this variable is not used in this shader.\n            return;\n        }\n        if (input.isUniform) {\n            // Upload the values of the tensor as uniform.\n            if (util.sizeFromShape(input.shape) < 2) {\n                gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);\n            }\n            else {\n                let vals = input.uniformValues;\n                if (!(vals instanceof Float32Array)) {\n                    vals = new Float32Array(vals);\n                }\n                gpgpu.gl.uniform1fv(varLoc, vals);\n            }\n            return;\n        }\n        // If the input was sliced, upload the flat offset index.\n        if (input.texData.slice != null && varOffsetLoc != null) {\n            gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);\n        }\n        gpgpu.setInputMatrixTexture(input.texData.texture, varLoc, i);\n    });\n    if (customSetup != null) {\n        customSetup(gpgpu, binary.webGLProgram);\n    }\n    gpgpu.executeProgram();\n}\nexport function makeShaderKey(program, inputs, output) {\n    let keyInputs = '';\n    inputs.concat(output).forEach(x => {\n        const hasOffset = x.texData != null && x.texData.slice != null &&\n            x.texData.slice.flatOffset > 0;\n        const texShape = x.isUniform ? 'uniform' : x.texData.texShape;\n        keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    });\n    const keyUserCode = program.userCode;\n    let key = program.constructor.name;\n    // Fast string concat. See https://jsperf.com/string-concatenation/14.\n    key += '_' + keyInputs + '_' + keyUserCode;\n    return key;\n}\n//# sourceMappingURL=gpgpu_math.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from './glsl_version';\nexport class Im2ColPackedProgram {\n    constructor(outputShape, inputShape, convInfo) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const { filterWidth, inChannels, strideWidth, strideHeight, padInfo, outWidth, dilationWidth, dilationHeight, dataFormat } = convInfo;\n        const { left, top } = padInfo;\n        const itemsPerBlockRow = inChannels * filterWidth;\n        const glsl = getGlslDifferences();\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const rowDim = isChannelsLast ? 0 : 1;\n        const colDim = isChannelsLast ? 1 : 2;\n        let unrolled = ``;\n        for (let row = 0; row <= 1; row++) {\n            for (let col = 0; col <= 1; col++) {\n                unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {\n            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};\n            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {\n\n              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);\n              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));\n\n              if(d1 < ${inputShape[colDim]} && d1 >= 0) {\n\n                ch = int(mod(float(pos), ${inChannels}.));\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n            }\n        }\n        this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=im2col_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNGradProgram {\n    constructor(inputShape, depthRadius, bias, alpha, beta) {\n        this.variableNames = ['inputImage', 'outputImage', 'dy'];\n        this.outputShape = [];\n        this.outputShape = inputShape;\n        this.depth = inputShape[3];\n        this.depthRadius = depthRadius;\n        this.bias = bias;\n        this.alpha = alpha;\n        this.beta = beta;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_grad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class LRNPackedProgram {\n    constructor(xShape, radius, bias, alpha, beta) {\n        this.variableNames = ['x'];\n        this.outputShape = [];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rad = radius;\n        const maxD = xShape[3] - 1;\n        this.outputShape = xShape;\n        // optimize pow(bias + alpha * sum, -beta)\n        // src: https://github.com/tensorflow/tensorflow/..\n        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n        // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n        let powOperator;\n        const basis = `float(${bias}) + float(${alpha}) * sum`;\n        if (beta === 0.5) {\n            powOperator = `inversesqrt(${basis})`;\n        }\n        else if (beta === 1.0) {\n            powOperator = `1.0/(${basis})`;\n        }\n        else {\n            powOperator = `exp(log(${basis}) * float(-${beta}));`;\n        }\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=lrn_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MaxPool2DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\nexport class MaxPool3DBackpropProgram {\n    constructor(convInfo) {\n        this.variableNames = ['dy', 'maxPos'];\n        this.outputShape = convInfo.inShape;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n        this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=max_pool_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MatMulPackedProgram {\n    constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false) {\n        this.variableNames = ['matrixA', 'matrixB'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        const sharedDim = transposeA ? aShape[1] : aShape[2];\n        const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n        const aSample = transposeA ? 'i * 2, rc.y' : 'rc.y, i * 2';\n        const bSample = transposeB ? 'rc.z, i * 2' : 'i * 2, rc.z';\n        const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n        const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n        let activationSnippet = '', applyActivationSnippet = '';\n        if (activation) {\n            if (hasPreluActivation) {\n                activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation}\n        }`;\n            }\n            else {\n                activationSnippet = `vec4 activation(vec4 x) {\n          ${activation}\n        }`;\n            }\n            applyActivationSnippet = `result = activation(result);`;\n        }\n        const addBiasSnippet = addBias ? 'result += getBiasAtOutCoords();' : '';\n        if (addBias) {\n            this.variableNames.push('bias');\n        }\n        if (hasPreluActivation) {\n            this.variableNames.push('preluActivationWeights');\n        }\n        let batchASnippet = 'rc.x';\n        let batchBSnippet = 'rc.x';\n        if (aShape[0] < bShape[0]) {\n            batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;\n        }\n        else if (bShape[0] < aShape[0]) {\n            batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;\n        }\n        this.userCode = `\n      ${activationSnippet}\n\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          int batchA = ${batchASnippet};\n          int batchB = ${batchBSnippet};\n          vec4 a = getMatrixA(batchA, ${aSample});\n          vec4 b = getMatrixB(batchB, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mulmat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class MultinomialProgram {\n    constructor(batchSize, numOutcomes, numSamples) {\n        this.variableNames = ['probs'];\n        this.outputShape = [batchSize, numSamples];\n        this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n    }\n    getCustomSetupFunc(seed) {\n        return (gpgpu, webGLProgram) => {\n            if (this.seedLoc == null) {\n                this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n            }\n            gpgpu.gl.uniform1f(this.seedLoc, seed);\n        };\n    }\n}\n//# sourceMappingURL=multinomial_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class OneHotProgram {\n    constructor(numIndices, depth, onValue, offValue) {\n        this.variableNames = ['indices'];\n        this.outputShape = [numIndices, depth];\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=onehot_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        // Only input / output 3D tensors.\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        if (rank === 0) {\n            this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n        }\n        else {\n            const channels = getChannels('rc', rank);\n            const dtype = getCoordsDataType(rank);\n            const outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);\n            const setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);\n            const output = getOutput(outputShape, channels);\n            this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n        }\n    }\n}\nfunction getSourceCoordsArr(rank, dims) {\n    const coords = [];\n    for (let row = 0; row <= 1; row++) {\n        for (let col = 0; col <= 1; col++) {\n            let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n            for (let d = 2; d < rank; d++) {\n                coord = `${dims[dims.length - 1 - d]},` + coord;\n            }\n            coords.push(coord);\n        }\n    }\n    return coords;\n}\nfunction getOutOfBoundsCondition(rank, shape, dims) {\n    if (rank === 1) {\n        return `rc > ${shape[0]}`;\n    }\n    let cond = '';\n    for (let i = rank - 2; i < rank; i++) {\n        cond += `${dims[i]} >= ${shape[i]}`;\n        if (i < rank - 1) {\n            cond += '||';\n        }\n    }\n    return cond;\n}\nfunction getSetup(rank, cols, rows, dims) {\n    if (rank === 1) {\n        return '';\n    }\n    const innerDims = dims.slice(-2);\n    return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\nfunction getOutput(shape, dims) {\n    const rank = shape.length;\n    const sourceCoords = getSourceCoordsArr(rank, dims);\n    if (rank === 1) {\n        return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}\n//# sourceMappingURL=pack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const type = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class PadPackedProgram {\n    constructor(xShape, paddings, constantValue) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const componentSetup = [\n            `${dtype} rc = outputLoc;`, `${coords[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n            rank === 1 ? '' : `}\n       rc = outputLoc;\n       ${coords[rank - 2]} += 1;\n       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n            rank === 1 ? '' : `  ${coords[rank - 1]} += 1;\n         if(${cLimit}) {`\n        ];\n        const paddingArea = rank === 1 ?\n            'rc < start || rc >= end' :\n            'any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))';\n        let mainLoop = '';\n        for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n            mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(${constantValue});\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        mainLoop += (rank === 1 ? `} ` : `}}`);\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class Pool2DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? (includeBatchInIndex ? batchFlattenPositionStr :\n                flattenPositionStr) :\n                `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\nexport class Pool3DProgram {\n    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n        this.variableNames = ['x'];\n        if (poolType === 'avg' && computePositions) {\n            throw new Error('Cannot compute positions for average pool.');\n        }\n        const filterWidth = convInfo.filterWidth;\n        const strideDepth = convInfo.strideDepth;\n        const strideHeight = convInfo.strideHeight;\n        const strideWidth = convInfo.strideWidth;\n        const dilationDepth = convInfo.dilationDepth;\n        const dilationHeight = convInfo.dilationHeight;\n        const dilationWidth = convInfo.dilationWidth;\n        const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n        const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n        const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n        const padFront = convInfo.padInfo.front;\n        const padTop = convInfo.padInfo.top;\n        const padLeft = convInfo.padInfo.left;\n        this.outputShape = convInfo.outShape;\n        const isAvgPool = poolType === 'avg';\n        let initializationValue = '0.0';\n        if (!isAvgPool) {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n        }\n        if (computePositions) {\n            const compareOp = '>=';\n            this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ?\n                (includeBatchInIndex ?\n                    `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` :\n                    `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`) :\n                `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n            return;\n        }\n        const compareOp = 'max';\n        let returnValue = `${poolType}(${poolType}(${poolType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (poolType === 'avg') {\n            returnValue = `avgValue / count`;\n        }\n        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n        const filterWidthVec4Remainder = filterWidth % 4;\n        const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=pool_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ReduceProgram {\n    constructor(reduceInfo, reduceType) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        let initializationValue = '0.0';\n        let compareOp = ``;\n        if (reduceType === 'prod') {\n            initializationValue = '1.0';\n        }\n        else if (reduceType === 'min') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '1.0 / 1e-20';\n            compareOp = `min`;\n        }\n        else if (reduceType === 'max') {\n            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.\n            initializationValue = '-1.0 / 1e-20';\n            compareOp = `max`;\n        }\n        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n        if (reduceType === 'sum') {\n            returnValue = `sumValue`;\n        }\n        else if (reduceType === 'prod') {\n            returnValue = `prodValue`;\n        }\n        else if (reduceType === 'all') {\n            returnValue = `allValue`;\n        }\n        else if (reduceType === 'any') {\n            returnValue = `anyValue`;\n        }\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n        let vecType = `vec4`;\n        if (reduceType === 'all') {\n            initializationValue = '1.0';\n            updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        else if (reduceType === 'any') {\n            initializationValue = '0.0';\n            updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n            vecType = `bvec4`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reduce_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as shader_util from './shader_compiler_util';\nexport class ReshapePackedProgram {\n    constructor(outputShape, inputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = outputShape;\n        let mainLoop = ``;\n        for (let i = 0; i < 4; i++) {\n            let thisRC = `thisRC = rc;`;\n            if (i % 2 === 1) {\n                thisRC += `thisRC.z += 1;`;\n            }\n            if (i > 1) {\n                thisRC += `thisRC.y += 1;`;\n            }\n            mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n        }\n        this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${shader_util.getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n    }\n}\nfunction getReshapedInputCoords(shape) {\n    const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n    return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n//# sourceMappingURL=reshape_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeBilinearPackedProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_bilinear_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeigborBackpropProgram {\n    constructor(dy, x, alignCorners) {\n        this.variableNames = ['dy'];\n        this.outputShape = [];\n        this.outputShape = x.shape;\n        const [, xHeight, xWidth,] = x.shape;\n        const [, yHeight, yWidth] = dy.shape;\n        // In the backwards pass, we want to find the pixels that were generated for\n        // each pixel in the input image the forward pass and add the corresponding\n        // coefficient from dy to the gradient (with some interpolation).\n        const effectiveXSize = [\n            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n        ];\n        const effectiveYSize = [\n            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n        ];\n        const heightScale = effectiveXSize[0] / effectiveYSize[0];\n        const widthScale = effectiveXSize[1] / effectiveYSize[1];\n        const invHeightScale = 1 / heightScale;\n        const invWidthScale = 1 / widthScale;\n        // This defines the size of the window of values around a particular\n        // index in dy that we want to search for contributions to dx.\n        const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n        const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n        this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class ResizeNearestNeighborProgram {\n    constructor(inputShape, newHeight, newWidth, alignCorners) {\n        this.variableNames = ['A'];\n        this.outputShape = [];\n        const [batch, oldHeight, oldWidth, depth] = inputShape;\n        this.outputShape = [batch, newHeight, newWidth, depth];\n        const effectiveInSize = [\n            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n        ];\n        const effectiveOutSize = [\n            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n        ];\n        // When align corners is false, we rounds the value with floor.\n        const roundBase = alignCorners ? '0.5' : '0.0';\n        this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReverseProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        if (rank === 1) {\n            this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n            return;\n        }\n        const getInCoord = (i) => {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - coords[${i}] - 1`;\n            }\n            return `coords[${i}]`;\n        };\n        const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n        const type = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=reverse_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ReversePackedProgram {\n    constructor(xShape, axis) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const rank = xShape.length;\n        if (rank > 4) {\n            throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n        }\n        this.outputShape = xShape;\n        const channels = getChannels('rc', rank);\n        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n        const type = getCoordsDataType(rank);\n        if (rank === 1) {\n            this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n        }\n        else {\n            this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n        }\n        function getR(channels) {\n            return getChannel(channels);\n        }\n        function getG(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getB(channels) {\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getA(channels) {\n            channels[rank - 1] = '(' + channels[rank - 1] + ` + 1)`;\n            channels[rank - 2] = '(' + channels[rank - 2] + ` + 1)`;\n            return getChannel(channels);\n        }\n        function getChannel(channels) {\n            const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels));\n            const inCoords = inCoordsArray.join(',');\n            const innerDims = inCoordsArray.slice(-2).join(',');\n            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n        }\n        function getInCoord(i, channels1) {\n            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n                return `${xShape[i]} - ${channels1[i]} - 1`;\n            }\n            else {\n                return `${channels1[i]}`;\n            }\n        }\n    }\n}\n//# sourceMappingURL=reverse_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class ScatterProgram {\n    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n        this.variableNames = ['updates', 'indices', 'defaultValue'];\n        this.outputShape = shape;\n        const stridesType = getCoordsDataType(strides.length);\n        const dtype = getCoordsDataType(shape.length);\n        let indicesString = '';\n        if (indicesRank === 1) {\n            indicesString = 'i';\n        }\n        else if (indicesRank === 2) {\n            indicesString = 'i, j';\n        }\n        const indicesSnippet = `getIndices(${indicesString})`;\n        let updatesString = '';\n        if (updatesRank === 1) {\n            updatesString = 'i';\n        }\n        else if (updatesRank === 2) {\n            updatesString = 'i, coords[1]';\n        }\n        const updatesSnippet = `getUpdates(${updatesString})`;\n        const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n        this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n    }\n}\n//# sourceMappingURL=scatter_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class SegmentOpProgram {\n    constructor(segOpInfo, segOpType) {\n        this.variableNames = ['x', 'segmentIds'];\n        const windowSize = segOpInfo.windowSize;\n        const batchSize = segOpInfo.batchSize;\n        const inSize = segOpInfo.inSize;\n        const numSegments = segOpInfo.numSegments;\n        const outSize = numSegments * Math.ceil(inSize / windowSize);\n        this.outputShape = [batchSize, outSize];\n        const initializationValue = '0.0';\n        const returnValue = `sumValue`;\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n        let checkValueOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n        }\n        let checkSegmentIdOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n    }\n}\n//# sourceMappingURL=segment_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SelectProgram {\n    constructor(cRank, shape, rank) {\n        this.variableNames = ['c', 'a', 'b'];\n        this.outputShape = shape;\n        let cCoords;\n        let abCoords;\n        if (rank > 4) {\n            throw Error(`Where for rank ${rank} is not yet supported`);\n        }\n        if (rank === 1) {\n            abCoords = `resRC`;\n            cCoords = `resRC`;\n        }\n        else {\n            const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n            const cCoordVars = [];\n            const abCoordVars = [];\n            for (let i = 0; i < shape.length; i++) {\n                abCoordVars.push(`${currentCoords[i]}`);\n                if (i < cRank) {\n                    cCoordVars.push(`${currentCoords[i]}`);\n                }\n            }\n            cCoords = cCoordVars.join();\n            abCoords = abCoordVars.join();\n        }\n        const dtype = getCoordsDataType(rank);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n    }\n}\n//# sourceMappingURL=select_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class SliceProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const uniformPart = `uniform int start[${this.rank}];`;\n        const sourceCoords = getCoords(this.rank);\n        let body;\n        const coordSum = destSize.map((_, i) => {\n            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n        });\n        body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join('\\n')}\n      `;\n        this.userCode = `\n      ${uniformPart}\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\nconst coords = ['x', 'y', 'z', 'w', 'u', 'v'];\nfunction getCoords(rank) {\n    if (rank === 1) {\n        return 'sourceLoc';\n    }\n    else if (rank <= 6) {\n        return coords.slice(0, rank).map(x => 'sourceLoc.' + x).join(',');\n    }\n    else {\n        throw Error(`Slicing for rank ${rank} is not yet supported`);\n    }\n}\n//# sourceMappingURL=slice_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class SlicePackedProgram {\n    constructor(destSize) {\n        this.variableNames = ['source'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = destSize;\n        this.rank = destSize.length;\n        const dtype = getCoordsDataType(this.rank);\n        const coords = getChannels('coords', this.rank);\n        const sourceLoc = getChannels('sourceLoc', this.rank);\n        const innerDims = this.rank === 1 ? 'sourceLoc' : `vec2(${sourceLoc.slice(-2).join()})`;\n        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n        const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n        const lowerRow = this.rank === 1 ? '' : `\n      --${coords[this.rank - 1]};\n      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n        const sourceLocSetup = this.rank <= 4 ?\n            `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` :\n            destSize.map((_, i) => `${sourceLoc[i]} = ${coords[i]} + start[${i}];`)\n                .join('\\n');\n        this.userCode = `\n      uniform int start[${this.rank}];\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n    }\n    getCustomSetupFunc(start) {\n        if (start.length !== this.rank) {\n            throw Error(`The rank (${this.rank}) of the program must match the ` +\n                `length of start (${start.length})`);\n        }\n        return (gpgpu, webGLProgram) => {\n            if (this.startLoc == null) {\n                this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n                if (this.startLoc == null) {\n                    // This means the compiler has optimized and realized it doesn't need\n                    // the uniform.\n                    return;\n                }\n            }\n            gpgpu.gl.uniform1iv(this.startLoc, start);\n        };\n    }\n}\n//# sourceMappingURL=slice_packed_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class StridedSliceProgram {\n    constructor(begin, strides, size) {\n        this.variableNames = ['x'];\n        this.outputShape = size;\n        const rank = size.length;\n        const inputDtype = getCoordsDataType(size.length);\n        const dtype = getCoordsDataType(size.length);\n        let newCoords = '';\n        if (rank === 1) {\n            newCoords = 'coords * strides + begin';\n        }\n        else {\n            let outputAxis = 0;\n            newCoords =\n                size.map((_, i) => {\n                    outputAxis++;\n                    return size.length === 1 ?\n                        `coords * strides[${i}] + begin[${i}]` :\n                        `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                })\n                    .join(',');\n        }\n        this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=strided_slice_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { getInternalFormatForFloat16MatrixTexture, getInternalFormatForFloat16PackedMatrixTexture, getInternalFormatForFloat32MatrixTexture, getInternalFormatForPackedMatrixTexture, getInternalFormatForUnsignedBytesMatrixTexture } from './gpgpu_util';\nimport { getPackedMatrixTextureShapeWidthHeight, getUnpackedMatrixTextureShapeWidthHeight, PhysicalTextureType, TextureUsage } from './tex_util';\nexport class TextureManager {\n    constructor(gpgpu) {\n        this.gpgpu = gpgpu;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0; // How many bytes that have been allocated\n        // are available for reuse.\n        this.freeTextures = {};\n        this.logEnabled = false;\n        this.usedTextures = {};\n    }\n    acquireTexture(shapeRC, usage, isPacked) {\n        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        if (!(shapeKey in this.usedTextures)) {\n            this.usedTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        if (this.freeTextures[shapeKey].length > 0) {\n            this.numFreeTextures--;\n            this.numUsedTextures++;\n            this._numBytesFree -= texBytes;\n            this.log();\n            const newTexture = this.freeTextures[shapeKey].shift();\n            this.usedTextures[shapeKey].push(newTexture);\n            return newTexture;\n        }\n        let newTexture;\n        if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n            newTexture =\n                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n            newTexture =\n                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n            newTexture =\n                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n        }\n        this.usedTextures[shapeKey].push(newTexture);\n        this.numUsedTextures++;\n        this._numBytesAllocated += texBytes;\n        this.log();\n        return newTexture;\n    }\n    releaseTexture(texture, shape, logicalTexType, isPacked) {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n        if (!(shapeKey in this.freeTextures)) {\n            this.freeTextures[shapeKey] = [];\n        }\n        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n        const deleteTexThreshold = env().get('WEBGL_DELETE_TEXTURE_THRESHOLD');\n        if (deleteTexThreshold !== -1 &&\n            this._numBytesAllocated > deleteTexThreshold) {\n            this.gpgpu.deleteMatrixTexture(texture);\n            this._numBytesAllocated -= texBytes;\n        }\n        else {\n            this.freeTextures[shapeKey].push(texture);\n            this.numFreeTextures++;\n            this._numBytesFree += texBytes;\n        }\n        this.numUsedTextures--;\n        const texList = this.usedTextures[shapeKey];\n        const texIndex = texList.indexOf(texture);\n        if (texIndex < 0) {\n            throw new Error('Cannot release a texture that was never provided by this ' +\n                'texture manager');\n        }\n        texList.splice(texIndex, 1);\n        this.log();\n    }\n    log() {\n        if (!this.logEnabled) {\n            return;\n        }\n        const total = this.numFreeTextures + this.numUsedTextures;\n        console.log('Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n        const freeRatio = this._numBytesFree / this._numBytesAllocated;\n        console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n    }\n    get numBytesAllocated() {\n        return this._numBytesAllocated;\n    }\n    get numBytesFree() {\n        return this._numBytesFree;\n    }\n    getNumUsedTextures() {\n        return this.numUsedTextures;\n    }\n    getNumFreeTextures() {\n        return this.numFreeTextures;\n    }\n    dispose() {\n        if (this.freeTextures == null) {\n            // Already disposed.\n            return;\n        }\n        for (const texShape in this.freeTextures) {\n            this.freeTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        for (const texShape in this.usedTextures) {\n            this.usedTextures[texShape].forEach(tex => {\n                this.gpgpu.deleteMatrixTexture(tex);\n            });\n        }\n        this.freeTextures = null;\n        this.usedTextures = null;\n        this.numUsedTextures = 0;\n        this.numFreeTextures = 0;\n        this._numBytesAllocated = 0;\n        this._numBytesFree = 0;\n    }\n}\nfunction numBytesForInternalFormat(gl, internalFormat) {\n    // tslint:disable-next-line:no-any\n    const glany = gl;\n    if (internalFormat === glany.R32F) {\n        return 4;\n    }\n    else if (internalFormat === glany.R16F) {\n        return 2;\n    }\n    else if (internalFormat === glany.RGBA32F) {\n        return 16;\n    }\n    else if (internalFormat === gl.RGBA) {\n        return 16;\n    }\n    else if (internalFormat === glany.RGBA16F) {\n        return 8;\n    }\n    throw new Error(`Unknown internal format ${internalFormat}`);\n}\nexport function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n    // It is not possible to infer packed status from the texture type because\n    // depending on the textureConfig, different  texture types may resolve to the\n    // same internal format (e.g. in WebGL1, the internal format for\n    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`\n    // explicitly.\n    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n    let numElements;\n    if (isPacked) {\n        const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = packedWidth * packedHeight;\n    }\n    else {\n        const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n        numElements = width * height;\n    }\n    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);\n    return numElements * bytesPerElement;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n    switch (physicalTexType) {\n        case PhysicalTextureType.PACKED_2X2_FLOAT32:\n            return getInternalFormatForPackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_2X2_FLOAT16:\n            return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT32:\n            return getInternalFormatForFloat32MatrixTexture(textureConfig);\n        case PhysicalTextureType.UNPACKED_FLOAT16:\n            return getInternalFormatForFloat16MatrixTexture(textureConfig);\n        case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n            return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n        default:\n            throw new Error(`Unknown physical texture type ${physicalTexType}`);\n    }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n    if (env().getBool('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        if (isPacked) {\n            return PhysicalTextureType.PACKED_2X2_FLOAT32;\n        }\n        return PhysicalTextureType.UNPACKED_FLOAT32;\n    }\n    if (isPacked) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n    if (logicalTexType === TextureUsage.UPLOAD) {\n        return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n        return getPhysicalTextureForRendering(isPacked);\n    }\n    else if (logicalTexType === TextureUsage.DOWNLOAD ||\n        logicalTexType === TextureUsage.PIXELS) {\n        return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n    }\n    throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n//# sourceMappingURL=texture_manager.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TileProgram {\n    constructor(aShape, reps) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[i] * reps[i];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const sourceCoords = getSourceCoords(aShape);\n        this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n    }\n}\nfunction getSourceCoords(aShape) {\n    const rank = aShape.length;\n    if (rank > 5) {\n        throw Error(`Tile for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n        return `imod(resRC, ${aShape[0]})`;\n    }\n    const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n    const sourceCoords = [];\n    for (let i = 0; i < aShape.length; i++) {\n        sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n    }\n    return sourceCoords.join();\n}\n//# sourceMappingURL=tile_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class UnaryOpProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.outputShape = aShape;\n        this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\nconst CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nexport const LINEAR = `return x;`;\nexport const ABS = `return abs(x);`;\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nexport const RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util.SELU_SCALEALPHA};\n  float scale = ${backend_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nexport function STEP(alpha = 0.0) {\n    return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\nexport const NEG = `return -x;`;\nexport const CEIL = `return ceil(x);`;\nexport const FLOOR = `return floor(x);`;\nexport const SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nexport const IS_NAN = `return float(isnan(x));`;\nexport const IS_INF = `return float(isinf(x));`;\nexport const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nexport const EXP = `return exp(x);`;\nexport const EXPM1 = `return exp(x) - 1.0;`;\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\nexport const LOG1P = `return log(1.0 + x);`;\nexport const SQRT = `return sqrt(x);`;\nexport const RSQRT = `return inversesqrt(x);`;\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nexport const ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nexport const ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nexport const ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util.ERF_P};\n  float a1 = ${backend_util.ERF_A1};\n  float a2 = ${backend_util.ERF_A2};\n  float a3 = ${backend_util.ERF_A3};\n  float a4 = ${backend_util.ERF_A4};\n  float a5 = ${backend_util.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nexport const RECIPROCAL = `return 1.0 / x;`;\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\nexport const CLONE = 'return x;';\n//# sourceMappingURL=unaryop_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const LINEAR = `return x;`;\nexport const LOG = `\n  vec4 result = log(x);\n  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n  result.r = isNaN.r == 1.0 ? NAN : result.r;\n  result.g = isNaN.g == 1.0 ? NAN : result.g;\n  result.b = isNaN.b == 1.0 ? NAN : result.b;\n  result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n  return result;\n`;\nexport const RELU = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const RELU6 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nexport const ELU = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nexport class UnaryOpPackedProgram {\n    constructor(aShape, opSnippet) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = aShape;\n        this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unaryop_packed_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels, getSourceCoords } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class UnpackProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = false;\n        this.outputShape = outputShape;\n        const rank = outputShape.length;\n        const channels = getChannels('rc', rank);\n        const dtype = getCoordsDataType(rank);\n        const sourceCoords = getSourceCoords(rank, channels);\n        const innerDims = channels.slice(-2);\n        const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n        this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=unpack_gpu.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Import webgl flags.\nimport './flags_webgl';\nimport * as tf from '@tensorflow/tfjs-core';\nimport { div, engine, env, max, range, reshape, scalar, softmax, tensor, tidy, transpose } from '@tensorflow/tfjs-core';\nimport { backend_util, buffer, kernel_impls, slice_util, util } from '@tensorflow/tfjs-core';\nimport { DataStorage, KernelBackend, upcastType } from '@tensorflow/tfjs-core';\nimport { ceilImplCPU, expImplCPU, expm1ImplCPU, floorImplCPU, logImplCPU, rsqrtImplCPU, simpleAbsImplCPU, sliceImplCPU } from './kernel_utils/shared';\nconst { segment_util } = backend_util;\nconst split = kernel_impls.split;\nconst tile = kernel_impls.tile;\nconst topkImpl = kernel_impls.topkImpl;\nconst whereImpl = kernel_impls.whereImpl;\nimport { AddNProgram } from './addn_gpu';\nimport { AddNPackedProgram } from './addn_packed_gpu';\nimport { ArgMinMaxProgram } from './argminmax_gpu';\nimport { ArgMinMaxPackedProgram } from './argminmax_packed_gpu';\nimport { AvgPool3DBackpropProgram } from './avg_pool_backprop_gpu';\nimport * as binaryop_gpu from './binaryop_gpu';\nimport { BinaryOpProgram } from './binaryop_gpu';\nimport * as binaryop_packed_gpu from './binaryop_packed_gpu';\nimport { BinaryOpPackedProgram } from './binaryop_packed_gpu';\nimport { getWebGLContext } from './canvas_util';\nimport { ClipProgram } from './clip_gpu';\nimport { ClipPackedProgram } from './clip_packed_gpu';\nimport { ComplexAbsProgram } from './complex_abs_gpu';\nimport { Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram } from './conv_backprop_gpu';\nimport { DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram } from './conv_backprop_gpu_depthwise';\nimport { Conv2DProgram, Conv3DProgram } from './conv_gpu';\nimport { DepthwiseConv2DProgram } from './conv_gpu_depthwise';\nimport { DepthwiseConvPacked2DProgram } from './conv_packed_gpu_depthwise';\nimport { CropAndResizeProgram } from './crop_and_resize_gpu';\nimport { CumSumProgram } from './cumsum_gpu';\nimport { DecodeMatrixProgram } from './decode_matrix_gpu';\nimport { DecodeMatrixPackedProgram } from './decode_matrix_packed_gpu';\nimport { DepthToSpaceProgram } from './depth_to_space_gpu';\nimport { DiagProgram } from './diag_gpu';\nimport { EncodeFloatProgram } from './encode_float_gpu';\nimport { EncodeFloatPackedProgram } from './encode_float_packed_gpu';\nimport { EncodeMatrixProgram } from './encode_matrix_gpu';\nimport { EncodeMatrixPackedProgram } from './encode_matrix_packed_gpu';\nimport { FillProgram } from './fill_gpu';\nimport { GatherProgram } from './gather_gpu';\nimport { GatherNDProgram } from './gather_nd_gpu';\nimport { GPGPUContext } from './gpgpu_context';\nimport * as gpgpu_math from './gpgpu_math';\nimport { Im2ColPackedProgram } from './im2col_packed_gpu';\nimport { LRNProgram } from './lrn_gpu';\nimport { LRNGradProgram } from './lrn_grad_gpu';\nimport { LRNPackedProgram } from './lrn_packed_gpu';\nimport { MaxPool3DBackpropProgram } from './max_pool_backprop_gpu';\nimport { MatMulPackedProgram } from './mulmat_packed_gpu';\nimport { MultinomialProgram } from './multinomial_gpu';\nimport { OneHotProgram } from './onehot_gpu';\nimport { PackProgram } from './pack_gpu';\nimport { PadProgram } from './pad_gpu';\nimport { PadPackedProgram } from './pad_packed_gpu';\nimport { Pool3DProgram } from './pool_gpu';\nimport { ReduceProgram } from './reduce_gpu';\nimport { ReshapePackedProgram } from './reshape_packed_gpu';\nimport { ResizeBilinearBackpropProgram } from './resize_bilinear_backprop_gpu';\nimport { ResizeBilinearProgram } from './resize_bilinear_gpu';\nimport { ResizeBilinearPackedProgram } from './resize_bilinear_packed_gpu';\nimport { ResizeNearestNeigborBackpropProgram } from './resize_nearest_neighbor_backprop_gpu';\nimport { ResizeNearestNeighborProgram } from './resize_nearest_neighbor_gpu';\nimport { ReverseProgram } from './reverse_gpu';\nimport { ReversePackedProgram } from './reverse_packed_gpu';\nimport { ScatterProgram } from './scatter_gpu';\nimport { SegmentOpProgram } from './segment_gpu';\nimport { SelectProgram } from './select_gpu';\nimport { SliceProgram } from './slice_gpu';\nimport { SlicePackedProgram } from './slice_packed_gpu';\nimport { StridedSliceProgram } from './strided_slice_gpu';\nimport * as tex_util from './tex_util';\nimport { TextureUsage } from './tex_util';\nimport { TextureManager } from './texture_manager';\nimport { TileProgram } from './tile_gpu';\nimport * as unary_op from './unaryop_gpu';\nimport { UnaryOpProgram } from './unaryop_gpu';\nimport * as unary_packed_op from './unaryop_packed_gpu';\nimport { UnaryOpPackedProgram } from './unaryop_packed_gpu';\nimport { UnpackProgram } from './unpack_gpu';\nimport * as webgl_util from './webgl_util';\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\nconst binaryCaches = {};\nexport function getBinaryCache(webGLVersion) {\n    if (webGLVersion in binaryCaches) {\n        return binaryCaches[webGLVersion];\n    }\n    binaryCaches[webGLVersion] = {};\n    return binaryCaches[webGLVersion];\n}\nfunction mapActivationToShaderProgram(activation, packed = false) {\n    if (activation === 'linear') {\n        if (packed) {\n            return unary_packed_op.LINEAR;\n        }\n        return unary_op.LINEAR;\n    }\n    else if (activation === 'relu') {\n        if (packed) {\n            return unary_packed_op.RELU;\n        }\n        return unary_op.RELU;\n    }\n    else if (activation === 'elu') {\n        if (packed) {\n            return unary_packed_op.ELU;\n        }\n        return unary_op.ELU;\n    }\n    else if (activation === 'relu6') {\n        if (packed) {\n            return unary_packed_op.RELU6;\n        }\n        return unary_op.RELU6;\n    }\n    else if (activation === 'prelu') {\n        if (packed) {\n            return binaryop_packed_gpu.PRELU;\n        }\n        return binaryop_gpu.PRELU;\n    }\n    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);\n}\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 128;\n// Empirically determined constant used to decide the number of MB on GPU\n// before we warn about high memory use. The MB are this constant * screen area\n// * dpi / 1024 / 1024.\nconst BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n    if (env().global.screen == null) {\n        return 1024; // 1 GB.\n    }\n    return (env().global.screen.height * env().global.screen.width *\n        window.devicePixelRatio) *\n        BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\nexport class MathBackendWebGL extends KernelBackend {\n    constructor(gpgpu) {\n        super();\n        // Maps data ids that have a pending read operation, to list of subscribers.\n        this.pendingRead = new WeakMap();\n        // List of data ids that are scheduled for disposal, but are waiting on a\n        // pending read operation.\n        this.pendingDisposal = new WeakSet();\n        // Used to count the number of 'shallow' sliced tensors that point to the\n        // same data id.\n        this.dataRefCount = new WeakMap();\n        this.numBytesInGPU = 0;\n        // Accumulated time spent (including blocking) in uploading data to webgl.\n        this.uploadWaitMs = 0;\n        // Accumulated time spent (including blocking in downloading data from webgl.\n        this.downloadWaitMs = 0;\n        this.warnedAboutMemory = false;\n        this.warnedAboutCPUBackend = false;\n        this.pendingDeletes = 0;\n        this.disposed = false;\n        if (!env().getBool('HAS_WEBGL')) {\n            throw new Error('WebGL is not supported on this device');\n        }\n        if (gpgpu == null) {\n            const gl = getWebGLContext(env().getNumber('WEBGL_VERSION'));\n            this.binaryCache = getBinaryCache(env().getNumber('WEBGL_VERSION'));\n            this.gpgpu = new GPGPUContext(gl);\n            this.canvas = gl.canvas;\n            this.gpgpuCreatedLocally = true;\n        }\n        else {\n            this.gpgpu = gpgpu;\n            this.binaryCache = {};\n            this.gpgpuCreatedLocally = false;\n            this.canvas = gpgpu.gl.canvas;\n        }\n        this.textureManager = new TextureManager(this.gpgpu);\n        this.numMBBeforeWarning = numMBBeforeWarning();\n        this.texData = new DataStorage(this, engine());\n    }\n    numDataIds() {\n        return this.texData.numDataIds() +\n            (this.cpuBackend ? this.cpuBackend.numDataIds() : 0) -\n            this.pendingDeletes;\n    }\n    write(values, shape, dtype) {\n        if (env().getBool('WEBGL_CHECK_NUMERICAL_PROBLEMS') ||\n            env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64' && values != null) {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        const dataId = {};\n        this.texData.set(dataId, {\n            shape,\n            dtype,\n            values,\n            usage: TextureUsage.UPLOAD,\n            refCount: 1,\n            complexParentRefCount: 0\n        });\n        return dataId;\n    }\n    /** Increase refCount of a `TextureData`. */\n    incRef(dataId) {\n        const texData = this.texData.get(dataId);\n        texData.refCount++;\n    }\n    /** Decrease refCount of a `TextureData`. */\n    decRef(dataId) {\n        if (this.texData.has(dataId)) {\n            const texData = this.texData.get(dataId);\n            texData.refCount--;\n        }\n    }\n    move(dataId, values, shape, dtype) {\n        if (env().getBool('DEBUG')) {\n            this.checkNumericalProblems(values);\n        }\n        if (dtype === 'complex64') {\n            throw new Error(`Cannot write to a complex64 dtype. ` +\n                `Please use tf.complex(real, imag).`);\n        }\n        this.texData.set(dataId, {\n            shape,\n            dtype,\n            values,\n            usage: TextureUsage.UPLOAD,\n            refCount: 1,\n            complexParentRefCount: 0\n        });\n    }\n    disposeIntermediateTensorInfo(tensorInfo) {\n        const dataId = tensorInfo.dataId;\n        if (this.texData.has(dataId)) {\n            const textureData = this.texData.get(dataId);\n            textureData.refCount--;\n            if (textureData.refCount < 1) {\n                this.disposeData(dataId);\n            }\n        }\n    }\n    readSync(dataId) {\n        const texData = this.texData.get(dataId);\n        const { values, dtype, complexTensorInfos, slice, shape, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.readSync(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (dtype === 'string') {\n            return values;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let result;\n        if (dtype === 'complex64') {\n            const realValues = this.readSync(complexTensorInfos.real.dataId);\n            const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n            result = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else {\n            result = this.getValuesFromTexture(dataId);\n        }\n        if (shouldTimeProgram) {\n            this.downloadWaitMs += util.now() - start;\n        }\n        return this.convertAndCacheOnCPU(dataId, result);\n    }\n    async read(dataId) {\n        if (this.pendingRead.has(dataId)) {\n            const subscribers = this.pendingRead.get(dataId);\n            return new Promise(resolve => subscribers.push(resolve));\n        }\n        const texData = this.texData.get(dataId);\n        const { values, shape, slice, dtype, complexTensorInfos, isPacked } = texData;\n        // The presence of `slice` indicates this tensor is a shallow slice of a\n        // different tensor, and is using that original tensor's texture. Run\n        // `clone` in order to copy that texture and read from it.\n        if (slice != null) {\n            let program;\n            if (isPacked) {\n                program = new UnaryOpPackedProgram(shape, unary_op.CLONE);\n            }\n            else {\n                program = new UnaryOpProgram(shape, unary_op.CLONE);\n            }\n            const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n            const data = this.read(res.dataId);\n            this.disposeIntermediateTensorInfo(res);\n            return data;\n        }\n        if (values != null) {\n            return this.convertAndCacheOnCPU(dataId);\n        }\n        if (!env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n            env().getNumber('WEBGL_VERSION') === 2) {\n            throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n                `WEBGL_VERSION=2 not yet supported.`);\n        }\n        let buffer = null;\n        let tmpDownloadTarget;\n        if (dtype !== 'complex64' && env().get('WEBGL_BUFFER_SUPPORTED')) {\n            // Possibly copy the texture into a buffer before inserting a fence.\n            tmpDownloadTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape));\n        }\n        this.pendingRead.set(dataId, []);\n        if (dtype !== 'complex64') {\n            // Create a fence and wait for it to resolve.\n            await this.gpgpu.createAndWaitForFence();\n        }\n        // Download the values from the GPU.\n        let vals;\n        if (dtype === 'complex64') {\n            const ps = await Promise.all([\n                this.read(complexTensorInfos.real.dataId),\n                this.read(complexTensorInfos.imag.dataId)\n            ]);\n            const realValues = ps[0];\n            const imagValues = ps[1];\n            vals = backend_util.mergeRealAndImagArrays(realValues, imagValues);\n        }\n        else if (buffer == null) {\n            vals = this.getValuesFromTexture(dataId);\n        }\n        else {\n            const size = util.sizeFromShape(shape);\n            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);\n        }\n        if (tmpDownloadTarget != null) {\n            this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n        }\n        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n        const subscribers = this.pendingRead.get(dataId);\n        this.pendingRead.delete(dataId);\n        // Notify all pending reads.\n        subscribers.forEach(resolve => resolve(dTypeVals));\n        if (this.pendingDisposal.has(dataId)) {\n            this.pendingDisposal.delete(dataId);\n            this.disposeData(dataId);\n            this.pendingDeletes--;\n        }\n        return dTypeVals;\n    }\n    checkNumericalProblems(values) {\n        if (values == null) {\n            return;\n        }\n        for (let i = 0; i < values.length; i++) {\n            const num = values[i];\n            if (!webgl_util.canBeRepresented(num)) {\n                if (env().getBool('WEBGL_RENDER_FLOAT32_CAPABLE')) {\n                    throw Error(`The value ${num} cannot be represented with your ` +\n                        `current settings. Consider enabling float32 rendering: ` +\n                        `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n                }\n                throw Error(`The value ${num} cannot be represented on this device.`);\n            }\n        }\n    }\n    getValuesFromTexture(dataId) {\n        const { shape, dtype, isPacked } = this.texData.get(dataId);\n        const size = util.sizeFromShape(shape);\n        if (env().getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n            const tmpTarget = this.decode(dataId);\n            const tmpData = this.texData.get(tmpTarget.dataId);\n            const vals = this.gpgpu\n                .downloadMatrixFromPackedTexture(tmpData.texture, ...tex_util.getDenseTexShape(shape))\n                .subarray(0, size);\n            this.disposeIntermediateTensorInfo(tmpTarget);\n            return vals;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK') && isPacked === true;\n        const outputShape = shouldUsePackedProgram ? webgl_util.getShapeAs3D(shape) : shape;\n        const program = shouldUsePackedProgram ?\n            new EncodeFloatPackedProgram(outputShape) :\n            new EncodeFloatProgram(outputShape);\n        const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], 'float32');\n        const tmpData = this.texData.get(output.dataId);\n        const vals = this.gpgpu\n            .downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1])\n            .subarray(0, size);\n        this.disposeIntermediateTensorInfo(output);\n        return vals;\n    }\n    async time(f) {\n        const oldActiveTimers = this.activeTimers;\n        const newActiveTimers = [];\n        let outerMostTime = false;\n        if (this.programTimersStack == null) {\n            this.programTimersStack = newActiveTimers;\n            outerMostTime = true;\n        }\n        else {\n            this.activeTimers.push(newActiveTimers);\n        }\n        this.activeTimers = newActiveTimers;\n        f();\n        // needing to split these up because util.flatten only accepts certain types\n        const flattenedActiveTimerQueries = util.flatten(this.activeTimers.map((d) => d.query))\n            .filter(d => d != null);\n        const flattenedActiveTimerNames = util.flatten(this.activeTimers.map((d) => d.name))\n            .filter(d => d != null);\n        this.activeTimers = oldActiveTimers;\n        if (outerMostTime) {\n            this.programTimersStack = null;\n        }\n        const res = {\n            uploadWaitMs: this.uploadWaitMs,\n            downloadWaitMs: this.downloadWaitMs,\n            kernelMs: null,\n            wallMs: null // will be filled by the engine\n        };\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n            res['kernelMs'] = util.sum(kernelMs);\n            res['getExtraProfileInfo'] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d }))\n                .map(d => `${d.name}: ${d.ms}`)\n                .join(', ');\n        }\n        else {\n            res['kernelMs'] = {\n                error: 'WebGL query timers are not supported in this environment.'\n            };\n        }\n        this.uploadWaitMs = 0;\n        this.downloadWaitMs = 0;\n        return res;\n    }\n    memory() {\n        return {\n            unreliable: false,\n            numBytesInGPU: this.numBytesInGPU,\n            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n            numBytesInGPUFree: this.textureManager.numBytesFree\n        };\n    }\n    startTimer() {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.beginQuery();\n        }\n        return { startMs: util.now(), endMs: null };\n    }\n    endTimer(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            this.gpgpu.endQuery();\n            return query;\n        }\n        query.endMs = util.now();\n        return query;\n    }\n    async getQueryTime(query) {\n        if (env().getNumber('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') > 0) {\n            return this.gpgpu.waitForQueryAndGetTime(query);\n        }\n        const timerQuery = query;\n        return timerQuery.endMs - timerQuery.startMs;\n    }\n    disposeData(dataId) {\n        if (this.pendingDisposal.has(dataId)) {\n            return;\n        }\n        if (this.pendingRead.has(dataId)) {\n            this.pendingDisposal.add(dataId);\n            this.pendingDeletes++;\n            return;\n        }\n        // No-op if already disposed.\n        if (!this.texData.has(dataId)) {\n            return;\n        }\n        // Trying to dispose a textureData that has a 'kept' refCount, e.g. trying\n        // to dispose a tensor whose data bucket is shared with a complex tensor. In\n        // this case we are removing a reference to the textureData, but we\n        // shouldn't actually dispose the texture.\n        if (this.texData.get(dataId).complexParentRefCount > 0) {\n            this.texData.get(dataId).refCount--;\n            return;\n        }\n        this.releaseGPUData(dataId);\n        const { complexTensorInfos } = this.texData.get(dataId);\n        if (complexTensorInfos != null) {\n            this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--;\n            this.disposeIntermediateTensorInfo(complexTensorInfos.real);\n            this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--;\n            this.disposeIntermediateTensorInfo(complexTensorInfos.imag);\n        }\n        this.texData.delete(dataId);\n    }\n    releaseGPUData(dataId) {\n        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);\n        const key = slice && slice.origDataId || dataId;\n        const refCount = this.dataRefCount.get(key);\n        if (refCount > 1) {\n            this.dataRefCount.set(key, refCount - 1);\n        }\n        else {\n            this.dataRefCount.delete(key);\n            if (texture != null) {\n                this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n            }\n        }\n        const texData = this.texData.get(dataId);\n        texData.texture = null;\n        texData.texShape = null;\n        texData.isPacked = false;\n        texData.slice = null;\n    }\n    getTexture(dataId) {\n        this.uploadToGPU(dataId);\n        return this.texData.get(dataId).texture;\n    }\n    /**\n     * Returns internal information for the specific data bucket. Used in unit\n     * tests.\n     */\n    getDataInfo(dataId) {\n        return this.texData.get(dataId);\n    }\n    getCPUBackend() {\n        if (!env().getBool('WEBGL_CPU_FORWARD')) {\n            return null;\n        }\n        if (this.cpuBackend == null) {\n            this.cpuBackend = engine().findBackend('cpu');\n        }\n        return this.cpuBackend;\n    }\n    /*\n    Tests whether all the inputs to an op are small and on the CPU. This heuristic\n    determines when it would be faster to execute a kernel on the CPU. WebGL\n    kernels opt into running this check and forwarding when appropriate.\n    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n    sustainable strategy for optimizing backend execution of ops.\n     */\n    shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n        const cpuBackend = this.getCPUBackend();\n        if (!this.warnedAboutCPUBackend && cpuBackend == null) {\n            console.warn('Your application contains ops that are small enough to be ' +\n                'executed on the CPU backend, however the CPU backend cannot ' +\n                'be found. Consider importing the CPU backend ' +\n                '(@tensorflow/tfjs-backend-cpu) for better performance.');\n            this.warnedAboutCPUBackend = true;\n        }\n        return cpuBackend != null &&\n            inputs.every(input => this.texData.get(input.dataId).texture == null &&\n                util.sizeFromShape(input.shape) < sizeThreshold);\n    }\n    getGPGPUContext() {\n        return this.gpgpu;\n    }\n    slice(x, begin, size) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = sliceImplCPU(this.texData.get(x.dataId).values, begin, size, x.shape, x.dtype);\n            return this.makeOutput(size, x.dtype, outValues);\n        }\n        // Short-circuit computation if the slice is zero-sized.\n        if (util.sizeFromShape(size) === 0) {\n            return tensor([], size, x.dtype);\n        }\n        const { isPacked } = this.texData.get(x.dataId);\n        const isContinous = slice_util.isSliceContinous(x.shape, begin, size);\n        if (isPacked || !isContinous) {\n            const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n                new SlicePackedProgram(size) :\n                new SliceProgram(size);\n            const customSetup = program.getCustomSetupFunc(begin);\n            return this.compileAndRun(program, [x], null, customSetup);\n        }\n        this.uploadToGPU(x.dataId);\n        return this.shallowSlice(x, begin, size);\n    }\n    shallowSlice(x, begin, size) {\n        const xTexData = this.texData.get(x.dataId);\n        const t = this.makeOutput(size, x.dtype);\n        const newTexData = this.texData.get(t.dataId);\n        // Copy texture data from the original tensor.\n        Object.assign(newTexData, xTexData);\n        newTexData.shape = size;\n        newTexData.dtype = x.dtype;\n        let flatOffset = slice_util.computeFlatOffset(begin, x.strides);\n        if (xTexData.slice) {\n            // We are slicing an already sliced tensor, so we have to accumulate\n            // the offset.\n            flatOffset += xTexData.slice.flatOffset;\n        }\n        newTexData.slice = {\n            flatOffset,\n            // Point to the original dataId, which is used to do ref counting.\n            origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n        };\n        // Increase the ref count for that data bucket.\n        const refCount = this.dataRefCount.get(newTexData.slice.origDataId) || 1;\n        this.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n        return t;\n    }\n    stridedSlice(x, begin, end, strides) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.stridedSlice(x, begin, end, strides));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const outShape = slice_util.computeOutShape(begin, end, strides);\n        if (outShape.some(axis => axis === 0)) {\n            return tensor([], outShape);\n        }\n        const program = new StridedSliceProgram(begin, strides, outShape);\n        return this.compileAndRun(program, [x]);\n    }\n    reverse(x, axis) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new ReversePackedProgram(x.shape, axis) :\n            new ReverseProgram(x.shape, axis);\n        return this.compileAndRun(program, [x]);\n    }\n    neg(x) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.neg(x));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.NEG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n        return this.compileAndRun(program, [x]);\n    }\n    batchMatMul(a, b, transposeA, transposeB) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n        const batch = Math.max(a.shape[0], b.shape[0]);\n        // Since the matrices are vectors, it is faster to call mul().sum()\n        // because sum() is O(sqrt(N)) due to divide-and-conquer.\n        if ((outerShapeA === 1 || outerShapeB === 1) &&\n            sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n            if (transposeA) {\n                a = transpose(a, [0, 2, 1]);\n            }\n            if (transposeB) {\n                b = transpose(b, [0, 2, 1]);\n            }\n            const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n            const axis = outerShapeB === 1 ? 2 : 1;\n            const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n            // TODO(annxingyuan): Call multiply directly as part of batchMatMul\n            // modularization.\n            const product = tf.mul(a3D, b3D);\n            return product.sum(axis, true /* keepDims */);\n        }\n        const dtype = upcastType(a.dtype, b.dtype);\n        const program = new MatMulPackedProgram(a.shape, b.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    fusedBatchMatMul({ a, b, transposeA, transposeB, bias, activation, preluActivationWeights }) {\n        const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n        const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n        const batch = Math.max(a.shape[0], b.shape[0]);\n        const dtype = upcastType(a.dtype, b.dtype);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const program = new MatMulPackedProgram(a.shape, b.shape, [batch, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [a, b];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs, dtype);\n    }\n    localResponseNormalization4D(x, radius, bias, alpha, beta) {\n        const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n            new LRNPackedProgram(x.shape, radius, bias, alpha, beta) :\n            new LRNProgram(x.shape, radius, bias, alpha, beta);\n        return this.compileAndRun(program, [x]);\n    }\n    LRNGrad(dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {\n        const program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n        return this.compileAndRun(program, [inputImage, outputImage, dy]);\n    }\n    tile(x, reps) {\n        if (x.dtype === 'string') {\n            const data = this.readSync(x.dataId);\n            const decodedData = data.map(d => util.decodeString(d));\n            const buf = buffer(x.shape, x.dtype, decodedData);\n            return tile(buf, reps);\n        }\n        const program = new TileProgram(x.shape, reps);\n        return this.compileAndRun(program, [x]);\n    }\n    pad(x, paddings, constantValue) {\n        const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n            new PadPackedProgram(x.shape, paddings, constantValue) :\n            new PadProgram(x.shape, paddings, constantValue);\n        return this.compileAndRun(program, [x]);\n    }\n    gather(x, indices, axis) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x, indices], () => this.cpuBackend.gather(x, indices, axis));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = new GatherProgram(x.shape, indices.size, axis);\n        return this.compileAndRun(program, [x, indices]);\n    }\n    batchToSpaceND(x, blockShape, crops) {\n        util.assert(x.rank <= 4, () => 'batchToSpaceND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const reshaped = backend_util.getReshaped(x.shape, blockShape, prod);\n        const permuted = backend_util.getPermuted(reshaped.length, blockShape.length);\n        const reshapedPermuted = backend_util.getReshapedPermuted(x.shape, blockShape, prod);\n        const sliceBeginCoords = backend_util.getSliceBeginCoords(crops, blockShape.length);\n        const sliceSize = backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n        return transpose(x.reshape(reshaped), permuted)\n            .reshape(reshapedPermuted)\n            .slice(sliceBeginCoords, sliceSize);\n    }\n    spaceToBatchND(x, blockShape, paddings) {\n        util.assert(x.rank <= 4, () => 'spaceToBatchND for rank > 4 with a WebGL backend not ' +\n            'implemented yet');\n        const prod = blockShape.reduce((a, b) => a * b);\n        const completePaddings = [[0, 0]];\n        completePaddings.push(...paddings);\n        for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n            completePaddings.push([0, 0]);\n        }\n        const paddedX = x.pad(completePaddings);\n        const reshapedPaddedShape = backend_util.getReshaped(paddedX.shape, blockShape, prod, false);\n        const permutedReshapedPaddedPermutation = backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n        const flattenShape = backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);\n        const paddedXT = transpose(paddedX.reshape(reshapedPaddedShape), permutedReshapedPaddedPermutation);\n        return reshape(paddedXT, flattenShape);\n    }\n    reduce(x, reduceType, dtype) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const outSize = Math.ceil(inSize / windowSize);\n        const reduceInfo = { windowSize, inSize, batchSize, outSize };\n        const program = new ReduceProgram(reduceInfo, reduceType);\n        const output = this.compileAndRun(program, [x], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.reduce(output, reduceType, dtype);\n    }\n    argReduce(x, reduceType, bestIndicesA = null) {\n        let batchSize = x.shape[0];\n        let inSize = x.shape[1];\n        if (bestIndicesA != null) {\n            batchSize = bestIndicesA.shape[0];\n            inSize = bestIndicesA.shape[1];\n        }\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const reduceInfo = {\n            windowSize,\n            inSize,\n            batchSize,\n            outSize: Math.ceil(inSize / windowSize)\n        };\n        const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n        const inputs = [x];\n        if (bestIndicesA != null) {\n            inputs.push(bestIndicesA);\n        }\n        const output = this.compileAndRun(program, inputs, 'int32');\n        // No need to run another GPGPU program.\n        if (output.shape[1] === 1) {\n            return output;\n        }\n        return this.argReduce(x, reduceType, output);\n    }\n    argReducePacked(x, reduceType, bestIndicesA = null) {\n        const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n        const inSize = inShape[inShape.length - 1];\n        const windowSize = backend_util.computeOptimalWindowSize(inSize);\n        const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n        const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n        const output = this.compileAndRun(program, inputs, 'int32');\n        if (output.rank === x.rank) {\n            return this.argReducePacked(x, reduceType, output);\n        }\n        return output;\n    }\n    sum(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n    }\n    prod(x, axes) {\n        const cpuRes = this.tryRunOnCpuOrThrow([x], () => this.cpuBackend.prod(x, axes));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n    }\n    unsortedSegmentSum(x, segmentIds, numSegments) {\n        let axis = 0;\n        const permutation = backend_util.getAxesPermutation([axis], x.rank);\n        let permutedX = x;\n        if (permutation != null) {\n            permutedX = transpose(x, permutation);\n            axis = backend_util.getInnerMostAxes(1, x.rank)[0];\n        }\n        const outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n        const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n        const a2D = permutedX.as2D(-1, inSize);\n        const outputDType = tf.sumOutType(x.dtype);\n        let result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n        if (permutation != null) {\n            result =\n                transpose(result, backend_util.getUndoAxesPermutation(permutation));\n        }\n        return result;\n    }\n    segOpCompute(x, segOpType, segmentIds, dtype, numSegments) {\n        const batchSize = x.shape[0];\n        const inSize = x.shape[1];\n        const windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n        const segOpInfo = { windowSize, inSize, batchSize, numSegments };\n        const program = new SegmentOpProgram(segOpInfo, segOpType);\n        const output = this.compileAndRun(program, [x, segmentIds], dtype);\n        // No need to run another GPGPU program.\n        if (output.shape[1] === numSegments) {\n            return output;\n        }\n        segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n    }\n    argMinMaxReduce(x, axis, reduceType) {\n        const axes = [axis];\n        backend_util.assertAxesAreInnerMostDims('arg' + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.rank);\n        if (!env().getBool('WEBGL_PACK_REDUCE') || x.rank <= 2) {\n            const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n            const inSize = util.sizeFromShape(reduceShape);\n            const a2D = x.as2D(-1, inSize);\n            return this.argReduce(a2D, reduceType).reshape(outShape);\n        }\n        return this.argReducePacked(x, reduceType);\n    }\n    argMin(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'min');\n    }\n    argMax(x, axis) {\n        return this.argMinMaxReduce(x, axis, 'max');\n    }\n    cumsum(x, axis, exclusive, reverse) {\n        if (axis !== x.rank - 1) {\n            throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n                `but got axis=${axis}`);\n        }\n        const size = x.shape[axis];\n        let result = x;\n        // Use cumsum parallel algorithm, ref:\n        // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda\n        for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n            const program = new CumSumProgram(x.shape, false, reverse);\n            const customSetup = program.getCustomSetupFunc(i);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result], result.dtype, customSetup);\n            prevResult.dispose();\n        }\n        // For exclusive cumsum, shift the end result in the direction of sum and\n        // add 0 to the front index.\n        if (exclusive) {\n            const program = new CumSumProgram(x.shape, exclusive, reverse);\n            const prevResult = result;\n            result = this.compileAndRun(program, [result]);\n            prevResult.dispose();\n        }\n        return result;\n    }\n    equal(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    less(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.less(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    lessEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LESS_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greater(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.greater(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    greaterEqual(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.GREATER_EQUAL, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalNot(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n        return this.compileAndRun(program, [x]);\n    }\n    logicalAnd(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_AND, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    logicalOr(a, b) {\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.LOGICAL_OR, 'bool');\n        }\n        const program = new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], 'bool');\n    }\n    select(condition, a, b) {\n        const program = new SelectProgram(condition.rank, a.shape, a.rank);\n        return this.compileAndRun(program, [condition, a, b], upcastType(a.dtype, b.dtype));\n    }\n    where(condition) {\n        backend_util.warn('tf.where() in webgl locks the UI thread. ' +\n            'Call tf.whereAsync() instead');\n        const condVals = condition.dataSync();\n        return whereImpl(condition.shape, condVals);\n    }\n    topk(x, k, sorted) {\n        const xVals = x.dataSync();\n        return topkImpl(xVals, x.shape, x.dtype, k, sorted);\n    }\n    min(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n    }\n    minimum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.minimum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MIN, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    mod(a, b) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MOD, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    maximum(a, b) {\n        const cpuRes = this.tryRunOnCpuOrThrow([a, b], () => this.cpuBackend.maximum(a, b));\n        if (cpuRes) {\n            return cpuRes;\n        }\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.MAX, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b]);\n    }\n    all(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n    }\n    any(x, axes) {\n        backend_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n        const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes(x.shape, axes);\n        const inSize = util.sizeFromShape(reduceShape);\n        const a2D = x.as2D(-1, inSize);\n        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n    }\n    floorDiv(a, b) {\n        const op = binaryop_gpu.INT_DIV;\n        const outputDtype = 'int32';\n        if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n            return this.packedBinaryOp(a, b, binaryop_packed_gpu.INT_DIV, outputDtype);\n        }\n        const program = new BinaryOpProgram(op, a.shape, b.shape);\n        return this.compileAndRun(program, [a, b], outputDtype);\n    }\n    packedUnaryOp(x, op, dtype) {\n        const program = new UnaryOpPackedProgram(x.shape, op);\n        return this.compileAndRun(program, [x], dtype);\n    }\n    packedBinaryOp(a, b, op, dtype, checkOutOfBounds = false) {\n        const program = new BinaryOpPackedProgram(op, a.shape, b.shape, checkOutOfBounds);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    // Returns a TensorInfo with the complex shape and the dataId of the\n    // underlying part. We need to do this because a reshaped complex tensor is\n    // not reflected in its parts.\n    makeComplexComponentTensorInfo(complexTensor, complexPart) {\n        return {\n            dataId: complexPart.dataId,\n            dtype: complexPart.dtype,\n            shape: complexTensor.shape\n        };\n    }\n    addN(tensors) {\n        if (tensors.length === 1) {\n            return tensors[0];\n        }\n        // Limit the number of uploaded textures for optimization.\n        if (tensors.length > env().get('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n            const midIndex = Math.floor(tensors.length / 2);\n            const leftSide = this.addN(tensors.slice(0, midIndex));\n            const rightSide = this.addN(tensors.slice(midIndex));\n            return this.addN([leftSide, rightSide]);\n        }\n        const dtype = tensors.map(t => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n        const shapes = tensors.map(t => t.shape);\n        // We can make sure shapes are identical in op level.\n        const usePackedOp = env().getBool('WEBGL_PACK');\n        const program = usePackedOp ?\n            new AddNPackedProgram(tensors[0].shape, shapes) :\n            new AddNProgram(tensors[0].shape, shapes);\n        return this.compileAndRun(program, tensors, dtype);\n    }\n    pow(a, b) {\n        const usePackedOp = env().getBool('WEBGL_PACK_BINARY_OPERATIONS');\n        const program = usePackedOp ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.POW, a.shape, b.shape) :\n            new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n        const dtype = upcastType(a.dtype, b.dtype);\n        return this.compileAndRun(program, [a, b], dtype);\n    }\n    ceil(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = ceilImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.CEIL, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n        return this.compileAndRun(program, [x]);\n    }\n    floor(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = floorImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.FLOOR, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n        return this.compileAndRun(program, [x]);\n    }\n    sign(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n        return this.compileAndRun(program, [x]);\n    }\n    isNaN(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_NAN);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isInf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_INF);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    isFinite(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.IS_FINITE);\n        return this.compileAndRun(program, [x], 'bool');\n    }\n    round(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n        return this.compileAndRun(program, [x]);\n    }\n    exp(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXP, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n        return this.compileAndRun(program, [x]);\n    }\n    expm1(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = expm1ImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.EXPM1, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n        return this.compileAndRun(program, [x]);\n    }\n    softmax(logits, dim) {\n        const axes = util.parseAxisParam([dim], logits.shape);\n        // TODO(annxingyuan): Call maxImpl rather than op as part of softmax kernel\n        // modularization.\n        const maxLogit = max(logits, axes);\n        const expandedShape = backend_util.expandShapeToKeepDim(maxLogit.shape, axes);\n        // TODO(annxingyuan): Call sub directly as part of softmax kernel\n        // modularization.\n        const a = tf.sub(logits, maxLogit.reshape(expandedShape));\n        const b = this.exp(a);\n        const sumExp = this.sum(b, axes).reshape(expandedShape);\n        // TODO(annxingyuan): Call divImpl rather than op as part of softmax kernel\n        // modularization.\n        return div(b, sumExp);\n    }\n    log(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = logImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.LOG, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n        return this.compileAndRun(program, [x]);\n    }\n    log1p(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n        return this.compileAndRun(program, [x]);\n    }\n    sqrt(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    rsqrt(x) {\n        if (this.shouldExecuteOnCPU([x])) {\n            const outValues = rsqrtImplCPU(this.texData.get(x.dataId).values, x.dtype);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n        return this.compileAndRun(program, [x]);\n    }\n    reciprocal(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n        return this.compileAndRun(program, [x]);\n    }\n    relu(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    relu6(x) {\n        let program;\n        if (env().getBool('WEBGL_PACK')) {\n            program = new UnaryOpPackedProgram(x.shape, unary_packed_op.RELU6);\n        }\n        else {\n            program = new UnaryOpProgram(x.shape, unary_op.RELU6);\n        }\n        return this.compileAndRun(program, [x]);\n    }\n    prelu(x, alpha) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.PRELU, x.shape, alpha.shape) :\n            new BinaryOpProgram(binaryop_gpu.PRELU, x.shape, alpha.shape);\n        return this.compileAndRun(program, [x, alpha]);\n    }\n    elu(x) {\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_packed_op.ELU, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n        return this.compileAndRun(program, [x]);\n    }\n    eluDer(dy, y) {\n        const program = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ?\n            new BinaryOpPackedProgram(binaryop_packed_gpu.ELU_DER, dy.shape, y.shape) :\n            new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n        return this.compileAndRun(program, [dy, y]);\n    }\n    selu(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n        return this.compileAndRun(program, [x]);\n    }\n    clip(x, min, max) {\n        let program;\n        if (env().getBool('WEBGL_PACK_CLIP')) {\n            program = new ClipPackedProgram(x.shape);\n        }\n        else {\n            program = new ClipProgram(x.shape);\n        }\n        const customSetup = program.getCustomSetupFunc(min, max);\n        return this.compileAndRun(program, [x], null, customSetup);\n    }\n    abs(x) {\n        // TODO: handle cases when x is complex.\n        if (this.shouldExecuteOnCPU([x]) && x.dtype !== 'complex64') {\n            const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n            return this.makeOutput(x.shape, x.dtype, outValues);\n        }\n        if (env().getBool('WEBGL_PACK_UNARY_OPERATIONS')) {\n            return this.packedUnaryOp(x, unary_op.ABS, x.dtype);\n        }\n        const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n        return this.compileAndRun(program, [x]);\n    }\n    complexAbs(x) {\n        const xData = this.texData.get(x.dataId);\n        const program = new ComplexAbsProgram(x.shape);\n        const inputs = [\n            this.makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),\n            this.makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag),\n        ];\n        return this.compileAndRun(program, inputs);\n    }\n    sigmoid(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n        return this.compileAndRun(program, [x]);\n    }\n    softplus(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n        return this.compileAndRun(program, [x]);\n    }\n    asin(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n        return this.compileAndRun(program, [x]);\n    }\n    acos(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n        return this.compileAndRun(program, [x]);\n    }\n    atan(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n        return this.compileAndRun(program, [x]);\n    }\n    sinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n        return this.compileAndRun(program, [x]);\n    }\n    cosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n        return this.compileAndRun(program, [x]);\n    }\n    tanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n        return this.compileAndRun(program, [x]);\n    }\n    asinh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n        return this.compileAndRun(program, [x]);\n    }\n    acosh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n        return this.compileAndRun(program, [x]);\n    }\n    atanh(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n        return this.compileAndRun(program, [x]);\n    }\n    erf(x) {\n        const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n        return this.compileAndRun(program, [x]);\n    }\n    step(x, alpha) {\n        const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n        return this.compileAndRun(program, [x]);\n    }\n    conv2dByMatMul(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n        // result from 2D to 4D.\n        const xShape = x.shape;\n        const xTexData = this.texData.get(x.dataId);\n        const sharedMatMulDim = convInfo.inChannels;\n        const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n        const outerShapeFilter = convInfo.outChannels;\n        const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n        const transposeA = false;\n        const transposeB = false;\n        // TODO: Once reduction ops are packed, batchMatMul will always be packed\n        // and we can remove this condition.\n        const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&\n            sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n        const reshapeWillBeExpensive = xShape[2] % 2 !== 0 && !!xTexData.isPacked;\n        if (batchMatMulWillBeUnpacked || !env().getBool('WEBGL_LAZILY_UNPACK') ||\n            !env().getBool('WEBGL_PACK_BINARY_OPERATIONS') ||\n            !reshapeWillBeExpensive) {\n            const targetShape = isChannelsLast ? xShape[0] * xShape[1] * xShape[2] :\n                xShape[0] * xShape[2] * xShape[3];\n            const xReshaped = reshape(x, [1, targetShape, convInfo.inChannels]);\n            const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n            const result = this.fusedBatchMatMul({\n                a: xReshaped,\n                b: filterReshaped,\n                transposeA,\n                transposeB,\n                bias,\n                activation,\n                preluActivationWeights\n            });\n            return reshape(result, convInfo.outShape);\n        }\n        // Following optimization is specific to packed |x| with odd row count\n        // (For example, in channelLast mode, 'row count' refers to x.shape[2]):\n        // we avoid expensive packed 2x2 reshape by padding row count to next,\n        // even number. When x.shape[2] is odd, the result of packed batchMatMul is\n        // the same (has the same texture layout and and values in the texture) as\n        // it is for even x.shape[2] + 1. We make the odd-rows tensor to look like\n        // even-rows tensor before the operation and, after the batchMatMul,\n        // fix the even-rows result to have odd number of rows.\n        const targetShape = isChannelsLast ?\n            xShape[0] * xShape[1] * (xShape[2] + 1) :\n            xShape[0] * xShape[2] * (xShape[3] + 1);\n        const xReshaped = {\n            dataId: x.dataId,\n            shape: [1, targetShape, convInfo.inChannels],\n            dtype: x.dtype\n        };\n        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n        // Decrementing row count, after batchMatMul->...->compileProgram leads to\n        // invalid row count within the reference in GPGPUBinary.inShapeInfos.\n        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n        // in compileProgram method, but that would affect compilation of all\n        // programs - instead, provide a copy here, with even row count, before\n        // calling batchMatMul->...->compileProgram and after that, the original\n        // xTexData.shape is restored.\n        const originalXTexDataShape = xTexData.shape;\n        xTexData.shape = xTexData.shape.slice();\n        xTexData.shape[xTexData.shape.length - 2]++;\n        util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n        const filterReshaped = reshape(filter, [1, convInfo.inChannels, convInfo.outChannels]);\n        const pointwiseConv = this.fusedBatchMatMul({\n            a: xReshaped,\n            b: filterReshaped,\n            transposeA,\n            transposeB,\n            bias,\n            activation,\n            preluActivationWeights\n        });\n        const pointwiseConvTexData = this.texData.get(pointwiseConv.dataId);\n        util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');\n        // Restore the input shape to original.\n        xTexData.shape = originalXTexDataShape;\n        // Set the output shape - there is no need for expensive reshape as data\n        // layout is already correct.\n        pointwiseConvTexData.shape = convInfo.outShape;\n        return engine().makeTensorFromDataId(pointwiseConv.dataId, convInfo.outShape, pointwiseConv.dtype);\n    }\n    conv2dWithIm2Row(x, filter, convInfo, bias, activation, preluActivationWeights) {\n        // Rearranges conv2d input so each block to be convolved over forms the\n        // column of a new matrix with shape [filterWidth * filterHeight *\n        // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n        // output channel forms a row of a new matrix with shape [outChannels,\n        // filterWidth * filterHeight * inChannels]. The convolution is then\n        // computed by multiplying these matrices and reshaping the result.\n        const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n        const isChannelsLast = dataFormat === 'channelsLast';\n        const sharedDim = filterWidth * filterHeight * inChannels;\n        const numCols = outHeight * outWidth;\n        const x2ColShape = [sharedDim, numCols];\n        const transposeA = true;\n        const transposeB = false;\n        const xSqueezed = x.squeeze([0]);\n        const w2Row = filter.reshape([1, sharedDim, -1]);\n        const im2ColProgram = new Im2ColPackedProgram(x2ColShape, xSqueezed.shape, convInfo);\n        const im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]).reshape([\n            1, x2ColShape[0], x2ColShape[1]\n        ]);\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;\n        const matmulProgram = new MatMulPackedProgram(im2Col.shape, w2Row.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [im2Col, w2Row];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        const product = this.compileAndRun(matmulProgram, inputs);\n        if (isChannelsLast) {\n            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n        }\n        else {\n            return product.reshape([1, convInfo.outChannels, outHeight, outWidth]);\n        }\n    }\n    fusedConv2d({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && input.shape[0] === 1) {\n            return this.conv2dWithIm2Row(input, filter, convInfo, bias, activation, preluActivationWeights);\n        }\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;\n        const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        const inputs = [input, filter];\n        if (bias) {\n            inputs.push(bias);\n        }\n        if (preluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        return this.compileAndRun(program, inputs);\n    }\n    conv2d(x, filter, convInfo) {\n        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n            (convInfo.padInfo.type === 'SAME' ||\n                convInfo.padInfo.type === 'VALID')) {\n            return this.conv2dByMatMul(x, filter, convInfo);\n        }\n        if (env().getBool('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n            return this.conv2dWithIm2Row(x, filter, convInfo);\n        }\n        const program = new Conv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv2dDerInput(dy, filter, convInfo) {\n        const program = new Conv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv2dDerFilter(x, dy, convInfo) {\n        const program = new Conv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    fusedDepthwiseConv2D({ input, filter, convInfo, bias, activation, preluActivationWeights }) {\n        const shouldPackDepthwiseConv = env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1;\n        const fusedActivation = activation ?\n            mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) :\n            null;\n        const inputs = [input, filter];\n        const hasBias = bias != null;\n        const hasPreluActivationWeights = preluActivationWeights != null;\n        if (hasBias) {\n            inputs.push(bias);\n        }\n        if (hasPreluActivationWeights) {\n            inputs.push(preluActivationWeights);\n        }\n        let program;\n        if (shouldPackDepthwiseConv) {\n            program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n            return this.compileAndRun(program, inputs);\n        }\n        program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights);\n        return this.compileAndRun(program, inputs);\n    }\n    depthwiseConv2D(x, filter, convInfo) {\n        let program;\n        if (env().getBool('WEBGL_PACK_DEPTHWISECONV') &&\n            convInfo.strideWidth <= 2 &&\n            convInfo.outChannels / convInfo.inChannels === 1) {\n            program = new DepthwiseConvPacked2DProgram(convInfo);\n            return this.compileAndRun(program, [x, filter]);\n        }\n        program = new DepthwiseConv2DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    depthwiseConv2DDerInput(dy, filter, convInfo) {\n        const program = new DepthwiseConv2DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    depthwiseConv2DDerFilter(x, dy, convInfo) {\n        const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    conv3d(x, filter, convInfo) {\n        const program = new Conv3DProgram(convInfo);\n        return this.compileAndRun(program, [x, filter]);\n    }\n    conv3dDerInput(dy, filter, convInfo) {\n        const program = new Conv3DDerInputProgram(convInfo);\n        return this.compileAndRun(program, [dy, filter]);\n    }\n    conv3dDerFilter(x, dy, convInfo) {\n        const program = new Conv3DDerFilterProgram(convInfo);\n        return this.compileAndRun(program, [x, dy]);\n    }\n    unstack(x, axis) {\n        const num = x.shape[axis];\n        const outShape = new Array(x.rank - 1);\n        let outIndex = 0;\n        for (let i = 0; i < x.rank; i++) {\n            if (i !== axis) {\n                outShape[outIndex++] = x.shape[i];\n            }\n        }\n        const begin = new Array(x.rank).fill(0);\n        const size = x.shape.slice();\n        size[axis] = 1;\n        const res = new Array(num);\n        for (let i = 0; i < res.length; i++) {\n            begin[axis] = i;\n            res[i] = this.slice(x, begin, size).reshape(outShape);\n        }\n        return res;\n    }\n    avgPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'avg', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    avgPool3dBackprop(dy, x, convInfo) {\n        const avgPool3dBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n        return this.compileAndRun(avgPool3dBackpropProgram, [dy], x.dtype);\n    }\n    maxPool3d(x, convInfo) {\n        const program = new Pool3DProgram(convInfo, 'max', false);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    maxPool3dBackprop(dy, x, y, convInfo) {\n        const getPositions = true;\n        const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, 'max', getPositions);\n        const maxPool3dPositions = this.compileAndRun(maxPool3dPositionsProgram, [x]);\n        const maxPool3dBackPropProgram = new MaxPool3DBackpropProgram(convInfo);\n        const result = this.compileAndRun(maxPool3dBackPropProgram, [dy, maxPool3dPositions], x.dtype);\n        maxPool3dPositions.dispose();\n        return result;\n    }\n    resizeBilinear(x, newHeight, newWidth, alignCorners) {\n        const program = env().getBool('WEBGL_PACK_IMAGE_OPERATIONS') ?\n            new ResizeBilinearPackedProgram(x.shape, newHeight, newWidth, alignCorners) :\n            new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x], 'float32');\n    }\n    resizeBilinearBackprop(dy, x, alignCorners) {\n        const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    resizeNearestNeighbor(x, newHeight, newWidth, alignCorners) {\n        const program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);\n        return this.compileAndRun(program, [x]);\n    }\n    resizeNearestNeighborBackprop(dy, x, alignCorners) {\n        const program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n        return this.compileAndRun(program, [dy]);\n    }\n    multinomial(logits, normalized, numSamples, seed) {\n        const probs = normalized ? logits : softmax(logits);\n        const batchSize = probs.shape[0];\n        const numOutcomes = probs.shape[1];\n        const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n        const customSetup = program.getCustomSetupFunc(seed);\n        return this.compileAndRun(program, [probs], 'int32', customSetup);\n    }\n    oneHot(indices, depth, onValue, offValue) {\n        const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n        return this.compileAndRun(program, [indices]);\n    }\n    diag(x) {\n        const program = new DiagProgram(x.size);\n        return this.compileAndRun(program, [x]);\n    }\n    cropAndResize(image, boxes, boxIndex, cropSize, method, extrapolationValue) {\n        const program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);\n        return this.compileAndRun(program, [image, boxes, boxIndex], 'float32');\n    }\n    depthToSpace(x, blockSize, dataFormat) {\n        util.assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n        const batchSize = x.shape[0];\n        const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n        const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n        const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n        const outputHeight = inputHeight * blockSize;\n        const outputWidth = inputWidth * blockSize;\n        const outputDepth = inputDepth / (blockSize * blockSize);\n        const outputShape = (dataFormat === 'NHWC') ?\n            [batchSize, outputHeight, outputWidth, outputDepth] :\n            [batchSize, outputDepth, outputHeight, outputWidth];\n        const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n        return this.compileAndRun(program, [x]);\n    }\n    split(x, sizeSplits, axis) {\n        return split(x, sizeSplits, axis);\n    }\n    scatterND(indices, updates, shape) {\n        const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util.calculateShapes(updates, indices, shape);\n        const flattenShape = [outputSize / sliceSize, sliceSize];\n        const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n        const flattenX = updates.reshape([numUpdates, sliceSize]);\n        if (outputSize === 0) {\n            return backend_util.reshapeTensor(tensor([]), shape);\n        }\n        const defaultValue = scalar(0);\n        const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices, defaultValue]);\n        return res.reshape(shape);\n    }\n    sparseToDense(sparseIndices, sparseValues, outputShape, defaultValue) {\n        const { sliceRank, numUpdates, strides, outputSize } = backend_util.calculateShapes(sparseValues, sparseIndices, outputShape);\n        const sumDupeIndices = false;\n        const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);\n        const res = this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue]);\n        return res.reshape(outputShape);\n    }\n    gatherND(x, indices) {\n        const indicesShape = indices.shape;\n        const sliceRank = indicesShape[indicesShape.length - 1];\n        const [resultShape, numSlices, sliceSize, strides] = backend_util.prepareAndValidate(x, indices);\n        const flattenIndices = indices.reshape([numSlices, sliceRank]);\n        const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n        const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n        const res = this.compileAndRun(program, [flattenX, flattenIndices]);\n        return res.reshape(resultShape);\n    }\n    fill(shape, value, dtype) {\n        dtype = dtype || util.inferDtype(value);\n        if (dtype === 'string') {\n            // String type should be handled in CPU memory.\n            const values = util.getArrayFromDType(dtype, util.sizeFromShape(shape));\n            values.fill(value);\n            return engine().makeTensor(values, shape, dtype, this);\n        }\n        else {\n            const program = new FillProgram(shape, value);\n            const customSetup = program.getCustomSetupFunc(value);\n            return this.compileAndRun(program, [], dtype, customSetup);\n        }\n    }\n    onesLike(x) {\n        if (x.dtype === 'string') {\n            throw new Error('onesLike is not supported under string dtype');\n        }\n        else {\n            // TODO(cais, smilkov): Add WebGL shader for onesLike:\n            //   https://github.com/tensorflow/tfjs/issues/1293\n            return this.fill(x.shape, 1, x.dtype);\n        }\n    }\n    zerosLike(x) {\n        return this.fill(x.shape, x.dtype === 'string' ? '' : 0, x.dtype);\n    }\n    linspace(start, stop, num) {\n        // TODO: Use CPU implementation due to the precision problem in Safari.\n        return backend_util.linspaceImpl(start, stop, num);\n    }\n    makeTensorInfo(shape, dtype, values) {\n        const dataId = this.write(values, shape, dtype);\n        this.texData.get(dataId).usage = null;\n        return { dataId, shape, dtype };\n    }\n    makeOutput(shape, dtype, values) {\n        const { dataId } = this.makeTensorInfo(shape, dtype, values);\n        return engine().makeTensorFromDataId(dataId, shape, dtype, this);\n    }\n    unpackTensor(input) {\n        const program = new UnpackProgram(input.shape);\n        return this.runWebGLProgram(program, [input], input.dtype);\n    }\n    packTensor(input) {\n        const program = new PackProgram(input.shape);\n        const preventEagerUnpackingOutput = true;\n        return this.runWebGLProgram(program, [input], input.dtype, null /* customSetup */, preventEagerUnpackingOutput);\n    }\n    packedReshape(input, afterShape) {\n        const input3DShape = [\n            webgl_util.getBatchDim(input.shape),\n            ...webgl_util.getRowsCols(input.shape)\n        ];\n        const input3D = {\n            dtype: input.dtype,\n            shape: input3DShape,\n            dataId: input.dataId\n        };\n        const afterShapeAs3D = [\n            webgl_util.getBatchDim(afterShape), ...webgl_util.getRowsCols(afterShape)\n        ];\n        const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n        const preventEagerUnpackingOfOutput = true;\n        const output = this.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n    }\n    decode(dataId) {\n        const texData = this.texData.get(dataId);\n        const { isPacked, shape, dtype } = texData;\n        const shapeAs3D = webgl_util.getShapeAs3D(shape);\n        let program;\n        if (isPacked) {\n            program = new DecodeMatrixPackedProgram(shapeAs3D);\n        }\n        else {\n            program = new DecodeMatrixProgram(shapeAs3D);\n        }\n        const preventEagerUnpackingOfOutput = true;\n        const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n        return { dtype, shape, dataId: out.dataId };\n    }\n    runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        const output = this.makeTensorInfo(program.outputShape, outputDtype);\n        const outData = this.texData.get(output.dataId);\n        if (program.packedOutput) {\n            outData.isPacked = true;\n        }\n        if (program.outPackingScheme === tex_util.PackingScheme.DENSE) {\n            const texelShape = tex_util.getDenseTexShape(program.outputShape);\n            // For a densely packed output, we explicitly set texShape\n            // so it doesn't get assigned later according to our typical packing\n            // scheme wherein a single texel can only contain values from adjacent\n            // rows/cols.\n            outData.texShape = texelShape.map(d => d * 2);\n        }\n        if (program.outTexUsage != null) {\n            outData.usage = program.outTexUsage;\n        }\n        if (util.sizeFromShape(output.shape) === 0) {\n            // Short-circuit the computation since the result is empty (has 0 in its\n            // shape).\n            outData.values =\n                util.getTypedArrayFromDType(output.dtype, 0);\n            return output;\n        }\n        const dataToDispose = [];\n        const inputsData = inputs.map(input => {\n            if (input.dtype === 'complex64') {\n                throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` +\n                    `dtypes, please separate the program into real and imaginary ` +\n                    `parts.`);\n            }\n            let texData = this.texData.get(input.dataId);\n            if (texData.texture == null) {\n                if (!program.packedInputs &&\n                    util.sizeFromShape(input.shape) <=\n                        env().getNumber('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n                    // Upload small tensors that live on the CPU as uniforms, not as\n                    // textures. Do this only when the environment supports 32bit floats\n                    // due to problems when comparing 16bit floats with 32bit floats.\n                    // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n                    // possible for packed shaders to sample from uniforms.\n                    return {\n                        shape: input.shape,\n                        texData: null,\n                        isUniform: true,\n                        uniformValues: texData.values\n                    };\n                }\n                // This ensures that if a packed program's inputs have not yet been\n                // uploaded to the GPU, they get uploaded as packed right off the bat.\n                if (program.packedInputs) {\n                    texData.isPacked = true;\n                    texData.shape = input.shape;\n                }\n            }\n            else if (!!texData.isPacked !== !!program.packedInputs) {\n                input = texData.isPacked ? this.unpackTensor(input) :\n                    this.packTensor(input);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n            }\n            else if (texData.isPacked &&\n                !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n                // This is a special case where a texture exists for a tensor\n                // but the shapes are incompatible (due to packing constraints) because\n                // the tensor did not have a chance to go through the packed reshape\n                // shader. This only happens when we reshape the *same* tensor to form\n                // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n                // case will disappear once packed uploading is the default.\n                const savedInput = input;\n                const targetShape = input.shape;\n                input.shape = texData.shape;\n                input = this.packedReshape(input, targetShape);\n                dataToDispose.push(input);\n                texData = this.texData.get(input.dataId);\n                savedInput.shape = targetShape;\n            }\n            this.uploadToGPU(input.dataId);\n            return { shape: input.shape, texData, isUniform: false };\n        });\n        this.uploadToGPU(output.dataId);\n        const outputData = { shape: output.shape, texData: outData, isUniform: false };\n        const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n        const binary = this.getAndSaveBinary(key, () => {\n            return gpgpu_math.compileProgram(this.gpgpu, program, inputsData, outputData);\n        });\n        const shouldTimeProgram = this.activeTimers != null;\n        let query;\n        if (shouldTimeProgram) {\n            query = this.startTimer();\n        }\n        gpgpu_math.runProgram(this.gpgpu, binary, inputsData, outputData, customSetup);\n        dataToDispose.forEach(info => this.disposeIntermediateTensorInfo(info));\n        if (shouldTimeProgram) {\n            query = this.endTimer(query);\n            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n        }\n        if (!env().getBool('WEBGL_LAZILY_UNPACK') && outData.isPacked &&\n            preventEagerUnpackingOfOutput === false) {\n            const unpacked = this.unpackTensor(output);\n            this.disposeIntermediateTensorInfo(output);\n            return unpacked;\n        }\n        return output;\n    }\n    compileAndRun(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput = false) {\n        outputDtype = outputDtype || inputs[0].dtype;\n        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customSetup, preventEagerUnpackingOfOutput);\n        return engine().makeTensorFromDataId(outInfo.dataId, outInfo.shape, outInfo.dtype);\n    }\n    getAndSaveBinary(key, getBinary) {\n        if (!(key in this.binaryCache)) {\n            this.binaryCache[key] = getBinary();\n        }\n        return this.binaryCache[key];\n    }\n    getTextureManager() {\n        return this.textureManager;\n    }\n    dispose() {\n        if (this.disposed) {\n            return;\n        }\n        // Avoid disposing the compiled webgl programs during unit testing because\n        // it slows down test execution.\n        if (!env().getBool('IS_TEST')) {\n            const allKeys = Object.keys(this.binaryCache);\n            allKeys.forEach(key => {\n                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n                delete this.binaryCache[key];\n            });\n        }\n        this.textureManager.dispose();\n        if (this.canvas != null &&\n            (typeof (HTMLCanvasElement) !== 'undefined' &&\n                this.canvas instanceof HTMLCanvasElement)) {\n            this.canvas.remove();\n        }\n        else {\n            this.canvas = null;\n        }\n        if (this.gpgpuCreatedLocally) {\n            this.gpgpu.program = null;\n            this.gpgpu.dispose();\n        }\n        this.disposed = true;\n    }\n    floatPrecision() {\n        if (this.floatPrecisionValue == null) {\n            this.floatPrecisionValue = tidy(() => {\n                if (!env().get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n                    // Momentarily switching DEBUG flag to false so we don't throw an\n                    // error trying to upload a small value.\n                    const debugFlag = env().getBool('DEBUG');\n                    env().set('DEBUG', false);\n                    const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n                    env().set('DEBUG', debugFlag);\n                    if (underflowCheckValue > 0) {\n                        return 32;\n                    }\n                }\n                return 16;\n            });\n        }\n        return this.floatPrecisionValue;\n    }\n    /** Returns the smallest representable number.  */\n    epsilon() {\n        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n    }\n    uploadToGPU(dataId) {\n        const texData = this.texData.get(dataId);\n        const { shape, dtype, values, texture, usage, isPacked } = texData;\n        if (texture != null) {\n            // Array is already on GPU. No-op.\n            return;\n        }\n        const shouldTimeProgram = this.activeTimers != null;\n        let start;\n        if (shouldTimeProgram) {\n            start = util.now();\n        }\n        let texShape = texData.texShape;\n        if (texShape == null) {\n            texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n            texData.texShape = texShape;\n        }\n        if (values != null) {\n            const shapeAs3D = webgl_util.getShapeAs3D(shape);\n            let program;\n            let width = texShape[1], height = texShape[0];\n            const isByteArray = values instanceof Uint8Array;\n            if (isPacked) {\n                [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n                program = new EncodeMatrixPackedProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            else {\n                program =\n                    new EncodeMatrixProgram(shapeAs3D, [height, width], isByteArray);\n            }\n            const tempDenseInputHandle = this.makeTensorInfo([height, width], dtype);\n            if (isByteArray) {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.PIXELS;\n            }\n            else {\n                this.texData.get(tempDenseInputHandle.dataId).usage =\n                    TextureUsage.UPLOAD;\n            }\n            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n            // We want the output to remain packed regardless of the value of\n            // WEBGL_PACK.\n            const preventEagerUnpacking = true;\n            const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, null, preventEagerUnpacking);\n            // Have the original texture assume the identity of the encoded output.\n            const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n            texData.texture = outputTexData.texture;\n            texData.texShape = outputTexData.texShape;\n            texData.isPacked = outputTexData.isPacked;\n            texData.usage = outputTexData.usage;\n            this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n            this.texData.delete(encodedOutputTarget.dataId);\n            // Once uploaded, don't store the values on cpu.\n            texData.values = null;\n            if (shouldTimeProgram) {\n                this.uploadWaitMs += util.now() - start;\n            }\n        }\n        else {\n            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n            texData.texture = newTexture;\n        }\n    }\n    convertAndCacheOnCPU(dataId, float32Values) {\n        const texData = this.texData.get(dataId);\n        const { dtype } = texData;\n        this.releaseGPUData(dataId);\n        if (float32Values != null) {\n            texData.values = float32ToTypedArray(float32Values, dtype);\n        }\n        return texData.values;\n    }\n    acquireTexture(texShape, texType, dtype, isPacked) {\n        this.numBytesInGPU += this.computeBytes(texShape, dtype);\n        if (!this.warnedAboutMemory &&\n            this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n            this.warnedAboutMemory = true;\n            console.warn(`High memory usage in GPU: ${mb} MB, ` +\n                `most likely due to a memory leak`);\n        }\n        return this.textureManager.acquireTexture(texShape, texType, isPacked);\n    }\n    computeBytes(shape, dtype) {\n        return shape[0] * shape[1] * util.bytesPerElement(dtype);\n    }\n    tryRunOnCpuOrThrow(inputs, fn) {\n        if (this.shouldExecuteOnCPU(inputs)) {\n            try {\n                return fn();\n            }\n            catch (e) {\n                if (env().getBool('IS_TEST')) {\n                    throw new Error('CPU forwarding failed');\n                }\n            }\n        }\n        return null;\n    }\n}\nfunction float32ToTypedArray(a, dtype) {\n    if (dtype === 'float32' || dtype === 'complex64') {\n        return a;\n    }\n    else if (dtype === 'int32' || dtype === 'bool') {\n        const result = (dtype === 'int32') ? new Int32Array(a.length) :\n            new Uint8Array(a.length);\n        for (let i = 0; i < result.length; ++i) {\n            result[i] = Math.round(a[i]);\n        }\n        return result;\n    }\n    else {\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n}\n//# sourceMappingURL=backend_webgl.js.map", "/** @license See the LICENSE file. */\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport { version };\n//# sourceMappingURL=version.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport * as gpgpu_util from './gpgpu_util';\nimport * as webgl_util from './webgl_util';\nexport { MathBackendWebGL } from './backend_webgl';\nexport { setWebGLContext } from './canvas_util';\nexport { GPGPUContext } from './gpgpu_context';\n// WebGL specific utils.\nexport { gpgpu_util, webgl_util };\n/**\n * Enforce use of half precision textures if available on the platform.\n *\n * @doc {heading: 'Environment', namespace: 'webgl'}\n */\nexport function forceHalfFloat() {\n    env().set('WEBGL_FORCE_F16_TEXTURES', true);\n}\n//# sourceMappingURL=webgl.js.map", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// base.ts is the webgl backend without auto kernel registration.\nimport { device_util, registerBackend } from '@tensorflow/tfjs-core';\nimport { MathBackendWebGL } from './backend_webgl';\nexport { version as version_webgl } from './version';\nif (device_util.isBrowser()) {\n    registerBackend('webgl', () => new MathBackendWebGL(), 2 /* priority */);\n}\n// Export webgl utilities\nexport * from './webgl';\n// Export forceHalfFlost under webgl namespace for the union bundle.\nimport { forceHalfFloat } from './webgl';\nexport const webgl = { forceHalfFloat };\n//# sourceMappingURL=base.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Identity } from '@tensorflow/tfjs-core';\nexport function identity(args) {\n    const { inputs, backend } = args;\n    const { x } = inputs;\n    backend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nexport const identityConfig = {\n    kernelName: Identity,\n    backendName: 'webgl',\n    kernelFunc: identity\n};\n//# sourceMappingURL=Identity.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Complex } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\n/**\n * In WebGL data is stored in GPU textures which can't be efficiently copied, so\n * complex tensors share data with their real and imaginary components. Complex\n * tensors increment the `complexParentRefCount` properties of the underlying\n * data buckets to prevent them from being disposed, as the engine's disposal\n * logic does not account for data sharing by complex tensors.\n *\n * When a complex tensor is disposed, it will explicitly decrease the\n * `complexParentRefCount` properties of its underlying components.\n */\nexport function complex(args) {\n    const { inputs, backend } = args;\n    const { real, imag } = inputs;\n    const complexInfo = backend.makeTensorInfo(real.shape, 'complex64');\n    const complex = backend.texData.get(complexInfo.dataId);\n    const realTensorInfo = identity({ inputs: { x: real }, backend });\n    const realData = backend.texData.get(realTensorInfo.dataId);\n    realData.complexParentRefCount++;\n    const imagTensorInfo = identity({ inputs: { x: imag }, backend });\n    const imagData = backend.texData.get(imagTensorInfo.dataId);\n    imagData.complexParentRefCount++;\n    complex.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n    return complexInfo;\n}\nexport const complexConfig = {\n    kernelName: Complex,\n    backendName: 'webgl',\n    kernelFunc: complex\n};\n//# sourceMappingURL=Complex.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, upcastType } from '@tensorflow/tfjs-core';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { complex } from '../kernels/Complex';\nimport { UnaryOpProgram } from '../unaryop_gpu';\nexport const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nexport const CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nexport const CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\n/**\n * Template that creates a `KernelFunc` for unary ops.\n * @param opSnippets Op snippet to create `UnaryOpProgram`.\n */\nexport function unaryKernelFunc(opSnippet) {\n    return ({ inputs, backend }) => {\n        const { x } = inputs;\n        const webglBackend = backend;\n        const program = new UnaryOpProgram(x.shape, opSnippet);\n        return webglBackend.runWebGLProgram(program, [x], x.dtype);\n    };\n}\n/**\n * Template that creates a `KernelFunc` for binary ops.\n * @param opSnippet Op snippet to create `BinaryOpProgram`.\n * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.\n * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true\n *     when creating BinaryOpPackedProgram.\n * @param dtype Optional. If set, the result has this dtype. Otherwise, the\n *     result has the same dtype as the first input. This is mainly used in\n *     comparison kernels, such as Equal, Less, Greater, etc.\n */\nexport function binaryKernelFunc({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {\n    return ({ inputs, backend }) => {\n        const { a, b } = inputs;\n        const webglBackend = backend;\n        if (supportsComplex && a.dtype === 'complex64') {\n            const aData = webglBackend.texData.get(a.dataId);\n            const bData = webglBackend.texData.get(b.dataId);\n            const [real, imag] = [\n                [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n                [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n            ].map(complexParts => {\n                const [aPart, bPart] = complexParts;\n                const aHandle = {\n                    dataId: aPart.dataId,\n                    dtype: aPart.dtype,\n                    shape: a.shape\n                };\n                const bHandle = {\n                    dataId: bPart.dataId,\n                    dtype: bPart.dtype,\n                    shape: b.shape\n                };\n                const program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n                return webglBackend.runWebGLProgram(program, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n            });\n            const complexOutput = complex({ inputs: { real, imag }, backend: webglBackend });\n            webglBackend.disposeIntermediateTensorInfo(real);\n            webglBackend.disposeIntermediateTensorInfo(imag);\n            // TODO(annxingyuan): Implement CPU forwarding for complex inputs.\n            return complexOutput;\n        }\n        const $dtype = dtype || upcastType(a.dtype, b.dtype);\n        if (webglBackend.shouldExecuteOnCPU([a, b]) && cpuKernelImpl != null) {\n            const aData = webglBackend.texData.get(a.dataId);\n            const bData = webglBackend.texData.get(b.dataId);\n            const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, aData.values, bData.values, $dtype);\n            const out = webglBackend.makeTensorInfo(outShape, $dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n            return out;\n        }\n        const shouldUsePackedProgram = env().getBool('WEBGL_PACK_BINARY_OPERATIONS') &&\n            packedOpSnippet != null;\n        let program;\n        if (shouldUsePackedProgram) {\n            program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);\n        }\n        else {\n            program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        }\n        return webglBackend.runWebGLProgram(program, [a, b], $dtype);\n    };\n}\n//# sourceMappingURL=kernel_funcs_utils.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Add } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nimport { addImplCPU as cpuAdd } from '../kernel_utils/shared';\nconst ADD = 'return a + b;';\nexport const addKernelFunc = binaryKernelFunc({\n    opSnippet: ADD,\n    packedOpSnippet: ADD,\n    supportsComplex: true,\n    cpuKernelImpl: cpuAdd\n});\nexport const addConfig = {\n    kernelName: Add,\n    backendName: 'webgl',\n    kernelFunc: addKernelFunc\n};\n//# sourceMappingURL=Add.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Atan2 } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc, CHECK_NAN_SNIPPET_BINARY, CHECK_NAN_SNIPPET_BINARY_PACKED } from '../kernel_utils/kernel_funcs_utils';\nconst ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nconst ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` +\n    CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nexport const atan2 = binaryKernelFunc({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });\nexport const atan2Config = {\n    kernelName: Atan2,\n    backendName: 'webgl',\n    kernelFunc: atan2,\n};\n//# sourceMappingURL=Atan2.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPool, backend_util, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function avgPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'avgPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const avgPoolProgram = new Pool2DProgram(convInfo, 'avg', false);\n    return backend.runWebGLProgram(avgPoolProgram, [x], 'float32');\n}\nexport const avgPoolConfig = {\n    kernelName: AvgPool,\n    backendName: 'webgl',\n    kernelFunc: avgPool\n};\n//# sourceMappingURL=AvgPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { AvgPoolBackprop, backend_util } from '@tensorflow/tfjs-core';\nimport { AvgPool2DBackpropProgram } from '../avg_pool_backprop_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function avgPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input } = inputs;\n    const x = input;\n    assertNotComplex([dy, input], 'avgPoolBackprop');\n    const { filterSize, strides, pad } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad);\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    return backend.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nexport const avgPoolBackpropConfig = {\n    kernelName: AvgPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: avgPoolBackprop\n};\n//# sourceMappingURL=AvgPoolBackprop.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.outputShape = [];\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = '0.0';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = '1.0';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class BatchNormPackedProgram {\n    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.variableNames = ['x', 'mean', 'variance'];\n        backend_util.assertAndGetBroadcastShape(xShape, meanShape);\n        backend_util.assertAndGetBroadcastShape(xShape, varianceShape);\n        let offsetSnippet = 'vec4(0.0)';\n        if (offsetShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, offsetShape);\n            this.variableNames.push('offset');\n            offsetSnippet = 'getOffsetAtOutCoords()';\n        }\n        let scaleSnippet = 'vec4(1.0)';\n        if (scaleShape != null) {\n            backend_util.assertAndGetBroadcastShape(xShape, scaleShape);\n            this.variableNames.push('scale');\n            scaleSnippet = 'getScaleAtOutCoords()';\n        }\n        this.outputShape = xShape;\n        this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=batchnorm_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, FusedBatchNorm, util } from '@tensorflow/tfjs-core';\nimport { BatchNormProgram } from '../batchnorm_gpu';\nimport { BatchNormPackedProgram } from '../batchnorm_packed_gpu';\nexport const batchNorm = ({ inputs, backend, attrs }) => {\n    const { x, mean, variance, offset, scale } = inputs;\n    util.assert(mean.shape.length === variance.shape.length, () => 'Batch normalization gradient requires mean and variance to have ' +\n        'equal ranks.');\n    util.assert(offset == null || mean.shape.length === offset.shape.length, () => 'Batch normalization gradient requires mean and offset to have ' +\n        'equal ranks.');\n    util.assert(scale == null || mean.shape.length === scale.shape.length, () => 'Batch normalization gradient requires mean and scale to have ' +\n        'equal ranks.');\n    let { varianceEpsilon } = attrs;\n    if (varianceEpsilon == null) {\n        varianceEpsilon = 0.001;\n    }\n    const finalInputs = [x, mean, variance];\n    let offsetShape = null;\n    if (offset != null) {\n        offsetShape = offset.shape;\n        finalInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale != null) {\n        scaleShape = scale.shape;\n        finalInputs.push(scale);\n    }\n    const program = env().getBool('WEBGL_PACK_NORMALIZATION') ?\n        new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) :\n        new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n    return output;\n};\nexport const batchNormConfig = {\n    kernelName: FusedBatchNorm,\n    backendName: 'webgl',\n    kernelFunc: batchNorm,\n};\n//# sourceMappingURL=BatchNorm.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { NotEqual } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst NOT_EQUAL = `return float(a != b);`;\nexport const notEqual = binaryKernelFunc({ opSnippet: NOT_EQUAL, dtype: 'bool' });\nexport const notEqualConfig = {\n    kernelName: NotEqual,\n    backendName: 'webgl',\n    kernelFunc: notEqual,\n};\n//# sourceMappingURL=NotEqual.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Real } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\nexport function real(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputData = backend.texData.get(input.dataId);\n    return identity({ inputs: { x: inputData.complexTensorInfos.real }, backend });\n}\nexport const realConfig = {\n    kernelName: Real,\n    backendName: 'webgl',\n    kernelFunc: real\n};\n//# sourceMappingURL=Real.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { UnaryOpProgram } from '../unaryop_gpu';\nconst TO_INT = `return float(int(x));`;\nexport function int(input, backend) {\n    const program = new UnaryOpProgram(input.shape, TO_INT);\n    const output = backend.runWebGLProgram(program, [input], 'int32');\n    return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n//# sourceMappingURL=int.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as tf from '@tensorflow/tfjs-core';\nimport { Cast, util } from '@tensorflow/tfjs-core';\nimport { complex } from './Complex';\nimport { identity } from './Identity';\nimport { notEqual } from './NotEqual';\nimport { real } from './Real';\nimport { int } from '../kernel_utils/int';\nexport function cast(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { dtype } = attrs;\n    // Casting to complex64.\n    if (dtype === 'complex64') {\n        if (x.dtype === 'complex64') {\n            return identity({ inputs: { x }, backend });\n        }\n        // TODO(annxingyuan): Import kernel function once zeros is modularized.\n        const zerosTensor = tf.zeros(x.shape);\n        const floatX = cast({ inputs: { x }, backend, attrs: { dtype: 'float32' } });\n        const result = complex({ inputs: { real: floatX, imag: zerosTensor }, backend });\n        zerosTensor.dispose();\n        backend.disposeIntermediateTensorInfo(floatX);\n        return result;\n    }\n    // Casting from complex64\n    if (x.dtype === 'complex64') {\n        const realPart = real({ inputs: { input: x }, backend });\n        const result = cast({ inputs: { x: realPart }, backend, attrs: { dtype } });\n        backend.disposeIntermediateTensorInfo(realPart);\n        return result;\n    }\n    if (!util.hasEncodingLoss(x.dtype, dtype)) {\n        // We don't change the underlying data, since we cast to higher\n        // precision.\n        const result = identity({ inputs: { x }, backend });\n        return { dataId: result.dataId, shape: result.shape, dtype };\n    }\n    if (dtype === 'int32') {\n        return int(x, backend);\n    }\n    if (dtype === 'bool') {\n        const zerosTensorInfo = backend.makeTensorInfo([], 'bool', util.getTypedArrayFromDType('bool', 1));\n        const binaryInputs = { a: x, b: zerosTensorInfo };\n        const result = notEqual({ inputs: binaryInputs, backend });\n        backend.disposeIntermediateTensorInfo(zerosTensorInfo);\n        return result;\n    }\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nexport const castConfig = {\n    kernelName: Cast,\n    backendName: 'webgl',\n    kernelFunc: cast\n};\n//# sourceMappingURL=Cast.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class ConcatProgram {\n    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n    constructor(shapes) {\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, 1 /* axis */);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][1];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][1];\n        }\n        const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            snippets.push(`else if (yC < ${offsets[i]}) ` +\n                `setOutput(getT${i}(yR, yC-${shift}));`);\n        }\n        const lastIndex = offsets.length;\n        const lastShift = offsets[offsets.length - 1];\n        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n        this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join('\\n        ')}\n      }\n    `;\n    }\n}\n//# sourceMappingURL=concat_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class ConcatPackedProgram {\n    constructor(shapes, axis) {\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = [];\n        this.outputShape = backend_util.computeOutShape(shapes, axis);\n        const shape = this.outputShape;\n        const rank = shape.length;\n        const dtype = getCoordsDataType(rank);\n        const coords = getChannels('coords', rank);\n        const channels = ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank);\n        this.variableNames = shapes.map((_, i) => `T${i}`);\n        const offsets = new Array(shapes.length - 1);\n        offsets[0] = shapes[0][axis];\n        for (let i = 1; i < offsets.length; i++) {\n            offsets[i] = offsets[i - 1] + shapes[i][axis];\n        }\n        const channel = channels[axis];\n        const lastChannels = channels.slice(-2);\n        const allChannels = channels.join();\n        let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n        for (let i = 1; i < offsets.length; i++) {\n            const shift = offsets[i - 1];\n            // Note: the >= comparison below may seem unnecessary given the check\n            // above but is needed to workaround branch execution issues on some\n            // devices. It makes all the conditions exclusive without relying on\n            // execution order.\n            getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift)}));\n        }`;\n        }\n        const lastIndex = offsets.length;\n        const shift = offsets[offsets.length - 1];\n        getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n        this.userCode = `\n      float getValue(${channels.map(x => 'int ' + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;\n        if (${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords});\n        }\n\n        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords});\n        }\n\n        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;\n        if (${coords[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords});\n        }\n        setOutput(result);\n      }\n    `;\n    }\n}\n/**\n * Return an expression for coordinates into a vector where a given channel\n * will be offset by [shift].\n *\n * @param channels the channels to consider\n * @param channel the channel we want shifted\n * @param shift  the amount to subtract from the channel.\n *\n * @returns a string of the form 'x, y-[shift], z' where any one channel can\n * have the shift applied.\n */\nfunction shiftedChannels(channels, channel, shift) {\n    const channelIdx = channels.indexOf(channel);\n    const res = channels.map((c, idx) => {\n        if (idx === channelIdx) {\n            return `${c} - ${shift}`;\n        }\n        else {\n            return c;\n        }\n    });\n    return res.join();\n}\n//# sourceMappingURL=concat_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Imag } from '@tensorflow/tfjs-core';\nimport { identity } from './Identity';\nexport function imag(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    const inputData = backend.texData.get(input.dataId);\n    return identity({ inputs: { x: inputData.complexTensorInfos.imag }, backend });\n}\nexport const imagConfig = {\n    kernelName: Imag,\n    backendName: 'webgl',\n    kernelFunc: imag\n};\n//# sourceMappingURL=Imag.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ReshapePackedProgram } from '../reshape_packed_gpu';\nimport { getBatchDim, getRowsCols } from '../webgl_util';\nexport function packedReshape(input, afterShape, backend) {\n    const input3DShape = [getBatchDim(input.shape),\n        ...getRowsCols(input.shape)];\n    const input3D = {\n        dtype: input.dtype,\n        shape: input3DShape,\n        dataId: input.dataId\n    };\n    const afterShapeAs3D = [getBatchDim(afterShape),\n        ...getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const output = backend.runWebGLProgram(program, [input3D], input.dtype, null /* customSetup */, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n//# sourceMappingURL=reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Reshape, util } from '@tensorflow/tfjs-core';\nimport { packedReshape } from '../kernel_utils/reshape';\nimport { isReshapeFree } from '../webgl_util';\nexport function reshape(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    const { shape } = attrs;\n    const webglBackend = backend;\n    const xSize = util.sizeFromShape(x.shape);\n    const $shape = util.inferFromImplicitShape(shape, xSize);\n    const $xSize = util.sizeFromShape($shape);\n    util.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old ` +\n        `shape (${x.shape}) has ${xSize} elements. The new shape and old ` +\n        `shape must have the same number of elements.`);\n    const xTexData = webglBackend.texData.get(x.dataId);\n    if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) &&\n        !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n        return packedReshape(x, $shape, webglBackend);\n    }\n    webglBackend.incRef(x.dataId);\n    return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nexport const reshapeConfig = {\n    kernelName: Reshape,\n    backendName: 'webgl',\n    kernelFunc: reshape\n};\n//# sourceMappingURL=Reshape.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, env, util } from '@tensorflow/tfjs-core';\nimport { ConcatProgram } from '../concat_gpu';\nimport { ConcatPackedProgram } from '../concat_packed_gpu';\nimport { complex } from './Complex';\nimport { imag } from './Imag';\nimport { real } from './Real';\nimport { reshape } from './Reshape';\nexport function concatImpl(inputs, axis, backend) {\n    const dtype = inputs[0].dtype;\n    if (dtype === 'complex64') {\n        const reals = inputs.map((t) => real({ inputs: { input: t }, backend }));\n        const imags = inputs.map((t) => imag({ inputs: { input: t }, backend }));\n        const realConcated = concatImpl(reals, axis, backend);\n        const imagConcated = concatImpl(imags, axis, backend);\n        const result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend });\n        reals.forEach(r => backend.disposeIntermediateTensorInfo(r));\n        imags.forEach(i => backend.disposeIntermediateTensorInfo(i));\n        backend.disposeIntermediateTensorInfo(realConcated);\n        backend.disposeIntermediateTensorInfo(imagConcated);\n        return result;\n    }\n    if (inputs.length > env().getNumber('WEBGL_MAX_TEXTURES_IN_SHADER')) {\n        const midIndex = Math.floor(inputs.length / 2);\n        const leftSide = concatImpl(inputs.slice(0, midIndex), axis, backend);\n        const rightSide = concatImpl(inputs.slice(midIndex), axis, backend);\n        const result = concatImpl([leftSide, rightSide], axis, backend);\n        backend.disposeIntermediateTensorInfo(leftSide);\n        backend.disposeIntermediateTensorInfo(rightSide);\n        return result;\n    }\n    if (env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') &&\n        inputs[0].shape.length > 1) {\n        const program = new ConcatPackedProgram(inputs.map(t => t.shape), axis);\n        return backend.runWebGLProgram(program, inputs, dtype);\n    }\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), axis);\n    const tensors2D = inputs.map(x => reshape({\n        inputs: { x },\n        attrs: { shape: [-1, util.sizeFromShape(x.shape.slice(axis))] },\n        backend\n    }));\n    const program = new ConcatProgram(tensors2D.map(t => t.shape));\n    const result = backend.runWebGLProgram(program, tensors2D, dtype);\n    tensors2D.forEach(r => backend.disposeIntermediateTensorInfo(r));\n    const reshapedResult = reshape({ inputs: { x: result }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n}\n//# sourceMappingURL=Concat_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Concat, util } from '@tensorflow/tfjs-core';\nimport { concatImpl } from './Concat_impl';\nexport function concat(args) {\n    const { inputs, backend, attrs } = args;\n    const { axis } = attrs;\n    const $axis = util.parseAxisParam(axis, inputs[0].shape)[0];\n    const outShape = backend_util.computeOutShape(inputs.map(t => t.shape), $axis);\n    if (util.sizeFromShape(outShape) === 0) {\n        return backend.makeTensorInfo(outShape, inputs[0].dtype, []);\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    const $inputs = inputs.filter(t => util.sizeFromShape(t.shape) > 0);\n    if ($inputs.length === 1) {\n        return $inputs[0];\n    }\n    const shapes = $inputs.map(t => t.shape);\n    backend_util.assertParamsConsistent(shapes, $axis);\n    return concatImpl($inputs, $axis, backend);\n}\nexport const concatConfig = {\n    kernelName: Concat,\n    backendName: 'webgl',\n    kernelFunc: concat\n};\n//# sourceMappingURL=Concat.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Cos } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nexport const cos = unaryKernelFunc(COS);\nexport const cosConfig = {\n    kernelName: Cos,\n    backendName: 'webgl',\n    kernelFunc: cos,\n};\n//# sourceMappingURL=Cos.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Div } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\n// Without the equality check div produces 0.9999 for a = b, which when\n// floored can cause errors.\nconst DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\n// We do the same as in ./binaryop_gpu, with vec4 and ivec4.\n// On Linux, the vectorized implementation produces NaNs when a and b are 0.\nconst DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nexport const div = binaryKernelFunc({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });\nexport const divConfig = {\n    kernelName: Div,\n    backendName: 'webgl',\n    kernelFunc: div,\n};\n//# sourceMappingURL=Div.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FFTProgram {\n    constructor(component, inputShape, inverse) {\n        this.variableNames = ['real', 'imag'];\n        const innerDim = inputShape[1];\n        this.outputShape = inputShape;\n        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n        const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n        let opString;\n        if (component === 'real') {\n            opString = 'return real * expR - imag * expI;';\n        }\n        else if (component === 'imag') {\n            opString = 'return real * expI + imag * expR;';\n        }\n        else {\n            throw new Error(`FFT component must be either \"real\" or \"imag\", got ${component}.`);\n        }\n        this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${opString}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=fft_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { FFTProgram } from '../fft_gpu';\nimport { complex } from './Complex';\nimport { reshape } from './Reshape';\nexport function fftImpl(x, inverse, backend) {\n    const xData = backend.texData.get(x.dataId);\n    const inputSize = util.sizeFromShape(x.shape);\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = x.shape[x.shape.length - 1];\n    const batch = inputSize / innerDimensionSize;\n    const input2D = reshape({ inputs: { x }, backend, attrs: { shape: [batch, innerDimensionSize] } });\n    const xShape = input2D.shape;\n    const realProgram = new FFTProgram('real', xShape, inverse);\n    const imagProgram = new FFTProgram('imag', xShape, inverse);\n    const inputs = [\n        {\n            dataId: xData.complexTensorInfos.real.dataId,\n            dtype: xData.complexTensorInfos.real.dtype,\n            shape: xShape\n        },\n        {\n            dataId: xData.complexTensorInfos.imag.dataId,\n            dtype: xData.complexTensorInfos.imag.dtype,\n            shape: xShape\n        }\n    ];\n    const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');\n    const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');\n    const complexOutput = complex({ inputs: { real: realPart, imag: imagPart }, backend });\n    backend.disposeIntermediateTensorInfo(realPart);\n    backend.disposeIntermediateTensorInfo(imagPart);\n    const complexOutputReshaped = reshape({ inputs: { x: complexOutput }, backend, attrs: { shape: x.shape } });\n    backend.disposeIntermediateTensorInfo(complexOutputReshaped);\n    return complexOutputReshaped;\n}\n//# sourceMappingURL=FFT_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FFT } from '@tensorflow/tfjs-core';\nimport { fftImpl } from './FFT_impl';\nexport function fft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    return fftImpl(input, false /* inverse */, backend);\n}\nexport const fftConfig = {\n    kernelName: FFT,\n    backendName: 'webgl',\n    kernelFunc: fft\n};\n//# sourceMappingURL=FFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class FlipLeftRightProgram {\n    constructor(imageShape) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageWidth = imageShape[2];\n        this.outputShape = imageShape;\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=flip_left_right_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { FlipLeftRight } from '@tensorflow/tfjs-core';\nimport { FlipLeftRightProgram } from '../flip_left_right_gpu';\nexport const flipLeftRightConfig = {\n    kernelName: FlipLeftRight,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend }) => {\n        const { image } = inputs;\n        const webglBackend = backend;\n        const program = new FlipLeftRightProgram(image.shape);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=FlipLeftRight.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_gpu.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlslDifferences } from '../../glsl_version';\nexport class FromPixelsPackedProgram {\n    constructor(outputShape) {\n        this.variableNames = ['A'];\n        this.packedInputs = false;\n        this.packedOutput = true;\n        const glsl = getGlslDifferences();\n        const [height, width,] = outputShape;\n        this.outputShape = outputShape;\n        this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n    }\n}\n//# sourceMappingURL=from_pixels_packed_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { FromPixels } from '@tensorflow/tfjs-core';\nimport { TextureUsage } from '../tex_util';\nimport { FromPixelsProgram } from './FromPixels_utils/from_pixels_gpu';\nimport { FromPixelsPackedProgram } from './FromPixels_utils/from_pixels_packed_gpu';\nexport const fromPixelsConfig = {\n    kernelName: FromPixels,\n    backendName: 'webgl',\n    kernelFunc: fromPixels,\n};\nlet fromPixels2DContext;\nfunction fromPixels(args) {\n    const { inputs, backend, attrs } = args;\n    let { pixels } = inputs;\n    const { numChannels } = attrs;\n    const isVideo = typeof (HTMLVideoElement) !== 'undefined' &&\n        pixels instanceof HTMLVideoElement;\n    const isImage = typeof (HTMLImageElement) !== 'undefined' &&\n        pixels instanceof HTMLImageElement;\n    const [width, height] = isVideo ?\n        [\n            pixels.videoWidth,\n            pixels.videoHeight\n        ] :\n        [pixels.width, pixels.height];\n    const texShape = [height, width];\n    const outShape = [height, width, numChannels];\n    if (isImage || isVideo) {\n        if (fromPixels2DContext == null) {\n            fromPixels2DContext = document.createElement('canvas').getContext('2d');\n        }\n        fromPixels2DContext.canvas.width = width;\n        fromPixels2DContext.canvas.height = height;\n        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = backend.makeTensorInfo(texShape, 'int32');\n    // This is a byte texture with pixels.\n    backend.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);\n    const program = env().getBool('WEBGL_PACK') ?\n        new FromPixelsPackedProgram(outShape) :\n        new FromPixelsProgram(outShape);\n    const res = backend.runWebGLProgram(program, [tempPixelHandle], 'int32');\n    backend.disposeData(tempPixelHandle.dataId);\n    return res;\n}\n//# sourceMappingURL=FromPixels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { IFFT } from '@tensorflow/tfjs-core';\nimport { fftImpl } from './FFT_impl';\nexport function ifft(args) {\n    const { inputs, backend } = args;\n    const { input } = inputs;\n    return fftImpl(input, true /* inverse */, backend);\n}\nexport const ifftConfig = {\n    kernelName: IFFT,\n    backendName: 'webgl',\n    kernelFunc: ifft\n};\n//# sourceMappingURL=IFFT.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nexport class MeanProgram {\n    constructor(reduceInfo, divisor) {\n        this.variableNames = ['x'];\n        const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n        this.outputShape = [batchSize, outSize];\n        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n        const windowSizeVec4Remainder = windowSize % 4;\n        let updateSnippet = `sumValue += dot(values, ones);`;\n        if (divisor != null) {\n            const denominator = 1 / divisor;\n            updateSnippet = `sumValue += dot(values * ${util.isInt(denominator) ? denominator.toPrecision(2) :\n                denominator}, ones);`;\n        }\n        let checkOutOfBounds = '';\n        if (inSize % windowSize > 0) {\n            checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return 0.0;\n        }\n      `;\n        }\n        this.userCode = `\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${updateSnippet}\n        }\n        setOutput(sumValue);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mean_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nimport { MeanProgram } from '../mean_gpu';\nimport { ReduceProgram } from '../reduce_gpu';\n// Returns an array of configuration objects that describe each stage of the\n// reduction.\nfunction getReductionStages(inShape) {\n    const stages = [];\n    while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n        const windowSize = backend_util.computeOptimalWindowSize(outSize);\n        stages.push({\n            inSize: outSize,\n            windowSize,\n            outSize: Math.ceil(outSize / windowSize)\n        });\n    }\n    return stages;\n}\nexport function reduce(x, dtype, reductionType, backend) {\n    const reductionStages = getReductionStages(x.shape);\n    let result = x;\n    for (let i = 0; i < reductionStages.length; i++) {\n        const { inSize, windowSize, outSize } = reductionStages[i];\n        let program;\n        let previousResult;\n        if (reductionType === 'mean') {\n            program = i === 0 ?\n                new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) :\n                new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });\n        }\n        else {\n            program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n        }\n        previousResult = result;\n        result = backend.runWebGLProgram(program, [result], dtype);\n        if (previousResult.dataId !== x.dataId) {\n            backend.disposeIntermediateTensorInfo(previousResult);\n        }\n    }\n    return result;\n}\n//# sourceMappingURL=reduce.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function maxImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, x.dtype, 'max', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Max_impl.js.map", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposeProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        const dtype = getCoordsDataType(this.rank);\n        const switched = getSwitchedCoords(newDim);\n        this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n    }\n}\nfunction getSwitchedCoords(newDim) {\n    const rank = newDim.length;\n    if (rank > 6) {\n        throw Error(`Transpose for rank ${rank} is not yet supported`);\n    }\n    const originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n    const switchedCoords = new Array(rank);\n    for (let i = 0; i < newDim.length; i++) {\n        switchedCoords[newDim[i]] = originalOrder[i];\n    }\n    return switchedCoords.join();\n}\n//# sourceMappingURL=transpose_gpu.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getVecChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\nexport class TransposePackedProgram {\n    constructor(aShape, newDim) {\n        this.variableNames = ['A'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        const outputShape = new Array(aShape.length);\n        for (let i = 0; i < outputShape.length; i++) {\n            outputShape[i] = aShape[newDim[i]];\n        }\n        this.outputShape = outputShape;\n        this.rank = outputShape.length;\n        if (this.rank > 6) {\n            throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n        }\n        const dtype = getCoordsDataType(this.rank);\n        const outputOrder = getVecChannels('rc', this.rank);\n        const switchedOrder = new Array(this.rank);\n        for (let i = 0; i < newDim.length; i++) {\n            switchedOrder[newDim[i]] = outputOrder[i];\n        }\n        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n        this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n    }\n}\n//# sourceMappingURL=transpose_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '@tensorflow/tfjs-core';\nimport { transposeImplCPU } from '../kernel_utils/shared';\nimport { TransposeProgram } from '../transpose_gpu';\nimport { TransposePackedProgram } from '../transpose_packed_gpu';\nexport function transposeImpl(x, perm, backend) {\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new TransposePackedProgram(x.shape, perm) :\n        new TransposeProgram(x.shape, perm);\n    return backend.runWebGLProgram(program, [x], x.dtype);\n}\nexport { transposeImplCPU };\n//# sourceMappingURL=Transpose_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Max } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxImplCPU } from '../kernel_utils/shared';\nimport { maxImpl } from './Max_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const maxConfig = {\n    kernelName: Max,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { reductionIndices, keepDims } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const maxInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        let maxInput = x;\n        if (maxInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(maxInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                maxInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const maxInputData = webglBackend.texData.get(maxInput.dataId);\n                maxInputData.values = maxInputValues;\n            }\n            else {\n                maxInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('max', axes, xRank);\n        const [maxOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(maxInput.shape, axes);\n        let outShape = maxOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(maxOutShape, origAxes);\n        }\n        let out;\n        if (shouldExecuteOnCPU) {\n            const xTexData = webglBackend.texData.get(maxInput.dataId);\n            const values = xTexData.values;\n            const outValues = maxImplCPU(values, util.sizeFromShape(reduceShape), outShape, x.dtype);\n            out = webglBackend.makeTensorInfo(outShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = maxImpl(maxInput, reduceShape, outShape, webglBackend);\n        }\n        if (maxInputIsTransposed) {\n            webglBackend.disposeIntermediateTensorInfo(maxInput);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Max.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPool, util } from '@tensorflow/tfjs-core';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nimport { identity } from './Identity';\nexport function maxPool(args) {\n    const { inputs, backend, attrs } = args;\n    const { x } = inputs;\n    assertNotComplex(x, 'maxPool');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const dilations = 1;\n    util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n        `Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n        return identity({ inputs: { x }, backend });\n    }\n    const maxPoolProgram = new Pool2DProgram(convInfo, 'max', false);\n    return backend.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nexport const maxPoolConfig = {\n    kernelName: MaxPool,\n    backendName: 'webgl',\n    kernelFunc: maxPool\n};\n//# sourceMappingURL=MaxPool.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, MaxPoolBackprop } from '@tensorflow/tfjs-core';\nimport { MaxPool2DBackpropProgram } from '../max_pool_backprop_gpu';\nimport { Pool2DProgram } from '../pool_gpu';\nimport { assertNotComplex } from '../webgl_util';\nexport function maxPoolBackprop(args) {\n    const { inputs, backend, attrs } = args;\n    const { dy, input, output } = inputs;\n    const x = input;\n    assertNotComplex([input, output], 'maxPoolBackprop');\n    const { filterSize, strides, pad, dimRoundingMode } = attrs;\n    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);\n    const getPositions = true;\n    const maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions], x.dtype);\n    backend.disposeIntermediateTensorInfo(maxPoolPositions);\n    return result;\n}\nexport const maxPoolBackpropConfig = {\n    kernelName: MaxPoolBackprop,\n    backendName: 'webgl',\n    kernelFunc: maxPoolBackprop\n};\n//# sourceMappingURL=MaxPoolBackprop.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Pool2DProgram } from '../pool_gpu';\nexport function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {\n    let program = new Pool2DProgram(convInfo, 'max', false);\n    const poolOutput = backend.runWebGLProgram(program, [x], 'float32');\n    program = new Pool2DProgram(convInfo, 'max', true, true, includeBatchInIndex);\n    const indexOutput = backend.runWebGLProgram(program, [x], 'float32');\n    return [poolOutput, indexOutput];\n}\n//# sourceMappingURL=MaxPoolWithArgmax_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { MaxPoolWithArgmax } from '@tensorflow/tfjs-core';\nimport { backend_util, util } from '@tensorflow/tfjs-core';\nimport { maxPoolWithArgmaxImpl } from './MaxPoolWithArgmax_impl';\nexport const maxPoolWithArgmaxConfig = {\n    kernelName: MaxPoolWithArgmax,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { filterSize, strides, pad, includeBatchInIndex } = attrs;\n        const webglBackend = backend;\n        util.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n        const dilations = [1, 1];\n        util.assert(backend_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n            `Got strides ${strides} and dilations '${dilations}'`);\n        const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad);\n        const [result, indexes] = maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, webglBackend);\n        return [result, indexes];\n    }\n};\n//# sourceMappingURL=MaxPoolWithArgmax.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { util } from '@tensorflow/tfjs-core';\nimport { reduce } from '../kernel_utils/reduce';\nimport { reshape } from '../kernels/Reshape';\nexport function meanImpl(x, reduceShape, outShape, backend) {\n    const inSize = util.sizeFromShape(reduceShape);\n    const xSize = util.sizeFromShape(x.shape);\n    const batchSize = xSize / inSize;\n    const reshapedInput = reshape({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend });\n    const reduced = reduce(reshapedInput, 'float32', 'mean', backend);\n    const reshapedOutput = reshape({ inputs: { x: reduced }, attrs: { shape: outShape }, backend });\n    backend.disposeIntermediateTensorInfo(reshapedInput);\n    backend.disposeIntermediateTensorInfo(reduced);\n    return reshapedOutput;\n}\n//# sourceMappingURL=Mean_impl.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, Mean, util } from '@tensorflow/tfjs-core';\nimport { meanImpl } from './Mean_impl';\nimport { transposeImpl, transposeImplCPU } from './Transpose_impl';\nexport const meanConfig = {\n    kernelName: Mean,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { keepDims, axis } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const origAxes = util.parseAxisParam(axis, x.shape);\n        let axes = origAxes;\n        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);\n        const meanInputIsTransposed = permutedAxes != null;\n        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n        const intermediates = [];\n        let meanInput = x;\n        if (meanInputIsTransposed) {\n            if (shouldExecuteOnCPU) {\n                const xTexData = webglBackend.texData.get(meanInput.dataId);\n                const values = xTexData.values;\n                const newShape = new Array(xRank);\n                for (let i = 0; i < newShape.length; i++) {\n                    newShape[i] = x.shape[permutedAxes[i]];\n                }\n                const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n                meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n                const meanInputData = webglBackend.texData.get(meanInput.dataId);\n                meanInputData.values = meanInputValues;\n            }\n            else {\n                meanInput = transposeImpl(x, permutedAxes, webglBackend);\n            }\n            intermediates.push(meanInput);\n            axes = backend_util.getInnerMostAxes(axes.length, xRank);\n        }\n        backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);\n        const [meanOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(meanInput.shape, axes);\n        let outShape = meanOutShape;\n        if (keepDims) {\n            // rather than reshape at the end, set the target shape here.\n            outShape = backend_util.expandShapeToKeepDim(meanOutShape, origAxes);\n        }\n        const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);\n        for (const i of intermediates) {\n            webglBackend.disposeIntermediateTensorInfo(i);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Mean.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getCoordsDataType } from './shader_compiler';\nexport class MirrorPadProgram {\n    constructor(xShape, paddings, mode) {\n        this.variableNames = ['x'];\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);\n        const offset = mode === 'reflect' ? 0 : 1;\n        if (rank === 1) {\n            this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${offset};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${offset};\n          }\n          setOutput(getX(outC - start));\n        }\n      `;\n            return;\n        }\n        this.userCode = `\n      ${dtype} start = ${dtype}(${start});\n      ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outC = getOutputCoords();\n        for (int i = 0; i < ${rank}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${offset};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};\n          }\n        }\n        ${dtype} coords = outC - start;\n        setOutput(getX(${unpackedCoords}));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mirror_pad_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getChannels } from './packing_util';\nimport { getCoordsDataType } from './shader_compiler';\n/**\n * Example shader code for\n * `mirrorPad(tf.tensor1d([1, 2, 3], 'int32'), [[2, 2]], 'reflect')`\n * ```\n *    const int start = int(2);\n *    const int end = int(5);\n *\n *    void main() {\n *       int outputLoc = getOutputCoords();\n *       vec4 result = vec4(0.);\n *\n *       int rc = outputLoc;\n *\n *       int source = rc;\n *       if (source < start) {\n *         source = start * 2 - source - 0;\n *       } else if (source >= end) {\n *         source = (end - 1) * 2 - source + 0;\n *       }\n *       source -= start;\n *\n *       result[0] = getChannel(getX(source), source);\n *       rc += 1;\n *       if(rc < 6) {\n *          int source = rc;\n *          if (source < start) {\n *            source = start * 2 - source - 0;\n *          } else if (source >= end) {\n *            source = (end - 1) * 2 - source + 0;\n *          }\n *          source -= start;\n *\n *         result[1] = getChannel(getX(source), source);\n *       }\n *\n *       setOutput(result);\n *     }\n * ```\n */\nexport class MirrorPadPackedProgram {\n    constructor(xShape, paddings, mode) {\n        this.variableNames = ['x'];\n        this.packedInputs = true;\n        this.packedOutput = true;\n        this.outputShape = paddings.map((p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n        const rank = xShape.length;\n        const dtype = getCoordsDataType(rank);\n        const start = paddings.map(p => p[0]).join(',');\n        const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n        const coords = getChannels('rc', rank);\n        const source = getChannels('source', rank);\n        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;\n        const innerDims = rank === 1 ? 'source' : `vec2(${source.slice(-2).join()})`;\n        const offset = mode === 'reflect' ? 0 : 1;\n        let mainLoop = '';\n        if (rank === 1) {\n            const padSetup = `\n        ${dtype} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${offset};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${offset};\n        }\n        source -= start;\n      `;\n            mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n        }\n        else {\n            const padSetup = `\n        ${dtype} source = rc;\n        ${dtype} lt = ${dtype}(lessThan(source, start));\n        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));\n        ${dtype} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${offset}) +\n                gte * ((end - 1) * 2 - source + ${offset});\n        source -= start;\n      `;\n            mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n        rc = outputLoc;\n        ${coords[rank - 2]} += 1;\n        if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {\n          ${padSetup}\n          result[2] = getChannel(getX(${source.join()}), ${innerDims});\n          ${coords[rank - 1]} += 1;\n          if(${cLimit}) {\n            ${padSetup}\n            result[3] = getChannel(getX(${source.join()}), ${innerDims});\n          }\n        }\n      `;\n        }\n        this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n    }\n}\n//# sourceMappingURL=mirror_pad_packed_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env, MirrorPad } from '@tensorflow/tfjs-core';\nimport { MirrorPadProgram } from '../mirror_pad_gpu';\nimport { MirrorPadPackedProgram } from '../mirror_pad_packed_gpu';\nexport const mirrorPadKernelFunc = ({ inputs, backend, attrs }) => {\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    const program = env().getBool('WEBGL_PACK_ARRAY_OPERATIONS') ?\n        new MirrorPadPackedProgram(x.shape, paddings, mode) :\n        new MirrorPadProgram(x.shape, paddings, mode);\n    const output = backend.runWebGLProgram(program, [x], x.dtype);\n    return output;\n};\nexport const mirrorPadConfig = {\n    kernelName: MirrorPad,\n    backendName: 'webgl',\n    kernelFunc: mirrorPadKernelFunc,\n};\n//# sourceMappingURL=MirrorPad.js.map", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n    REAL: 'return areal * breal - aimag * bimag;',\n    IMAG: 'return areal * bimag + aimag * breal;'\n};\nexport class BinaryOpComplexProgram {\n    constructor(op, aShape, bShape) {\n        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n        this.outputShape = backend_util.assertAndGetBroadcastShape(aShape, bShape);\n        this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n    }\n}\n//# sourceMappingURL=binaryop_complex_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, env, Multiply } from '@tensorflow/tfjs-core';\nimport * as binaryop_complex_gpu from '../binaryop_complex_gpu';\nimport { BinaryOpComplexProgram } from '../binaryop_complex_gpu';\nimport { BinaryOpProgram } from '../binaryop_gpu';\nimport { BinaryOpPackedProgram } from '../binaryop_packed_gpu';\nimport { multiplyImplCPU as cpuMultiply } from '../kernel_utils/shared';\nimport { complex } from './Complex';\nconst MUL = 'return a * b;';\nexport function multiply(args) {\n    const { inputs, backend } = args;\n    const { a, b } = inputs;\n    const dtype = backend_util.upcastType(a.dtype, b.dtype);\n    if (a.dtype === 'complex64') {\n        const aData = backend.texData.get(a.dataId);\n        const bData = backend.texData.get(b.dataId);\n        const realProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n        const imagProgram = new BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n        const inputs = [\n            {\n                dataId: aData.complexTensorInfos.real.dataId,\n                dtype: aData.complexTensorInfos.real.dtype,\n                shape: a.shape\n            },\n            {\n                dataId: aData.complexTensorInfos.imag.dataId,\n                dtype: aData.complexTensorInfos.imag.dtype,\n                shape: a.shape\n            },\n            {\n                dataId: bData.complexTensorInfos.real.dataId,\n                dtype: bData.complexTensorInfos.real.dtype,\n                shape: b.shape\n            },\n            {\n                dataId: bData.complexTensorInfos.imag.dataId,\n                dtype: bData.complexTensorInfos.imag.dtype,\n                shape: b.shape\n            }\n        ];\n        const realPart = backend.runWebGLProgram(realProgram, inputs, 'float32');\n        const imagPart = backend.runWebGLProgram(imagProgram, inputs, 'float32');\n        const complexOutput = complex({ inputs: { real: realPart, imag: imagPart }, backend });\n        backend.disposeIntermediateTensorInfo(realPart);\n        backend.disposeIntermediateTensorInfo(imagPart);\n        // TODO(annxingyuan): CPU forwarding for complex inputs.\n        return complexOutput;\n    }\n    if (backend.shouldExecuteOnCPU([a, b])) {\n        const aData = backend.texData.get(a.dataId);\n        const bData = backend.texData.get(b.dataId);\n        const [outValues, outShape] = cpuMultiply(a.shape, b.shape, aData.values, bData.values, dtype);\n        const out = backend.makeTensorInfo(outShape, dtype);\n        const outData = backend.texData.get(out.dataId);\n        outData.values = outValues;\n        return out;\n    }\n    let program;\n    if (env().getBool('WEBGL_PACK_BINARY_OPERATIONS')) {\n        program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);\n    }\n    else {\n        program = new BinaryOpProgram(MUL, a.shape, b.shape);\n    }\n    return backend.runWebGLProgram(program, [a, b], dtype);\n}\nexport const multiplyConfig = {\n    kernelName: Multiply,\n    backendName: 'webgl',\n    kernelFunc: multiply\n};\n//# sourceMappingURL=Multiply.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV3 } from '@tensorflow/tfjs-core';\nexport const nonMaxSuppressionV3Config = {\n    kernelName: NonMaxSuppressionV3,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        return kernel_impls.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal);\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV3.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls, NonMaxSuppressionV4 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV4Impl = kernel_impls.nonMaxSuppressionV4Impl;\nexport const nonMaxSuppressionV4Config = {\n    kernelName: NonMaxSuppressionV4,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n        return [selectedIndices, validOutputs];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV4.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util, kernel_impls } from '@tensorflow/tfjs-core';\nimport { NonMaxSuppressionV5 } from '@tensorflow/tfjs-core';\nconst nonMaxSuppressionV5Impl = kernel_impls.nonMaxSuppressionV5Impl;\nexport const nonMaxSuppressionV5Config = {\n    kernelName: NonMaxSuppressionV5,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, backend, attrs }) => {\n        backend_util.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n            'Call tf.nonMaxSuppressionAsync() instead');\n        const { boxes, scores } = inputs;\n        const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n        const gpuBackend = backend;\n        const boxesVals = gpuBackend.readSync(boxes.dataId);\n        const scoresVals = gpuBackend.readSync(scores.dataId);\n        const maxOutputSizeVal = maxOutputSize;\n        const iouThresholdVal = iouThreshold;\n        const scoreThresholdVal = scoreThreshold;\n        const softNmsSigmaVal = softNmsSigma;\n        const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n        return [selectedIndices, selectedScores];\n    }\n};\n//# sourceMappingURL=NonMaxSuppressionV5.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { backend_util } from '@tensorflow/tfjs-core';\nexport class RotateProgram {\n    constructor(imageShape, radians, fillValue, center) {\n        this.variableNames = ['Image'];\n        this.outputShape = [];\n        const imageHeight = imageShape[1];\n        const imageWidth = imageShape[2];\n        const sinFactor = Math.sin(radians).toFixed(3);\n        const cosFactor = Math.cos(radians).toFixed(3);\n        this.outputShape = imageShape;\n        const [centerX, centerY] = backend_util.getImageCenter(center, imageHeight, imageWidth);\n        const centerXString = centerX.toFixed(3);\n        const centerYString = centerY.toFixed(3);\n        let fillSnippet = '';\n        if (typeof fillValue === 'number') {\n            fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n        }\n        else {\n            fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(',')});\n        float outputValue = fill[coords[3]];`;\n        }\n        this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};\n          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};\n          int coordX = int(round(coordXFloat + ${centerXString}));\n          int coordY = int(round(coordYFloat + ${centerYString}));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n    }\n}\n//# sourceMappingURL=rotate_gpu.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { RotateWithOffset } from '@tensorflow/tfjs-core';\nimport { RotateProgram } from '../rotate_gpu';\nexport const rotateWithOffsetConfig = {\n    kernelName: RotateWithOffset,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { image } = inputs;\n        const { radians, fillValue, center } = attrs;\n        const webglBackend = backend;\n        const program = new RotateProgram(image.shape, radians, fillValue, center);\n        const output = webglBackend.runWebGLProgram(program, [image], image.dtype);\n        return output;\n    }\n};\n//# sourceMappingURL=RotateWithOffset.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sin } from '@tensorflow/tfjs-core';\nimport { CHECK_NAN_SNIPPET_UNARY, unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nexport const sin = unaryKernelFunc(SIN);\nexport const sinConfig = {\n    kernelName: Sin,\n    backendName: 'webgl',\n    kernelFunc: sin,\n};\n//# sourceMappingURL=Sin.js.map", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Square } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARE = `return x * x;`;\nexport const square = unaryKernelFunc(SQUARE);\nexport const squareConfig = {\n    kernelName: Square,\n    backendName: 'webgl',\n    kernelFunc: square,\n};\n//# sourceMappingURL=Square.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { SquaredDifference } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\nexport const squaredDifference = binaryKernelFunc({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });\nexport const squaredDifferenceConfig = {\n    kernelName: SquaredDifference,\n    backendName: 'webgl',\n    kernelFunc: squaredDifference,\n};\n//# sourceMappingURL=SquaredDifference.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Sub } from '@tensorflow/tfjs-core';\nimport { binaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nimport { subImplCPU as cpuSub } from '../kernel_utils/shared';\nconst SUB = 'return a - b;';\nexport const subKernelFunc = binaryKernelFunc({\n    opSnippet: SUB,\n    packedOpSnippet: SUB,\n    supportsComplex: true,\n    cpuKernelImpl: cpuSub\n});\nexport const subConfig = {\n    kernelName: Sub,\n    backendName: 'webgl',\n    kernelFunc: subKernelFunc\n};\n//# sourceMappingURL=Sub.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tan } from '@tensorflow/tfjs-core';\nimport { unaryKernelFunc } from '../kernel_utils/kernel_funcs_utils';\nconst TAN = `return tan(x);`;\nexport const tan = unaryKernelFunc(TAN);\nexport const tanConfig = {\n    kernelName: Tan,\n    backendName: 'webgl',\n    kernelFunc: tan,\n};\n//# sourceMappingURL=Tan.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Transpose } from '@tensorflow/tfjs-core';\nimport { transposeImpl } from './Transpose_impl';\nimport { transposeImplCPU as cpuTranspose } from './Transpose_impl';\nexport const transposeConfig = {\n    kernelName: Transpose,\n    backendName: 'webgl',\n    kernelFunc: ({ inputs, attrs, backend }) => {\n        const { x } = inputs;\n        const { perm } = attrs;\n        const webglBackend = backend;\n        const xRank = x.shape.length;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n            newShape[i] = x.shape[perm[i]];\n        }\n        let out;\n        if (webglBackend.shouldExecuteOnCPU([x])) {\n            const xTexData = webglBackend.texData.get(x.dataId);\n            const values = xTexData.values;\n            const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape);\n            out = webglBackend.makeTensorInfo(newShape, x.dtype);\n            const outData = webglBackend.texData.get(out.dataId);\n            outData.values = outValues;\n        }\n        else {\n            out = transposeImpl(x, perm, webglBackend);\n        }\n        return out;\n    }\n};\n//# sourceMappingURL=Transpose.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Unique } from '@tensorflow/tfjs-core';\nimport { uniqueImplCPU } from '../kernel_utils/shared';\nimport { assertNotComplex } from '../webgl_util';\nexport function unique(args) {\n    const { inputs, attrs, backend } = args;\n    const { axis } = attrs;\n    const { x } = inputs;\n    assertNotComplex(x, 'unique');\n    // For now, always forward calculation to the CPU backend.\n    console.warn('WARNING: ', 'UI might be locked temporarily as data is being downloaded');\n    const values = backend.readSync(x.dataId);\n    const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n    return [\n        backend.makeTensorInfo(outputShape, x.dtype, outputValues),\n        backend.makeTensorInfo([indices.length], 'int32', indices),\n    ];\n}\nexport const uniqueConfig = {\n    kernelName: Unique,\n    backendName: 'webgl',\n    kernelFunc: unique,\n};\n//# sourceMappingURL=Unique.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { registerKernel } from '@tensorflow/tfjs-core';\nimport { addConfig } from './kernels/Add';\nimport { atan2Config } from './kernels/Atan2';\nimport { avgPoolConfig } from './kernels/AvgPool';\nimport { avgPoolBackpropConfig } from './kernels/AvgPoolBackprop';\nimport { batchNormConfig } from './kernels/BatchNorm';\nimport { castConfig } from './kernels/Cast';\nimport { complexConfig } from './kernels/Complex';\nimport { concatConfig } from './kernels/Concat';\nimport { cosConfig } from './kernels/Cos';\nimport { divConfig } from './kernels/Div';\nimport { fftConfig } from './kernels/FFT';\nimport { flipLeftRightConfig } from './kernels/FlipLeftRight';\nimport { fromPixelsConfig } from './kernels/FromPixels';\nimport { identityConfig } from './kernels/Identity';\nimport { ifftConfig } from './kernels/IFFT';\nimport { imagConfig } from './kernels/Imag';\nimport { maxConfig } from './kernels/Max';\nimport { maxPoolConfig } from './kernels/MaxPool';\nimport { maxPoolBackpropConfig } from './kernels/MaxPoolBackprop';\nimport { maxPoolWithArgmaxConfig } from './kernels/MaxPoolWithArgmax';\nimport { meanConfig } from './kernels/Mean';\nimport { mirrorPadConfig } from './kernels/MirrorPad';\nimport { multiplyConfig } from './kernels/Multiply';\nimport { nonMaxSuppressionV3Config } from './kernels/NonMaxSuppressionV3';\nimport { nonMaxSuppressionV4Config } from './kernels/NonMaxSuppressionV4';\nimport { nonMaxSuppressionV5Config } from './kernels/NonMaxSuppressionV5';\nimport { notEqualConfig } from './kernels/NotEqual';\nimport { realConfig } from './kernels/Real';\nimport { reshapeConfig } from './kernels/Reshape';\nimport { rotateWithOffsetConfig } from './kernels/RotateWithOffset';\nimport { sinConfig } from './kernels/Sin';\nimport { squareConfig } from './kernels/Square';\nimport { squaredDifferenceConfig } from './kernels/SquaredDifference';\nimport { subConfig } from './kernels/Sub';\nimport { tanConfig } from './kernels/Tan';\nimport { transposeConfig } from './kernels/Transpose';\nimport { uniqueConfig } from './kernels/Unique';\n// List all kernel configs here\nconst kernelConfigs = [\n    addConfig,\n    atan2Config,\n    avgPoolConfig,\n    avgPoolBackpropConfig,\n    batchNormConfig,\n    castConfig,\n    complexConfig,\n    concatConfig,\n    cosConfig,\n    divConfig,\n    fftConfig,\n    flipLeftRightConfig,\n    fromPixelsConfig,\n    identityConfig,\n    ifftConfig,\n    imagConfig,\n    maxConfig,\n    maxPoolConfig,\n    maxPoolBackpropConfig,\n    maxPoolWithArgmaxConfig,\n    meanConfig,\n    mirrorPadConfig,\n    multiplyConfig,\n    nonMaxSuppressionV3Config,\n    nonMaxSuppressionV4Config,\n    nonMaxSuppressionV5Config,\n    notEqualConfig,\n    realConfig,\n    reshapeConfig,\n    rotateWithOffsetConfig,\n    sinConfig,\n    squareConfig,\n    subConfig,\n    squaredDifferenceConfig,\n    tanConfig,\n    transposeConfig,\n    uniqueConfig,\n];\nfor (const kernelConfig of kernelConfigs) {\n    registerKernel(kernelConfig);\n}\n//# sourceMappingURL=register_all_kernels.js.map", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// All exports from this package should be in base.\nexport * from './base';\nimport './register_all_kernels';\n//# sourceMappingURL=index.js.map", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport * from '@tensorflow/tfjs-core';\nexport * from '@tensorflow/tfjs-layers';\nexport * from '@tensorflow/tfjs-converter';\n\n// Export data api as tf.data\nimport * as data from '@tensorflow/tfjs-data';\nexport {data};\n\n// Import and register backends.\nimport '@tensorflow/tfjs-backend-cpu';\nimport '@tensorflow/tfjs-backend-webgl';\n\n// Import versions of all sub-packages.\nimport {version_core} from '@tensorflow/tfjs-core';\nimport {version_cpu} from '@tensorflow/tfjs-backend-cpu';\nimport {version_webgl} from '@tensorflow/tfjs-backend-webgl';\nimport {version_data} from '@tensorflow/tfjs-data';\nimport {version_layers} from '@tensorflow/tfjs-layers';\nimport {version_converter} from '@tensorflow/tfjs-converter';\nimport {version as version_union} from './version';\n\nexport const version = {\n  'tfjs-core': version_core,\n  'tfjs-backend-cpu': version_cpu,\n  'tfjs-backend-webgl': version_webgl,\n  'tfjs-data': version_data,\n  'tfjs-layers': version_layers,\n  'tfjs-converter': version_converter,\n  'tfjs': version_union\n};\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport {FusedBatchMatMulConfig, FusedConv2DConfig} from '../ops/fused_types';\nimport {Backend, DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {BackendValues, DataType, Rank, ShapeMap} from '../types';\n\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n  kernelMs: number|{error: string};\n  getExtraProfileInfo?(): string;  // a field for additional timing information\n                                   // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n  read(dataId: DataId): Promise<BackendValues>;\n  readSync(dataId: DataId): BackendValues;\n  disposeData(dataId: DataId): void;\n  write(values: BackendValues, shape: number[], dtype: DataType): DataId;\n  move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType):\n      void;\n  memory(): {unreliable: boolean;};  // Backend-specific information.\n  /** Returns number of data ids currently in the storage. */\n  numDataIds(): number;\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage<T> {\n  private data = new WeakMap<DataId, T>();\n  private dataIdsCount = 0;\n\n  constructor(private backend: KernelBackend, private dataMover: DataMover) {}\n\n  get(dataId: DataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(this.backend, dataId);\n    }\n    return this.data.get(dataId);\n  }\n\n  set(dataId: DataId, value: T): void {\n    this.dataIdsCount++;\n    this.data.set(dataId, value);\n  }\n\n  has(dataId: DataId): boolean {\n    return this.data.has(dataId);\n  }\n\n  delete(dataId: DataId): boolean {\n    this.dataIdsCount--;\n    return this.data.delete(dataId);\n  }\n\n  numDataIds(): number {\n    return this.dataIdsCount;\n  }\n}\n\nexport interface DataMover {\n  /**\n   * To be called by backends whenever they see a dataId that they don't own.\n   * Upon calling this method, the mover will fetch the tensor from another\n   * backend and register it with the current active backend.\n   */\n  moveData(backend: KernelBackend, dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n  time(f: () => void): Promise<BackendTimingInfo>;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, Backend, BackendTimer {\n  time(f: () => void): Promise<BackendTimingInfo> {\n    return notYetImplemented('time');\n  }\n  read(dataId: object): Promise<BackendValues> {\n    return notYetImplemented('read');\n  }\n  readSync(dataId: object): BackendValues {\n    return notYetImplemented('readSync');\n  }\n  numDataIds(): number {\n    return notYetImplemented('numDataIds');\n  }\n  disposeData(dataId: object): void {\n    return notYetImplemented('disposeData');\n  }\n  write(values: BackendValues, shape: number[], dtype: DataType): DataId {\n    return notYetImplemented('write');\n  }\n  move(dataId: DataId, values: BackendValues, shape: number[], dtype: DataType):\n      void {\n    return notYetImplemented('move');\n  }\n  memory(): {unreliable: boolean; reasons?: string[]} {\n    return notYetImplemented('memory');\n  }\n  /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n  floatPrecision(): 16|32 {\n    return notYetImplemented('floatPrecision');\n  }\n  /** Returns the smallest representable number.  */\n  epsilon(): number {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    return notYetImplemented('batchMatMul');\n  }\n\n  fusedBatchMatMul(\n      {a, b, transposeA, transposeB, bias, activation, preluActivationWeights}:\n          FusedBatchMatMulConfig): Tensor3D {\n    return notYetImplemented('fusedBatchMatMul');\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    return notYetImplemented('slice');\n  }\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[]): T {\n    return notYetImplemented('stridedSlice');\n  }\n  unstack(x: Tensor, axis: number): Tensor[] {\n    return notYetImplemented('unstack');\n  }\n  reverse<T extends Tensor>(a: T, axis: number[]): T {\n    return notYetImplemented('reverse');\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    return notYetImplemented('concat');\n  }\n\n  neg<T extends Tensor>(a: T): T {\n    return notYetImplemented('neg');\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('add');\n  }\n  addN<T extends Tensor>(tensors: T[]): T {\n    return notYetImplemented('addN');\n  }\n  subtract(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('subtract');\n  }\n  multiply(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('multiply');\n  }\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('realDivide');\n  }\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('floorDiv');\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    return notYetImplemented('sum');\n  }\n  prod(x: Tensor, axes: number[]): Tensor {\n    return notYetImplemented('prod');\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    return notYetImplemented('unsortedSegmentSum');\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    return notYetImplemented('argMin');\n  }\n  argMax(x: Tensor, axis: number): Tensor {\n    return notYetImplemented('argMax');\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('equal');\n  }\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('notEqual');\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('less');\n  }\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('lessEqual');\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('greater');\n  }\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('greaterEqual');\n  }\n\n  logicalNot<T extends Tensor>(a: T): T {\n    return notYetImplemented('logicalNot');\n  }\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('logicalAnd');\n  }\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('logicalOr');\n  }\n\n  where(condition: Tensor): Tensor2D {\n    return notYetImplemented('where');\n  }\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('select');\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    return notYetImplemented('topk');\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    return notYetImplemented('min');\n  }\n  minimum(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('minimum');\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('mod');\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    return notYetImplemented('max');\n  }\n  maximum(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('maximum');\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    return notYetImplemented('all');\n  }\n  any(x: Tensor, axes: number[]): Tensor {\n    return notYetImplemented('any');\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    return notYetImplemented('squaredDifference');\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    return notYetImplemented('ceil');\n  }\n  floor<T extends Tensor>(x: T): T {\n    return notYetImplemented('floor');\n  }\n  round<T extends Tensor>(x: T): T {\n    return notYetImplemented('round');\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    return notYetImplemented('sign');\n  }\n\n  isNaN<T extends Tensor>(x: T): T {\n    return notYetImplemented('isNaN');\n  }\n  isInf<T extends Tensor>(x: T): T {\n    return notYetImplemented('isInf');\n  }\n  isFinite<T extends Tensor>(x: T): T {\n    return notYetImplemented('isFinite');\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    return notYetImplemented('pow');\n  }\n  exp<T extends Tensor>(x: T): T {\n    return notYetImplemented('exp');\n  }\n  expm1<T extends Tensor>(x: T): T {\n    return notYetImplemented('expm1');\n  }\n  softmax<T extends Tensor>(x: T, dim: number): T {\n    return notYetImplemented('softmax');\n  }\n  log<T extends Tensor>(x: T): T {\n    return notYetImplemented('log');\n  }\n  log1p<T extends Tensor>(x: T): T {\n    return notYetImplemented('log1p');\n  }\n  sqrt<T extends Tensor>(x: T): T {\n    return notYetImplemented('sqrt');\n  }\n  rsqrt<T extends Tensor>(x: T): T {\n    return notYetImplemented('rsqrt');\n  }\n  square<T extends Tensor>(x: T): T {\n    return notYetImplemented('square');\n  }\n  reciprocal<T extends Tensor>(x: T): T {\n    return notYetImplemented('reciprocal');\n  }\n  relu<T extends Tensor>(x: T): T {\n    return notYetImplemented('relu');\n  }\n  relu6<T extends Tensor>(x: T): T {\n    return notYetImplemented('relu6');\n  }\n  prelu<T extends Tensor>(x: T, a: T): T {\n    return notYetImplemented('prelu');\n  }\n  elu<T extends Tensor>(x: T): T {\n    return notYetImplemented('elu');\n  }\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    return notYetImplemented('eluDer');\n  }\n  selu<T extends Tensor>(x: T): T {\n    return notYetImplemented('selu');\n  }\n  int<T extends Tensor>(x: T): T {\n    return notYetImplemented('int');\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    return notYetImplemented('clip');\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    return notYetImplemented('abs');\n  }\n  complexAbs<T extends Tensor>(x: T): T {\n    return notYetImplemented('complexAbs');\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    return notYetImplemented('sigmoid');\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    return notYetImplemented('softplus');\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    return notYetImplemented('sin');\n  }\n  cos<T extends Tensor>(x: T): T {\n    return notYetImplemented('cos');\n  }\n  tan<T extends Tensor>(x: T): T {\n    return notYetImplemented('tan');\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    return notYetImplemented('asin');\n  }\n  acos<T extends Tensor>(x: T): T {\n    return notYetImplemented('acos');\n  }\n  atan<T extends Tensor>(x: T): T {\n    return notYetImplemented('atan');\n  }\n  atan2<T extends Tensor>(a: T, b: T): T {\n    return notYetImplemented('atan2');\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    return notYetImplemented('sinh');\n  }\n  cosh<T extends Tensor>(x: T): T {\n    return notYetImplemented('cosh');\n  }\n  tanh<T extends Tensor>(x: T): T {\n    return notYetImplemented('tanh');\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    return notYetImplemented('asinh');\n  }\n  acosh<T extends Tensor>(x: T): T {\n    return notYetImplemented('acosh');\n  }\n  atanh<T extends Tensor>(x: T): T {\n    return notYetImplemented('atanh');\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    return notYetImplemented('erf');\n  }\n\n  step<T extends Tensor>(x: T, alpha: number): T {\n    return notYetImplemented('step');\n  }\n\n  fusedConv2d(\n      {input, filter, convInfo, bias, activation, preluActivationWeights}:\n          FusedConv2DConfig): Tensor4D {\n    return notYetImplemented('fusedConv2d');\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    return notYetImplemented('conv2d');\n  }\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    return notYetImplemented('conv2dDerInput');\n  }\n  conv2dDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    return notYetImplemented('conv2dDerFilter');\n  }\n\n  fusedDepthwiseConv2D(\n      {input, filter, convInfo, bias, activation, preluActivationWeights}:\n          FusedConv2DConfig): Tensor4D {\n    return notYetImplemented('fusedDepthwiseConv2D');\n  }\n\n  depthwiseConv2D(input: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    return notYetImplemented('depthwiseConv2D');\n  }\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    return notYetImplemented('depthwiseConv2DDerInput');\n  }\n  depthwiseConv2DDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    return notYetImplemented('depthwiseConv2DDerFilter');\n  }\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    return notYetImplemented('conv3d');\n  }\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    return notYetImplemented('conv3dDerInput');\n  }\n  conv3dDerFilter(x: Tensor5D, dY: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    return notYetImplemented('conv3dDerFilter');\n  }\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    return notYetImplemented('maxPool');\n  }\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    return notYetImplemented('maxPoolBackprop');\n  }\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    return notYetImplemented('avgPool');\n  }\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    return notYetImplemented('avgPoolBackprop');\n  }\n  avgPool3d(x: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    return notYetImplemented('avgPool3d');\n  }\n  avgPool3dBackprop(dy: Tensor5D, x: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    return notYetImplemented('avgPool3dBackprop');\n  }\n  maxPool3d(x: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    return notYetImplemented('maxPool3d');\n  }\n  maxPool3dBackprop(\n      dy: Tensor5D, x: Tensor5D, y: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    return notYetImplemented('maxPool3dBackprop');\n  }\n\n  reshape<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]):\n      Tensor<R> {\n    return notYetImplemented('reshape');\n  }\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    return notYetImplemented('cast');\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    return notYetImplemented('tile');\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    return notYetImplemented('pad');\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    return notYetImplemented('transpose');\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    return notYetImplemented('gather');\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor {\n    return notYetImplemented('gatherND');\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    return notYetImplemented('scatterND');\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    return notYetImplemented('batchToSpaceND');\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: number[][]): T {\n    return notYetImplemented('spaceToBatchND');\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    return notYetImplemented('resizeBilinear');\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n      Tensor4D {\n    return notYetImplemented('resizeBilinearBackprop');\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHEight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    return notYetImplemented('resizeNearestNeighbor');\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n    return notYetImplemented('resizeNearestNeighborBackprop');\n  }\n\n  batchNorm(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D, scale?: Tensor4D|Tensor1D,\n      varianceEpsilon?: number): Tensor4D {\n    return notYetImplemented('batchNorm');\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, radius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    return notYetImplemented('localResponseNormalization4D');\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number,\n      bias: number, alpha: number, beta: number): Tensor4D {\n    return notYetImplemented('LRNGrad');\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    return notYetImplemented('multinomial');\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    return notYetImplemented('oneHot');\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    return notYetImplemented('cumsum');\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold?: number): Tensor1D {\n    return notYetImplemented('nonMaxSuppression');\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    return notYetImplemented('fft');\n  }\n  ifft(x: Tensor2D): Tensor2D {\n    return notYetImplemented('ifft');\n  }\n  complex<T extends Tensor>(real: T, imag: T): T {\n    return notYetImplemented('complex');\n  }\n  real<T extends Tensor>(input: T): T {\n    return notYetImplemented('real');\n  }\n  imag<T extends Tensor>(input: T): T {\n    return notYetImplemented('imag');\n  }\n\n  cropAndResize(\n      image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n      cropSize: [number, number], method: 'bilinear'|'nearest',\n      extrapolationValue: number): Tensor4D {\n    return notYetImplemented('cropAndResize');\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D {\n    return notYetImplemented('depthToSpace');\n  }\n\n  // Aligns with the \"SplitV\" kernel in TensorFlow.\n  split<T extends Tensor>(value: T, sizeSplits: number[], axis: number): T[] {\n    return notYetImplemented('split');\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    return notYetImplemented('sparseToDense');\n  }\n\n  diag(x: Tensor): Tensor {\n    return notYetImplemented('diag');\n  }\n\n  fill<R extends Rank>(\n      shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor<R> {\n    return notYetImplemented('fill');\n  }\n\n  onesLike<R extends Rank>(x: Tensor<R>): Tensor<R> {\n    return notYetImplemented('onesLike');\n  }\n\n  zerosLike<R extends Rank>(x: Tensor<R>): Tensor<R> {\n    return notYetImplemented('zerosLike');\n  }\n\n  linspace(start: number, stop: number, num: number): Tensor1D {\n    return notYetImplemented('linspace');\n  }\n\n  dispose(): void {\n    return notYetImplemented('dispose');\n  }\n}\n\nfunction notYetImplemented(kernelName: string): never {\n  throw new Error(\n      `'${kernelName}' not yet implemented or not found in the registry. ` +\n      `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';\n\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array: any[]|Uint32Array|Int32Array|\n                        Float32Array): void {\n  let counter = array.length;\n  let temp = 0;\n  let index = 0;\n  // While there are elements in the array\n  while (counter > 0) {\n    // Pick a random index\n    index = (Math.random() * counter) | 0;\n    // Decrease counter by 1\n    counter--;\n    // And swap the last element with it\n    temp = array[counter];\n    array[counter] = array[index];\n    array[index] = temp;\n  }\n}\n\n/** Clamps a value to a specified range. */\nexport function clamp(min: number, x: number, max: number): number {\n  return Math.max(min, Math.min(x, max));\n}\n\nexport function nearestLargerEven(val: number): number {\n  return val % 2 === 0 ? val : val + 1;\n}\n\nexport function sum(arr: number[]): number {\n  let sum = 0;\n  for (let i = 0; i < arr.length; i++) {\n    sum += arr[i];\n  }\n  return sum;\n}\n\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a: number, b: number) {\n  const r = Math.random();\n  return (b * r) + (1 - r) * a;\n}\n\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a: FlatVector, b: FlatVector): number {\n  let result = 0;\n  for (let i = 0; i < a.length; i++) {\n    const diff = Number(a[i]) - Number(b[i]);\n    result += diff * diff;\n  }\n  return result;\n}\n\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n *     error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr: boolean, msg: () => string) {\n  if (!expr) {\n    throw new Error(typeof msg === 'string' ? msg : msg());\n  }\n}\n\nexport function assertShapesMatch(\n    shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {\n  assert(\n      arraysEqual(shapeA, shapeB),\n      () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\n\nexport function assertNonNull(a: TensorLike): void {\n  assert(\n      a != null,\n      () => `The input to the tensor constructor must be a non-null value.`);\n}\n\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\n/**\n *  Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n *  @param arr The nested array to flatten.\n *  @param result The destination array which holds the elements.\n *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n *      to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function\nflatten<T extends number|boolean|string|Promise<number>|TypedArray>(\n    arr: T|RecursiveArray<T>, result: T[] = [], skipTypedArray = false): T[] {\n  if (result == null) {\n    result = [];\n  }\n  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n    for (let i = 0; i < arr.length; ++i) {\n      flatten(arr[i], result, skipTypedArray);\n    }\n  } else {\n    result.push(arr as T);\n  }\n  return result;\n}\n\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape: number[]): number {\n  if (shape.length === 0) {\n    // Scalar.\n    return 1;\n  }\n  let size = shape[0];\n  for (let i = 1; i < shape.length; i++) {\n    size *= shape[i];\n  }\n  return size;\n}\n\nexport function isScalarShape(shape: number[]): boolean {\n  return shape.length === 0;\n}\n\nexport function arraysEqual(n1: FlatVector, n2: FlatVector) {\n  if (n1 === n2) {\n    return true;\n  }\n  if (n1 == null || n2 == null) {\n    return false;\n  }\n\n  if (n1.length !== n2.length) {\n    return false;\n  }\n  for (let i = 0; i < n1.length; i++) {\n    if (n1[i] !== n2[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function isInt(a: number): boolean {\n  return a % 1 === 0;\n}\n\nexport function tanh(x: number): number {\n  // tslint:disable-next-line:no-any\n  if ((Math as any).tanh != null) {\n    // tslint:disable-next-line:no-any\n    return (Math as any).tanh(x);\n  }\n  if (x === Infinity) {\n    return 1;\n  } else if (x === -Infinity) {\n    return -1;\n  } else {\n    const e2x = Math.exp(2 * x);\n    return (e2x - 1) / (e2x + 1);\n  }\n}\n\nexport function sizeToSquarishShape(size: number): [number, number] {\n  const width = Math.ceil(Math.sqrt(size));\n  return [width, Math.ceil(size / width)];\n}\n\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n: number): Uint32Array {\n  const shuffledIndices = new Uint32Array(n);\n  for (let i = 0; i < n; ++i) {\n    shuffledIndices[i] = i;\n  }\n  shuffle(shuffledIndices);\n  return shuffledIndices;\n}\n\nexport function rightPad(a: string, size: number): string {\n  if (size <= a.length) {\n    return a;\n  }\n  return a + ' '.repeat(size - a.length);\n}\n\nexport function repeatedTry(\n    checkFn: () => boolean, delayFn = (counter: number) => 0,\n    maxCounter?: number): Promise<void> {\n  return new Promise<void>((resolve, reject) => {\n    let tryCount = 0;\n\n    const tryFn = () => {\n      if (checkFn()) {\n        resolve();\n        return;\n      }\n\n      tryCount++;\n\n      const nextBackoff = delayFn(tryCount);\n\n      if (maxCounter != null && tryCount >= maxCounter) {\n        reject();\n        return;\n      }\n      setTimeout(tryFn, nextBackoff);\n    };\n\n    tryFn();\n  });\n}\n\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(\n    shape: number[], size: number): number[] {\n  let shapeProd = 1;\n  let implicitIdx = -1;\n\n  for (let i = 0; i < shape.length; ++i) {\n    if (shape[i] >= 0) {\n      shapeProd *= shape[i];\n    } else if (shape[i] === -1) {\n      if (implicitIdx !== -1) {\n        throw Error(\n            `Shapes can only have 1 implicit size. ` +\n            `Found -1 at dim ${implicitIdx} and dim ${i}`);\n      }\n      implicitIdx = i;\n    } else if (shape[i] < 0) {\n      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n    }\n  }\n\n  if (implicitIdx === -1) {\n    if (size > 0 && size !== shapeProd) {\n      throw Error(`Size(${size}) must match the product of shape ${shape}`);\n    }\n    return shape;\n  }\n\n  if (shapeProd === 0) {\n    throw Error(\n        `Cannot infer the missing size in [${shape}] when ` +\n        `there are 0 elements`);\n  }\n  if (size % shapeProd !== 0) {\n    throw Error(\n        `The implicit shape can't be a fractional number. ` +\n        `Got ${size} / ${shapeProd}`);\n  }\n\n  const newShape = shape.slice();\n  newShape[implicitIdx] = size / shapeProd;\n  return newShape;\n}\n\nexport function parseAxisParam(\n    axis: number|number[], shape: number[]): number[] {\n  const rank = shape.length;\n\n  // Normalize input\n  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n\n  // Check for valid range\n  assert(\n      axis.every(ax => ax >= -rank && ax < rank),\n      () =>\n          `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n          `got axis ${axis}`);\n\n  // Check for only integers\n  assert(\n      axis.every(ax => isInt(ax)),\n      () => `All values in axis param must be integers but ` +\n          `got axis ${axis}`);\n\n  // Handle negative axis.\n  return axis.map(a => a < 0 ? rank + a : a);\n}\n\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape: number[], axis?: number[]):\n    {newShape: number[], keptDims: number[]} {\n  const newShape: number[] = [];\n  const keptDims: number[] = [];\n  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n  const axes = (axis == null || isEmptyArray) ?\n      null :\n      parseAxisParam(axis, shape).sort();\n  let j = 0;\n  for (let i = 0; i < shape.length; ++i) {\n    if (axes != null) {\n      if (axes[j] === i && shape[i] !== 1) {\n        throw new Error(\n            `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n      }\n      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n        newShape.push(shape[i]);\n        keptDims.push(i);\n      }\n      if (axes[j] <= i) {\n        j++;\n      }\n    }\n    if (shape[i] !== 1) {\n      newShape.push(shape[i]);\n      keptDims.push(i);\n    }\n  }\n  return {newShape, keptDims};\n}\n\nexport function getTypedArrayFromDType<D extends NumericDataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values as DataTypeMap[D];\n}\n\nexport function getArrayFromDType<D extends DataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else if (dtype === 'string') {\n    values = new Array<'string'>(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values as DataTypeMap[D];\n}\n\nexport function checkConversionForErrors<D extends DataType>(\n    vals: DataTypeMap[D]|number[], dtype: D): void {\n  for (let i = 0; i < vals.length; i++) {\n    const num = vals[i] as number;\n    if (isNaN(num) || !isFinite(num)) {\n      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n    }\n  }\n}\n\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype: DataType): boolean {\n  return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n      dtype === 'int32' || dtype === 'string';\n}\n\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {\n  if (newType === 'complex64') {\n    return false;\n  }\n  if (newType === 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'bool' && oldType === 'bool') {\n    return false;\n  }\n  return true;\n}\n\nexport function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array {\n  return a instanceof Float32Array || a instanceof Int32Array ||\n      a instanceof Uint8Array;\n}\n\nexport function bytesPerElement(dtype: DataType): number {\n  if (dtype === 'float32' || dtype === 'int32') {\n    return 4;\n  } else if (dtype === 'complex64') {\n    return 8;\n  } else if (dtype === 'bool') {\n    return 1;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr: Uint8Array[]): number {\n  if (arr == null) {\n    return 0;\n  }\n  let bytes = 0;\n  arr.forEach(x => bytes += x.length);\n  return bytes;\n}\n\n/** Returns true if the value is a string. */\nexport function isString(value: {}): value is string {\n  return typeof value === 'string' || value instanceof String;\n}\n\nexport function isBoolean(value: {}): boolean {\n  return typeof value === 'boolean';\n}\n\nexport function isNumber(value: {}): boolean {\n  return typeof value === 'number';\n}\n\nexport function inferDtype(values: TensorLike): DataType {\n  if (Array.isArray(values)) {\n    return inferDtype(values[0]);\n  }\n  if (values instanceof Float32Array) {\n    return 'float32';\n  } else if (values instanceof Int32Array || values instanceof Uint8Array) {\n    return 'int32';\n  } else if (isNumber(values)) {\n    return 'float32';\n  } else if (isString(values)) {\n    return 'string';\n  } else if (isBoolean(values)) {\n    return 'bool';\n  }\n  return 'float32';\n}\n\nexport function isFunction(f: Function) {\n  return !!(f && f.constructor && f.call && f.apply);\n}\n\nexport function nearestDivisor(size: number, start: number): number {\n  for (let i = start; i < size; ++i) {\n    if (size % i === 0) {\n      return i;\n    }\n  }\n  return size;\n}\n\nexport function computeStrides(shape: number[]): number[] {\n  const rank = shape.length;\n  if (rank < 2) {\n    return [];\n  }\n\n  // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n  // strides.\n  const strides = new Array(rank - 1);\n  strides[rank - 2] = shape[rank - 1];\n  for (let i = rank - 3; i >= 0; --i) {\n    strides[i] = strides[i + 1] * shape[i + 1];\n  }\n  return strides;\n}\n\nfunction createNestedArray(offset: number, shape: number[], a: TypedArray) {\n  const ret = new Array();\n  if (shape.length === 1) {\n    const d = shape[0];\n    for (let i = 0; i < d; i++) {\n      ret[i] = a[offset + i];\n    }\n  } else {\n    const d = shape[0];\n    const rest = shape.slice(1);\n    const len = rest.reduce((acc, c) => acc * c);\n    for (let i = 0; i < d; i++) {\n      ret[i] = createNestedArray(offset + i * len, rest, a);\n    }\n  }\n  return ret;\n}\n\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape: number[], a: TypedArray) {\n  if (shape.length === 0) {\n    // Scalar type should return a single number.\n    return a[0];\n  }\n  const size = shape.reduce((acc, c) => acc * c);\n  if (size === 0) {\n    // A tensor with shape zero should be turned into empty list.\n    return [];\n  }\n  if (size !== a.length) {\n    throw new Error(`[${shape}] does not match the input size ${a.length}.`);\n  }\n\n  return createNestedArray(0, shape, a);\n}\n\nexport function makeOnesTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  const array = makeZerosTypedArray(size, dtype);\n  for (let i = 0; i < array.length; i++) {\n    array[i] = 1;\n  }\n  return array;\n}\n\nexport function makeZerosTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(size) as DataTypeMap[D];\n  } else if (dtype === 'int32') {\n    return new Int32Array(size) as DataTypeMap[D];\n  } else if (dtype === 'bool') {\n    return new Uint8Array(size) as DataTypeMap[D];\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray<D extends DataType>(\n    shape: number[], dtype: D) {\n  const size = shape.reduce((prev, curr) => prev * curr, 1);\n  if (dtype == null || dtype === 'float32') {\n    return toNestedArray(shape, new Float32Array(size));\n  } else if (dtype === 'int32') {\n    return toNestedArray(shape, new Int32Array(size));\n  } else if (dtype === 'bool') {\n    return toNestedArray(shape, new Uint8Array(size));\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\nexport function assertNonNegativeIntegerDimensions(shape: number[]) {\n  shape.forEach(dimSize => {\n    assert(\n        Number.isInteger(dimSize) && dimSize >= 0,\n        () =>\n            `Tensor must have a shape comprised of positive integers but got ` +\n            `shape [${shape}].`);\n  });\n}\n\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(\n    locs: number[], rank: number, strides: number[]): number {\n  if (rank === 0) {\n    return 0;\n  } else if (rank === 1) {\n    return locs[0];\n  }\n  let index = locs[locs.length - 1];\n  for (let i = 0; i < locs.length - 1; ++i) {\n    index += strides[i] * locs[i];\n  }\n  return index;\n}\n\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(\n    index: number, rank: number, strides: number[]): number[] {\n  if (rank === 0) {\n    return [];\n  } else if (rank === 1) {\n    return [index];\n  }\n  const locs: number[] = new Array(rank);\n  for (let i = 0; i < locs.length - 1; ++i) {\n    locs[i] = Math.floor(index / strides[i]);\n    index -= locs[i] * strides[i];\n  }\n  locs[locs.length - 1] = index;\n  return locs;\n}\n\n/**\n * This method asserts whether an object is a Promise instance.\n * @param object\n */\n// tslint:disable-next-line: no-any\nexport function isPromise(object: any) {\n  //  We chose to not use 'obj instanceOf Promise' for two reasons:\n  //  1. It only reliably works for es6 Promise, not other Promise\n  //  implementations.\n  //  2. It doesn't work with framework that uses zone.js. zone.js monkey patch\n  //  the async calls, so it is possible the obj (patched) is comparing to a\n  //  pre-patched Promise.\n  return object && object.then && typeof object.then === 'function';\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Platform} from './platforms/platform';\nimport {isPromise} from './util_base';\n\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n\ntype FlagValue = number|boolean;\ntype FlagEvaluationFn = (() => FlagValue)|(() => Promise<FlagValue>);\nexport type Flags = {\n  [featureName: string]: FlagValue\n};\nexport type FlagRegistryEntry = {\n  evaluationFn: FlagEvaluationFn;\n  setHook?: (value: FlagValue) => void;\n};\n\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n  private flags: Flags = {};\n  private flagRegistry: {[flagName: string]: FlagRegistryEntry} = {};\n\n  private urlFlags: Flags = {};\n\n  platformName: string;\n  platform: Platform;\n\n  // tslint:disable-next-line: no-any\n  constructor(public global: any) {\n    this.populateURLFlags();\n  }\n\n  setPlatform(platformName: string, platform: Platform) {\n    if (this.platform != null) {\n      console.warn(\n          `Platform ${this.platformName} has already been set. ` +\n          `Overwriting the platform with ${platform}.`);\n    }\n    this.platformName = platformName;\n    this.platform = platform;\n  }\n\n  registerFlag(\n      flagName: string, evaluationFn: FlagEvaluationFn,\n      setHook?: (value: FlagValue) => void) {\n    this.flagRegistry[flagName] = {evaluationFn, setHook};\n\n    // Override the flag value from the URL. This has to happen here because the\n    // environment is initialized before flags get registered.\n    if (this.urlFlags[flagName] != null) {\n      const flagValue = this.urlFlags[flagName];\n      console.warn(\n          `Setting feature override from URL ${flagName}: ${flagValue}.`);\n      this.set(flagName, flagValue);\n    }\n  }\n\n  async getAsync(flagName: string): Promise<FlagValue> {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n\n    this.flags[flagName] = await this.evaluateFlag(flagName);\n    return this.flags[flagName];\n  }\n\n  get(flagName: string): FlagValue {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n\n    const flagValue = this.evaluateFlag(flagName);\n    if (isPromise(flagValue)) {\n      throw new Error(\n          `Flag ${flagName} cannot be synchronously evaluated. ` +\n          `Please use getAsync() instead.`);\n    }\n\n    this.flags[flagName] = flagValue as number | boolean;\n\n    return this.flags[flagName];\n  }\n\n  getNumber(flagName: string): number {\n    return this.get(flagName) as number;\n  }\n\n  getBool(flagName: string): boolean {\n    return this.get(flagName) as boolean;\n  }\n\n  getFlags(): Flags {\n    return this.flags;\n  }\n  // For backwards compatibility.\n  get features(): Flags {\n    return this.flags;\n  }\n\n  set(flagName: string, value: FlagValue): void {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(\n          `Cannot set flag ${flagName} as it has not been registered.`);\n    }\n    this.flags[flagName] = value;\n    if (this.flagRegistry[flagName].setHook != null) {\n      this.flagRegistry[flagName].setHook(value);\n    }\n  }\n\n  private evaluateFlag(flagName: string): FlagValue|Promise<FlagValue> {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(\n          `Cannot evaluate flag '${flagName}': no evaluation function found.`);\n    }\n    return this.flagRegistry[flagName].evaluationFn();\n  }\n\n  setFlags(flags: Flags) {\n    this.flags = Object.assign({}, flags);\n  }\n\n  reset() {\n    this.flags = {};\n    this.urlFlags = {};\n    this.populateURLFlags();\n  }\n\n  private populateURLFlags(): void {\n    if (typeof this.global === 'undefined' ||\n        typeof this.global.location === 'undefined' ||\n        typeof this.global.location.search === 'undefined') {\n      return;\n    }\n\n    const urlParams = getQueryParams(this.global.location.search);\n    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n      keyValues.forEach(keyValue => {\n        const [key, value] = keyValue.split(':') as [string, string];\n        this.urlFlags[key] = parseValue(key, value);\n      });\n    }\n  }\n}\n\nexport function getQueryParams(queryString: string): {[key: string]: string} {\n  const params = {};\n  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n    decodeParam(params, t[0], t[1]);\n    return t.join('=');\n  });\n  return params;\n}\n\nfunction decodeParam(\n    params: {[key: string]: string}, name: string, value?: string) {\n  params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\n\nfunction parseValue(flagName: string, value: string): FlagValue {\n  value = value.toLowerCase();\n  if (value === 'true' || value === 'false') {\n    return value === 'true';\n  } else if (`${+ value}` === value) {\n    return +value;\n  }\n  throw new Error(\n      `Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n  return ENV;\n}\n\nexport let ENV: Environment = null;\nexport function setEnvironmentGlobal(environment: Environment) {\n  ENV = environment;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace: {_tfGlobals: Map<string, any>};\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace(): {_tfGlobals: Map<string, any>} {\n  if (globalNameSpace == null) {\n    // tslint:disable-next-line:no-any\n    let ns: any;\n    if (typeof (window) !== 'undefined') {\n      ns = window;\n    } else if (typeof (global) !== 'undefined') {\n      ns = global;\n    } else if (typeof (process) !== 'undefined') {\n      ns = process;\n    } else if (typeof (self) !== 'undefined') {\n      ns = self;\n    } else {\n      throw new Error('Could not find a global object');\n    }\n    globalNameSpace = ns;\n  }\n  return globalNameSpace;\n}\n\n// tslint:disable-next-line:no-any\nfunction getGlobalMap(): Map<string, any> {\n  const ns = getGlobalNamespace();\n  if (ns._tfGlobals == null) {\n    ns._tfGlobals = new Map();\n  }\n  return ns._tfGlobals;\n}\n\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n *             the first time it is fetched.\n */\nexport function getGlobal<T>(key: string, init: () => T): T {\n  const globalMap = getGlobalMap();\n  if (globalMap.has(key)) {\n    return globalMap.get(key);\n  } else {\n    const singleton = init();\n    globalMap.set(key, singleton);\n    return globalMap.get(key);\n  }\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Allow UpperCamelCase variable names\n// tslint:disable: variable-name\n// Unfortunately just enabling PascalCase per file (tslint:enable:\n// allow-pascal-case) doesn't work.\nimport {NamedTensorInfoMap, TensorInfo} from './kernel_registry';\nimport {ExplicitPadding} from './ops/conv_util';\nimport {Activation} from './ops/fused_types';\nimport {DataType, PixelData} from './types';\n\nexport const Abs = 'Abs';\nexport type AbsInputs = UnaryInputs;\n\nexport const Acos = 'Acos';\nexport type AcosInputs = UnaryInputs;\n\nexport const Acosh = 'Acosh';\nexport type AcoshInputs = UnaryInputs;\n\nexport const Add = 'Add';\nexport type AddInputs = BinaryInputs;\n\nexport const AddN = 'AddN';\nexport type AddNInputs = TensorInfo[];\n\nexport const All = 'All';\nexport type AllInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface AllAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const Any = 'Any';\nexport type AnyInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface AnyAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const ArgMax = 'ArgMax';\nexport type ArgMaxInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ArgMaxAttrs {\n  axis: number;\n}\n\nexport const ArgMin = 'ArgMin';\nexport type ArgMinInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ArgMinAttrs {\n  axis: number;\n}\n\nexport const Asin = 'Asin';\nexport type AsinInputs = UnaryInputs;\n\nexport const Asinh = 'Asinh';\nexport type AsinhInputs = UnaryInputs;\n\nexport const Atan = 'Atan';\nexport type AtanInputs = UnaryInputs;\n\nexport const Atanh = 'Atanh';\nexport type AtanhInputs = UnaryInputs;\n\nexport const Atan2 = 'Atan2';\nexport type Atan2Inputs = BinaryInputs;\n\nexport const AvgPool = 'AvgPool';\nexport type AvgPoolInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface AvgPoolAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const AvgPoolBackprop = 'AvgPoolBackprop';\nexport type AvgPoolBackpropInputs = Pick<NamedTensorInfoMap, 'dy'|'input'>;\nexport interface AvgPoolBackpropAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n}\n\nexport const AvgPool3D = 'AvgPool3D';\nexport type AvgPool3DInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface AvgPool3DAttrs {\n  filterSize: [number, number, number]|number;\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  dataFormat: 'NDHWC'|'NCDHW';\n  dilations?: [number, number, number]|number;\n}\n\nexport const AvgPool3DBackprop = 'AvgPool3DBackprop';\nexport type AvgPool3DBackpropInputs = Pick<NamedTensorInfoMap, 'dy'|'input'>;\nexport interface AvgPool3DBackpropAttrs {\n  filterSize: [number, number, number]|number;\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same'|number;\n  dilations: [number, number, number]|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const BatchMatMul = 'BatchMatMul';\nexport type BatchMatMulInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;\nexport interface BatchMatMulAttrs {\n  transposeA: boolean;\n  transposeB: boolean;\n}\n\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport type BatchToSpaceNDInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface BatchToSpaceNDAttrs {\n  blockShape: number[];\n  crops: number[][];\n}\n\nexport type BinaryInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;\n\nexport const BroadcastTo = 'BroadcastTo';\nexport type BroadcastToInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface BroadCastToAttrs {\n  shape: number[];\n  inputShape: number[];  // for gradient\n}\n\nexport const Cast = 'Cast';\nexport type CastInputs = UnaryInputs;\nexport interface CastAttrs {\n  dtype: DataType;\n}\n\nexport const Ceil = 'Ceil';\nexport type CeilInputs = UnaryInputs;\n\nexport const ClipByValue = 'ClipByValue';\nexport type ClipByValueInputs = UnaryInputs;\nexport interface ClipByValueAttrs {\n  clipValueMin: number;\n  clipValueMax: number;\n}\n\nexport const Complex = 'Complex';\nexport type ComplexInputs = Pick<NamedTensorInfoMap, 'real'|'imag'>;\n\nexport const Concat = 'Concat';\nexport type ConcatInputs = TensorInfo[];\nexport interface ConcatAttrs {\n  axis: number;\n}\n\nexport const Conv2D = 'Conv2D';\nexport type Conv2DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;\nexport interface Conv2DAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dilations: [number, number]|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport type Conv2DBackpropFilterInputs = Pick<NamedTensorInfoMap, 'x'|'dy'>;\nexport interface Conv2DBackpropFilterAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  filterShape: [number, number, number, number];\n}\n\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport type Conv2DBackpropInputInputs = Pick<NamedTensorInfoMap, 'dy'|'filter'>;\nexport interface Conv2DBackpropInputAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  inputShape: [number, number, number, number];\n}\n\nexport const Conv3D = 'Conv3D';\nexport type Conv3DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;\nexport interface Conv3DAttrs {\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same';\n  dataFormat: 'NDHWC'|'NCDHW';\n  dilations: [number, number, number]|number;\n}\n\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport type Conv3DBackpropFilterInputs = Pick<NamedTensorInfoMap, 'x'|'dy'>;\n\nexport interface Conv3DBackpropFilterAttrs {\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same';\n  filterShape: [number, number, number, number, number];\n}\n\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport type Conv3DBackpropInputInputs = Pick<NamedTensorInfoMap, 'dy'|'filter'>;\nexport interface Conv3DBackpropInputAttrs {\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same';\n  inputShape: [number, number, number, number, number];\n}\n\nexport const Cos = 'Cos';\nexport type CosInputs = UnaryInputs;\n\nexport const Cosh = 'Cosh';\nexport type CoshInputs = UnaryInputs;\n\nexport const Cumsum = 'Cumsum';\nexport type CumsumInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface CumsumAttrs {\n  axis: number;\n  exclusive: boolean;\n  reverse: boolean;\n}\n\nexport const CropAndResize = 'CropAndResize';\nexport type CropAndResizeInputs =\n    Pick<NamedTensorInfoMap, 'image'|'boxes'|'boxInd'>;\nexport interface CropAndResizeAttrs {\n  cropSize: [number, number];\n  method: 'bilinear'|'nearest';\n  extrapolationValue: number;\n}\n\nexport const DepthToSpace = 'DepthToSpace';\nexport type DepthToSpaceInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface DepthToSpaceAttrs {\n  blockSize: number;\n  dataFormat: 'NHWC'|'NCHW';\n}\n\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport type DepthwiseConv2dNativeInputs =\n    Pick<NamedTensorInfoMap, 'x'|'filter'>;\nexport interface DepthwiseConv2dNativeAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dataFormat: 'NHWC'|'NCHW';\n  dilations: [number, number]|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const DepthwiseConv2dNativeBackpropFilter =\n    'DepthwiseConv2dNativeBackpropFilter';\nexport type DepthwiseConv2dNativeBackpropFilterInputs =\n    Pick<NamedTensorInfoMap, 'x'|'dy'>;\nexport interface DepthwiseConv2dNativeBackpropFilterAttrs {\n  strides: [number, number]|number;\n  dilations: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  filterShape: [number, number, number, number];\n}\n\nexport const DepthwiseConv2dNativeBackpropInput =\n    'DepthwiseConv2dNativeBackpropInput';\nexport type DepthwiseConv2dNativeBackpropInputInputs =\n    Pick<NamedTensorInfoMap, 'dy'|'filter'>;\nexport interface DepthwiseConv2dNativeBackpropInputAttrs {\n  strides: [number, number]|number;\n  dilations: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n  inputShape: [number, number, number, number];\n}\n\nexport const Diag = 'Diag';\nexport type DiagInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Dilation2D = 'Dilation2D';\nexport type Dilation2DInputs = Pick<NamedTensorInfoMap, 'x'|'filter'>;\nexport interface Dilation2DAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dilations: [number, number]|number;\n}\n\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport type Dilation2DBackpropInputInputs =\n    Pick<NamedTensorInfoMap, 'x'|'filter'|'dy'>;\n\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport type Dilation2DBackpropFilterInputs =\n    Pick<NamedTensorInfoMap, 'x'|'filter'|'dy'>;\n\nexport const Div = 'Div';\nexport type DivInputs = BinaryInputs;\n\nexport const Elu = 'Elu';\nexport type EluInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const EluGrad = 'EluGrad';\nexport type EluGradInputs = Pick<NamedTensorInfoMap, 'dy'|'y'>;\n\nexport const Erf = 'Erf';\nexport type ErfInputs = UnaryInputs;\n\nexport const Equal = 'Equal';\nexport type EqualInputs = BinaryInputs;\n\nexport const Exp = 'Exp';\nexport type ExpInputs = UnaryInputs;\n\nexport const Expm1 = 'Expm1';\nexport type Expm1Inputs = UnaryInputs;\n\nexport const FFT = 'FFT';\nexport type FFTInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const Fill = 'Fill';\nexport interface FillAttrs {\n  shape: number[];\n  value: number|string;\n  dtype: DataType;\n}\n\nexport const FlipLeftRight = 'FlipLeftRight';\nexport type FlipLeftRightInputs = Pick<NamedTensorInfoMap, 'image'>;\n\nexport const Floor = 'Floor';\nexport type FloorInputs = UnaryInputs;\n\nexport const FloorDiv = 'FloorDiv';\nexport type FloorDivInputs = BinaryInputs;\n\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport type FusedBatchNormInputs =\n    Pick<NamedTensorInfoMap, 'x'|'scale'|'offset'|'mean'|'variance'>;\nexport interface FusedBatchNormAttrs {\n  varianceEpsilon: number;\n}\n\nexport const GatherV2 = 'GatherV2';\nexport type GatherV2Inputs = Pick<NamedTensorInfoMap, 'x'|'indices'>;\nexport interface GatherV2Attrs {\n  axis: number;\n}\n\nexport const GatherNd = 'GatherNd';\nexport type GatherNdInputs = Pick<NamedTensorInfoMap, 'params'|'indices'>;\n\nexport const Greater = 'Greater';\nexport type GreaterInputs = BinaryInputs;\n\nexport const GreaterEqual = 'GreaterEqual';\nexport type GreaterEqualInputs = BinaryInputs;\n\nexport const Identity = 'Identity';\nexport type IdentityInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const IFFT = 'IFFT';\nexport type IFFTInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const Imag = 'Imag';\nexport type ImagInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const IsFinite = 'IsFinite';\nexport type IsFiniteInputs = UnaryInputs;\n\nexport const IsInf = 'IsInf';\nexport type IsInfInputs = UnaryInputs;\n\nexport const IsNan = 'IsNan';\nexport type IsNanInputs = UnaryInputs;\n\nexport const Less = 'Less';\nexport type LessInputs = BinaryInputs;\n\nexport const LessEqual = 'LessEqual';\nexport type LessEqualInputs = BinaryInputs;\n\nexport const LinSpace = 'LinSpace';\nexport interface LinSpaceAttrs {\n  start: number;\n  stop: number;\n  num: number;\n}\nexport const Log = 'Log';\nexport type LogInputs = UnaryInputs;\n\nexport const Log1p = 'Log1p';\nexport type Log1pInputs = UnaryInputs;\n\nexport const LogicalAnd = 'LogicalAnd';\nexport type LogicalAndInputs = BinaryInputs;\n\nexport const LogicalNot = 'LogicalNot';\nexport type LogicalNotInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const LogicalOr = 'LogicalOr';\nexport type LogicalOrInputs = BinaryInputs;\n\nexport const LogSoftmax = 'LogSoftmax';\nexport type LogSoftmaxInputs = Pick<NamedTensorInfoMap, 'logits'>;\nexport interface LogSoftmaxAttrs {\n  axis: number;\n}\n\nexport const LRN = 'LRN';\nexport type LRNInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface LRNAttrs {\n  depthRadius: number;\n  bias: number;\n  alpha: number;\n  beta: number;\n}\n\nexport const LRNBackprop = 'LRNBackprop';\nexport type LRNBackpropInputs = Pick<NamedTensorInfoMap, 'x'|'y'|'dy'>;\nexport interface LRNBackpropAttrs {\n  depthRadius: number;\n  bias: number;\n  alpha: number;\n  beta: number;\n}\n\nexport const Max = 'Max';\nexport type MaxInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MaxAttrs {\n  reductionIndices: number|number[];\n  keepDims: boolean;\n}\n\nexport const Maximum = 'Maximum';\nexport type MaximumInputs = BinaryInputs;\n\nexport const MaxPool = 'MaxPool';\nexport type MaxPoolInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MaxPoolAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPoolBackprop = 'MaxPoolBackprop';\nexport type MaxPoolBackpropInputs =\n    Pick<NamedTensorInfoMap, 'dy'|'input'|'output'>;\nexport interface MaxPoolBackpropAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPool3D = 'MaxPool3D';\nexport type MaxPool3DInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MaxPool3DAttrs {\n  filterSize: [number, number, number]|number;\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same'|number;\n  dataFormat: 'NDHWC'|'NCDHW';\n  dilations?: [number, number, number]|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPool3DBackprop = 'MaxPool3DBackprop';\nexport type MaxPool3DBackpropInputs =\n    Pick<NamedTensorInfoMap, 'dy'|'input'|'output'>;\nexport interface MaxPool3DBackpropAttrs {\n  filterSize: [number, number, number]|number;\n  strides: [number, number, number]|number;\n  pad: 'valid'|'same'|number;\n  dilations?: [number, number, number]|number;\n  dimRoundingMode?: 'floor'|'round'|'ceil';\n}\n\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport type MaxPoolWithArgmaxInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MaxPoolWithArgmaxAttrs {\n  filterSize: [number, number]|number;\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  includeBatchInIndex: boolean;\n}\n\nexport const Mean = 'Mean';\nexport type MeanInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MeanAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const Min = 'Min';\nexport type MinInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MinAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const Minimum = 'Minimum';\nexport type MinimumInputs = BinaryInputs;\n\nexport const MirrorPad = 'MirrorPad';\nexport type MirrorPadInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface MirrorPadAttrs {\n  paddings: Array<[number, number]>;\n  mode: 'reflect'|'symmetric';\n}\n\nexport const Mod = 'Mod';\nexport type ModInputs = BinaryInputs;\n\nexport const Multiply = 'Multiply';\nexport type MultiplyInputs = BinaryInputs;\n\nexport const Negate = 'Negate';\nexport type NegateInputs = UnaryInputs;\n\nexport const NotEqual = 'NotEqual';\nexport type NotEqualInputs = BinaryInputs;\n\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport type NonMaxSuppressionV3Inputs =\n    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;\nexport interface NonMaxSuppressionV3Attrs {\n  maxOutputSize: number;\n  iouThreshold: number;\n  scoreThreshold: number;\n}\n\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport type NonMaxSuppressionV4Inputs =\n    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;\nexport interface NonMaxSuppressionV4Attrs {\n  maxOutputSize: number;\n  iouThreshold: number;\n  scoreThreshold: number;\n  padToMaxOutputSize: boolean;\n}\n\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport type NonMaxSuppressionV5Inputs =\n    Pick<NamedTensorInfoMap, 'boxes'|'scores'>;\nexport interface NonMaxSuppressionV5Attrs {\n  maxOutputSize: number;\n  iouThreshold: number;\n  scoreThreshold: number;\n  softNmsSigma: number;\n}\n\nexport const OnesLike = 'OnesLike';\nexport type OnesLikeInputs = UnaryInputs;\n\nexport const OneHot = 'OneHot';\nexport type OneHotInputs = Pick<NamedTensorInfoMap, 'indices'>;\nexport interface OneHotAttrs {\n  depth: number;\n  onValue: number;\n  offValue: number;\n}\n\nexport const PadV2 = 'PadV2';\nexport type PadV2Inputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface PadV2Attrs {\n  paddings: Array<[number, number]>;\n  constantValue: number;\n}\n\nexport const Pool = 'Pool';\nexport type PoolInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const Pow = 'Pow';\nexport type PowInputs = BinaryInputs;\n\nexport const Prelu = 'Prelu';\nexport type PreluInputs = Pick<NamedTensorInfoMap, 'x'|'alpha'>;\n\nexport const Prod = 'Prod';\nexport type ProdInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ProdAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const Range = 'Range';\nexport interface RangeAttrs {\n  start: number;\n  stop: number;\n  step: number;\n  dtype: 'float32'|'int32';\n}\n\nexport const Real = 'Real';\nexport type RealInputs = Pick<NamedTensorInfoMap, 'input'>;\n\nexport const Reciprocal = 'Reciprocal';\nexport type ReciprocalInputs = UnaryInputs;\n\nexport const Relu = 'Relu';\nexport type ReluInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Reshape = 'Reshape';\nexport type ReshapeInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ReshapeAttrs {\n  shape: number[];\n}\n\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport type ResizeNearestNeighborInputs = Pick<NamedTensorInfoMap, 'images'>;\nexport interface ResizeNearestNeighborAttrs {\n  alignCorners: boolean;\n  size: [number, number];\n}\n\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport type ResizeNearestNeighborGradInputs =\n    Pick<NamedTensorInfoMap, 'images'>;\n\nexport const ResizeBilinear = 'ResizeBilinear';\nexport type ResizeBilinearInputs = Pick<NamedTensorInfoMap, 'images'>;\nexport interface ResizeBilinearAttrs {\n  alignCorners: boolean;\n  size: [number, number];\n}\n\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport type ResizeBilinearGradInputs = Pick<NamedTensorInfoMap, 'images'>;\n\nexport const Relu6 = 'Relu6';\nexport type Relu6Inputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Reverse = 'Reverse';\nexport type ReverseInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface ReverseAttrs {\n  dims: number|number[];\n}\n\nexport const Round = 'Round';\nexport type RoundInputs = UnaryInputs;\n\nexport const Rsqrt = 'Rsqrt';\nexport type RsqrtInputs = UnaryInputs;\n\nexport const ScatterNd = 'ScatterNd';\nexport type ScatterNdInputs = Pick<NamedTensorInfoMap, 'indices'|'updates'>;\nexport interface ScatterNdAttrs {\n  shape: number[];\n}\n\nexport const SelectV2 = 'SelectV2';\nexport type SelectV2Inputs = Pick<NamedTensorInfoMap, 'condition'|'t'|'e'>;\n\nexport const Selu = 'Selu';\nexport type SeluInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Slice = 'Slice';\nexport type SliceInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface SliceAttrs {\n  begin: number|number[];\n  size: number|number[];\n}\nexport const Sin = 'Sin';\nexport type SinInputs = UnaryInputs;\n\nexport const Sinh = 'Sinh';\nexport type SinhInputs = UnaryInputs;\n\nexport const Sign = 'Sign';\nexport type SignInputs = UnaryInputs;\n\nexport const Sigmoid = 'Sigmoid';\nexport type SigmoidInputs = UnaryInputs;\n\nexport const Softplus = 'Softplus';\nexport type SoftplusInputs = UnaryInputs;\n\nexport const Sqrt = 'Sqrt';\nexport type SqrtInputs = UnaryInputs;\n\nexport const Sum = 'Sum';\nexport type SumInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface SumAttrs {\n  axis: number|number[];\n  keepDims: boolean;\n}\n\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport type SpaceToBatchNDInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface SpaceToBatchNDAttrs {\n  blockShape: number[];\n  paddings: number[][];\n}\n\nexport const SplitV = 'SplitV';\nexport type SplitVInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface SplitVAttrs {\n  numOrSizeSplits: number[]|number;\n  axis: number;\n}\n\nexport const Softmax = 'Softmax';\nexport type SoftmaxInputs = Pick<NamedTensorInfoMap, 'logits'>;\nexport interface SoftmaxAttrs {\n  dim: number;\n}\n\nexport const SquaredDifference = 'SquaredDifference';\nexport type SquaredDifferenceInputs = BinaryInputs;\n\nexport const Square = 'Square';\nexport type SquareInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Sub = 'Sub';\nexport type SubInputs = BinaryInputs;\n\nexport const SparseToDense = 'SparseToDense';\nexport type SparseToDenseInputs =\n    Pick<NamedTensorInfoMap, 'sparseIndices'|'sparseValues'|'defaultValue'>;\nexport interface SparseToDenseAttrs {\n  outputShape: number[];\n}\n\nexport const StridedSlice = 'StridedSlice';\nexport type StridedSliceInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface StridedSliceAttrs {\n  begin: number[];\n  end: number[];\n  strides: number[];\n  beginMask: number;\n  endMask: number;\n  ellipsisMask: number;\n  newAxisMask: number;\n  shrinkAxisMask: number;\n}\n\nexport const Tan = 'Tan';\nexport type TanInputs = UnaryInputs;\n\nexport const Tanh = 'Tanh';\nexport type TanhInputs = UnaryInputs;\n\nexport const Tile = 'Tile';\nexport type TileInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface TileAttrs {\n  reps: number[];\n}\n\nexport const TopK = 'TopK';\nexport type TopKInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface TopKAttrs {\n  k: number;\n  sorted: boolean;\n}\n\nexport const Transpose = 'Transpose';\nexport type TransposeInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface TransposeAttrs {\n  perm: number[];\n}\n\nexport const Unique = 'Unique';\nexport type UniqueInputs = Pick<NamedTensorInfoMap, 'x'>;\nexport interface UniqueAttrs {\n  axis: number;\n}\n\nexport type UnaryInputs = Pick<NamedTensorInfoMap, 'x'>;\n\nexport const Unpack = 'Unpack';\nexport type UnpackInputs = Pick<NamedTensorInfoMap, 'value'>;\nexport interface UnpackAttrs {\n  axis: number;\n}\n\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport type UnsortedSegmentSumInputs =\n    Pick<NamedTensorInfoMap, 'x'|'segmentIds'>;\nexport interface UnsortedSegmentSumAttrs {\n  numSegments: number;\n}\n\nexport const ZerosLike = 'ZerosLike';\nexport type ZerosLikeInputs = UnaryInputs;\n\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport type StepInputs = UnaryInputs;\nexport interface StepAttrs {\n  alpha: number;\n}\n\nexport const FromPixels = 'FromPixels';\nexport interface FromPixelsInputs {\n  pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n      HTMLVideoElement;\n}\nexport interface FromPixelsAttrs {\n  numChannels: number;\n}\n\nexport const RotateWithOffset = 'RotateWithOffset';\nexport type RotateWithOffsetInputs = Pick<NamedTensorInfoMap, 'image'>;\nexport interface RotateWithOffsetAttrs {\n  radians: number;\n  fillValue: number|[number, number, number];\n  center: number|[number, number];\n}\n\nexport const _FusedMatMul = '_FusedMatMul';\n// tslint:disable-next-line: class-name\nexport interface _FusedMatMulInputs extends NamedTensorInfoMap {\n  a: TensorInfo;\n  b: TensorInfo;\n  bias?: TensorInfo;\n  preluActivationWeights?: TensorInfo;\n}\n// tslint:disable-next-line: class-name\nexport interface _FusedMatMulAttrs {\n  transposeA: boolean;\n  transposeB: boolean;\n  activation: Activation;\n}\n\nexport const FusedConv2D = 'FusedConv2D';\nexport interface FusedConv2DInputs extends NamedTensorInfoMap {\n  x: TensorInfo;\n  filter: TensorInfo;\n  bias?: TensorInfo;\n  preluActivationWeights?: TensorInfo;\n}\nexport interface FusedConv2DAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number|ExplicitPadding;\n  dataFormat: 'NHWC'|'NCHW';\n  dilations: [number, number]|number;\n  dimRoundingMode: 'floor'|'round'|'ceil';\n  activation: Activation;\n}\n\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\nexport interface FusedDepthwiseConv2DInputs extends NamedTensorInfoMap {\n  x: TensorInfo;\n  filter: TensorInfo;\n  bias?: TensorInfo;\n  preluActivationWeights?: TensorInfo;\n}\nexport interface FusedDepthwiseConv2DAttrs {\n  strides: [number, number]|number;\n  pad: 'valid'|'same'|number;\n  dataFormat: 'NHWC'|'NCHW';\n  dilations: [number, number]|number;\n  dimRoundingMode: 'floor'|'round'|'ceil';\n  activation: Activation;\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from './environment';\n\nimport {getGlobal} from './global_util';\nimport {NamedGradientMap} from './tape';\nimport {Tensor} from './tensor';\nimport {DataType, RecursiveArray} from './types';\n\nconst kernelRegistry =\n    getGlobal('kernelRegistry', () => new Map<string, KernelConfig>());\nconst gradRegistry =\n    getGlobal('gradRegistry', () => new Map<string, GradConfig>());\n\nexport type DataId = object;\n\ntype AttributeValue =\n    number|number[]|boolean|boolean[]|string|string[]|NamedAttrMap;\n\n/** These are extra non-tensor/primitive params passed to kernel functions. */\nexport type Attribute = AttributeValue|RecursiveArray<AttributeValue>;\n\n/** Specifies the code to run when executing a kernel. */\nexport type KernelFunc = (params: {\n  inputs: NamedTensorInfoMap,\n  backend: {},\n  attrs?: NamedAttrMap,\n}) => TensorInfo|TensorInfo[];\n\n/** The function to run when computing a gradient during backprop. */\nexport type GradFunc =\n    (dy: Tensor|Tensor[], saved: Tensor[], attrs: NamedAttrMap) =>\n        NamedGradientMap;\n\n/** Function that gets called after the backend initializes. */\nexport type KernelSetupFunc = (backend: {}) => void;\n/** Function that gets called right before the backend is disposed. */\nexport type KernelDisposeFunc = KernelSetupFunc;\n\n/** Config object for registering a kernel in the global registry. */\nexport interface KernelConfig {\n  kernelName: string;\n  backendName: string;\n  kernelFunc: KernelFunc;\n  setupFunc?: KernelSetupFunc;\n  disposeFunc?: KernelDisposeFunc;\n}\n\n/** Config object for registering a gradient in the global registry. */\nexport interface GradConfig {\n  kernelName: string;\n  inputsToSave?: string[];\n  // When saveAllInputs is true, all inputs will be saved. Only use this flag\n  // if inputs is an array of Tensors.\n  saveAllInputs?: boolean;\n  outputsToSave?: boolean[];\n  gradFunc: GradFunc;\n}\n\n/** Holds metadata for a given tensor. */\nexport interface TensorInfo {\n  dataId: DataId;\n  shape: number[];\n  dtype: DataType;\n}\n\nexport interface NamedTensorInfoMap {\n  [name: string]: TensorInfo;\n}\n\nexport interface NamedAttrMap {\n  [name: string]: Attribute;\n}\n\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(\n    kernelName: string, backendName: string): KernelConfig {\n  const key = makeKey(kernelName, backendName);\n  return kernelRegistry.get(key);\n}\n\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName: string): GradConfig {\n  return gradRegistry.get(kernelName);\n}\n\nexport function getKernelsForBackend(backendName: string): KernelConfig[] {\n  const it = kernelRegistry.entries();\n  const result: KernelConfig[] = [];\n\n  while (true) {\n    const {done, value} = it.next();\n    if (done) {\n      break;\n    }\n    const [key, config] = value;\n    const [backend, ] = key.split('_');\n    if (backend === backendName) {\n      result.push(config);\n    }\n  }\n  return result;\n}\n\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config: KernelConfig) {\n  const {kernelName, backendName} = config;\n  const key = makeKey(kernelName, backendName);\n  if (kernelRegistry.has(key)) {\n    console.warn(\n        `The kernel '${kernelName}' for backend ` +\n        `'${backendName}' is already registered`);\n  }\n  kernelRegistry.set(key, config);\n}\n\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config: GradConfig) {\n  const {kernelName} = config;\n\n  if (gradRegistry.has(kernelName)) {\n    // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n    // to debug mode.\n    if (env().getBool('DEBUG')) {\n      console.warn(`Overriding the gradient for '${kernelName}'`);\n    }\n  }\n  gradRegistry.set(kernelName, config);\n}\n\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(\n    kernelName: string, backendName: string): void {\n  const key = makeKey(kernelName, backendName);\n  if (!kernelRegistry.has(key)) {\n    throw new Error(\n        `The kernel '${kernelName}' for backend ` +\n        `'${backendName}' is not registered`);\n  }\n  kernelRegistry.delete(key);\n}\n\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName: string): void {\n  if (!gradRegistry.has(kernelName)) {\n    throw new Error(\n        `The gradient '${kernelName}' for backend is not registered`);\n  }\n  gradRegistry.delete(kernelName);\n}\n\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(\n    registeredBackendName: string, newBackendName: string): void {\n  const kernels = getKernelsForBackend(registeredBackendName);\n  kernels.forEach(kernelConfig => {\n    const newKernelConfig =\n        Object.assign({}, kernelConfig, {backendName: newBackendName});\n    registerKernel(newKernelConfig);\n  });\n}\n\nfunction makeKey(kernelName: string, backendName: string) {\n  return `${backendName}_${kernelName}`;\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from './environment';\nimport {BackendValues, DataType, TensorLike, TypedArray} from './types';\nimport * as base from './util_base';\nexport * from './util_base';\n\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(\n    value: DataType, dtype: DataType): BackendValues {\n  if (dtype === 'string') {\n    return encodeString(value);\n  }\n\n  return toTypedArray([value], dtype);\n}\n\nfunction noConversionNeeded(a: TensorLike, dtype: DataType): boolean {\n  return (a instanceof Float32Array && dtype === 'float32') ||\n      (a instanceof Int32Array && dtype === 'int32') ||\n      (a instanceof Uint8Array && dtype === 'bool');\n}\n\nexport function toTypedArray(a: TensorLike, dtype: DataType): TypedArray {\n  if (dtype === 'string') {\n    throw new Error('Cannot convert a string[] to a TypedArray');\n  }\n  if (Array.isArray(a)) {\n    a = base.flatten(a);\n  }\n\n  if (env().getBool('DEBUG')) {\n    base.checkConversionForErrors(a as number[], dtype);\n  }\n  if (noConversionNeeded(a, dtype)) {\n    return a as TypedArray;\n  }\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(a as number[]);\n  } else if (dtype === 'int32') {\n    return new Int32Array(a as number[]);\n  } else if (dtype === 'bool') {\n    const bool = new Uint8Array((a as number[]).length);\n    for (let i = 0; i < bool.length; ++i) {\n      if (Math.round((a as number[])[i]) !== 0) {\n        bool[i] = 1;\n      }\n    }\n    return bool;\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now(): number {\n  return env().platform.now();\n}\n\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(\n    path: string, requestInits?: RequestInit): Promise<Response> {\n  return env().platform.fetch(path, requestInits);\n}\n\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s: string, encoding = 'utf-8'): Uint8Array {\n  encoding = encoding || 'utf-8';\n  return env().platform.encode(s, encoding);\n}\n\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes: Uint8Array, encoding = 'utf-8'): string {\n  encoding = encoding || 'utf-8';\n  return env().platform.decode(bytes, encoding);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimer} from './backends/backend';\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport {DataType, DataTypeMap, TypedArray} from './types';\nimport * as util from './util';\n\nexport type KernelProfile = {\n  kernelName: string,\n  outputs: Tensor[],\n  inputs: NamedTensorMap,\n  timeMs: Promise<number|{error: string}>,\n  extraInfo: Promise<string>\n};\n\nexport class Profiler {\n  constructor(private backendTimer: BackendTimer, private logger?: Logger) {\n    if (logger == null) {\n      this.logger = new Logger();\n    }\n  }\n\n  profileKernel(kernelName: string, inputs: NamedTensorMap, f: () => Tensor[]):\n      KernelProfile {\n    let outputs: Tensor[];\n    const holdResultWrapperFn = () => {\n      outputs = f();\n    };\n    const timer = this.backendTimer.time(holdResultWrapperFn);\n\n    for (let i = 0; i < outputs.length; i++) {\n      const output = outputs[i];\n      // Dangling promise here because we don't want to propagate up\n      // asynchronicity.\n      output.data().then(tensorVals => {\n        checkComputationForErrors(tensorVals, output.dtype, kernelName);\n      });\n    }\n\n    const kernelProfile = {\n      kernelName,\n      outputs,\n      inputs,\n      timeMs: timer.then(timing => timing.kernelMs),\n      extraInfo: timer.then(\n          timing => timing.getExtraProfileInfo != null ?\n              timing.getExtraProfileInfo() :\n              '')\n    };\n    return kernelProfile;\n  }\n\n  logKernelProfile(kernelProfile: KernelProfile): void {\n    const {kernelName, outputs, timeMs, inputs, extraInfo} = kernelProfile;\n\n    outputs.forEach(result => {\n      Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n        this.logger.logKernelProfile(\n            kernelName, result, valueContainer[0], valueContainer[1], inputs,\n            valueContainer[2]);\n      });\n    });\n  }\n}\n\nexport function checkComputationForErrors<D extends DataType>(\n    vals: DataTypeMap[D], dtype: D, kernelName: string): boolean {\n  if (dtype !== 'float32') {\n    // Only floating point computations will generate NaN values\n    return false;\n  }\n  for (let i = 0; i < vals.length; i++) {\n    const num = vals[i] as number;\n    if (isNaN(num) || !isFinite(num)) {\n      // Throwing custom exception so behavior is testable.\n      console.warn(`Found ${num} in the result of '${kernelName}'`);\n      return true;\n    }\n  }\n  return false;\n}\n\nexport class Logger {\n  logKernelProfile(\n      name: string, result: Tensor, vals: TypedArray,\n      timeMs: number|{error: string}, inputs: NamedTensorMap,\n      extraInfo?: string) {\n    const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n                                              timeMs['error'];\n    const paddedName = util.rightPad(name, 25);\n    const rank = result.rank;\n    const size = result.size;\n    const shape = util.rightPad(result.shape.toString(), 14);\n    let inputShapesDescription = '';\n\n    for (const name in inputs) {\n      const input = inputs[name];\n      if (input != null) {\n        // The input might be a non-tensor (e.g HTMLImageElement), in which case\n        // we claim the output shape as input shape.\n        const inputShape = input.shape || result.shape;\n        const inputRank = inputShape.length;\n        inputShapesDescription +=\n            `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n      }\n    }\n\n    console.log(\n        `%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${\n            inputShapesDescription}\\t%c${extraInfo}`,\n        'font-weight:bold', 'color:red', 'color:blue', 'color: orange',\n        'color: green', 'color: steelblue');\n  }\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n  id: number;\n  kernelName: string;\n  outputs: Tensor[];\n  inputs: NamedTensorMap;\n  // Optional params, defined only for ops with gradient impl.\n  gradient?: (dys: Tensor[]) => NamedGradientMap;\n  saved?: Tensor[];\n}\n\nexport type NamedGradientMap = {\n  [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n    tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n  // Forward pass to compute all the nodes and Tensors that are transitively a\n  // function of x.\n  const tensorsFromX: {[tensorId: number]: boolean} = {};\n  const nodesFromX: {[nodeId: number]: boolean} = {};\n  for (let i = 0; i < xs.length; i++) {\n    tensorsFromX[xs[i].id] = true;\n  }\n\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input = nodeInputs[inputName];\n\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input.id]) {\n          node.outputs.forEach(output => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n\n  // Backward pass to find all of the nodes and Tensors that lead to y.\n  const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY: {[nodeId: number]: boolean} = {};\n\n  for (let i = tape.length - 1; i >= 0; i--) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n\n    // If any of the outputs lead to y, mark all of the inputs as leading to y.\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n\n  // Return the paths that come from x and lead to y.\n  const filteredTape: TapeNode[] = [];\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      // Prune the inputs from the node that aren't a function of x.\n      const prunedInputs: {[inputName: string]: Tensor} = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n\n      // Copy the node and overwrite inputsAndArgs to the pruned version.\n      const prunedNode = Object.assign({}, node);\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n\n      filteredTape.push(prunedNode);\n    }\n  }\n\n  return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n    tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n    filteredTape: TapeNode[], tidy: (f: Function) => Tensor,\n    add: (a: Tensor, b: Tensor) => Tensor) {\n  // Walk the tape backward and keep a map of Tensor to its gradient.\n  for (let i = filteredTape.length - 1; i >= 0; i--) {\n    const node = filteredTape[i];\n\n    const dys: Tensor[] = [];\n    node.outputs.forEach(o => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        // This particular output is not in the back-propagation subgraph, so it\n        // does not affect the final output, thus we put null for its dy.\n        dys.push(null);\n      }\n    });\n\n    if (node.gradient == null) {\n      throw new Error(\n          `Cannot compute gradient: gradient function not found ` +\n          `for ${node.kernelName}.`);\n    }\n\n    // Backprop dy through this node and accumulate gradients over the inputs.\n    const inputGradients = node.gradient(dys);\n\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(\n            `Cannot backprop through input ${inputName}. ` +\n            `Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n\n      // Call the gradient function.\n      const dx = tidy(() => inputGradients[inputName]());\n      if (dx.dtype !== 'float32') {\n        throw new Error(\n            `Error in gradient for op ${\n                node.kernelName}. The gradient of input ` +\n            `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!util.arraysEqual(dx.shape, x.shape)) {\n        throw new Error(\n            `Error in gradient for op ${\n                node.kernelName}. The gradient of input ` +\n            `'${inputName}' has shape '${dx.shape}', which does not match ` +\n            `the shape of the input '${x.shape}'`);\n      }\n\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TypedArray} from './types';\nimport {computeStrides, isString, rightPad, sizeFromShape} from './util';\n\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\n\nexport function tensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    verbose: boolean) {\n  const strides = computeStrides(shape);\n  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n  const rank = shape.length;\n  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n  const lines = ['Tensor'];\n  if (verbose) {\n    lines.push(`  dtype: ${dtype}`);\n    lines.push(`  rank: ${rank}`);\n    lines.push(`  shape: [${shape}]`);\n    lines.push(`  values:`);\n  }\n  lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n  return lines.join('\\n');\n}\n\nfunction computeMaxSizePerColumn(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[]): number[] {\n  const n = sizeFromShape(shape);\n  const numCols = strides[strides.length - 1];\n  const padPerCol = new Array(numCols).fill(0);\n  const rank = shape.length;\n  const valuesOrTuples =\n      dtype === 'complex64' ? createComplexTuples(vals) : vals;\n\n  if (rank > 1) {\n    for (let row = 0; row < n / numCols; row++) {\n      const offset = row * numCols;\n      for (let j = 0; j < numCols; j++) {\n        padPerCol[j] = Math.max(\n            padPerCol[j],\n            valToString(valuesOrTuples[offset + j], 0, dtype).length);\n      }\n    }\n  }\n  return padPerCol;\n}\n\nfunction valToString(\n    val: number|string|[number, number], pad: number, dtype: DataType) {\n  let valStr: string;\n  if (Array.isArray(val)) {\n    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n        `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n  } else if (isString(val)) {\n    valStr = `'${val}'`;\n  } else if (dtype === 'bool') {\n    valStr = boolNumToString(val);\n  } else {\n    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n  }\n\n  return rightPad(valStr, pad);\n}\n\nfunction boolNumToString(v: number): string {\n  return v === 0 ? 'false' : 'true';\n}\n\nfunction subTensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[], padPerCol: number[], isLast = true): string[] {\n  const storagePerElement = dtype === 'complex64' ? 2 : 1;\n\n  const size = shape[0];\n  const rank = shape.length;\n  if (rank === 0) {\n    if (dtype === 'complex64') {\n      const complexTuple = createComplexTuples(vals);\n      return [valToString(complexTuple[0], 0, dtype)];\n    }\n    if (dtype === 'bool') {\n      return [boolNumToString(vals[0] as number)];\n    }\n    return [vals[0].toString()];\n  }\n\n  if (rank === 1) {\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n\n      let firstVals = Array.from<number|string|[number, number]>(\n          vals.slice(0, firstValsSize));\n      let lastVals = Array.from<number|string|[number, number]>(vals.slice(\n          (size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement,\n          size * storagePerElement));\n      if (dtype === 'complex64') {\n        firstVals = createComplexTuples(firstVals);\n        lastVals = createComplexTuples(lastVals);\n      }\n      return [\n        '[' +\n        firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n            .join(', ') +\n        ', ..., ' +\n        lastVals\n            .map(\n                (x, i) => valToString(\n                    x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n            .join(', ') +\n        ']'\n      ];\n    }\n    const displayVals: Array<number|string|[number, number]> =\n        dtype === 'complex64' ? createComplexTuples(vals) :\n                                Array.from<number|string>(vals);\n\n    return [\n      '[' +\n      displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n          .join(', ') +\n      ']'\n    ];\n  }\n\n  // The array is rank 2 or more.\n  const subshape = shape.slice(1);\n  const substrides = strides.slice(1);\n  const stride = strides[0] * storagePerElement;\n  const lines: string[] = [];\n  if (size > FORMAT_LIMIT_NUM_VALS) {\n    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          false /* isLast */));\n    }\n    lines.push('...');\n    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  } else {\n    for (let i = 0; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  }\n  const sep = rank === 2 ? ',' : '';\n  lines[0] = '[' + lines[0] + sep;\n  for (let i = 1; i < lines.length - 1; i++) {\n    lines[i] = ' ' + lines[i] + sep;\n  }\n  let newLineSep = ',\\n';\n  for (let i = 2; i < rank; i++) {\n    newLineSep += '\\n';\n  }\n  lines[lines.length - 1] =\n      ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n  return lines;\n}\n\nfunction createComplexTuples(vals: Array<{}>|\n                             TypedArray): Array<[number, number]> {\n  const complexTuples: Array<[number, number]> = [];\n  for (let i = 0; i < vals.length; i += 2) {\n    complexTuples.push([vals[i], vals[i + 1]] as [number, number]);\n  }\n  return complexTuples;\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensorToString} from './tensor_format';\nimport {ArrayMap, BackendValues, DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TypedArray} from './types';\nimport * as util from './util';\nimport {computeStrides, toNestedArray} from './util';\n\nexport interface TensorData<D extends DataType> {\n  dataId?: DataId;\n  values?: DataTypeMap[D];\n}\n\n// This interface mimics KernelBackend (in backend.ts), which would create a\n// circular dependency if imported.\nexport interface Backend {}\n\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {\n  size: number;\n  shape: ShapeMap[R];\n  strides: number[];\n  values: DataTypeMap[D];\n\n  constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {\n    this.shape = shape.slice() as ShapeMap[R];\n    this.size = util.sizeFromShape(shape);\n\n    if (values != null) {\n      const n = values.length;\n      util.assert(\n          n === this.size,\n          () => `Length of values '${n}' does not match the size ` +\n              `inferred by the shape '${this.size}'.`);\n    }\n    if (dtype === 'complex64') {\n      throw new Error(\n          `complex64 dtype TensorBuffers are not supported. Please create ` +\n          `a TensorBuffer for the real and imaginary parts separately and ` +\n          `call tf.complex(real, imag).`);\n    }\n    this.values = values || util.getArrayFromDType(dtype, this.size);\n    this.strides = computeStrides(shape);\n  }\n\n  /**\n   * Sets a value in the buffer at a given location.\n   *\n   * @param value The value to set.\n   * @param locs  The location indices.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Creation'}\n   */\n  set(value: SingleValueMap[D], ...locs: number[]): void {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    util.assert(\n        locs.length === this.rank,\n        () => `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n\n    const index = this.locToIndex(locs);\n    this.values[index] = value as number;\n  }\n\n  /**\n   * Returns the value in the buffer at the provided location.\n   *\n   * @param locs The location indices.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Creation'}\n   */\n  get(...locs: number[]): SingleValueMap[D] {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let i = 0;\n    for (const loc of locs) {\n      if (loc < 0 || loc >= this.shape[i]) {\n        const msg = `Requested out of range element at ${locs}. ` +\n            `  Buffer shape=${this.shape}`;\n        throw new Error(msg);\n      }\n      i++;\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return this.values[index] as SingleValueMap[D];\n  }\n\n  locToIndex(locs: number[]): number {\n    if (this.rank === 0) {\n      return 0;\n    } else if (this.rank === 1) {\n      return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return index;\n  }\n\n  indexToLoc(index: number): number[] {\n    if (this.rank === 0) {\n      return [];\n    } else if (this.rank === 1) {\n      return [index];\n    }\n    const locs: number[] = new Array(this.shape.length);\n    for (let i = 0; i < locs.length - 1; ++i) {\n      locs[i] = Math.floor(index / this.strides[i]);\n      index -= locs[i] * this.strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n  }\n\n  get rank() {\n    return this.shape.length;\n  }\n\n  /**\n   * Creates an immutable `tf.Tensor` object from the buffer.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Creation'}\n   */\n  toTensor(): Tensor<R> {\n    return trackerFn().makeTensor(this.values, this.shape, this.dtype) as\n        Tensor<R>;\n  }\n}\n\nexport interface TensorTracker {\n  makeTensor(\n      values: DataValues, shape: number[], dtype: DataType,\n      backend?: Backend): Tensor;\n  makeVariable(\n      initialValue: Tensor, trainable?: boolean, name?: string,\n      dtype?: DataType): Variable;\n  incRef(a: Tensor, backend: Backend): void;\n  disposeTensor(t: Tensor): void;\n  disposeVariable(v: Variable): void;\n  read(dataId: DataId): Promise<BackendValues>;\n  readSync(dataId: DataId): BackendValues;\n}\n\n/**\n * The Tensor class calls into this handler to delegate chaining operations.\n */\nexport interface OpHandler {\n  cast<T extends Tensor>(x: T, dtype: DataType): T;\n  buffer<R extends Rank, D extends DataType>(\n      shape: ShapeMap[R], dtype: D,\n      values?: DataTypeMap[D]): TensorBuffer<R, D>;\n  print<T extends Tensor>(x: T, verbose: boolean): void;\n  clone<T extends Tensor>(x: T): T;\n  // TODO(yassogba) bring reshape back?\n}\n\n// For tracking tensor creation and disposal.\nlet trackerFn: () => TensorTracker = null;\n// Used by chaining methods to call into ops.\nlet opHandler: OpHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn: (msg: string) => void = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn: () => TensorTracker) {\n  trackerFn = fn;\n}\n\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler: OpHandler) {\n  opHandler = handler;\n}\n\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn: (msg: string) => void) {\n  deprecationWarningFn = fn;\n}\n\n/**\n * We wrap data id since we use weak map to avoid memory leaks.\n * Since we have our own memory management, we have a reference counter\n * mapping a tensor to its data, so there is always a pointer (even if that\n * data is otherwise garbage collectable).\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/\n * Global_Objects/WeakMap\n */\nexport type DataId = object;  // object instead of {} to force non-primitive.\n\n// Declare this namespace to make Tensor class augmentation work in google3.\nexport declare namespace Tensor {}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor<R extends Rank = Rank> {\n  /** Unique id of this tensor. */\n  readonly id: number;\n  /**\n   * Id of the bucket holding the data for this tensor. Multiple arrays can\n   * point to the same bucket (e.g. when calling array.reshape()).\n   */\n  dataId: DataId;\n  /** The shape of the tensor. */\n  readonly shape: ShapeMap[R];\n  /** Number of elements in the tensor. */\n  readonly size: number;\n  /** The data type for the array. */\n  readonly dtype: DataType;\n  /** The rank type for the array (see `Rank` enum). */\n  readonly rankType: R;\n\n  /** Whether this tensor has been globally kept. */\n  kept = false;\n  /** The id of the scope this tensor is being tracked in. */\n  scopeId: number;\n\n  /**\n   * Number of elements to skip in each dimension when indexing. See\n   * https://docs.scipy.org/doc/numpy/reference/generated/\\\n   * numpy.ndarray.strides.html\n   */\n  readonly strides: number[];\n\n  constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number) {\n    this.shape = shape.slice() as ShapeMap[R];\n    this.dtype = dtype || 'float32';\n    this.size = util.sizeFromShape(shape);\n    this.strides = computeStrides(shape);\n    this.dataId = dataId;\n    this.id = id;\n    this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;\n  }\n\n  get rank(): number {\n    return this.shape.length;\n  }\n\n  /**\n   * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  async buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>> {\n    const vals = await this.data<D>();\n    return opHandler.buffer(this.shape, this.dtype as D, vals);\n  }\n\n  /**\n   * Returns a `tf.TensorBuffer` that holds the underlying data.\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D> {\n    return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n  }\n\n  /**\n   * Returns the tensor data as a nested array. The transfer of data is done\n   * asynchronously.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  async array(): Promise<ArrayMap[R]> {\n    const vals = await this.data();\n    return toNestedArray(this.shape, vals) as ArrayMap[R];\n  }\n\n  /**\n   * Returns the tensor data as a nested array. The transfer of data is done\n   * synchronously.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  arraySync(): ArrayMap[R] {\n    return toNestedArray(this.shape, this.dataSync()) as ArrayMap[R];\n  }\n\n  /**\n   * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n   * promise of `TypedArray` that resolves when the computation has finished.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  async data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]> {\n    this.throwIfDisposed();\n    const data = trackerFn().read(this.dataId);\n    if (this.dtype === 'string') {\n      const bytes = await data as Uint8Array[];\n      try {\n        return bytes.map(b => util.decodeString(b)) as DataTypeMap[D];\n      } catch {\n        throw new Error(\n            'Failed to decode the string bytes into utf-8. ' +\n            'To get the original bytes, call tensor.bytes().');\n      }\n    }\n    return data as Promise<DataTypeMap[D]>;\n  }\n\n  /**\n   * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n   * UI thread until the values are ready, which can cause performance issues.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D] {\n    this.throwIfDisposed();\n    const data = trackerFn().readSync(this.dataId);\n    if (this.dtype === 'string') {\n      try {\n        return (data as Uint8Array[]).map(b => util.decodeString(b)) as\n            DataTypeMap[D];\n      } catch {\n        throw new Error(\n            'Failed to decode the string bytes into utf-8. ' +\n            'To get the original bytes, call tensor.bytes().');\n      }\n    }\n    return data as DataTypeMap[D];\n  }\n\n  /** Returns the underlying bytes of the tensor's data. */\n  async bytes(): Promise<Uint8Array[]|Uint8Array> {\n    this.throwIfDisposed();\n    const data = await trackerFn().read(this.dataId);\n    if (this.dtype === 'string') {\n      return data as Uint8Array[];\n    } else {\n      return new Uint8Array((data as TypedArray).buffer);\n    }\n  }\n\n  /**\n   * Disposes `tf.Tensor` from memory.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  dispose(): void {\n    if (this.isDisposed) {\n      return;\n    }\n    trackerFn().disposeTensor(this);\n    this.isDisposedInternal = true;\n  }\n\n  protected isDisposedInternal = false;\n  get isDisposed(): boolean {\n    return this.isDisposedInternal;\n  }\n\n  throwIfDisposed() {\n    if (this.isDisposed) {\n      throw new Error(`Tensor is disposed.`);\n    }\n  }\n\n  /**\n   * Prints the `tf.Tensor`. See `tf.print` for details.\n   *\n   * @param verbose Whether to print verbose information about the tensor,\n   *    including dtype and size.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  print(verbose = false): void {\n    return opHandler.print(this, verbose);\n  }\n\n  /**\n   * Returns a copy of the tensor. See `tf.clone` for details.\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  clone<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.clone(this);\n  }\n\n  /**\n   * Returns a human-readable description of the tensor. Useful for logging.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  toString(verbose = false): string {\n    const vals = this.dataSync();\n    return tensorToString(vals, this.shape, this.dtype, verbose);\n  }\n\n  cast<T extends this>(dtype: DataType): T {\n    this.throwIfDisposed();\n    return opHandler.cast(this as T, dtype);\n  }\n  variable(trainable = true, name?: string, dtype?: DataType): Variable<R> {\n    this.throwIfDisposed();\n    return trackerFn().makeVariable(this, trainable, name, dtype) as\n        Variable<R>;\n  }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n  value: (instance: Tensor) => {\n    // Implementation note: we should use properties of the object that will be\n    // defined before the constructor body has finished executing (methods).\n    // This is because when this code is transpiled by babel, babel will call\n    // classCallCheck before the constructor body is run.\n    // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n    return !!instance && instance.data != null && instance.dataSync != null &&\n        instance.throwIfDisposed != null;\n  }\n});\n\nexport interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: NumericDataType;\n  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];\n  data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;\n}\n\nexport interface StringTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: 'string';\n  dataSync<D extends DataType = 'string'>(): DataTypeMap[D];\n  data<D extends DataType = 'string'>(): Promise<DataTypeMap[D]>;\n}\n\n/** @doclink Tensor */\nexport type Scalar = Tensor<Rank.R0>;\n/** @doclink Tensor */\nexport type Tensor1D = Tensor<Rank.R1>;\n/** @doclink Tensor */\nexport type Tensor2D = Tensor<Rank.R2>;\n/** @doclink Tensor */\nexport type Tensor3D = Tensor<Rank.R3>;\n/** @doclink Tensor */\nexport type Tensor4D = Tensor<Rank.R4>;\n/** @doclink Tensor */\nexport type Tensor5D = Tensor<Rank.R5>;\n/** @doclink Tensor */\nexport type Tensor6D = Tensor<Rank.R6>;\n\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable<R extends Rank = Rank> extends Tensor<R> {\n  name: string;\n\n  constructor(\n      initialValue: Tensor<R>, public trainable: boolean, name: string,\n      tensorId: number) {\n    super(\n        initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n    this.name = name;\n  }\n\n  /**\n   * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n   * the same shape and dtype as the old `tf.Tensor`.\n   *\n   * @param newValue New tensor to be assigned to this variable.\n   *\n   * @doc {heading: 'Tensors', subheading: 'Classes'}\n   */\n  assign(newValue: Tensor<R>): void {\n    if (newValue.dtype !== this.dtype) {\n      throw new Error(\n          `dtype of the new value (${newValue.dtype}) and ` +\n          `previous value (${this.dtype}) must match`);\n    }\n    if (!util.arraysEqual(newValue.shape, this.shape)) {\n      throw new Error(\n          `shape of the new value (${newValue.shape}) and ` +\n          `previous value (${this.shape}) must match`);\n    }\n    trackerFn().disposeTensor(this);\n    this.dataId = newValue.dataId;\n    trackerFn().incRef(this, null /* backend */);\n  }\n\n  dispose(): void {\n    trackerFn().disposeVariable(this);\n    this.isDisposedInternal = true;\n  }\n}\n\nObject.defineProperty(Variable, Symbol.hasInstance, {\n  value: (instance: Variable) => {\n    return instance instanceof Tensor && instance.assign != null &&\n        instance.assign instanceof Function;\n  }\n});\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n  R0: number[];\n  R1: [number];\n  R2: [number, number];\n  R3: [number, number, number];\n  R4: [number, number, number, number];\n  R5: [number, number, number, number, number];\n  R6: [number, number, number, number, number, number];\n}\n\n/** @docalias number[] */\nexport interface ArrayMap {\n  R0: number;\n  R1: number[];\n  R2: number[][];\n  R3: number[][][];\n  R4: number[][][][];\n  R5: number[][][][][];\n  R6: number[][][][][][];\n}\n\nexport interface DataTypeMap {\n  float32: Float32Array;\n  int32: Int32Array;\n  bool: Uint8Array;\n  complex64: Float32Array;\n  string: string[];\n}\n\nexport interface SingleValueMap {\n  bool: boolean;\n  int32: number;\n  float32: number;\n  complex64: number;\n  string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\n/** Tensor data used in tensor creation and user-facing API. */\nexport type DataValues = DataTypeMap[DataType];\n/** The underlying tensor data that gets stored in a backend. */\nexport type BackendValues = Float32Array|Int32Array|Uint8Array|Uint8Array[];\n\nexport enum Rank {\n  R0 = 'R0',\n  R1 = 'R1',\n  R2 = 'R2',\n  R3 = 'R3',\n  R4 = 'R4',\n  R5 = 'R5',\n  R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray<T> =\n    T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray<T extends any> {\n  [index: number]: T|RecursiveArray<T>;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'int32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'bool',\n  'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n  'float32' = 'float32',\n  'int32' = 'float32',\n  'bool' = 'float32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n  'float32' = 'complex64',\n  'int32' = 'complex64',\n  'bool' = 'complex64',\n  'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n  'float32': UpcastFloat32AndMap,\n  'int32': UpcastInt32AndMap,\n  'bool': UpcastBoolAndMap,\n  'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n  if (typeA === 'string' || typeB === 'string') {\n    if (typeA === 'string' && typeB === 'string') {\n      return 'string';\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n  return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n    TypedArray|number|boolean|string|RecursiveArray<number|number[]|TypedArray>|\n    RecursiveArray<boolean>|RecursiveArray<string>|Uint8Array[];\nexport type ScalarLike = number|boolean|string|Uint8Array;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[]|Uint8Array[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D = TypedArray|number[]|number[][]|boolean[]|boolean[][]|\n    string[]|string[][]|Uint8Array[]|Uint8Array[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n    boolean[][][]|string[]|string[][][]|Uint8Array[]|Uint8Array[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n    boolean[][][][]|string[]|string[][][][]|Uint8Array[]|Uint8Array[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D =\n    TypedArray|number[]|number[][][][][]|boolean[]|boolean[][][][][]|string[]|\n    string[][][][][]|Uint8Array[]|Uint8Array[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D =\n    TypedArray|number[]|number[][][][][][]|boolean[]|boolean[][][][][][]|\n    string[]|string[][][][][][]|Uint8Array[]|Uint8Array[][][][][];\n\n/** Type for representing image dat in Uint8Array type. */\nexport interface PixelData {\n  width: number;\n  height: number;\n  data: Uint8Array;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {TensorContainer, TensorContainerArray} from './tensor_types';\nimport {upcastType} from './types';\nimport {assert} from './util';\n\nexport function makeTypesMatch<T extends Tensor>(a: T, b: T): [T, T] {\n  if (a.dtype === b.dtype) {\n    return [a, b];\n  }\n  const dtype = upcastType(a.dtype, b.dtype);\n  return [a.cast(dtype), b.cast(dtype)];\n}\n\nexport function assertTypesMatch(a: Tensor, b: Tensor): void {\n  assert(\n      a.dtype === b.dtype,\n      () => `The dtypes of the first(${a.dtype}) and` +\n          ` second(${b.dtype}) input must match`);\n}\n\nexport function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {\n  return tensorList.some(x => x.id === tensor.id);\n}\n\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result: TensorContainer): Tensor[] {\n  const list: Tensor[] = [];\n  const seen = new Set<{}|void>();\n  walkTensorContainer(result, list, seen);\n  return list;\n}\n\nfunction walkTensorContainer(\n    container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {\n  if (container == null) {\n    return;\n  }\n  if (container instanceof Tensor) {\n    list.push(container);\n    return;\n  }\n  if (!isIterable(container)) {\n    return;\n  }\n  // Iteration over keys works also for arrays.\n  const iterable = container as TensorContainerArray;\n  for (const k in iterable) {\n    const val = iterable[k];\n    if (!seen.has(val)) {\n      seen.add(val);\n      walkTensorContainer(val, list, seen);\n    }\n  }\n}\n\n// tslint:disable-next-line:no-any\nfunction isIterable(obj: any): boolean {\n  return Array.isArray(obj) || typeof obj === 'object';\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimingInfo, DataMover, KernelBackend} from './backends/backend';\nimport {Environment, setEnvironmentGlobal} from './environment';\nimport {getGlobalNamespace} from './global_util';\nimport {Add, Cast} from './kernel_names';\nimport {getGradient, getKernel, getKernelsForBackend, GradFunc, NamedAttrMap, TensorInfo} from './kernel_registry';\nimport {KernelProfile, Profiler} from './profiler';\nimport {backpropagateGradients, getFilteredNodesXToY, TapeNode} from './tape';\nimport {DataId, setTensorTracker, Tensor, TensorTracker, Variable} from './tensor';\nimport {GradSaveFunc, NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\nimport {BackendValues, DataType, DataValues} from './types';\nimport * as util from './util';\nimport {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';\n\n/**\n * A function that computes an output. The save function is for saving tensors\n * computed in the forward pass, that we need in the backward pass.\n */\nexport type ForwardFunc<T> = (backend: KernelBackend, save?: GradSaveFunc) => T;\n\n/**\n * @docalias (a: Tensor, b: Tensor,..., save?: Function) => {\n *   value: Tensor,\n *   gradFunc: (dy: Tensor, saved?: NamedTensorMap) => Tensor | Tensor[]\n * }\n */\nexport type CustomGradientFunc<T extends Tensor> =\n    (...inputs: Array<Tensor|GradSaveFunc>) => {\n      value: T;\n      gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[];\n    };\n\nexport type MemoryInfo = {\n  numTensors: number; numDataBuffers: number; numBytes: number;\n  unreliable?: boolean; reasons: string[];\n};\n\ntype KernelInfo = {\n  name: string; bytesAdded: number; totalBytesSnapshot: number;\n  tensorsAdded: number;\n  totalTensorsSnapshot: number;\n  inputShapes: number[][];\n  outputShapes: number[][];\n  kernelTimeMs: number | {error: string} | Promise<number|{error: string}>;\n  extraInfo: string | Promise<string>;\n};\n\nexport type ProfileInfo = {\n  newBytes: number; newTensors: number; peakBytes: number;\n  kernels: KernelInfo[];\n  result: TensorContainer;\n};\n\nexport interface TimingInfo extends BackendTimingInfo {\n  wallMs: number;\n}\n\n/** @docalias Function */\nexport type ScopeFn<T extends TensorContainer> = () => T;\n\ninterface ScopeState {\n  track: Tensor[];\n  name: string;\n  id: number;\n}\n\nclass EngineState {\n  // Public since optimizers will use it.\n  registeredVariables: NamedVariableMap = {};\n\n  nextTapeNodeId = 0;\n  numBytes = 0;\n  numTensors = 0;\n  numStringTensors = 0;\n  numDataBuffers = 0;\n\n  activeTape: TapeNode[];\n  // Number of nested tf.grad() statements when computing higher-order\n  // gradients. E.g. `1` for first-order gradients and `2` for second-order\n  // gradients. Used to track if the tape should be removed after a backprop.\n  gradientDepth = 0;\n  // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n  // off the tape.\n  kernelDepth = 0;\n\n  // Keep Tensors that parallel the tapes.\n  activeScope: ScopeState;\n  scopeStack: ScopeState[] = [];\n  /**\n   * Keeps track of the number of data moves during a kernel execution. We\n   * maintain a stack since kernels can call other kernels, recursively.\n   */\n  numDataMovesStack: number[] = [];\n  nextScopeId = 0;\n\n  tensorInfo = new WeakMap<DataId, {\n    backend: KernelBackend,\n    bytes: number,\n    dtype: DataType,\n    shape: number[],\n    refCount: number\n  }>();\n\n  profiling = false;\n  activeProfile: ProfileInfo =\n      {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};\n\n  dispose() {\n    for (const variableName in this.registeredVariables) {\n      this.registeredVariables[variableName].dispose();\n    }\n  }\n}\n\nexport class Engine implements TensorTracker, DataMover {\n  state: EngineState;\n  backendName: string;\n  registry: {[id: string]: KernelBackend} = {};\n  registryFactory: {\n    [id: string]: {\n      factory: () => KernelBackend | Promise<KernelBackend>,\n      priority: number\n    }\n  } = {};\n\n  private profiler: Profiler;\n  private backendInstance: KernelBackend;\n  private pendingBackendInit: Promise<boolean>;\n  private pendingBackendInitId = 0;\n\n  constructor(public ENV: Environment) {\n    this.state = new EngineState();\n  }\n\n  async ready(): Promise<void> {\n    if (this.pendingBackendInit != null) {\n      return this.pendingBackendInit.then(() => {});\n    }\n    if (this.backendInstance != null) {\n      return;\n    }\n    const sortedBackends = this.getSortedBackends();\n\n    for (let i = 0; i < sortedBackends.length; i++) {\n      const backendName = sortedBackends[i];\n      const success = await this.initializeBackend(backendName).success;\n      if (success) {\n        await this.setBackend(backendName);\n        return;\n      }\n    }\n\n    throw new Error(\n        `Could not initialize any backends, all backend initializations ` +\n        `failed.`);\n  }\n\n  get backend(): KernelBackend {\n    if (this.pendingBackendInit != null) {\n      throw new Error(\n          `Backend '${this.backendName}' has not yet been initialized. Make ` +\n          `sure to await tf.ready() or await tf.setBackend() before calling ` +\n          `other methods`);\n    }\n    if (this.backendInstance == null) {\n      const {name, asyncInit} = this.initializeBackendsAndReturnBest();\n      if (asyncInit) {\n        throw new Error(\n            `The highest priority backend '${name}' has not yet been ` +\n            `initialized. Make sure to await tf.ready() or ` +\n            `await tf.setBackend() before calling other methods`);\n      }\n      this.setBackend(name);\n    }\n    return this.backendInstance;\n  }\n\n  backendNames(): string[] {\n    return Object.keys(this.registryFactory);\n  }\n\n  findBackend(backendName: string): KernelBackend {\n    if (!(backendName in this.registry)) {\n      // If the backend hasn't been initialized but we have a registry entry for\n      // it, initialize it and return it.\n      if (backendName in this.registryFactory) {\n        const {asyncInit} = this.initializeBackend(backendName);\n        if (asyncInit) {\n          // Backend is not ready yet.\n          return null;\n        }\n      } else {\n        return null;\n      }\n    }\n    return this.registry[backendName];\n  }\n\n  findBackendFactory(backendName: string):\n      () => KernelBackend | Promise<KernelBackend> {\n    if (!(backendName in this.registryFactory)) {\n      return null;\n    }\n    return this.registryFactory[backendName].factory;\n  }\n\n  registerBackend(\n      backendName: string,\n      factory: () => KernelBackend | Promise<KernelBackend>,\n      priority = 1): boolean {\n    if (backendName in this.registryFactory) {\n      console.warn(\n          `${backendName} backend was already registered. ` +\n          `Reusing existing backend factory.`);\n      return false;\n    }\n    this.registryFactory[backendName] = {factory, priority};\n    return true;\n  }\n\n  async setBackend(backendName: string): Promise<boolean> {\n    if (this.registryFactory[backendName] == null) {\n      throw new Error(`Backend name '${backendName}' not found in registry`);\n    }\n    this.backendName = backendName;\n    if (this.registry[backendName] == null) {\n      this.backendInstance = null;\n      const {success, asyncInit} = this.initializeBackend(backendName);\n      const result = asyncInit ? await success : success;\n      if (!result) {\n        return false;\n      }\n    }\n    this.backendInstance = this.registry[backendName];\n    this.setupRegisteredKernels();\n    // Reset the profiler.\n    this.profiler = new Profiler(this.backendInstance);\n\n    return true;\n  }\n\n  private setupRegisteredKernels(): void {\n    const kernels = getKernelsForBackend(this.backendName);\n    kernels.forEach(kernel => {\n      if (kernel.setupFunc != null) {\n        kernel.setupFunc(this.backendInstance);\n      }\n    });\n  }\n\n  private disposeRegisteredKernels(backendName: string): void {\n    const kernels = getKernelsForBackend(backendName);\n    kernels.forEach(kernel => {\n      if (kernel.disposeFunc != null) {\n        kernel.disposeFunc(this.registry[backendName]);\n      }\n    });\n  }\n\n  /**\n   * Initializes a backend by looking up the backend name in the factory\n   * registry and calling the factory method. Returns a boolean representing\n   * whether the initialization of the backend suceeded. Throws an error if\n   * there is no backend in the factory registry.\n   */\n  private initializeBackend(backendName: string):\n      {success: boolean|Promise<boolean>, asyncInit: boolean} {\n    const registryFactoryEntry = this.registryFactory[backendName];\n    if (registryFactoryEntry == null) {\n      throw new Error(\n          `Cannot initialize backend ${backendName}, no registration found.`);\n    }\n\n    try {\n      const backend = registryFactoryEntry.factory();\n      /* Test if the factory returns a promise.\n      Done in a more liberal way than\n      previous 'Promise.resolve(backend)===backend'\n      as we needed to account for custom Promise\n      implementations (e.g. Angular) */\n      if (backend && !(backend instanceof KernelBackend)\n          && typeof backend.then === 'function') {\n        const promiseId = ++this.pendingBackendInitId;\n        const success =\n            backend\n                .then(backendInstance => {\n                  // Outdated promise. Another backend was set in the meantime.\n                  if (promiseId < this.pendingBackendInitId) {\n                    return false;\n                  }\n                  this.registry[backendName] = backendInstance;\n                  this.pendingBackendInit = null;\n                  return true;\n                })\n                .catch(err => {\n                  // Outdated promise. Another backend was set in the meantime.\n                  if (promiseId < this.pendingBackendInitId) {\n                    return false;\n                  }\n                  this.pendingBackendInit = null;\n                  console.warn(\n                      `Initialization of backend ${backendName} failed`);\n                  console.warn(err.stack || err.message);\n                  return false;\n                });\n        this.pendingBackendInit = success;\n        return {success, asyncInit: true};\n      } else {\n        this.registry[backendName] = backend as KernelBackend;\n        return {success: true, asyncInit: false};\n      }\n    } catch (err) {\n      console.warn(`Initialization of backend ${backendName} failed`);\n      console.warn(err.stack || err.message);\n      return {success: false, asyncInit: false};\n    }\n  }\n\n  removeBackend(backendName: string): void {\n    if (!(backendName in this.registryFactory)) {\n      throw new Error(`${backendName} backend not found in registry`);\n    }\n    if (this.backendName === backendName && this.pendingBackendInit != null) {\n      // There is a pending promise of the backend we want to remove. Make it\n      // obsolete.\n      this.pendingBackendInitId++;\n    }\n\n    if (backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n\n    delete this.registryFactory[backendName];\n\n    // Unset the backend if it is active.\n    if (this.backendName === backendName) {\n      this.pendingBackendInit = null;\n      this.backendName = null;\n      this.backendInstance = null;\n    }\n  }\n\n  private getSortedBackends(): string[] {\n    if (Object.keys(this.registryFactory).length === 0) {\n      throw new Error('No backend found in registry.');\n    }\n    return Object.keys(this.registryFactory).sort((a: string, b: string) => {\n      // Highest priority comes first.\n      return this.registryFactory[b].priority -\n          this.registryFactory[a].priority;\n    });\n  }\n\n  private initializeBackendsAndReturnBest():\n      {name: string, asyncInit: boolean} {\n    const sortedBackends = this.getSortedBackends();\n\n    for (let i = 0; i < sortedBackends.length; i++) {\n      const backendName = sortedBackends[i];\n      const {success, asyncInit} = this.initializeBackend(backendName);\n      if (asyncInit || success) {\n        return {name: backendName, asyncInit};\n      }\n    }\n    throw new Error(\n        `Could not initialize any backends, all backend initializations ` +\n        `failed.`);\n  }\n\n  moveData(backend: KernelBackend, dataId: DataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    const srcBackend = info.backend;\n    const values = this.readSync(dataId);\n    // Delete the tensor from the old backend and move it to the new\n    // backend.\n    srcBackend.disposeData(dataId);\n    info.backend = backend;\n    backend.move(dataId, values, info.shape, info.dtype);\n    if (this.shouldCheckForMemLeaks()) {\n      // Track the number of moves during a kernel execution to correctly\n      // detect memory leaks.\n      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n    }\n  }\n\n  tidy<T extends TensorContainer>(nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>):\n      T {\n    let name: string = null;\n    if (fn == null) {\n      // Called with only 1 argument.\n      if (typeof nameOrFn !== 'function') {\n        throw new Error('Please provide a function to tidy()');\n      }\n      fn = nameOrFn;\n    } else {\n      // Called with 2 arguments.\n      if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n        throw new Error(\n            'When calling with two arguments, the first argument ' +\n            'to tidy() must be a string');\n      }\n      if (typeof fn !== 'function') {\n        throw new Error(\n            'When calling with two arguments, the 2nd argument ' +\n            'to tidy() must be a function');\n      }\n      name = nameOrFn as string;\n      // TODO(nsthorat,smilkov): Do operation logging and performance\n      // profiling.\n    }\n    let result: T;\n    return this.scopedRun(\n        () => this.startScope(name), () => this.endScope(result), () => {\n          result = fn();\n          if (result instanceof Promise) {\n            console.error('Cannot return a Promise inside of tidy.');\n          }\n          return result;\n        });\n  }\n\n  private scopedRun<T>(start: () => void, end: () => void, f: () => T): T {\n    start();\n    try {\n      const res = f();\n      end();\n      return res;\n    } catch (ex) {\n      end();\n      throw ex;\n    }\n  }\n\n  private static nextTensorId = 0;\n  private nextTensorId(): number {\n    return Engine.nextTensorId++;\n  }\n\n  private static nextVariableId = 0;\n  private nextVariableId(): number {\n    return Engine.nextVariableId++;\n  }\n\n  /**\n   * This method is called instead of the public-facing tensor.clone() when\n   * saving a tensor for backwards pass. It makes sure to add the clone\n   * operation to the tape regardless of being called inside a kernel\n   * execution.\n   *\n   * This method will go away once all kernels are modularized since we won't\n   * need to turn off the tape inside runKernel().\n   */\n  private clone(x: Tensor): Tensor {\n    const y = this.makeTensorFromDataId(x.dataId, x.shape, x.dtype);\n    const inputs = {x};\n    const grad = (dy: Tensor) => ({\n      x: () => {\n        const dtype = 'float32';\n        const gradInputs = {x: dy};\n        const attrs = {dtype};\n\n        return ENGINE.runKernelFunc(\n            backend => backend.cast(dy, dtype),\n            gradInputs as {} as NamedTensorMap, null /* grad */, Cast,\n            attrs as {} as NamedAttrMap);\n      }\n    });\n    const saved: Tensor[] = [];\n    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n    return y;\n  }\n\n  /**\n   * Execute a kernel with the given name and return the output tensor.\n   *\n   * @param kernelName The name of the kernel to execute.\n   * @param inputs A map of input names to tensors.\n   * @param attrs A map of attribute names to their values. An attribute is a\n   *     primitive (non-tensor) input to the kernel.\n   * @param inputsToSave A list of tensors, inputs to save for the backprop\n   *     computation.\n   * @param outputsToSave A list of booleans, specifying which output to save\n   *     for the backprop computation. These are booleans since the output\n   * tensors are not visible to the user.\n   */\n  runKernel(\n      kernelName: string, inputs: NamedTensorMap, attrs: NamedAttrMap,\n      inputsToSave?: Tensor[], outputsToSave?: boolean[]): Tensor|Tensor[] {\n    const forwardFunc: null = null;\n    const backwardsFunc: null = null;\n    // Call runKernel as a stop-gap until we modularize all kernels.\n    // Once we modularize all kernels, we will remove the existing\n    // `runKernelFunc`.\n    return this.runKernelFunc(\n        forwardFunc, inputs, backwardsFunc, kernelName, attrs, inputsToSave,\n        outputsToSave);\n  }\n\n  private shouldCheckForMemLeaks(): boolean {\n    return this.ENV.getBool('IS_TEST');\n  }\n\n  private checkKernelForMemLeak(\n      kernelName: string, numDataIdsBefore: number,\n      outInfos: TensorInfo[]): void {\n    const numDataIdsAfter = this.backend.numDataIds();\n\n    // Count the number of data ids associated with the result of the kernel.\n    let numOutputDataIds = 0;\n    outInfos.forEach(info => {\n      // Complex numbers allocate 3 data ids, one for 'real', one for\n      // 'imaginary', and one for the container that holds the former two.\n      numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n    });\n\n    // Account for the number of moves during kernel execution. A \"data move\"\n    // can happen in the middle of a kernel execution, placing a new (key,value)\n    // pair in the data storage. Since data moves have net zero effect (we\n    // always remove the data from the old backend), we have to cancel them out\n    // when detecting memory leaks.\n    const numMoves =\n        this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n    const dataIdsLeaked =\n        numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n    if (dataIdsLeaked > 0) {\n      throw new Error(\n          `Backend '${this.backendName}' has an internal memory leak ` +\n          `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n    }\n  }\n\n  /**\n   * @deprecated Use `runKernel` for newly added kernels. Keep using this method\n   *     only for kernels that are not yet fully modularized.\n   */\n  runKernelFunc<T extends Tensor|Tensor[], I extends NamedTensorMap>(\n      forwardFunc: ForwardFunc<T>, inputs: I,\n      backwardsFunc?: (dy: T, saved: Tensor[]) => {[P in keyof I]: () => I[P]},\n      kernelName?: string, attrs?: NamedAttrMap, inputsToSave?: Tensor[],\n      outputsToSave?: boolean[]): T {\n    let outputs: Tensor[];\n    let saved: Tensor[] = [];\n    const isTapeOn = this.isTapeOn();\n    if (kernelName == null) {\n      kernelName =\n          this.state.activeScope != null ? this.state.activeScope.name : '';\n    }\n\n    const startingBytecount = this.state.numBytes;\n    const startingNumTensors = this.state.numTensors;\n\n    if (this.shouldCheckForMemLeaks()) {\n      this.state.numDataMovesStack.push(0);\n    }\n\n    let kernelFunc: () => Tensor[];\n    const kernel = getKernel(kernelName, this.backendName);\n    let out: TensorInfo|TensorInfo[];\n    if (kernel != null) {\n      kernelFunc = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = kernel.kernelFunc({inputs, attrs, backend: this.backend});\n        const outInfos = Array.isArray(out) ? out : [out];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n        }\n        const outTensors = outInfos.map(\n            ({dataId, shape, dtype}) =>\n                this.makeTensorFromDataId(dataId, shape, dtype));\n\n        // Save the inputs and outputs.\n        // Do not save unless we are recording to the tape. Otherwise it would\n        // cause a mem leak since we would never run backprop, which disposes\n        // the kept tensors.\n        if (isTapeOn) {\n          let tensorsToSave =\n              this.getTensorsForGradient(kernelName, inputs, outTensors);\n          if (tensorsToSave == null) {\n            // Fallback for ops that call runKernelFunc and pass in\n            // inputsToSave and outputsToSave. Currently this is the set of ops\n            // with kernel support in the WASM backend. Once those ops and\n            // respective gradients are modularised we can remove this path.\n            if (outputsToSave == null) {\n              outputsToSave = [];\n            }\n            const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);\n            tensorsToSave = (inputsToSave || []).slice().concat(outsToSave);\n          }\n          saved = this.saveTensorsForBackwardMode(tensorsToSave);\n        }\n        return outTensors;\n      };\n    } else {\n      const saveFunc: GradSaveFunc = (tensors) => {\n        // Do not save unless we are recording to the tape. Otherwise it would\n        // cause a mem leak since we would never run backprop, which disposes\n        // the kept tensors.\n        if (!isTapeOn) {\n          return;\n        }\n        saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n      };\n\n      kernelFunc = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n        const outs = (Array.isArray(out) ? out : [out]) as Tensor[];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outs);\n        }\n        return outs;\n      };\n    }\n\n    // Stop recording to a tape when running a kernel.\n    let kernelProfile: KernelProfile;\n    this.scopedRun(\n        () => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n          if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n            outputs = kernelFunc();\n          } else {\n            kernelProfile = this.profiler.profileKernel(\n                kernelName, inputs, () => kernelFunc());\n            if (this.ENV.getBool('DEBUG')) {\n              this.profiler.logKernelProfile(kernelProfile);\n            }\n            outputs = kernelProfile.outputs;\n          }\n        });\n\n    if (isTapeOn) {\n      this.addTapeNode(\n          kernelName, inputs, outputs, backwardsFunc, saved, attrs);\n    }\n\n    if (this.state.profiling) {\n      this.state.activeProfile.kernels.push({\n        name: kernelName,\n        bytesAdded: this.state.numBytes - startingBytecount,\n        totalBytesSnapshot: this.state.numBytes,\n        tensorsAdded: this.state.numTensors - startingNumTensors,\n        totalTensorsSnapshot: this.state.numTensors,\n        inputShapes: Object.keys(inputs).map(\n            key => inputs[key] != null ? inputs[key].shape : null),\n        outputShapes: outputs.map(item => item.shape),\n        kernelTimeMs: kernelProfile.timeMs,\n        extraInfo: kernelProfile.extraInfo\n      });\n    }\n    return (Array.isArray(out) ? outputs : outputs[0]) as T;\n  }\n\n  /**\n   * Saves tensors used in forward mode for use in backward mode.\n   *\n   * @param tensors the list of tensors to save.\n   */\n  private saveTensorsForBackwardMode(tensors: Tensor[]): Tensor[] {\n    const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n    return saved;\n  }\n\n  /**\n   * Returns a list of tensors to save for a given gradient calculation.\n   *\n   * Returns undefined if their is no registered gradient for this kernel in the\n   * gradient registry.\n   *\n   * @param kernelName name of kernel to look up gradient for.\n   * @param inputs a map of input tensors.\n   * @param outputs an array of output tensors from forward mode of kernel.\n   */\n  private getTensorsForGradient(\n      kernelName: string, inputs: NamedTensorMap,\n      outputs: Tensor[]): Tensor[]|null {\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      const inputsToSave: string[] = gradConfig.inputsToSave || [];\n      const outputsToSave: boolean[] = gradConfig.outputsToSave || [];\n\n      // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n      // specified in inputsToSave will be saved.\n      let inputTensorsToSave: Tensor[];\n      if (gradConfig.saveAllInputs) {\n        util.assert(\n            Array.isArray(inputs),\n            () => 'saveAllInputs is true, expected inputs to be an array.');\n\n        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n      } else {\n        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n      }\n\n      const outputTensorsToSave: Tensor[] =\n          outputs.filter((_, i) => outputsToSave[i]);\n\n      return inputTensorsToSave.concat(outputTensorsToSave);\n    }\n    // TODO(yassogba) throw exception here once all runkernelFunc calls with\n    // inputsToSave/outputsToSave are removed\n    return null;\n  }\n\n  /**\n   * Internal method used by public APIs for tensor creation. Makes a new\n   * tensor with the provided shape, dtype and values. It always\n   * creates a new data id and writes the values to the underlying backend.\n   */\n  makeTensor(\n      values: DataValues, shape: number[], dtype: DataType,\n      backend?: KernelBackend): Tensor {\n    if (values == null) {\n      throw new Error('Values passed to engine.makeTensor() are null');\n    }\n    dtype = dtype || 'float32';\n    backend = backend || this.backend;\n    let backendVals = values as BackendValues;\n    if (dtype === 'string' && util.isString(values[0])) {\n      backendVals = (values as string[]).map(d => util.encodeString(d));\n    }\n    const dataId = backend.write(backendVals, shape, dtype);\n    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.incRef(t, backend);\n\n    // Count bytes for string tensors.\n    if (dtype === 'string') {\n      const info = this.state.tensorInfo.get(dataId);\n      const newBytes = bytesFromStringArray(backendVals as Uint8Array[]);\n      this.state.numBytes += newBytes - info.bytes;\n      info.bytes = newBytes;\n    }\n    return t;\n  }\n\n  /**\n   * Internal method used by backends. Makes a new tensor\n   * that is a wrapper around an existing data id. It doesn't create\n   * a new data id, only increments the ref count used in memory tracking.\n   */\n  makeTensorFromDataId(\n      dataId: DataId, shape: number[], dtype: DataType,\n      backend?: KernelBackend): Tensor {\n    dtype = dtype || 'float32';\n    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.incRef(t, backend);\n    return t;\n  }\n\n  makeVariable(\n      initialValue: Tensor, trainable = true, name?: string,\n      dtype?: DataType): Variable {\n    name = name || this.nextVariableId().toString();\n    if (dtype != null && dtype !== initialValue.dtype) {\n      initialValue = initialValue.cast(dtype);\n    }\n    const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n    if (this.state.registeredVariables[v.name] != null) {\n      throw new Error(`Variable with name ${v.name} was already registered`);\n    }\n    this.state.registeredVariables[v.name] = v;\n    this.incRef(v, this.backend);\n    return v;\n  }\n\n  incRef(a: Tensor, backend: KernelBackend): void {\n    const refCount = this.state.tensorInfo.has(a.dataId) ?\n        this.state.tensorInfo.get(a.dataId).refCount :\n        0;\n    this.state.numTensors++;\n    if (a.dtype === 'string') {\n      this.state.numStringTensors++;\n    }\n    if (refCount === 0) {\n      this.state.numDataBuffers++;\n\n      // Bytes for complex numbers are counted by their components. Bytes for\n      // string tensors are counted when writing values.\n      let bytes = 0;\n      if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n        bytes = a.size * util.bytesPerElement(a.dtype);\n      }\n      this.state.tensorInfo.set(a.dataId, {\n        backend: backend || this.backend,\n        dtype: a.dtype,\n        shape: a.shape,\n        bytes,\n        refCount: 0\n      });\n      this.state.numBytes += bytes;\n    }\n\n    this.state.tensorInfo.get(a.dataId).refCount++;\n\n    if (!(a instanceof Variable)) {\n      this.track(a);\n    }\n  }\n\n  disposeTensor(a: Tensor): void {\n    if (!this.state.tensorInfo.has(a.dataId)) {\n      return;\n    }\n\n    this.state.numTensors--;\n    if (a.dtype === 'string') {\n      this.state.numStringTensors--;\n    }\n    const info = this.state.tensorInfo.get(a.dataId);\n    const refCount = info.refCount;\n\n    if (refCount <= 1) {\n      // Don't count bytes for complex numbers as they are counted by their\n      // components.\n      if (a.dtype !== 'complex64') {\n        this.state.numBytes -= info.bytes;\n      }\n      this.state.numDataBuffers--;\n\n      info.backend.disposeData(a.dataId);\n      this.state.tensorInfo.delete(a.dataId);\n    } else {\n      this.state.tensorInfo.get(a.dataId).refCount--;\n    }\n    // TODO(nsthorat): Construct an error and save the stack trace for\n    // debugging when in debug mode. Creating a stack trace is too expensive\n    // to do unconditionally.\n  }\n\n  disposeVariables(): void {\n    for (const varName in this.state.registeredVariables) {\n      const v = this.state.registeredVariables[varName];\n      this.disposeVariable(v);\n    }\n  }\n\n  disposeVariable(v: Variable): void {\n    this.disposeTensor(v);\n    if (this.state.registeredVariables[v.name] != null) {\n      delete this.state.registeredVariables[v.name];\n    }\n  }\n\n  memory(): MemoryInfo {\n    const info = this.backend.memory() as MemoryInfo;\n    info.numTensors = this.state.numTensors;\n    info.numDataBuffers = this.state.numDataBuffers;\n    info.numBytes = this.state.numBytes;\n    if (this.state.numStringTensors > 0) {\n      info.unreliable = true;\n      if (info.reasons == null) {\n        info.reasons = [];\n      }\n      info.reasons.push(\n          'Memory usage by string tensors is approximate ' +\n          '(2 bytes per character)');\n    }\n    return info;\n  }\n\n  async profile(query: () => (TensorContainer | Promise<TensorContainer>)):\n      Promise<ProfileInfo> {\n    this.state.profiling = true;\n\n    const startBytes = this.state.numBytes;\n    const startNumTensors = this.state.numTensors;\n\n    this.state.activeProfile.kernels = [];\n    this.state.activeProfile.result = await query();\n\n    this.state.profiling = false;\n\n    this.state.activeProfile.peakBytes = Math.max(\n        ...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n    this.state.activeProfile.newTensors =\n        this.state.numTensors - startNumTensors;\n    for (const kernel of this.state.activeProfile.kernels) {\n      kernel.kernelTimeMs = await kernel.kernelTimeMs;\n      kernel.extraInfo = await kernel.extraInfo;\n    }\n    return this.state.activeProfile;\n  }\n\n  isTapeOn(): boolean {\n    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n  }\n\n  private addTapeNode(\n      kernelName: string, inputs: NamedTensorMap, outputs: Tensor[],\n      gradientsFunc: GradFunc, saved: Tensor[], attrs: NamedAttrMap): void {\n    const tapeNode: TapeNode =\n        {id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved};\n\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      gradientsFunc = gradConfig.gradFunc;\n    }\n    if (gradientsFunc != null) {\n      tapeNode.gradient = (dys: Tensor[]) => {\n        // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n        // the backprop graph to the user as null instead of zeros\n        dys = dys.map((dy, i) => {\n          if (dy == null) {\n            const output = outputs[i];\n            const vals = util.makeZerosTypedArray(output.size, output.dtype);\n            return this.makeTensor(vals, output.shape, output.dtype);\n          }\n          return dy;\n        });\n        // Grad functions of ops with single outputs expect a dy, while ops\n        // with multiple outputs expect dys (array of dy).\n        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n      };\n    }\n    this.state.activeTape.push(tapeNode);\n  }\n\n  keep<T extends Tensor>(result: T): T {\n    result.kept = true;\n    return result;\n  }\n\n  private startTape() {\n    if (this.state.gradientDepth === 0) {\n      this.state.activeTape = [];\n    }\n    this.state.gradientDepth++;\n  }\n\n  private endTape() {\n    this.state.gradientDepth--;\n  }\n\n  /**\n   * Start a scope. Use this with endScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  startScope(name?: string) {\n    const scopeInfo: ScopeState = {\n      track: [],\n      name: 'unnamed scope',\n      id: this.state.nextScopeId++\n    };\n    if (name) {\n      scopeInfo.name = name;\n    }\n    this.state.scopeStack.push(scopeInfo);\n    this.state.activeScope = scopeInfo;\n  }\n\n  /**\n   * End a scope. Use this with startScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  endScope(result?: TensorContainer) {\n    const tensorsToTrackInParent = getTensorsInContainer(result);\n    const tensorsToTrackInParentSet =\n        new Set(tensorsToTrackInParent.map(t => t.id));\n\n    // Dispose the arrays tracked in this scope.\n    for (let i = 0; i < this.state.activeScope.track.length; i++) {\n      const tensor = this.state.activeScope.track[i];\n      if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n        tensor.dispose();\n      }\n    }\n\n    const oldScope = this.state.scopeStack.pop();\n    this.state.activeScope = this.state.scopeStack.length === 0 ?\n        null :\n        this.state.scopeStack[this.state.scopeStack.length - 1];\n\n    // Track the current result in the parent scope.\n    tensorsToTrackInParent.forEach(tensor => {\n      // Only track the tensor if was allocated in the inner scope and is not\n      // globally kept.\n      if (!tensor.kept && tensor.scopeId === oldScope.id) {\n        this.track(tensor);\n      }\n    });\n  }\n\n  /**\n   * Returns gradients of `f` with respect to each of the `xs`. The gradients\n   * returned are of the same length as `xs`, but some might be null if `f`\n   * was not a function of that `x`. It also takes optional dy to multiply the\n   * gradient, which defaults to `1`.\n   */\n  gradients<T extends Tensor>(\n      f: () => T, xs: Tensor[], dy?: T,\n      allowNoGradients = false): {value: T, grads: Tensor[]} {\n    util.assert(\n        xs.length > 0, () => 'gradients() received an empty list of xs.');\n    if (dy != null && dy.dtype !== 'float32') {\n      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n    }\n\n    const y = this.scopedRun(\n        () => this.startTape(), () => this.endTape(),\n        () => this.tidy('forward', f));\n\n    util.assert(\n        y instanceof Tensor,\n        () => 'The result y returned by f() must be a tensor.');\n    // Filter out the nodes that don't connect x => y.\n    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n      throw new Error(\n          'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n          'that the f you passed encloses all operations that lead from x ' +\n          'to y.');\n    }\n\n    return this.tidy('backward', () => {\n      const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};\n      accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n\n      // Backprop gradients through the filtered nodes.\n      backpropagateGradients(\n          accumulatedGradientMap, filteredTape,\n          // Pass the tidy function to avoid circular dep with `tape.ts`.\n          f => this.tidy(f as ScopeFn<Tensor>),\n          // Pass an add function to avoide a circular dep with `tape.ts`.\n          add);\n      const grads = xs.map(x => accumulatedGradientMap[x.id]);\n\n      if (this.state.gradientDepth === 0) {\n        // This means that we are not computing higher-order gradients\n        // and can clean up the tape.\n        this.state.activeTape.forEach(node => {\n          for (const tensor of node.saved) {\n            tensor.dispose();\n          }\n        });\n        this.state.activeTape = null;\n      }\n      return {value: y, grads};\n    });\n  }\n\n  customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n      (...args: Array<Tensor|GradSaveFunc>) => T {\n    util.assert(\n        util.isFunction(f),\n        () => 'The f passed in customGrad(f) must be a function.');\n    return (...inputs: Tensor[]): T => {\n      util.assert(\n          inputs.every(t => t instanceof Tensor),\n          () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n              'tensors');\n\n      let res: {\n        value: T,\n        gradFunc: (dy: T, saved: Tensor[]) => Tensor | Tensor[],\n      };\n      const inputMap: NamedTensorMap = {};\n      inputs.forEach((input, i) => {\n        inputMap[i] = input;\n      });\n      return this.runKernelFunc(\n          (_, save) => {\n            res = f(...[...inputs, save]);\n            util.assert(\n                res.value instanceof Tensor,\n                () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.value` is a tensor');\n            util.assert(\n                util.isFunction(res.gradFunc),\n                () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function.');\n            return res.value;\n          },\n          inputMap,\n          (dy: T, saved: Tensor[]) => {\n            const gradRes = res.gradFunc(dy, saved);\n            const grads: Tensor[] =\n                Array.isArray(gradRes) ? gradRes : [gradRes];\n            util.assert(\n                grads.length === inputs.length,\n                () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'the same number of tensors as inputs passed to f(...).');\n            util.assert(\n                grads.every(t => t instanceof Tensor),\n                () => 'The function f passed in customGrad(f) must return an ' +\n                    'object where `obj.gradFunc` is a function that returns ' +\n                    'a list of only tensors.');\n            const gradMap: {[key: string]: () => Tensor} = {};\n            grads.forEach((grad, i) => {\n              gradMap[i] = () => grad;\n            });\n            return gradMap;\n          });\n    };\n  }\n\n  readSync(dataId: DataId): BackendValues {\n    // Route the read to the correct backend.\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.readSync(dataId);\n  }\n  read(dataId: DataId): Promise<BackendValues> {\n    // Route the read to the correct backend.\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.read(dataId);\n  }\n\n  async time(query: () => void): Promise<TimingInfo> {\n    const start = now();\n    const timingInfo = await this.backend.time(query) as TimingInfo;\n    timingInfo.wallMs = now() - start;\n    return timingInfo;\n  }\n\n  /**\n   * Tracks a Tensor in the current scope to be automatically cleaned up\n   * when the current scope ends, and returns the value.\n   *\n   * @param result The Tensor to track in the current scope.\n   */\n  private track<T extends Tensor>(result: T): T {\n    if (this.state.activeScope != null) {\n      result.scopeId = this.state.activeScope.id;\n      this.state.activeScope.track.push(result);\n    }\n\n    return result;\n  }\n\n  get registeredVariables(): NamedVariableMap {\n    return this.state.registeredVariables;\n  }\n\n  /**\n   * Resets the engine state. Removes all backends but does not remove\n   * registered backend factories.\n   */\n  reset(): void {\n    // Make any pending promise obsolete.\n    this.pendingBackendInitId++;\n\n    this.state.dispose();\n    this.ENV.reset();\n    this.state = new EngineState();\n\n    for (const backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n    this.backendName = null;\n    this.backendInstance = null;\n    this.pendingBackendInit = null;\n  }\n}\n\nfunction ones(shape: number[]): Tensor {\n  const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n  return ENGINE.makeTensor(values, shape, 'float32');\n}\n\nexport function getOrMakeEngine(): Engine {\n  const ns = getGlobalNamespace() as {} as {_tfengine: Engine};\n  if (ns._tfengine == null) {\n    const environment = new Environment(ns);\n    ns._tfengine = new Engine(environment);\n  }\n  setEnvironmentGlobal(ns._tfengine.ENV);\n\n  // Tell the current tensor interface that the global engine is responsible\n  // for tracking.\n  setTensorTracker(() => ns._tfengine);\n  return ns._tfengine;\n}\n\nexport const ENGINE = getOrMakeEngine();\n\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a: Tensor, b: Tensor): Tensor {\n  // We duplicate Add here to avoid a circular dependency with add.ts.\n  const inputs = {a, b};\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.add(a, b);\n    save([a, b]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* gradient */, Add);\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined(): boolean {\n  return typeof navigator !== 'undefined' && navigator != null;\n}\n\nexport function isMobile(): boolean {\n  if (_isNavigatorDefined()) {\n    // tslint:disable-next-line:no-any\n    const a = navigator.userAgent || navigator.vendor || (window as any).opera;\n    // tslint:disable-next-line:max-line-length\n    return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n               .test(a) ||\n        // tslint:disable-next-line:max-line-length\n        /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n            .test(a.substr(0, 4));\n  }\n  return false;\n}\n\nexport function isBrowser(): boolean {\n  return (typeof window !== 'undefined' && window.document != null) ||\n      //@ts-ignore\n      (typeof WorkerGlobalScope !== 'undefined');\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\n\nimport * as device_util from './device_util';\nimport {env} from './environment';\n\nconst ENV = env();\n\n/**\n * This file contains environment-related flag registrations.\n */\n\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n  if (debugValue) {\n    console.warn(\n        'Debugging mode is ON. The output of every math call will ' +\n        'be downloaded to CPU and checked for NaNs. ' +\n        'This significantly impacts performance.');\n  }\n});\n\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag(\n    'IS_NODE',\n    () => (typeof process !== 'undefined') &&\n        (typeof process.versions !== 'undefined') &&\n        (typeof process.versions.node !== 'undefined'));\n\n/** Whether this browser is Chrome. */\nENV.registerFlag(\n    'IS_CHROME',\n    () => typeof navigator !== 'undefined' && navigator != null &&\n        navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n        /Google Inc/.test(navigator.vendor));\n\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag(\n    'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from './engine';\nimport {env} from './environment';\nimport {Tensor} from './tensor';\nimport {DataType, TensorLike} from './types';\nimport {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';\n\nexport function inferShape(val: TensorLike, dtype?: DataType): number[] {\n  let firstElem: typeof val = val;\n\n  if (isTypedArray(val)) {\n    return dtype === 'string' ? [] : [val.length];\n  }\n  if (!Array.isArray(val)) {\n    return [];  // Scalar.\n  }\n  const shape: number[] = [];\n\n  while (Array.isArray(firstElem) ||\n         isTypedArray(firstElem) && dtype !== 'string') {\n    shape.push(firstElem.length);\n    firstElem = firstElem[0];\n  }\n  if (Array.isArray(val) &&\n      env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n    deepAssertShapeConsistency(val, shape, []);\n  }\n\n  return shape;\n}\n\nfunction deepAssertShapeConsistency(\n    val: TensorLike, shape: number[], indices: number[]) {\n  indices = indices || [];\n  if (!(Array.isArray(val)) && !isTypedArray(val)) {\n    assert(\n        shape.length === 0,\n        () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n    return;\n  }\n  assert(\n      shape.length > 0,\n      () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n          `but is an array of ${val.length} elements`);\n  assert(\n      val.length === shape[0],\n      () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n          `elements, but has ${val.length} elements`);\n  const subShape = shape.slice(1);\n  for (let i = 0; i < val.length; ++i) {\n    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n  }\n}\n\nfunction assertDtype(\n    expectedDtype: DataType|'numeric', actualDType: DataType, argName: string,\n    functionName: string) {\n  if (expectedDtype == null) {\n    return;\n  }\n  if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n      expectedDtype === 'numeric' && actualDType === 'string') {\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must ` +\n        `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n  }\n}\n\nexport function convertToTensor<T extends Tensor>(\n    x: T|TensorLike, argName: string, functionName: string,\n    parseAsDtype: DataType|'numeric' = 'numeric'): T {\n  if (x instanceof Tensor) {\n    assertDtype(parseAsDtype, x.dtype, argName, functionName);\n    return x;\n  }\n  let inferredDtype = inferDtype(x);\n  // If the user expects a bool/int/float, use that info to update the\n  // inferredDtype when it is not a string.\n  if (inferredDtype !== 'string' &&\n      ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n    inferredDtype = parseAsDtype as DataType;\n  }\n  assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n\n  if ((x == null) ||\n      (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n       typeof x !== 'boolean' && typeof x !== 'string')) {\n    const type = x == null ? 'null' : (x as {}).constructor.name;\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must be a ` +\n        `Tensor or TensorLike, but got '${type}'`);\n  }\n  const inferredShape = inferShape(x, inferredDtype);\n  if (!isTypedArray(x) && !Array.isArray(x)) {\n    x = [x] as number[];\n  }\n  const skipTypedArray = true;\n  const values = inferredDtype !== 'string' ?\n      toTypedArray(x, inferredDtype as DataType) :\n      flatten(x as string[], [], skipTypedArray) as string[];\n  return ENGINE.makeTensor(values, inferredShape, inferredDtype) as T;\n}\n\nexport function convertToTensorArray<T extends Tensor>(\n    arg: Array<T|TensorLike>, argName: string, functionName: string,\n    parseAsDtype: DataType|'numeric' = 'numeric'): T[] {\n  if (!Array.isArray(arg)) {\n    throw new Error(\n        `Argument ${argName} passed to ${functionName} must be a ` +\n        '`Tensor[]` or `TensorLike[]`');\n  }\n  const tensors = arg as T[];\n  return tensors.map(\n      (t, i) => convertToTensor(t, `${argName}[${i}]`, functionName),\n      parseAsDtype);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {isPromise} from '../util';\n\nexport const OP_SCOPE_SUFFIX = '__op';\n\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op<T extends Function>(f: {[name: string]: T}): T {\n  const keys = Object.keys(f);\n  if (keys.length !== 1) {\n    throw new Error(\n        `Please provide an object with a single key ` +\n        `(operation name) mapping to a function. Got an object with ` +\n        `${keys.length} keys.`);\n  }\n\n  let opName = keys[0];\n  const fn = f[opName];\n\n  // Strip the underscore from the end of the function name.\n  if (opName.endsWith('_')) {\n    opName = opName.substring(0, opName.length - 1);\n  }\n\n  // add an __op suffix to distinguish ops from kernels in tf.profile\n  opName = opName + OP_SCOPE_SUFFIX;\n\n  // tslint:disable-next-line:no-any\n  const f2 = (...args: any[]) => {\n    ENGINE.startScope(opName);\n    try {\n      const result = fn(...args);\n      if (isPromise(result)) {\n        console.error('Cannot return a Promise inside of tidy.');\n      }\n      ENGINE.endScope(result);\n      return result;\n    } catch (ex) {\n      ENGINE.endScope(null);\n      throw ex;\n    }\n  };\n  Object.defineProperty(f2, 'name', {value: opName, configurable: true});\n\n  // tslint:disable-next-line:no-any\n  return f2 as any as T;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Complex, ComplexInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_<T extends Tensor>(real: T|TensorLike, imag: T|TensorLike): T {\n  const $real = convertToTensor(real, 'real', 'complex');\n  const $imag = convertToTensor(imag, 'imag', 'complex');\n  util.assertShapesMatch(\n      $real.shape, $imag.shape,\n      `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n          `must match in call to tf.complex().`);\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    return backend.complex($real, $imag);\n  };\n  const inputs: ComplexInputs = {real: $real, imag: $imag};\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Complex) as T;\n}\n\nexport const complex = op({complex_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {TensorLike, TypedArray} from '../types';\nimport {DataType} from '../types';\nimport {assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray} from '../util';\n\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(\n    values: TensorLike, shape: number[], inferredShape: number[],\n    dtype?: DataType): Tensor {\n  if (dtype == null) {\n    dtype = inferDtype(values);\n  }\n  if (dtype === 'complex64') {\n    throw new Error(\n        `Cannot construct a complex64 tensor directly. ` +\n        `Please use tf.complex(real, imag).`);\n  }\n  if (!isTypedArray(values) && !Array.isArray(values) &&\n      typeof values !== 'number' && typeof values !== 'boolean' &&\n      typeof values !== 'string') {\n    throw new Error(\n        'values passed to tensor(values) must be a number/boolean/string or ' +\n        'an array of numbers/booleans/strings, or a TypedArray');\n  }\n  if (shape != null) {\n    assertNonNegativeIntegerDimensions(shape);\n\n    const providedSize = sizeFromShape(shape);\n    const inferredSize = sizeFromShape(inferredShape);\n    assert(\n        providedSize === inferredSize,\n        () =>\n            `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n\n    for (let i = 0; i < inferredShape.length; ++i) {\n      const inferred = inferredShape[i];\n      const flatDimsDontMatch = i === inferredShape.length - 1 ?\n          inferred !== sizeFromShape(shape.slice(i)) :\n          true;\n      assert(\n          inferredShape[i] === shape[i] || !flatDimsDontMatch,\n          () => `Error creating a new Tensor. Inferred shape ` +\n              `(${inferredShape}) does not match the provided ` +\n              `shape (${shape}). `);\n    }\n  }\n\n  if (!isTypedArray(values) && !Array.isArray(values)) {\n    values = [values] as number[];\n  }\n\n  shape = shape || inferredShape;\n  values = dtype !== 'string' ?\n      toTypedArray(values, dtype) :\n      flatten(values as string[], [], true) as string[];\n  return ENGINE.makeTensor(values as TypedArray, shape, dtype);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`. If the values are strings,\n *     they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor<R extends Rank>(\n    values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor<R> {\n  const inferredShape = inferShape(values, dtype);\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor<R>;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n  'float32': 4,\n  'float16': 2,\n  'int32': 4,\n  'uint16': 2,\n  'uint8': 1,\n  'bool': 1,\n  'complex64': 8\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n  /**\n   * An ordered list of paths.\n   *\n   * Paths are intentionally abstract in order to be general. For example, they\n   * can be relative URL paths or relative paths on the file system.\n   */\n  paths: string[];\n\n  /**\n   * Specifications of the weights stored in the paths.\n   */\n  weights: WeightsManifestEntry[];\n}\n\n/**\n * Group to which the weight belongs.\n *\n * - 'optimizer': Weight from a stateful optimizer.\n */\nexport type WeightGroup = 'model'|'optimizer';\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n  /**\n   * Name of the weight, e.g., 'Dense_1/bias'\n   */\n  name: string;\n\n  /**\n   * Shape of the weight.\n   */\n  shape: number[];\n\n  /**\n   * Data type of the weight.\n   */\n  dtype: 'float32'|'int32'|'bool'|'string'|'complex64';\n\n  /**\n   * Type of the weight.\n   *\n   * Optional.\n   *\n   * The value 'optimizer' indicates the weight belongs to an optimizer\n   * (i.e., used only during model training and not during inference).\n   */\n  group?: WeightGroup;\n\n  /**\n   * Information for dequantization of the weight.\n   */\n  quantization?: {\n    scale?: number,  // The scaling constant to multiply by.\n    min?: number,    // The (possibly nudged) minimum weight to add.\n       dtype: 'uint16'|'uint8'|'float16'  // The dtype of the quantized weights.\n  };\n}\n\n/**\n * Options for saving a model.\n * @innamespace io\n */\nexport interface SaveConfig {\n  /**\n   * Whether to save only the trainable weights of the model, ignoring the\n   * non-trainable ones.\n   */\n  trainableOnly?: boolean;\n\n  /**\n   * Whether the optimizer will be saved (if exists).\n   *\n   * Default: `false`.\n   */\n  includeOptimizer?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n  /**\n   * Information about the model artifacts saved.\n   */\n  modelArtifactsInfo: ModelArtifactsInfo;\n\n  /**\n   * HTTP responses from the server that handled the model-saving request (if\n   * any). This is applicable only to server-based saving routes.\n   */\n  responses?: Response[];\n\n  /**\n   * Error messages and related data (if any).\n   */\n  errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n  /**\n   * Timestamp for when the model is saved.\n   */\n  dateSaved: Date;\n\n  /**\n   * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now\n   * come in a JSON format and none of our IOHandlers support a non json\n   * format. We could conder replacing this with 'Binary' if we want to\n   * allow future handlers to save to non json formats (though they will\n   * probably want more information than 'Binary').\n   * Type of the model topology\n   *\n   * Type of the model topology\n   *\n   * Possible values:\n   *   - JSON: JSON config (human-readable, e.g., Keras JSON).\n   *   - GraphDef: TensorFlow\n   *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n   *     protocol buffer (binary).\n   */\n  modelTopologyType: 'JSON'|'GraphDef';\n\n  /**\n   * Size of model topology (Keras JSON or GraphDef), in bytes.\n   */\n  modelTopologyBytes?: number;\n\n  /**\n   * Size of weight specification or manifest, in bytes.\n   */\n  weightSpecsBytes?: number;\n\n  /**\n   * Size of weight value data, in bytes.\n   */\n  weightDataBytes?: number;\n}\n\n/** Model training configuration. */\nexport declare interface TrainingConfig {\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  // See\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tfjs-layers/blob/master/src/keras_format/training_config.ts\n  /** Optimizer used for the model training. */\n  optimizer_config: {};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  /** Loss function(s) for the model's output(s). */\n  loss: string|string[]|{[key: string]: string};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  /** Metric function(s) for the model's output(s). */\n  metrics?: string[]|{[key: string]: string};\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  weighted_metrics?: string[];\n\n  // TODO(cais): Tighten the typing once keras spec is available to tfjs-core.\n  sample_weight_mode?: string;\n\n  loss_weights?: number[]|{[key: string]: number};\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields\n * of this interface are optional, in order to support topology- or weights-only\n * saving and loading.\n *\n * Note this interface is used internally in IOHandlers.  For the file format\n * written to disk as `model.json`, see `ModelJSON`.\n */\nexport declare interface ModelArtifacts {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n   * encoding of the `GraphDef` protocol buffer.\n   */\n  modelTopology?: {}|ArrayBuffer;\n\n  /**\n   * Serialized configuration for the model's training.\n   */\n  trainingConfig?: TrainingConfig;\n\n  /**\n   * Weight specifications.\n   *\n   * This corresponds to the weightsData below.\n   */\n  weightSpecs?: WeightsManifestEntry[];\n\n  /**\n   * Binary buffer for all weight values concatenated in the order specified\n   * by `weightSpecs`.\n   */\n  weightData?: ArrayBuffer;\n\n  /**\n   * Hard-coded format name for models saved from TensorFlow.js or converted\n   * by TensorFlow.js Converter.\n   */\n  format?: string;\n\n  /**\n   * What library is responsible for originally generating this artifact.\n   *\n   * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n   */\n  generatedBy?: string;\n\n  /**\n   * What library or tool is responsible for converting the original model\n   * to this format, applicable only if the model is output by a converter.\n   *\n   * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.\n   *\n   * A value of `null` means the model artifacts are generated without any\n   * conversion process (e.g., saved directly from a TensorFlow.js\n   * `tf.LayersModel` instance.)\n   */\n  convertedBy?: string|null;\n\n  /**\n   * User-defined metadata about the model.\n   */\n  userDefinedMetadata?: {};\n\n  /**\n   * Initializer for the model.\n   */\n  modelInitializer?: {};\n}\n\n/**\n * The on-disk format of the `model.json` file.\n *\n * TF.js 1.0 always populates the optional fields when writing model.json.\n * Prior versions did not provide those fields.\n */\nexport declare interface ModelJSON {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON\n   * encoding of the `GraphDef` protocol buffer.\n   */\n  modelTopology: {};\n\n  /** Model training configuration. */\n  trainingConfig?: TrainingConfig;\n\n  /**\n   * Weights manifest.\n   *\n   * The weights manifest consists of an ordered list of weight-manifest\n   * groups. Each weight-manifest group consists of a number of weight values\n   * stored in a number of paths. See the documentation of\n   * `WeightsManifestConfig` for more details.\n   */\n  weightsManifest: WeightsManifestConfig;\n\n  /**\n   * Hard-coded format name for models saved from TensorFlow.js or converted\n   * by TensorFlow.js Converter.\n   */\n  format?: string;\n\n  /**\n   * What library is responsible for originally generating this artifact.\n   *\n   * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.\n   */\n  generatedBy?: string;\n\n  /**\n   * What library or tool is responsible for converting the original model\n   * to this format, applicable only if the model is output by a converter.\n   *\n   * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.\n   *\n   * A value of `null` means the model artifacts are generated without any\n   * conversion process (e.g., saved directly from a TensorFlow.js\n   * `tf.LayersModel` instance.)\n   */\n  convertedBy?: string|null;\n\n  /**\n   * User-defined metadata about the model.\n   */\n  userDefinedMetadata?: {};\n\n  /**\n   * Initializer for the model.\n   */\n  modelInitializer?: {};\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise<ModelArtifacts>;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n    Promise<SaveResult>;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n  save?: SaveHandler;\n  load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n  /**\n   * List all models in the model store.\n   *\n   * @returns A dictionary mapping paths of existing models to their\n   *   model artifacts info. Model artifacts info include type of the model's\n   *   topology, byte sizes of the topology, weights, etc.\n   */\n  listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n  /**\n   * Remove a model specified by `path`.\n   *\n   * @param path\n   * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n   *   is successful).\n   * @throws Error if deletion fails, e.g., if no model exists at `path`.\n   */\n  removeModel(path: string): Promise<ModelArtifactsInfo>;\n}\n\n/**\n * Callback for the progress of a long-running action such as an HTTP\n * request for a large binary object.\n *\n * `fraction` should be a number in the [0, 1] interval, indicating how\n * much of the action has completed.\n */\nexport type OnProgressCallback = (fraction: number) => void;\n\n/** @innamespace io */\nexport interface LoadOptions {\n  /**\n   * RequestInit (options) for HTTP requests.\n   *\n   * For detailed information on the supported fields, see\n   * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](\n   *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)\n   */\n  requestInit?: RequestInit;\n\n  /**\n   * Progress callback.\n   */\n  onProgress?: OnProgressCallback;\n\n  /**\n   * A function used to override the `window.fetch` function.\n   */\n  fetchFunc?: Function;\n\n  /**\n   * Strict loading model: whether extraneous weights or missing\n   * weights should trigger an `Error`.\n   *\n   * If `true`, require that the provided weights exactly match those\n   * required by the layers. `false` means that both extra weights\n   * and missing weights will be silently ignored.\n   *\n   * Default: `true`.\n   */\n  strict?: boolean;\n\n  /**\n   * Path prefix for weight files, by default this is calculated from the\n   * path of the model JSON file.\n   *\n   * For instance, if the path to the model JSON file is\n   * `http://localhost/foo/model.json`, then the default path prefix will be\n   * `http://localhost/foo/`. If a weight file has the path value\n   * `group1-shard1of2` in the weight manifest, then the weight file will be\n   * loaded from `http://localhost/foo/group1-shard1of2` by default. However,\n   * if you provide a `weightPathPrefix` value of\n   * `http://localhost/foo/alt-weights`, then the weight file will be loaded\n   * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.\n   */\n  weightPathPrefix?: string;\n\n  /**\n   * Whether the module or model is to be loaded from TF Hub.\n   *\n   * Setting this to `true` allows passing a TF-Hub module URL, omitting the\n   * standard model file name and the query parameters.\n   *\n   * Default: `false`.\n   */\n  fromTFHub?: boolean;\n\n  /**\n   * An async function to convert weight file name to URL. The weight file\n   * names are stored in model.json's weightsManifest.paths field. By default we\n   * consider weight files are colocated with the model.json file. For example:\n   *     model.json URL: https://www.google.com/models/1/model.json\n   *     group1-shard1of1.bin url:\n   *        https://www.google.com/models/1/group1-shard1of1.bin\n   *\n   * With this func you can convert the weight file name to any URL.\n   */\n  weightUrlConverter?: (weightFileName: string) => Promise<string>;\n}\n\n/**\n * Additional options for Platform.fetch\n */\nexport interface RequestDetails {\n  /**\n   * Is this request for a binary file (as opposed to a json file)\n   */\n  isBinary?: boolean;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {complex} from '../ops/complex';\n\nimport {tensor} from '../ops/tensor';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\nimport {sizeFromShape} from '../util';\n\nimport {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, WeightGroup, WeightsManifestEntry} from './types';\n\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(\n    tensors: NamedTensorMap|NamedTensor[], group?: WeightGroup):\n    Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {\n  // TODO(adarob, cais): Support quantization.\n  const specs: WeightsManifestEntry[] = [];\n  const dataPromises: Array<Promise<TypedArray>> = [];\n\n  const names: string[] = Array.isArray(tensors) ?\n      tensors.map(tensor => tensor.name) :\n      Object.keys(tensors);\n\n  for (let i = 0; i < names.length; ++i) {\n    const name = names[i];\n    const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n    if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n        t.dtype !== 'string' && t.dtype !== 'complex64') {\n      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n    }\n    const spec: WeightsManifestEntry = {name, shape: t.shape, dtype: t.dtype};\n    if (t.dtype === 'string') {\n      const utf8bytes = new Promise<TypedArray>(async resolve => {\n        const vals = await t.bytes() as Uint8Array[];\n        const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n            NUM_BYTES_STRING_LENGTH * vals.length;\n        const bytes = new Uint8Array(totalNumBytes);\n        let offset = 0;\n        for (let i = 0; i < vals.length; i++) {\n          const val = vals[i];\n          const bytesOfLength =\n              new Uint8Array(new Uint32Array([val.length]).buffer);\n          bytes.set(bytesOfLength, offset);\n          offset += NUM_BYTES_STRING_LENGTH;\n          bytes.set(val, offset);\n          offset += val.length;\n        }\n        resolve(bytes);\n      });\n      dataPromises.push(utf8bytes);\n    } else {\n      dataPromises.push(t.data());\n    }\n    if (group != null) {\n      spec.group = group;\n    }\n    specs.push(spec);\n  }\n\n  const tensorValues = await Promise.all(dataPromises);\n  return {data: concatenateTypedArrays(tensorValues), specs};\n}\n\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(\n    buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap {\n  // TODO(adarob, cais): Support quantization.\n  const out: NamedTensorMap = {};\n  let float16Decode: (buffer: Uint16Array) => Float32Array | undefined;\n  let offset = 0;\n  for (const spec of specs) {\n    const name = spec.name;\n    const dtype = spec.dtype;\n    const shape = spec.shape;\n    const size = sizeFromShape(shape);\n    let values: TypedArray|string[]|Uint8Array[];\n\n    if ('quantization' in spec) {\n      const quantization = spec.quantization;\n      if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n        if (!('min' in quantization && 'scale' in quantization)) {\n          throw new Error(\n              `Weight ${spec.name} with quantization ${quantization.dtype} ` +\n              `doesn't have corresponding metadata min and scale.`);\n        }\n      } else if (quantization.dtype === 'float16') {\n        if (dtype !== 'float32') {\n          throw new Error(\n              `Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n              `which only supports weights of type float32 not ${dtype}.`);\n        }\n      } else {\n        throw new Error(\n            `Weight ${spec.name} has unknown ` +\n            `quantization dtype ${quantization.dtype}. ` +\n            `Supported quantization dtypes are: ` +\n            `'uint8', 'uint16', and 'float16'.`);\n      }\n      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n      const byteBuffer =\n          buffer.slice(offset, offset + size * quantizationSizeFactor);\n      const quantizedArray = (quantization.dtype === 'uint8') ?\n          new Uint8Array(byteBuffer) :\n          new Uint16Array(byteBuffer);\n      if (dtype === 'float32') {\n        if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n          values = new Float32Array(quantizedArray.length);\n          for (let i = 0; i < quantizedArray.length; i++) {\n            const v = quantizedArray[i];\n            values[i] = v * quantization.scale + quantization.min;\n          }\n        } else if (quantization.dtype === 'float16') {\n          if (float16Decode === undefined) {\n            float16Decode = getFloat16Decoder();\n          }\n          values = float16Decode(quantizedArray as Uint16Array);\n        } else {\n          throw new Error(\n              `Unsupported quantization type ${quantization.dtype} ` +\n              `for weight type float32.`);\n        }\n      } else if (dtype === 'int32') {\n        if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n          throw new Error(\n              `Unsupported quantization type ${quantization.dtype} ` +\n              `for weight type int32.`);\n        }\n        values = new Int32Array(quantizedArray.length);\n        for (let i = 0; i < quantizedArray.length; i++) {\n          const v = quantizedArray[i];\n          values[i] = Math.round(v * quantization.scale + quantization.min);\n        }\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * quantizationSizeFactor;\n    } else if (dtype === 'string') {\n      const size = sizeFromShape(spec.shape);\n      values = [];\n      for (let i = 0; i < size; i++) {\n        const byteLength = new Uint32Array(\n            buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n        offset += NUM_BYTES_STRING_LENGTH;\n        const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n        (values as Uint8Array[]).push(bytes);\n        offset += byteLength;\n      }\n    } else {\n      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n      const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n\n      if (dtype === 'float32') {\n        values = new Float32Array(byteBuffer);\n      } else if (dtype === 'int32') {\n        values = new Int32Array(byteBuffer);\n      } else if (dtype === 'bool') {\n        values = new Uint8Array(byteBuffer);\n      } else if (dtype === 'complex64') {\n        values = new Float32Array(byteBuffer);\n        const real = new Float32Array(values.length / 2);\n        const image = new Float32Array(values.length / 2);\n        for (let i = 0; i < real.length; i++) {\n          real[i] = values[i * 2];\n          image[i] = values[i * 2 + 1];\n        }\n        const realTensor = tensor(real, shape, 'float32');\n        const imageTensor = tensor(image, shape, 'float32');\n        out[name] = complex(realTensor, imageTensor);\n        realTensor.dispose();\n        imageTensor.dispose();\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * dtypeFactor;\n    }\n    if (dtype !== 'complex64') {\n      out[name] = tensor(values, shape, dtype);\n    }\n  }\n  return out;\n}\n\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {\n  // TODO(adarob, cais): Support quantization.\n  if (xs === null) {\n    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n  }\n\n  let totalByteLength = 0;\n\n  // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n  // can have a different byte length from that of the `TypedArray` itself,\n  // for example, when the `TypedArray` is created from an offset in an\n  // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n  // the `TypedArray` in byte length. If an element of `xs` does not show\n  // this property, a new `TypedArray` that satisfy this property will be\n  // constructed and pushed into `normalizedXs`.\n  const normalizedXs: TypedArray[] = [];\n  xs.forEach((x: TypedArray) => {\n    totalByteLength += x.byteLength;\n    // tslint:disable:no-any\n    normalizedXs.push(\n        x.byteLength === x.buffer.byteLength ? x :\n                                               new (x.constructor as any)(x));\n    if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||\n          x as any instanceof Uint8Array)) {\n      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n    }\n    // tslint:enable:no-any\n  });\n\n  const y = new Uint8Array(totalByteLength);\n  let offset = 0;\n  normalizedXs.forEach((x: TypedArray) => {\n    y.set(new Uint8Array(x.buffer), offset);\n    offset += x.byteLength;\n  });\n\n  return y.buffer;\n}\n\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n     typeof btoa === 'undefined');\n\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str: string): number {\n  if (useNodeBuffer) {\n    return Buffer.byteLength(str);\n  }\n  return new Blob([str]).size;\n}\n\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer: ArrayBuffer): string {\n  if (useNodeBuffer) {\n    return Buffer.from(buffer).toString('base64');\n  }\n  const buf = new Uint8Array(buffer);\n  let s = '';\n  for (let i = 0, l = buf.length; i < l; i++) {\n    s += String.fromCharCode(buf[i]);\n  }\n  return btoa(s);\n}\n\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str: string): ArrayBuffer {\n  if (useNodeBuffer) {\n    const buf = Buffer.from(str, 'base64');\n    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n  }\n  const s = atob(str);\n  const buffer = new Uint8Array(s.length);\n  for (let i = 0; i < s.length; ++i) {\n    buffer.set([s.charCodeAt(i)], i);\n  }\n  return buffer.buffer;\n}\n\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer {\n  if (buffers.length === 1) {\n    return buffers[0];\n  }\n\n  let totalByteLength = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    totalByteLength += buffer.byteLength;\n  });\n\n  const temp = new Uint8Array(totalByteLength);\n  let offset = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    temp.set(new Uint8Array(buffer), offset);\n    offset += buffer.byteLength;\n  });\n  return temp.buffer;\n}\n\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path: string): string {\n  const SEPARATOR = '/';\n  path = path.trim();\n  while (path.endsWith(SEPARATOR)) {\n    path = path.slice(0, path.length - 1);\n  }\n  const items = path.split(SEPARATOR);\n  return items[items.length - 1];\n}\n\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):\n    ModelArtifactsInfo {\n  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n    throw new Error('Expected JSON model topology, received ArrayBuffer.');\n  }\n\n  return {\n    dateSaved: new Date(),\n    modelTopologyType: 'JSON',\n    modelTopologyBytes: modelArtifacts.modelTopology == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n    weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n    weightDataBytes: modelArtifacts.weightData == null ?\n        0 :\n        modelArtifacts.weightData.byteLength,\n  };\n}\n\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable(): Uint32Array {\n  const convertMantissa = (i: number): number => {\n    let m = i << 13;\n    let e = 0;\n\n    while ((m & 0x00800000) === 0) {\n      e -= 0x00800000;\n      m <<= 1;\n    }\n    m &= ~0x00800000;\n    e += 0x38800000;\n\n    return m | e;\n  };\n\n  const mantisaTable = new Uint32Array(2048);\n\n  mantisaTable[0] = 0;\n  for (let i = 1; i < 1024; i++) {\n    mantisaTable[i] = convertMantissa(i);\n  }\n  for (let i = 1024; i < 2048; i++) {\n    mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n  }\n\n  return mantisaTable;\n}\n\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable(): Uint32Array {\n  const exponentTable = new Uint32Array(64);\n\n  exponentTable[0] = 0;\n  exponentTable[31] = 0x47800000;\n  exponentTable[32] = 0x80000000;\n  exponentTable[63] = 0xc7800000;\n  for (let i = 1; i < 31; i++) {\n    exponentTable[i] = i << 23;\n  }\n  for (let i = 33; i < 63; i++) {\n    exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n  }\n\n  return exponentTable;\n}\n\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable(): Uint32Array {\n  const offsetTable = new Uint32Array(64);\n\n  for (let i = 0; i < 64; i++) {\n    offsetTable[i] = 1024;\n  }\n  offsetTable[0] = offsetTable[32] = 0;\n\n  return offsetTable;\n}\n\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n *          the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder(): (buffer: Uint16Array) => Float32Array {\n  // Algorithm is based off of\n  // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n\n  // Cache lookup tables\n  const mantisaTable = computeFloat16MantisaTable();\n  const exponentTable = computeFloat16ExponentTable();\n  const offsetTable = computeFloat16OffsetTable();\n\n  return (quantizedArray: Uint16Array) => {\n    const buffer = new ArrayBuffer(4 * quantizedArray.length);\n    const bufferUint32View = new Uint32Array(buffer);\n    for (let index = 0; index < quantizedArray.length; index++) {\n      const float16Bits = quantizedArray[index];\n      const float32Bits =\n          mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n          exponentTable[float16Bits >> 10];\n      bufferUint32View[index] = float32Bits;\n    }\n    return new Float32Array(buffer);\n  };\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IOHandler, LoadOptions} from './types';\n\nexport type IORouter = (url: string|string[], loadOptions?: LoadOptions) =>\n    IOHandler;\n\nexport class IORouterRegistry {\n  // Singleton instance.\n  private static instance: IORouterRegistry;\n\n  private saveRouters: IORouter[];\n  private loadRouters: IORouter[];\n\n  private constructor() {\n    this.saveRouters = [];\n    this.loadRouters = [];\n  }\n\n  private static getInstance(): IORouterRegistry {\n    if (IORouterRegistry.instance == null) {\n      IORouterRegistry.instance = new IORouterRegistry();\n    }\n    return IORouterRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerSaveRouter(saveRouter: IORouter) {\n    IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n  }\n\n  /**\n   * Register a load-handler router.\n   *\n   * @param loadRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `load` method defined or `null`.\n   */\n  static registerLoadRouter(loadRouter: IORouter) {\n    IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n  }\n\n  /**\n   * Look up IOHandler for saving, given a URL-like string.\n   *\n   * @param url\n   * @returns If only one match is found, an instance of IOHandler with the\n   * `save` method defined. If no match is found, `null`.\n   * @throws Error, if more than one match is found.\n   */\n  static getSaveHandlers(url: string|string[]): IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'save');\n  }\n\n  /**\n   * Look up IOHandler for loading, given a URL-like string.\n   *\n   * @param url\n   * @param loadOptions Optional, custom load options.\n   * @returns All valid handlers for `url`, given the currently registered\n   *   handler routers.\n   */\n  static getLoadHandlers(url: string|string[], loadOptions?: LoadOptions):\n      IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n  }\n\n  private static getHandlers(\n      url: string|string[], handlerType: 'save'|'load',\n      loadOptions?: LoadOptions): IOHandler[] {\n    const validHandlers: IOHandler[] = [];\n    const routers = handlerType === 'load' ?\n        IORouterRegistry.getInstance().loadRouters :\n        IORouterRegistry.getInstance().saveRouters;\n    routers.forEach(router => {\n      const handler = router(url, loadOptions);\n      if (handler !== null) {\n        validHandlers.push(handler);\n      }\n    });\n    return validHandlers;\n  }\n}\n\nexport const registerSaveRouter = (loudRouter: IORouter) =>\n    IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter: IORouter) =>\n    IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url: string|string[]) =>\n    IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers =\n    (url: string|string[], loadOptions?: LoadOptions) =>\n        IORouterRegistry.getLoadHandlers(url, loadOptions);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\n\nimport {env} from '../environment';\n\nimport {getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase(): Promise<void> {\n  const idbFactory = getIndexedDBFactory();\n\n  return new Promise<void>((resolve, reject) => {\n    const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n    deleteRequest.onsuccess = () => resolve();\n    deleteRequest.onerror = error => reject(error);\n  });\n}\n\nfunction getIndexedDBFactory(): IDBFactory {\n  if (!env().getBool('IS_BROWSER')) {\n    // TODO(cais): Add more info about what IOHandler subtypes are available.\n    //   Maybe point to a doc page on the web and/or automatically determine\n    //   the available IOHandlers and print them in the error message.\n    throw new Error(\n        'Failed to obtain IndexedDB factory because the current environment' +\n        'is not a web browser.');\n  }\n  // tslint:disable-next-line:no-any\n  const theWindow: any = typeof window === 'undefined' ? self : window;\n  const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n      theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n      theWindow.shimIndexedDB;\n  if (factory == null) {\n    throw new Error(\n        'The current browser does not appear to support IndexedDB.');\n  }\n  return factory;\n}\n\nfunction setUpDatabase(openRequest: IDBRequest) {\n  const db = openRequest.result as IDBDatabase;\n  db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});\n  db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});\n}\n\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB implements IOHandler {\n  protected readonly indexedDB: IDBFactory;\n  protected readonly modelPath: string;\n\n  static readonly URL_SCHEME = 'indexeddb://';\n\n  constructor(modelPath: string) {\n    this.indexedDB = getIndexedDBFactory();\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For IndexedDB, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    // TODO(cais): Support saving GraphDef models.\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    return this.databaseAction(this.modelPath, modelArtifacts) as\n        Promise<SaveResult>;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    return this.databaseAction(this.modelPath) as Promise<ModelArtifacts>;\n  }\n\n  /**\n   * Perform database action to put model artifacts into or read model artifacts\n   * from IndexedDB object store.\n   *\n   * Whether the action is put or get depends on whether `modelArtifacts` is\n   * specified. If it is specified, the action will be put; otherwise the action\n   * will be get.\n   *\n   * @param modelPath A unique string path for the model.\n   * @param modelArtifacts If specified, it will be the model artifacts to be\n   *   stored in IndexedDB.\n   * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n   *   of `ModelArtifacts`, if the action is get.\n   */\n  private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):\n      Promise<ModelArtifacts|SaveResult> {\n    return new Promise<ModelArtifacts|SaveResult>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n\n        if (modelArtifacts == null) {\n          // Read model out from object store.\n          const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n          const getRequest = modelStore.get(this.modelPath);\n          getRequest.onsuccess = () => {\n            if (getRequest.result == null) {\n              db.close();\n              return reject(new Error(\n                  `Cannot find model with path '${this.modelPath}' ` +\n                  `in IndexedDB.`));\n            } else {\n              resolve(getRequest.result.modelArtifacts);\n            }\n          };\n          getRequest.onerror = error => {\n            db.close();\n            return reject(getRequest.error);\n          };\n          modelTx.oncomplete = () => db.close();\n        } else {\n          // Put model into object store.\n          const modelArtifactsInfo: ModelArtifactsInfo =\n              getModelArtifactsInfoForJSON(modelArtifacts);\n          // First, put ModelArtifactsInfo into info store.\n          const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n          let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n          const putInfoRequest =\n              infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});\n          let modelTx: IDBTransaction;\n          putInfoRequest.onsuccess = () => {\n            // Second, put model data into model store.\n            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n            const putModelRequest = modelStore.put({\n              modelPath: this.modelPath,\n              modelArtifacts,\n              modelArtifactsInfo\n            });\n            putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});\n            putModelRequest.onerror = error => {\n              // If the put-model request fails, roll back the info entry as\n              // well.\n              infoStore = infoTx.objectStore(INFO_STORE_NAME);\n              const deleteInfoRequest = infoStore.delete(this.modelPath);\n              deleteInfoRequest.onsuccess = () => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n              deleteInfoRequest.onerror = error => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n            };\n          };\n          putInfoRequest.onerror = error => {\n            db.close();\n            return reject(putInfoRequest.error);\n          };\n          infoTx.oncomplete = () => {\n            if (modelTx == null) {\n              db.close();\n            } else {\n              modelTx.oncomplete = () => db.close();\n            }\n          };\n        }\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n\nexport const indexedDBRouter: IORouter = (url: string|string[]) => {\n  if (!env().getBool('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath: string): IOHandler {\n  return new BrowserIndexedDB(modelPath);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n      key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n      key;\n}\n\nexport class BrowserIndexedDBManager implements ModelStoreManager {\n  private indexedDB: IDBFactory;\n\n  constructor() {\n    this.indexedDB = getIndexedDBFactory();\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    return new Promise<{[path: string]: ModelArtifactsInfo}>(\n        (resolve, reject) => {\n          const openRequest =\n              this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n          openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n          openRequest.onsuccess = () => {\n            const db = openRequest.result;\n            const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n            const store = tx.objectStore(INFO_STORE_NAME);\n            // tslint:disable:max-line-length\n            // Need to cast `store` as `any` here because TypeScript's DOM\n            // library does not have the `getAll()` method even though the\n            // method is supported in the latest version of most mainstream\n            // browsers:\n            // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n            // tslint:enable:max-line-length\n            // tslint:disable-next-line:no-any\n            const getAllInfoRequest = (store as any).getAll() as IDBRequest;\n            getAllInfoRequest.onsuccess = () => {\n              const out: {[path: string]: ModelArtifactsInfo} = {};\n              for (const item of getAllInfoRequest.result) {\n                out[item.modelPath] = item.modelArtifactsInfo;\n              }\n              resolve(out);\n            };\n            getAllInfoRequest.onerror = error => {\n              db.close();\n              return reject(getAllInfoRequest.error);\n            };\n            tx.oncomplete = () => db.close();\n          };\n          openRequest.onerror = error => reject(openRequest.error);\n        });\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    return new Promise<ModelArtifactsInfo>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n        const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n\n        const getInfoRequest = infoStore.get(path);\n        let modelTx: IDBTransaction;\n        getInfoRequest.onsuccess = () => {\n          if (getInfoRequest.result == null) {\n            db.close();\n            return reject(new Error(\n                `Cannot find model with path '${path}' ` +\n                `in IndexedDB.`));\n          } else {\n            // First, delete the entry in the info store.\n            const deleteInfoRequest = infoStore.delete(path);\n            const deleteModelData = () => {\n              // Second, delete the entry in the model store.\n              modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n              const deleteModelRequest = modelStore.delete(path);\n              deleteModelRequest.onsuccess = () =>\n                  resolve(getInfoRequest.result.modelArtifactsInfo);\n              deleteModelRequest.onerror = error =>\n                  reject(getInfoRequest.error);\n            };\n            // Proceed with deleting model data regardless of whether deletion\n            // of info data succeeds or not.\n            deleteInfoRequest.onsuccess = deleteModelData;\n            deleteInfoRequest.onerror = error => {\n              deleteModelData();\n              db.close();\n              return reject(getInfoRequest.error);\n            };\n          }\n        };\n        getInfoRequest.onerror = error => {\n          db.close();\n          return reject(getInfoRequest.error);\n        };\n\n        infoTx.oncomplete = () => {\n          if (modelTx == null) {\n            db.close();\n          } else {\n            modelTx.oncomplete = () => db.close();\n          }\n        };\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\nimport {env} from '../environment';\n\nimport {assert} from '../util';\nimport {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts(): string[] {\n  if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n      typeof window.localStorage === 'undefined') {\n    throw new Error(\n        'purgeLocalStorageModels() cannot proceed because local storage is ' +\n        'unavailable in the current environment.');\n  }\n  const LS = window.localStorage;\n  const purgedModelPaths: string[] = [];\n  for (let i = 0; i < LS.length; ++i) {\n    const key = LS.key(i);\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    if (key.startsWith(prefix) && key.length > prefix.length) {\n      LS.removeItem(key);\n      const modelName = getModelPathFromKey(key);\n      if (purgedModelPaths.indexOf(modelName) === -1) {\n        purgedModelPaths.push(modelName);\n      }\n    }\n  }\n  return purgedModelPaths;\n}\n\nfunction getModelKeys(path: string): {\n  info: string,\n  topology: string,\n  weightSpecs: string,\n  weightData: string,\n  modelMetadata: string\n} {\n  return {\n    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n    modelMetadata:\n        [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n  };\n}\n\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key: string) {\n  const items = key.split(PATH_SEPARATOR);\n  if (items.length < 3) {\n    throw new Error(`Invalid key format: ${key}`);\n  }\n  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n      key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n      key;\n}\n\ndeclare type LocalStorageKeys = {\n  info: string,\n  topology: string,\n  weightSpecs: string,\n  weightData: string,\n  modelMetadata: string\n};\n\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage implements IOHandler {\n  protected readonly LS: Storage;\n  protected readonly modelPath: string;\n  protected readonly keys: LocalStorageKeys;\n\n  static readonly URL_SCHEME = 'localstorage://';\n\n  constructor(modelPath: string) {\n    if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n        typeof window.localStorage === 'undefined') {\n      // TODO(cais): Add more info about what IOHandler subtypes are\n      // available.\n      //   Maybe point to a doc page on the web and/or automatically determine\n      //   the available IOHandlers and print them in the error message.\n      throw new Error(\n          'The current environment does not support local storage.');\n    }\n    this.LS = window.localStorage;\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For local storage, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n    this.keys = getModelKeys(this.modelPath);\n  }\n\n  /**\n   * Save model artifacts to browser local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @param modelArtifacts The model artifacts to be stored.\n   * @returns An instance of SaveResult.\n   */\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const topology = JSON.stringify(modelArtifacts.modelTopology);\n      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n\n      const modelArtifactsInfo: ModelArtifactsInfo =\n          getModelArtifactsInfoForJSON(modelArtifacts);\n\n      try {\n        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n        this.LS.setItem(this.keys.topology, topology);\n        this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n        this.LS.setItem(\n            this.keys.weightData,\n            arrayBufferToBase64String(modelArtifacts.weightData));\n        this.LS.setItem(this.keys.modelMetadata, JSON.stringify({\n          format: modelArtifacts.format,\n          generatedBy: modelArtifacts.generatedBy,\n          convertedBy: modelArtifacts.convertedBy,\n          userDefinedMetadata: modelArtifacts.userDefinedMetadata\n        }));\n\n        return {modelArtifactsInfo};\n      } catch (err) {\n        // If saving failed, clean up all items saved so far.\n        this.LS.removeItem(this.keys.info);\n        this.LS.removeItem(this.keys.topology);\n        this.LS.removeItem(this.keys.weightSpecs);\n        this.LS.removeItem(this.keys.weightData);\n        this.LS.removeItem(this.keys.modelMetadata);\n\n        throw new Error(\n            `Failed to save model '${this.modelPath}' to local storage: ` +\n            `size quota being exceeded is a possible cause of this failure: ` +\n            `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n            `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n            `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n      }\n    }\n  }\n\n  /**\n   * Load a model from local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    const info =\n        JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;\n    if (info == null) {\n      throw new Error(\n          `In local storage, there is no model with name '${this.modelPath}'`);\n    }\n\n    if (info.modelTopologyType !== 'JSON') {\n      throw new Error(\n          'BrowserLocalStorage does not support loading non-JSON model ' +\n          'topology yet.');\n    }\n\n    const out: ModelArtifacts = {};\n\n    // Load topology.\n    const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n    if (topology == null) {\n      throw new Error(\n          `In local storage, the topology of model '${this.modelPath}' ` +\n          `is missing.`);\n    }\n    out.modelTopology = topology;\n\n    // Load weight specs.\n    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n    if (weightSpecs == null) {\n      throw new Error(\n          `In local storage, the weight specs of model '${this.modelPath}' ` +\n          `are missing.`);\n    }\n    out.weightSpecs = weightSpecs;\n\n    // Load meta-data fields.\n    const metadataString = this.LS.getItem(this.keys.modelMetadata);\n    if (metadataString != null) {\n      const metadata = JSON.parse(metadataString) as ModelArtifacts;\n      out.format = metadata['format'];\n      out.generatedBy = metadata['generatedBy'];\n      out.convertedBy = metadata['convertedBy'];\n      out.userDefinedMetadata = metadata['userDefinedMetadata'];\n    }\n\n    // Load weight data.\n    const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n    if (weightDataBase64 == null) {\n      throw new Error(\n          `In local storage, the binary weight values of model ` +\n          `'${this.modelPath}' are missing.`);\n    }\n    out.weightData = base64StringToArrayBuffer(weightDataBase64);\n\n    return out;\n  }\n}\n\nexport const localStorageRouter: IORouter = (url: string|string[]) => {\n  if (!env().getBool('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n      return browserLocalStorage(\n          url.slice(BrowserLocalStorage.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath: string): IOHandler {\n  return new BrowserLocalStorage(modelPath);\n}\n\nexport class BrowserLocalStorageManager implements ModelStoreManager {\n  private readonly LS: Storage;\n\n  constructor() {\n    assert(\n        env().getBool('IS_BROWSER'),\n        () => 'Current environment is not a web browser');\n    assert(\n        typeof window === 'undefined' ||\n            typeof window.localStorage !== 'undefined',\n        () => 'Current browser does not appear to support localStorage');\n    this.LS = window.localStorage;\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    const out: {[path: string]: ModelArtifactsInfo} = {};\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n    for (let i = 0; i < this.LS.length; ++i) {\n      const key = this.LS.key(i);\n      if (key.startsWith(prefix) && key.endsWith(suffix)) {\n        const modelPath = getModelPathFromKey(key);\n        out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;\n      }\n    }\n    return out;\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    const keys = getModelKeys(path);\n    if (this.LS.getItem(keys.info) == null) {\n      throw new Error(`Cannot find model at path '${path}'`);\n    }\n    const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;\n\n    this.LS.removeItem(keys.info);\n    this.LS.removeItem(keys.topology);\n    this.LS.removeItem(keys.weightSpecs);\n    this.LS.removeItem(keys.weightData);\n    return info;\n  }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\n\nimport {assert} from '../util';\n\nimport {IORouterRegistry} from './router_registry';\nimport {ModelArtifactsInfo, ModelStoreManager} from './types';\n\nconst URL_SCHEME_SUFFIX = '://';\n\nexport class ModelStoreManagerRegistry {\n  // Singleton instance.\n  private static instance: ModelStoreManagerRegistry;\n\n  private managers: {[scheme: string]: ModelStoreManager};\n\n  private constructor() {\n    this.managers = {};\n  }\n\n  private static getInstance(): ModelStoreManagerRegistry {\n    if (ModelStoreManagerRegistry.instance == null) {\n      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n    }\n    return ModelStoreManagerRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerManager(scheme: string, manager: ModelStoreManager) {\n    assert(scheme != null, () => 'scheme must not be undefined or null.');\n    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n    }\n    assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n    const registry = ModelStoreManagerRegistry.getInstance();\n    assert(\n        registry.managers[scheme] == null,\n        () => `A model store manager is already registered for scheme '${\n            scheme}'.`);\n    registry.managers[scheme] = manager;\n  }\n\n  static getManager(scheme: string): ModelStoreManager {\n    const manager = this.getInstance().managers[scheme];\n    if (manager == null) {\n      throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n    }\n    return manager;\n  }\n\n  static getSchemes(): string[] {\n    return Object.keys(this.getInstance().managers);\n  }\n}\n\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url: string): {scheme: string, path: string} {\n  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n    throw new Error(\n        `The url string provided does not contain a scheme. ` +\n        `Supported schemes are: ` +\n        `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n  }\n  return {\n    scheme: url.split(URL_SCHEME_SUFFIX)[0],\n    path: url.split(URL_SCHEME_SUFFIX)[1],\n  };\n}\n\nasync function cloneModelInternal(\n    sourceURL: string, destURL: string,\n    deleteSource = false): Promise<ModelArtifactsInfo> {\n  assert(\n      sourceURL !== destURL,\n      () => `Old path and new path are the same: '${sourceURL}'`);\n\n  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n  assert(\n      loadHandlers.length > 0,\n      () => `Copying failed because no load handler is found for source URL ${\n          sourceURL}.`);\n  assert(\n      loadHandlers.length < 2,\n      () => `Copying failed because more than one (${loadHandlers.length}) ` +\n          `load handlers for source URL ${sourceURL}.`);\n  const loadHandler = loadHandlers[0];\n\n  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n  assert(\n      saveHandlers.length > 0,\n      () => `Copying failed because no save handler is found for destination ` +\n          `URL ${destURL}.`);\n  assert(\n      saveHandlers.length < 2,\n      () => `Copying failed because more than one (${loadHandlers.length}) ` +\n          `save handlers for destination URL ${destURL}.`);\n  const saveHandler = saveHandlers[0];\n\n  const sourceScheme = parseURL(sourceURL).scheme;\n  const sourcePath = parseURL(sourceURL).path;\n  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n\n  const modelArtifacts = await loadHandler.load();\n\n  // If moving within the same storage medium, remove the old model as soon as\n  // the loading is done. Without doing this, it is possible that the combined\n  // size of the two models will cause the cloning to fail.\n  if (deleteSource && sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  const saveResult = await saveHandler.save(modelArtifacts);\n\n  // If moving between mediums, the deletion is done after the save succeeds.\n  // This guards against the case in which saving to the destination medium\n  // fails.\n  if (deleteSource && !sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  return saveResult.modelArtifactsInfo;\n}\n\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {\n  const schemes = ModelStoreManagerRegistry.getSchemes();\n  const out: {[url: string]: ModelArtifactsInfo} = {};\n  for (const scheme of schemes) {\n    const schemeOut =\n        await ModelStoreManagerRegistry.getManager(scheme).listModels();\n    for (const path in schemeOut) {\n      const url = scheme + URL_SCHEME_SUFFIX + path;\n      out[url] = schemeOut[path];\n    }\n  }\n  return out;\n}\n\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function removeModel(url: string): Promise<ModelArtifactsInfo> {\n  const schemeAndPath = parseURL(url);\n  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n  return manager.removeModel(schemeAndPath.path);\n}\n\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function copyModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = false;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Management',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nasync function moveModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = true;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\nexport {moveModel, copyModel, removeModel, listModels};\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport '../flags';\n\nimport {env} from '../environment';\nimport {BrowserIndexedDB, BrowserIndexedDBManager} from '../io/indexed_db';\nimport {BrowserLocalStorage, BrowserLocalStorageManager} from '../io/local_storage';\nimport {ModelStoreManagerRegistry} from '../io/model_management';\n\nimport {Platform} from './platform';\n\nexport class PlatformBrowser implements Platform {\n  // According to the spec, the built-in encoder can do only UTF-8 encoding.\n  // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n  private textEncoder: TextEncoder;\n\n  fetch(path: string, init?: RequestInit): Promise<Response> {\n    return fetch(path, init);\n  }\n\n  now(): number {\n    return performance.now();\n  }\n\n  encode(text: string, encoding: string): Uint8Array {\n    if (encoding !== 'utf-8' && encoding !== 'utf8') {\n      throw new Error(\n          `Browser's encoder only supports utf-8, but got ${encoding}`);\n    }\n    if (this.textEncoder == null) {\n      this.textEncoder = new TextEncoder();\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes: Uint8Array, encoding: string): string {\n    return new TextDecoder(encoding).decode(bytes);\n  }\n}\n\nif (env().get('IS_BROWSER')) {\n  env().setPlatform('browser', new PlatformBrowser());\n\n  // Register LocalStorage IOHandler\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n  } catch (err) {\n  }\n\n  // Register IndexedDB IOHandler\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n  } catch (err) {\n  }\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {env} from '../environment';\n\nimport {Platform} from './platform';\n\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n  // tslint:disable-next-line:no-require-imports\n  importFetch: () => require('node-fetch')\n};\n\ntype FetchFn = (url: string, init?: RequestInit) => Promise<Response>;\nlet systemFetch: FetchFn;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n  systemFetch = null;\n}\nexport function setSystemFetch(fetchFn: FetchFn) {\n  systemFetch = fetchFn;\n}\nexport function getSystemFetch(): FetchFn {\n  return systemFetch;\n}\n\nexport class PlatformNode implements Platform {\n  private textEncoder: TextEncoder;\n  // tslint:disable-next-line:no-any\n  util: any;\n\n  constructor() {\n    // tslint:disable-next-line:no-require-imports\n    this.util = require('util');\n    // According to the spec, the built-in encoder can do only UTF-8 encoding.\n    // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n    this.textEncoder = new this.util.TextEncoder();\n  }\n\n  fetch(path: string, requestInits?: RequestInit): Promise<Response> {\n    if (env().global.fetch != null) {\n      return env().global.fetch(path, requestInits);\n    }\n\n    if (systemFetch == null) {\n      systemFetch = getNodeFetch.importFetch();\n    }\n    return systemFetch(path, requestInits);\n  }\n\n  now(): number {\n    const time = process.hrtime();\n    return time[0] * 1000 + time[1] / 1000000;\n  }\n\n  encode(text: string, encoding: string): Uint8Array {\n    if (encoding !== 'utf-8' && encoding !== 'utf8') {\n      throw new Error(\n          `Node built-in encoder only supports utf-8, but got ${encoding}`);\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes: Uint8Array, encoding: string): string {\n    if (bytes.length === 0) {\n      return '';\n    }\n    return new this.util.TextDecoder(encoding).decode(bytes);\n  }\n}\n\nif (env().get('IS_NODE')) {\n  env().setPlatform('node', new PlatformNode());\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorBuffer} from '../tensor';\nimport {DataType, DataTypeMap, Rank, ShapeMap} from '../types';\nimport * as util from '../util';\n\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer<R extends Rank, D extends DataType = 'float32'>(\n    shape: ShapeMap[R], dtype: D = 'float32' as D,\n    values?: DataTypeMap[D]): TensorBuffer<R, D> {\n  dtype = dtype || 'float32' as D;\n  util.assertNonNegativeIntegerDimensions(shape);\n  return new TensorBuffer<R, D>(shape, dtype, values);\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Cast, CastAttrs, CastInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {DataType, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_<T extends Tensor>(x: T|TensorLike, dtype: DataType): T {\n  const $x = convertToTensor(x, 'x', 'cast');\n\n  // Sanity checks.\n  if (!util.isValidDtype(dtype)) {\n    throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n  }\n  if (dtype === 'string' && $x.dtype !== 'string' ||\n      dtype !== 'string' && $x.dtype === 'string') {\n    throw new Error('Only strings can be casted to strings');\n  }\n\n  const inputs: CastInputs = {x: $x};\n  const attrs: CastAttrs = {dtype};\n\n  return ENGINE.runKernelFunc(\n      backend => backend.cast($x, dtype), inputs as {} as NamedTensorMap,\n      null /* grad */, Cast, attrs as {} as NamedAttrMap);\n}\n\nexport const cast = op({cast_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Identity, IdentityInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'clone', null);\n  const forward = () =>\n      ENGINE.makeTensorFromDataId($x.dataId, $x.shape, $x.dtype) as T;\n\n  const inputs: IdentityInputs = {x: $x};\n\n  // Note this op is called tf.identity in python. Hence the kernel name used\n  // here.\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, Identity);\n}\n\nexport const clone = op({clone_});\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print<T extends Tensor>(x: T, verbose = false): void {\n  console.log(x.toString(verbose));\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Required side effectful code for tfjs-core\n\n// Set up Engine and ENV\nimport {getOrMakeEngine} from './engine';\ngetOrMakeEngine();\n\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n\n// Set up OpHandler\nimport {buffer} from './ops/buffer';\nimport {cast} from './ops/cast';\nimport {clone} from './ops/clone';\nimport {print} from './ops/print';\nimport {OpHandler, setOpHandler} from './tensor';\nconst opHandler: OpHandler = {\n  buffer,\n  cast,\n  clone,\n  print\n};\nsetOpHandler(opHandler);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\n\nimport '../flags';\nimport {env} from '../environment';\n\nimport {basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelJSON, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\n\nfunction defer<T>(f: () => T): Promise<T> {\n  return new Promise(resolve => setTimeout(resolve)).then(f);\n}\n\nexport class BrowserDownloads implements IOHandler {\n  private readonly modelTopologyFileName: string;\n  private readonly weightDataFileName: string;\n  private readonly jsonAnchor: HTMLAnchorElement;\n  private readonly weightDataAnchor: HTMLAnchorElement;\n\n  static readonly URL_SCHEME = 'downloads://';\n\n  constructor(fileNamePrefix?: string) {\n    if (!env().getBool('IS_BROWSER')) {\n      // TODO(cais): Provide info on what IOHandlers are available under the\n      //   current environment.\n      throw new Error(\n          'browserDownloads() cannot proceed because the current environment ' +\n          'is not a browser.');\n    }\n\n    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n    }\n    if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n    }\n\n    this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n    this.weightDataFileName =\n        fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (typeof (document) === 'undefined') {\n      throw new Error(\n          'Browser downloads are not supported in ' +\n          'this environment since `document` is not present');\n    }\n    const weightsURL = window.URL.createObjectURL(new Blob(\n        [modelArtifacts.weightData], {type: 'application/octet-stream'}));\n\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserDownloads.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const weightsManifest: WeightsManifestConfig = [{\n        paths: ['./' + this.weightDataFileName],\n        weights: modelArtifacts.weightSpecs\n      }];\n      const modelTopologyAndWeightManifest: ModelJSON = {\n        modelTopology: modelArtifacts.modelTopology,\n        format: modelArtifacts.format,\n        generatedBy: modelArtifacts.generatedBy,\n        convertedBy: modelArtifacts.convertedBy,\n        weightsManifest\n      };\n      const modelTopologyAndWeightManifestURL =\n          window.URL.createObjectURL(new Blob(\n              [JSON.stringify(modelTopologyAndWeightManifest)],\n              {type: 'application/json'}));\n\n      // If anchor elements are not provided, create them without attaching them\n      // to parents, so that the downloaded file names can be controlled.\n      const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                                                   this.jsonAnchor;\n      jsonAnchor.download = this.modelTopologyFileName;\n      jsonAnchor.href = modelTopologyAndWeightManifestURL;\n      // Trigger downloads by evoking a click event on the download anchors.\n      // When multiple downloads are started synchronously, Firefox will only\n      // save the last one.\n      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n\n      if (modelArtifacts.weightData != null) {\n        const weightDataAnchor = this.weightDataAnchor == null ?\n            document.createElement('a') :\n            this.weightDataAnchor;\n        weightDataAnchor.download = this.weightDataFileName;\n        weightDataAnchor.href = weightsURL;\n        await defer(\n            () => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n      }\n\n      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};\n    }\n  }\n}\n\nclass BrowserFiles implements IOHandler {\n  private readonly files: File[];\n\n  constructor(files: File[]) {\n    if (files == null || files.length < 1) {\n      throw new Error(\n          `When calling browserFiles, at least 1 file is required, ` +\n          `but received ${files}`);\n    }\n    this.files = files;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    const jsonFile = this.files[0];\n    const weightFiles = this.files.slice(1);\n\n    return new Promise<ModelArtifacts>((resolve, reject) => {\n      const jsonReader = new FileReader();\n      jsonReader.onload = (event: Event) => {\n        // tslint:disable-next-line:no-any\n        const modelJSON = JSON.parse((event.target as any).result) as ModelJSON;\n        const modelTopology = modelJSON.modelTopology;\n        if (modelTopology == null) {\n          reject(new Error(\n              `modelTopology field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        if (weightFiles.length === 0) {\n          resolve({modelTopology});\n        }\n\n        const weightsManifest = modelJSON.weightsManifest;\n        if (weightsManifest == null) {\n          reject(new Error(\n              `weightManifest field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        let pathToFile: {[path: string]: File};\n        try {\n          pathToFile =\n              this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n        } catch (err) {\n          reject(err);\n          return;\n        }\n\n        const weightSpecs: WeightsManifestEntry[] = [];\n        const paths: string[] = [];\n        const perFileBuffers: ArrayBuffer[] = [];\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            paths.push(path);\n            perFileBuffers.push(null);\n          });\n          weightSpecs.push(...weightsGroup.weights);\n        });\n\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            const weightFileReader = new FileReader();\n            weightFileReader.onload = (event: Event) => {\n              // tslint:disable-next-line:no-any\n              const weightData = (event.target as any).result as ArrayBuffer;\n              const index = paths.indexOf(path);\n              perFileBuffers[index] = weightData;\n              if (perFileBuffers.indexOf(null) === -1) {\n                resolve({\n                  modelTopology,\n                  weightSpecs,\n                  weightData: concatenateArrayBuffers(perFileBuffers),\n                  format: modelJSON.format,\n                  generatedBy: modelJSON.generatedBy,\n                  convertedBy: modelJSON.convertedBy,\n                  userDefinedMetadata: modelJSON.userDefinedMetadata\n                });\n              }\n            };\n            weightFileReader.onerror = error =>\n                reject(`Failed to weights data from file of path '${path}'.`);\n            weightFileReader.readAsArrayBuffer(pathToFile[path]);\n          });\n        });\n      };\n      jsonReader.onerror = error => reject(\n          `Failed to read model topology and weights manifest JSON ` +\n          `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n          `Keras-style tf.Model artifacts only.`);\n      jsonReader.readAsText(jsonFile);\n    });\n  }\n\n  /**\n   * Check the compatibility between weights manifest and weight files.\n   */\n  private checkManifestAndWeightFiles(\n      manifest: WeightsManifestConfig, files: File[]): {[path: string]: File} {\n    const basenames: string[] = [];\n    const fileNames = files.map(file => basename(file.name));\n    const pathToFile: {[path: string]: File} = {};\n    for (const group of manifest) {\n      group.paths.forEach(path => {\n        const pathBasename = basename(path);\n        if (basenames.indexOf(pathBasename) !== -1) {\n          throw new Error(\n              `Duplicate file basename found in weights manifest: ` +\n              `'${pathBasename}'`);\n        }\n        basenames.push(pathBasename);\n        if (fileNames.indexOf(pathBasename) === -1) {\n          throw new Error(\n              `Weight file with basename '${pathBasename}' is not provided.`);\n        } else {\n          pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n        }\n      });\n    }\n\n    if (basenames.length !== files.length) {\n      throw new Error(\n          `Mismatch in the number of files in weights manifest ` +\n          `(${basenames.length}) and the number of weight files provided ` +\n          `(${files.length}).`);\n    }\n    return pathToFile;\n  }\n}\n\nexport const browserDownloadsRouter: IORouter = (url: string|string[]) => {\n  if (!env().getBool('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model'): IOHandler {\n  return new BrowserDownloads(fileNamePrefix);\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function browserFiles(files: File[]): IOHandler {\n  return new BrowserFiles(files);\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from '../util';\n\nimport {OnProgressCallback} from './types';\n\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(\n    promises: Array<Promise<{}|void>>, onProgress: OnProgressCallback,\n    startFraction?: number, endFraction?: number) {\n  checkPromises(promises);\n  startFraction = startFraction == null ? 0 : startFraction;\n  endFraction = endFraction == null ? 1 : endFraction;\n  checkFraction(startFraction, endFraction);\n  let resolvedPromise = 0;\n\n  const registerMonitor = (promise: Promise<{}>) => {\n    promise.then(value => {\n      const fraction = startFraction +\n          ++resolvedPromise / promises.length * (endFraction - startFraction);\n      // pass fraction as parameter to callback function.\n      onProgress(fraction);\n      return value;\n    });\n    return promise;\n  };\n\n  function checkPromises(promises: Array<Promise<{}|void>>): void {\n    assert(\n        promises != null && Array.isArray(promises) && promises.length > 0,\n        () => 'promises must be a none empty array');\n  }\n\n  function checkFraction(startFraction: number, endFraction: number): void {\n    assert(\n        startFraction >= 0 && startFraction <= 1,\n        () => `Progress fraction must be in range [0, 1], but ` +\n            `got startFraction ${startFraction}`);\n    assert(\n        endFraction >= 0 && endFraction <= 1,\n        () => `Progress fraction must be in range [0, 1], but ` +\n            `got endFraction ${endFraction}`);\n    assert(\n        endFraction >= startFraction,\n        () => `startFraction must be no more than endFraction, but ` +\n            `got startFraction ${startFraction} and endFraction ` +\n            `${endFraction}`);\n  }\n\n  return Promise.all(promises.map(registerMonitor));\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from '../environment';\n\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\nimport {decodeWeights} from './io_utils';\nimport {monitorPromisesProgress} from './progress';\nimport {DTYPE_VALUE_SIZE_MAP, LoadOptions, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n *   before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(\n    fetchURLs: string[], loadOptions?: LoadOptions): Promise<ArrayBuffer[]> {\n  if (loadOptions == null) {\n    loadOptions = {};\n  }\n\n  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n                                                    loadOptions.fetchFunc;\n\n  // Create the requests for all of the weights in parallel.\n  const requests = fetchURLs.map(\n      fetchURL =>\n          fetchFunc(fetchURL, loadOptions.requestInit, {isBinary: true}));\n\n  const fetchStartFraction = 0;\n  const fetchEndFraction = 0.5;\n\n  const responses = loadOptions.onProgress == null ?\n      await Promise.all(requests) :\n      await monitorPromisesProgress(\n          requests, loadOptions.onProgress, fetchStartFraction,\n          fetchEndFraction);\n\n  const bufferPromises = responses.map(response => response.arrayBuffer());\n\n  const bufferStartFraction = 0.5;\n  const bufferEndFraction = 1;\n\n  const buffers = loadOptions.onProgress == null ?\n      await Promise.all(bufferPromises) :\n      await monitorPromisesProgress(\n          bufferPromises, loadOptions.onProgress, bufferStartFraction,\n          bufferEndFraction);\n  return buffers;\n}\n\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(\n    manifest: WeightsManifestConfig, filePathPrefix = '',\n    weightNames?: string[],\n    requestInit?: RequestInit): Promise<NamedTensorMap> {\n  // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n  // single weight from a group, the whole group will be fetched. At a future\n  // date, we should support fetching only the individual shards within a\n  // group that are needed to reconstruct the requested weight.\n  // TODO(cais): Use `decodeWeights` for implementation.\n\n  const fetchWeights = (fetchUrls: string[]) =>\n      loadWeightsAsArrayBuffer(fetchUrls, {requestInit});\n  const loadWeights = weightsLoaderFactory(fetchWeights);\n\n  return loadWeights(manifest, filePathPrefix, weightNames);\n}\n\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(\n    fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>):\n    (manifest: WeightsManifestConfig, filePathPrefix?: string,\n     weightNames?: string[]) => Promise<NamedTensorMap> {\n  return async(\n             manifest: WeightsManifestConfig, filePathPrefix = '',\n             weightNames?: string[]): Promise<NamedTensorMap> => {\n    // Collect all the groups, weights, and their relative offsets to be\n    // fetched.\n    const groupIndicesToFetchMap = manifest.map(() => false);\n    const groupWeightsToFetch: {\n      [group: number]: Array<{\n        manifestEntry: WeightsManifestEntry; groupOffset: number;\n        sizeBytes: number;\n      }>\n    } = {};\n    const weightsFound =\n        weightNames != null ? weightNames.map(() => false) : [];\n    const allManifestWeightNames: string[] = [];\n    manifest.forEach((manifestGroupConfig, groupIndex) => {\n      let groupOffset = 0;\n      manifestGroupConfig.weights.forEach(weightsEntry => {\n        const rawDtype = ('quantization' in weightsEntry) ?\n            weightsEntry.quantization.dtype :\n            weightsEntry.dtype;\n\n        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n            util.sizeFromShape(weightsEntry.shape);\n\n        const enqueueWeightsForFetchingFn = () => {\n          groupIndicesToFetchMap[groupIndex] = true;\n          if (groupWeightsToFetch[groupIndex] == null) {\n            groupWeightsToFetch[groupIndex] = [];\n          }\n\n          groupWeightsToFetch[groupIndex].push({\n            manifestEntry: weightsEntry,\n            groupOffset,\n            sizeBytes: weightsBytes\n          });\n        };\n\n        if (weightNames != null) {\n          weightNames.forEach((weightName, weightIndex) => {\n            if (weightName === weightsEntry.name) {\n              enqueueWeightsForFetchingFn();\n              weightsFound[weightIndex] = true;\n            }\n          });\n        } else {\n          enqueueWeightsForFetchingFn();\n        }\n\n        allManifestWeightNames.push(weightsEntry.name);\n        groupOffset += weightsBytes;\n      });\n    });\n\n    if (!weightsFound.every(found => found)) {\n      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n      throw new Error(\n          `Could not find weights in manifest with names: ` +\n          `${weightsNotFound.join(', ')}. \\n` +\n          `Manifest JSON has weights with names: ` +\n          `${allManifestWeightNames.join(', ')}.`);\n    }\n\n    // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n    // IDs.\n    const groupIndicesToFetch =\n        groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n          if (shouldFetch) {\n            accumulator.push(i);\n          }\n          return accumulator;\n        }, []);\n\n    const fetchUrls: string[] = [];\n    groupIndicesToFetch.forEach(i => {\n      manifest[i].paths.forEach(filepath => {\n        const fetchUrl = filePathPrefix +\n            (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n        fetchUrls.push(fetchUrl);\n      });\n    });\n    const buffers = await fetchWeightsFunction(fetchUrls);\n\n    const weightsTensorMap: NamedTensorMap = {};\n    let bufferIndexOffset = 0;\n    groupIndicesToFetch.forEach(i => {\n      const numBuffers = manifest[i].paths.length;\n\n      let groupBytes = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        groupBytes += buffers[bufferIndexOffset + i].byteLength;\n      }\n\n      // Create a buffer for the whole group.\n      const groupBuffer = new ArrayBuffer(groupBytes);\n      const groupByteBuffer = new Uint8Array(groupBuffer);\n      let groupBufferOffset = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n        groupByteBuffer.set(buffer, groupBufferOffset);\n        groupBufferOffset += buffer.byteLength;\n      }\n\n      const weightsEntries = groupWeightsToFetch[i];\n      weightsEntries.forEach(weightsEntry => {\n        const byteBuffer = groupBuffer.slice(\n            weightsEntry.groupOffset,\n            weightsEntry.groupOffset + weightsEntry.sizeBytes);\n        const nameToTensorMap =\n            decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n        for (const name in nameToTensorMap) {\n          weightsTensorMap[name] = nameToTensorMap[name];\n        }\n      });\n\n      bufferIndexOffset += numBuffers;\n    });\n\n    return weightsTensorMap;\n  };\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\n\nimport {env} from '../environment';\n\nimport {assert} from '../util';\nimport {concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, LoadOptions, ModelArtifacts, ModelJSON, OnProgressCallback, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeightsAsArrayBuffer} from './weights_loader';\n\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest implements IOHandler {\n  protected readonly path: string;\n  protected readonly requestInit: RequestInit;\n\n  private readonly fetch: Function;\n  private readonly weightUrlConverter: (weightName: string) => Promise<string>;\n\n  readonly DEFAULT_METHOD = 'POST';\n\n  static readonly URL_SCHEME_REGEX = /^https?:\\/\\//;\n\n  private readonly weightPathPrefix: string;\n  private readonly onProgress: OnProgressCallback;\n\n  constructor(path: string, loadOptions?: LoadOptions) {\n    if (loadOptions == null) {\n      loadOptions = {};\n    }\n    this.weightPathPrefix = loadOptions.weightPathPrefix;\n    this.onProgress = loadOptions.onProgress;\n    this.weightUrlConverter = loadOptions.weightUrlConverter;\n\n    if (loadOptions.fetchFunc != null) {\n      assert(\n          typeof loadOptions.fetchFunc === 'function',\n          () => 'Must pass a function that matches the signature of ' +\n              '`fetch` (see ' +\n              'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n      this.fetch = loadOptions.fetchFunc;\n    } else {\n      this.fetch = env().platform.fetch;\n    }\n\n    assert(\n        path != null && path.length > 0,\n        () => 'URL path for http must not be null, undefined or ' +\n            'empty.');\n\n    if (Array.isArray(path)) {\n      assert(\n          path.length === 2,\n          () => 'URL paths for http must have a length of 2, ' +\n              `(actual length is ${path.length}).`);\n    }\n    this.path = path;\n\n    if (loadOptions.requestInit != null &&\n        loadOptions.requestInit.body != null) {\n      throw new Error(\n          'requestInit is expected to have no pre-existing body, but has one.');\n    }\n    this.requestInit = loadOptions.requestInit || {};\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserHTTPRequest.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);\n    init.body = new FormData();\n\n    const weightsManifest: WeightsManifestConfig = [{\n      paths: ['./model.weights.bin'],\n      weights: modelArtifacts.weightSpecs,\n    }];\n    const modelTopologyAndWeightManifest: ModelJSON = {\n      modelTopology: modelArtifacts.modelTopology,\n      format: modelArtifacts.format,\n      generatedBy: modelArtifacts.generatedBy,\n      convertedBy: modelArtifacts.convertedBy,\n      userDefinedMetadata: modelArtifacts.userDefinedMetadata,\n      weightsManifest\n    };\n\n    init.body.append(\n        'model.json',\n        new Blob(\n            [JSON.stringify(modelTopologyAndWeightManifest)],\n            {type: JSON_TYPE}),\n        'model.json');\n\n    if (modelArtifacts.weightData != null) {\n      init.body.append(\n          'model.weights.bin',\n          new Blob([modelArtifacts.weightData], {type: OCTET_STREAM_MIME_TYPE}),\n          'model.weights.bin');\n    }\n\n    const response = await this.fetch(this.path, init);\n\n    if (response.ok) {\n      return {\n        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n        responses: [response],\n      };\n    } else {\n      throw new Error(\n          `BrowserHTTPRequest.save() failed due to HTTP response status ` +\n          `${response.status}.`);\n    }\n  }\n\n  /**\n   * Load model artifacts via HTTP request(s).\n   *\n   * See the documentation to `tf.io.http` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model artifacts (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n\n    if (!modelConfigRequest.ok) {\n      throw new Error(\n          `Request to ${this.path} failed with status code ` +\n          `${modelConfigRequest.status}. Please verify this URL points to ` +\n          `the model JSON of the model to load.`);\n    }\n    let modelConfig: ModelJSON;\n    try {\n      modelConfig = await modelConfigRequest.json();\n    } catch (e) {\n      let message = `Failed to parse model JSON of response from ${this.path}.`;\n      // TODO(nsthorat): Remove this after some time when we're comfortable that\n      // .pb files are mostly gone.\n      if (this.path.endsWith('.pb')) {\n        message += ' Your path contains a .pb file extension. ' +\n            'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n            'in favor of .json models. You can re-convert your Python ' +\n            'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n            'or you can convert your.pb models with the \\'pb2json\\'' +\n            'NPM script in the tensorflow/tfjs-converter repository.';\n      } else {\n        message += ' Please make sure the server is serving valid ' +\n            'JSON for this request.';\n      }\n      throw new Error(message);\n    }\n    const modelTopology = modelConfig.modelTopology;\n    const weightsManifest = modelConfig.weightsManifest;\n    const generatedBy = modelConfig.generatedBy;\n    const convertedBy = modelConfig.convertedBy;\n    const format = modelConfig.format;\n    const userDefinedMetadata = modelConfig.userDefinedMetadata;\n\n    // We do not allow both modelTopology and weightsManifest to be missing.\n    if (modelTopology == null && weightsManifest == null) {\n      throw new Error(\n          `The JSON from HTTP path ${this.path} contains neither model ` +\n          `topology or manifest for weights.`);\n    }\n\n    let weightSpecs: WeightsManifestEntry[];\n    let weightData: ArrayBuffer;\n    if (weightsManifest != null) {\n      const results = await this.loadWeights(weightsManifest);\n      [weightSpecs, weightData] = results;\n    }\n\n    const artifacts: ModelArtifacts = {\n      modelTopology,\n      weightSpecs,\n      weightData,\n      userDefinedMetadata,\n      generatedBy,\n      convertedBy,\n      format\n    };\n\n    const initializer = modelConfig.modelInitializer;\n    if (initializer) {\n      artifacts.modelInitializer = initializer;\n    }\n\n    return artifacts;\n  }\n\n  private async loadWeights(weightsManifest: WeightsManifestConfig):\n      Promise<[WeightsManifestEntry[], ArrayBuffer]> {\n    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n    const [prefix, suffix] = parseUrl(weightPath);\n    const pathPrefix = this.weightPathPrefix || prefix;\n\n    const weightSpecs = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n    }\n\n    const fetchURLs: string[] = [];\n    const urlPromises: Array<Promise<string>> = [];\n    for (const weightsGroup of weightsManifest) {\n      for (const path of weightsGroup.paths) {\n        if (this.weightUrlConverter != null) {\n          urlPromises.push(this.weightUrlConverter(path));\n        } else {\n          fetchURLs.push(pathPrefix + path + suffix);\n        }\n      }\n    }\n\n    if (this.weightUrlConverter) {\n      fetchURLs.push(...await Promise.all(urlPromises));\n    }\n\n    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n      requestInit: this.requestInit,\n      fetchFunc: this.fetch,\n      onProgress: this.onProgress\n    });\n    return [weightSpecs, concatenateArrayBuffers(buffers)];\n  }\n}\n\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url: string): [string, string] {\n  const lastSlash = url.lastIndexOf('/');\n  const lastSearchParam = url.lastIndexOf('?');\n  const prefix = url.substring(0, lastSlash);\n  const suffix =\n      lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n  return [prefix + '/', suffix];\n}\n\nexport function isHTTPScheme(url: string): boolean {\n  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\n\nexport const httpRouter: IORouter =\n    (url: string, loadOptions?: LoadOptions) => {\n      if (typeof fetch === 'undefined' &&\n          (loadOptions == null || loadOptions.fetchFunc == null)) {\n        // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n        // an environment that is not the browser or node they have to setup a\n        // global fetch polyfill.\n        return null;\n      } else {\n        let isHTTP = true;\n        if (Array.isArray(url)) {\n          isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n        } else {\n          isHTTP = isHTTPScheme(url);\n        }\n        if (isHTTP) {\n          return http(url, loadOptions);\n        }\n      }\n      return null;\n    };\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n *   following fields:\n *   - weightPathPrefix Optional, this specifies the path prefix for weight\n *     files, by default this is calculated from the path param.\n *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *     the `fetch` from node-fetch can be used here.\n *   - onProgress Optional, progress callback function, fired periodically\n *     before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n *   heading: 'Models',\n *   subheading: 'Loading',\n *   namespace: 'io',\n *   ignoreCI: true\n * }\n */\nexport function http(path: string, loadOptions?: LoadOptions): IOHandler {\n  return new HTTPRequest(path, loadOptions);\n}\n\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(\n    path: string, loadOptions?: LoadOptions): IOHandler {\n  return http(path, loadOptions);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, ModelArtifacts, SaveResult, TrainingConfig, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandler {\n  constructor(private readonly modelArtifacts?: ModelArtifacts) {}\n\n  async load(): Promise<ModelArtifacts> {\n    return this.modelArtifacts;\n  }\n}\n\nclass PassthroughSaver implements IOHandler {\n  constructor(\n      private readonly saveHandler:\n          (artifacts: ModelArtifacts) => Promise<SaveResult>) {}\n\n  async save(modelArtifacts: ModelArtifacts) {\n    return this.saveHandler(modelArtifacts);\n  }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n    modelArtifacts: {}|ModelArtifacts, weightSpecs?: WeightsManifestEntry[],\n    weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler {\n  if (arguments.length === 1) {\n    const isModelArtifacts =\n        (modelArtifacts as ModelArtifacts).modelTopology != null ||\n        (modelArtifacts as ModelArtifacts).weightSpecs != null;\n    if (isModelArtifacts) {\n      return new PassthroughLoader(modelArtifacts as ModelArtifacts);\n    } else {\n      // Legacy support: with only modelTopology.\n      // TODO(cais): Remove this deprecated API.\n      console.warn(\n          'Please call tf.io.fromMemory() with only one argument. ' +\n          'The argument should be of type ModelArtifacts. ' +\n          'The multi-argument signature of tf.io.fromMemory() has been ' +\n          'deprecated and will be removed in a future release.');\n      return new PassthroughLoader({modelTopology: modelArtifacts as {}});\n    }\n  } else {\n    // Legacy support.\n    // TODO(cais): Remove this deprecated API.\n    console.warn(\n        'Please call tf.io.fromMemory() with only one argument. ' +\n        'The argument should be of type ModelArtifacts. ' +\n        'The multi-argument signature of tf.io.fromMemory() has been ' +\n        'deprecated and will be removed in a future release.');\n    return new PassthroughLoader({\n      modelTopology: modelArtifacts as {},\n      weightSpecs,\n      weightData,\n      trainingConfig\n    });\n  }\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(\n    saveHandler: (artifacts: ModelArtifacts) =>\n        Promise<SaveResult>): IOHandler {\n  return new PassthroughSaver(saveHandler);\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\n\nimport {browserFiles} from './browser_files';\nimport {browserHTTPRequest, http, isHTTPScheme} from './http';\nimport {concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON} from './io_utils';\nimport {fromMemory, withSaveHandler} from './passthrough';\nimport {getLoadHandlers, getSaveHandlers, registerLoadRouter, registerSaveRouter} from './router_registry';\nimport {IOHandler, LoadHandler, LoadOptions, ModelArtifacts, ModelArtifactsInfo, ModelJSON, ModelStoreManager, OnProgressCallback, RequestDetails, SaveConfig, SaveHandler, SaveResult, WeightGroup, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeights, weightsLoaderFactory} from './weights_loader';\n\nexport {copyModel, listModels, moveModel, removeModel} from './model_management';\nexport {\n  browserFiles,\n  browserHTTPRequest,\n  concatenateArrayBuffers,\n  decodeWeights,\n  encodeWeights,\n  fromMemory,\n  getLoadHandlers,\n  getModelArtifactsInfoForJSON,\n  getSaveHandlers,\n  http,\n  IOHandler,\n  isHTTPScheme,\n  LoadHandler,\n  LoadOptions,\n  loadWeights,\n  ModelArtifacts,\n  ModelArtifactsInfo,\n  ModelJSON,\n  ModelStoreManager,\n  OnProgressCallback,\n  registerLoadRouter,\n  registerSaveRouter,\n  RequestDetails,\n  SaveConfig,\n  SaveHandler,\n  SaveResult,\n  WeightGroup,\n  weightsLoaderFactory,\n  WeightsManifestConfig,\n  WeightsManifestEntry,\n  withSaveHandler\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from '../backends/backend';\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Reshape, ReshapeAttrs, ReshapeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction reshape_<R extends Rank>(\n    x: Tensor|TensorLike, shape: ShapeMap[R]): Tensor<R> {\n  const $x = convertToTensor(x, 'x', 'reshape', null);\n\n  const inputs: ReshapeInputs = {x: $x};\n  const attrs: ReshapeAttrs = {shape};\n  const forward: ForwardFunc<\n      Tensor<R>> = (backend: KernelBackend, save: GradSaveFunc) => {\n    shape = util.inferFromImplicitShape(shape, $x.size) as ShapeMap[R];\n    util.assert(\n        $x.size === util.sizeFromShape(shape),\n        () => 'new shape and old shape must have the same number of elements.');\n    save([$x]);\n    return backend.reshape($x, shape);\n  };\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, Reshape,\n      attrs as {} as NamedAttrMap);\n}\nexport const reshape = op({reshape_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike, transposeA = false,\n    transposeB = false): T {\n  let $a = convertToTensor(a, 'a', 'matMul');\n  let $b = convertToTensor(b, 'b', 'matMul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    save([$a, $b]);\n\n    const innerShapeA =\n        transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n    const innerShapeB =\n        transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n    const outerShapeA =\n        transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n    const outerShapeB =\n        transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n    const outerDimsA = $a.shape.slice(0, -2);\n    const outerDimsB = $b.shape.slice(0, -2);\n    const batchDimA = util.sizeFromShape(outerDimsA);\n    const batchDimB = util.sizeFromShape(outerDimsB);\n\n    const batchDimsCompatible =\n        batchDimA === batchDimB || batchDimA === 1 || batchDimB === 1;\n\n    util.assert(\n        $a.rank >= 2 && $b.rank >= 2 && batchDimsCompatible,\n        () =>\n            `Error in matMul: the input batch dimensions must either be the ` +\n            `same or at least one input batch dimension must be 1. Got input ` +\n            `batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);\n\n    util.assert(\n        innerShapeA === innerShapeB,\n        () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n            `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n            `${$b.shape} and transposeA=${transposeA}` +\n            ` and transposeB=${transposeB} must match.`);\n\n    const outShapeOuterDims = batchDimA > batchDimB ? outerDimsA : outerDimsB;\n    const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n\n    const a3D = transposeA ?\n        reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n        reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n    const b3D = transposeB ?\n        reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n        reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n\n    const res3d = backend.batchMatMul(\n        a3D as Tensor3D, b3D as Tensor3D, transposeA, transposeB);\n    return reshape(res3d, outShape);\n  };\n\n  const inputs: BatchMatMulInputs = {a: $a, b: $b};\n  const attrs: BatchMatMulAttrs = {transposeA, transposeB};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             BatchMatMul, attrs as {} as NamedAttrMap) as T;\n}\n\nexport const matMul = op({matMul_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {OneHot, OneHotAttrs, OneHotInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(\n    indices: Tensor|TensorLike, depth: number, onValue = 1,\n    offValue = 0): Tensor {\n  if (depth < 2) {\n    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n  }\n  const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n  const outShape = [...$indices.shape, depth];\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    save([$indices]);\n    return reshape(\n        backend.oneHot(\n            reshape($indices, [$indices.size]), depth, onValue, offValue),\n        outShape);\n  };\n\n  const inputs: OneHotInputs = {indices: $indices};\n  const attrs: OneHotAttrs = {depth, onValue, offValue};\n\n  return ENGINE.runKernelFunc(\n      forward, inputs as unknown as NamedTensorMap, null /* grad */, OneHot,\n      attrs as unknown as NamedAttrMap);\n}\n\nexport const oneHot = op({oneHot_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Transpose, TransposeAttrs, TransposeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_<T extends Tensor>(x: T|TensorLike, perm?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'transpose');\n\n  if (perm == null) {\n    perm = $x.shape.map((s, i) => i).reverse();\n  }\n  util.assert(\n      $x.rank === perm.length,\n      () => `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of perm ${perm}.`);\n  perm.forEach(axis => {\n    util.assert(\n        axis >= 0 && axis < $x.rank,\n        () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n  });\n\n  if ($x.rank <= 1) {\n    return $x.clone();\n  }\n\n  const inputs: TransposeInputs = {x: $x};\n  const attrs: TransposeAttrs = {perm};\n\n  return ENGINE.runKernelFunc(\n      backend => backend.transpose($x, perm), inputs as {} as NamedTensorMap,\n      null /* gradient */, Transpose, attrs as {} as NamedAttrMap);\n}\n\nexport const transpose = op({transpose_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport {matMul} from './mat_mul';\nimport {oneHot} from './one_hot';\nimport {op} from './operation';\nimport {transpose} from './transpose';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(\n    labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n    numClasses: number): Tensor2D {\n  const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n  util.assert(\n      numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n      () => `If provided, numClasses must be a positive integer, ` +\n          `but got ${numClasses}`);\n  util.assert(\n      $labels.rank === 1,\n      () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n  util.assert(\n      $predictions.rank === 1,\n      () => `Expected the rank of predictions to be 1, ` +\n          `but got ${$predictions.rank}`);\n  util.assert(\n      $labels.shape[0] === $predictions.shape[0],\n      () => `Mismatch in the number of examples: ` +\n          `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n          `Labels and predictions should have the same number of elements.`);\n  util.assert(\n      numClasses > 0 && Number.isInteger(numClasses),\n      () => `numClasses is required to be a positive integer, but got ` +\n          `${numClasses}`);\n  // TODO(cais): In the future, if oneHot supports tensors inputs for\n  //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n  const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses) as Tensor2D;\n  const oneHotPredictions =\n      oneHot(cast($predictions, 'int32'), numClasses) as Tensor2D;\n  const oneHotLabelsT: Tensor2D = transpose(oneHotLabels);\n  const product: Tensor2D = matMul(oneHotLabelsT, oneHotPredictions);\n  return cast(product, 'int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Exports under the tf.math.* namespace.\n */\n\nimport {confusionMatrix} from './ops/confusion_matrix';\n\nexport {confusionMatrix};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike3D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(\n    values: TensorLike3D, shape?: [number, number, number],\n    dtype?: DataType): Tensor3D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 3) {\n    throw new Error('tensor3d() requires shape to have three numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor3d() requires values to be number[][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor3d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor3D;\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {FromPixels, FromPixelsAttrs, FromPixelsInputs} from '../kernel_names';\nimport {getKernel, NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor2D, Tensor3D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {PixelData, TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {op} from './operation';\nimport {tensor3d} from './tensor3d';\n\nlet fromPixels2DContext: CanvasRenderingContext2D;\n\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(\n    pixels: PixelData|ImageData|HTMLImageElement|HTMLCanvasElement|\n    HTMLVideoElement,\n    numChannels = 3): Tensor3D {\n  // Sanity checks.\n  if (numChannels > 4) {\n    throw new Error(\n        'Cannot construct Tensor with more than 4 channels from pixels.');\n  }\n  if (pixels == null) {\n    throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n  }\n  let isPixelData = false;\n  let isImageData = false;\n  let isVideo = false;\n  let isImage = false;\n  let isCanvasLike = false;\n  if ((pixels as PixelData).data instanceof Uint8Array) {\n    isPixelData = true;\n  } else if (\n      typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n    isImageData = true;\n  } else if (\n      typeof (HTMLVideoElement) !== 'undefined' &&\n      pixels instanceof HTMLVideoElement) {\n    isVideo = true;\n  } else if (\n      typeof (HTMLImageElement) !== 'undefined' &&\n      pixels instanceof HTMLImageElement) {\n    isImage = true;\n    // tslint:disable-next-line: no-any\n  } else if ((pixels as any).getContext != null) {\n    isCanvasLike = true;\n  } else {\n    throw new Error(\n        'pixels passed to tf.browser.fromPixels() must be either an ' +\n        `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n        `in browser, or OffscreenCanvas, ImageData in webworker` +\n        ` or {data: Uint32Array, width: number, height: number}, ` +\n        `but was ${(pixels as {}).constructor.name}`);\n  }\n  if (isVideo) {\n    const HAVE_CURRENT_DATA_READY_STATE = 2;\n    if (isVideo &&\n        (pixels as HTMLVideoElement).readyState <\n            HAVE_CURRENT_DATA_READY_STATE) {\n      throw new Error(\n          'The video element has not loaded data yet. Please wait for ' +\n          '`loadeddata` event on the <video> element.');\n    }\n  }\n  // If the current backend has 'FromPixels' registered, it has a more\n  // efficient way of handling pixel uploads, so we call that.\n  const kernel = getKernel(FromPixels, ENGINE.backendName);\n  if (kernel != null) {\n    const inputs: FromPixelsInputs = {pixels};\n    const attrs: FromPixelsAttrs = {numChannels};\n    return ENGINE.runKernel(\n               FromPixels, inputs as {} as NamedTensorMap,\n               attrs as {} as NamedAttrMap) as Tensor3D;\n  }\n\n  const [width, height] = isVideo ?\n      [\n        (pixels as HTMLVideoElement).videoWidth,\n        (pixels as HTMLVideoElement).videoHeight\n      ] :\n      [pixels.width, pixels.height];\n  let vals: Uint8ClampedArray|Uint8Array;\n\n  if (isCanvasLike) {\n    vals =\n        // tslint:disable-next-line:no-any\n        (pixels as any).getContext('2d').getImageData(0, 0, width, height).data;\n  } else if (isImageData || isPixelData) {\n    vals = (pixels as PixelData | ImageData).data;\n  } else if (isImage || isVideo) {\n    if (fromPixels2DContext == null) {\n      fromPixels2DContext = document.createElement('canvas').getContext('2d');\n    }\n    fromPixels2DContext.canvas.width = width;\n    fromPixels2DContext.canvas.height = height;\n    fromPixels2DContext.drawImage(\n        pixels as HTMLVideoElement, 0, 0, width, height);\n    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n  }\n  let values: Int32Array;\n  if (numChannels === 4) {\n    values = new Int32Array(vals);\n  } else {\n    const numPixels = width * height;\n    values = new Int32Array(numPixels * numChannels);\n    for (let i = 0; i < numPixels; i++) {\n      for (let channel = 0; channel < numChannels; ++channel) {\n        values[i * numChannels + channel] = vals[i * 4 + channel];\n      }\n    }\n  }\n  const outShape: [number, number, number] = [height, width, numChannels];\n  return tensor3d(values, outShape, 'int32');\n}\n\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n *\n * @doc {heading: 'Browser', namespace: 'browser'}\n */\nexport async function toPixels(\n    img: Tensor2D|Tensor3D|TensorLike,\n    canvas?: HTMLCanvasElement): Promise<Uint8ClampedArray> {\n  let $img = convertToTensor(img, 'img', 'toPixels');\n  if (!(img instanceof Tensor)) {\n    // Assume int32 if user passed a native array.\n    const originalImgTensor = $img;\n    $img = cast(originalImgTensor, 'int32');\n    originalImgTensor.dispose();\n  }\n  if ($img.rank !== 2 && $img.rank !== 3) {\n    throw new Error(\n        `toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n  }\n  const [height, width] = $img.shape.slice(0, 2);\n  const depth = $img.rank === 2 ? 1 : $img.shape[2];\n\n  if (depth > 4 || depth === 2) {\n    throw new Error(\n        `toPixels only supports depth of size ` +\n        `1, 3 or 4 but got ${depth}`);\n  }\n\n  if ($img.dtype !== 'float32' && $img.dtype !== 'int32') {\n    throw new Error(\n        `Unsupported type for toPixels: ${$img.dtype}.` +\n        ` Please use float32 or int32 tensors.`);\n  }\n\n  const data = await $img.data();\n  const multiplier = $img.dtype === 'float32' ? 255 : 1;\n  const bytes = new Uint8ClampedArray(width * height * 4);\n\n  for (let i = 0; i < height * width; ++i) {\n    const rgba = [0, 0, 0, 255];\n\n    for (let d = 0; d < depth; d++) {\n      const value = data[i * depth + d];\n\n      if ($img.dtype === 'float32') {\n        if (value < 0 || value > 1) {\n          throw new Error(\n              `Tensor values for a float32 Tensor must be in the ` +\n              `range [0 - 1] but encountered ${value}.`);\n        }\n      } else if ($img.dtype === 'int32') {\n        if (value < 0 || value > 255) {\n          throw new Error(\n              `Tensor values for a int32 Tensor must be in the ` +\n              `range [0 - 255] but encountered ${value}.`);\n        }\n      }\n\n      if (depth === 1) {\n        rgba[0] = value * multiplier;\n        rgba[1] = value * multiplier;\n        rgba[2] = value * multiplier;\n      } else {\n        rgba[d] = value * multiplier;\n      }\n    }\n\n    const j = i * 4;\n    bytes[j + 0] = Math.round(rgba[0]);\n    bytes[j + 1] = Math.round(rgba[1]);\n    bytes[j + 2] = Math.round(rgba[2]);\n    bytes[j + 3] = Math.round(rgba[3]);\n  }\n\n  if (canvas != null) {\n    canvas.width = width;\n    canvas.height = height;\n    const ctx = canvas.getContext('2d');\n    const imageData = new ImageData(bytes, width, height);\n    ctx.putImageData(imageData, 0, 0);\n  }\n  if ($img !== img) {\n    $img.dispose();\n  }\n  return bytes;\n}\n\nexport const fromPixels = op({fromPixels_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides} from '../util';\n\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(\n    tensor: Tensor, indices: Tensor): [number[], number, number, number[]] {\n  if (tensor.rank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the input to be rank 1 or higher,' +\n        ` but the rank was ${tensor.rank}.`);\n  }\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(\n        'tf.gatherND() expects the indices to be int32 type,' +\n        ` but the dtype was ${indices.dtype}.`);\n  }\n  if (indices.shape[indices.rank - 1] > tensor.rank) {\n    throw new Error(\n        'index innermost dimension length must be <= tensor rank; saw: ' +\n        `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n  }\n\n  if (tensor.size === 0) {\n    throw new Error(\n        'Requested more than 0 entries, but input is empty.' +\n        ` Input shape: ${tensor.shape}.`);\n  }\n\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n\n  // The result shape is\n  //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n  let nResult = 1;\n  for (let i = 0; i < indicesShape.length - 1; ++i) {\n    nResult *= indicesShape[i];\n  }\n\n  const inputShape = tensor.shape;\n\n  const resultShape = indicesShape.slice();\n  resultShape.pop();\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < tensor.rank; ++i) {\n    sliceSize *= inputShape[i];\n    resultShape.push(inputShape[i]);\n  }\n\n  const strides =\n      [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n       1].slice(0, sliceRank);\n\n  return [resultShape, nResult, sliceSize, strides];\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {TensorInfo} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(\n    shape: number[], indices: Tensor, updates: Tensor) {\n  const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n\n  const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n      `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n      `, indices.shape: ${indices.shape}, shape: ${shape}` +\n      `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(\n        shapeError +\n        ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(\n        shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${\n              indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d + batchDim}] (${\n              updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${\n              shape[d + batchDim]})`);\n    }\n  }\n}\n\nexport interface ScatterShapeInfo {\n  sliceRank: number;\n  numUpdates: number;\n  sliceSize: number;\n  strides: number[];\n  outputSize: number;\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(\n    updates: Tensor, indices: Tensor, shape: number[]) {\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the updates to be rank 1 or higher,' +\n        ` but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${\n        indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(\n        `Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${\n          indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${\n          updates.shape}`);\n    }\n  }\n\n  validateUpdateShape(shape, indices, updates);\n}\n\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(\n    updates: TensorInfo, indices: TensorInfo,\n    shape: number[]): ScatterShapeInfo {\n  // Calculate the number of dimensions in indices\n  const indicesRank = indices.shape.length;\n  const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n\n  // Calculate the number of elements that make up each slice of our updated\n  // tensor. This allows us to work with flattened tensors and copy over whole\n  // slices at a time.\n  const totalNd = shape.length;\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < totalNd; ++i) {\n    sliceSize *= shape[i];\n  }\n\n  const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n\n  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n  const outputSize = sizeFromShape(shape);\n  return {sliceRank, numUpdates, sliceSize, strides, outputSize};\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TensorInfo} from '../kernel_registry';\nimport * as util from '../util';\n\nexport function assertParamsValid(\n    input: TensorInfo, begin: number[], size: number[]): void {\n  const inputRank = input.shape.length;\n  util.assert(\n      inputRank === begin.length,\n      () => `Error in slice${inputRank}D: Length of begin ${begin} must ` +\n          `match the rank of the array (${inputRank}).`);\n  util.assert(\n      inputRank === size.length,\n      () => `Error in slice${inputRank}D: Length of size ${size} must ` +\n          `match the rank of the array (${inputRank}).`);\n\n  for (let i = 0; i < inputRank; ++i) {\n    util.assert(\n        begin[i] + size[i] <= input.shape[i],\n        () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${\n                  input.shape[i]})`);\n  }\n}\n\n/** Converts a binary mask to an array of axes. Used in stridedSlice(). */\nexport function maskToAxes(mask: number): number[] {\n  const axes = [];\n  let axis = 0;\n  while (mask > 0) {\n    if (mask & 1) {\n      axes.push(axis);\n    }\n    mask /= 2;\n    axis++;\n  }\n  return axes;\n}\n\n/** Computes the output shape given the strided slice params. */\nexport function computeOutShape(\n    begin: number[], end: number[], strides: number[]): number[] {\n  const size = [];\n  for (let axis = 0; axis < begin.length; axis++) {\n    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n  }\n  return size;\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stride value. Otherwise, insert.\nexport function stridesWithElidedDims(\n    strides: number[], ellipsisInsertionIndex: number, numElidedAxes: number,\n    inputShape: number[]): number[] {\n  const newStrides = [...strides];\n  for (let i = newStrides.length; i < inputShape.length; i++) {\n    newStrides.push(1);\n  }\n  for (let i = 0; i < numElidedAxes; i++) {\n    if (i === 0) {\n      newStrides[ellipsisInsertionIndex] = 1;\n    } else {\n      newStrides.splice(\n          ellipsisInsertionIndex, 0 /* num elements to delete */,\n          1 /* element to add */);\n      newStrides.pop();\n    }\n  }\n  return newStrides;\n}\n\nfunction unnormalizeAxis(\n    ellipsisInsertionIndex: number, numElidedAxes: number,\n    normalizedAxis: number): number {\n  if (normalizedAxis <= ellipsisInsertionIndex) {\n    return normalizedAxis;\n  }\n\n  return normalizedAxis - (numElidedAxes - 1);\n}\n\nfunction getElidedAxes(numElidedAxes: number, ellipsisInsertionIndex: number) {\n  const elidedAxes = [];\n  for (let i = 0; i < numElidedAxes; i++) {\n    elidedAxes.push(ellipsisInsertionIndex + i);\n  }\n  return elidedAxes;\n}\n\n// Normalize the start, end and strides.\nexport function getNormalizedAxes(\n    inputShape: number[], ellipsisAxes: number[], numInterpolatedAxes: number,\n    begin: number[], end: number[], strides: number[], beginMask: number,\n    endMask: number,\n    ellipsisMask: number): {begin: number[], end: number[], strides: number[]} {\n  const inputRank = inputShape.length;\n  let normalizedBegin = new Array(inputRank),\n      normalizedEnd = new Array(inputRank),\n      normalizedStrides = new Array(inputRank);\n  if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n    const fullIndex = ellipsisAxes[0];\n\n    // The ellipsis applies to the masked index as well as any dimensions\n    // that are interpolated.\n    const numElidedAxes = numInterpolatedAxes + 1;\n    normalizedBegin = startIndicesWithElidedDims(\n        beginMask, fullIndex, numElidedAxes, begin, inputShape);\n    normalizedEnd = stopIndicesWithElidedDims(\n        endMask, fullIndex, numElidedAxes, end, inputShape);\n    normalizedStrides =\n        stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n  } else {\n    for (let axis = 0; axis < inputRank; axis++) {\n      normalizedBegin[axis] = startForAxis(\n          beginMask, begin, strides, inputShape, axis, ellipsisMask);\n      normalizedEnd[axis] =\n          stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n    }\n  }\n\n  return {\n    begin: normalizedBegin,\n    end: normalizedEnd,\n    strides: normalizedStrides\n  };\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current start value. Otherwise, insert.\nexport function startIndicesWithElidedDims(\n    beginMask: number, ellipsisInsertionIndex: number, numElidedAxes: number,\n    originalBegin: number[], inputShape: number[]): number[] {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = 0;\n    } else {\n      const originalAxis =\n          unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalBegin[originalAxis];\n      if (beginMask & 1 << originalAxis) {\n        originalValue = 0;\n      }\n\n      newIndices[axis] = originalValue;\n    }\n  }\n  return newIndices;\n}\n\n// Creates full selection at the elided dimensions. If the dimension matches\n// the ellipsis mask, override the current stop value. Otherwise, insert.\nexport function stopIndicesWithElidedDims(\n    endMask: number, ellipsisInsertionIndex: number, numElidedAxes: number,\n    originalEnd: number[], inputShape: number[]): number[] {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = Number.MAX_SAFE_INTEGER;\n    } else {\n      const originalAxis =\n          unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalEnd[originalAxis];\n      if (endMask & 1 << originalAxis) {\n        originalValue = Number.MAX_SAFE_INTEGER;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n\n  for (let i = 0; i < newIndices.length; i++) {\n    // Handle negative indices\n    const axisSize = inputShape[i];\n    if (newIndices[i] < 0) {\n      newIndices[i] += axisSize;\n    }\n    newIndices[i] = util.clamp(0, newIndices[i], inputShape[i]);\n  }\n  return newIndices;\n}\n\nexport function stridesForAxis(\n    strides: number[], axis: number, ellipsisMask: number): number {\n  let stride = strides[axis];\n  if (ellipsisMask & (1 << axis) || stride == null) {\n    stride = 1;\n  }\n\n  return stride;\n}\n\nexport function startForAxis(\n    beginMask: number, startIndices: number[], strides: number[],\n    inputShape: number[], axis: number, ellipsisMask: number): number {\n  // Begin with the specified index\n  let start = startIndices[axis];\n  const stride = strides[axis] || 1;\n\n  // Check the axis bit from right of masked axes, or the begin index is not set\n  // for the axis.\n  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n    if (stride > 0) {\n      // Forward iteration - use the first element. These values will get\n      // clamped below (Note: We could have set them to 0 and axis_size-1, but\n      // use lowest() and max() to maintain symmetry with StopForAxis())\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the last element.\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n\n  // Clamping\n  start = util.clamp(0, start, axisSize - 1);\n\n  return start;\n}\n\nexport function stopForAxis(\n    endMask: number, stopIndices: number[], strides: number[],\n    inputShape: number[], axis: number, ellipsisMask: number): number {\n  // Begin with the specified index\n  let stop = stopIndices[axis];\n  const stride = strides[axis] || 1;\n\n  // Check the axis bit from right of masked axes, or if the stop index is not\n  // set for this axis.\n  if (endMask & (1 << axis) || ellipsisMask & (1 << axis) || stop == null) {\n    if (stride > 0) {\n      // Forward iteration - use the last element. These values will get\n      // clamped below\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the first element.\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n\n  // Clamping\n  // Because the end index points one past the last element, we need slightly\n  // different clamping ranges depending on the direction.\n  if (stride > 0) {\n    // Forward iteration\n    stop = util.clamp(0, stop, axisSize);\n  } else {\n    // Backward iteration\n    stop = util.clamp(-1, stop, axisSize - 1);\n  }\n\n  return stop;\n}\n\n/**\n * Returns true if the slice occupies a continous set of elements in the\n * 'flat' space.\n */\nexport function isSliceContinous(\n    shape: number[], begin: number[], size: number[]) {\n  // Index of the first axis that has size > 1.\n  let firstNonOneAxis = size.length;\n  for (let i = 0; i < size.length; i++) {\n    if (size[i] > 1) {\n      firstNonOneAxis = i;\n      break;\n    }\n  }\n\n  for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n    if (begin[i] > 0 || size[i] !== shape[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function computeFlatOffset(begin: number[], strides: number[]): number {\n  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n  for (let i = 0; i < begin.length - 1; i++) {\n    flatOffset += begin[i] * strides[i];\n  }\n  return flatOffset;\n}\n\nexport function parseSliceParams(\n    x: TensorInfo, begin: number|number[], size?: number|number[]) {\n  // The following logic allows for more ergonomic calls.\n  let begin_: number[];\n  const xRank = x.shape.length;\n  if (typeof begin === 'number') {\n    begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n  } else if (begin.length < xRank) {\n    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  begin_.forEach(d => {\n    util.assert(\n        d !== -1, () => 'slice() does not support negative begin indexing.');\n  });\n  let size_: number[];\n  if (size == null) {\n    size_ = new Array(xRank).fill(-1);\n  } else if (typeof size === 'number') {\n    size_ = [size, ...new Array(xRank - 1).fill(-1)];\n  } else if (size.length < xRank) {\n    size_ = size.concat(new Array(xRank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      util.assert(\n          d === -1,\n          () => `Negative size values should be exactly -1 but got ` +\n              `${d} for the slice() size at index ${i}.`);\n      return x.shape[i] - begin_[i];\n    }\n  });\n  return [begin_, size_];\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from './util';\n\n/**\n * Types to support JSON-esque data structures internally.\n *\n * Internally ConfigDict's use camelCase keys and values where the\n * values are class names to be instantiated.  On the python side, these\n * will be snake_case.  Internally we allow Enums into the values for better\n * type safety, but these need to be converted to raw primitives (usually\n * strings) for round-tripping with python.\n *\n * toConfig returns the TS-friendly representation. model.toJSON() returns\n * the pythonic version as that's the portable format.  If you need to\n * python-ify a non-model level toConfig output, you'll need to use a\n * convertTsToPythonic from serialization_utils in -Layers.\n *\n */\nexport declare type ConfigDictValue =\n    boolean | number | string | null | ConfigDictArray | ConfigDict;\nexport declare interface ConfigDict {\n  [key: string]: ConfigDictValue;\n}\nexport declare interface ConfigDictArray extends Array<ConfigDictValue> {}\n\n/**\n * Type to represent the class-type of Serializable objects.\n *\n * Ie the class prototype with access to the constructor and any\n * static members/methods. Instance methods are not listed here.\n *\n * Source for this idea: https://stackoverflow.com/a/43607255\n */\nexport declare type SerializableConstructor<T extends Serializable> = {\n  // tslint:disable-next-line:no-any\n  new (...args: any[]): T; className: string; fromConfig: FromConfigMethod<T>;\n};\nexport declare type FromConfigMethod<T extends Serializable> =\n    (cls: SerializableConstructor<T>, config: ConfigDict) => T;\n\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport abstract class Serializable {\n  /**\n   * Return the class name for this class to use in serialization contexts.\n   *\n   * Generally speaking this will be the same thing that constructor.name\n   * would have returned.  However, the class name needs to be robust\n   * against minification for serialization/deserialization to work properly.\n   *\n   * There's also places such as initializers.VarianceScaling, where\n   * implementation details between different languages led to different\n   * class hierarchies and a non-leaf node is used for serialization purposes.\n   */\n  getClassName(): string {\n    return (this.constructor as SerializableConstructor<Serializable>)\n        .className;\n  }\n\n  /**\n   * Return all the non-weight state needed to serialize this object.\n   */\n  abstract getConfig(): ConfigDict;\n\n  /**\n   * Creates an instance of T from a ConfigDict.\n   *\n   * This works for most descendants of serializable.  A few need to\n   * provide special handling.\n   * @param cls A Constructor for the class to instantiate.\n   * @param config The Configuration for the object.\n   */\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config);\n  }\n}\n\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n  private static instance: SerializationMap;\n  classNameMap: {\n    [className: string]:\n        [SerializableConstructor<Serializable>, FromConfigMethod<Serializable>]\n  };\n\n  private constructor() {\n    this.classNameMap = {};\n  }\n\n  /**\n   * Returns the singleton instance of the map.\n   */\n  static getMap(): SerializationMap {\n    if (SerializationMap.instance == null) {\n      SerializationMap.instance = new SerializationMap();\n    }\n    return SerializationMap.instance;\n  }\n\n  /**\n   * Registers the class as serializable.\n   */\n  static register<T extends Serializable>(cls: SerializableConstructor<T>) {\n    SerializationMap.getMap().classNameMap[cls.className] =\n        [cls, cls.fromConfig];\n  }\n}\n\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n *\n * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}\n */\nexport function registerClass<T extends Serializable>(\n    cls: SerializableConstructor<T>) {\n  assert(\n      cls.className != null,\n      () => `Class being registered does not have the static className ` +\n          `property defined.`);\n  assert(\n      typeof cls.className === 'string',\n      () => `className is required to be a string, but got type ` +\n          typeof cls.className);\n  assert(\n      cls.className.length > 0,\n      () => `Class being registered has an empty-string as its className, ` +\n          `which is disallowed.`);\n\n  SerializationMap.register(cls);\n}\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from './engine';\nimport {inferShape} from './tensor_util_env';\nimport {RecursiveArray, TensorLike, TypedArray} from './types';\nimport {arraysEqual, flatten, isString, isTypedArray} from './util';\n\nconst TEST_EPSILON_FLOAT32 = 1e-3;\nexport const TEST_EPSILON_FLOAT16 = 1e-1;\n\nexport function expectArraysClose(\n    actual: TypedArray|number|RecursiveArray<number>,\n    expected: TypedArray|number|RecursiveArray<number>, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, b as number, epsilon));\n}\n\nexport function testEpsilon() {\n  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n                                                  TEST_EPSILON_FLOAT16;\n}\n\nfunction expectArraysPredicate(\n    actual: TensorLike, expected: TensorLike,\n    predicate: (a: {}, b: {}) => boolean) {\n  let checkClassType = true;\n  if (isTypedArray(actual) || isTypedArray(expected)) {\n    checkClassType = false;\n  }\n  if (isTypedArray(actual) && isTypedArray(expected)) {\n    checkClassType = true;\n  }\n  if (checkClassType) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n\n    if (aType !== bType) {\n      throw new Error(\n          `Arrays are of different type. Actual: ${aType}. ` +\n          `Expected: ${bType}`);\n    }\n  }\n\n  if (Array.isArray(actual) && Array.isArray(expected)) {\n    const actualShape = inferShape(actual);\n    const expectedShape = inferShape(expected);\n    if (!arraysEqual(actualShape, expectedShape)) {\n      throw new Error(\n          `Arrays have different shapes. ` +\n          `Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n    }\n  }\n\n  const actualFlat =\n      isTypedArray(actual) ? actual : flatten(actual as RecursiveArray<number>);\n  const expectedFlat = isTypedArray(expected) ?\n      expected :\n      flatten(expected as RecursiveArray<number>);\n\n  if (actualFlat.length !== expectedFlat.length) {\n    throw new Error(\n        `Arrays have different lengths actual: ${actualFlat.length} vs ` +\n        `expected: ${expectedFlat.length}.\\n` +\n        `Actual:   ${actualFlat}.\\n` +\n        `Expected: ${expectedFlat}.`);\n  }\n  for (let i = 0; i < expectedFlat.length; ++i) {\n    const a = actualFlat[i];\n    const e = expectedFlat[i];\n\n    if (!predicate(a, e)) {\n      throw new Error(\n          `Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n          `Actual:   ${actualFlat}.\\n` +\n          `Expected: ${expectedFlat}.`);\n    }\n  }\n}\n\nexport interface DoneFn {\n  (): void;\n  fail: (message?: Error|string) => void;\n}\n\nexport function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void {\n  fn().then(() => done.fail(), () => done());\n}\n\nexport function expectArraysEqual(actual: TensorLike, expected: TensorLike) {\n  const exp = typeof expected === 'string' || typeof expected === 'number' ||\n          typeof expected === 'boolean' ?\n      [expected] as number[] :\n      expected as number[];\n  if (isString(actual) || isString((actual as string[])[0]) ||\n      isString(expected) || isString((expected as string[])[0])) {\n    // tslint:disable-next-line: triple-equals\n    return expectArraysPredicate(actual, exp, (a, b) => a == b);\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, b as number, 0));\n}\n\nexport function expectNumbersClose(a: number, e: number, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  if (!areClose(a, e, epsilon)) {\n    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n  }\n}\n\nfunction areClose(a: number, e: number, epsilon: number): boolean {\n  if (!isFinite(a) && !isFinite(e)) {\n    return true;\n  }\n  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n    return false;\n  }\n  return true;\n}\n\nexport function expectValuesInRange(\n    actual: TypedArray|number[], low: number, high: number) {\n  for (let i = 0; i < actual.length; i++) {\n    if (actual[i] < low || actual[i] > high) {\n      throw new Error(\n          `Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n    }\n  }\n}\n\nexport function expectArrayBuffersEqual(\n    actual: ArrayBuffer, expected: ArrayBuffer) {\n  // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n  // a Float32Array solves this issue.\n  expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n", "/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '2.7.0';\nexport {version};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from './backends/backend';\nimport {ENGINE, Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo} from './engine';\nimport {env} from './environment';\n\nimport {Platform} from './platforms/platform';\nimport {setDeprecationWarningFn, Tensor} from './tensor';\nimport {TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\n\n/**\n * Enables production mode which disables correctness checks in favor of\n * performance.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableProdMode(): void {\n  env().set('PROD', true);\n}\n\n/**\n * Enables debug mode which will log information about all executed kernels:\n * the elapsed time of the kernel execution, as well as the rank, shape, and\n * size of the output tensor.\n *\n * Debug mode will significantly slow down your application as it will\n * download the result of every operation to the CPU. This should not be used in\n * production. Debug mode does not affect the timing information of the kernel\n * execution as we do not measure download time in the kernel execution time.\n *\n * See also: `tf.profile`, `tf.memory`.\n *\n * @doc {heading: 'Environment'}\n */\nexport function enableDebugMode(): void {\n  env().set('DEBUG', true);\n}\n\n/** Globally disables deprecation warnings */\nexport function disableDeprecationWarnings(): void {\n  env().set('DEPRECATION_WARNINGS_ENABLED', false);\n  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\n\n/** Warn users about deprecated functionality. */\nexport function deprecationWarn(msg: string) {\n  if (env().getBool('DEPRECATION_WARNINGS_ENABLED')) {\n    console.warn(\n        msg + ' You can disable deprecation warnings with ' +\n        'tf.disableDeprecationWarnings().');\n  }\n}\nsetDeprecationWarningFn(deprecationWarn);\n\n/**\n * Dispose all variables kept in backend engine.\n *\n * @doc {heading: 'Environment'}\n */\nexport function disposeVariables(): void {\n  ENGINE.disposeVariables();\n}\n\n/**\n * It returns the global engine that keeps track of all tensors and backends.\n *\n * @doc {heading: 'Environment'}\n */\nexport function engine(): Engine {\n  return ENGINE;\n}\n\n/**\n * Returns memory info at the current time in the program. The result is an\n * object with the following properties:\n *\n * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n * - `numTensors`: Number of unique tensors allocated.\n * - `numDataBuffers`: Number of unique data buffers allocated\n *   (undisposed) at this time, which is \u2264 the number of tensors\n *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n *   data buffer with `a`).\n * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n *    `unreliable` is true.\n * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n *    `unreliable` is true.\n *\n * WebGL Properties:\n * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at\n *     this time.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function memory(): MemoryInfo {\n  return ENGINE.memory();\n}\n\n/**\n * Executes the provided function `f()` and returns a promise that resolves\n * with information about the function's memory use:\n * - `newBytes`: the number of new bytes allocated\n * - `newTensors`: the number of new tensors created\n * - `peakBytes`: the peak number of bytes allocated\n * - `kernels`: an array of objects for each kernel involved that reports\n * their input and output shapes, number of bytes used, and number of new\n * tensors created.\n *\n * ```js\n * const profile = await tf.profile(() => {\n *   const x = tf.tensor1d([1, 2, 3]);\n *   let x2 = x.square();\n *   x2.dispose();\n *   x2 = x.square();\n *   x2.dispose();\n *   return x;\n * });\n *\n * console.log(`newBytes: ${profile.newBytes}`);\n * console.log(`newTensors: ${profile.newTensors}`);\n * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n * k.totalBytesSnapshot)}`);\n * ```\n *\n *\n * @doc {heading: 'Performance', subheading: 'Profile'}\n */\nexport function profile(f: () => (TensorContainer | Promise<TensorContainer>)):\n    Promise<ProfileInfo> {\n  return ENGINE.profile(f);\n}\n\n/**\n * Executes the provided function `fn` and after it is executed, cleans up all\n * intermediate tensors allocated by `fn` except those returned by `fn`.\n * `fn` must not return a Promise (async functions not allowed). The returned\n * result can be a complex object.\n *\n * Using this method helps avoid memory leaks. In general, wrap calls to\n * operations in `tf.tidy` for automatic memory cleanup.\n *\n * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to\n * dispose variables, please use `tf.disposeVariables` or call dispose()\n * directly on variables.\n *\n * ```js\n * // y = 2 ^ 2 + 1\n * const y = tf.tidy(() => {\n *   // a, b, and one will be cleaned up when the tidy ends.\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *   const b = a.square();\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * y.print();\n * ```\n *\n * @param nameOrFn The name of the closure, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If debug mode is on, the timing and the memory usage of the function\n *     will be tracked and displayed on the console using the provided name.\n * @param fn The function to execute.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function tidy<T extends TensorContainer>(\n    nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>): T {\n  return ENGINE.tidy(nameOrFn, fn);\n}\n\n/**\n * Disposes any `tf.Tensor`s found within the provided object.\n *\n * @param container an object that may be a `tf.Tensor` or may directly\n *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n *     happens. In general it is safe to pass any object here, except that\n *     `Promise`s are not supported.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function dispose(container: TensorContainer) {\n  const tensors = getTensorsInContainer(container);\n  tensors.forEach(tensor => tensor.dispose());\n}\n\n/**\n * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n * automatically.\n *\n * ```js\n * let b;\n * const y = tf.tidy(() => {\n *   const one = tf.scalar(1);\n *   const a = tf.scalar(2);\n *\n *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n *   // when the tidy ends.\n *   b = tf.keep(a.square());\n *\n *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n *\n *   // The value returned inside the tidy function will return\n *   // through the tidy, in this case to the variable y.\n *   return b.add(one);\n * });\n *\n * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n * console.log('y:');\n * y.print();\n * console.log('b:');\n * b.print();\n * ```\n *\n * @param result The tensor to keep from being disposed.\n *\n * @doc {heading: 'Performance', subheading: 'Memory'}\n */\nexport function keep<T extends Tensor>(result: T): T {\n  return ENGINE.keep(result);\n}\n\n/**\n * Executes `f()` and returns a promise that resolves with timing\n * information.\n *\n * The result is an object with the following properties:\n *\n * - `wallMs`: Wall execution time.\n * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the\n * WebGL backend and the query timer extension is not available, this will\n * return an error object.\n * - On `WebGL` The following additional properties exist:\n *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n *\n * ```js\n * const x = tf.randomNormal([20, 20]);\n * const time = await tf.time(() => x.matMul(x));\n *\n * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n * ```\n *\n * @param f The function to execute and time.\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nexport function time(f: () => void): Promise<TimingInfo> {\n  return ENGINE.time(f);\n}\n\n/**\n * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and\n * executing operations on those tensors. Returns a promise that resolves\n * to a boolean if the backend initialization was successful.\n *\n * Note this disposes the current backend, if any, as well as any tensors\n * associated with it. A new backend is initialized, even if it is of the\n * same type as the previous one.\n *\n * @param backendName The name of the backend. Currently supports\n *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js\n *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).\n *\n * @doc {heading: 'Backends'}\n */\nexport function setBackend(backendName: string): Promise<boolean> {\n  return ENGINE.setBackend(backendName);\n}\n\n/**\n * Returns a promise that resolves when the currently selected backend (or the\n * highest priority one) has initialized. Await this promise when you are using\n * a backend that has async initialization.\n *\n * @doc {heading: 'Backends'}\n */\nexport function ready(): Promise<void> {\n  return ENGINE.ready();\n}\n\n/**\n * Returns the current backend name (cpu, webgl, etc). The backend is\n * responsible for creating tensors and executing operations on those tensors.\n *\n * @doc {heading: 'Backends'}\n */\nexport function getBackend(): string {\n  return ENGINE.backendName;\n}\n\n/**\n * Removes a backend and the registered factory.\n *\n * @doc {heading: 'Backends'}\n */\nexport function removeBackend(name: string): void {\n  ENGINE.removeBackend(name);\n}\n\n/**\n * Finds the backend registered under the provided name. Returns null if the\n * name is not in the registry, or the registration hasn't finished yet.\n */\nexport function findBackend(name: string): KernelBackend {\n  return ENGINE.findBackend(name);\n}\n\n/**\n * Finds the backend factory registered under the provided name. Returns a\n * function that produces a new backend when called. Returns null if the name\n * is not in the registry.\n */\nexport function findBackendFactory(name: string): () =>\n    KernelBackend | Promise<KernelBackend> {\n  return ENGINE.findBackendFactory(name);\n}\n\n/**\n * Registers a global backend. The registration should happen when importing\n * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n * modular builds (e.g. custom tfjs bundle with only webgl support).\n *\n * @param factory The backend factory function. When called, it should\n * return a backend instance, or a promise of an instance.\n * @param priority The priority of the backend (higher = more important).\n *     In case multiple backends are registered, the priority is used to find\n *     the best backend. Defaults to 1.\n * @return False if there is already a registered backend under this name, true\n *     if not.\n *\n * @doc {heading: 'Backends'}\n */\nexport function registerBackend(\n    name: string, factory: () => KernelBackend | Promise<KernelBackend>,\n    priority = 1): boolean {\n  return ENGINE.registerBackend(name, factory, priority);\n}\n\n/**\n * Gets the current backend. If no backends have been initialized, this will\n * attempt to initialize the best backend. Will throw an error if the highest\n * priority backend has async initialization, in which case, you should call\n * 'await tf.ready()' before running other code.\n *\n * @doc {heading: 'Backends'}\n */\nexport function backend(): KernelBackend {\n  return ENGINE.backend;\n}\n\n/**\n * Sets the global platform.\n *\n * @param platformName The name of this platform.\n * @param platform A platform implementation.\n */\nexport function setPlatform(platformName: string, platform: Platform) {\n  env().setPlatform(platformName, platform);\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Add, AddInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'add');\n  let $b = convertToTensor(b, 'b', 'add');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.add($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n\n  const inputs: AddInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Add) as T;\n}\n\nexport const add = op({add_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {FloorDiv, FloorDivInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction floorDiv_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'floorDiv');\n  let $b = convertToTensor(b, 'b', 'floorDiv');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.floorDiv($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n  const inputs: FloorDivInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             FloorDiv) as T;\n}\n\nexport const floorDiv = op({floorDiv_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Div, DivInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {floorDiv} from './floorDiv';\nimport {op} from './operation';\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction div_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'div');\n  let $b = convertToTensor(b, 'b', 'div');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n    return floorDiv($a, $b);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.realDivide($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n\n  const inputs: DivInputs = {a: $a, b: $b};\n  const attrs = {};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */, Div,\n             attrs) as T;\n}\n\nexport const div = op({div_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Multiply, MultiplyInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mul');\n  let $b = convertToTensor(b, 'b', 'mul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.multiply($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n  const inputs: MultiplyInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Multiply) as T;\n}\nexport const mul = op({mul_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Abs, AbsInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction abs_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'abs');\n\n  const inputs: AbsInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    save([$x]);\n    if ($x.dtype === 'complex64') {\n      return backend.complexAbs($x);\n    }\n\n    return backend.abs($x);\n  }, inputs as {} as NamedTensorMap, null /* grad */, Abs);\n}\n\nexport const abs = op({abs_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Acos, AcosInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acos');\n  const inputs: AcosInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.acos($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Acos);\n}\nexport const acos = op({acos_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Acosh, AcoshInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction acosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acosh');\n  const inputs: AcoshInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.acosh($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Acosh);\n}\nexport const acosh = op({acosh_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {AddN, AddNInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {\n  util.assert(\n      Array.isArray(tensors),\n      () => 'The argument passed to tf.addN() must be a list of tensors');\n  util.assert(\n      tensors.length >= 1,\n      () => `Must pass at least one tensor to tf.addN(), but got ` +\n          `${tensors.length}`);\n\n  const $tensors =\n      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n\n  const firstTensor = $tensors[0];\n  $tensors.forEach(t => {\n    if (t.dtype !== firstTensor.dtype) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same dtype');\n    }\n  });\n\n  $tensors.forEach(t => {\n    if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same shape');\n    }\n  });\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.addN($tensors);\n    save($tensors);\n    return res;\n  };\n\n  const inputs: AddNInputs = $tensors;\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, AddN) as\n      T;\n}\n\nexport const addN = op({addN_});\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes: number[], rank: number): boolean {\n  for (let i = 0; i < axes.length; ++i) {\n    if (axes[axes.length - i - 1] !== rank - 1 - i) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function combineLocations(\n    outputLoc: number[], reduceLoc: number[], axes: number[]): number[] {\n  const rank = outputLoc.length + reduceLoc.length;\n  const loc = [];\n  let outIdx = 0;\n  let reduceIdx = 0;\n  \u00A0 for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      loc.push(outputLoc[outIdx++]);\n    } else {\n      loc.push(reduceLoc[reduceIdx++]);\n    }\n  }\n  return loc;\n}\n\nexport function computeOutAndReduceShapes(\n    aShape: number[], axes: number[]): [number[], number[]] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      outShape.push(aShape[dim]);\n    }\n  }\n  const reduceShape = axes.map(dim => aShape[dim]);\n  return [outShape, reduceShape];\n}\n\nexport function expandShapeToKeepDim(\n    shape: number[], axes: number[]): number[] {\n  const reduceSubShape = axes.map(x => 1);\n  return combineLocations(shape, reduceSubShape, axes);\n}\n\nexport function assertAxesAreInnerMostDims(\n    msg: string, axes: number[], rank: number): void {\n  util.assert(\n      axesAreInnerMostDims(axes, rank),\n      () => `${msg} supports only inner-most axes for now. ` +\n          `Got axes ${axes} and rank-${rank} input.`);\n}\n\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes: number[], rank: number): number[]|\n    null {\n  if (axesAreInnerMostDims(axes, rank)) {\n    return null;\n  }\n  const result: number[] = [];\n  for (let i = 0; i < rank; ++i) {\n    if (axes.indexOf(i) === -1) {\n      result.push(i);\n    }\n  }\n  axes.forEach(axis => result.push(axis));\n  return result;\n}\n\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes: number[]): number[] {\n  return axes.map((axis, i) => [i, axis])\n      .sort((a, b) => a[1] - b[1])\n      .map(x => x[0]);\n}\n\nexport function getInnerMostAxes(numAxes: number, rank: number): number[] {\n  const res: number[] = [];\n  for (let i = rank - numAxes; i < rank; ++i) {\n    res.push(i);\n  }\n  return res;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {All, AllAttrs, AllInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes} from './axis_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {transpose} from './transpose';\n\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction all_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'all', 'bool');\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    const origAxes = parseAxisParam(axis, $x.shape);\n    let axes = origAxes;\n    const permutedAxes = getAxesPermutation(axes, $x.rank);\n    if (permutedAxes != null) {\n      $x = transpose($x, permutedAxes);\n      axes = getInnerMostAxes(axes.length, $x.rank);\n    }\n    const res = backend.all($x, axes);\n    if (keepDims) {\n      const newShape = expandShapeToKeepDim(res.shape, origAxes);\n      return reshape(res, newShape);\n    }\n    return res as T;\n  };\n\n  const inputs: AllInputs = {x: $x};\n  const attrs: AllAttrs = {axis, keepDims};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, All,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const all = op({all_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Any, AnyAttrs, AnyInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes} from './axis_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {transpose} from './transpose';\n\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1], 'bool');\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction any_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'any', 'bool');\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    const origAxes = parseAxisParam(axis, $x.shape);\n    let axes = origAxes;\n    const permutedAxes = getAxesPermutation(axes, $x.rank);\n    if (permutedAxes != null) {\n      $x = transpose($x, permutedAxes);\n      axes = getInnerMostAxes(axes.length, $x.rank);\n    }\n    const res = backend.any($x, axes);\n    if (keepDims) {\n      const newShape = expandShapeToKeepDim(res.shape, origAxes);\n      return reshape(res, newShape);\n    }\n    return res as T;\n  };\n\n  const inputs: AnyInputs = {x: $x};\n  const attrs: AnyAttrs = {axis, keepDims};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Any,\n             attrs as {} as NamedAttrMap) as T;\n}\n\n// tslint:disable-next-line:variable-name\nexport const any = op({any_});\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {ArgMax, ArgMaxAttrs, ArgMaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {transpose} from './transpose';\n\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMax_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  let $x = convertToTensor(x, 'x', 'argMax');\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    save([$x]);\n\n    let axes = util.parseAxisParam(axis, $x.shape);\n    const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n    if (permutedAxes != null) {\n      $x = transpose($x, permutedAxes);\n      axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n    }\n    return backend.argMax($x, axes[0]);\n  };\n\n  const inputs: ArgMaxInputs = {x: $x};\n  const attrs: ArgMaxAttrs = {axis};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, ArgMax,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const argMax = op({argMax_});\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {ArgMin, ArgMinAttrs, ArgMinInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {transpose} from './transpose';\n\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction argMin_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  let $x = convertToTensor(x, 'x', 'argMin');\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    save([$x]);\n\n    if (axis == null) {\n      axis = 0;\n    }\n    let axes = util.parseAxisParam(axis, $x.shape);\n    const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n    if (permutedAxes != null) {\n      $x = transpose($x, permutedAxes);\n      axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n    }\n    return backend.argMin($x, axes[0]);\n  };\n\n  const inputs: ArgMinInputs = {x: $x};\n  const attrs: ArgMinAttrs = {axis};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, ArgMin,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const argMin = op({argMin_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Asin, AsinInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asin');\n  const inputs: AsinInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.asin($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Asin);\n}\nexport const asin = op({asin_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Asinh, AsinhInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction asinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asinh');\n\n  const inputs: AsinhInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.asinh($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Asinh);\n}\nexport const asinh = op({asinh_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Atan, AtanInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atan');\n\n  const inputs: AtanInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.atan($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Atan);\n}\nexport const atan = op({atan_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Atan2, Atan2Inputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atan2_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'atan2');\n  let $b = convertToTensor(b, 'b', 'atan2');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.atan2($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n  const inputs: Atan2Inputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Atan2) as T;\n}\n\nexport const atan2 = op({atan2_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Atanh, AtanhInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction atanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atanh');\n\n  const inputs: AtanhInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.atanh($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Atanh);\n}\nexport const atanh = op({atanh_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\ntype PadType = 'SAME'|'VALID'|'NUMBER'|'EXPLICIT';\n\n// For NHWC should be in the following form:\n//  [[0, 0], [pad_top,pad_bottom], [pad_left, pad_right], [0, 0]]\n// For NCHW should be in the following form:\n//  [[0, 0], [0, 0], [pad_top,pad_bottom], [pad_left, pad_right]]\n// Reference: https://www.tensorflow.org/api_docs/python/tf/nn/conv2d\nexport type ExplicitPadding =\n    [[number, number], [number, number], [number, number], [number, number]];\n\nexport type PadInfo = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  type: PadType\n};\n\nexport type PadInfo3D = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  front: number,\n  back: number,\n  type: PadType\n};\n\n/**\n * Information about the forward pass of a convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv2DInfo = {\n  batchSize: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideHeight: number,\n  strideWidth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterHeight: number,\n  filterWidth: number,\n  effectiveFilterHeight: number,\n  effectiveFilterWidth: number,\n  padInfo: PadInfo,\n  inShape: [number, number, number, number],\n  outShape: [number, number, number, number],\n  filterShape: [number, number, number, number]\n};\n\n/**\n *\n * @param inputShape Input tensor shape is of the following dimensions:\n *     `[batch, height, width, inChannels]`.\n * @param filterShape The filter shape is of the following dimensions:\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat The data format of the input and output data.\n *     Defaults to 'NHWC'.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.\n *     Defaults to `[1, 1]`. If `dilations` is a single number, then\n *     `dilationHeight == dilationWidth`.\n */\nexport function computeDilation2DInfo(\n    inputShape: [number, number, number, number],\n    filterShape: [number, number, number], strides: number|[number, number],\n    pad: 'same'|'valid'|number, dataFormat: 'NHWC' = 'NHWC',\n    dilations: number|[number, number]) {\n  // `computerConv2DInfo` require filterShape to be in the dimension of:\n  // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have\n  // outDepth, it should have the same depth as the input.\n  // Input shape: [batch, height, width, inChannels]\n  const inputChannels = inputShape[3];\n  const $filterShape =\n      [...filterShape, inputChannels] as [number, number, number, number];\n  const $dataFormat = convertConv2DDataFormat(dataFormat);\n\n  return computeConv2DInfo(\n      inputShape, $filterShape, strides, dilations, pad,\n      null /* roundingMode */, null /* depthWise */, $dataFormat);\n}\n\nexport function computePool2DInfo(\n    inShape: [number, number, number, number],\n    filterSize: [number, number]|number, strides: number|[number, number],\n    dilations: number|[number, number], pad: 'same'|'valid'|number,\n    roundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n\n  let filterShape: [number, number, number, number];\n  if (dataFormat === 'channelsLast') {\n    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n  } else if (dataFormat === 'channelsFirst') {\n    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  return computeConv2DInfo(\n      inShape, filterShape, strides, dilations, pad, roundingMode, false,\n      dataFormat);\n}\n\n/**\n * Computes the information for a forward pass of a pooling3D operation.\n */\nexport function computePool3DInfo(\n    inShape: [number, number, number, number, number],\n    filterSize: number|[number, number, number],\n    strides: number|[number, number, number],\n    dilations: number|[number, number, number], pad: 'same'|'valid'|number,\n    roundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC'): Conv3DInfo {\n  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n\n  let filterShape: [number, number, number, number, number];\n  let $dataFormat: 'channelsFirst'|'channelsLast';\n  if (dataFormat === 'NDHWC') {\n    $dataFormat = 'channelsLast';\n    filterShape =\n        [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n  } else if (dataFormat === 'NCDHW') {\n    $dataFormat = 'channelsFirst';\n    filterShape =\n        [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  return computeConv3DInfo(\n      inShape, filterShape, strides, dilations, pad, false, $dataFormat,\n      roundingMode);\n}\n\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(\n    inShape: [number, number, number, number],\n    filterShape: [number, number, number, number],\n    strides: number|[number, number], dilations: number|[number, number],\n    pad: 'same'|'valid'|number|ExplicitPadding,\n    roundingMode?: 'floor'|'round'|'ceil', depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideHeight, strideWidth] = parseTupleParam(strides);\n  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(\n      pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight,\n      effectiveFilterWidth, roundingMode, dataFormat);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inHeight,\n    inWidth,\n    inChannels,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideHeight,\n    strideWidth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\n/**\n * Information about the forward pass of a 3D convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv3DInfo = {\n  batchSize: number,\n  inDepth: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideDepth: number,\n  strideHeight: number,\n  strideWidth: number,\n  dilationDepth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterDepth: number,\n  filterHeight: number,\n  filterWidth: number,\n  effectiveFilterDepth: number,\n  effectiveFilterHeight: number,\n  effectiveFilterWidth: number,\n  padInfo: PadInfo3D,\n  inShape: [number, number, number, number, number],\n  outShape: [number, number, number, number, number],\n  filterShape: [number, number, number, number, number]\n};\n\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(\n    inShape: [number, number, number, number, number],\n    filterShape: [number, number, number, number, number],\n    strides: number|[number, number, number],\n    dilations: number|[number, number, number], pad: 'same'|'valid'|number,\n    depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast',\n    roundingMode?: 'floor'|'round'|'ceil'): Conv3DInfo {\n  let [batchSize, inDepth, inHeight, inWidth, inChannels] =\n      [-1, -1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterDepth, filterHeight, filterWidth, , filterChannels] =\n      filterShape;\n  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n  const [dilationDepth, dilationHeight, dilationWidth] =\n      parse3TupleParam(dilations);\n\n  const effectiveFilterDepth =\n      getEffectiveFilterSize(filterDepth, dilationDepth);\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(\n      pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth,\n      effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth,\n      roundingMode);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inDepth,\n    inHeight,\n    inWidth,\n    inChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideDepth,\n    strideHeight,\n    strideWidth,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterDepth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\nfunction computeOutputShape2D(\n    inShape: [number, number], fieldSize: number, stride: number,\n    zeroPad?: number, roundingMode?: 'floor'|'round'|'ceil'): [number, number] {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputRows = inShape[0];\n  const inputCols = inShape[1];\n\n  const outputRows = conditionalRound(\n      (inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputRows),\n      () => `The output # of rows (${outputRows}) must be an integer. ` +\n          `Change the stride and/or zero pad parameters`);\n\n  const outputCols = conditionalRound(\n      (inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputCols),\n      () => `The output # of columns (${outputCols}) must be an integer. ` +\n          `Change the stride and/or zero pad parameters`);\n\n  return [outputRows, outputCols];\n}\n\nfunction computeOutputShape4D(\n    inShape: [number, number, number, number], fieldSize: number,\n    outChannels: number, stride: number, zeroPad?: number,\n    roundingMode?: 'floor'|'round'|'ceil'): [number, number, number, number] {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputDepth = inShape[0];\n  const inputRows = inShape[1];\n  const inputCols = inShape[2];\n\n  const outputDepths = conditionalRound(\n      (inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputDepths),\n      () => `The output # of depths (${outputDepths}) must be an integer. ` +\n          `Change the stride and/or zero pad parameters`);\n\n  const outputRows = conditionalRound(\n      (inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputRows),\n      () => `The output # of rows (${outputRows}) must be an integer. ` +\n          `Change the stride and/or zero pad parameters`);\n\n  const outputCols = conditionalRound(\n      (inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputCols),\n      () => `The output # of columns (${outputCols}) must be an integer. ` +\n          `Change the stride and/or zero pad parameters`);\n\n  return [outputDepths, outputRows, outputCols, outChannels];\n}\n\nexport function computeDefaultPad(\n    inputShape: [number, number]|[number, number, number, number],\n    fieldSize: number, stride: number, dilation = 1): number {\n  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n  return Math.floor(\n      (inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\n\nfunction parseTupleParam(param: number|number[]): [number, number, number] {\n  if (typeof param === 'number') {\n    return [param, param, param];\n  }\n  if (param.length === 2) {\n    return [param[0], param[1], 1];\n  }\n  return param as [number, number, number];\n}\n\nfunction parse3TupleParam(param: number|[number, number, number]):\n    [number, number, number] {\n  return typeof param === 'number' ? [param, param, param] : param;\n}\n\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize: number, dilation: number) {\n  if (dilation <= 1) {\n    return filterSize;\n  }\n\n  return filterSize + (filterSize - 1) * (dilation - 1);\n}\n\nfunction getPadAndOutInfo(\n    pad: 'same'|'valid'|number|ExplicitPadding, inHeight: number,\n    inWidth: number, strideHeight: number, strideWidth: number,\n    filterHeight: number, filterWidth: number,\n    roundingMode: 'floor'|'round'|'ceil',\n    dataFormat: 'channelsFirst'|\n    'channelsLast'): {padInfo: PadInfo, outHeight: number, outWidth: number} {\n  let padInfo: PadInfo;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (typeof pad === 'number') {\n    const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n    padInfo = {top: pad, bottom: pad, left: pad, right: pad, type: padType};\n    const outShape = computeOutputShape2D(\n        [inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode);\n    outHeight = outShape[0];\n    outWidth = outShape[1];\n  } else if (pad === 'same') {\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongHeight =\n        Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n    const padAlongWidth =\n        Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = {top, bottom, left, right, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: 'VALID'};\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else if (typeof pad === 'object') {\n    const top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0];\n    const bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1];\n    const left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0];\n    const right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1];\n    const padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ?\n        'VALID' :\n        'EXPLICIT';\n    padInfo = {top, bottom, left, right, type: padType};\n    outHeight = conditionalRound(\n        (inHeight - filterHeight + top + bottom) / strideHeight + 1,\n        roundingMode);\n    outWidth = conditionalRound(\n        (inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outHeight, outWidth};\n}\n\nfunction get3DPadAndOutInfo(\n    pad: 'same'|'valid'|number, inDepth: number, inHeight: number,\n    inWidth: number, strideDepth: number, strideHeight: number,\n    strideWidth: number, filterDepth: number, filterHeight: number,\n    filterWidth: number, roundingMode?: 'floor'|'round'|'ceil'): {\n  padInfo: PadInfo3D,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number\n} {\n  let padInfo: PadInfo3D;\n  let outDepth: number;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (typeof pad === 'number') {\n    const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n    padInfo = {\n      top: pad,\n      bottom: pad,\n      left: pad,\n      right: pad,\n      front: pad,\n      back: pad,\n      type: padType\n    };\n    const outShape = computeOutputShape4D(\n        [inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad,\n        roundingMode);\n    outDepth = outShape[0];\n    outHeight = outShape[1];\n    outWidth = outShape[2];\n  } else if (pad === 'same') {\n    outDepth = Math.ceil(inDepth / strideDepth);\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n    const padAlongHeight =\n        (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const front = Math.floor(padAlongDepth / 2);\n    const back = padAlongDepth - front;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n\n    padInfo = {top, bottom, left, right, front, back, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {\n      top: 0,\n      bottom: 0,\n      left: 0,\n      right: 0,\n      front: 0,\n      back: 0,\n      type: 'VALID'\n    };\n    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outDepth, outHeight, outWidth};\n}\n\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(\n    value: number, roundingMode?: 'floor'|'round'|'ceil') {\n  if (!roundingMode) {\n    return value;\n  }\n  switch (roundingMode) {\n    case 'round':\n      // used for Caffe Conv\n      return Math.round(value);\n    case 'ceil':\n      // used for Caffe Pool\n      return Math.ceil(value);\n    case 'floor':\n      return Math.floor(value);\n    default:\n      throw new Error(`Unknown roundingMode ${roundingMode}`);\n  }\n}\n\nexport function tupleValuesAreOne(param: number|number[]): boolean {\n  const [dimA, dimB, dimC] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1 && dimC === 1;\n}\n\nexport function eitherStridesOrDilationsAreOne(\n    strides: number|number[], dilations: number|number[]): boolean {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n\n/**\n * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to\n *    'channelsLast'|'channelsFirst'\n * @param dataFormat in 'NHWC'|'NCHW' mode\n * @return dataFormat in 'channelsLast'|'channelsFirst' mode\n * @throws unknown dataFormat\n */\nexport function convertConv2DDataFormat(dataFormat: 'NHWC'|'NCHW'):\n    'channelsLast'|'channelsFirst' {\n  if (dataFormat === 'NHWC') {\n    return 'channelsLast';\n  } else if (dataFormat === 'NCHW') {\n    return 'channelsFirst';\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {AvgPool, AvgPoolAttrs, AvgPoolInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n  const dilations = 1;\n\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in avgPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const convInfo = conv_util.computePool2DInfo(\n        x4D.shape, filterSize, strides, 1 /* dilations */, pad,\n        dimRoundingMode);\n\n    save([x4D]);\n\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n      return x4D.clone();\n    }\n\n    return backend.avgPool(x4D, convInfo);\n  };\n\n  const inputs: AvgPoolInputs = {x: x4D};\n\n  const attrs: AvgPoolAttrs = {filterSize, strides, pad, dimRoundingMode};\n\n  let res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, AvgPool,\n      attrs as {} as NamedAttrMap);\n\n  res = cast(res, $x.dtype);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n\n  return res as T;\n}\n\nexport const avgPool = op({avgPool_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {deprecationWarn} from '../globals';\nimport {AvgPool3D, AvgPool3DAttrs, AvgPool3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 3D average pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.avgPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates:\n *     `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction avgPool3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filterSize: [number, number, number]|number,\n    strides: [number, number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC',\n    dilations?: [number, number, number]|number): T {\n  if (dilations == null) {\n    dilations = [1, 1, 1];\n  } else {\n    deprecationWarn(\n        'dilations is deprecated, this field will be gone in ' +\n        'v3.0.0.');\n  }\n\n  const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in avgPool3d: Only NDHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in avgPool3d: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in avgPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    if (dilations == null) {\n      dilations = [1, 1, 1];\n    }\n\n    const convInfo = conv_util.computePool3DInfo(\n        x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode,\n        dataFormat);\n\n    save([x5D]);\n\n    return backend.avgPool3d(x5D, convInfo);\n  };\n\n  const inputs: AvgPool3DInputs = {x: x5D};\n\n  const attrs: AvgPool3DAttrs =\n      {filterSize, strides, pad, dimRoundingMode, dataFormat, dilations};\n\n  let res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, AvgPool3D,\n      attrs as {} as NamedAttrMap);\n\n  res = cast(res, x5D.dtype);\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n\n  return res as T;\n}\n\nexport const avgPool3d = op({avgPool3d_});\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport function assertParamsConsistent(shapes: number[][], axis: number) {\n  const rank = shapes[0].length;\n  shapes.forEach((shape, i) => {\n    util.assert(\n        shape.length === rank,\n        () =>\n            `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n  });\n\n  util.assert(\n      axis >= 0 && axis < rank,\n      () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n\n  const firstShape = shapes[0];\n  shapes.forEach((shape, i) => {\n    for (let r = 0; r < rank; r++) {\n      util.assert(\n          (r === axis) || (shape[r] === firstShape[r]),\n          () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n              `does not match the shape of the rest (${firstShape}) ` +\n              `along the non-concatenated axis ${i}.`);\n    }\n  });\n}\n\nexport function computeOutShape(shapes: number[][], axis: number): number[] {\n  const outputShape = shapes[0].slice();\n  for (let i = 1; i < shapes.length; i++) {\n    outputShape[axis] += shapes[i][axis];\n  }\n  return outputShape;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Concat, ConcatAttrs, ConcatInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, parseAxisParam, sizeFromShape} from '../util';\n\nimport {assertParamsConsistent, computeOutShape} from './concat_util';\nimport {op} from './operation';\nimport {tensor} from './tensor';\n\n/**\n * Concatenates a list of `tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction concat_<T extends Tensor>(tensors: Array<T|TensorLike>, axis = 0): T {\n  assert(tensors.length >= 1, () => 'Pass at least one tensor to concat');\n\n  let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n\n  if ($tensors[0].dtype === 'complex64') {\n    $tensors.forEach(tensor => {\n      if (tensor.dtype !== 'complex64') {\n        throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor.dtype}. `);\n      }\n    });\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const $axis = parseAxisParam(axis, $tensors[0].shape)[0];\n    const outShape = computeOutShape($tensors.map(t => t.shape), $axis);\n    if (sizeFromShape(outShape) === 0) {\n      return tensor([], outShape) as T;\n    }\n    // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n    $tensors = $tensors.filter(t => t.size > 0);\n    if ($tensors.length === 1) {\n      return $tensors[0];\n    }\n\n    const shapes = $tensors.map(t => t.shape);\n    assertParamsConsistent(shapes, $axis);\n\n    const res = backend.concat($tensors, $axis);\n    save($tensors);\n    return res;\n  };\n\n  const inputs: ConcatInputs = $tensors;\n  const attr: ConcatAttrs = {axis};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Concat,\n             attr as {} as NamedAttrMap) as T;\n}\n\nexport const concat = op({concat_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sigmoid, SigmoidInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sigmoid');\n\n  const inputs: SigmoidInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.sigmoid($x);\n    save([res]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Sigmoid);\n}\nexport const sigmoid = op({sigmoid_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Slice, SliceAttrs, SliceInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\n\nimport {op} from './operation';\nimport * as slice_util from './slice_util';\n\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction slice_<R extends Rank, T extends Tensor<R>>(\n    x: T|TensorLike, begin: number|number[], size?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'slice');\n\n  if ($x.rank === 0) {\n    throw new Error('Slicing scalar is not possible');\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const [begin_, size_] = slice_util.parseSliceParams($x, begin, size);\n    slice_util.assertParamsValid($x, begin_, size_);\n    save([$x]);\n    return backend.slice($x, begin_, size_);\n  };\n\n  const inputs: SliceInputs = {x: $x};\n  const attrs: SliceAttrs = {begin, size};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Slice,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const slice = op({slice_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tanh, TanhInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tanh');\n\n  const inputs: TanhInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const y = backend.tanh($x);\n    save([y]);\n    return y;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Tanh);\n}\nexport const tanh = op({tanh_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {add} from './add';\nimport {concat} from './concat';\nimport {matMul} from './mat_mul';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {sigmoid} from './sigmoid';\nimport {slice} from './slice';\nimport {tanh} from './tanh';\n\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction basicLSTMCell_(\n    forgetBias: Scalar|TensorLike, lstmKernel: Tensor2D|TensorLike,\n    lstmBias: Tensor1D|TensorLike, data: Tensor2D|TensorLike,\n    c: Tensor2D|TensorLike, h: Tensor2D|TensorLike): [Tensor2D, Tensor2D] {\n  const $forgetBias =\n      convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n  const $lstmKernel =\n      convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n  const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n  const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n  const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n  const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n\n  const combined = concat([$data, $h], 1);\n  const weighted = matMul(combined, $lstmKernel);\n  const res: Tensor2D = add(weighted, $lstmBias);\n\n  // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n  const batchSize = res.shape[0];\n  const sliceCols = res.shape[1] / 4;\n  const sliceSize: [number, number] = [batchSize, sliceCols];\n  const i = slice(res, [0, 0], sliceSize);\n  const j = slice(res, [0, sliceCols], sliceSize);\n  const f = slice(res, [0, sliceCols * 2], sliceSize);\n  const o = slice(res, [0, sliceCols * 3], sliceSize);\n\n  const newC: Tensor2D =\n      add(mul(sigmoid(i), tanh(j)),\n          mul($c, sigmoid(add($forgetBias, f)) as Tensor2D));\n  const newH: Tensor2D = mul(tanh(newC), sigmoid(o));\n  return [newC, newH];\n}\n\nexport const basicLSTMCell = op({basicLSTMCell_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {BatchToSpaceND, BatchToSpaceNDAttrs, BatchToSpaceNDInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction batchToSpaceND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], crops: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n  const prod = blockShape.reduce((a, b) => a * b);\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      () => `input rank is ${$x.rank} but should be > than blockShape.length ${\n          blockShape.length}`);\n\n  util.assert(\n      crops.length === blockShape.length,\n      () => `crops.length is ${\n          crops.length} but should be equal to blockShape.length  ${\n          blockShape.length}`);\n\n  util.assert(\n      $x.shape[0] % prod === 0,\n      () => `input tensor batch is ${\n                $x.shape[0]} but is not divisible by the product of ` +\n          `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n\n  const forward: ForwardFunc<T> = backend => {\n    return backend.batchToSpaceND($x, blockShape, crops);\n  };\n\n  const inputs: BatchToSpaceNDInputs = {x: $x};\n  const attrs: BatchToSpaceNDAttrs = {blockShape, crops};\n\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* gradient */,\n      BatchToSpaceND, attrs as {} as NamedAttrMap);\n}\n\nexport const batchToSpaceND = op({batchToSpaceND_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor, Tensor4D} from '../tensor';\nimport {Rank} from '../types';\nimport {reshape} from './reshape';\n\nexport function xAs4D<R extends Rank>(x: Tensor<R>) {\n  let x4D: Tensor4D;\n  if (x.rank === 0 || x.rank === 1) {\n    x4D = reshape(x, [1, 1, 1, x.size]);\n  } else if (x.rank === 2) {\n    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n  } else if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  } else {\n    x4D = x as Tensor4D;\n  }\n\n  return x4D;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {FusedBatchNorm, FusedBatchNormAttrs, FusedBatchNormInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor1D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {xAs4D} from './batchnorm_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNorm2d`\n *   - `tf.batchNorm3d`\n *   - `tf.batchNorm4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction batchNorm_<R extends Rank>(\n    x: Tensor<R>|TensorLike, mean: Tensor<R>|Tensor1D|TensorLike,\n    variance: Tensor<R>|Tensor1D|TensorLike,\n    offset?: Tensor<R>|Tensor1D|TensorLike,\n    scale?: Tensor<R>|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor<R> {\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 0.001;\n  }\n  const $x = convertToTensor(x, 'x', 'batchNorm');\n  const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n  const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n  let $scale: Tensor<R>|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNorm');\n  }\n  let $offset: Tensor<R>|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNorm');\n  }\n\n  util.assert(\n      $mean.rank === $variance.rank,\n      () => 'Batch normalization gradient requires mean and variance to have ' +\n          'equal ranks.');\n  util.assert(\n      $offset == null || $mean.rank === $offset.rank,\n      () => 'Batch normalization gradient requires mean and offset to have ' +\n          'equal ranks.');\n  util.assert(\n      $scale == null || $mean.rank === $scale.rank,\n      () => 'Batch normalization gradient requires mean and scale to have ' +\n          'equal ranks.');\n\n  const x4D: Tensor4D = xAs4D($x);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    save([x4D, $mean, $variance, $scale]);\n\n    return backend.batchNorm(\n        x4D, as1DOr4D($mean), as1DOr4D($variance), as1DOr4D($offset),\n        as1DOr4D($scale), varianceEpsilon);\n  };\n\n  const inputs: FusedBatchNormInputs = {\n    x: x4D,\n    scale: $scale,\n    offset: $offset,\n    mean: $mean,\n    variance: $variance\n  };\n\n  const attrs: FusedBatchNormAttrs = {varianceEpsilon};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* gradient */,\n      FusedBatchNorm, attrs as {} as NamedAttrMap);\n\n  return reshape(res, $x.shape);\n}\n\nfunction as1DOr4D(x: Tensor): Tensor4D|Tensor1D {\n  if (x == null) {\n    return null;\n  }\n  if (x.rank === 0) {\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return reshape(x, [x.size]) as Tensor1D;\n  } else if (x.rank === 1) {\n    return x as Tensor1D;\n  } else if (x.rank === 2) {\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return reshape(x, [1, 1, x.shape[0], x.shape[1]]) as Tensor4D;\n  } else if (x.rank === 3) {\n    // tslint:disable-next-line:no-unnecessary-type-assertion\n    return reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]) as Tensor4D;\n  }\n  return x as Tensor4D;\n}\n\nexport const batchNorm = op({batchNorm_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {batchNorm} from './batchnorm';\nimport {op} from './operation';\n\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm2d_(\n    x: Tensor2D|TensorLike, mean: Tensor2D|Tensor1D|TensorLike,\n    variance: Tensor2D|Tensor1D|TensorLike,\n    offset?: Tensor2D|Tensor1D|TensorLike, scale?: Tensor2D|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'batchNorm');\n  const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n  const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n  let $scale: Tensor2D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNorm');\n  }\n  let $offset: Tensor2D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNorm');\n  }\n  util.assert(\n      $x.rank === 2,\n      () => `Error in batchNorm2D: x must be rank 2 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 2 || $mean.rank === 1,\n      () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 2 || $variance.rank === 1,\n      () => `Error in batchNorm2D: variance must be rank 2 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 2 || $scale.rank === 1,\n        () => `Error in batchNorm2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 2 || $offset.rank === 1,\n        () => `Error in batchNorm2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\nexport const batchNorm2d = op({batchNorm2d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D, Tensor3D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {batchNorm} from './batchnorm';\nimport {op} from './operation';\n\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm3d_(\n    x: Tensor3D|TensorLike, mean: Tensor3D|Tensor1D|TensorLike,\n    variance: Tensor3D|Tensor1D|TensorLike,\n    offset?: Tensor3D|Tensor1D|TensorLike, scale?: Tensor3D|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'batchNorm');\n  const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n  const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n  let $scale: Tensor3D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNorm');\n  }\n  let $offset: Tensor3D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNorm');\n  }\n  util.assert(\n      $x.rank === 3,\n      () => `Error in batchNorm3D: x must be rank 3 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 3 || $mean.rank === 1,\n      () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 3 || $variance.rank === 1,\n      () => `Error in batchNorm3D: variance must be rank 3 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 3 || $scale.rank === 1,\n        () => `Error in batchNorm3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 3 || $offset.rank === 1,\n        () => `Error in batchNorm3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\nexport const batchNorm3d = op({batchNorm3d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {batchNorm} from './batchnorm';\nimport {op} from './operation';\n\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNorm`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param offset An offset Tensor.\n * @param scale A scale Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n */\nfunction batchNorm4d_(\n    x: Tensor4D|TensorLike, mean: Tensor4D|Tensor1D|TensorLike,\n    variance: Tensor4D|Tensor1D|TensorLike,\n    offset?: Tensor4D|Tensor1D|TensorLike, scale?: Tensor4D|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'batchNorm');\n  const $mean = convertToTensor(mean, 'mean', 'batchNorm');\n  const $variance = convertToTensor(variance, 'variance', 'batchNorm');\n  let $scale: Tensor4D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNorm');\n  }\n  let $offset: Tensor4D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNorm');\n  }\n  util.assert(\n      $x.rank === 4,\n      () => `Error in batchNorm4D: x must be rank 4 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 4 || $mean.rank === 1,\n      () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 4 || $variance.rank === 1,\n      () => `Error in batchNorm4D: variance must be rank 4 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 4 || $scale.rank === 1,\n        () => `Error in batchNorm4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 4 || $offset.rank === 1,\n        () => `Error in batchNorm4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\n\nexport const batchNorm4d = op({batchNorm4d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from '../backends/backend';\nimport {ENGINE} from '../engine';\nimport {BroadcastTo, BroadCastToAttrs, BroadcastToInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {clone} from './clone';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Broadcast an array to a compatible shape NumPy-style.\n *\n * The tensor's shape is compared to the broadcast shape from end to beginning.\n * Ones are prepended to the tensor's shape until is has the same length as\n * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is\n * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then\n * the input tensor is tiled N times along that axis (using tf.tile).\n *\n * @param input The tensor that is to be broadcasted.\n * @param shape The input is to be broadcast to this shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction broadcastTo_<R extends Rank>(\n    x: Tensor|TensorLike, shape: ShapeMap[R]): Tensor<R> {\n  let input = convertToTensor(x, 'broadcastTo', 'x');\n  const xShape = input.shape;\n\n  if (shape.some(d => !(d > 0) || d % 1 !== 0)) {\n    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n  }\n\n  if (shape.length < input.rank) {\n    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${\n        input.rank}.`);\n  }\n\n  if (shape.length > input.rank) {\n    const newShape = input.shape.slice();\n    while (newShape.length < shape.length) {\n      newShape.unshift(1);\n    }\n    input = reshape(input, newShape);\n  }\n\n  const inputShape = input.shape;\n  const reps: number[] = Array.from(shape);\n  for (let i = shape.length - 1; i >= 0; i--) {\n    if (inputShape[i] === shape[i]) {\n      reps[i] = 1;\n    } else if (input.shape[i] !== 1) {\n      throw new Error(\n          `broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n    }\n  }\n  const axes = reps.map((n, i) => n > 1 ? i : -1).filter(i => i >= 0);\n\n  if (axes.length === 0) {\n    return clone(input) as Tensor<R>;\n  }\n\n  const forward = (backend: KernelBackend) => backend.tile(input, reps);\n\n  const inputs: BroadcastToInputs = {x: input};\n  const attrs: BroadCastToAttrs = {shape, inputShape};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as unknown as NamedTensorMap, null /* grad */,\n             BroadcastTo, attrs as unknown as NamedAttrMap) as Tensor<R>;\n}\n\nexport const broadcastTo = op({broadcastTo_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Ceil, CeilInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction ceil_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'ceil');\n\n  const inputs: CeilInputs = {x: $x};\n  return ENGINE.runKernelFunc(\n      backend => backend.ceil($x), inputs as {} as NamedTensorMap,\n      null /* grad */, Ceil);\n}\nexport const ceil = op({ceil_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {ClipByValue, ClipByValueAttrs, ClipByValueInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction clipByValue_<T extends Tensor>(\n    x: T|TensorLike, clipValueMin: number, clipValueMax: number): T {\n  const $x = convertToTensor(x, 'x', 'clipByValue');\n  util.assert(\n      (clipValueMin <= clipValueMax),\n      () => `Error in clip: min (${clipValueMin}) must be ` +\n          `less than or equal to max (${clipValueMax}).`);\n\n  const inputs: ClipByValueInputs = {x: $x};\n  const attrs: ClipByValueAttrs = {clipValueMin, clipValueMax};\n\n  return ENGINE.runKernelFunc(\n      (backend, save) => {\n        const res = backend.clip($x, clipValueMin, clipValueMax);\n        save([$x]);\n        return res;\n      },\n      inputs as {} as NamedTensorMap, null /* grad */, ClipByValue,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const clipByValue = op({clipByValue_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D} from '../tensor';\nimport {TensorLike} from '../types';\n\nimport {concat} from './concat';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors: Array<Tensor1D|TensorLike>): Tensor1D {\n  return concat(tensors, 0 /* axis */);\n}\n\nexport const concat1d = op({concat1d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D} from '../tensor';\nimport {TensorLike} from '../types';\n\nimport {concat} from './concat';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(\n    tensors: Array<Tensor2D|TensorLike>, axis: number): Tensor2D {\n  return concat(tensors, axis);\n}\n\nexport const concat2d = op({concat2d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D} from '../tensor';\nimport {TensorLike} from '../types';\n\nimport {concat} from './concat';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of `tf.Tensor3D`s along an axis.\n * See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(\n    tensors: Array<Tensor3D|TensorLike>, axis: number): Tensor3D {\n  return concat(tensors, axis);\n}\n\nexport const concat3d = op({concat3d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor4D} from '../tensor';\nimport {TensorLike} from '../types';\n\nimport {concat} from './concat';\nimport {op} from './operation';\n\n/**\n * Concatenates a list of `tf.Tensor4D`s along an axis.\n * See `concat` for details.\n *\n * @param tensors A list of `tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(\n    tensors: Array<Tensor4D|TensorLike>, axis: number): Tensor4D {\n  return concat(tensors, axis);\n}\n\nexport const concat4d = op({concat4d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Conv2D, Conv2DAttrs, Conv2DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2d');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n  util.assert(\n      inDepth === $filter.shape[2],\n      () => `Error in conv2d: depth of input (${inDepth}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = conv_util.computeConv2DInfo(\n        x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n        false, $dataFormat);\n    const res = backend.conv2d(x4D, $filter, convInfo);\n\n    save([x4D, $filter]);\n\n    return res;\n  };\n\n  const inputs: Conv2DInputs = {x: x4D, filter: $filter};\n  const attrs:\n      Conv2DAttrs = {strides, pad, dataFormat, dilations, dimRoundingMode};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, Conv2D,\n      attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const conv2d = op({conv2d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {conv2d} from './conv2d';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv1d_<T extends Tensor2D|Tensor3D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv1d');\n  const $filter = convertToTensor(filter, 'filter', 'conv1d');\n\n  let x3D = $x as Tensor3D;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n  }\n\n  util.assert(\n      x3D.rank === 3,\n      () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      () => `Error in conv1d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x3D.shape[2] === $filter.shape[1],\n      () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n          `input depth for filter ${$filter.shape[1]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(stride, dilation),\n      () => 'Error in conv1D: Either stride or dilation must be 1. ' +\n          `Got stride ${stride} and dilation '${dilation}'`);\n  util.assert(\n      dataFormat === 'NWC',\n      () => `Error in conv1d: got dataFormat of ${\n          dataFormat} but only NWC is currently supported.`);\n\n  const filter4D = reshape(\n      $filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n  const strides: [number, number] = [1, stride];\n  const dilations: [number, number] = [1, dilation];\n\n  const conv2dDataFormat = 'NHWC';\n\n  const res = conv2d(\n      (input4D as Tensor4D), (filter4D as Tensor4D), strides, pad,\n      conv2dDataFormat, dilations, dimRoundingMode);\n\n  if (reshapedTo3D) {\n    return reshape(res, [res.shape[2], res.shape[3]]) as T;\n  }\n\n  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]) as T;\n}\n\nexport const conv1d = op({conv1d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2DBackpropInput_<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      () => `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape4D = xShape as [number, number, number, number];\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n\n  util.assert(\n      xShape4D.length === 4,\n      () =>\n          `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n          `${xShape4D.length}.`);\n  util.assert(\n      dy4D.rank === 4,\n      () => `Error in conv2dDerInput: dy must be rank 4, but got ` +\n          `rank ${dy4D.rank}`);\n  util.assert(\n      filter.rank === 4,\n      () => `Error in conv2dDerInput: filter must be rank 4, but got ` +\n          `rank ${filter.rank}`);\n  const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];\n  const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n  util.assert(\n      inDepth === filter.shape[2],\n      () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[2]}.`);\n  util.assert(\n      outDepth === filter.shape[3],\n      () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[3]}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const dilations = 1;\n\n    const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = conv_util.computeConv2DInfo(\n        xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false,\n        $dataFormat);\n\n    const res = backend.conv2dDerInput(dy4D, filter, convInfo);\n\n    save([dy4D, filter]);\n\n    return res;\n  };\n\n  const inputs: Conv2DBackpropInputInputs = {dy: dy4D, filter};\n\n  const attrs: Conv2DBackpropInputAttrs =\n      {strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */,\n      Conv2DBackpropInput, attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const conv2DBackpropInput = op({conv2DBackpropInput_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {conv2DBackpropInput} from './conv2d_backprop_input';\nimport {op} from './operation';\n\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv2dTranspose_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    outputShape: [number, number, number, number]|[number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n  const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n\n  return conv2DBackpropInput(\n      outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);\n}\n\nexport const conv2dTranspose = op({conv2dTranspose_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Conv3D, Conv3DAttrs, Conv3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {eitherStridesOrDilationsAreOne} from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    strides: [number, number, number]|number, pad: 'valid'|'same',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC',\n    dilations: [number, number, number]|number = [1, 1, 1]): T {\n  const $x = convertToTensor(x, 'x', 'conv3d');\n  const $filter = convertToTensor(filter, 'filter', 'conv3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  util.assert(\n      $filter.rank === 5,\n      () => `Error in conv3d: filter must be rank 5, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x5D.shape[4] === $filter.shape[3],\n      () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n          `input depth for filter ${$filter.shape[3]}.`);\n  util.assert(\n      eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv3D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in conv3d: got dataFormat of ${\n          dataFormat} but only NDHWC is currently supported.`);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const convInfo = conv_util.computeConv3DInfo(\n        x5D.shape, $filter.shape, strides, dilations, pad);\n    const res = backend.conv3d(x5D, $filter, convInfo);\n\n    save([x5D, $filter]);\n\n    return res;\n  };\n\n  const inputs: Conv3DInputs = {x: x5D, filter: $filter};\n\n  const attrs: Conv3DAttrs = {strides, pad, dataFormat, dilations};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, Conv3D,\n      attrs as {} as NamedAttrMap);\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n  return res as T;\n}\n\nexport const conv3d = op({conv3d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Conv3DBackpropInputAttrs, Conv3DBackpropInputInputs, Conv3DBackpropInputV2} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3DBackpropInput_<T extends Tensor4D|Tensor5D>(\n    xShape:\n        [number, number, number, number,\n         number]|[number, number, number, number],\n    dy: T, filter: Tensor5D, strides: [number, number, number]|number,\n    pad: 'valid'|'same'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      () => `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape5D = xShape as [number, number, number, number, number];\n  let dy5D = dy as Tensor5D;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  util.assert(\n      xShape5D.length === 5,\n      () =>\n          `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n          `${xShape5D.length}.`);\n  util.assert(\n      dy5D.rank === 5,\n      () => `Error in conv3dDerInput: dy must be rank 5, but got ` +\n          `rank ${dy5D.rank}`);\n  util.assert(\n      filter.rank === 5,\n      () => `Error in conv3dDerInput: filter must be rank 5, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[3],\n      () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[3]}.`);\n  util.assert(\n      outDepth === filter.shape[4],\n      () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[4]}.`);\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const dilations = 1;\n\n    const convInfo = conv_util.computeConv3DInfo(\n        xShape5D, filter.shape, strides, dilations, pad);\n\n    return backend.conv3dDerInput(dy5D, filter, convInfo);\n  };\n\n  const inputs: Conv3DBackpropInputInputs = {dy: dy5D, filter};\n\n  const attrs: Conv3DBackpropInputAttrs = {pad, strides, inputShape: xShape5D};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null, Conv3DBackpropInputV2,\n      attrs as {} as NamedAttrMap);\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n  return res as T;\n}\n\nexport const conv3DBackpropInput = op({conv3DBackpropInput_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor4D, Tensor5D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {conv3DBackpropInput} from './conv3d_backprop_input';\nimport {op} from './operation';\n\n/**\n * Computes the transposed 3D convolution of a volume, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 5 or rank 4, of shape\n *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 5 or rank 4:\n *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is\n *    assumed.\n * @param strides The strides of the original convolution:\n *     `[strideDepth, strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction conv3dTranspose_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    outputShape:\n        [number, number, number, number,\n         number]|[number, number, number, number],\n    strides: [number, number, number]|number, pad: 'valid'|'same'): T {\n  const $x = convertToTensor(x, 'x', 'conv3dTranspose');\n  const $filter = convertToTensor(filter, 'filter', 'conv3dTranspose');\n\n  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad);\n}\n\nexport const conv3dTranspose = op({conv3dTranspose_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Cos, CosInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cos');\n\n  const inputs: CosInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.cos($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Cos);\n}\nexport const cos = op({cos_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Cosh, CoshInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction cosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cosh');\n  const inputs: CoshInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.cosh($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Cosh);\n}\nexport const cosh = op({cosh_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from '../backends/backend';\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Cumsum, CumsumAttrs, CumsumInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {getAxesPermutation, getInnerMostAxes, getUndoAxesPermutation} from './axis_util';\nimport {op} from './operation';\nimport {transpose} from './transpose';\n\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Scan'}\n */\nfunction cumsum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis = 0, exclusive = false, reverse = false): T {\n  const $x = convertToTensor(x, 'x', 'cumsum');\n\n  const forward: ForwardFunc<Tensor> =\n      (backend: KernelBackend, save: GradSaveFunc) => {\n        const permutation = getAxesPermutation([axis], $x.rank);\n        let permutedX = $x;\n        if (permutation != null) {\n          permutedX = transpose($x, permutation);\n        }\n        const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n        let value = backend.cumsum(permutedX, permutedAxis, exclusive, reverse);\n        save([$x]);\n\n        if (permutation != null) {\n          const reversePermutation = getUndoAxesPermutation(permutation);\n          value = transpose(value, reversePermutation);\n        }\n        return value;\n      };\n\n  const inputs: CumsumInputs = {x: $x};\n  const attrs: CumsumAttrs = {axis, exclusive, reverse};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Cumsum,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const cumsum = op({cumsum_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {DepthToSpace, DepthToSpaceAttrs, DepthToSpaceInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike4D} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction depthToSpace_(\n    x: Tensor4D|TensorLike4D, blockSize: number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'depthToSpace') as Tensor4D;\n\n  const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n  const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n  const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n\n  util.assert(\n      inputHeight * blockSize >= 0,\n      () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n\n  util.assert(\n      inputWidth * blockSize >= 0,\n      () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n\n  util.assert(\n      (inputDepth % (blockSize * blockSize) === 0),\n      () => `Dimension size must be evenly divisible by ${\n          blockSize * blockSize} but is ${\n          inputDepth} for depthToSpace with input shape ${$x.shape}`);\n\n  const forward: ForwardFunc<Tensor4D> = backend =>\n      backend.depthToSpace($x, blockSize, dataFormat);\n\n  const inputs: DepthToSpaceInputs = {x: $x};\n  const attrs: DepthToSpaceAttrs = {blockSize, dataFormat};\n\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* gradient */,\n      DepthToSpace, attrs as {} as NamedAttrMap);\n}\n\nexport const depthToSpace = op({depthToSpace_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction depthwiseConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n  const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    if (dilations == null) {\n      dilations = [1, 1];\n    }\n\n    util.assert(\n        conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n        () => 'Error in depthwiseConv2d: Either strides or dilations must be ' +\n            `1. Got strides ${strides} and dilations '${dilations}'`);\n\n    const convInfo = conv_util.computeConv2DInfo(\n        x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n        true /* depthwise */);\n    const res = backend.depthwiseConv2D(x4D, $filter, convInfo);\n    save([x4D, $filter]);\n    return res;\n  };\n\n  const inputs: DepthwiseConv2dNativeInputs = {x: x4D, filter: $filter};\n  const attrs: DepthwiseConv2dNativeAttrs =\n      {strides, pad, dataFormat, dilations, dimRoundingMode};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */,\n      DepthwiseConv2dNative, attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const depthwiseConv2d = op({depthwiseConv2d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Diag, DiagInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Returns a diagonal tensor with a given diagonal values.\n *\n * Given a diagonal, this operation returns a tensor with the diagonal and\n * everything else padded with zeros.\n *\n * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor\n * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * tf.diag(x).print()\n * ```\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])\n *\n * tf.diag(x).print()\n * ```\n * @param x The input tensor.\n */\nfunction diag_(x: Tensor): Tensor {\n  const $x = convertToTensor(x, 'x', 'diag');\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const flat = reshape($x, [$x.size]);\n    const result = backend.diag(flat);\n    const outShape = [...x.shape, ...x.shape];\n\n    return reshape(result, outShape);\n  };\n\n  const inputs: DiagInputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, Diag);\n}\n\nexport const diag = op({diag_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Dilation2D, Dilation2DAttrs, Dilation2DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the grayscale dilation over the input `x`.\n *\n * @param x The input tensor, rank 3 or rank 4 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter tensor, rank 3, of shape\n *     `[filterHeight, filterWidth, depth]`.\n * @param strides The strides of the sliding window for each dimension of the\n *     input tensor: `[strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat Specify the data format of the input and output data.\n *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the\n *      default format \"NHWC\", the data is stored in the order of: [batch,\n *      height, width, channels].\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`\n *     is a single number, then `dilationHeight == dilationWidth`. If it is\n *     greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction dilation2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same',\n    dilations: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'dilation2d');\n  const $filter = convertToTensor(filter, 'filter', 'dilation2d');\n\n  util.assert(\n      $x.rank === 3 || $x.rank === 4,\n      () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      () => `Error in dilation2d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      dataFormat === 'NHWC',\n      () => `Error in dilation2d: Only NHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    reshapedTo4D = true;\n  }\n\n  const inputs: Dilation2DInputs = {x: x4D, filter: $filter};\n  const attrs: Dilation2DAttrs = {strides, pad, dilations};\n\n  const res = ENGINE.runKernel(\n                  Dilation2D, inputs as {} as NamedTensorMap,\n                  attrs as {} as NamedAttrMap) as T;\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n\n  return res;\n}\n\nexport const dilation2d = op({dilation2d_});\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(\n    inShape: number[], outShape: number[]): number[] {\n  const inRank = inShape.length;\n  const dims: number[] = [];\n  for (let i = 0; i < inRank; i++) {\n    const dim = inRank - 1 - i;\n    const a = inShape[dim] || 1;\n    const b = outShape[outShape.length - 1 - i] || 1;\n    if (b > 1 && a === 1) {\n      dims.unshift(dim);\n    }\n  }\n  return dims;\n}\n\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(\n    inShape: number[], outShape: number[]): number[] {\n  const result: number[] = [];\n  for (let i = 0; i < outShape.length; i++) {\n    const inDim = inShape[inShape.length - i - 1];\n    const outAxis = outShape.length - i - 1;\n    const outDim = outShape[outAxis];\n    if (inDim == null || (inDim === 1 && outDim > 1)) {\n      result.unshift(outAxis);\n    }\n  }\n  return result;\n}\n\nexport function assertAndGetBroadcastShape(\n    shapeA: number[], shapeB: number[]): number[] {\n  const result: number[] = [];\n  const l = Math.max(shapeA.length, shapeB.length);\n\n  for (let i = 0; i < l; i++) {\n    let a = shapeA[shapeA.length - i - 1];\n    if (a == null) {\n      a = 1;\n    }\n    let b = shapeB[shapeB.length - i - 1];\n    if (b == null) {\n      b = 1;\n    }\n    if (a === 1) {\n      result.unshift(b);\n    } else if (b === 1) {\n      result.unshift(a);\n    } else if (a !== b) {\n      const errMsg = `Operands could not be broadcast together with shapes ` +\n          `${shapeA} and ${shapeB}.`;\n      throw Error(errMsg);\n    } else {\n      result.unshift(a);\n    }\n  }\n  return result;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Equal, EqualInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction equal_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'equal');\n  let $b = convertToTensor(b, 'b', 'equal');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = backend => backend.equal($a, $b);\n\n  const inputs: EqualInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null, Equal) as T;\n}\n\nexport const equal = op({equal_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {SelectV2, SelectV2Inputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch} from '../util';\n\nimport {broadcastTo} from './broadcast_to';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same dtype as `a` and with shape that is\n *     compatible with `a`.\n * @return A tensor with same dtype as `a` and `b`, and shape that is\n *     broadcastable from `a` and `b`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction where_<T extends Tensor>(\n    condition: Tensor|TensorLike, a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'where');\n  const $b = convertToTensor(b, 'b', 'where');\n  const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n  // TODO: move this logic to forward function when the broadcastTo op is\n  // implemented in WASM.\n  // Find the broadcastable shape for $a and $b.\n  const broadcastShape = assertAndGetBroadcastShape($a.shape, $b.shape);\n  const $broadcastedA = broadcastTo($a, broadcastShape);\n  const $broadcastedB = broadcastTo($b, broadcastShape);\n  if ($condition.rank === 1) {\n    // If condition rank is 1, then the first dimension must match the size of\n    // condition.\n    assert(\n        $condition.shape[0] === $a.shape[0],\n        () => 'The first dimension of `a` must match the size of `condition`.');\n  }\n\n  if ($condition.rank !== 1) {\n    // A must have the same shape as condition.\n    assertShapesMatch(\n        $condition.shape, $broadcastedB.shape, 'Error in where: ');\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.select($condition, $broadcastedA, $broadcastedB);\n    save([$condition]);\n    return res;\n  };\n  const inputs: SelectV2Inputs = {\n    condition: $condition,\n    t: $broadcastedA,\n    e: $broadcastedB\n  };\n  return ENGINE.runKernelFunc(\n             forward, inputs as unknown as NamedTensorMap, null /* gradient */,\n             SelectV2) as T;\n}\n\nexport const where = op({where_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {ZerosLike, ZerosLikeInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction zerosLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'zerosLike');\n  const inputs: ZerosLikeInputs = {x: $x};\n  return ENGINE.runKernelFunc(\n             backend => backend.zerosLike($x), inputs as {} as NamedTensorMap,\n             null /* grad */, ZerosLike) as T;\n}\nexport const zerosLike = op({zerosLike_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {div} from './div';\nimport {equal} from './equal';\nimport {op} from './operation';\nimport {where} from './where';\nimport {zerosLike} from './zeros_like';\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0\n * if denominator is 0.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n * const c = tf.tensor1d([0, 0, 0, 0]);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n * const c = tf.scalar(0);\n *\n * a.divNoNan(b).print();  // or tf.divNoNan(a, b)\n * a.divNoNan(c).print();  // or tf.divNoNan(a, c)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction divNoNan_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  // TODO: Make this into its own kernel.\n  let $a = convertToTensor(a, 'a', 'div');\n  let $b = convertToTensor(b, 'b', 'div');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const divResult = div($a, $b);\n  const zeros = zerosLike(divResult);\n  const bEqualsZero = equal($b, zeros);\n  return where(bEqualsZero, zeros, divResult) as T;\n}\n\nexport const divNoNan = op({divNoNan_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor,} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {matMul} from './mat_mul';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction dot_(t1: Tensor|TensorLike, t2: Tensor|TensorLike): Tensor {\n  const $t1 = convertToTensor(t1, 't1', 'dot');\n  const $t2 = convertToTensor(t2, 't2', 'dot');\n\n  util.assert(\n      ($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2),\n      () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n          `${$t1.rank} and ${$t2.rank}.`);\n\n  const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n  const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n\n  util.assert(\n      t1Inner === t2Inner,\n      () => `Error in dot: inner dimensions of inputs must match, but got ` +\n          `${t1Inner} and ${t2Inner}.`);\n\n  if ($t1.rank === 1 && $t2.rank === 1) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, []);\n  } else if ($t1.rank === 1 && $t2.rank === 2) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else if ($t1.rank === 2 && $t2.rank === 1) {\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul($t1, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else {\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul($t1, t22D);\n    return t1t2;\n  }\n}\n\nexport const dot = op({dot_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Elu, EluInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction elu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'elu');\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const y = backend.elu($x);\n    save([y]);\n    return y;\n  };\n\n  const inputs: EluInputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Elu) as\n      T;\n}\n\nexport const elu = op({elu_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Erf, ErfInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction erf_<T extends Tensor>(x: T|TensorLike): T {\n  let $x = convertToTensor(x, 'x', 'erf');\n  util.assert(\n      $x.dtype === 'int32' || $x.dtype === 'float32',\n      () => 'Input dtype must be `int32` or `float32`.');\n\n  if ($x.dtype === 'int32') {\n    $x = cast($x, 'float32');\n  }\n\n  const inputs: ErfInputs = {x: $x};\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.erf($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Erf);\n}\nexport const erf = op({erf_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Exp, ExpInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction exp_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'exp');\n\n  const inputs: ExpInputs = {x: $x};\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.exp($x);\n    save([res]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Exp);\n}\nexport const exp = op({exp_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {DataType, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction expandDims_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  const parseAs: DataType = null;\n  const $x = convertToTensor(x, 'x', 'expandDims', parseAs);\n\n  util.assert(axis <= $x.rank, () => 'Axis must be <= rank of the tensor');\n  const newShape = $x.shape.slice();\n  if (axis < 0) {\n    // Negative value is counted from the tail of rank.\n    util.assert(\n        -($x.rank + 1) <= axis,\n        () => `Axis must be in the interval [${- ($x.rank + 1)}, ${$x.rank}]`);\n    axis = $x.rank + axis + 1;\n  }\n  newShape.splice(axis, 0, 1);\n  return reshape($x, newShape) as T;\n}\n\nexport const expandDims = op({expandDims_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Expm1, Expm1Inputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction expm1_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'expm1');\n\n  const inputs: Expm1Inputs = {x: $x};\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.expm1($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Expm1);\n}\nexport const expm1 = op({expm1_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Tile, TileAttrs, TileInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {DataType, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction tile_<T extends Tensor>(x: T|TensorLike, reps: number[]): T {\n  const parseAs: DataType = null;\n  const $x = convertToTensor(x, 'x', 'tile', parseAs);\n  util.assert(\n      $x.rank === reps.length,\n      () => `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of reps ${reps}.`);\n\n  const forward: ForwardFunc<T> = (backend, save) => {\n    const res = backend.tile($x, reps);\n    save([$x]);\n    return res;\n  };\n\n  const inputsToSave = [$x];\n  const inputs: TileInputs = {x: $x};\n  const attrs: TileAttrs = {reps};\n\n  return ENGINE.runKernelFunc(\n      forward, inputs as unknown as NamedTensorMap, null /* grad */, Tile,\n      attrs as unknown as NamedAttrMap, inputsToSave);\n}\n\nexport const tile = op({tile_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor2D} from '../tensor';\nimport {DataType} from '../types';\n\nimport {buffer} from './buffer';\nimport {expandDims} from './expand_dims';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {tile} from './tile';\n\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction eye_(\n    numRows: number, numColumns?: number,\n    batchShape?:\n        [\n          number\n        ]|[number,\n           number]|[number, number, number]|[number, number, number, number],\n    dtype: DataType = 'float32'): Tensor2D {\n  if (numColumns == null) {\n    numColumns = numRows;\n  }\n  const buff = buffer([numRows, numColumns], dtype);\n  const n = numRows <= numColumns ? numRows : numColumns;\n  for (let i = 0; i < n; ++i) {\n    buff.set(1, i, i);\n  }\n  const out: Tensor2D = reshape(buff.toTensor(), [numRows, numColumns]);\n  if (batchShape == null) {\n    return out;\n  } else {\n    if (batchShape.length === 1) {\n      return tile(expandDims(out, 0), [batchShape[0], 1, 1]) as Tensor2D;\n    } else if (batchShape.length === 2) {\n      // tslint:disable-next-line:no-unnecessary-type-assertion\n      return tile(\n                 expandDims(expandDims(out, 0), 0),\n                 [batchShape[0], batchShape[1], 1, 1]) as Tensor2D;\n    } else if (batchShape.length === 3) {\n      // tslint:disable-next-line:no-unnecessary-type-assertion\n      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n               batchShape[0], batchShape[1], batchShape[2], 1, 1\n             ]) as Tensor2D;\n    } else {\n      throw new Error(\n          `eye() currently supports only 1D and 2D ` +\n          // tslint:disable-next-line:no-any\n          `batchShapes, but received ${(batchShape as any).length}D.`);\n    }\n  }\n}\n\nexport const eye = op({eye_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Fill, FillAttrs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction fill<R extends Rank>(\n    shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor<R> {\n  const attrs: FillAttrs = {shape, value, dtype};\n\n  return ENGINE.runKernelFunc(\n      backend => backend.fill(shape, value, dtype), {}, null, Fill,\n      attrs as {} as NamedAttrMap);\n}\n\nexport {fill};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Floor, FloorInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction floor_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'floor');\n\n  const inputs: FloorInputs = {x: $x};\n  return ENGINE.runKernelFunc(\n      backend => backend.floor($x), inputs as {} as NamedTensorMap,\n      null /* grad */, Floor);\n}\nexport const floor = op({floor_});\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport {nearestDivisor} from '../util';\n\nexport const PARALLELIZE_THRESHOLD = 30;\n\nexport interface ReduceInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n  outSize: number;\n}\n\nexport function computeOptimalWindowSize(inSize: number): number {\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    return inSize;\n  }\n  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {nearestDivisor} from '../util';\n\nimport {PARALLELIZE_THRESHOLD} from './reduce_util';\n\nexport interface SegOpInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n  numSegments: number;\n}\n\nexport function segOpComputeOptimalWindowSize(\n    inSize: number, numSegments: number): number {\n  let done = false;\n  let res;\n\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    res = inSize;\n    done = true;\n  } else {\n    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n  }\n\n  while (!done) {\n    if (res > numSegments || res === inSize) {\n      done = true;\n    } else {\n      res = nearestDivisor(inSize, res + 1);\n    }\n  }\n  return res;\n}\n\nexport function computeOutShape(\n    aShape: number[], axis: number, numSegments: number): number[] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (dim !== axis) {\n      outShape.push(aShape[dim]);\n    } else {\n      outShape.push(numSegments);\n    }\n  }\n  return outShape;\n}\n\nexport interface GatherOpShapeInfo {\n  batchSize: number;\n  sliceSize: number;\n  dimSize: number;\n  outputShape: number[];\n}\nexport function collectGatherOpShapeInfo(\n    x: Tensor, indices: Tensor, axis: number): GatherOpShapeInfo {\n  const dimSize = x.shape[axis];\n\n  const outputShape: number[] = [];\n  let batchSize = 1;\n  let sliceSize = 1;\n  for (let i = 0; i < axis; i++) {\n    outputShape.push(x.shape[i]);\n    batchSize *= x.shape[i];\n  }\n\n  for (let i = 0; i < indices.rank; i++) {\n    outputShape.push(indices.shape[i]);\n  }\n\n  for (let i = axis + 1; i < x.rank; i++) {\n    outputShape.push(x.shape[i]);\n    sliceSize *= x.shape[i];\n  }\n\n  return {batchSize, sliceSize, dimSize, outputShape};\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {GatherV2, GatherV2Attrs, GatherV2Inputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {collectGatherOpShapeInfo} from './segment_util';\n\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction gather_<T extends Tensor>(\n    x: T|TensorLike, indices: Tensor|TensorLike, axis = 0): T {\n  const $x = convertToTensor(x, 'x', 'gather');\n  const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n\n  const inputs: GatherV2Inputs = {x: $x, indices: $indices};\n  const attrs: GatherV2Attrs = {axis};\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const parsedAxis = parseAxisParam(axis, $x.shape)[0];\n    const shapeInfo = collectGatherOpShapeInfo($x, $indices, parsedAxis);\n\n    const res =\n        backend.gather($x, reshape($indices, [$indices.size]), parsedAxis);\n    save([$x, $indices]);\n\n    return reshape(res, shapeInfo.outputShape);\n  };\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, GatherV2,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const gather = op({gather_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Greater, GreaterInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greater_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greater');\n  let $b = convertToTensor(b, 'b', 'greater');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = backend => backend.greater($a, $b);\n\n  const inputs: GreaterInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             Greater) as T;\n}\n\nexport const greater = op({greater_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {GreaterEqual, GreaterEqualInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction greaterEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greaterEqual');\n  let $b = convertToTensor(b, 'b', 'greaterEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.greaterEqual($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n\n  const inputs: GreaterEqualInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             GreaterEqual) as T;\n}\n\nexport const greaterEqual = op({greaterEqual_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Imag, ImagInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction imag_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'imag');\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    return backend.imag($input);\n  };\n\n  const inputs: ImagInputs = {input: $input};\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Imag) as T;\n}\n\nexport const imag = op({imag_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {IsFinite, IsFiniteInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns which elements of x are finite.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isFinite().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isFinite_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'isFinite');\n\n  const inputs: IsFiniteInputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n      (backend) => backend.isFinite($x), inputs as {} as NamedTensorMap,\n      null /* grad */, IsFinite);\n}\nexport const isFinite = op({isFinite_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {IsInf, IsInfInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns which elements of x are Infinity or -Infinity.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isInf().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isInf_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'isInf');\n\n  const inputs: IsInfInputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n      (backend) => backend.isInf($x), inputs as {} as NamedTensorMap,\n      null /* grad */, IsInf);\n}\nexport const isInf = op({isInf_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {IsNan, IsNanInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * RReturns which elements of x are NaN.\n *\n * ```js\n * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);\n *\n * x.isNaN().print();  // or tf.isNaN(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction isNaN_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'isNaN');\n  const inputs: IsNanInputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n      backend => backend.isNaN($x), inputs as {} as NamedTensorMap,\n      null /* grad */, IsNan);\n}\nexport const isNaN = op({isNaN_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Maximum, MaximumInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction maximum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'maximum');\n  let $b = convertToTensor(b, 'b', 'maximum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = cast($a, 'int32');\n    $b = cast($b, 'int32');\n  }\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.maximum($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n\n  const inputs: MaximumInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Maximum) as T;\n}\n\nexport const maximum = op({maximum_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar} from '../tensor';\nimport {DataType} from '../types';\nimport {isTypedArray} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function scalar(\n    value: number|boolean|string|Uint8Array, dtype?: DataType): Scalar {\n  if (((isTypedArray(value) && dtype !== 'string') || Array.isArray(value)) &&\n      dtype !== 'complex64') {\n    throw new Error(\n        'Error creating a new Scalar: value must be a primitive ' +\n        '(number|boolean|string)');\n  }\n  if (dtype === 'string' && isTypedArray(value) &&\n      !(value instanceof Uint8Array)) {\n    throw new Error(\n        'When making a scalar from encoded string, ' +\n        'the value must be `Uint8Array`.');\n  }\n  const shape: number[] = [];\n  const inferredShape: number[] = [];\n  return makeTensor(value, shape, inferredShape, dtype) as Scalar;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {maximum} from './maximum';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {scalar} from './scalar';\n\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction leakyRelu_<T extends Tensor>(x: T|TensorLike, alpha = 0.2): T {\n  const $x = convertToTensor(x, 'x', 'leakyRelu');\n  return maximum(mul(scalar(alpha), $x), $x);\n}\n\nexport const leakyRelu = op({leakyRelu_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Less, LessInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction less_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'less');\n  let $b = convertToTensor(b, 'b', 'less');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = backend => backend.less($a, $b);\n\n  const inputs: LessInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Less) as\n      T;\n}\n\nexport const less = op({less_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {LessEqual, LessEqualInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction lessEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'lessEqual');\n  let $b = convertToTensor(b, 'b', 'lessEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.lessEqual($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n\n  const inputs: LessEqualInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             LessEqual) as T;\n}\n\nexport const lessEqual = op({lessEqual_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {LinSpace, LinSpaceAttrs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor1D} from '../tensor';\n\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function linspace(start: number, stop: number, num: number): Tensor1D {\n  if (num <= 0) {\n    throw new Error('The number of values should be positive.');\n  }\n\n  const attrs: LinSpaceAttrs = {start, stop, num};\n  return ENGINE.runKernelFunc(\n      backend => backend.linspace(start, stop, num), {} /* inputs */,\n      null /* grad */, LinSpace, attrs as {} as NamedAttrMap);\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {LRN, LRNAttrs, LRNInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction localResponseNormalization_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n  const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n  util.assert(\n      $x.rank === 4 || $x.rank === 3,\n      () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n  util.assert(\n      util.isInt(depthRadius),\n      () => `Error in localResponseNormalization: depthRadius must be an ` +\n          `integer but got depthRadius ${depthRadius}.`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const y = backend.localResponseNormalization4D(\n        x4D, depthRadius, bias, alpha, beta);\n\n    save([x4D, y]);\n\n    return y;\n  };\n\n  const inputs: LRNInputs = {x: x4D};\n\n  const attrs: LRNAttrs = {depthRadius, bias, alpha, beta};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, LRN,\n      attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  } else {\n    return res as T;\n  }\n}\n\nexport const localResponseNormalization = op({localResponseNormalization_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Log, LogInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log');\n\n  const inputs: LogInputs = {x: $x};\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.log($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Log);\n}\nexport const log = op({log_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Log1p, Log1pInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction log1p_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log1p');\n\n  const inputs: Log1pInputs = {x: $x};\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.log1p($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Log1p);\n}\nexport const log1p = op({log1p_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {CustomGradientFunc, ENGINE} from './engine';\nimport {Scalar, Tensor, Variable} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport {convertToTensor, convertToTensorArray} from './tensor_util_env';\nimport {TensorLike} from './types';\nimport * as util from './util';\n\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grad(f: (x: Tensor) => Tensor): (\n    x: TensorLike|Tensor, dy?: TensorLike|Tensor) => Tensor {\n  util.assert(\n      util.isFunction(f), () => 'The f passed in grad(f) must be a function');\n  return (x: TensorLike|Tensor, dy?: TensorLike|Tensor): Tensor => {\n    // x can be of any dtype, thus null as the last argument.\n    const $x = convertToTensor(x, 'x', 'tf.grad', null);\n    const $dy: Tensor =\n        (dy != null) ? convertToTensor(dy, 'dy', 'tf.grad') : null;\n    return ENGINE.tidy(() => {\n      const {value, grads} = ENGINE.gradients(() => f($x), [$x], $dy);\n      if ($dy != null) {\n        util.assertShapesMatch(\n            value.shape, $dy.shape,\n            'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                'returned by f(x)');\n      }\n      checkGrads(grads);\n      return grads[0];\n    });\n  };\n}\n\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction grads(f: (...args: Tensor[]) => Tensor): (\n    args: Array<Tensor|TensorLike>, dy?: Tensor|TensorLike) => Tensor[] {\n  util.assert(\n      util.isFunction(f), () => 'The f passed in grads(f) must be a function');\n  return (args: Array<Tensor|TensorLike>, dy?: Tensor|TensorLike): Tensor[] => {\n    util.assert(\n        Array.isArray(args),\n        () => 'The args passed in grads(f)(args) must be an array ' +\n            'of `Tensor`s or `TensorLike`s');\n    // args can be of any dtype, thus null as the last argument.\n    const $args = convertToTensorArray(args, 'args', 'tf.grads', null);\n    const $dy: Tensor =\n        (dy != null) ? convertToTensor(dy, 'dy', 'tf.grads') : null;\n    return ENGINE.tidy(() => {\n      const {value, grads} = ENGINE.gradients(() => f(...$args), $args, $dy);\n      if ($dy != null) {\n        util.assertShapesMatch(\n            value.shape, $dy.shape,\n            'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n      }\n      checkGrads(grads);\n      return grads;\n    });\n  };\n}\n\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (\n    x: I, dy?: O) => {\n  value: O;\n  grad: I;\n} {\n  util.assert(\n      util.isFunction(f),\n      () => 'The f passed in valueAndGrad(f) must be a function');\n  return (x: I, dy?: O) => {\n    util.assert(\n        x instanceof Tensor,\n        () => 'The x passed in valueAndGrad(f)(x) must be a tensor');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        () => 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n    const {grads, value} = ENGINE.gradients(() => f(x), [x], dy);\n    checkGrads(grads);\n    return {grad: grads[0] as I, value};\n  };\n}\n\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction valueAndGrads<O extends Tensor>(f: (...args: Tensor[]) => O): (\n    args: Tensor[], dy?: O) => {\n  grads: Tensor[];\n  value: O;\n} {\n  util.assert(\n      util.isFunction(f),\n      () => 'The f passed in valueAndGrads(f) must be a function');\n  return (args: Tensor[], dy?: O) => {\n    util.assert(\n        Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n        () => 'The args passed in valueAndGrads(f)(args) must be array of ' +\n            'tensors');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        () => 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n    const res = ENGINE.gradients(() => f(...args), args, dy);\n    if (dy != null) {\n      util.assertShapesMatch(\n          res.value.shape, dy.shape,\n          'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n              'match the shape returned by f([x1,...])');\n    }\n    checkGrads(res.grads);\n    return res;\n  };\n}\n\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of variables to compute the gradients with respect\n *     to. Defaults to all trainable variables.\n * @returns An object with the following keys and values:\n *   - `value`: The value of the function `f`.\n *   - `grads`: A map from the names of the variables to the gradients.\n *     If the `varList` argument is provided explicitly and contains a subset of\n *     non-trainable variables, this map in the return value will contain keys\n *     that map the names of the non-trainable variables to `null`.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction variableGrads(f: () => Scalar, varList?: Variable[]):\n    {value: Scalar, grads: NamedTensorMap} {\n  util.assert(\n      util.isFunction(f),\n      () => 'The f passed in variableGrads(f) must be a function');\n  util.assert(\n      varList == null ||\n          Array.isArray(varList) && varList.every(v => v instanceof Variable),\n      () =>\n          'The varList passed in variableGrads(f, varList) must be an array ' +\n          'of variables');\n\n  const specifiedVarList = varList != null;\n  if (!specifiedVarList) {\n    // Get all of the trainable variables.\n    varList = [];\n    for (const varName in ENGINE.registeredVariables) {\n      varList.push(ENGINE.registeredVariables[varName]);\n    }\n  }\n\n  const specifiedNonTrainable: Variable[] =\n      specifiedVarList ? varList.filter(variable => !variable.trainable) : null;\n\n  // Prune non-trainable variables.\n  const originalVarCount = varList.length;\n  varList = varList.filter(variable => variable.trainable);\n  util.assert(\n      varList.length > 0,\n      () => `variableGrads() expects at least one of the input variables to ` +\n          `be trainable, but none of the ${originalVarCount} variables is ` +\n          `trainable.`);\n\n  const allowNoGradients = true;\n  const {value, grads} = ENGINE.gradients(f, varList, null, allowNoGradients);\n\n  util.assert(\n      grads.some(g => g != null),\n      () => 'Cannot find a connection between any variable and the result of ' +\n          'the loss function y=f(x). Please make sure the operations that ' +\n          'use variables are inside the function f passed to minimize().');\n  util.assert(\n      value.rank === 0,\n      () => `The f passed in variableGrads(f) must return a scalar, but it ` +\n          `returned a rank-${value.rank} tensor`);\n\n  const namedGrads: NamedTensorMap = {};\n  varList.forEach((v, i) => {\n    if (grads[i] != null) {\n      namedGrads[v.name] = grads[i];\n    }\n  });\n  if (specifiedNonTrainable != null) {\n    // If varList is explicitly provided and contains non-trainable values,\n    // add them to the returned gradients with `null` values.\n    specifiedNonTrainable.forEach(v => namedGrads[v.name] = null);\n  }\n  return {value, grads: namedGrads};\n}\n\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`\n * and returns another function `g(...inputs)` which takes the same inputs as\n * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients\n * with respect to each input of `f` are computed using `f().gradFunc`.\n *\n * The `save` function passsed to `f` should be used for saving tensors needed\n * in the gradient. And the `saved` passed to the `gradFunc` is a\n * `NamedTensorMap`, which contains those saved tensor.\n *\n * ```js\n * const customOp = tf.customGrad((x, save) => {\n *   // Save x to make sure it's available later for the gradient.\n *   save([x]);\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {\n *     value: x.square(),\n *     // Note `saved.x` which points to the `x` we saved earlier.\n *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]\n *   };\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`\n *     returns the custom gradients of `f` with respect to its inputs.\n *\n * @doc {heading: 'Training', subheading: 'Gradients'}\n */\nfunction customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n    (...args: Tensor[]) => T {\n  return ENGINE.customGrad(f);\n}\n\nfunction checkGrads(grads: Tensor[]) {\n  const numNullGradients = grads.filter(g => g == null).length;\n  if (numNullGradients > 0) {\n    throw new Error(\n        `Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n  }\n}\n\nexport {\n  customGrad,\n  variableGrads,\n  valueAndGrad,\n  valueAndGrads,\n  grad,\n  grads,\n};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Negate, NegateInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction neg_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'neg');\n\n  const inputs: NegateInputs = {x: $x};\n  return ENGINE.runKernelFunc(\n      backend => backend.neg($x), inputs as {} as NamedTensorMap,\n      null /* grad */, Negate);\n}\nexport const neg = op({neg_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Softplus, SoftplusInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction softplus_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'softplus');\n\n  const inputs: SoftplusInputs = {x: $x};\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.softplus($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Softplus);\n}\nexport const softplus = op({softplus_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../gradients';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {mul} from './mul';\nimport {neg} from './neg';\nimport {op} from './operation';\nimport {sigmoid} from './sigmoid';\nimport {softplus} from './softplus';\n\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction logSigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logSigmoid');\n\n  // Use a custom gradient to maintain previous implementation.\n  // There is no LogSigmoid kernel in TF so we can't use engine.runKernel\n  // directly\n  const customOp = customGrad((x: Tensor) => {\n    // TODO(yassogba) we can remove the chained softplus call here only\n    // after backends have modualrized softplus at which point we can call\n    // engine runKernel(..., Sotfplus, ...) directly.\n    const value = neg(softplus(neg(x)));\n\n    const gradFunc = (dy: T) => {\n      const derX = mul(dy, sigmoid(neg(x)));\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\nexport const logSigmoid = op({logSigmoid_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from '../backends/backend';\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Max, MaxAttrs, MaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {transpose} from './transpose';\n\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction max_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'max');\n  const forward: ForwardFunc<Tensor> =\n      (backend: KernelBackend, save: GradSaveFunc) => {\n        const origAxes = util.parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let maxInput = $x;\n        if (permutedAxes != null) {\n          maxInput = transpose($x, permutedAxes);\n          axes = axis_util.getInnerMostAxes(axes.length, maxInput.rank);\n        }\n\n        const y = backend.max(maxInput, axes);\n        if (permutedAxes != null) {\n          maxInput.dispose();\n        }\n\n        let res = y;\n        if (keepDims) {\n          const expandedShape = axis_util.expandShapeToKeepDim(\n              res.shape, util.parseAxisParam(axis, $x.shape));\n          res = reshape(res, expandedShape) as T;\n          y.dispose();\n        }\n\n        save([$x, res]);\n        return res;\n      };\n\n  const inputs: MaxInputs = {x: $x};\n  const attrs: MaxAttrs = {reductionIndices: axis, keepDims};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */, Max,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const max = op({max_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Sub, SubInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction sub_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'sub');\n  let $b = convertToTensor(b, 'b', 'sub');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.subtract($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n\n  const inputs: SubInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Sub) as\n      T;\n}\n\nexport const sub = op({sub_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Sum, SumAttrs, SumInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes} from './axis_util';\nimport {cast} from './cast';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {transpose} from './transpose';\n\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction sum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'sum');\n  if ($x.dtype === 'bool') {\n    $x = cast($x, 'int32');\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    save([$x]);\n    const axes = parseAxisParam(axis, $x.shape);\n\n    const permutation = getAxesPermutation(axes, $x.rank);\n    let reductionAxes = axes;\n    let permutedX = $x;\n    if (permutation != null) {\n      permutedX = transpose($x, permutation);\n      reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n    }\n    let value = backend.sum(permutedX, reductionAxes);\n    if (keepDims) {\n      const newShape = expandShapeToKeepDim(value.shape, axes);\n      value = reshape(value, newShape);\n    }\n    return value;\n  };\n\n  const inputs: SumInputs = {x: $x};\n  const attrs: SumAttrs = {axis, keepDims};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Sum,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const sum = op({sum_});\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {LogSoftmax, LogSoftmaxAttrs, LogSoftmaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {exp} from './exp';\nimport {log} from './log';\nimport {max} from './max';\nimport {op} from './operation';\nimport {sub} from './sub';\nimport {sum} from './sum';\n\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction logSoftmax_<T extends Tensor>(logits: T|TensorLike, axis = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n\n  if (axis === -1) {\n    axis = $logits.rank - 1;\n  }\n  if (axis !== $logits.rank - 1) {\n    throw Error(\n        'Log Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and axis was ${axis}`);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const keepDims = true;\n    const xMax = max(logits, axis, true);\n    const shifted = sub(logits, xMax);\n    const value =\n        sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims)));\n    save([value]);\n    return value;\n  };\n\n  const inputs: LogSoftmaxInputs = {logits: $logits};\n  const attrs: LogSoftmaxAttrs = {axis};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             LogSoftmax, attrs as {} as NamedAttrMap) as T;\n}\n\nexport const logSoftmax = op({logSoftmax_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {add} from './add';\nimport {expandShapeToKeepDim} from './axis_util';\nimport {exp} from './exp';\nimport {log} from './log';\nimport {max} from './max';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {sub} from './sub';\nimport {sum} from './sum';\n\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction logSumExp_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'logSumExp');\n\n  const axes = parseAxisParam(axis, $x.shape);\n  const xMax = max($x, axes, true /* keepDims */);\n  const a = sub($x, xMax);\n  const b = exp(a);\n  const c = sum(b, axes);\n  const d = log(c);\n  const res = add(reshape(xMax, d.shape), d);\n\n  if (keepDims) {\n    const newShape = expandShapeToKeepDim(res.shape, axes);\n    return reshape(res, newShape) as T;\n  }\n  return res as T;\n}\n\nexport const logSumExp = op({logSumExp_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {LogicalAnd, LogicalAndInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalAnd_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: LogicalAndInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             backend => backend.logicalAnd($a, $b),\n             inputs as {} as NamedTensorMap, null /* grad */, LogicalAnd) as T;\n}\n\nexport const logicalAnd = op({logicalAnd_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {LogicalNot, LogicalNotInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalNot_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n  const inputs: LogicalNotInputs = {x: $x};\n  return ENGINE.runKernelFunc(\n      backend => backend.logicalNot($x), inputs as {} as NamedTensorMap,\n      null /* grad */, LogicalNot);\n}\n\nexport const logicalNot = op({logicalNot_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {LogicalOr, LogicalOrInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalOr_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const inputs: LogicalOrInputs = {a: $a, b: $b};\n  return ENGINE.runKernelFunc(\n             backend => backend.logicalOr($a, $b),\n             inputs as {} as NamedTensorMap, null /* grad */, LogicalOr) as T;\n}\nexport const logicalOr = op({logicalOr_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {logicalAnd} from './logical_and';\nimport {logicalNot} from './logical_not';\nimport {logicalOr} from './logical_or';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction logicalXor_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  // x ^ y = (x | y) & ~(x & y)\n  return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\n\nexport const logicalXor = op({logicalXor_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {MaxPool, MaxPoolAttrs, MaxPoolInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'maxPool');\n  const dilations = 1;\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in maxPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const convInfo = conv_util.computePool2DInfo(\n        x4D.shape, filterSize, strides, 1 /* dilations */, pad,\n        dimRoundingMode);\n\n    let y;\n\n    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 &&\n        util.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n      y = x4D.clone();\n    } else {\n      y = backend.maxPool(x4D, convInfo);\n    }\n\n    save([x4D, y]);\n\n    return y;\n  };\n\n  const inputs: MaxPoolInputs = {x: x4D};\n\n  const attrs: MaxPoolAttrs = {filterSize, strides, pad, dimRoundingMode};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, MaxPool,\n      attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const maxPool = op({maxPool_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {deprecationWarn} from '../globals';\nimport {MaxPool3D, MaxPool3DAttrs, MaxPool3DInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the 3D max pooling.\n *\n * ```js\n * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);\n * const result = tf.maxPool3d(x, 2, 1, 'valid');\n * result.print();\n * ```\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     `[batch, depth, height, width, inChannels]`.\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     If `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`.\n *     If `strides` is a single number,\n *     then `strideDepth == strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1*1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPool3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filterSize: [number, number, number]|number = [1, 1, 1],\n    strides: [number, number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'NDHWC'|'NCDHW' = 'NDHWC',\n    dilations?: [number, number, number]|number): T {\n  if (dilations == null) {\n    dilations = [1, 1, 1];\n  } else {\n    deprecationWarn(\n        'dilations is deprecated, this field will be gone in ' +\n        'v3.0.0.');\n  }\n\n  const $x = convertToTensor(x, 'x', 'maxPool3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  util.assert(\n      dataFormat === 'NDHWC',\n      () => `Error in maxPool3d: Only NDHWC is currently supported, ` +\n          `but got dataFormat of ${dataFormat}`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in maxPool3d: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in maxPool3d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    if (dilations == null) {\n      dilations = [1, 1, 1];\n    }\n    const convInfo = conv_util.computePool3DInfo(\n        x5D.shape, filterSize, strides, dilations, pad, dimRoundingMode,\n        dataFormat);\n    const y = backend.maxPool3d(x5D, convInfo);\n    save([x5D, y]);\n    return y;\n  };\n\n  const inputs: MaxPool3DInputs = {x: x5D};\n\n  const attrs: MaxPool3DAttrs =\n      {filterSize, strides, pad, dimRoundingMode, dataFormat, dilations};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, MaxPool3D,\n      attrs as {} as NamedAttrMap);\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n\n  return res as T;\n}\n\nexport const maxPool3d = op({maxPool3d_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {MaxPoolWithArgmax, MaxPoolWithArgmaxAttrs, MaxPoolWithArgmaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the 2D max pooling of an image with Argmax index.\n * The indices in argmax are flattened, so that a maximum value at position `[b,\n * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if\n * include_batch_in_index is False; `((b * height + y) * width + x) * channels\n * +c` if include_batch_in_index is True.\n *\n * The indices returned are always in `[0, height) x [0, width)` before\n * flattening.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dataFormat An optional string from: \"NDHWC\", \"NCDHW\". Defaults to\n *     \"NDHWC\". Specify the data format of the input and output data. With the\n *     default format \"NDHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NDHWC\" is currently supported.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param includeBatchIndex Defaults to False. Whether to include batch\n *    dimension in flattened index of argmax.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction maxPoolWithArgmax_<T extends Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    includeBatchInIndex = false): NamedTensorMap {\n  const $x = convertToTensor(x, 'x', 'maxPoolWithArgmax');\n\n  const inputs: MaxPoolWithArgmaxInputs = {x: $x};\n  const attrs:\n      MaxPoolWithArgmaxAttrs = {filterSize, strides, pad, includeBatchInIndex};\n\n  const result = ENGINE.runKernel(\n                     MaxPoolWithArgmax, inputs as {} as NamedTensorMap,\n                     attrs as {} as NamedAttrMap) as Tensor[];\n\n  return {result: result[0], indexes: result[1]};\n}\n\nexport const maxPoolWithArgmax = op({maxPoolWithArgmax_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {makeZerosTypedArray, sizeFromShape} from '../util';\n\nimport {complex} from './complex';\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function zeros<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = zeros(shape, 'float32');\n    const imag = zeros(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {makeOnesTypedArray, sizeFromShape} from '../util';\n\nimport {complex} from './complex';\nimport {zeros} from './zeros';\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function ones<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = ones(shape, 'float32');\n    const imag = zeros(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;\n}\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {customGrad} from '../gradients';\nimport {Mean, MeanAttrs, MeanInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam, sizeFromShape} from '../util';\n\nimport {computeOutAndReduceShapes} from './axis_util';\nimport {cast} from './cast';\nimport {div} from './div';\nimport {mul} from './mul';\nimport {ones} from './ones';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {scalar} from './scalar';\nimport {sum} from './sum';\n\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction mean_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'mean');\n\n  const axes = parseAxisParam(axis, $x.shape);\n  const shapes = computeOutAndReduceShapes($x.shape, axes);\n  const reduceShape = shapes[1];\n  const reduceSize = sizeFromShape(reduceShape);\n\n  const inputs: MeanInputs = {x: $x};\n  const attrs: MeanAttrs = {axis, keepDims};\n  const forward: ForwardFunc<Tensor> = () => {\n    const reduceSizeScalar = scalar(reduceSize);\n    // Cast if needed.\n    const xReduce = reduceSizeScalar.dtype === $x.dtype ?\n        $x :\n        cast($x, reduceSizeScalar.dtype);\n    const res = div(xReduce, reduceSizeScalar);\n    return sum(res, axis, keepDims);\n  };\n\n  // Use a custom gradient to bypass 2 gradient backprops since mean is used\n  // extremely often.\n  const customOp = customGrad((x: Tensor) => {\n    const value = ENGINE.runKernelFunc(\n        forward, inputs as {} as NamedTensorMap, null /* grad */, Mean,\n        attrs as {} as NamedAttrMap);\n\n    const gradFunc = (dy: Tensor) => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach(axis => {\n        expandedDyShape[axis] = 1;\n      });\n      const expandedDy = reshape(dy, expandedDyShape);\n      const derX = div(mul(expandedDy, ones(x.shape, 'float32')), reduceSize);\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\n\nexport const mean = op({mean_});\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {KernelBackend} from '../backends/backend';\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Min, MinAttrs, MinInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {transpose} from './transpose';\n\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction min_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'min');\n\n  const forward: ForwardFunc<Tensor> =\n      (backend: KernelBackend, save: GradSaveFunc) => {\n        const origAxes = parseAxisParam(axis, $x.shape);\n        let axes = origAxes;\n        const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n        let minInput = $x;\n        if (permutedAxes != null) {\n          minInput = transpose($x, permutedAxes);\n          axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n        }\n\n        const y = backend.min(minInput, axes);\n        if (permutedAxes != null) {\n          minInput.dispose();\n        }\n\n        let res = y;\n        if (keepDims) {\n          const expandedShape =\n              axis_util.expandShapeToKeepDim(res.shape, origAxes);\n          res = reshape(y, expandedShape) as T;\n          y.dispose();\n        }\n\n        save([$x, res]);\n        return res;\n      };\n\n  const inputs: MinInputs = {x: $x};\n  const attrs: MinAttrs = {axis, keepDims};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */, Min,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const min = op({min_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Minimum, MinimumInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction minimum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'minimum');\n  let $b = convertToTensor(b, 'b', 'minimum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = cast($a, 'int32');\n    $b = cast($b, 'int32');\n  }\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.minimum($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n  const inputs: MinimumInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Minimum) as T;\n}\n\nexport const minimum = op({minimum_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {MirrorPad, MirrorPadAttrs, MirrorPadInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Pads a `tf.Tensor` using mirror padding.\n *\n * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.\n *\n * ```js\n * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);\n * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * In \"reflect\" mode, the padded regions do not include the borders,\n * while in \"symmetric\" mode the padded regions do include the borders.\n * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,\n * then the output is `[1, 2, 3, 2, 1]` in \"reflect\" mode, and\n * `[1, 2, 3, 3, 2]` in \"symmetric\" mode.\n * If `mode` is \"reflect\" then both `paddings[D, 0]` and `paddings[D, 1]`\n * must be no greater than `x.shape[D] - 1`. If mode is \"symmetric\"\n * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than\n * `x.shape[D]`\n * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction mirrorPad_<T extends Tensor>(\n    x: T|TensorLike, paddings: Array<[number, number]>,\n    mode: 'reflect'|'symmetric'): T {\n  util.assert(\n      mode === 'reflect' || mode === 'symmetric',\n      () => `Invalid mode. Mode must be either reflect or symmetric. ` +\n          `Got ${mode}.`);\n\n  const $x = convertToTensor(x, 'x', 'mirrorPad');\n  if ($x.rank === 0) {\n    throw new Error(\n        'mirrorPad(scalar) is not defined. ' +\n        'Pass non-scalar to mirrorPad');\n  }\n  util.assert(\n      paddings.length === $x.rank,\n      () => `Padding doesn't match input. Must be ${$x.rank}. ` +\n          `Got ${paddings.length}.`);\n  const shapeOffset = mode === 'reflect' ? 1 : 0;\n  for (let i = 0; i < $x.rank; i++) {\n    util.assert(\n        paddings[i].length === 2,\n        () => `Invalid number of paddings. Must be length of 2 each.`);\n    util.assert(\n        paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset &&\n            paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset,\n        () => `Padding in dimension ${i} cannot be greater than or equal ` +\n            `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` +\n            `shape ${$x.shape}`);\n  }\n\n  const attrs: MirrorPadAttrs = {paddings, mode};\n  const inputs: MirrorPadInputs = {x: $x};\n  return ENGINE.runKernel(\n             MirrorPad, inputs as {} as NamedTensorMap,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const mirrorPad = op({mirrorPad_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Mod, ModInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction mod_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mod');\n  let $b = convertToTensor(b, 'b', 'mod');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.mod($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n  const inputs: ModInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Mod) as T;\n}\n\nexport const mod = op({mod_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction square_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'square');\n  const attrs = {};\n  const inputsToSave = [$x];\n  const outputsToSave: boolean[] = [];\n  return ENGINE.runKernelFunc((backend, save) => {\n    save([$x]);\n    return backend.square($x);\n  }, {x: $x}, null /* grad */, 'Square', attrs, inputsToSave, outputsToSave);\n}\n\nexport const square = op({square_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {expandShapeToKeepDim} from './axis_util';\nimport {cast} from './cast';\nimport {mean} from './mean';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {square} from './square';\nimport {sub} from './sub';\n\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction moments_(\n    x: Tensor|TensorLike, axis: number|number[] = null,\n    keepDims = false): {mean: Tensor, variance: Tensor} {\n  x = convertToTensor(x, 'x', 'moments');\n  const axes = parseAxisParam(axis, x.shape);\n  const xMean = mean(x, axes, keepDims);\n  let keepDimsShape = xMean.shape;\n  if (!keepDims) {\n    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n  }\n  const devSquared =\n      square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape)));\n  const variance = mean(devSquared, axes, keepDims);\n  return {mean: xMean, variance};\n}\n\nexport const moments = op({moments_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * @docalias (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D]\n */\nexport type LSTMCellFunc = {\n  (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D];\n};\n\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n *\n * @doc {heading: 'Operations', subheading: 'RNN'}\n */\nfunction multiRNNCell_(\n    lstmCells: LSTMCellFunc[], data: Tensor2D|TensorLike,\n    c: Array<Tensor2D|TensorLike>,\n    h: Array<Tensor2D|TensorLike>): [Tensor2D[], Tensor2D[]] {\n  const $data = convertToTensor(data, 'data', 'multiRNNCell');\n  const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n  const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n\n  let input = $data;\n  const newStates = [];\n  for (let i = 0; i < lstmCells.length; i++) {\n    const output = lstmCells[i](input, $c[i], $h[i]);\n    newStates.push(output[0]);\n    newStates.push(output[1]);\n    input = output[1];\n  }\n  const newC: Tensor2D[] = [];\n  const newH: Tensor2D[] = [];\n  for (let i = 0; i < newStates.length; i += 2) {\n    newC.push(newStates[i]);\n    newH.push(newStates[i + 1]);\n  }\n  return [newC, newH];\n}\nexport const multiRNNCell = op({multiRNNCell_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction multinomial_(\n    logits: Tensor1D|Tensor2D|TensorLike, numSamples: number, seed?: number,\n    normalized = false): Tensor1D|Tensor2D {\n  const $logits = convertToTensor(logits, 'logits', 'multinomial');\n  const numOutcomes = $logits.size;\n  const origRank = $logits.rank;\n  if (numOutcomes < 2) {\n    throw new Error(\n        `Error in multinomial: you need at least 2 outcomes, but got ` +\n        `${numOutcomes}.`);\n  }\n  if (origRank > 2) {\n    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n  }\n  seed = seed || Math.random();\n  const logits2D: Tensor2D =\n      origRank === 1 ? reshape($logits, [1, -1]) : $logits as Tensor2D;\n  const res = ENGINE.runKernelFunc(\n      backend => backend.multinomial(logits2D, normalized, numSamples, seed),\n      {logits2D});\n  // tslint:disable-next-line:no-unnecessary-type-assertion\n  return origRank === 1 ? reshape(res, [res.size]) as Tensor1D : res;\n}\n\nexport const multinomial = op({multinomial_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {NotEqual, NotEqualInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nfunction notEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'notEqual');\n  let $b = convertToTensor(b, 'b', 'notEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = (backend) => backend.notEqual($a, $b);\n\n  const inputs: NotEqualInputs = {a: $a, b: $b};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             NotEqual) as T;\n}\n\nexport const notEqual = op({notEqual_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Real, RealInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction real_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'real');\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    return backend.real($input);\n  };\n\n  const inputs: RealInputs = {input: $input};\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Real) as T;\n}\n\nexport const real = op({real_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {OnesLike, OnesLikeInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {complex} from './complex';\nimport {imag} from './imag';\nimport {op} from './operation';\nimport {real} from './real';\nimport {zerosLike} from './zeros_like';\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction onesLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'onesLike');\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    if ($x.dtype === 'complex64') {\n      const r = onesLike(real($x));\n      const i = zerosLike(imag($x));\n      return complex(r, i);\n    }\n\n    return backend.onesLike($x);\n  };\n\n  const inputs: OnesLikeInputs = {x: $x};\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             OnesLike) as T;\n}\n\nexport const onesLike = op({onesLike_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {matMul} from './mat_mul';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction outerProduct_(\n    v1: Tensor1D|TensorLike, v2: Tensor1D|TensorLike): Tensor2D {\n  const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n  const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n\n  util.assert(\n      $v1.rank === 1 && $v2.rank === 1,\n      () => `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n          `${$v1.rank} and ${$v2.rank}.`);\n\n  const v12D = reshape($v1, [-1, 1]);\n  const v22D = reshape($v2, [1, -1]);\n  return matMul(v12D, v22D);\n}\n\nexport const outerProduct = op({outerProduct_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {PadV2, PadV2Attrs, PadV2Inputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,\n * refer to `tf.mirrorPad`\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction pad_<T extends Tensor>(\n    x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {\n  const $x = convertToTensor(x, 'x', 'pad');\n  if ($x.rank === 0) {\n    throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n  }\n  const forward: ForwardFunc<T> = (backend, save) => {\n    save([$x]);\n    return backend.pad($x, paddings, constantValue);\n  };\n\n  const attrs: PadV2Attrs = {paddings, constantValue};\n  const inputs: PadV2Inputs = {x: $x};\n  return ENGINE.runKernelFunc(\n      forward, inputs as unknown as NamedTensorMap, null /* grad */, PadV2,\n      attrs as unknown as NamedAttrMap);\n}\n\nexport const pad = op({pad_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor1D} from '../tensor';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\nimport {op} from './operation';\nimport {pad} from './pad';\n\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(\n    x: Tensor1D|TensorLike, paddings: [number, number],\n    constantValue = 0): Tensor1D {\n  assert(\n      paddings.length === 2,\n      () => 'Invalid number of paddings. Must be length of 2.');\n  return pad(x, [paddings], constantValue);\n}\n\nexport const pad1d = op({pad1d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor2D} from '../tensor';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\nimport {op} from './operation';\nimport {pad} from './pad';\n\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(\n    x: Tensor2D|TensorLike, paddings: [[number, number], [number, number]],\n    constantValue = 0): Tensor2D {\n  assert(\n      paddings.length === 2 && paddings[0].length === 2 &&\n          paddings[1].length === 2,\n      () => 'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\nexport const pad2d = op({pad2d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D} from '../tensor';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\nimport {op} from './operation';\nimport {pad} from './pad';\n\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(\n    x: Tensor3D|TensorLike,\n    paddings: [[number, number], [number, number], [number, number]],\n    constantValue = 0): Tensor3D {\n  assert(\n      paddings.length === 3 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2,\n      () => 'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\nexport const pad3d = op({pad3d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor4D} from '../tensor';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\nimport {op} from './operation';\nimport {pad} from './pad';\n\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(\n    x: Tensor4D|TensorLike,\n    paddings:\n        [\n          [number, number], [number, number], [number, number], [number, number]\n        ],\n    constantValue = 0): Tensor4D {\n  assert(\n      paddings.length === 4 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2 &&\n          paddings[3].length === 2,\n      () => 'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\nexport const pad4d = op({pad4d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {SpaceToBatchND, SpaceToBatchNDAttrs, SpaceToBatchNDInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction spaceToBatchND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], paddings: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      () => `input rank ${$x.rank} should be > than [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      paddings.length === blockShape.length,\n      () => `paddings.shape[0] ${\n          paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n\n  util.assert(\n      $x.shape.reduce(\n          (a, b, i) => {\n            if (i > 0 && i <= blockShape.length) {\n              return a &&\n                  ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                       blockShape[i - 1] ===\n                   0);\n            }\n            return a;\n          },\n          true),\n      () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${\n          paddings.toString()} must be divisible by blockShapes ${\n          blockShape.toString()}`);\n\n  const forward: ForwardFunc<T> = backend =>\n      backend.spaceToBatchND($x, blockShape, paddings);\n\n  const inputs: SpaceToBatchNDInputs = {x: $x};\n  const attrs: SpaceToBatchNDAttrs = {blockShape, paddings};\n\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* gradient */,\n      SpaceToBatchND, attrs as {} as NamedAttrMap);\n}\n\nexport const spaceToBatchND = op({spaceToBatchND_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {avgPool} from './avg_pool';\nimport {batchToSpaceND} from './batch_to_space_nd';\nimport * as conv_util from './conv_util';\nimport {maxPool} from './max_pool';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {spaceToBatchND} from './space_to_batch_nd';\n\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction pool_<T extends Tensor3D|Tensor4D>(\n    input: T|TensorLike, windowShape: [number, number]|number,\n    poolingType: 'avg'|'max', pad: 'valid'|'same'|number,\n    dilations?: [number, number]|number, strides?: [number, number]|number) {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad === 0) {\n    pad = 'valid';\n  }\n\n  const $x = convertToTensor(input, 'x', 'maxPool');\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in pool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, windowShape, strides, dilations, pad);\n  const dilation: [number, number] =\n      [convInfo.dilationHeight, convInfo.dilationWidth];\n\n  // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n  // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n\n  let basePadding: number[][];\n  if (pad === 'same') {\n    basePadding = withSpaceToBatchBasePaddings(\n        [convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(\n      [convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad : 'valid';\n  const convertedX =\n      isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n\n  const forwardOp = poolingType === 'avg' ?\n      () => avgPool(convertedX, windowShape, strides, convertedPad) :\n      () => maxPool(convertedX, windowShape, strides, convertedPad);\n  const y = forwardOp();\n\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n\n  return res as T;\n}\n\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(\n    inputShape: [number, number], blockShape: [number, number],\n    basePadding: number[][]) {\n  const padStart = basePadding.map(b => b[0]);\n  const origPadEnd = basePadding.map(b => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n  return [paddings, crops];\n}\n\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(\n    filterShape: [number, number], dilation: [number, number]) {\n  // Spatial dimensions of the filters and the upsampled filters in which we\n  // introduce (rate - 1) zeros between consecutive filter values.\n  const dilatedFilterShape = filterShape.map((s, i) => {\n    return s + (s - 1) * (dilation[i] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map(s => s - 1);\n\n  // When padding is odd, we pad more at end, following the same\n  // convention as conv2d.\n  const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n  return padExtraShape.map((_, i) => {\n    return [padExtraStart[i], padExtraEnd[i]];\n  });\n}\n\nexport const pool = op({pool_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Pow, PowInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction pow_<T extends Tensor>(\n    base: Tensor|TensorLike, exp: Tensor|TensorLike): T {\n  let $base = convertToTensor(base, 'base', 'pow');\n  let $exp = convertToTensor(exp, 'exp', 'pow');\n  [$base, $exp] = makeTypesMatch($base, $exp);\n\n  const inputs: PowInputs = {a: $base, b: $exp};\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const y = backend.pow($base, $exp);\n    save([$base, $exp, y]);\n    return y;\n  };\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Pow) as T;\n}\n\nexport const pow = op({pow_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Prelu, PreluInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction prelu_<T extends Tensor>(x: T|TensorLike, alpha: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'prelu');\n  const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.prelu($x, $alpha);\n    save([$x, $alpha]);\n    return res;\n  };\n\n  const inputs: PreluInputs = {x: $x, alpha: $alpha};\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Prelu) as\n      T;\n}\n\nexport const prelu = op({prelu_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Prod, ProdAttrs, ProdInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {expandShapeToKeepDim, getAxesPermutation, getInnerMostAxes} from './axis_util';\nimport {cast} from './cast';\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {transpose} from './transpose';\n\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n *\n * @doc {heading: 'Operations', subheading: 'Reduction'}\n */\nfunction prod_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'prod');\n  if ($x.dtype === 'bool') {\n    // bool is not an allowed type for the underlying kernel.\n    $x = cast($x, 'int32');\n  }\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    const axes = parseAxisParam(axis, $x.shape);\n\n    const permutation = getAxesPermutation(axes, $x.rank);\n    let reductionAxes = axes;\n    let permutedX = $x;\n    if (permutation != null) {\n      permutedX = transpose($x, permutation);\n      reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);\n    }\n    let value = backend.prod(permutedX, reductionAxes);\n    if (keepDims) {\n      const newShape = expandShapeToKeepDim(value.shape, axes);\n      value = reshape(value, newShape);\n    }\n\n    return value as T;\n  };\n\n  const inputs: ProdInputs = {x: $x};\n  const attrs: ProdAttrs = {axis, keepDims};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Prod,\n             attrs as {} as NamedAttrMap) as T;\n}\n\nexport const prod = op({prod_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {sizeFromShape} from '../util';\n\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_<R extends Rank>(\n    shape: ShapeMap[R], randFunction: () => number,\n    dtype?: DataType): Tensor<R> {\n  const size = sizeFromShape(shape);\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  for (let i = 0; i < size; i++) {\n    values[i] = randFunction();\n  }\n  return ENGINE.makeTensor(values, shape, dtype) as Tensor<R>;\n}\n\nexport const rand = op({rand_});\n", "// 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", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\n\nimport {expectNumbersClose, testEpsilon} from '../test_util';\nimport {TypedArray} from '../types';\n\nexport interface RandomBase {\n  nextValue(): number;\n}\n\nexport interface RandomGamma {\n  nextValue(): number;\n}\n\nexport interface RandNormalDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\nexport interface RandGammaDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss implements RandomBase {\n  private mean: number;\n  private stdDev: number;\n  private nextVal: number;\n  private dtype?: keyof RandNormalDataTypes;\n  private truncated?: boolean;\n  private upper?: number;\n  private lower?: number;\n  private random: seedrandom.prng;\n\n  constructor(\n      mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,\n      truncated?: boolean, seed?: number) {\n    this.mean = mean;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n\n  /** Returns next sample from a Gaussian distribution. */\n  public nextValue(): number {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n\n    let resultX: number, resultY: number;\n    let isValid = false;\n    while (!isValid) {\n      let v1: number, v2: number, s: number;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s = v1 * v1 + v2 * v2;\n      } while (s >= 1 || s === 0);\n\n      const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n      resultX = this.mean + this.stdDev * v1 * mul;\n      resultY = this.mean + this.stdDev * v2 * mul;\n\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n\n  /** Handles proper rounding for non-floating-point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype == null || this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  /** Returns true if less than 2-standard-deviations from the mean. */\n  private isValidTruncated(value: number): boolean {\n    return value <= this.upper && value >= this.lower;\n  }\n}\n\n// Marsaglia, George, and Wai Wan Tsang. 2000. \"A Simple Method for Generating\n// Gamma Variables.\"\nexport class RandGamma implements RandomGamma {\n  private alpha: number;\n  private beta: number;\n  private d: number;\n  private c: number;\n  private dtype?: keyof RandGammaDataTypes;\n  private randu: seedrandom.prng;\n  private randn: MPRandGauss;\n\n  constructor(\n      alpha: number, beta: number, dtype: keyof RandGammaDataTypes,\n      seed?: number) {\n    this.alpha = alpha;\n    this.beta = 1 / beta;  // convert rate to scale parameter\n    this.dtype = dtype;\n\n    const seedValue = seed ? seed : Math.random();\n    this.randu = seedrandom.alea(seedValue.toString());\n    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n\n    if (alpha < 1) {\n      this.d = alpha + (2 / 3);\n    } else {\n      this.d = alpha - (1 / 3);\n    }\n    this.c = 1 / Math.sqrt(9 * this.d);\n  }\n\n  /** Returns next sample from a gamma distribution. */\n  public nextValue(): number {\n    let x2: number, v0: number, v1: number, x: number, u: number, v: number;\n    while (true) {\n      do {\n        x = this.randn.nextValue();\n        v = 1 + (this.c * x);\n      } while (v <= 0);\n      v *= v * v;\n      x2 = x * x;\n      v0 = 1 - (0.331 * x2 * x2);\n      v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));\n      u = this.randu();\n      if (u < v0 || Math.log(u) < v1) {\n        break;\n      }\n    }\n    v = (1 / this.beta) * this.d * v;\n    if (this.alpha < 1) {\n      v *= Math.pow(this.randu(), 1 / this.alpha);\n    }\n    return this.convertValue(v);\n  }\n  /** Handles proper rounding for non-floating-point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n}\n\nexport class UniformRandom implements RandomBase {\n  private min: number;\n  private range: number;\n  private random: seedrandom.prng;\n  private dtype?: keyof RandNormalDataTypes;\n\n  constructor(\n      min = 0, max = 1, dtype?: keyof RandNormalDataTypes,\n      seed?: string|number) {\n    this.min = min;\n    this.range = max - min;\n    this.dtype = dtype;\n    if (seed == null) {\n      seed = Math.random();\n    }\n    if (typeof seed === 'number') {\n      seed = seed.toString();\n    }\n\n    if (!this.canReturnFloat() && this.range <= 1) {\n      throw new Error(\n          `The difference between ${min} - ${max} <= 1 and dtype is not float`);\n    }\n    this.random = seedrandom.alea(seed);\n  }\n\n  /** Handles proper rounding for non floating point numbers. */\n  private canReturnFloat = () =>\n      (this.dtype == null || this.dtype === 'float32');\n\n  private convertValue(value: number): number {\n    if (this.canReturnFloat()) {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  nextValue() {\n    return this.convertValue(this.min + this.range * this.random());\n  }\n}\n\nexport function jarqueBeraNormalityTest(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test\n  const n = values.length;\n  const s = skewness(values);\n  const k = kurtosis(values);\n  const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));\n  // JB test requires 2-degress of freedom from Chi-Square @ 0.95:\n  // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm\n  const CHI_SQUARE_2DEG = 5.991;\n  if (jb > CHI_SQUARE_2DEG) {\n    throw new Error(`Invalid p-value for JB: ${jb}`);\n  }\n}\n\nexport function expectArrayInMeanStdRange(\n    actual: TypedArray|number[], expectedMean: number, expectedStdDev: number,\n    epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = testEpsilon();\n  }\n  const actualMean = mean(actual);\n  expectNumbersClose(actualMean, expectedMean, epsilon);\n  expectNumbersClose(\n      standardDeviation(actual, actualMean), expectedStdDev, epsilon);\n}\n\nfunction mean(values: TypedArray|number[]) {\n  let sum = 0;\n  for (let i = 0; i < values.length; i++) {\n    sum += values[i];\n  }\n  return sum / values.length;\n}\n\nfunction standardDeviation(values: TypedArray|number[], mean: number) {\n  let squareDiffSum = 0;\n  for (let i = 0; i < values.length; i++) {\n    const diff = values[i] - mean;\n    squareDiffSum += diff * diff;\n  }\n  return Math.sqrt(squareDiffSum / values.length);\n}\n\nfunction kurtosis(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Kurtosis\n  const valuesMean = mean(values);\n  const n = values.length;\n  let sum2 = 0;\n  let sum4 = 0;\n  for (let i = 0; i < n; i++) {\n    const v = values[i] - valuesMean;\n    sum2 += Math.pow(v, 2);\n    sum4 += Math.pow(v, 4);\n  }\n  return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);\n}\n\nfunction skewness(values: TypedArray|number[]) {\n  // https://en.wikipedia.org/wiki/Skewness\n  const valuesMean = mean(values);\n  const n = values.length;\n  let sum2 = 0;\n  let sum3 = 0;\n  for (let i = 0; i < n; i++) {\n    const v = values[i] - valuesMean;\n    sum2 += Math.pow(v, 2);\n    sum3 += Math.pow(v, 3);\n  }\n  return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {RandGamma} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a gamma distribution.\n *\n * ```js\n * tf.randomGamma([2, 2], 1).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param alpha The shape parameter of the gamma distribution.\n * @param beta The inverse scale parameter of the gamma distribution. Defaults\n *     to 1.\n * @param dtype The data type of the output. Defaults to float32.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomGamma_<R extends Rank>(\n    shape: ShapeMap[R], alpha: number, beta = 1,\n    dtype: 'float32'|'int32' = 'float32', seed?: number): Tensor<R> {\n  if (beta == null) {\n    beta = 1;\n  }\n  if (dtype == null) {\n    dtype = 'float32';\n  }\n  if (dtype !== 'float32' && dtype !== 'int32') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const rgamma = new RandGamma(alpha, beta, dtype, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = rgamma.nextValue();\n  }\n  return res.toTensor();\n}\n\nexport const randomGamma = op({randomGamma_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\nexport const randomNormal = op({randomNormal_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {UniformRandom} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Random'}\n */\nfunction randomUniform_<R extends Rank>(\n    shape: ShapeMap[R], minval = 0, maxval = 1, dtype: DataType = 'float32',\n    seed?: number|string): Tensor<R> {\n  const res = buffer(shape, dtype);\n  const random = new UniformRandom(minval, maxval, null, seed);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = random.nextValue();\n  }\n  return res.toTensor();\n}\n\nexport const randomUniform = op({randomUniform_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike1D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor1d(values: TensorLike1D, dtype?: DataType): Tensor1D {\n  assertNonNull(values);\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 1) {\n    throw new Error('tensor1d() requires values to be a flat/TypedArray');\n  }\n  const shape: number[] = null;\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor1D;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Range, RangeAttrs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {makeZerosTypedArray} from '../util';\n\nimport {tensor1d} from './tensor1d';\nimport {zeros} from './zeros';\n\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.sv\n *\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function range(\n    start: number, stop: number, step = 1,\n    dtype: 'float32'|'int32' = 'float32'): Tensor1D {\n  if (step === 0) {\n    throw new Error('Cannot have a step of zero');\n  }\n\n  const forward: ForwardFunc<Tensor> = () => {\n    const sameStartStop = start === stop;\n    const increasingRangeNegativeStep = start < stop && step < 0;\n    const decreasingRangePositiveStep = stop < start && step > 1;\n\n    if (sameStartStop || increasingRangeNegativeStep ||\n        decreasingRangePositiveStep) {\n      return zeros([0], dtype);\n    }\n\n    const numElements = Math.abs(Math.ceil((stop - start) / step));\n    const values = makeZerosTypedArray(numElements, dtype);\n\n    if (stop < start && step === 1) {\n      // Auto adjust the step's sign if it hasn't been set\n      // (or was set to 1)\n      step = -1;\n    }\n\n    values[0] = start;\n    for (let i = 1; i < values.length; i++) {\n      values[i] = values[i - 1] + step;\n    }\n\n    return tensor1d(values, dtype);\n  };\n\n  const attrs: RangeAttrs = {start, stop, step, dtype};\n\n  return ENGINE.runKernelFunc(\n             forward, {} /* inputs */, null /* grad */, Range,\n             attrs as {} as NamedAttrMap) as Tensor1D;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Reciprocal, ReciprocalInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction reciprocal_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'reciprocal');\n\n  const inputs: ReciprocalInputs = {x: $x};\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.reciprocal($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Reciprocal);\n}\nexport const reciprocal = op({reciprocal_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Relu, ReluInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\nimport { cast } from './cast';\n\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'relu');\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    save([$x]);\n\n    if ($x.dtype === 'bool') {\n      return cast($x, 'int32');\n    }\n\n    return backend.relu($x);\n  };\n\n  const inputs: ReluInputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Relu) as\n      T;\n}\n\nexport const relu = op({relu_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Relu6, Relu6Inputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {cast} from './cast';\nimport {op} from './operation';\n\n/**\n * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 8]);\n *\n * x.relu6().print();  // or tf.relu6(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction relu6_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'relu6');\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    save([$x]);\n\n    if ($x.dtype === 'bool') {\n      return cast($x, 'int32');\n    }\n\n    return backend.relu6($x);\n  };\n\n  const inputs: Relu6Inputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Relu6) as\n      T;\n}\n\nexport const relu6 = op({relu6_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Reverse, ReverseAttrs, ReverseInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {clone} from './clone';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction reverse_<T extends Tensor>(\n    x: T|TensorLike, axis?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'reverse');\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    const axes = parseAxisParam(axis, $x.shape);\n    if ($x.rank === 0) {\n      return clone($x);\n    }\n    const res = backend.reverse($x, axes);\n    return reshape(res, $x.shape);\n  };\n\n  const inputs: ReverseInputs = {x: $x};\n  const attrs: ReverseAttrs = {dims: axis};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* gradient */,\n             Reverse, attrs as {} as NamedAttrMap) as T;\n}\n\nexport const reverse = op({reverse_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport {reverse} from './reverse';\n\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x: Tensor1D|TensorLike): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert(\n      $x.rank === 1,\n      () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n  return reverse($x, 0);\n}\n\nexport const reverse1d = op({reverse1d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport {reverse} from './reverse';\n\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x: Tensor2D|TensorLike, axis?: number|number[]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert(\n      $x.rank === 2,\n      () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\nexport const reverse2d = op({reverse2d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport {reverse} from './reverse';\n\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x: Tensor3D|TensorLike, axis?: number|number[]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert(\n      $x.rank === 3,\n      () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\nexport const reverse3d = op({reverse3d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport {reverse} from './reverse';\n\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x: Tensor4D|TensorLike, axis?: number|number[]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert(\n      $x.rank === 4,\n      () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\nexport const reverse4d = op({reverse4d_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Round, RoundInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction round_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'round');\n  const inputs: RoundInputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n      (backend) => backend.round($x), inputs as {} as NamedTensorMap,\n      null /* grad */, Round);\n}\n\nexport const round = op({round_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Rsqrt, RsqrtInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction rsqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'rsqrt');\n\n  const inputs: RsqrtInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.rsqrt($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Rsqrt);\n}\nexport const rsqrt = op({rsqrt_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Selu, SeluInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction selu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'selu');\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.selu($x);\n    save([$x]);\n    return res;\n  };\n\n  const inputs: SeluInputs = {x: $x};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, Selu) as\n      T;\n}\n\nexport const selu = op({selu_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {conv2d} from './conv2d';\nimport {depthwiseConv2d} from './depthwise_conv2d';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n *\n * @doc {heading: 'Operations', subheading: 'Convolution'}\n */\nfunction separableConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,\n    pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n    pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'separableConv2d');\n  const $depthwiseFilter =\n      convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n  const $pointwiseFilter =\n      convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n\n  if (dataFormat === 'NCHW') {\n    throw new Error(\n        'separableConv2d currently does not support dataFormat NCHW; only ' +\n        'NHWC is supported');\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in separableConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $depthwiseFilter.rank === 4,\n      () => `Error in separableConv2d: depthwise filter must be rank 4, but ` +\n          `got rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.rank === 4,\n      () => `Error in separableConv2d: pointwise filter must be rank 4, but ` +\n          `got rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.shape[0] === 1,\n      () =>\n          `Error in separableConv2d: the first dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  util.assert(\n      $pointwiseFilter.shape[1] === 1,\n      () => `Error in separableConv2d: the second dimension of pointwise ` +\n          `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  util.assert(\n      $pointwiseFilter.shape[2] === inChannels * channelMultiplier,\n      () =>\n          `Error in separableConv2d: the third dimension of pointwise filter ` +\n          `must be ${inChannels * channelMultiplier}, ` +\n          `but got ${$pointwiseFilter.shape[2]}.`);\n\n  const depthwise = depthwiseConv2d(\n      x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res =\n      conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const separableConv2d = op({separableConv2d_});\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor, TensorBuffer} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nasync function setdiff1dAsync_(\n    x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {\n  const $x = convertToTensor(x, 'x', 'setdiff1d');\n  const $y = convertToTensor(y, 'y', 'setdiff1d');\n\n  util.assert(\n      $x.dtype === $y.dtype,\n      () => `x and y should have the same dtype, but got x (${\n          $x.dtype}) and y (${$y.dtype}).`);\n\n  util.assert(\n      $x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n\n  util.assert(\n      $y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n\n  const xVals = await $x.data();\n  const yVals = await $y.data();\n  const ySet = new Set(yVals);\n\n  let outputSize = 0;\n  for (let i = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      outputSize++;\n    }\n  }\n\n  const buffer = new TensorBuffer([outputSize], $x.dtype);\n  const indices = new TensorBuffer([outputSize], 'int32');\n  for (let i = 0, p = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      buffer.values[p] = xVals[i];\n      indices.values[p] = i;\n      p++;\n    }\n  }\n  return [buffer.toTensor(), indices.toTensor()];\n}\nexport const setdiff1dAsync = setdiff1dAsync_;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sign, SignInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sign_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sign');\n  const inputs: SignInputs = {x: $x};\n  return ENGINE.runKernelFunc(\n      backend => backend.sign($x), inputs as {} as NamedTensorMap,\n      null /* grad */, Sign);\n}\nexport const sign = op({sign_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sin, SinInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sin');\n\n  const inputs: SinInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.sin($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Sin);\n}\nexport const sin = op({sin_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sinh, SinhInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sinh');\n  const inputs: SinhInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.sinh($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Sinh);\n}\nexport const sinh = op({sinh_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {slice} from './slice';\n\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(\n    x: Tensor1D|TensorLike, begin: number, size: number): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'slice1d');\n  util.assert(\n      $x.rank === 1,\n      () =>\n          `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, [begin], [size]);\n}\nexport const slice1d = op({slice1d_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {slice} from './slice';\n\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(\n    x: Tensor2D|TensorLike, begin: [number, number],\n    size: [number, number]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'slice2d');\n  util.assert(\n      $x.rank === 2,\n      () =>\n          `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nexport const slice2d = op({slice2d_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor3D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {slice} from './slice';\n\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(\n    x: Tensor3D|TensorLike, begin: [number, number, number],\n    size: [number, number, number]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'slice3d');\n  util.assert(\n      $x.rank === 3,\n      () =>\n          `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nexport const slice3d = op({slice3d_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {slice} from './slice';\n\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(\n    x: Tensor4D|TensorLike, begin: [number, number, number, number],\n    size: [number, number, number, number]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'slice4d');\n  util.assert(\n      $x.rank === 4,\n      () =>\n          `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nexport const slice4d = op({slice4d_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Softmax, SoftmaxAttrs, SoftmaxInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction softmax_<T extends Tensor>(logits: T|TensorLike, dim = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'softmax', 'float32');\n\n  if (dim === -1) {\n    dim = $logits.rank - 1;\n  }\n  if (dim !== $logits.rank - 1) {\n    throw Error(\n        'Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and dim was ${dim}`);\n  }\n\n  const inputs: SoftmaxInputs = {logits: $logits};\n  const attrs: SoftmaxAttrs = {dim};\n\n  return ENGINE.runKernelFunc(\n      (backend, save) => {\n        const y = backend.softmax($logits, dim);\n        save([y]);\n        return y;\n      },\n      inputs as {} as NamedTensorMap, null /* grad */, Softmax,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const softmax = op({softmax_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {FFT, FFTInputs} from '../../kernel_names';\nimport {Tensor} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {assert} from '../../util';\nimport {op} from '../operation';\n\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      () => `The dtype for tf.spectral.fft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  const inputs: FFTInputs = {input};\n\n  return ENGINE.runKernelFunc(backend => {\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n\n    const input2D = input.as2D(batch, innerDimensionSize);\n    const result = backend.fft(input2D);\n    return result.reshape(input.shape);\n  }, inputs as {} as NamedTensorMap, null /* gradient */, FFT);\n}\n\nexport const fft = op({fft_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {IFFT, IFFTInputs} from '../../kernel_names';\nimport {Tensor, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {assert} from '../../util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      () => `The dtype for tf.spectral.ifft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  const inputs: IFFTInputs = {input};\n\n  return ENGINE.runKernelFunc(backend => {\n    // Collapse all outer dimensions to a single batch dimension.\n    const innerDimensionSize = input.shape[input.shape.length - 1];\n    const batch = input.size / innerDimensionSize;\n\n    const input2D: Tensor2D = reshape(input, [batch, innerDimensionSize]);\n    const result = backend.ifft(input2D);\n    return reshape(result, input.shape);\n  }, inputs as {} as NamedTensorMap, null /* gradient */, IFFT);\n}\n\nexport const ifft = op({ifft_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor2D} from '../../tensor';\nimport {complex} from '../complex';\nimport {concat} from '../concat';\nimport {imag} from '../imag';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {real} from '../real';\nimport {reshape} from '../reshape';\nimport {reverse} from '../reverse';\nimport {scalar} from '../scalar';\nimport {slice} from '../slice';\n\nimport {ifft} from './ifft';\n\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([0, 0, 0]);\n * const x = tf.complex(real, imag);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input: Tensor): Tensor {\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n  let ret: Tensor;\n  if (innerDimensionSize <= 2) {\n    const complexInput = reshape(input, [batch, innerDimensionSize]);\n    ret = ifft(complexInput);\n  } else {\n    // The length of unique components of the DFT of a real-valued signal\n    // is 2 * (input_len - 1)\n    const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n    const realInput = reshape(real(input), [batch, innerDimensionSize]);\n    const imagInput = reshape(imag(input), [batch, innerDimensionSize]);\n\n    const realConjugate =\n        reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n    const imagConjugate: Tensor2D = mul(\n        reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1),\n        scalar(-1));\n\n    const r = concat([realInput, realConjugate], 1);\n    const i = concat([imagInput, imagConjugate], 1);\n    const complexInput =\n        reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n    ret = ifft(complexInput);\n  }\n  ret = real(ret);\n  // reshape the result if the input is 3D tensor.\n  if (input.rank === 3 && input.shape[0] !== 0) {\n    const temp = ret;\n    const batch = input.shape[0];\n    ret = reshape(ret, [batch, ret.shape[0] / batch, ret.shape[1]]);\n    temp.dispose();\n  }\n  return ret;\n}\n\nexport const irfft = op({irfft_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {TensorInfo} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {assert} from '../util';\n\n/**\n * Prepare the split size array. When the input is a number, the axis is evenly\n * divided among the split size. When the input contains the negative value, the\n * rest of the axis is allocated toward that.\n */\nexport function prepareSplitSize(\n    x: Tensor|TensorInfo, numOrSizeSplits: number[]|number,\n    axis = 0): number[] {\n  let splitSizes = [];\n  if (typeof (numOrSizeSplits) === 'number') {\n    assert(\n        x.shape[axis] % numOrSizeSplits === 0,\n        () => 'Number of splits must evenly divide the axis.');\n    splitSizes =\n        new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n  } else {\n    const numOfNegs = numOrSizeSplits.reduce((count, value) => {\n      if (value === -1) {\n        count += 1;\n      }\n      return count;\n    }, 0);\n    assert(\n        numOfNegs <= 1,\n        () => 'There should be only one negative value in split array.');\n    const negIndex = numOrSizeSplits.indexOf(-1);\n    // Allow the number of split array to be -1, which indicates the rest\n    // of dimension is allocated to that split.\n    if (negIndex !== -1) {\n      const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n      numOrSizeSplits[negIndex] = x.shape[axis] - total;\n    }\n    assert(\n        x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b),\n        () => 'The sum of sizes must match the size of the axis dimension.');\n    splitSizes = numOrSizeSplits;\n  }\n\n  return splitSizes;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {SplitV, SplitVAttrs, SplitVInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {op} from './operation';\nimport {prepareSplitSize} from './split_util';\n\n/**\n * Splits a `tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * Can contain one -1 indicating that dimension is to be inferred.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction split_<T extends Tensor>(\n    x: Tensor|TensorLike, numOrSizeSplits: number[]|number, axis = 0): T[] {\n  const $x = convertToTensor(x, 'x', 'split');\n\n  const forward: ForwardFunc<Tensor> = (backend, _) => {\n    const $axis = parseAxisParam(axis, $x.shape)[0];\n    const splitSizes = prepareSplitSize($x, numOrSizeSplits, $axis);\n    return backend.split($x, splitSizes, $axis) as {} as T;\n  };\n\n  const inputs: SplitVInputs = {x: $x};\n  const attr: SplitVAttrs = {numOrSizeSplits, axis};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */, SplitV,\n             attr as {} as NamedAttrMap) as {} as T[];\n}\n\nexport const split = op({split_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {assert} from '../../util';\nimport {complex} from '../complex';\nimport {concat} from '../concat';\nimport {imag} from '../imag';\nimport {op} from '../operation';\nimport {real} from '../real';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {split} from '../split';\nimport {zeros} from '../zeros';\nimport {zerosLike} from '../zeros_like';\n\nimport {fft} from './fft';\n\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * real.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n *\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input: Tensor, fftLength?: number): Tensor {\n  assert(\n      input.dtype === 'float32',\n      () => `The dtype for rfft() must be real value but got ${input.dtype}`);\n\n  let innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  let adjustedInput: Tensor;\n  if (fftLength != null && fftLength < innerDimensionSize) {\n    // Need to crop\n    const begin = input.shape.map(v => 0);\n    const size = input.shape.map(v => v);\n    size[input.shape.length - 1] = fftLength;\n    adjustedInput = slice(input, begin, size);\n    innerDimensionSize = fftLength;\n  } else if (fftLength != null && fftLength > innerDimensionSize) {\n    // Need to pad with zeros\n    const zerosShape = input.shape.map(v => v);\n    zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;\n    adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1);\n    innerDimensionSize = fftLength;\n  } else {\n    adjustedInput = input;\n  }\n\n  // Complement the input with zero imaginary numbers.\n  const zerosInput = zerosLike(adjustedInput);\n  const complexInput =\n      reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n\n  const ret = fft(complexInput);\n\n  // Exclude complex conjugations. These conjugations are put symmetrically.\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = split(\n      realValues, [half, innerDimensionSize - half],\n      realValues.shape.length - 1);\n  const imagComplexConjugate = split(\n      imagValues, [half, innerDimensionSize - half],\n      imagValues.shape.length - 1);\n\n  const outputShape = adjustedInput.shape.slice();\n  outputShape[adjustedInput.shape.length - 1] = half;\n\n  return reshape(\n      complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\n\nexport const rfft = op({rfft_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Sqrt, SqrtInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction sqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sqrt');\n\n  const inputs: SqrtInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.sqrt($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Sqrt);\n}\nexport const sqrt = op({sqrt_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {SquaredDifference, SquaredDifferenceInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n *\n * @doc {heading: 'Operations', subheading: 'Arithmetic'}\n */\nfunction squaredDifference_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'squaredDifference');\n  let $b = convertToTensor(b, 'b', 'squaredDifference');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.squaredDifference($a, $b);\n    save([$a, $b]);\n    return res;\n  };\n\n  const inputs: SquaredDifferenceInputs = {a: $a, b: $b};\n  const attrs = {};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as unknown as NamedTensorMap, null /* grad */,\n             SquaredDifference, attrs) as T;\n}\n\nexport const squaredDifference = op({squaredDifference_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {squeezeShape} from '../util';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction squeeze_<T extends Tensor>(x: Tensor|TensorLike, axis?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'squeeze');\n  return reshape($x, squeezeShape($x.shape, axis).newShape) as T;\n}\n\nexport const squeeze = op({squeeze_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {concat} from './concat';\nimport {expandDims} from './expand_dims';\nimport {op} from './operation';\n\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction stack_<T extends Tensor>(\n    tensors: Array<T|TensorLike>, axis = 0): Tensor {\n  const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n\n  util.assert(\n      $tensors.length >= 1, () => 'Pass at least one tensor to tf.stack');\n\n  if ($tensors.length === 1) {\n    return expandDims($tensors[0], axis);\n  }\n\n  const rank = $tensors[0].rank;\n  const shape = $tensors[0].shape;\n  const dtype = $tensors[0].dtype;\n\n  util.assert(axis <= rank, () => 'Axis must be <= rank of the tensor');\n\n  $tensors.forEach(t => {\n    util.assertShapesMatch(\n        shape, t.shape,\n        'All tensors passed to stack must have matching shapes');\n    util.assert(\n        dtype === t.dtype,\n        () => 'All tensors passed to stack must have matching dtypes');\n  });\n\n  const expandedTensors = $tensors.map(t => expandDims(t, axis));\n  // Stack exists in the TensorFlow C++ API\n  // (https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/stack) but not\n  // in\n  // https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/core/ops/ops.pbtxt.\n  // Therefore we are treating it like a high-level op rather than\n  // creating a dedicated stack kernel.\n  return concat(expandedTensors, axis);\n}\n\nexport const stack = op({stack_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Step, StepAttrs, StepInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction step_<T extends Tensor>(x: T|TensorLike, alpha = 0.0): T {\n  const $x = convertToTensor(x, 'x', 'step');\n\n  const inputs: StepInputs = {x: $x};\n  const attrs: StepAttrs = {alpha};\n\n  return ENGINE.runKernelFunc(\n      backend => backend.step($x, alpha), inputs as {} as NamedTensorMap,\n      null /* grad */, Step, attrs as {} as NamedAttrMap);\n}\nexport const step = op({step_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {StridedSlice, StridedSliceAttrs, StridedSliceInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\nimport {reshape} from './reshape';\nimport {slice} from './slice';\nimport {computeOutShape, getNormalizedAxes, maskToAxes} from './slice_util';\n\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input tensor (x). Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of endMask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction stridedSlice_(\n    x: Tensor|TensorLike, begin: number[], end: number[], strides?: number[],\n    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n    shrinkAxisMask = 0): Tensor {\n  let $x = convertToTensor(x, 'x', 'stridedSlice');\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    if (strides == null) {\n      strides = new Array(begin.length);\n    }\n\n    const ellipsisAxes = maskToAxes(ellipsisMask);\n    if (ellipsisAxes.length > 1) {\n      throw new Error('Multiple ellipses in slice is not allowed.');\n    }\n\n    if (ellipsisMask !== 0 && newAxisMask !== 0) {\n      throw new Error(\n          'Using both ellipsisMask and newAxisMask is not yet supported.');\n    }\n\n    if (ellipsisMask !== 0 && shrinkAxisMask !== 0) {\n      throw new Error(\n          'Using both ellipsisMask and shrinkAxisMask is not yet supported.');\n    }\n\n    const numInterpolatedAxes = $x.rank - begin.length;\n\n    // Expand the dims of x based on the newAxisMask.\n    const expandAxes = maskToAxes(newAxisMask);\n    const newShape = $x.shape.slice();\n    expandAxes.forEach(axis => {\n      begin[axis] = 0;\n      end[axis] = 1;\n      newShape.splice(axis, 0, 1);\n    });\n    $x = reshape($x, newShape);\n\n    const {\n      begin: normalizedBegin,\n      end: normalizedEnd,\n      strides: normalizedStrides\n    } =\n        getNormalizedAxes(\n            $x.shape, ellipsisAxes, numInterpolatedAxes, begin, end, strides,\n            beginMask, endMask, ellipsisMask);\n    begin = normalizedBegin;\n    end = normalizedEnd;\n    strides = normalizedStrides;\n\n    const shrinkAxes = maskToAxes(shrinkAxisMask);\n    // Adjust the ends based on the shrink mask.\n    shrinkAxes.forEach(axis => {\n      end[axis] = begin[axis] + 1;\n      strides[axis] = 1;\n    });\n\n    // Figure out the output shape.\n    const size = computeOutShape(begin, end, strides);\n    // Remove the axes based on shrinkMask.\n    const outShape = size.filter((_, axis) => shrinkAxes.indexOf(axis) === -1);\n\n    const nonStrided = strides.every(v => v === 1);\n    if (nonStrided) {\n      return reshape(slice($x, begin, size), outShape);\n    }\n\n    const res = backend.stridedSlice($x, begin, end, strides);\n    return reshape(res, outShape);\n  };\n\n  const inputs: StridedSliceInputs = {x: $x};\n  const attrs: StridedSliceAttrs = {\n    begin,\n    end,\n    strides,\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, StridedSlice,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const stridedSlice = op({stridedSlice_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tan, TanInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Basic math'}\n */\nfunction tan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tan');\n\n  const inputs: TanInputs = {x: $x};\n\n  return ENGINE.runKernelFunc((backend, save) => {\n    const res = backend.tan($x);\n    save([$x]);\n    return res;\n  }, inputs as {} as NamedTensorMap, null /* grad */, Tan);\n}\nexport const tan = op({tan_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor2D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike2D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor2d(\n    values: TensorLike2D, shape?: [number, number],\n    dtype?: DataType): Tensor2D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 2) {\n    throw new Error('tensor2d() requires shape to have two numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor2d() requires values to be number[][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor2d() requires shape to be provided when `values` ' +\n        'are a flat/TypedArray');\n  }\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor2D;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike4D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor4d(\n    values: TensorLike4D, shape?: [number, number, number, number],\n    dtype?: DataType): Tensor4D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 4) {\n    throw new Error('tensor4d() requires shape to have four numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor4d() requires values to be number[][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor4d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor4D;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor5D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike5D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor5d(\n    values: TensorLike5D, shape?: [number, number, number, number, number],\n    dtype?: DataType): Tensor5D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 5) {\n    throw new Error('tensor5d() requires shape to have five numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor5d() requires values to be ' +\n        'number[][][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor5d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor5D;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor6D} from '../tensor';\nimport {inferShape} from '../tensor_util_env';\nimport {TensorLike6D} from '../types';\nimport {DataType} from '../types';\nimport {assertNonNull} from '../util';\nimport {makeTensor} from './tensor_ops_util';\n\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor6d(\n    values: TensorLike6D,\n    shape?: [number, number, number, number, number, number],\n    dtype?: DataType): Tensor6D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 6) {\n    throw new Error('tensor6d() requires shape to have six numbers');\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor6d() requires values to be number[][][][][][] or ' +\n        'flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor6d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape ||\n      inferredShape as [number, number, number, number, number, number];\n  return makeTensor(values, shape, inferredShape, dtype) as Tensor6D;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {TopK, TopKAttrs, TopKInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {NumericTensor, Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction topk_<T extends Tensor>(\n    x: T|TensorLike, k = 1, sorted = true): {values: T, indices: T} {\n  const $x = convertToTensor(x, 'x', 'topk');\n  if ($x.rank === 0) {\n    throw new Error('topk() expects the input to be of rank 1 or higher');\n  }\n  const lastDim = $x.shape[$x.shape.length - 1];\n  if (k > lastDim) {\n    throw new Error(\n        `'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n        `but got ${k}`);\n  }\n\n  const inputs: TopKInputs = {x: $x};\n  const attrs: TopKAttrs = {k, sorted};\n\n  const [values, indices] = ENGINE.runKernelFunc(\n      b => b.topk($x as NumericTensor, k, sorted),\n      inputs as {} as NamedTensorMap, null /* grad */, TopK,\n      attrs as {} as NamedAttrMap);\n\n  return {values, indices} as {values: T, indices: T};\n}\n\nexport const topk = op({topk_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {DataType, Rank, ShapeMap} from '../types';\n\nimport {buffer} from './buffer';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand_util';\n\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction truncatedNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type $ { dtype }`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\nexport const truncatedNormal = op({truncatedNormal_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Unique, UniqueAttrs, UniqueInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert} from '../util';\n\nimport {op} from './operation';\n\n/**\n * Finds unique elements along an axis of a tensor.\n *\n * It returns a tensor `values` containing all of the unique elements along the\n * `axis` of the given tensor `x` in the same order that they occur along the\n * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor\n * `indices` the same size as the number of the elements in `x` along the `axis`\n * dimension. It contains the index in the unique output `values`.\n *\n * ```js\n * // A 1-D tensor\n * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);\n * const {values, indices} = tf.unique(a);\n * values.print();   // [1, 2, 4, 7, 8,]\n * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=0\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 0)\n * values.print();   // [[1, 0, 0],\n *                   //  [2, 0, 0]]\n * indices.print();  // [0, 0, 1]\n * ```\n *\n * ```js\n * // A 2-D tensor with axis=1\n * //\n * // 'a' is: [[1, 0, 0],\n * //          [1, 0, 0],\n * //          [2, 0, 0]]\n * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);\n * const {values, indices} = tf.unique(a, 1)\n * values.print();   // [[1, 0],\n *                   //  [1, 0],\n *                   //  [2, 0]]\n * indices.print();  // [0, 1, 1]\n * ```\n * @param x A tensor (int32, string, bool).\n * @param axis The axis of the tensor to find the unique elements.\n * @returns [uniqueElements, indices] (see above for details)\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nfunction unique_<T extends Tensor>(\n    x: T|TensorLike, axis = 0): {values: T, indices: Tensor1D} {\n  // x can be of any dtype, thus null as the last argument.\n  const $x = convertToTensor(x, 'x', 'unique', null);\n  assert($x.rank > 0, () => 'The input tensor must be at least 1D');\n\n  const inputs: UniqueInputs = {x: $x};\n  const attrs: UniqueAttrs = {axis};\n  const [values, indices] = ENGINE.runKernel(\n                                Unique, inputs as {} as NamedTensorMap,\n                                attrs as {} as NamedAttrMap) as [T, Tensor1D];\n  return {values, indices};\n}\n\nexport const unique = op({unique_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {UnsortedSegmentSum, UnsortedSegmentSumAttrs, UnsortedSegmentSumInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, isInt} from '../util';\n\nimport {op} from './operation';\n\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n *\n * @doc {heading: 'Operations', subheading: 'Segment'}\n */\nfunction unsortedSegmentSum_<T extends Tensor>(\n    x: T|TensorLike, segmentIds: Tensor1D|TensorLike, numSegments: number): T {\n  const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n  const $segmentIds =\n      convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n  assert(isInt(numSegments), () => 'numSegments must be of dtype int');\n\n  const inputs: UnsortedSegmentSumInputs = {x: $x, segmentIds: $segmentIds};\n  const attrs: UnsortedSegmentSumAttrs = {numSegments};\n\n  const forward: ForwardFunc<Tensor> = (backend, save) => {\n    const res = backend.unsortedSegmentSum($x, $segmentIds, numSegments);\n    save([$segmentIds]);\n    return res;\n  };\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             UnsortedSegmentSum, attrs as {} as NamedAttrMap) as T;\n}\n\nexport const unsortedSegmentSum = op({unsortedSegmentSum_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelBackend} from '../backends/backend';\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Unpack, UnpackAttrs, UnpackInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\n\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nfunction unstack_(x: Tensor|TensorLike, axis = 0): Tensor[] {\n  const $x = convertToTensor(x, 'x', 'unstack');\n  util.assert(\n      axis >= -$x.shape.length && axis < $x.shape.length,\n      () =>\n          `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n  if (axis < 0) {\n    axis += $x.shape.length;\n  }\n  const inputs: UnpackInputs = {value: $x};\n  const attrs: UnpackAttrs = {axis};\n  const forward: ForwardFunc<Tensor[]> = (backend: KernelBackend) =>\n      backend.unstack($x, axis);\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, Unpack,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const unstack = op({unstack_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {Tensor, Variable} from '../tensor';\nimport {DataType, Rank} from '../types';\n\n/**\n * Creates a new variable with the provided initial value.\n * ```js\n * const x = tf.variable(tf.tensor([1, 2, 3]));\n * x.assign(tf.tensor([4, 5, 6]));\n *\n * x.print();\n * ```\n *\n * @param initialValue Initial value for the tensor.\n * @param trainable If true, optimizers are allowed to update it.\n * @param name Name of the variable. Defaults to a unique id.\n * @param dtype If set, initialValue will be converted to the given type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function variable<R extends Rank>(\n    initialValue: Tensor<R>, trainable = true, name?: string,\n    dtype?: DataType): Variable<R> {\n  return ENGINE.makeVariable(initialValue, trainable, name, dtype) as\n      Variable<R>;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the Where kernel shared between cpu and webgl */\n\nimport {buffer} from '../ops/buffer';\nimport {Tensor2D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function whereImpl(condShape: number[], condVals: TypedArray): Tensor2D {\n  const indices = [];\n  for (let i = 0; i < condVals.length; i++) {\n    if (condVals[i]) {\n      indices.push(i);\n    }\n  }\n\n  const inBuffer = buffer(condShape, 'int32');\n\n  const out = buffer([indices.length, condShape.length], 'int32');\n  for (let i = 0; i < indices.length; i++) {\n    const loc = inBuffer.indexToLoc(indices[i]);\n    const offset = i * condShape.length;\n    out.values.set(loc, offset);\n  }\n  return out.toTensor() as Tensor2D;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {whereImpl} from '../backends/where_impl';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\n\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const result = await tf.whereAsync(cond);\n * result.print();\n * ```\n *\n * @doc {heading: 'Operations', subheading: 'Logical'}\n */\nasync function whereAsync_(condition: Tensor|TensorLike): Promise<Tensor2D> {\n  const $condition =\n      convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n  const vals = await $condition.data();\n  const res = whereImpl($condition.shape, vals);\n  if (condition !== $condition) {\n    $condition.dispose();\n  }\n  return res;\n}\n\nexport const whereAsync = whereAsync_;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {gather} from './gather';\nimport {reshape} from './reshape';\nimport {squeeze} from './squeeze';\nimport {whereAsync} from './where_async';\n\n/**\n * Apply boolean mask to tensor.\n *\n * ```js\n * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);\n * const mask = tf.tensor1d([1, 0, 1], 'bool');\n * const result = await tf.booleanMaskAsync(tensor, mask);\n * result.print();\n * ```\n *\n * @param tensor N-D tensor.\n * @param mask K-D boolean tensor, K <= N and K must be known statically.\n * @param axis A 0-D int Tensor representing the axis in tensor to mask from.\n *     By default, axis is 0 which will mask from the first dimension.\n *     Otherwise K + axis <= N.\n *\n * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}\n */\nasync function booleanMaskAsync_(\n    tensor: Tensor|TensorLike, mask: Tensor|TensorLike,\n    axis?: number): Promise<Tensor> {\n  const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');\n  const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');\n\n  const axisFrom = axis == null ? 0 : axis;\n  const maskDim = $mask.rank;\n  const tensorShape = $tensor.shape;\n\n  util.assert(maskDim > 0, () => 'mask cannot be scalar');\n  util.assertShapesMatch(\n      tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape,\n      `mask's shape must match the first K dimensions of tensor's shape,`);\n\n  let leadingSize = 1;\n  for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n    leadingSize *= tensorShape[i];\n  }\n  const targetTensorShape =\n      tensorShape.slice(0, axisFrom)\n          .concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n  const reshapedTensor = reshape($tensor, targetTensorShape);\n  const reshapedMask = reshape($mask, [-1]);\n  const positivePositions = await whereAsync(reshapedMask);\n  const indices = squeeze(positivePositions, [1]);\n\n  const res = gather(reshapedTensor, indices, axisFrom);\n\n  // Ensure no memory leak.\n  if (tensor !== $tensor) {\n    $tensor.dispose();\n  }\n  if (mask !== $mask) {\n    $mask.dispose();\n  }\n  indices.dispose();\n  reshapedTensor.dispose();\n  reshapedMask.dispose();\n  positivePositions.dispose();\n\n  return res;\n}\n\nexport const booleanMaskAsync = booleanMaskAsync_;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {deprecationWarn} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch} from '../util';\n\nimport {equal} from './equal';\nimport {greater} from './greater';\nimport {greaterEqual} from './greater_equal';\nimport {less} from './less';\nimport {lessEqual} from './less_equal';\nimport {notEqual} from './not_equal';\nimport {op} from './operation';\n\n/**\n * @deprecated\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n  const $a = convertToTensor(a, 'a', 'notEqualStrict');\n  const $b = convertToTensor(b, 'b', 'notEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n  return notEqual($a, $b);\n}\n\n/**\n * @deprecated\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n  const $a = convertToTensor(a, 'a', 'lessStrict');\n  const $b = convertToTensor(b, 'b', 'lessStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n  return less($a, $b);\n}\n\nfunction equalStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n  const $a = convertToTensor(a, 'a', 'equalStrict');\n  const $b = convertToTensor(b, 'b', 'equalStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n  return equal($a, $b);\n}\n\nfunction lessEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n  const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n  const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n  return lessEqual($a, $b);\n}\n\nfunction greaterStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n  const $a = convertToTensor(a, 'a', 'greaterStrict');\n  const $b = convertToTensor(b, 'b', 'greaterStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n  return greater($a, $b);\n}\n\nfunction greaterEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n  const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n  const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n  return greaterEqual($a, $b);\n}\n\nexport const equalStrict = op({equalStrict_});\nexport const greaterEqualStrict = op({greaterEqualStrict_});\nexport const greaterStrict = op({greaterStrict_});\nexport const lessEqualStrict = op({lessEqualStrict_});\nexport const lessStrict = op({lessStrict_});\nexport const notEqualStrict = op({notEqualStrict_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {deprecationWarn} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {add} from './add';\nimport {div} from './div';\nimport {maximum} from './maximum';\nimport {minimum} from './minimum';\nimport {mod} from './mod';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {pow} from './pow';\nimport {squaredDifference} from './squared_difference';\nimport {sub} from './sub';\n\n/**\n * @deprecated\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n  const $a = convertToTensor(a, 'a', 'addStrict');\n  const $b = convertToTensor(b, 'b', 'addStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n  return add($a, $b);\n}\n\n/**\n * @deprecated\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n\n  const $a = convertToTensor(a, 'a', 'subStrict');\n  const $b = convertToTensor(b, 'b', 'subStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n  return sub($a, $b);\n}\n\n/**\n * @deprecated\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_<T extends Tensor>(base: T, exp: Tensor): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n\n  util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n  return pow(base, exp);\n}\n\n/**\n * @deprecated\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n\n  const $a = convertToTensor(a, 'a', 'mul');\n  const $b = convertToTensor(b, 'b', 'mul');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n  return mul($a, $b);\n}\n\n/**\n * @deprecated\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n\n  const $a = convertToTensor(a, 'a', 'div');\n  const $b = convertToTensor(b, 'b', 'div');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n  return div($a, $b);\n}\n\n/**\n * @deprecated\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n\n  const $a = convertToTensor(a, 'a', 'modStrict');\n  const $b = convertToTensor(b, 'b', 'modStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n  return mod($a, $b);\n}\n\n/**\n * @deprecated\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n\n  const $a = convertToTensor(a, 'a', 'minimumStrict');\n  const $b = convertToTensor(b, 'b', 'minimumStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n  return minimum($a, $b);\n}\n\n/**\n * @deprecated\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n\n  const $a = convertToTensor(a, 'a', 'maximumStrict');\n  const $b = convertToTensor(b, 'b', 'maximumStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n  return maximum($a, $b);\n}\n\n/**\n * @deprecated\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  deprecationWarn(\n      'strict variants of ops have been deprecated ' +\n      'and will be removed in future');\n  const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n  const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n  util.assertShapesMatch(\n      $a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n  return squaredDifference($a, $b);\n}\n\nexport const addStrict = op({addStrict_});\nexport const divStrict = op({divStrict_});\nexport const maximumStrict = op({maximumStrict_});\nexport const minimumStrict = op({minimumStrict_});\nexport const modStrict = op({modStrict_});\nexport const mulStrict = op({mulStrict_});\nexport const powStrict = op({powStrict_});\nexport const squaredDifferenceStrict = op({squaredDifferenceStrict_});\nexport const subStrict = op({subStrict_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {parseAxisParam} from '../util';\n\nimport {abs} from './abs';\nimport * as axis_util from './axis_util';\nimport {max} from './max';\nimport {min} from './min';\nimport {op} from './operation';\nimport {pow} from './pow';\nimport {reshape} from './reshape';\nimport {scalar} from './scalar';\nimport {sqrt} from './sqrt';\nimport {square} from './square';\nimport {sum} from './sum';\n\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction norm_(\n    x: Tensor|TensorLike, ord: number|'euclidean'|'fro' = 'euclidean',\n    axis: number|number[] = null, keepDims = false): Tensor {\n  x = convertToTensor(x, 'x', 'norm');\n\n  const norm = normImpl(x, ord, axis);\n  let keepDimsShape = norm.shape;\n  if (keepDims) {\n    const axes = parseAxisParam(axis, x.shape);\n    keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n  }\n  return reshape(norm, keepDimsShape);\n}\n\nfunction normImpl(\n    x: Tensor, p: number|string, axis: number|number[] = null): Tensor {\n  if (x.rank === 0) {\n    return abs(x);\n  }\n\n  // consider vector when no axis is specified\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(reshape(x, [-1]), p, axis);\n  }\n\n  // vector\n  if (x.rank === 1 || typeof axis === 'number' ||\n      Array.isArray(axis) && axis.length === 1) {\n    if (p === 1) {\n      return sum(abs(x), axis);\n    }\n    if (p === Infinity) {\n      return max(abs(x), axis);\n    }\n    if (p === -Infinity) {\n      return min(abs(x), axis);\n    }\n    if (p === 'euclidean' || p === 2) {\n      // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n      return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis));\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  // matrix (assumption axis[0] < axis[1])\n  if (Array.isArray(axis) && axis.length === 2) {\n    if (p === 1) {\n      return max(sum(abs(x), axis[0]), axis[1] - 1);\n    }\n    if (p === Infinity) {\n      return max(sum(abs(x), axis[1]), axis[0]);\n    }\n    if (p === -Infinity) {\n      return min(sum(abs(x), axis[1]), axis[0]);\n    }\n    if (p === 'fro' || p === 'euclidean') {\n      // norm(x) = sqrt(sum(pow(x, 2)))\n      return sqrt(sum(square(x), axis));\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\n\nexport const norm = op({norm_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor} from '../tensor';\nimport {assertTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {add} from './add';\nimport {div} from './div';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {pow} from './pow';\nimport {scalar} from './scalar';\nimport {sub} from './sub';\n\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n *\n * @doc {heading: 'Operations', subheading: 'Moving Average'}\n */\nfunction movingAverage_<T extends Tensor>(\n    v: T|TensorLike, x: T|TensorLike, decay: number|Scalar,\n    step?: number|Scalar, zeroDebias = true): T {\n  const $v = convertToTensor(v, 'v', 'movingAverage');\n  const $x = convertToTensor(x, 'x', 'movingAverage');\n  const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n\n  assertTypesMatch($v, $x);\n  util.assert(\n      util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');\n\n  const one = scalar(1);\n  const oneMinusDecay = sub(one, $decay);\n\n  let update = mul(sub($x, $v), oneMinusDecay);\n  if (zeroDebias) {\n    util.assert(\n        step != null, () => 'When using zeroDebias: true, step is required.');\n    const $step = convertToTensor(step, 'step', 'movingAverage');\n    update = div(update, sub(one, pow($decay, $step)));\n  }\n  return add($v, update);\n}\n\nexport const movingAverage = op({movingAverage_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {ScatterNd, ScatterNdAttrs, ScatterNdInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');\n * const updates = tf.tensor1d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction scatterND_<R extends Rank>(\n    indices: Tensor|TensorLike, updates: Tensor|TensorLike,\n    shape: ShapeMap[R]): Tensor<R> {\n  const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n  const $updates = convertToTensor(updates, 'updates', 'scatterND');\n  scatter_nd_util.validateInput($updates, $indices, shape);\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    return backend.scatterND($indices, $updates, shape);\n  };\n\n  const inputs: ScatterNdInputs = {indices: $indices, updates: $updates};\n  const attrs: ScatterNdAttrs = {shape};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             ScatterNd, attrs as {} as NamedAttrMap) as Tensor<R>;\n}\n\nexport const scatterND = op({scatterND_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\n\n/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(\n    sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[],\n    defaultValues: Tensor) {\n  if (sparseIndices.dtype !== 'int32') {\n    throw new Error(\n        'tf.sparseToDense() expects the indices to be int32 type,' +\n        ` but the dtype was ${sparseIndices.dtype}.`);\n  }\n  if (sparseIndices.rank > 2) {\n    throw new Error(\n        'sparseIndices should be a scalar, vector, or matrix,' +\n        ` but got shape ${sparseIndices.shape}.`);\n  }\n\n  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n\n  if (outputShape.length !== numDims) {\n    throw new Error(\n        'outputShape has incorrect number of elements:,' +\n        ` ${outputShape.length}, should be: ${numDims}.`);\n  }\n\n  const numValues = sparseValues.size;\n  if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n    throw new Error(\n        'sparseValues has incorrect shape ' +\n        `${sparseValues.shape}, should be [] or [${numElems}]`);\n  }\n\n  if (sparseValues.dtype !== defaultValues.dtype) {\n    throw new Error('sparseValues.dtype must match defaultValues.dtype');\n  }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {SparseToDense, SparseToDenseAttrs, SparseToDenseInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport {Scalar, Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ScalarLike, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * If indices are repeated the final value is summed over all values for those\n * indices.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n *\n * @doc {heading: 'Operations', subheading: 'Normalization'}\n */\nfunction sparseToDense_<R extends Rank>(\n    sparseIndices: Tensor|TensorLike, sparseValues: Tensor|TensorLike,\n    outputShape: ShapeMap[R], defaultValue: Scalar|ScalarLike = 0): Tensor<R> {\n  const $sparseIndices =\n      convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n  const $sparseValues =\n      convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n  const $defaultValue = convertToTensor(\n      defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n\n  sparse_to_dense.validateInput(\n      $sparseIndices, $sparseValues, outputShape, $defaultValue);\n\n  const inputs: SparseToDenseInputs = {\n    sparseIndices: $sparseIndices,\n    sparseValues: $sparseValues,\n    defaultValue: $defaultValue\n  };\n\n  const attrs: SparseToDenseAttrs = {outputShape};\n\n  return ENGINE.runKernelFunc(\n      backend => backend.sparseToDense(\n          $sparseIndices, $sparseValues, outputShape, $defaultValue),\n      inputs as {} as NamedTensorMap, null /* grad */, SparseToDense,\n      attrs as {} as NamedAttrMap);\n}\n\nexport const sparseToDense = op({sparseToDense_});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {GatherNd, GatherNdInputs} from '../kernel_names';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() // [10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n *\n * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}\n */\nfunction gatherND_(x: Tensor|TensorLike, indices: Tensor|TensorLike): Tensor {\n  const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n  const $x = convertToTensor(x, 'x', 'gatherND');\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    return backend.gatherND($x, $indices);\n  };\n\n  const inputs: GatherNdInputs = {params: $x, indices: $indices};\n\n  return ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* gradient */, GatherNd);\n}\n\nexport const gatherND = op({gatherND_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport * as util from '../util';\n\n/**\n * Normalize noise shape based on provided tensor and noise shape.\n *\n * @param x Tensor.\n * @param noiseShape The shape for the randomly generated keep/drop flags, as\n *   an array of numbers. Optional.\n * @returns Normalized noise shape.\n */\nexport function getNoiseShape(x: Tensor, noiseShape?: number[]): number[] {\n  if (noiseShape == null) {\n    return x.shape.slice();\n  }\n  if (util.arraysEqual(x.shape, noiseShape)) {\n    return noiseShape;\n  }\n  if (x.shape.length === noiseShape.length) {\n    const newDimension: number[] = [];\n    for (let i = 0; i < x.shape.length; i++) {\n      if (noiseShape[i] == null && x.shape[i] != null) {\n        newDimension.push(x.shape[i]);\n      } else {\n        newDimension.push(noiseShape[i]);\n      }\n    }\n    return newDimension;\n  }\n\n  return noiseShape;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {add} from './add';\nimport {div} from './div';\nimport {getNoiseShape} from './dropout_util';\nimport {floor} from './floor';\nimport {mul} from './mul';\nimport {op} from './operation';\nimport {randomUniform} from './random_uniform';\n\n/**\n * Computes dropout.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 2, 1]);\n * const rate = 0.75;\n * const output = tf.dropout(x, rate);\n * output.print();\n * ```\n *\n * @param x A floating point Tensor or TensorLike.\n * @param rate A float in the range [0, 1). The probability that each element\n *   of x is discarded.\n * @param noiseShape An array of numbers of type int32, representing the\n * shape for randomly generated keep/drop flags. If the noiseShape has null\n * value, it will be automatically replaced with the x's relative dimension\n * size. Optional.\n * @param seed Used to create random seeds. Optional.\n * @returns A Tensor of the same shape of x.\n *\n * @doc {heading: 'Operations', subheading: 'Dropout'}\n */\nfunction dropout_(\n    x: Tensor|TensorLike, rate: number, noiseShape?: number[],\n    seed?: number|string): Tensor {\n  const $x = convertToTensor(x, 'x', 'dropout');\n\n  util.assert(\n      $x.dtype === 'float32',\n      () => `x has to be a floating point tensor since it's going to be ` +\n          `scaled, but got a ${$x.dtype} tensor instead.`);\n  util.assert(\n      rate >= 0 && rate < 1,\n      () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n\n  if (rate === 0) {\n    return x instanceof Tensor ? $x.clone() : $x;\n  }\n\n  const $noiseShape = getNoiseShape($x, noiseShape);\n  const keepProb = 1 - rate;\n  const multiplier = div(\n      floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)),\n      keepProb);\n\n  return mul($x, multiplier);\n}\n\nexport const dropout = op({dropout_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../tensor';\nimport {tensor1d} from './tensor1d';\n\nexport function enclosingPowerOfTwo(value: number) {\n  // Return 2**N for integer N such that 2**N >= value.\n  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));\n}\n\nexport function cosineWindow(\n    windowLength: number, a: number, b: number): Tensor1D {\n  const even = 1 - windowLength % 2;\n  const newValues = new Float32Array(windowLength);\n  for (let i = 0; i < windowLength; ++i) {\n    const cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1);\n    newValues[i] = a - b * Math.cos(cosArg);\n  }\n  return tensor1d(newValues, 'float32');\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch, getTypedArrayFromDType} from '../util';\nimport {tensor} from './tensor';\n\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_<T extends Tensor, U extends Tensor>(\n    predictions: T|TensorLike, targets: U|TensorLike, k = 1): Promise<U> {\n  const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n  const $targets = convertToTensor(targets, 'targets', 'inTopK');\n\n  assert(\n      $predictions.rank > 1,\n      () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n          `but got ${$predictions.rank}`);\n  assert(\n      $predictions.rank - 1 === $targets.rank,\n      () => `predictions rank should be 1 larger than ` +\n          `targets rank, but got predictions rank ` +\n          `${$predictions.rank} and targets rank ${$targets.rank}`);\n  assertShapesMatch(\n      $predictions.shape.slice(0, $predictions.shape.length - 1),\n      $targets.shape,\n      `predictions's shape should be align with the targets' shape, ` +\n          'except the last dimension.');\n  const lastDim = $predictions.shape[$predictions.shape.length - 1];\n  assert(\n      k > 0 && k <= lastDim,\n      () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n          `dimension (${lastDim}), but got ${k}`);\n\n  const predictionsVals = await $predictions.data();\n  const targetsVals = await $targets.data();\n\n  // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n  // and look up topK along lastDim.\n  const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n  const precision = getTypedArrayFromDType('bool', batch);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = predictionsVals.subarray(offset, offset + size);\n    const valAndInd: Array<{value: number, index: number}> = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({value: vals[i], index: i});\n    }\n    valAndInd.sort((a, b) => b.value - a.value);\n\n    precision[b] = 0;\n    for (let i = 0; i < k; i++) {\n      if (valAndInd[i].index === targetsVals[b]) {\n        precision[b] = 1;\n        break;\n      }\n    }\n  }\n\n  if (predictions !== $predictions) {\n    $predictions.dispose();\n  }\n  if (targets !== $targets) {\n    $targets.dispose();\n  }\n\n  // Output precision has the same shape as targets.\n  return tensor(precision, $targets.shape, 'bool') as U;\n}\n\nexport const inTopKAsync = inTopKAsync_;\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Conv2DBackpropFilter, Conv2DBackpropFilterAttrs, Conv2DBackpropFilterInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels].\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2DBackpropFilter_<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    strides: [number, number]|number,\n    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n          `${x4D.shape}.`);\n  util.assert(\n      dy4D.rank === 4,\n      () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n          `${dy4D.shape}.`);\n  util.assert(\n      filterShape.length === 4,\n      () => `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n          `${filterShape}.`);\n  const inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n  const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];\n  util.assert(\n      inDepth === filterShape[2],\n      () => `Error in conv2dDerFilter: depth of input ${inDepth}) must ` +\n          `match input depth in filter (${filterShape[2]}.`);\n  util.assert(\n      outDepth === filterShape[3],\n      () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must ` +\n          `match output depth for filter (${filterShape[3]}).`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const dilations = 1;\n    const $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);\n    const convInfo = conv_util.computeConv2DInfo(\n        x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false,\n        $dataFormat);\n\n    return backend.conv2dDerFilter(x4D, dy4D, convInfo);\n  };\n\n  const inputs: Conv2DBackpropFilterInputs = {x: x4D, dy: dy4D};\n  const attrs: Conv2DBackpropFilterAttrs =\n      {strides, pad, dataFormat, dimRoundingMode, filterShape};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null,\n             Conv2DBackpropFilter, attrs as {} as NamedAttrMap) as Tensor4D;\n}\n\nexport const conv2DBackpropFilter = op({conv2DBackpropFilter_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\nimport * as broadcast_util from './broadcast_util';\nimport {elu} from './elu';\nimport {Activation} from './fused_types';\nimport {mul} from './mul';\nimport {prelu} from './prelu';\nimport {relu} from './relu';\nimport {relu6} from './relu6';\nimport {reshape} from './reshape';\nimport {step} from './step';\nimport {sum} from './sum';\n\n// Returns gradient for fused activation.\nexport function getFusedDyActivation(\n    dy: Tensor, y: Tensor, activation: Activation): Tensor {\n  if (activation == null || activation === 'linear') {\n    return dy;\n  }\n  if (activation === 'relu') {\n    return mul(dy, step(y));\n  }\n  throw new Error(\n      `Cannot compute gradient for fused activation ${activation}.`);\n}\n\n// Returns gradient for fused bias.\nexport function getFusedBiasGradient(\n    bias: Tensor, dyActivation: Tensor): Tensor {\n  let res = dyActivation;\n  const reduceAxes =\n      broadcast_util.getReductionAxes(bias.shape, dyActivation.shape);\n  if (reduceAxes.length > 0) {\n    res = sum(res, reduceAxes);\n  }\n  return reshape(res, bias.shape);\n}\n\nexport function applyActivation(\n    x: Tensor, activation: Activation,\n    preluActivationWeights?: Tensor): Tensor {\n  if (activation === 'linear') {\n    return x;\n  } else if (activation === 'relu') {\n    return relu(x);\n  } else if (activation === 'elu') {\n    return elu(x);\n  } else if (activation === 'relu6') {\n    return relu6(x);\n  } else if (activation === 'prelu') {\n    return prelu(x, preluActivationWeights);\n  }\n  throw new Error(`Unknown fused activation ${activation}.`);\n}\n\n// Whether we should call fused ops.\nexport const shouldFuse = (gradientDepth: number, activation: Activation) => {\n  const gradientMode = gradientDepth > 0;\n  return !gradientMode || activation === 'linear';\n};\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport {conv2d as unfusedConv2d} from '../conv2d';\nimport {conv2DBackpropFilter} from '../conv2d_backprop_filter';\nimport {conv2DBackpropInput} from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedConv2d_<T extends Tensor3D|Tensor4D>({\n  x,\n  filter,\n  strides,\n  pad,\n  dataFormat = 'NHWC',\n  dilations = [1, 1],\n  dimRoundingMode,\n  bias,\n  activation = 'linear',\n  preluActivationWeights\n}: {\n  x: T|TensorLike,\n  filter: Tensor4D|TensorLike,\n  strides: [number, number]|number,\n  pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n  dataFormat?: 'NHWC'|'NCHW',\n  dilations?: [number, number]|number,\n  dimRoundingMode?: 'floor'|'round'|'ceil',\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor\n}): T {\n  activation = activation || 'linear';\n\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    let result = unfusedConv2d(\n        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(result, activation, preluActivationWeights) as T;\n  }\n\n  const $x = convertToTensor(x, 'x', 'conv2d');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n          `${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in fused conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      () => `Error in conv2d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n    [$bias] = makeTypesMatch($bias, $x);\n\n    broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused conv2d');\n  }\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    const [$filter, x4D, y, $bias] =\n        saved as [Tensor4D, Tensor4D, Tensor4D, Tensor];\n\n    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;\n\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n\n    const xDer =\n        conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n    const filterDer =\n        conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n    const der: Tensor[] = [xDer, filterDer];\n\n    if ($bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      der.push(biasDer);\n    }\n    return der;\n  };\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    const res = backend.fusedConv2d({\n      input: x4D,\n      filter: $filter,\n      convInfo,\n      bias: $bias,\n      activation,\n      preluActivationWeights: $preluActivationWeights\n    });\n    return res;\n  };\n\n  const inputs: FusedConv2DInputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n\n  const attrs: FusedConv2DAttrs =\n      {strides, pad, dataFormat, dilations, dimRoundingMode, activation};\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {\n          let res = ENGINE.runKernelFunc(\n              forward, inputs as {} as NamedTensorMap, null /* grad */,\n              FusedConv2D, attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res]);\n\n          if (reshapedTo4D) {\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n    return customOp(x4D, $filter) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {\n          let res = ENGINE.runKernelFunc(\n              forward, inputs as {} as NamedTensorMap, null /* grad */,\n              FusedConv2D, attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res, bias]);\n\n          if (reshapedTo4D) {\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n\n    return customOpWithBias(x4D, $filter, $bias) as T;\n  }\n}\nexport const conv2d = op({fusedConv2d_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropFilterAttrs, DepthwiseConv2dNativeBackpropFilterInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\nfunction depthwiseConv2dNativeBackpropFilter_<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const convInfo = conv_util.computeConv2DInfo(\n        x.shape as [number, number, number, number], filterShape, strides,\n        dilations, pad, dimRoundingMode, true /* depthwise */);\n\n    return backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo);\n  };\n\n  const inputs: DepthwiseConv2dNativeBackpropFilterInputs = {x: x4D, dy: dy4D};\n  const attrs: DepthwiseConv2dNativeBackpropFilterAttrs =\n      {strides, pad, dimRoundingMode, dilations, filterShape};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null,\n             DepthwiseConv2dNativeBackpropFilter,\n             attrs as {} as NamedAttrMap) as Tensor4D;\n}\n\nexport const depthwiseConv2dNativeBackpropFilter =\n    op({depthwiseConv2dNativeBackpropFilter_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {DepthwiseConv2dNativeBackpropInput, DepthwiseConv2dNativeBackpropInputAttrs, DepthwiseConv2dNativeBackpropInputInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\nfunction depthwiseConv2dNativeBackpropInput_<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number], dy: T, filter: Tensor4D,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const convInfo = conv_util.computeConv2DInfo(\n        xShape, filter.shape, strides, dilations, pad, dimRoundingMode,\n        true /* depthwise */);\n\n    return backend.depthwiseConv2DDerInput(dy4D, filter, convInfo);\n  };\n\n  const inputs: DepthwiseConv2dNativeBackpropInputInputs = {dy: dy4D, filter};\n  const attrs: DepthwiseConv2dNativeBackpropInputAttrs =\n      {strides, pad, dimRoundingMode, dilations, inputShape: xShape};\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null,\n      DepthwiseConv2dNativeBackpropInput, attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const depthwiseConv2dNativeBackpropInput =\n    op({depthwiseConv2dNativeBackpropInput_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport * as conv_util from '../conv_util';\nimport {depthwiseConv2d as unfusedDepthwiseConv2d} from '../depthwise_conv2d';\nimport {depthwiseConv2dNativeBackpropFilter} from '../depthwise_conv2d_native_backprop_filter';\nimport {depthwiseConv2dNativeBackpropInput} from '../depthwise_conv2d_native_backprop_input';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes depthwise 2D convolution, optionally fused with adding a\n * bias and applying an activation.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`).\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n */\nfunction fusedDepthwiseConv2d_<T extends Tensor3D|Tensor4D>({\n  x,\n  filter,\n  strides,\n  pad,\n  dataFormat = 'NHWC',\n  dilations = [1, 1],\n  dimRoundingMode,\n  bias,\n  activation = 'linear',\n  preluActivationWeights\n}: {\n  x: T|TensorLike,\n  filter: Tensor4D|TensorLike,\n  strides: [number, number]|number,\n  pad: 'valid'|'same'|number,\n  dataFormat?: 'NHWC'|'NCHW',\n  dilations?: [number, number]|number,\n  dimRoundingMode?: 'floor'|'round'|'ceil',\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor\n}): T {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    let result = unfusedDepthwiseConv2d(\n        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(result, activation, preluActivationWeights) as T;\n  }\n\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n  const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in fused depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in fused depthwiseConv2d: filter must be rank 4, ` +\n          `but got rank ${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      () => `Error in fused depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () =>\n          'Error in fused depthwiseConv2d: Either strides or dilations must ' +\n          `be 1. Got strides ${strides} and dilations '${dilations}'`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in fused depthwiseConv2d: pad must be an integer when ` +\n            `using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n      true /* depthwise */);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n    [$bias] = makeTypesMatch($bias, $x);\n\n    broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');\n  }\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${dilations}'`);\n    const [$filter, x4D, y, bias] = saved;\n\n    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;\n\n    const xDer = depthwiseConv2dNativeBackpropInput(\n        (x4D as Tensor4D).shape, dyActivation, $filter as Tensor4D, strides,\n        pad, dilations, dimRoundingMode);\n    const filterDer = depthwiseConv2dNativeBackpropFilter(\n        x4D as Tensor4D, dyActivation, ($filter as Tensor4D).shape, strides,\n        pad, dilations, dimRoundingMode);\n\n    if (bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      return [xDer, filterDer, biasDer];\n    }\n    return [xDer, filterDer];\n  };\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    const res = backend.fusedDepthwiseConv2D({\n      input: x4D,\n      filter: $filter,\n      convInfo,\n      bias: $bias,\n      activation,\n      preluActivationWeights: $preluActivationWeights\n    });\n    return res;\n  };\n\n  const inputs: FusedDepthwiseConv2DInputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs: FusedDepthwiseConv2DAttrs =\n      {strides, pad, dataFormat, dilations, dimRoundingMode, activation};\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {\n          let res = ENGINE.runKernelFunc(\n              forward, inputs as {} as NamedTensorMap, null /* grad */,\n              FusedDepthwiseConv2D, attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res]);\n\n          if (reshapedTo4D) {\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n    return customOp(x4D, $filter) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {\n          let res = ENGINE.runKernelFunc(\n              forward, inputs as {} as NamedTensorMap, null /* grad */,\n              FusedDepthwiseConv2D, attrs as {} as NamedAttrMap);\n\n          save([filter, x4D, res, bias]);\n\n          if (reshapedTo4D) {\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n\n    return customOpWithBias(x4D, $filter, $bias) as T;\n  }\n}\nexport const depthwiseConv2d = op({fusedDepthwiseConv2d_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {_FusedMatMul, _FusedMatMulAttrs, _FusedMatMulInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {matMul as unfusedMatMul} from '../mat_mul';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes the dot product of two matrices with optional activation and bias.\n *\n * ```js\n * const a = tf.tensor2d([-1, -2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const bias = tf.tensor2d([1, 2], [1, 2]);\n *\n * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();\n * ```\n *\n * @param obj An object with the following properties:\n * - `a` First matrix in dot product operation.\n * - `b` Second matrix in dot product operation.\n * - `transposeA` If true, `a` is transposed before multiplication.\n * - `transposeB` If true, `b` is transposed before multiplication.\n * - `bias` Matrix to be added to the result.\n * - `activation` Name of activation kernel (defaults to `linear`).\n * - `preluActivationWeights` Tensor of prelu weights.\n */\nfunction fusedMatMul_<T extends Tensor>({\n  a,\n  b,\n  transposeA = false,\n  transposeB = false,\n  bias,\n  activation = 'linear',\n  preluActivationWeights\n}: {\n  a: T|TensorLike,\n  b: T|TensorLike,\n  transposeA?: boolean,\n  transposeB?: boolean,\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor\n}): T {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    let result = unfusedMatMul(a, b, transposeA, transposeB);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(result, activation, preluActivationWeights) as T;\n  }\n\n  let $a = convertToTensor(a, 'a', 'fused matMul');\n  let $b = convertToTensor(b, 'b', 'fused matMul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const innerShapeA =\n      transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n  const innerShapeB =\n      transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n  const outerShapeA =\n      transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n  const outerShapeB =\n      transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n  const outerDimsA = $a.shape.slice(0, -2);\n  const outerDimsB = $b.shape.slice(0, -2);\n  const batchDimA = util.sizeFromShape(outerDimsA);\n  const batchDimB = util.sizeFromShape(outerDimsB);\n\n  util.assert(\n      $a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,\n      () =>\n          `Error in fused matMul: inputs must have the same rank of at least ` +\n          `2, got ranks ${$a.rank} and ${$b.rank}.`);\n\n  util.assert(\n      util.arraysEqual(outerDimsA, outerDimsB),\n      () => `Error in fused matMul: outer dimensions (${outerDimsA}) and (` +\n          `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n          `${$b.shape} must match.`);\n\n  util.assert(\n      innerShapeA === innerShapeB,\n      () => `Error in fused matMul: inner shapes (${innerShapeA}) and (` +\n          `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n          `${$b.shape} and transposeA=${transposeA}` +\n          ` and transposeB=${transposeB} must match.`);\n\n  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n\n  const a3D: Tensor3D = transposeA ?\n      reshape($a, [batchDimA, innerShapeA, outerShapeA]) :\n      reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n  const b3D: Tensor3D = transposeB ?\n      reshape($b, [batchDimB, outerShapeB, innerShapeB]) :\n      reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused matMul');\n    [$bias] = makeTypesMatch($bias, $a);\n\n    broadcast_util.assertAndGetBroadcastShape(outShape, $bias.shape);\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused matMul');\n  }\n\n  const grad = (dy: Tensor3D, saved: Tensor[]) => {\n    const [a3D, b3D, y, $bias] = saved;\n    // we reshape dy because the result of the forward is not\n    // necessarily going to be a 3d tensor due to a reshape done at the end of\n    // the customOp.\n    const dyActivation =\n        getFusedDyActivation(reshape(dy, y.shape), y, activation);\n    let aDer: Tensor;\n    let bDer: Tensor;\n\n    if (!transposeA && !transposeB) {\n      aDer = unfusedMatMul(dyActivation, b3D, false, true);\n      bDer = unfusedMatMul(a3D, dyActivation, true, false);\n    } else if (!transposeA && transposeB) {\n      aDer = unfusedMatMul(dyActivation, b3D, false, false);\n      bDer = unfusedMatMul(dyActivation, a3D, true, false);\n    } else if (transposeA && !transposeB) {\n      aDer = unfusedMatMul(b3D, dyActivation, false, true);\n      bDer = unfusedMatMul(a3D, dyActivation, false, false);\n    } else {\n      aDer = unfusedMatMul(b3D, dyActivation, true, true);\n      bDer = unfusedMatMul(dyActivation, a3D, true, true);\n    }\n\n    if (bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      return [aDer, bDer, biasDer];\n    } else {\n      return [aDer, bDer];\n    }\n  };\n\n  const forward: ForwardFunc<Tensor> = (backend) => {\n    const y = backend.fusedBatchMatMul({\n      a: a3D,\n      b: b3D,\n      transposeA,\n      transposeB,\n      bias: $bias,\n      activation,\n      preluActivationWeights: $preluActivationWeights\n    });\n    return y;\n  };\n\n  const inputs: _FusedMatMulInputs = {\n    a: a3D,\n    b: b3D,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs: _FusedMatMulAttrs = {transposeA, transposeB, activation};\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((a3D: Tensor3D, b3D: Tensor3D, save: GradSaveFunc) => {\n          const res = ENGINE.runKernelFunc(\n              forward, inputs as {} as NamedTensorMap, null /* grad */,\n              _FusedMatMul, attrs as {} as NamedAttrMap);\n\n          save([a3D, b3D, res]);\n\n          return {value: reshape(res, outShape), gradFunc: grad};\n        });\n    return customOp(a3D, b3D) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (a3D: Tensor3D, b3D: Tensor3D, $bias: Tensor, save: GradSaveFunc) => {\n          const res = ENGINE.runKernelFunc(\n              forward, inputs as {} as NamedTensorMap, null /* grad */,\n              _FusedMatMul, attrs as {} as NamedAttrMap);\n\n          save([a3D, b3D, res, $bias]);\n\n          return {value: reshape(res, outShape), gradFunc: grad};\n        });\n\n    return customOpWithBias(a3D, b3D, $bias) as T;\n  }\n}\n\nexport const matMul = op({fusedMatMul_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {conv2d} from './fused/conv2d';\nimport {depthwiseConv2d} from './fused/depthwise_conv2d';\nimport {matMul} from './fused/mat_mul';\nimport {Activation} from './fused_types';\n\nexport {Activation, conv2d, depthwiseConv2d, matMul};\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../../tensor';\nimport {op} from '../operation';\nimport {cosineWindow} from '../signal_ops_util';\n\n/**\n * Generate a hamming window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hammingWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hammingWindow_(windowLength: number): Tensor1D {\n  return cosineWindow(windowLength, 0.54, 0.46);\n}\nexport const hammingWindow = op({hammingWindow_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D} from '../../tensor';\nimport {op} from '../operation';\nimport {cosineWindow} from '../signal_ops_util';\n\n/**\n * Generate a Hann window.\n *\n * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows\n *\n * ```js\n * tf.signal.hannWindow(10).print();\n * ```\n * @param The length of window\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction hannWindow_(windowLength: number): Tensor1D {\n  return cosineWindow(windowLength, 0.5, 0.5);\n}\n\nexport const hannWindow = op({hannWindow_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {concat} from '../concat';\nimport {fill} from '../fill';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {tensor2d} from '../tensor2d';\n\n/**\n * Expands input into frames of frameLength.\n * Slides a window size with frameStep.\n *\n * ```js\n * tf.signal.frame([1, 2, 3], 2, 1).print();\n * ```\n * @param signal The input tensor to be expanded\n * @param frameLength Length of each frame\n * @param frameStep The frame hop size in samples.\n * @param padEnd Whether to pad the end of signal with padValue.\n * @param padValue An number to use where the input signal does\n *     not exist when padEnd is True.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction frame_(\n    signal: Tensor1D, frameLength: number, frameStep: number, padEnd = false,\n    padValue = 0): Tensor {\n  let start = 0;\n  const output: Tensor[] = [];\n  while (start + frameLength <= signal.size) {\n    output.push(slice(signal, start, frameLength));\n    start += frameStep;\n  }\n\n  if (padEnd) {\n    while (start < signal.size) {\n      const padLen = (start + frameLength) - signal.size;\n      const pad = concat([\n        slice(signal, start, frameLength - padLen), fill([padLen], padValue)\n      ]);\n      output.push(pad);\n      start += frameStep;\n    }\n  }\n\n  if (output.length === 0) {\n    return tensor2d([], [0, frameLength]);\n  }\n\n  return reshape(concat(output), [output.length, frameLength]);\n}\nexport const frame = op({frame_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {concat} from '../concat';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {enclosingPowerOfTwo} from '../signal_ops_util';\nimport {slice} from '../slice';\nimport {rfft} from '../spectral/rfft';\n\nimport {frame} from './frame';\nimport {hannWindow} from './hann_window';\n\n/**\n * Computes the Short-time Fourier Transform of signals\n * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform\n *\n * ```js\n * const input = tf.tensor1d([1, 1, 1, 1, 1])\n * tf.signal.stft(input, 3, 1).print();\n * ```\n * @param signal 1-dimensional real value tensor.\n * @param frameLength The window length of samples.\n * @param frameStep The number of samples to step.\n * @param fftLength The size of the FFT to apply.\n * @param windowFn A callable that takes a window length and returns 1-d tensor.\n *\n * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}\n */\nfunction stft_(\n    signal: Tensor1D, frameLength: number, frameStep: number,\n    fftLength?: number,\n    windowFn: (length: number) => Tensor1D = hannWindow): Tensor {\n  if (fftLength == null) {\n    fftLength = enclosingPowerOfTwo(frameLength);\n  }\n  const framedSignal = frame(signal, frameLength, frameStep);\n  const windowedSignal = mul(framedSignal, windowFn(frameLength));\n  const output: Tensor[] = [];\n  for (let i = 0; i < framedSignal.shape[0]; i++) {\n    output.push(\n        rfft(slice(windowedSignal, [i, 0], [1, frameLength]), fftLength));\n  }\n  return concat(output);\n}\nexport const stft = op({stft_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../../engine';\nimport {CropAndResize, CropAndResizeAttrs, CropAndResizeInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor1D, Tensor2D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\n\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by cropSize.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction cropAndResize_(\n    image: Tensor4D|TensorLike,\n    boxes: Tensor2D|TensorLike,\n    boxInd: Tensor1D|TensorLike,\n    cropSize: [number, number],\n    method?: 'bilinear'|'nearest',\n    extrapolationValue?: number,\n    ): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'cropAndResize');\n  const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n  const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n  method = method || 'bilinear';\n  extrapolationValue = extrapolationValue || 0;\n\n  const numBoxes = $boxes.shape[0];\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in cropAndResize: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n  util.assert(\n      $boxes.rank === 2 && $boxes.shape[1] === 4,\n      () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,\n      () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      cropSize.length === 2,\n      () => `Error in cropAndResize: cropSize must be of length 2, but got ` +\n          `length ${cropSize.length}.`);\n  util.assert(\n      cropSize[0] >= 1 && cropSize[1] >= 1,\n      () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n  util.assert(\n      method === 'bilinear' || method === 'nearest',\n      () => `method must be bilinear or nearest, but was ${method}`);\n\n  const forward: ForwardFunc<Tensor4D> = (backend) => backend.cropAndResize(\n      $image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n\n  const inputs:\n      CropAndResizeInputs = {image: $image, boxes: $boxes, boxInd: $boxInd};\n  const attrs: CropAndResizeAttrs = {method, extrapolationValue, cropSize};\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */, CropAndResize,\n      attrs as {} as NamedAttrMap);\n  return res;\n}\n\nexport const cropAndResize = op({cropAndResize_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {FlipLeftRight, FlipLeftRightInputs} from '../../kernel_names';\nimport {Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\nimport {op} from '../operation';\n\n/**\n * Flips the image left to right. Currently available in the CPU, WebGL, and\n * WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction flipLeftRight_(image: Tensor4D|TensorLike): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32');\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in flipLeftRight: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n\n  const inputs: FlipLeftRightInputs = {image: $image};\n  const res =\n      ENGINE.runKernel(FlipLeftRight, inputs as {} as NamedTensorMap, {});\n  return res as Tensor4D;\n}\n\nexport const flipLeftRight = op({flipLeftRight_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {RotateWithOffset, RotateWithOffsetAttrs, RotateWithOffsetInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\n\n/**\n * Rotates the input image tensor counter-clockwise with an optional offset\n * center of rotation. Currently available in the CPU, WebGL, and WASM backends.\n *\n * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.\n * @param radians The amount of rotation.\n * @param fillValue The value to fill in the empty space leftover\n *     after rotation. Can be either a single grayscale value (0-255), or an\n *     array of three numbers `[red, green, blue]` specifying the red, green,\n *     and blue channels. Defaults to `0` (black).\n * @param center The center of rotation. Can be either a single value (0-1), or\n *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates\n *     the image around its center).\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction rotateWithOffset_(\n    image: Tensor4D|TensorLike, radians: number,\n    fillValue: number|[number, number, number] = 0,\n    center: number|[number, number] = 0.5): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32');\n\n  util.assert(\n      $image.rank === 4,\n      () => 'Error in rotateWithOffset: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n\n  const inputs: RotateWithOffsetInputs = {image: $image};\n  const attrs: RotateWithOffsetAttrs = {radians, fillValue, center};\n  const res = ENGINE.runKernel(\n      RotateWithOffset, inputs as {} as NamedTensorMap,\n      attrs as {} as NamedAttrMap);\n  return res as Tensor4D;\n}\n\nexport const rotateWithOffset = op({rotateWithOffset_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport * as util from '../util';\n\nfunction nonMaxSuppSanityCheck(\n    boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number, softNmsSigma?: number): {\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number,\n  softNmsSigma: number\n} {\n  if (iouThreshold == null) {\n    iouThreshold = 0.5;\n  }\n  if (scoreThreshold == null) {\n    scoreThreshold = Number.NEGATIVE_INFINITY;\n  }\n  if (softNmsSigma == null) {\n    softNmsSigma = 0.0;\n  }\n\n  const numBoxes = boxes.shape[0];\n  maxOutputSize = Math.min(maxOutputSize, numBoxes);\n\n  util.assert(\n      0 <= iouThreshold && iouThreshold <= 1,\n      () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n  util.assert(\n      boxes.rank === 2,\n      () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n  util.assert(\n      boxes.shape[1] === 4,\n      () =>\n          `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n  util.assert(scores.rank === 1, () => 'scores must be a 1D tensor');\n  util.assert(\n      scores.shape[0] === numBoxes,\n      () => `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n          `but was ${scores.shape[0]}`);\n  util.assert(\n      0 <= softNmsSigma && softNmsSigma <= 1,\n      () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n  return {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};\n}\n\nexport {nonMaxSuppSanityCheck};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {NonMaxSuppressionV3} from '../../kernel_names';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\n\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {op} from '../operation';\n\nfunction nonMaxSuppression_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Tensor1D {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  const attrs = {maxOutputSize, iouThreshold, scoreThreshold};\n  return ENGINE.runKernelFunc(\n      b => b.nonMaxSuppression(\n          $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold),\n      {boxes: $boxes, scores: $scores}, null /* grad */, NonMaxSuppressionV3,\n      attrs);\n}\n\nexport const nonMaxSuppression = op({nonMaxSuppression_});\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inserts a value into a sorted array. This method allows duplicate, meaning it\n * allows inserting duplicate value, in which case, the element will be inserted\n * at the lowest index of the value.\n * @param arr The array to modify.\n * @param element The element to insert.\n * @param comparator Optional. If no comparator is specified, elements are\n * compared using array_util.defaultComparator, which is suitable for Strings\n * and Numbers in ascending arrays. If the array contains multiple instances of\n * the target value, the left-most instance will be returned. To provide a\n * comparator, it should take 2 arguments to compare and return a negative,\n * zero, or a positive number.\n */\nexport function binaryInsert<T>(\n    arr: T[], element: T, comparator?: (a: T, b: T) => number) {\n  const index = binarySearch(arr, element, comparator);\n  const insertionPoint = index < 0 ? -(index + 1) : index;\n  arr.splice(insertionPoint, 0, element);\n}\n\n/**\n * Searches the array for the target using binary search, returns the index\n * of the found element, or position to insert if element not found. If no\n * comparator is specified, elements are compared using array_\n * util.defaultComparator, which is suitable for Strings and Numbers in\n * ascending arrays. If the array contains multiple instances of the target\n * value, the left-most instance will be returned.\n * @param arr The array to be searched in.\n * @param target The target to be searched for.\n * @param comparator Should take 2 arguments to compare and return a negative,\n *    zero, or a positive number.\n * @return Lowest index of the target value if found, otherwise the insertion\n *    point where the target should be inserted, in the form of\n *    (-insertionPoint - 1).\n */\nexport function binarySearch<T>(\n    arr: T[], target: T, comparator?: (a: T, b: T) => number) {\n  return binarySearch_(arr, target, comparator || defaultComparator);\n}\n\n/**\n * Compares its two arguments for order.\n * @param a The first element to be compared.\n * @param b The second element to be compared.\n * @return A negative number, zero, or a positive number as the first\n *     argument is less than, equal to, or greater than the second.\n */\nfunction defaultComparator<T>(a: T, b: T): number {\n  return a > b ? 1 : a < b ? -1 : 0;\n}\n\nfunction binarySearch_<T>(\n    arr: T[], target: T, comparator: (a: T, b: T) => number) {\n  let left = 0;\n  let right = arr.length;\n  let middle = 0;\n  let found = false;\n  while (left < right) {\n    middle = left + ((right - left) >>> 1);\n    const compareResult = comparator(target, arr[middle]);\n    if (compareResult > 0) {\n      left = middle + 1;\n    } else {\n      right = middle;\n      // If compareResult is 0, the value is found. We record it is found,\n      // and then keep looking because there may be duplicate.\n      found = !compareResult;\n    }\n  }\n\n  return found ? left : -left - 1;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\n\nimport {scalar} from '../ops/scalar';\nimport {tensor1d} from '../ops/tensor1d';\nimport {Tensor1D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\n\nimport {binaryInsert} from './array_util';\n\ninterface Candidate {\n  score: number;\n  boxIndex: number;\n  suppressBeginIndex: number;\n}\n\nexport function nonMaxSuppressionV3Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number): Tensor1D {\n  return nonMaxSuppressionImpl_(\n             boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n             0 /* softNmsSigma */)\n             .selectedIndices as Tensor1D;\n}\n\nexport function nonMaxSuppressionV4Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number,\n    padToMaxOutputSize: boolean): NamedTensorMap {\n  return nonMaxSuppressionImpl_(\n      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold,\n      0 /* softNmsSigma */, false /* returnScoresTensor */,\n      padToMaxOutputSize /* padToMaxOutputSize */, true\n      /* returnValidOutputs */);\n}\n\nexport function nonMaxSuppressionV5Impl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number,\n    softNmsSigma: number): NamedTensorMap {\n  return nonMaxSuppressionImpl_(\n      boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma,\n      true /* returnScoresTensor */);\n}\n\nfunction nonMaxSuppressionImpl_(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number, softNmsSigma: number,\n    returnScoresTensor = false, padToMaxOutputSize = false,\n    returnValidOutputs = false): NamedTensorMap {\n  // The list is sorted in ascending order, so that we can always pop the\n  // candidate with the largest score in O(1) time.\n  const candidates = [];\n\n  for (let i = 0; i < scores.length; i++) {\n    if (scores[i] > scoreThreshold) {\n      candidates.push({score: scores[i], boxIndex: i, suppressBeginIndex: 0});\n    }\n  }\n\n  candidates.sort(ascendingComparator);\n\n  // If softNmsSigma is 0, the outcome of this algorithm is exactly same as\n  // before.\n  const scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0;\n\n  const selectedIndices: number[] = [];\n  const selectedScores: number[] = [];\n\n  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n    const candidate = candidates.pop();\n    const {score: originalScore, boxIndex, suppressBeginIndex} = candidate;\n\n    if (originalScore < scoreThreshold) {\n      break;\n    }\n\n    // Overlapping boxes are likely to have similar scores, therefore we\n    // iterate through the previously selected boxes backwards in order to\n    // see if candidate's score should be suppressed. We use\n    // suppressBeginIndex to track and ensure a candidate can be suppressed\n    // by a selected box no more than once. Also, if the overlap exceeds\n    // iouThreshold, we simply ignore the candidate.\n    let ignoreCandidate = false;\n    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n\n      candidate.score =\n          candidate.score * suppressWeight(iouThreshold, scale, iou);\n\n      if (candidate.score <= scoreThreshold) {\n        break;\n      }\n    }\n\n    // At this point, if `candidate.score` has not dropped below\n    // `scoreThreshold`, then we know that we went through all of the\n    // previous selections and can safely update `suppressBeginIndex` to the\n    // end of the selected array. Then we can re-insert the candidate with\n    // the updated score and suppressBeginIndex back in the candidate list.\n    // If on the other hand, `candidate.score` has dropped below the score\n    // threshold, we will not add it back to the candidates list.\n    candidate.suppressBeginIndex = selectedIndices.length;\n\n    if (!ignoreCandidate) {\n      // Candidate has passed all the tests, and is not suppressed, so\n      // select the candidate.\n      if (candidate.score === originalScore) {\n        selectedIndices.push(boxIndex);\n        selectedScores.push(candidate.score);\n      } else if (candidate.score > scoreThreshold) {\n        // Candidate's score is suppressed but is still high enough to be\n        // considered, so add back to the candidates list.\n        binaryInsert(candidates, candidate, ascendingComparator);\n      }\n    }\n  }\n\n  // NonMaxSuppressionV4 feature: padding output to maxOutputSize.\n  const validOutputs = selectedIndices.length;\n  const elemsToPad = maxOutputSize - validOutputs;\n\n  if (padToMaxOutputSize && elemsToPad > 0) {\n    selectedIndices.push(...new Array(elemsToPad).fill(0));\n    selectedScores.push(...new Array(elemsToPad).fill(0.0));\n  }\n\n  const result:\n      NamedTensorMap = {selectedIndices: tensor1d(selectedIndices, 'int32')};\n\n  if (returnScoresTensor) {\n    result['selectedScores'] = tensor1d(selectedScores, 'float32');\n  }\n\n  if (returnValidOutputs) {\n    result['validOutputs'] = scalar(validOutputs, 'int32');\n  }\n\n  return result;\n}\n\nfunction intersectionOverUnion(boxes: TypedArray, i: number, j: number) {\n  const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n\n// A Gaussian penalty function, this method always returns values in [0, 1].\n// The weight is a function of similarity, the more overlap two boxes are, the\n// smaller the weight is, meaning highly overlapping boxe will be significantly\n// penalized. On the other hand, a non-overlapping box will not be penalized.\nfunction suppressWeight(iouThreshold: number, scale: number, iou: number) {\n  const weight = Math.exp(scale * iou * iou);\n  return iou <= iouThreshold ? weight : 0.0;\n}\n\nfunction ascendingComparator(c1: Candidate, c2: Candidate) {\n  // For objects with same scores, we make the object with the larger index go\n  // first. In an array that pops from the end, this means that the object with\n  // the smaller index will be popped first. This ensures the same output as\n  // the TensorFlow python version.\n  return (c1.score - c2.score) ||\n      ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex));\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {nonMaxSuppressionV3Impl} from '../../backends/non_max_suppression_impl';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This is the async version of `nonMaxSuppression`\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Promise<Tensor1D> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n\n  // We call a cpu based impl directly with the typedarray data  here rather\n  // than a kernel because all kernels are synchronous (and thus cannot await\n  // .data()).\n  const res = nonMaxSuppressionV3Impl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return res;\n}\n\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {NonMaxSuppressionV5, NonMaxSuppressionV5Attrs, NonMaxSuppressionV5Inputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\n\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {op} from '../operation';\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionWithScore_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY,\n    softNmsSigma = 0.0): NamedTensorMap {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const params = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,\n      softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n\n  const inputs: NonMaxSuppressionV5Inputs = {boxes: $boxes, scores: $scores};\n  const attrs: NonMaxSuppressionV5Attrs =\n      {maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma};\n\n  const result = ENGINE.runKernel(\n                     NonMaxSuppressionV5, inputs as {} as NamedTensorMap,\n                     attrs as {} as NamedAttrMap) as Tensor[];\n\n  return {selectedIndices: result[0], selectedScores: result[1]};\n}\n\nexport const nonMaxSuppressionWithScore = op({nonMaxSuppressionWithScore_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {nonMaxSuppressionV5Impl} from '../../backends/non_max_suppression_impl';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\n\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union).\n *\n * This op also supports a Soft-NMS mode (c.f.\n * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score\n * of other overlapping boxes, therefore favoring different regions of the image\n * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`\n * parameter to be larger than 0.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param softNmsSigma A float representing the sigma parameter for Soft NMS.\n *     When sigma is 0, it falls back to nonMaxSuppression.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - selectedScores: A 1D tensor with the corresponding scores for each\n *       selected box.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionWithScoreAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY,\n    softNmsSigma = 0.0): Promise<NamedTensorMap> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const params = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,\n      softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n\n  // We call a cpu based impl directly with the typedarray data  here rather\n  // than a kernel because all kernels are synchronous (and thus cannot await\n  // .data()).\n  const res = nonMaxSuppressionV5Impl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold,\n      softNmsSigma);\n\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return res;\n}\n\nexport const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\n\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\nimport {op} from '../operation';\n\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction nonMaxSuppressionPadded_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY,\n    padToMaxOutputSize = false): NamedTensorMap {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const params = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,\n      null /* softNmsSigma */);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n\n  const inputs: NonMaxSuppressionV4Inputs = {boxes: $boxes, scores: $scores};\n  const attrs: NonMaxSuppressionV4Attrs = {\n    maxOutputSize: $maxOutputSize,\n    iouThreshold: $iouThreshold,\n    scoreThreshold: $scoreThreshold,\n    padToMaxOutputSize\n  };\n\n  const result = ENGINE.runKernel(\n                     NonMaxSuppressionV4, inputs as {} as NamedTensorMap,\n                     attrs as {} as NamedAttrMap) as Tensor[];\n\n  return {selectedIndices: result[0], validOutputs: result[1]};\n}\n\nexport const nonMaxSuppressionPadded = op({nonMaxSuppressionPadded_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {nonMaxSuppressionV4Impl} from '../../backends/non_max_suppression_impl';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {nonMaxSuppSanityCheck} from '../nonmax_util';\n\n/**\n * Asynchronously performs non maximum suppression of bounding boxes based on\n * iou (intersection over union), with an option to pad results.\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @param padToMaxOutputSize Defalts to false. If true, size of output\n *     `selectedIndices` is padded to maxOutputSize.\n * @return A map with the following properties:\n *     - selectedIndices: A 1D tensor with the selected box indices.\n *     - validOutputs: A scalar denoting how many elements in `selectedIndices`\n *       are valid. Valid elements occur first, then padding.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nasync function nonMaxSuppressionPaddedAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY,\n    padToMaxOutputSize = false): Promise<NamedTensorMap> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const params = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold,\n      null /* softNmsSigma */);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n\n  const [boxesVals, scoresVals] =\n      await Promise.all([$boxes.data(), $scores.data()]);\n\n  // We call a cpu based impl directly with the typedarray data here rather\n  // than a kernel because all kernels are synchronous (and thus cannot await\n  // .data()).\n  const res = nonMaxSuppressionV4Impl(\n      boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold,\n      padToMaxOutputSize);\n\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return res;\n}\n\nexport const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../../engine';\nimport {ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Bilinear resize a single 3D image or a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeBilinear_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeBilinear');\n\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      () => `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      () => `Error in resizeBilinear: new shape must 2D, but got shape ` +\n          `${size}.`);\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape(\n        $images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n\n  const [newHeight, newWidth] = size;\n  const forward: ForwardFunc<Tensor4D> = (backend, save) => {\n    save([batchImages]);\n    return backend.resizeBilinear(\n        batchImages, newHeight, newWidth, alignCorners);\n  };\n\n  const inputs: ResizeBilinearInputs = {images: batchImages};\n  const attrs: ResizeBilinearAttrs = {alignCorners, size};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* gradient */,\n      ResizeBilinear, attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const resizeBilinear = op({resizeBilinear_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../../engine';\nimport {ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor3D, Tensor4D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\n\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n *\n * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}\n */\nfunction resizeNearestNeighbor_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      () =>\n          `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n          `${size}.`);\n  util.assert(\n      $images.dtype === 'float32' || $images.dtype === 'int32',\n      () => '`images` must have `int32` or `float32` as dtype');\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape(\n        $images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n  const [newHeight, newWidth] = size;\n\n  const inputs: ResizeNearestNeighborInputs = {images: batchImages};\n  const attrs: ResizeNearestNeighborAttrs = {alignCorners, size};\n\n  const forward: ForwardFunc<Tensor4D> = (backend, save) => {\n    save([batchImages]);\n    return backend.resizeNearestNeighbor(\n        batchImages, newHeight, newWidth, alignCorners);\n  };\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* gradient */,\n      ResizeNearestNeighbor, attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const resizeNearestNeighbor = op({resizeNearestNeighbor_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assert} from '../../util';\n\nimport {greaterEqual} from '../greater_equal';\nimport {lessEqual} from '../less_equal';\nimport {logicalAnd} from '../logical_and';\nimport {op} from '../operation';\nimport {range} from '../range';\nimport {reshape} from '../reshape';\nimport {scalar} from '../scalar';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {unstack} from '../unstack';\nimport {where} from '../where';\nimport {zeros} from '../zeros';\n\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_<T extends Tensor>(\n    a: T|TensorLike, numLower: number, numUpper: number): T {\n  assert(\n      numLower % 1 === 0,\n      () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n  assert(\n      numUpper % 1 === 0,\n      () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n\n  const $a = convertToTensor(a, 'a', 'bandPart');\n\n  assert(\n      $a.rank >= 2,\n      () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n\n  const shape = $a.shape;\n  const [M, N] = $a.shape.slice(-2);\n\n  if (!(numLower <= M)) {\n    throw new Error(\n        `bandPart(): numLower (${numLower})` +\n        ` must not be greater than the number of rows (${M}).`);\n  }\n  if (!(numUpper <= N)) {\n    throw new Error(\n        `bandPart(): numUpper (${numUpper})` +\n        ` must not be greater than the number of columns (${N}).`);\n  }\n\n  if (numLower < 0) {\n    numLower = M;\n  }\n  if (numUpper < 0) {\n    numUpper = N;\n  }\n\n  const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n  const j = range(0, N, 1, 'int32');\n  const ij = sub(i, j);\n\n  const inBand = logicalAnd(\n      lessEqual(ij, scalar(+numLower, 'int32')),\n      greaterEqual(ij, scalar(-numUpper, 'int32')));\n\n  const zero = zeros([M, N], $a.dtype);\n\n  return reshape(\n             stack(unstack(reshape($a, [-1, M, N]))\n                       .map(mat => where(inBand, mat, zero))),\n             shape) as T;\n}\n\nexport const bandPart = op({bandPart_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {Tensor1D, Tensor2D} from '../../tensor';\nimport {assert} from '../../util';\n\nimport {div} from '../div';\nimport {mul} from '../mul';\nimport {norm} from '../norm';\nimport {op} from '../operation';\nimport {split} from '../split';\nimport {squeeze} from '../squeeze';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {sum} from '../sum';\n\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * const data = await y.array();\n * console.log(data[0][1] / data[0][0]);  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction gramSchmidt_(xs: Tensor1D[]|Tensor2D): Tensor1D[]|Tensor2D {\n  let inputIsTensor2D: boolean;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(\n        xs != null && xs.length > 0,\n        () => 'Gram-Schmidt process: input must not be null, undefined, or ' +\n            'empty');\n    const dim = xs[0].shape[0];\n    for (let i = 1; i < xs.length; ++i) {\n      assert(\n          xs[i].shape[0] === dim,\n          () =>\n              'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n              `(${(xs as Tensor1D[])[i].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n  }\n\n  assert(\n      xs.length <= xs[0].shape[0],\n      () => `Gram-Schmidt: Number of vectors (${\n                (xs as Tensor1D[]).length}) exceeds ` +\n          `number of dimensions (${(xs as Tensor1D[])[0].shape[0]}).`);\n\n  const ys: Tensor1D[] = [];\n  const xs1d = xs;\n  for (let i = 0; i < xs.length; ++i) {\n    ys.push(ENGINE.tidy(() => {\n      let x = xs1d[i];\n      if (i > 0) {\n        for (let j = 0; j < i; ++j) {\n          const proj = mul(sum(mul(ys[j], x)), ys[j]);\n          x = sub(x, proj);\n        }\n      }\n      return div(x, norm(x, 'euclidean'));\n    }));\n  }\n\n  if (inputIsTensor2D) {\n    return stack(ys, 0) as Tensor2D;\n  } else {\n    return ys;\n  }\n}\n\nexport const gramSchmidt = op({gramSchmidt_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../../engine';\nimport {dispose} from '../../globals';\nimport {Tensor, Tensor2D} from '../../tensor';\nimport {assert} from '../../util';\n\nimport {clone} from '../clone';\nimport {concat} from '../concat';\nimport {div} from '../div';\nimport {eye} from '../eye';\nimport {greater} from '../greater';\nimport {matMul} from '../mat_mul';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {norm} from '../norm';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\nimport {slice} from '../slice';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {tensor2d} from '../tensor2d';\nimport {transpose} from '../transpose';\nimport {unstack} from '../unstack';\nimport {where} from '../where';\n\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n *\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x: Tensor, fullMatrices = false): [Tensor, Tensor] {\n  assert(\n      x.rank >= 2,\n      () => `qr() requires input tensor to have a rank >= 2, but got rank ${\n          x.rank}`);\n\n  if (x.rank === 2) {\n    return qr2d(x as Tensor2D, fullMatrices);\n  } else {\n    // Rank > 2.\n    // TODO(cais): Below we split the input into individual 2D tensors,\n    //   perform QR decomposition on them and then stack the results back\n    //   together. We should explore whether this can be parallelized.\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n                              .reduce((value, prev) => value * prev);\n    const x2ds = unstack(\n        reshape(\n            x,\n            [\n              outerDimsProd, x.shape[x.shape.length - 2],\n              x.shape[x.shape.length - 1]\n            ]),\n        0);\n    const q2ds: Tensor2D[] = [];\n    const r2ds: Tensor2D[] = [];\n    x2ds.forEach(x2d => {\n      const [q2d, r2d] = qr2d(x2d as Tensor2D, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = reshape(stack(q2ds, 0), x.shape);\n    const r = reshape(stack(r2ds, 0), x.shape);\n    return [q, r];\n  }\n}\n\nfunction qr2d(x: Tensor2D, fullMatrices = false): [Tensor2D, Tensor2D] {\n  return ENGINE.tidy(() => {\n    assert(\n        x.shape.length === 2,\n        () => `qr2d() requires a 2D Tensor, but got a ${\n            x.shape.length}D Tensor.`);\n\n    const m = x.shape[0];\n    const n = x.shape[1];\n\n    let q = eye(m);    // Orthogonal transform so far.\n    let r = clone(x);  // Transformed matrix so far.\n\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w: Tensor2D = clone(one2D);\n\n    const iters = m >= n ? n : m;\n    for (let j = 0; j < iters; ++j) {\n      // This tidy within the for-loop ensures we clean up temporary\n      // tensors as soon as they are no longer needed.\n      const rTemp = r;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r, q] = ENGINE.tidy((): [Tensor2D, Tensor2D, Tensor2D] => {\n        // Find H = I - tau * w * w', to put zeros below R(j, j).\n        const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n        const normX = norm(rjEnd1);\n        const rjj = slice(r, [j, j], [1, 1]);\n\n        // The sign() function returns 0 on 0, which causes division by zero.\n        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n\n        const u1 = sub(rjj, mul(s, normX));\n        const wPre = div(rjEnd1, u1);\n        if (wPre.shape[0] === 1) {\n          w = clone(one2D);\n        } else {\n          w = concat(\n              [\n                one2D,\n                slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) as\n                    Tensor2D\n              ],\n              0);\n        }\n        const tau = neg(div(matMul(s, u1), normX)) as Tensor2D;\n\n        // -- R := HR, Q := QH.\n        const rjEndAll = slice(r, [j, 0], [m - j, n]);\n        const tauTimesW: Tensor2D = mul(tau, w);\n        const wT: Tensor2D = transpose(w);\n        if (j === 0) {\n          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n        } else {\n          const rTimesTau: Tensor2D =\n              sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n        }\n        const tawTimesWT: Tensor2D = transpose(tauTimesW);\n        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n        } else {\n          const qTimesTau: Tensor2D =\n              sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n        }\n        return [w, r, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n\n    if (!fullMatrices && m > n) {\n      q = slice(q, [0, 0], [m, n]);\n      r = slice(r, [0, 0], [n, n]);\n    }\n\n    return [q, r];\n  }) as [Tensor2D, Tensor2D];\n}\n\nexport const qr = op({qr_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport enum Reduction {\n  NONE,\n  MEAN,\n  SUM,\n  SUM_BY_NONZERO_WEIGHTS\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\n\nimport {cast} from '../cast';\nimport {div} from '../div';\nimport {Reduction} from '../loss_ops_utils';\nimport {mean} from '../mean';\nimport {mul} from '../mul';\nimport {notEqual} from '../not_equal';\nimport {ones} from '../ones';\nimport {op} from '../operation';\nimport {scalar} from '../scalar';\nimport {sum} from '../sum';\n\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction computeWeightedLoss_<T extends Tensor, O extends Tensor>(\n    losses: T|TensorLike, weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n  }\n\n  const weightedLoss = ($weights == null) ? $losses : mul($losses, $weights);\n\n  if (reduction === Reduction.NONE) {\n    return weightedLoss as O;\n  }\n  if (reduction === Reduction.SUM) {\n    return sum(weightedLoss);\n  }\n  if (reduction === Reduction.MEAN) {\n    if ($weights == null) {\n      return mean(weightedLoss);\n    } else {\n      const broadcastFactor = $losses.size / $weights.size;\n      const result = div(sum(weightedLoss), sum($weights));\n      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) :\n                                   result as O;\n    }\n  }\n  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    if ($weights == null) {\n      return div(sum(weightedLoss), scalar($losses.size));\n    } else {\n      const broadcastedWeights = mul($weights, ones($losses.shape));\n\n      const numNonZeros =\n          cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32');\n      return div(sum(weightedLoss), numNonZeros);\n    }\n  }\n\n  throw Error(`Unknown reduction: ${reduction}`);\n}\nexport const computeWeightedLoss = op({computeWeightedLoss_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {abs} from '../abs';\nimport {Reduction} from '../loss_ops_utils';\nimport {op} from '../operation';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction absoluteDifference_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'absoluteDifference');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n\n  const losses = abs(sub($labels, $predictions));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const absoluteDifference = op({absoluteDifference_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\nimport {sum} from '../sum';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction cosineDistance_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike, axis: number,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'cosineDistance');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n\n  const one = scalar(1);\n  const losses = sub(one, sum(mul($labels, $predictions), axis, true));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const cosineDistance = op({cosineDistance_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {relu} from '../relu';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction hingeLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n\n  const one = scalar(1);\n  // Convert binary labels to (-1, 1)\n  $labels = sub(mul(scalar(2), $labels), one);\n  const losses = relu(sub(one, mul($labels, $predictions)));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const hingeLoss = op({hingeLoss_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {abs} from '../abs';\nimport {add} from '../add';\nimport {Reduction} from '../loss_ops_utils';\nimport {minimum} from '../minimum';\nimport {mul} from '../mul';\nimport {op} from '../operation';\nimport {scalar} from '../scalar';\nimport {square} from '../square';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction huberLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, delta = 1.0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'huberLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n\n  const deltaScalar = scalar(delta);\n  const error = abs(sub($predictions, $labels));\n  const quadratic = minimum(error, deltaScalar);\n  const linear = sub(error, quadratic);\n\n  const losses =\n      add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const huberLoss = op({huberLoss_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {add} from '../add';\nimport {log} from '../log';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction logLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, epsilon = 1e-7,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'logLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'logLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n\n  const one = scalar(1);\n  const epsilonScalar = scalar(epsilon);\n\n  const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));\n  const l2 =\n      mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));\n  const losses = sub(l1, l2);\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const logLoss = op({logLoss_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {Reduction} from '../loss_ops_utils';\nimport {op} from '../operation';\nimport {squaredDifference} from '../squared_difference';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}\n */\nfunction meanSquaredError_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'meanSquaredError');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n\n  const losses = squaredDifference($labels, $predictions);\n  return computeWeightedLoss(losses, $weights, reduction);\n}\nexport const meanSquaredError = op({meanSquaredError_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {abs} from '../abs';\nimport {add} from '../add';\nimport {exp} from '../exp';\nimport {log1p} from '../log1p';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {relu} from '../relu';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\nfunction sigmoidCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, logits: T|TensorLike): O {\n  const $labels =\n      convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n  const $logits =\n      convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n  assertShapesMatch(\n      $labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n\n  /**\n   * Implementation Details:\n   *\n   * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n   *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n   *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n   *   = (1 - z) * x + log(1 + exp(-x))\n   *   = x - x * z + log(1 + exp(-x))\n   *\n   *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n   *     x - x * z + log(1 + exp(-x))\n   *   = log(exp(x)) - x * z + log(1 + exp(-x))\n   *   = - x * z + log(1 + exp(x))\n   *\n   * Hence, to ensure stability and avoid overflow, the implementation uses\n   * this equivalent formulation:\n   *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n   */\n  const maxOutput = relu($logits);\n  const outputXTarget = mul($logits, $labels);\n  const sigmoidOutput = log1p(exp(neg(abs($logits))));\n\n  return add(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\n\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction sigmoidCrossEntropy_<T extends Tensor, O extends Tensor>(\n    multiClassLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $multiClassLabels = convertToTensor(\n      multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n  }\n  assertShapesMatch(\n      $multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n\n    $multiClassLabels =\n        add(mul($multiClassLabels, sub(one, labelSmoothingScalar)),\n            mul(half, labelSmoothingScalar));\n  }\n  const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {customGrad} from '../../gradients';\nimport {Tensor} from '../../tensor';\nimport {GradSaveFunc} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assertShapesMatch} from '../../util';\nimport {add} from '../add';\nimport {expandShapeToKeepDim} from '../axis_util';\nimport {cast} from '../cast';\nimport {div} from '../div';\nimport {exp} from '../exp';\nimport {logSumExp} from '../log_sum_exp';\nimport {Reduction} from '../loss_ops_utils';\nimport {mul} from '../mul';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\nimport {scalar} from '../scalar';\nimport {sub} from '../sub';\nimport {sum} from '../sum';\n\nimport {computeWeightedLoss} from './compute_weighted_loss';\n\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T, logits: T, dim = -1): O {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n\n  if (dim !== logits.rank - 1) {\n    throw Error(\n        `Softmax cross entropy along a non-last dimension is not yet ` +\n        `supported. Labels / logits was rank ${logits.rank} ` +\n        `and dim was ${dim}`);\n  }\n  // Use a custom gradient for numerical stability.\n  const customOp =\n      customGrad((labels: Tensor, logits: Tensor, save: GradSaveFunc) => {\n        // Reference:\n        //   1. http://cs231n.github.io/linear-classify/#softmax\n        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n        const keepDims = true;\n        const lse = logSumExp(logits, [dim], keepDims);\n        const logResult = sub(cast(logits, 'float32'), lse);\n        save([labels, logResult]);\n\n        const costVector = neg(mul(logResult, labels));\n        const value: O = sum(costVector, [dim]);\n\n        const gradFunc = (dy: O, saved: Tensor[]) => {\n          const [labels, logResult] = saved;\n          const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n          return [\n            mul(reshape(dy, dyShape),\n                sub(cast(labels, 'float32'), exp(logResult))),\n            mul(reshape(dy, dyShape),\n                sub(exp(logResult), cast(labels, 'float32'))),\n          ];\n        };\n        return {value, gradFunc};\n      });\n\n  return customOp(labels, logits);\n}\n\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n *\n * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }\n */\nfunction softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(\n    onehotLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $onehotLabels =\n      convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n  let $weights: Tensor = null;\n\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n  }\n\n  assertShapesMatch(\n      $onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n\n    $onehotLabels =\n        add(mul($onehotLabels, sub(one, labelSmoothingScalar)),\n            div(labelSmoothingScalar, numClasses));\n  }\n\n  const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const softmaxCrossEntropy = op({softmaxCrossEntropy_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Modularized ops.\nexport {abs} from './abs';\nexport {acos} from './acos';\nexport {acosh} from './acosh';\nexport {add} from './add';\nexport {addN} from './add_n';\nexport {all} from './all';\nexport {any} from './any';\nexport {argMax} from './arg_max';\nexport {argMin} from './arg_min';\nexport {asin} from './asin';\nexport {asinh} from './asinh';\nexport {atan} from './atan';\nexport {atan2} from './atan2';\nexport {atanh} from './atanh';\nexport {avgPool} from './avg_pool';\nexport {avgPool3d} from './avg_pool_3d';\nexport {basicLSTMCell} from './basic_lstm_cell';\nexport {batchToSpaceND} from './batch_to_space_nd';\nexport {batchNorm} from './batchnorm';\nexport {batchNorm2d} from './batchnorm2d';\nexport {batchNorm3d} from './batchnorm3d';\nexport {batchNorm4d} from './batchnorm4d';\nexport {broadcastTo} from './broadcast_to';\nexport {buffer} from './buffer';\nexport {cast} from './cast';\nexport {ceil} from './ceil';\nexport {clipByValue} from './clip_by_value';\nexport {clone} from './clone';\nexport {complex} from './complex';\nexport {concat} from './concat';\nexport {concat1d} from './concat_1d';\nexport {concat2d} from './concat_2d';\nexport {concat3d} from './concat_3d';\nexport {concat4d} from './concat_4d';\nexport {conv1d} from './conv1d';\nexport {conv2d} from './conv2d';\nexport {conv2dTranspose} from './conv2d_transpose';\nexport {conv3d} from './conv3d';\nexport {conv3dTranspose} from './conv3d_transpose';\nexport {cos} from './cos';\nexport {cosh} from './cosh';\nexport {cumsum} from './cumsum';\nexport {depthToSpace} from './depth_to_space';\nexport {depthwiseConv2d} from './depthwise_conv2d';\nexport {diag} from './diag';\nexport {dilation2d} from './dilation2d';\nexport {div} from './div';\nexport {divNoNan} from './div_no_nan';\nexport {dot} from './dot';\nexport {elu} from './elu';\nexport {equal} from './equal';\nexport {erf} from './erf';\nexport {exp} from './exp';\nexport {expandDims} from './expand_dims';\nexport {expm1} from './expm1';\nexport {eye} from './eye';\nexport {fill} from './fill';\nexport {floor} from './floor';\nexport {floorDiv} from './floorDiv';\nexport {gather} from './gather';\nexport {greater} from './greater';\nexport {greaterEqual} from './greater_equal';\nexport {imag} from './imag';\nexport {isFinite} from './is_finite';\nexport {isInf} from './is_inf';\nexport {isNaN} from './is_nan';\nexport {leakyRelu} from './leaky_relu';\nexport {less} from './less';\nexport {lessEqual} from './less_equal';\nexport {linspace} from './linspace';\nexport {localResponseNormalization} from './local_response_normalization';\nexport {log} from './log';\nexport {log1p} from './log1p';\nexport {logSigmoid} from './log_sigmoid';\nexport {logSoftmax} from './log_softmax';\nexport {logSumExp} from './log_sum_exp';\nexport {logicalAnd} from './logical_and';\nexport {logicalNot} from './logical_not';\nexport {logicalOr} from './logical_or';\nexport {logicalXor} from './logical_xor';\nexport {matMul} from './mat_mul';\nexport {max} from './max';\nexport {maxPool} from './max_pool';\nexport {maxPool3d} from './max_pool_3d';\nexport {maxPoolWithArgmax} from './max_pool_with_argmax';\nexport {maximum} from './maximum';\nexport {mean} from './mean';\nexport {min} from './min';\nexport {minimum} from './minimum';\nexport {mirrorPad} from './mirror_pad';\nexport {mod} from './mod';\nexport {moments} from './moments';\nexport {mul} from './mul';\nexport {LSTMCellFunc, multiRNNCell} from './multi_rnn_cell';\nexport {multinomial} from './multinomial';\nexport {neg} from './neg';\nexport {notEqual} from './not_equal';\nexport {oneHot} from './one_hot';\nexport {ones} from './ones';\nexport {onesLike} from './ones_like';\nexport {outerProduct} from './outer_product';\nexport {pad} from './pad';\nexport {pad1d} from './pad1d';\nexport {pad2d} from './pad2d';\nexport {pad3d} from './pad3d';\nexport {pad4d} from './pad4d';\nexport {pool} from './pool';\nexport {pow} from './pow';\nexport {prelu} from './prelu';\nexport {print} from './print';\nexport {prod} from './prod';\nexport {rand} from './rand';\nexport {randomGamma} from './random_gamma';\nexport {randomNormal} from './random_normal';\nexport {randomUniform} from './random_uniform';\nexport {range} from './range';\nexport {real} from './real';\nexport {reciprocal} from './reciprocal';\nexport {relu} from './relu';\nexport {relu6} from './relu6';\nexport {reshape} from './reshape';\nexport {reverse} from './reverse';\nexport {reverse1d} from './reverse_1d';\nexport {reverse2d} from './reverse_2d';\nexport {reverse3d} from './reverse_3d';\nexport {reverse4d} from './reverse_4d';\nexport {round} from './round';\nexport {rsqrt} from './rsqrt';\nexport {scalar} from './scalar';\nexport {selu} from './selu';\nexport {separableConv2d} from './separable_conv2d';\nexport {setdiff1dAsync} from './setdiff1d_async';\nexport {sigmoid} from './sigmoid';\nexport {sign} from './sign';\nexport {sin} from './sin';\nexport {sinh} from './sinh';\nexport {slice} from './slice';\nexport {slice1d} from './slice1d';\nexport {slice2d} from './slice2d';\nexport {slice3d} from './slice3d';\nexport {slice4d} from './slice4d';\nexport {softmax} from './softmax';\nexport {softplus} from './softplus';\nexport {spaceToBatchND} from './space_to_batch_nd';\nexport {fft} from './spectral/fft';\nexport {ifft} from './spectral/ifft';\nexport {irfft} from './spectral/irfft';\nexport {rfft} from './spectral/rfft';\nexport {split} from './split';\nexport {sqrt} from './sqrt';\nexport {square} from './square';\nexport {squaredDifference} from './squared_difference';\nexport {squeeze} from './squeeze';\nexport {stack} from './stack';\nexport {step} from './step';\nexport {stridedSlice} from './strided_slice';\nexport {sub} from './sub';\nexport {sum} from './sum';\nexport {tan} from './tan';\nexport {tanh} from './tanh';\nexport {tensor} from './tensor';\nexport {tensor1d} from './tensor1d';\nexport {tensor2d} from './tensor2d';\nexport {tensor3d} from './tensor3d';\nexport {tensor4d} from './tensor4d';\nexport {tensor5d} from './tensor5d';\nexport {tensor6d} from './tensor6d';\nexport {tile} from './tile';\nexport {topk} from './topk';\nexport {truncatedNormal} from './truncated_normal';\nexport {unique} from './unique';\nexport {unsortedSegmentSum} from './unsorted_segment_sum';\nexport {unstack} from './unstack';\nexport {variable} from './variable';\nexport {where} from './where';\nexport {whereAsync} from './where_async';\nexport {zeros} from './zeros';\nexport {zerosLike} from './zeros_like';\n\nexport * from './boolean_mask';\nexport * from './compare';\nexport * from './binary_ops';\nexport * from './transpose';\nexport * from './norm';\nexport * from './moving_average';\nexport * from './scatter_nd';\nexport * from './sparse_to_dense';\nexport * from './gather_nd';\nexport * from './dropout';\nexport * from './signal_ops_util';\nexport * from './in_top_k';\n\nexport {op, OP_SCOPE_SUFFIX} from './operation';\n\nimport {rfft} from './spectral/rfft';\nimport {fft} from './spectral/fft';\nimport {ifft} from './spectral/ifft';\nimport {irfft} from './spectral/irfft';\nconst spectral = {\n  fft,\n  ifft,\n  rfft,\n  irfft\n};\n\nimport * as fused from './fused_ops';\n\nimport {hammingWindow} from './signal/hamming_window';\nimport {hannWindow} from './signal/hann_window';\nimport {frame} from './signal/frame';\nimport {stft} from './signal/stft';\nconst signal = {\n  hammingWindow,\n  hannWindow,\n  frame,\n  stft,\n};\n\n// Image Ops namespace\nimport {cropAndResize} from './image/crop_and_resize';\nimport {flipLeftRight} from './image/flip_left_right';\nimport {rotateWithOffset} from './image/rotate_with_offset';\nimport {nonMaxSuppression} from './image/non_max_suppression';\nimport {nonMaxSuppressionAsync} from './image/non_max_suppression_async';\nimport {nonMaxSuppressionWithScore} from './image/non_max_suppression_with_score';\nimport {nonMaxSuppressionWithScoreAsync} from './image/non_max_suppression_with_score_async';\nimport {nonMaxSuppressionPadded} from './image/non_max_suppression_padded';\nimport {nonMaxSuppressionPaddedAsync} from './image/non_max_suppression_padded_async';\nimport {resizeBilinear} from './image/resize_bilinear';\nimport {resizeNearestNeighbor} from './image/resize_nearest_neighbor';\nconst image = {\n  flipLeftRight,\n  resizeNearestNeighbor,\n  resizeBilinear,\n  rotateWithOffset,\n  cropAndResize,\n  nonMaxSuppression,\n  nonMaxSuppressionAsync,\n  nonMaxSuppressionWithScore,\n  nonMaxSuppressionWithScoreAsync,\n  nonMaxSuppressionPadded,\n  nonMaxSuppressionPaddedAsync\n};\n\n// linalg namespace\nimport {bandPart} from './linalg/band_part';\nimport {gramSchmidt} from './linalg/gram_schmidt';\nimport {qr} from './linalg/qr';\nconst linalg = {\n  bandPart,\n  gramSchmidt,\n  qr\n};\n\n// losses namespace;\nimport {absoluteDifference} from './losses/absolute_difference';\nimport {computeWeightedLoss} from './losses/compute_weighted_loss';\nimport {cosineDistance} from './losses/cosine_distance';\nimport {hingeLoss} from './losses/hinge_loss';\nimport {huberLoss} from './losses/huber_loss';\nimport {logLoss} from './losses/log_loss';\nimport {meanSquaredError} from './losses/mean_squared_error';\nimport {sigmoidCrossEntropy} from './losses/sigmoid_cross_entropy';\nimport {softmaxCrossEntropy} from './losses/softmax_cross_entropy';\nconst losses = {\n  absoluteDifference,\n  computeWeightedLoss,\n  cosineDistance,\n  hingeLoss,\n  huberLoss,\n  logLoss,\n  meanSquaredError,\n  sigmoidCrossEntropy,\n  softmaxCrossEntropy\n};\n\n// Second level exports.\nexport {image, linalg, losses, spectral, fused, signal};\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {dispose} from '../globals';\nimport {variableGrads} from '../gradients';\nimport {scalar} from '../ops/ops';\nimport {Serializable} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\n\n/**\n * A variable that belongs to an optimizer.\n *\n * The `originalName` field is required for keeping track of the canonical\n * name of the variable, which is usually the name of the model weight that\n * the variable is related to plus a suffix, e.g., 'dense1/kernel/momentum'.\n * The name of the `Variable` object itself cannot be used directly due to\n * possible deduplication: Every `Variable` must have a unique name but more\n * than one optimizer objects of the same type may be created for the same model\n * or the same `Variable`.\n */\nexport interface OptimizerVariable {\n  originalName: string;\n  variable: Variable;\n}\n\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport abstract class Optimizer extends Serializable {\n  protected iterations_: number;\n\n  /**\n   * Executes `f()` and minimizes the scalar output of `f()` by computing\n   * gradients of y with respect to the list of trainable variables provided by\n   * `varList`. If no list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to minimize.\n   * @param returnCost Whether to return the scalar cost value produced by\n   * executing `f()`.\n   * @param varList An optional list of variables to update. If specified, only\n   * the trainable variables in varList will be updated by minimize. Defaults to\n   * all trainable variables.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers'}\n   */\n  minimize(f: () => Scalar, returnCost = false, varList?: Variable[]): Scalar\n      |null {\n    const {value, grads} = this.computeGradients(f, varList);\n\n    if (varList != null) {\n      const gradArray: NamedTensor[] =\n          varList.map(v => ({name: v.name, tensor: grads[v.name]}));\n      this.applyGradients(gradArray);\n    } else {\n      this.applyGradients(grads);\n    }\n\n    // Dispose gradients.\n    dispose(grads);\n\n    if (returnCost) {\n      return value;\n    } else {\n      value.dispose();\n      return null;\n    }\n  }\n\n  /**\n   * The number of iterations that this optimizer instance has been invoked for.\n   */\n  get iterations(): number {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return this.iterations_;\n  }\n\n  protected incrementIterations() {\n    this.iterations_ = this.iterations + 1;\n  }\n\n  /**\n   * Executes f() and computes the gradient of the scalar output of f() with\n   * respect to the list of trainable variables provided by `varList`. If no\n   * list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to use for computing\n   * gradients with respect to variables.\n   * @param varList An optional list of variables to compute gradients with\n   * respect to. If specified, only the trainable variables in varList will have\n   * gradients computed with respect to. Defaults to all trainable variables.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers'}\n   */\n  computeGradients(f: () => Scalar, varList?: Variable[]):\n      {value: Scalar, grads: NamedTensorMap} {\n    return variableGrads(f, varList);\n  }\n\n  /**\n   * Updates variables by using the computed gradients.\n   *\n   * @param variableGradients A mapping of variable name to its gradient value.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers'}\n   */\n  abstract applyGradients(variableGradients: NamedTensorMap|\n                          NamedTensor[]): void;\n\n  /**\n   * Dispose the variables (if any) owned by this optimizer instance.\n   */\n  dispose(): void {\n    if (this.iterations_ != null) {\n      dispose(this.iterations_);\n    }\n  }\n\n  async saveIterations(): Promise<NamedTensor> {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return {\n      name: 'iter',  // Named for Python compatibility.\n      // TODO(cais): Use 'int64' type when available.\n      tensor: scalar(this.iterations_, 'int32')\n    };\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    throw new Error('getWeights() is not implemented for this optimizer yet.');\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    throw new Error(\n        `setWeights() is not implemented for this optimizer class ` +\n        `${this.getClassName()}`);\n  }\n\n  /**\n   * Extract the first element of the weight values and set it\n   * as the iterations counter variable of this instance of optimizer.\n   *\n   * @param weightValues\n   * @returns Weight values with the first element consumed and excluded.\n   */\n  protected async extractIterations(weightValues: NamedTensor[]):\n      Promise<NamedTensor[]> {\n    this.iterations_ = (await weightValues[0].tensor.data())[0];\n    return weightValues.slice(1);\n  }\n}\n\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n  value: (instance: Optimizer) => {\n    return instance.minimize != null && instance.computeGradients != null &&\n        instance.applyGradients != null;\n  }\n});\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/ops';\nimport {square} from '../ops/square';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'Adadelta';  // Name matters for Python compatibility.\n  private accumulatedGrads: OptimizerVariable[] = [];\n  private accumulatedUpdates: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected rho: number,\n      protected epsilon: number = null) {\n    super();\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedGrads[i] == null) {\n        this.accumulatedGrads[i] = {\n          originalName: `${name}/accum_grad`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedUpdates[i] == null) {\n        this.accumulatedUpdates[i] = {\n          originalName: `${name}/accum_var`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      const accumulatedGrad = this.accumulatedGrads[i].variable;\n      const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n\n      tidy(() => {\n        const newAccumulatedGrad =\n            add(mul(accumulatedGrad, this.rho),\n                mul(square(gradient), 1 - this.rho));\n\n        const updates =\n            mul(div(sqrt(add(accumulatedUpdate, this.epsilon)),\n                    sqrt(add(accumulatedGrad, this.epsilon))),\n                gradient);\n\n        const newAccumulatedUpdate =\n            add(mul(accumulatedUpdate, this.rho),\n                mul(square(updates), 1 - this.rho));\n\n        accumulatedGrad.assign(newAccumulatedGrad);\n        accumulatedUpdate.assign(newAccumulatedUpdate);\n\n        const newValue = add(mul(updates, -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    if (this.accumulatedUpdates != null) {\n      dispose(this.accumulatedGrads.map(v => v.variable));\n      dispose(this.accumulatedUpdates.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    const variables: OptimizerVariable[] =\n        [...this.accumulatedGrads, ...this.accumulatedUpdates];\n    return [await this.saveIterations()].concat(\n        variables.map(v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedGrads =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedUpdates =\n        weightValues.slice(variableCount, variableCount * 2)\n            .map(v => ({\n                   originalName: v.name,\n                   variable: v.tensor.variable(trainable)\n                 }));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'rho': this.rho,\n      'epsilon': this.epsilon\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config['learningRate'], config['rho'], config['epsilon']);\n  }\n}\nregisterClass(AdadeltaOptimizer);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {fill} from '../ops/fill';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'Adagrad';  // Note: Name matters for Python compatibility.\n\n  private accumulatedGrads: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, private initialAccumulatorValue = 0.1) {\n    super();\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulatedGrads[i] == null) {\n        const trainable = false;\n        this.accumulatedGrads[i] = {\n          originalName: `${name}/accumulator`,\n          variable: tidy(\n              () => fill(value.shape, this.initialAccumulatorValue)\n                        .variable(trainable))\n        };\n      }\n\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      const accumulatedGrad = this.accumulatedGrads[i].variable;\n\n      tidy(() => {\n        const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n        accumulatedGrad.assign(newAccumulatedGrad);\n\n        const newValue = add(\n            mul(div(gradient,\n                    sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))),\n                -this.learningRate),\n            value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    if (this.accumulatedGrads != null) {\n      dispose(this.accumulatedGrads.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    return [await this.saveIterations()].concat(this.accumulatedGrads.map(\n        v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulatedGrads = weightValues.map(\n        v => ({originalName: v.name, variable: v.tensor.variable(trainable)}));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'initialAccumulatorValue': this.initialAccumulatorValue,\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config['learningRate'], config['initialAccumulatorValue']);\n  }\n}\nregisterClass(AdagradOptimizer);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {pow} from '../ops/pow';\nimport {scalar} from '../ops/scalar';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Variable} from '../tensor';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\nexport class AdamOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'Adam';  // Note: Name matters for Python compatibility.\n  private accBeta1: Variable;\n  private accBeta2: Variable;\n\n  private accumulatedFirstMoment: OptimizerVariable[] = [];\n  private accumulatedSecondMoment: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null) {\n    super();\n    tidy(() => {\n      // accB* will be updated by batch.\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const varNames = Array.isArray(variableGradients) ?\n        variableGradients.map(v => v.name) :\n        Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const oneMinusAccBeta2 = sub(1, this.accBeta2);\n\n      varNames.forEach((name, i) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i] == null) {\n          this.accumulatedFirstMoment[i] = {\n            originalName: `${name}/m`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        if (this.accumulatedSecondMoment[i] == null) {\n          this.accumulatedSecondMoment[i] = {\n            originalName: `${name}/v`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n\n        const gradient = Array.isArray(variableGradients) ?\n            variableGradients[i].tensor :\n            variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n\n        const firstMoment = this.accumulatedFirstMoment[i].variable;\n        const secondMoment = this.accumulatedSecondMoment[i].variable;\n\n        const newFirstMoment =\n            add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const newSecondMoment =\n            add(mul(secondMoment, this.beta2),\n                mul(square(gradient), 1 - this.beta2));\n\n        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n        const biasCorrectedSecondMoment =\n            div(newSecondMoment, oneMinusAccBeta2);\n\n        firstMoment.assign(newFirstMoment);\n        secondMoment.assign(newSecondMoment);\n\n        const newValue =\n            add(mul(div(biasCorrectedFirstMoment,\n                        add(sqrt(biasCorrectedSecondMoment), this.epsilon)),\n                    -this.learningRate),\n                value);\n        value.assign(newValue);\n      });\n\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n      this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map(v => v.variable));\n    }\n    if (this.accumulatedSecondMoment != null) {\n      dispose(this.accumulatedSecondMoment.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    const variables: OptimizerVariable[] =\n        [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n    return [await this.saveIterations()].concat(\n        variables.map(v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    tidy(() => {\n      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n    });\n\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedFirstMoment =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedSecondMoment =\n        weightValues.slice(variableCount, variableCount * 2)\n            .map(v => ({\n                   originalName: v.name,\n                   variable: v.tensor.variable(trainable)\n                 }));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'beta1': this.beta1,\n      'beta2': this.beta2,\n      'epsilon': this.epsilon,\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['beta1'], config['beta2'],\n        config['epsilon']);\n  }\n}\nregisterClass(AdamOptimizer);\n", "\uFEFF/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {abs} from '../ops/abs';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {maximum} from '../ops/maximum';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {sub} from '../ops/sub';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Variable} from '../tensor';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\nexport class AdamaxOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'Adamax';  // Note: Name matters for Python compatbility.\n  private accBeta1: Variable;\n  private iteration: Variable;\n\n  private accumulatedFirstMoment: OptimizerVariable[] = [];\n  private accumulatedWeightedInfNorm: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null,\n      protected decay = 0.0) {\n    super();\n\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const lr =\n          div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n\n      variableNames.forEach((name, i) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i] == null) {\n          this.accumulatedFirstMoment[i] = {\n            originalName: `${name}/m`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        if (this.accumulatedWeightedInfNorm[i] == null) {\n          this.accumulatedWeightedInfNorm[i] = {\n            originalName: `${name}/v`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n\n        const gradient = Array.isArray(variableGradients) ?\n            variableGradients[i].tensor :\n            variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n\n        const firstMoment = this.accumulatedFirstMoment[i].variable;\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n\n        const newFirstMoment =\n            add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n\n        const ut0 = mul(weightedInfNorm, this.beta2);\n        const ut1 = abs(gradient);\n\n        const newWeightedInfNorm = maximum(ut0, ut1);\n\n        firstMoment.assign(newFirstMoment);\n        weightedInfNorm.assign(newWeightedInfNorm);\n\n        const newValue =\n            add(mul(div(lr, oneMinusAccBeta1),\n                    div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))),\n                value);\n\n        value.assign(newValue);\n      });\n\n      this.iteration.assign(add(this.iteration, 1));\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    this.accBeta1.dispose();\n    this.iteration.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map(v => v.variable));\n    }\n    if (this.accumulatedWeightedInfNorm != null) {\n      dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    throw new Error('getWeights() is not implemented for Adamax yet.');\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    throw new Error('setWeights() is not implemented for Adamax yet.');\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'beta1': this.beta1,\n      'beta2': this.beta2,\n      'epsilon': this.epsilon,\n      'decay': this.decay\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['beta1'], config['beta2'],\n        config['epsilon'], config['decay']);\n  }\n}\nregisterClass(AdamaxOptimizer);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {keep, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\n\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'SGD';  // Note: Name matters for Python compatibility.\n  protected c: Scalar;\n\n  constructor(protected learningRate: number) {\n    super();\n    this.setLearningRate(learningRate);\n  }\n\n  applyGradients(variableGradients: NamedTensorMap|NamedTensor[]) {\n    const varNames = Array.isArray(variableGradients) ?\n        variableGradients.map(v => v.name) :\n        Object.keys(variableGradients);\n    varNames.forEach((name, i) => {\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const value = ENGINE.registeredVariables[name];\n      tidy(() => {\n        const newValue = add(mul(this.c, gradient), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  /**\n   * Sets the learning rate of the optimizer.\n   */\n  setLearningRate(learningRate: number) {\n    this.learningRate = learningRate;\n    if (this.c != null) {\n      this.c.dispose();\n    }\n    this.c = keep(scalar(-learningRate));\n  }\n\n  dispose() {\n    this.c.dispose();\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    return [await this.saveIterations()];\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    if (weightValues.length !== 0) {\n      throw new Error('SGD optimizer does not have settable weights.');\n    }\n  }\n\n  getConfig(): ConfigDict {\n    return {'learningRate': this.learningRate};\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config['learningRate']);\n  }\n}\nregisterClass(SGDOptimizer);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Tensor} from '../tensor';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {OptimizerVariable} from './optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n  /** @nocollapse */\n  static className = 'Momentum';  // Name matters for Python compatibility.\n  private m: Scalar;\n  private accumulations: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, private momentum: number,\n      private useNesterov = false) {\n    super(learningRate);\n    this.m = scalar(this.momentum);\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulations[i] == null) {\n        const trainable = false;\n        this.accumulations[i] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n\n      const accumulation = this.accumulations[i].variable;\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      tidy(() => {\n        let newValue: Tensor;\n        const newAccumulation = add(mul(this.m, accumulation), gradient);\n        if (this.useNesterov) {\n          newValue = add(\n              mul(this.c, add(gradient, mul(newAccumulation, this.m))), value);\n        } else {\n          newValue = add(mul(this.c, newAccumulation), value);\n        }\n        accumulation.assign(newAccumulation);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    this.m.dispose();\n    if (this.accumulations != null) {\n      dispose(this.accumulations.map(v => v.variable));\n    }\n  }\n\n  /**\n   * Sets the momentum of the optimizer.\n   *\n   * @param momentum\n   */\n  setMomentum(momentum: number) {\n    this.momentum = momentum;\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    return [await this.saveIterations()].concat(this.accumulations.map(\n        v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulations = weightValues.map(\n        v => ({originalName: v.name, variable: v.tensor.variable(trainable)}));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'momentum': this.momentum,\n      'useNesterov': this.useNesterov\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['momentum'], config['useNesterov']);\n  }\n}\nregisterClass(MomentumOptimizer);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedTensorMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'RMSProp';  // Note: Name matters for Python compatibility.\n  private centered: boolean;\n\n  private accumulatedMeanSquares: OptimizerVariable[] = [];\n  private accumulatedMoments: OptimizerVariable[] = [];\n  private accumulatedMeanGrads: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected decay = 0.9,\n      protected momentum = 0.0, protected epsilon: number = null,\n      centered = false) {\n    super();\n\n    this.centered = centered;\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n    if (learningRate == null) {\n      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n    }\n  }\n\n  applyGradients(variableGradients: NamedTensorMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedMeanSquares[i] == null) {\n        this.accumulatedMeanSquares[i] = {\n          originalName: `${name}/rms`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMoments[i] == null) {\n        this.accumulatedMoments[i] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMeanGrads[i] == null && this.centered) {\n        this.accumulatedMeanGrads[i] = {\n          originalName: `${name}/mg`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n      const accumulatedMoments = this.accumulatedMoments[i].variable;\n      tidy(() => {\n        const newAccumulatedMeanSquare =\n            add(mul(accumulatedMeanSquare, this.decay),\n                mul(square(gradient), 1 - this.decay));\n\n        if (this.centered) {\n          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n          // Centered gradient\n          const newAccumulatedMeanGrad =\n              add(mul(accumulatedMeanGrad, this.decay),\n                  mul(gradient, 1 - this.decay));\n\n          const gradContribution =\n              div(mul(gradient, this.learningRate),\n                  sqrt(\n                      sub(newAccumulatedMeanSquare,\n                          add(square(newAccumulatedMeanGrad), this.epsilon))));\n          const newAccumulatedMoments =\n              add(mul(accumulatedMoments, this.momentum), gradContribution);\n\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n          accumulatedMoments.assign(newAccumulatedMoments);\n\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          // Plain gradient\n          const newAccumulatedMeanSquare =\n              add(mul(accumulatedMeanSquare, this.decay),\n                  mul(square(gradient), 1 - this.decay));\n\n          const newAccumulatedMoments =\n              add(mul(accumulatedMoments, this.momentum),\n                  div(mul(gradient, this.learningRate),\n                      sqrt(add(newAccumulatedMeanSquare, this.epsilon))));\n\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMoments.assign(newAccumulatedMoments);\n\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    });\n    this.incrementIterations();\n  }\n\n  dispose(): void {\n    if (this.accumulatedMeanSquares != null) {\n      dispose(this.accumulatedMeanSquares.map(v => v.variable));\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      dispose(this.accumulatedMeanGrads.map(v => v.variable));\n    }\n    if (this.accumulatedMoments != null) {\n      dispose(this.accumulatedMoments.map(v => v.variable));\n    }\n  }\n\n  async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    const variables: OptimizerVariable[] =\n        [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n    if (this.centered) {\n      variables.push(...this.accumulatedMeanGrads);\n    }\n    return [await this.saveIterations()].concat(\n        variables.map(v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount =\n        this.centered ? weightValues.length / 3 : weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedMeanSquares =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedMoments =\n        weightValues.slice(variableCount, variableCount * 2)\n            .map(v => ({\n                   originalName: v.name,\n                   variable: v.tensor.variable(trainable)\n                 }));\n    if (this.centered) {\n      this.accumulatedMeanGrads =\n          weightValues.slice(variableCount * 2, variableCount * 3)\n              .map(v => ({\n                     originalName: v.name,\n                     variable: v.tensor.variable(trainable)\n                   }));\n    }\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'decay': this.decay,\n      'momentum': this.momentum,\n      'epsilon': this.epsilon,\n      'centered': this.centered\n    };\n  }\n\n  /** @nocollapse */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['decay'], config['momentum'],\n        config['epsilon'], config['centered']);\n  }\n}\nregisterClass(RMSPropOptimizer);\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AdadeltaOptimizer} from './adadelta_optimizer';\nimport {AdagradOptimizer} from './adagrad_optimizer';\nimport {AdamOptimizer} from './adam_optimizer';\nimport {AdamaxOptimizer} from './adamax_optimizer';\nimport {MomentumOptimizer} from './momentum_optimizer';\nimport {RMSPropOptimizer} from './rmsprop_optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\nexport class OptimizerConstructors {\n  /**\n   * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n   *\n   * ```js\n   * // Fit a quadratic function by learning the coefficients a, b, c.\n   * const xs = tf.tensor1d([0, 1, 2, 3]);\n   * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n   *\n   * const a = tf.scalar(Math.random()).variable();\n   * const b = tf.scalar(Math.random()).variable();\n   * const c = tf.scalar(Math.random()).variable();\n   *\n   * // y = a * x^2 + b * x + c.\n   * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n   * const loss = (pred, label) => pred.sub(label).square().mean();\n   *\n   * const learningRate = 0.01;\n   * const optimizer = tf.train.sgd(learningRate);\n   *\n   * // Train the model.\n   * for (let i = 0; i < 10; i++) {\n   *   optimizer.minimize(() => loss(f(xs), ys));\n   * }\n   *\n   * // Make predictions.\n   * console.log(\n   *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n   * const preds = f(xs).dataSync();\n   * preds.forEach((pred, i) => {\n   *   console.log(`x: ${i}, pred: ${pred}`);\n   * });\n   * ```\n   *\n   * @param learningRate The learning rate to use for the SGD algorithm.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static sgd(learningRate: number): SGDOptimizer {\n    return new SGDOptimizer(learningRate);\n  }\n\n  /**\n   * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n   * descent.\n   *\n   * See\n   * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n   * http://proceedings.mlr.press/v28/sutskever13.pdf)\n   *\n   * @param learningRate The learning rate to use for the Momentum gradient\n   * descent algorithm.\n   * @param momentum The momentum to use for the momentum gradient descent\n   * algorithm.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static momentum(learningRate: number, momentum: number, useNesterov = false):\n      MomentumOptimizer {\n    return new MomentumOptimizer(learningRate, momentum, useNesterov);\n  }\n\n  /**\n   * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n   * descent. This implementation uses plain momentum and is not centered\n   * version of RMSProp.\n   *\n   * See\n   * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n   * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n   *\n   * @param learningRate The learning rate to use for the RMSProp gradient\n   * descent algorithm.\n   * @param decay The discounting factor for the history/coming gradient.\n   * @param momentum The momentum to use for the RMSProp gradient descent\n   * algorithm.\n   * @param epsilon Small value to avoid zero denominator.\n   * @param centered If true, gradients are normalized by the estimated\n   * variance of the gradient.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static rmsprop(\n      learningRate: number, decay = .9, momentum = 0.0, epsilon: number = null,\n      centered = false): RMSPropOptimizer {\n    return new RMSPropOptimizer(\n        learningRate, decay, momentum, epsilon, centered);\n  }\n\n  /**\n   * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adam gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adam(\n      learningRate = 0.001, beta1 = 0.9, beta2 = 0.999,\n      epsilon: number = null): AdamOptimizer {\n    return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n   * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n   *\n   * @param learningRate The learning rate to use for the Adadelta gradient\n   * descent algorithm.\n   * @param rho The learning rate decay over each update.\n   * @param epsilon A constant epsilon used to better condition the grad\n   * update.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adadelta(learningRate = .001, rho = .95, epsilon: number = null):\n      AdadeltaOptimizer {\n    return new AdadeltaOptimizer(learningRate, rho, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adamax gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   * @param decay The learning rate decay over each update.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adamax(\n      learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon: number = null,\n      decay = 0.0): AdamaxOptimizer {\n    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n  }\n\n  /**\n   * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n   * See\n   * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n   * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n   * or\n   * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n   * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n   *\n   * @param learningRate The learning rate to use for the Adagrad gradient\n   * descent algorithm.\n   * @param initialAccumulatorValue Starting value for the accumulators, must be\n   * positive.\n   *\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adagrad(learningRate: number, initialAccumulatorValue = 0.1):\n      AdagradOptimizer {\n    return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n  }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// So typings can propagate.\nimport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nimport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nimport {AdamOptimizer} from './optimizers/adam_optimizer';\nimport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nimport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nimport {OptimizerConstructors} from './optimizers/optimizer_constructors';\nimport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nimport {SGDOptimizer} from './optimizers/sgd_optimizer';\n\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\n\nexport const train = {\n  sgd: OptimizerConstructors.sgd,\n  momentum: OptimizerConstructors.momentum,\n  adadelta: OptimizerConstructors.adadelta,\n  adagrad: OptimizerConstructors.adagrad,\n  rmsprop: OptimizerConstructors.rmsprop,\n  adamax: OptimizerConstructors.adamax,\n  adam: OptimizerConstructors.adam\n};\n", "/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst delayCallback: Function = (() => {\n  if (typeof requestAnimationFrame !== 'undefined') {\n    return requestAnimationFrame;\n  } else if (typeof setImmediate !== 'undefined') {\n    return setImmediate;\n  }\n  return (f: Function) => f();  // no delays\n})();\n\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n *\n * @doc {heading: 'Performance', subheading: 'Timing'}\n */\nfunction nextFrame(): Promise<void> {\n  return new Promise<void>(resolve => delayCallback(() => resolve()));\n}\n\nexport {nextFrame};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Returns the image center in pixels.\nexport function getImageCenter(\n    center: number|[number, number], imageHeight: number,\n    imageWidth: number): [number, number] {\n  const centerX =\n      imageWidth * (typeof center === 'number' ? center : center[0]);\n  const centerY =\n      imageHeight * (typeof center === 'number' ? center : center[1]);\n  return [centerX, centerY];\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  let reshaped: number[] = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i = 0; i < spatialLength; ++i) {\n      reshaped =\n          reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\n\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(\n    reshapedRank: number, blockShapeRank: number,\n    batchToSpace = true): number[] {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n      if (i <= 2 * blockShapeRank) {\n        permuted.push(i);\n        permuted.push(i - (blockShapeRank + 1));\n      } else {\n        permuted.push(i);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i = 1; i < reshapedRank; ++i) {\n      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n        permutedAfterBatch.push(i);\n      } else {\n        permutedBeforeBatch.push(i);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\n\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  const reshapedPermuted = [];\n\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod);\n  }\n\n  for (let i = 1; i < inputShape.length; ++i) {\n    if (i <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n      } else {\n        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i]);\n    }\n  }\n\n  return reshapedPermuted;\n}\n\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(\n    crops: number[][], blockShape: number): number[] {\n  const sliceBeginCoords = [0];\n  for (let i = 0; i < blockShape; ++i) {\n    sliceBeginCoords.push(crops[i][0]);\n  }\n  return sliceBeginCoords;\n}\n\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(\n    uncroppedShape: number[], crops: number[][], blockShape: number): number[] {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i = 0; i < blockShape; ++i) {\n    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n  }\n\n  return sliceSize;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {env} from './environment';\n\nexport function warn(...msg: Array<{}>): void {\n  if (!env().getBool('IS_TEST')) {\n    console.warn(...msg);\n  }\n}\n\nexport function log(...msg: Array<{}>): void {\n  if (!env().getBool('IS_TEST')) {\n    console.log(...msg);\n  }\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(\n    real: Float32Array, imag: Float32Array): Float32Array {\n  if (real.length !== imag.length) {\n    throw new Error(\n        `Cannot merge real and imag arrays of different lengths. real:` +\n        `${real.length}, imag: ${imag.length}.`);\n  }\n  const result = new Float32Array(real.length * 2);\n  for (let i = 0; i < result.length; i += 2) {\n    result[i] = real[i / 2];\n    result[i + 1] = imag[i / 2];\n  }\n  return result;\n}\n\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(complex.length / 2);\n  const imag = new Float32Array(complex.length / 2);\n  for (let i = 0; i < complex.length; i += 2) {\n    real[i / 2] = complex[i];\n    imag[i / 2] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.ceil(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 0; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.floor(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 2; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(\n    complex: Float32Array, index: number): {real: number, imag: number} {\n  const real = complex[index * 2];\n  const imag = complex[index * 2 + 1];\n  return {real, imag};\n}\n\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(\n    data: TypedArray, real: number, imag: number, index: number) {\n  data[index * 2] = real;\n  data[index * 2 + 1] = imag;\n}\n\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(\n    n: number, inverse: boolean): {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(n / 2);\n  const imag = new Float32Array(n / 2);\n  for (let i = 0; i < Math.ceil(n / 2); i++) {\n    const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n    real[i] = Math.cos(x);\n    imag[i] = Math.sin(x);\n  }\n  return {real, imag};\n}\n\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(\n    k: number, n: number, inverse: boolean): {real: number, imag: number} {\n  const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n  const real = Math.cos(x);\n  const imag = Math.sin(x);\n  return {real, imag};\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {cast} from '../ops/cast';\nimport {scalar} from '../ops/scalar';\nimport {tensor1d} from '../ops/tensor1d';\nimport {zeros} from '../ops/zeros';\nimport {Tensor} from '../tensor';\nimport {Rank} from '../types';\nimport {DataType, ShapeMap} from '../types';\nimport {hasEncodingLoss, makeZerosTypedArray} from '../util';\n\nimport {KernelBackend} from './backend';\n\n// Utilities needed by backend consumers of tf-core.\nexport * from '../ops/axis_util';\nexport * from '../ops/broadcast_util';\nexport * from '../ops/concat_util';\nexport * from '../ops/conv_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/fused_types';\nexport * from '../ops/reduce_util';\n\nimport * as slice_util from '../ops/slice_util';\nexport {slice_util};\n\nexport {BackendValues, TypedArray, upcastType, PixelData} from '../types';\nexport {MemoryInfo, TimingInfo} from '../engine';\nexport * from '../ops/rotate_util';\nexport * from '../ops/array_ops_util';\nexport * from '../ops/gather_nd_util';\nexport * from '../ops/scatter_nd_util';\nexport * from '../ops/selu_util';\nexport * from '../ops/fused_util';\nexport * from '../ops/erf_util';\nexport * from '../log';\nexport * from '../backends/complex_util';\nexport * from '../ops/split_util';\n\nimport * as segment_util from '../ops/segment_util';\nexport {segment_util};\n\nexport function castTensor<T extends Tensor>(\n    x: T, dtype: DataType, backend: KernelBackend): T {\n  if (dtype === 'complex64') {\n    if (x.dtype === 'complex64') {\n      return x.clone();\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = cast(x, 'float32');\n    const result = backend.complex(floatX, zerosTensor);\n    zerosTensor.dispose();\n    floatX.dispose();\n    return result as T;\n  }\n\n  if (!hasEncodingLoss(x.dtype, dtype)) {\n    // We don't change the underlying data, since we cast to higher\n    // precision.\n    return ENGINE.makeTensorFromDataId(x.dataId, x.shape, dtype) as T;\n  }\n  if (x.dtype === 'complex64') {\n    const real = backend.real(x);\n    const result = cast(real, dtype);\n    real.dispose();\n    return result;\n  }\n  if (dtype === 'int32') {\n    return backend.int(x);\n  } else if (dtype === 'bool') {\n    const zero = scalar(0, x.dtype);\n    const result = backend.notEqual(x, zero) as T;\n    zero.dispose();\n    return result;\n  } else {\n    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n  }\n}\n\nexport function reshapeTensor<T extends Tensor, R extends Rank>(\n    x: T, shape: ShapeMap[R]): Tensor<R> {\n  return ENGINE.makeTensorFromDataId(x.dataId, shape, x.dtype) as Tensor<R>;\n}\n\nexport function linspaceImpl(start: number, stop: number, num: number) {\n  const step = (stop - start) / (num - 1);\n\n  const values = makeZerosTypedArray(num, 'float32');\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step;\n  }\n\n  return tensor1d(values, 'float32');\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {slice} from '../ops/slice';\nimport {Tensor} from '../tensor';\n\n// TODO(annxingyuan): Use this helper in WASM Split kernel once intermediate\n// kernels have been modularized in WebGL and CPU\n// https://github.com/tensorflow/tfjs/issues/2822.\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split<T extends Tensor>(\n    x: T, sizeSplits: number[], axis: number): T[] {\n  const begin = new Array(x.rank).fill(0);\n  const size = x.shape.slice();\n  return sizeSplits.map(s => {\n    const sliceSize = [...size];\n    sliceSize[axis] = s;\n    const sliceT = slice(x, begin, sliceSize);\n    begin[axis] += s;\n    return sliceT;\n  });\n}\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * An implementation of the tile kernel shared between webgl and cpu for string\n * tensors only.\n */\n\nimport {buffer} from '../ops/buffer';\nimport {Tensor, TensorBuffer} from '../tensor';\nimport {DataType, Rank} from '../types';\n\nexport function tile<R extends Rank>(\n    xBuf: TensorBuffer<R, DataType>, reps: number[]): Tensor<R> {\n  const newShape: number[] = new Array(xBuf.rank);\n  for (let i = 0; i < newShape.length; i++) {\n    newShape[i] = xBuf.shape[i] * reps[i];\n  }\n  const result = buffer(newShape, xBuf.dtype);\n  for (let i = 0; i < result.values.length; ++i) {\n    const newLoc = result.indexToLoc(i);\n\n    const originalLoc: number[] = new Array(xBuf.rank);\n    for (let j = 0; j < originalLoc.length; j++) {\n      originalLoc[j] = newLoc[j] % xBuf.shape[j];\n    }\n\n    const originalIndex = xBuf.locToIndex(originalLoc);\n\n    result.values[i] = xBuf.values[originalIndex];\n  }\n  return result.toTensor() as Tensor<R>;\n}\n", "/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the TopK kernel shared between webgl and cpu. */\n\nimport {tensor} from '../ops/tensor';\nimport {Tensor} from '../tensor';\nimport {NumericDataType, TypedArray} from '../types';\nimport {getTypedArrayFromDType} from '../util';\n\nexport function topkImpl<T extends Tensor>(\n    x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,\n    sorted: boolean): [T, T] {\n  // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n  const lastDim = xShape[xShape.length - 1];\n  const [batch, size] = [x.length / lastDim, lastDim];\n  const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n  const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = x.subarray(offset, offset + size);\n    const valAndInd: Array<{value: number, index: number}> = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({value: vals[i], index: i});\n    }\n    valAndInd.sort((a, b) => b.value - a.value);\n\n    const outOffset = b * k;\n    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n    for (let i = 0; i < k; i++) {\n      topKVals[i] = valAndInd[i].value;\n      topKIndices[i] = valAndInd[i].index;\n    }\n  }\n  // Reshape back to the original input shape, except that the last\n  // dimension is k.\n  const outputShape = xShape.slice();\n  outputShape[outputShape.length - 1] = k;\n  return [\n    tensor(allTopKVals, outputShape, xDtype) as T,\n    tensor(allTopKIndices, outputShape, 'int32') as T\n  ];\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport {nonMaxSuppressionV3Impl, nonMaxSuppressionV4Impl, nonMaxSuppressionV5Impl} from './non_max_suppression_impl';\nexport {split} from './split_shared';\nexport {tile} from './tile_impl';\nexport {topkImpl} from './topk_impl';\nexport {whereImpl} from './where_impl';\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Abs} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {mul} from '../ops/mul';\nimport {step} from '../ops/step';\nimport {Tensor} from '../tensor';\n\nexport const absGradConfig: GradConfig = {\n  kernelName: Abs,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => mul(dy, step(cast(x, 'float32'), -1))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Acos} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {neg} from '../ops/neg';\nimport {scalar} from '../ops/scalar';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {Tensor} from '../tensor';\n\nexport const acosGradConfig: GradConfig = {\n  kernelName: Acos,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {\n      x: () => {\n        const a = square(cast(x, 'float32'));\n        const b = sqrt(sub(scalar(1), a));\n        return neg(div(dy, b));\n      }\n\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Acosh} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {Tensor} from '../tensor';\n\nexport const acoshGradConfig: GradConfig = {\n  kernelName: Acosh,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {\n      x: () => {\n        const a = sqrt(sub(square(cast(x, 'float32')), 1));\n        return div(dy, a);\n      }\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Add} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport {reshape} from '../ops/reshape';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const addGradConfig: GradConfig = {\n  kernelName: Add,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const outShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n      }\n      return reshape(res, b.shape);\n    };\n\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AddN} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {Tensor} from '../tensor';\n\nexport const addNGradConfig: GradConfig = {\n  kernelName: AddN,\n  saveAllInputs: true,\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const ders: {[key: string]: () => Tensor} = {};\n    saved.forEach((_, i) => {\n      ders[i] = () => dy.clone();\n    });\n    return ders;\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ArgMax} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const argMaxGradConfig: GradConfig = {\n  kernelName: ArgMax,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => zerosLike(x)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ArgMin} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const argMinGradConfig: GradConfig = {\n  kernelName: ArgMin,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => zerosLike(x)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Asin} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {scalar} from '../ops/scalar';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {Tensor} from '../tensor';\n\nexport const asinGradConfig: GradConfig = {\n  kernelName: Asin,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, 'float32')))))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Asinh} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {add} from '../ops/add';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {scalar} from '../ops/scalar';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {Tensor} from '../tensor';\n\nexport const asinhGradConfig: GradConfig = {\n  kernelName: Asinh,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {\n      x: () => {\n        const a = sqrt(add(scalar(1), square(cast(x, 'float32'))));\n        return div(dy, a);\n      }\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atan2} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {add} from '../ops/add';\nimport {assertAndGetBroadcastShape, getReductionAxes} from '../ops/broadcast_util';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {neg} from '../ops/neg';\nimport {reshape} from '../ops/reshape';\nimport {square} from '../ops/square';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const atan2GradConfig: GradConfig = {\n  kernelName: Atan2,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n\n    const derA = () => {\n      const d = add(square(a), square(b));\n      let res = mul(dy, div(b, d));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      const d = add(square(a), square(b));\n      let res = neg(mul(dy, div(a, d)));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n      }\n      return reshape(res, b.shape);\n    };\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atan} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {add} from '../ops/add';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {square} from '../ops/square';\nimport {Tensor} from '../tensor';\n\nexport const atanGradConfig: GradConfig = {\n  kernelName: Atan,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => div(dy, add(square(cast(x, 'float32')), 1))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Atanh} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {scalar} from '../ops/scalar';\nimport {Tensor} from '../tensor';\n\nexport const atanhGradConfig: GradConfig = {\n  kernelName: Atanh,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => div(dy, sub(scalar(1), square(cast(x, 'float32'))))};\n  }\n};\n", "\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {AvgPool3DBackprop, AvgPool3DBackpropAttrs, AvgPool3DBackpropInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the backprop of a 3d avg pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0. The dilation\n *     rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction avgPool3dBackprop_<T extends Tensor4D|Tensor5D>(\n    dy: T|TensorLike, input: T|TensorLike,\n    filterSize: [number, number, number]|number,\n    strides: [number, number, number]|number,\n    dilations: [number, number, number]|number = [1, 1, 1],\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $dy = convertToTensor(dy, 'dy', 'avgPool3dBackprop');\n  const $input = convertToTensor(input, 'input', 'avgPool3dBackprop');\n\n  let dy5D = $dy as Tensor5D;\n  let input5D = $input as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($input.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape(\n        $dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n    input5D = reshape($input, [\n      1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n    ]);\n  }\n\n  util.assert(\n      dy5D.rank === 5,\n      () => `Error in avgPool3dBackprop: dy must be rank 5 but got rank ` +\n          `${dy5D.rank}.`);\n  util.assert(\n      input5D.rank === 5,\n      () => `Error in avgPool3dBackprop: input must be rank 5 but got rank ` +\n          `${input5D.rank}.`);\n\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in avgPool3dBackprop: Either strides or dilations ' +\n          `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const convInfo = conv_util.computePool3DInfo(\n        input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n\n    return backend.avgPool3dBackprop(dy5D, input5D, convInfo);\n  };\n\n  const inputs: AvgPool3DBackpropInputs = {dy: dy5D, input: input5D};\n\n  const attrs: AvgPool3DBackpropAttrs =\n      {filterSize, strides, dilations, pad, dimRoundingMode};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */,\n      AvgPool3DBackprop, attrs as {} as NamedAttrMap);\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n\n  return res as T;\n}\n\nexport const avgPool3dBackprop = op({avgPool3dBackprop_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AvgPool3D, AvgPool3DAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {avgPool3dBackprop} from '../ops/avg_pool_3d_backprop';\nimport {Tensor, Tensor5D} from '../tensor';\n\nexport const avgPool3DGradConfig: GradConfig = {\n  kernelName: AvgPool3D,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x] = saved as [Tensor5D];\n    const {filterSize, strides, dilations, pad, dimRoundingMode} =\n        attrs as {} as AvgPool3DAttrs;\n\n    const $dilations =\n        dilations == null ? [1, 1, 1] as [number, number, number] : dilations;\n\n    return {\n      x: () => avgPool3dBackprop(\n          dy as Tensor5D, x, filterSize, strides, $dilations, pad,\n          dimRoundingMode)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {AvgPoolBackprop, AvgPoolBackpropAttrs, AvgPoolBackpropInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the backprop of an 2D avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop_<T extends Tensor3D|Tensor4D>(\n    dy: T|TensorLike, input: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number): T {\n  const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n  const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n\n  util.assert(\n      $input.rank === $dy.rank,\n      () => `Rank of input (${$input.rank}) does not match rank of dy (${\n          $dy.rank})`);\n\n  let input4D = $input as Tensor4D;\n  let dy4D = $dy as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($input.rank === 3) {\n    reshapedTo4D = true;\n    input4D =\n        reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n  }\n\n  util.assert(\n      dy4D.rank === 4,\n      () => `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n          `${dy4D.rank}.`);\n  util.assert(\n      input4D.rank === 4,\n      () => `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n          `${input4D.rank}.`);\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const convInfo = conv_util.computePool2DInfo(\n        input4D.shape, filterSize, strides, 1 /* dilations */, pad);\n\n    return backend.avgPoolBackprop(dy4D, input4D, convInfo);\n  };\n\n  const inputs: AvgPoolBackpropInputs = {dy: dy4D, input: input4D};\n\n  const attrs: AvgPoolBackpropAttrs = {filterSize, strides, pad};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null, AvgPoolBackprop,\n      attrs as {} as NamedAttrMap);\n\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;\n  }\n  return res as T;\n}\n\nexport const avgPoolBackprop = op({avgPoolBackprop_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AvgPool, AvgPoolAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {avgPoolBackprop} from '../ops/avg_pool_backprop';\nimport {Tensor, Tensor4D} from '../tensor';\n\nexport const avgPoolGradConfig: GradConfig = {\n  kernelName: AvgPool,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x] = saved as [Tensor4D];\n    const {filterSize, strides, pad} = attrs as {} as AvgPoolAttrs;\n    return {\n      x: () => avgPoolBackprop(dy as Tensor4D, x, filterSize, strides, pad)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BatchMatMul, BatchMatMulAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {matMul} from '../ops/mat_mul';\nimport {Tensor} from '../tensor';\n\nexport const batchMatMulGradConfig: GradConfig = {\n  kernelName: BatchMatMul,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [a, b] = saved;\n\n    const {transposeA, transposeB} = attrs as {} as BatchMatMulAttrs;\n\n    if (!transposeA && !transposeB) {\n      return {\n        a: () => matMul(dy, b, false, true),\n        b: () => matMul(a, dy, true, false)\n      };\n    } else if (!transposeA && transposeB) {\n      return {\n        a: () => matMul(dy, b, false, false),\n        b: () => matMul(dy, a, true, false)\n      };\n    } else if (transposeA && !transposeB) {\n      return {\n        a: () => matMul(b, dy, false, true),\n        b: () => matMul(a, dy, false, false)\n      };\n    } else {\n      return {\n        a: () => matMul(b, dy, true, true),\n        b: () => matMul(dy, a, true, true)\n      };\n    }\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BatchToSpaceND, BatchToSpaceNDAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {spaceToBatchND} from '../ops/space_to_batch_nd';\nimport {Tensor} from '../tensor';\n\nexport const batchToSpaceNDGradConfig: GradConfig = {\n  kernelName: BatchToSpaceND,\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const {blockShape, crops} = attrs as {} as BatchToSpaceNDAttrs;\n    return {x: () => spaceToBatchND(dy, blockShape, crops)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BroadcastTo, BroadCastToAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const broadcastToGradConfig: GradConfig = {\n  kernelName: BroadcastTo,\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const broadCastToAttrs: BroadCastToAttrs =\n        attrs as unknown as BroadCastToAttrs;\n\n    const inputShape = broadCastToAttrs.inputShape;\n    const outputShape = broadCastToAttrs.shape;\n\n    const reps: number[] = Array.from(outputShape);\n    for (let i = inputShape.length - 1; i >= 0; i--) {\n      if (inputShape[i] === outputShape[i]) {\n        reps[i] = 1;\n      } else if (inputShape[i] !== 1) {\n        throw new Error(`broadcastTo(): [${\n            inputShape}] cannot be broadcast to [${outputShape}].`);\n      }\n    }\n    const axes: number[] = [];\n    for (let i = 0; i < reps.length; i++) {\n      if (reps[i] > 1) {\n        axes.push(i);\n      }\n    }\n\n    return {x: () => sum(dy, axes, true /* keepDims */)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cast} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {Tensor} from '../tensor';\n\nexport const castGradConfig: GradConfig = {\n  kernelName: Cast,\n  gradFunc: (dy: Tensor) => {\n    return {x: () => dy.clone()};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Ceil} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const ceilGradConfig: GradConfig = {\n  kernelName: Ceil,\n  gradFunc: (dy: Tensor) => {\n    // TODO(manrajgrover): Return null for gradients when backprop supports it.\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ClipByValue, ClipByValueAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {greaterEqual} from '../ops/greater_equal';\nimport {lessEqual} from '../ops/less_equal';\nimport {logicalAnd} from '../ops/logical_and';\nimport {where} from '../ops/where';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const clipByValueGradConfig: GradConfig = {\n  kernelName: ClipByValue,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x] = saved;\n    const {clipValueMin, clipValueMax} = attrs as {} as ClipByValueAttrs;\n    return {\n      x: () => where(\n          logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)),\n          dy, zerosLike(dy)),\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Concat, ConcatAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {split} from '../ops/split';\nimport {Tensor} from '../tensor';\nimport {parseAxisParam} from '../util';\n\nexport const concatGradConfig: GradConfig = {\n  kernelName: Concat,\n  saveAllInputs: true,\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const shapes = saved.map(t => t.shape);\n    const {axis} = attrs as {} as ConcatAttrs;\n    const $axis = parseAxisParam(axis, saved[0].shape)[0];\n    const sizeSplits = shapes.map(s => s[$axis]);\n    const derTensors = split(dy, sizeSplits, $axis);\n    return derTensors.map(t => () => t) as {};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Conv2D, Conv2DAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {conv2DBackpropFilter} from '../ops/conv2d_backprop_filter';\nimport {conv2DBackpropInput} from '../ops/conv2d_backprop_input';\nimport * as conv_util from '../ops/conv_util';\nimport {Tensor, Tensor4D} from '../tensor';\nimport * as util from '../util';\n\nexport const conv2DGradConfig: GradConfig = {\n  kernelName: Conv2D,\n  inputsToSave: ['x', 'filter'],\n  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x4D, $filter] = saved as [Tensor4D, Tensor4D];\n    const {dilations, strides, pad, dataFormat} = attrs as {} as Conv2DAttrs;\n\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of conv2D: dilation rates greater than 1 ' +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n\n    return {\n      x: () =>\n          conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad, dataFormat),\n      filter: () =>\n          conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad, dataFormat)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Conv2DBackpropInput, Conv2DBackpropInputAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {conv2d} from '../ops/conv2d';\nimport {conv2DBackpropFilter} from '../ops/conv2d_backprop_filter';\nimport {Tensor, Tensor4D} from '../tensor';\n\nexport const conv2DBackpropInputGradConfig: GradConfig = {\n  kernelName: Conv2DBackpropInput,\n  inputsToSave: ['dy', 'filter'],\n  gradFunc: (ddx: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [dy, filter] = saved as [Tensor4D, Tensor4D];\n\n    const {strides, pad, dataFormat, dimRoundingMode} =\n        attrs as {} as Conv2DBackpropInputAttrs;\n\n    return {\n      dy: () => conv2d(\n          ddx, filter, strides, pad, dataFormat, 1 /* dilations */,\n          dimRoundingMode),\n      filter: () => conv2DBackpropFilter(\n          ddx, dy, filter.shape, strides, pad, dataFormat, dimRoundingMode)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Conv3DBackpropFilterAttrs, Conv3DBackpropFilterInputs, Conv3DBackpropFilterV2} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3DBackpropFilter_<T extends Tensor4D|Tensor5D>(\n    x: T, dy: T, filterShape: [number, number, number, number, number],\n    strides: [number, number, number]|number, pad: 'valid'|'same'): Tensor5D {\n  let x5D = x as Tensor5D;\n  if (x.rank === 4) {\n    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n  }\n  let dy5D = dy as Tensor5D;\n  if (dy5D.rank === 4) {\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      () => `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n          `${x5D.shape}.`);\n  util.assert(\n      dy5D.rank === 5,\n      () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n          `${dy5D.shape}.`);\n  util.assert(\n      filterShape.length === 5,\n      () => `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n          `${filterShape}.`);\n  util.assert(\n      x5D.shape[4] === filterShape[3],\n      () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n          `match input depth in filter (${filterShape[3]}.`);\n  util.assert(\n      dy5D.shape[4] === filterShape[4],\n      () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n          `match output depth for filter (${filterShape[4]}).`);\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const dilations = 1;\n\n    const convInfo = conv_util.computeConv3DInfo(\n        x5D.shape, filterShape, strides, dilations, pad);\n\n    return backend.conv3dDerFilter(x5D, dy5D, convInfo);\n  };\n\n  const inputs: Conv3DBackpropFilterInputs = {x: x5D, dy: dy5D};\n\n  const attrs: Conv3DBackpropFilterAttrs = {strides, pad, filterShape};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null,\n             Conv3DBackpropFilterV2, attrs as {} as NamedAttrMap) as Tensor5D;\n}\n\nexport const conv3DBackpropFilter = op({conv3DBackpropFilter_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Conv3D, Conv3DAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {conv3DBackpropFilter} from '../ops/conv3d_backprop_filter';\nimport {conv3DBackpropInput} from '../ops/conv3d_backprop_input';\nimport {tupleValuesAreOne} from '../ops/conv_util';\nimport {Tensor, Tensor5D} from '../tensor';\nimport * as util from '../util';\n\nexport const conv3DGradConfig: GradConfig = {\n  kernelName: Conv3D,\n  inputsToSave: ['x', 'filter'],\n  gradFunc: (dy: Tensor5D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const {dilations, strides, pad} = attrs as {} as Conv3DAttrs;\n    util.assert(\n        tupleValuesAreOne(dilations),\n        () =>\n            'Error in gradient of conv3D: dilation rates greater than 1 are ' +\n            `not yet supported in gradients. Got dilations '${dilations}'`);\n\n    const [x5D, $filter] = saved;\n\n    return {\n      x: () => conv3DBackpropInput(\n          (x5D as Tensor5D).shape, dy, $filter as Tensor5D, strides, pad),\n      filter: () => conv3DBackpropFilter(\n          x5D as Tensor5D, dy, ($filter as Tensor5D).shape, strides, pad)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cos} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {mul} from '../ops/mul';\nimport {neg} from '../ops/neg';\nimport {sin} from '../ops/sin';\nimport {Tensor} from '../tensor';\n\nexport const cosGradConfig: GradConfig = {\n  kernelName: Cos,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => mul(neg(sin(cast(x, 'float32'))), dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cosh} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {mul} from '../ops/mul';\nimport {sinh} from '../ops/sinh';\nimport {Tensor} from '../tensor';\n\nexport const coshGradConfig: GradConfig = {\n  kernelName: Cosh,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => mul(sinh(cast(x, 'float32')), dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Cumsum, CumsumAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {getAxesPermutation} from '../ops/axis_util';\nimport {cumsum} from '../ops/cumsum';\nimport {transpose} from '../ops/transpose';\nimport {Tensor} from '../tensor';\n\nexport const cumsumGradConfig: GradConfig = {\n  kernelName: Cumsum,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x] = saved;\n    const {axis, exclusive, reverse}: CumsumAttrs = attrs as {} as CumsumAttrs;\n\n    return {\n      x: () => {\n        const permutation = getAxesPermutation([axis], x.rank);\n\n        let out = cumsum(dy, axis, exclusive, !reverse);\n\n        if (permutation != null) {\n          out = transpose(out, permutation);\n        }\n\n        return out;\n      }\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport * as conv_util from '../ops/conv_util';\nimport {depthwiseConv2dNativeBackpropFilter} from '../ops/depthwise_conv2d_native_backprop_filter';\nimport {depthwiseConv2dNativeBackpropInput} from '../ops/depthwise_conv2d_native_backprop_input';\nimport {Tensor, Tensor4D} from '../tensor';\nimport * as util from '../util';\n\nexport const depthwiseConv2dNativeGradConfig: GradConfig = {\n  kernelName: DepthwiseConv2dNative,\n  inputsToSave: ['x', 'filter'],\n  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const {dilations, strides, pad, dimRoundingMode} =\n        attrs as {} as DepthwiseConv2dNativeAttrs;\n\n    const $dilations = dilations == null ? [1, 1] : dilations;\n\n    util.assert(\n        conv_util.tupleValuesAreOne($dilations),\n        () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +\n            `greater than 1 are not yet supported. Got dilations ` +\n            `'${$dilations}'`);\n\n    const [x, filter] = saved as [Tensor4D, Tensor4D];\n\n    util.assert(\n        x.rank === 4,\n        () => `Error in gradient of depthwiseConv2dNative: input must be ` +\n            `rank 4, but got rank ${x.rank}.`);\n    util.assert(\n        filter.rank === 4,\n        () => `Error in gradient of depthwiseConv2dNative: filter must be ` +\n            `rank 4, but got rank ${filter.rank}.`);\n    util.assert(\n        x.shape[3] === filter.shape[2],\n        () => `Error in gradient of depthwiseConv2d: number of input ` +\n            `channels (${x.shape[3]}) must match the inChannels dimension ` +\n            `in filter ${filter.shape[2]}.`);\n\n    util.assert(\n        conv_util.eitherStridesOrDilationsAreOne(strides, $dilations),\n        () => 'Error in gradient of depthwiseConv2d: Either strides or ' +\n            `dilations must be  1. Got strides ${strides} and dilations ` +\n            `'${$dilations}'.`);\n\n    if (dimRoundingMode != null) {\n      util.assert(\n          util.isInt(pad as number),\n          () =>\n              `Error in depthwiseConv2d: pad must be an integer when using, ` +\n              `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n    }\n\n    return {\n      x: () => depthwiseConv2dNativeBackpropInput(\n          x.shape, dy, filter, strides, pad, dilations, dimRoundingMode),\n      filter: () => depthwiseConv2dNativeBackpropFilter(\n          x, dy, filter.shape, strides, pad, dilations, dimRoundingMode),\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE} from '../engine';\nimport {Dilation2D, Dilation2DBackpropFilter, Dilation2DBackpropFilterInputs, Dilation2DBackpropInput, Dilation2DBackpropInputInputs} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nexport const dilation2dGradConfig: GradConfig = {\n  kernelName: Dilation2D,\n  inputsToSave: ['x', 'filter'],\n  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x, filter] = saved as [Tensor4D, Tensor3D];\n\n    const inputInputs: Dilation2DBackpropInputInputs = {x, filter, dy};\n    const filterInputs: Dilation2DBackpropFilterInputs = {x, filter, dy};\n\n    return {\n      x: () => ENGINE.runKernel(\n                   Dilation2DBackpropInput, inputInputs as {} as NamedTensorMap,\n                   attrs) as Tensor,\n      filter: () => ENGINE.runKernel(\n                        Dilation2DBackpropFilter,\n                        filterInputs as {} as NamedTensorMap, attrs) as Tensor\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Div} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {neg} from '../ops/neg';\nimport {reshape} from '../ops/reshape';\nimport {square} from '../ops/square';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const divGradConfig: GradConfig = {\n  kernelName: Div,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const outShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const res = div(dy, cast(b, 'float32'));\n      const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = mul(dy, cast(a, 'float32'));\n      const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = reshape(sum(res, reduceAxes), b.shape);\n      }\n      const tmp = square(b);\n      return neg(div(res, cast(tmp, 'float32')));\n    };\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {Elu, EluGrad, EluGradInputs} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nexport const eluGradConfig: GradConfig = {\n  kernelName: Elu,\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [y] = saved;\n\n    const backPropKernelFunc: ForwardFunc<Tensor> = (backend) => {\n      return backend.eluDer(dy, y);\n    };\n\n    const inputs: EluGradInputs = {dy, y};\n\n    return {\n      x: () => ENGINE.runKernelFunc(\n          backPropKernelFunc, inputs as {} as NamedTensorMap, null /* grad */,\n          EluGrad)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Erf} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {exp} from '../ops/exp';\nimport {mul} from '../ops/mul';\nimport {neg} from '../ops/neg';\nimport {square} from '../ops/square';\nimport {Tensor} from '../tensor';\n\nexport const erfGradConfig: GradConfig = {\n  kernelName: Erf,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n    return {x: () => mul(dy, a)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Exp} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {mul} from '../ops/mul';\nimport {Tensor} from '../tensor';\n\nexport const expGradConfig: GradConfig = {\n  kernelName: Exp,\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [y] = saved;\n    return {x: () => mul(dy, y)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Expm1} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {exp} from '../ops/exp';\nimport {mul} from '../ops/mul';\nimport {Tensor} from '../tensor';\n\nexport const expm1GradConfig: GradConfig = {\n  kernelName: Expm1,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => mul(dy, exp(x))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Floor} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const floorGradConfig: GradConfig = {\n  kernelName: Floor,\n  gradFunc: (dy: Tensor) => {\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {FloorDiv} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {assertAndGetBroadcastShape, getReductionAxes} from '../ops/broadcast_util';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {neg} from '../ops/neg';\nimport {reshape} from '../ops/reshape';\nimport {square} from '../ops/square';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const floorDivGradConfig: GradConfig = {\n  kernelName: FloorDiv,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n\n    const derA = () => {\n      const res = div(dy, cast(b, 'float32'));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = mul(dy, cast(a, 'float32'));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = reshape(sum(res, reduceAxes), b.shape);\n      }\n      const tmp = square(b);\n      return neg(div(res, cast(tmp, 'float32')));\n    };\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {FusedBatchNorm, FusedBatchNormAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {add} from '../ops/add';\nimport {getReductionAxes} from '../ops/broadcast_util';\nimport {mul} from '../ops/mul';\nimport {reshape} from '../ops/reshape';\nimport {rsqrt} from '../ops/rsqrt';\nimport {scalar} from '../ops/scalar';\nimport {sub} from '../ops/sub';\nimport {sum} from '../ops/sum';\nimport {tile} from '../ops/tile';\nimport {Tensor} from '../tensor';\nimport {Rank, ShapeMap} from '../types';\n\nexport const fusedBatchNormGradConfig: GradConfig = {\n  kernelName: FusedBatchNorm,\n  inputsToSave: ['x', 'mean', 'variance', 'scale'],\n  gradFunc: <R extends Rank>(\n      dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const {varianceEpsilon} = attrs as {} as FusedBatchNormAttrs;\n    const [x, mean, variance, scale] = saved;\n\n    const scaleValue = scale == null ? scalar(1) : scale;\n    const reductionAxes = getReductionAxes(mean.shape, x.shape);\n    const tileShape: number[] = [];\n    if (mean.rank === 1) {\n      for (let i = 0; i < x.shape.length - 1; ++i) {\n        tileShape.push(x.shape[i]);\n      }\n      tileShape.push(1);\n    }\n\n    const xMinusMean = sub(x, mean);\n    const dyTimesScaleValue = mul(dy, scaleValue);\n    const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));\n    const minusHalfRCube = mul(\n        mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance),\n        scalar(-0.5));\n\n    const derX = () => {\n      if (mean.rank === 1) {\n        return reshape(\n            mul(mul(dy,\n                    tile(\n                        reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]),\n                        tileShape)),\n                scaleValue),\n            x.shape);\n      } else {\n        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n      }\n    };\n    const derMean = () => {\n      let meanDer =\n          mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n      if (mean.rank === 1) {\n        meanDer = sum(meanDer, reductionAxes);\n      }\n      return reshape(meanDer, mean.shape as ShapeMap[R]);\n    };\n    const derVariance = () => {\n      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n\n      if (mean.rank === 1) {\n        varianceDer = sum(varianceDer, reductionAxes);\n      }\n      return reshape(varianceDer, mean.shape as ShapeMap[R]);\n    };\n    const derScale = () => {\n      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n\n      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n      if (mean.rank === 1) {\n        scaleDer = sum(scaleDer, reductionAxes);\n      }\n      return reshape(scaleDer, mean.shape as ShapeMap[R]);\n    };\n    const derOffset = () => {\n      let offsetDer = dy;\n      if (mean.rank === 1) {\n        offsetDer = sum(offsetDer, reductionAxes);\n      }\n      return reshape(offsetDer, mean.shape as ShapeMap[R]);\n    };\n\n    return {\n      x: derX,\n      mean: derMean,\n      variance: derVariance,\n      scale: derScale,\n      offset: derOffset\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GatherV2, GatherV2Attrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {getUndoAxesPermutation} from '../ops/axis_util';\nimport {reshape} from '../ops/reshape';\nimport {transpose} from '../ops/transpose';\nimport {unsortedSegmentSum} from '../ops/unsorted_segment_sum';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {parseAxisParam} from '../util';\n\nexport const gatherGradConfig: GradConfig = {\n  kernelName: GatherV2,\n  inputsToSave: ['x', 'indices'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x, indices] = saved;\n    const {axis} = attrs as {} as GatherV2Attrs;\n\n    const parsedAxis = parseAxisParam(axis, x.shape)[0];\n\n    const derX = () => {\n      const paramsShape = x.shape;\n      const indicesSize = indices.size;\n\n      const outerShape = paramsShape.slice(0, parsedAxis);\n      const outerDims = outerShape.length;\n      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n      const innerDims = innerShape.length;\n\n      const outerAxesIndices = arrayRange(0, outerDims);\n      const innerAxesIndices =\n          arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n\n      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n\n      const values = reshape(dy, valuesShape);\n      const reshapedIndices = reshape(indices, [indicesSize]);\n\n      const transposeDims =\n          arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n      const valuesTranspose = transpose(values, transposeDims);\n      let paramsGrad = unsortedSegmentSum(\n          valuesTranspose, reshapedIndices as Tensor1D, x.shape[parsedAxis]);\n\n      const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n      paramsGrad = transpose(paramsGrad, invertTransposeDims);\n\n      return paramsGrad;\n    };\n    return {x: derX, indices: () => indices};\n  }\n};\n\nfunction arrayRange(start: number, stop: number): number[] {\n  const result = [];\n  for (let i = start; i < stop; ++i) {\n    result.push(i);\n  }\n  return result;\n}\n\nfunction arrayConcat(arrays: number[][]): number[] {\n  const result = [];\n  for (let i = 0; i < arrays.length; ++i) {\n    for (let j = 0; j < arrays[i].length; ++j) {\n      result.push(arrays[i][j]);\n    }\n  }\n  return result;\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {GreaterEqual} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const greaterEqualGradConfig: GradConfig = {\n  kernelName: GreaterEqual,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    return {a: () => zerosLike(a), b: () => zerosLike(b)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Identity} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {Tensor} from '../tensor';\n\nexport const identityGradConfig: GradConfig = {\n  kernelName: Identity,\n  gradFunc: (dy: Tensor) => {\n    return {x: () => cast(dy, 'float32')};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsFinite} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const isFiniteGradConfig: GradConfig = {\n  kernelName: IsFinite,\n  gradFunc: (dy: Tensor) => {\n    // TODO(nsthorat): Let gradients be null for cases where we want to stop\n    // backpropgation.\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsInf} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const isInfGradConfig: GradConfig = {\n  kernelName: IsInf,\n  gradFunc: (dy: Tensor) => {\n    // TODO(nsthorat): Let gradients be null for cases where we want to stop\n    // backpropgation.\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IsNan} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const isNanGradConfig: GradConfig = {\n  kernelName: IsNan,\n  gradFunc: (dy: Tensor) => {\n    // TODO(nsthorat): Let gradients be null for cases where we want to stop\n    // backpropgation.\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Log1p} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {Tensor} from '../tensor';\n\nexport const log1pGradConfig: GradConfig = {\n  kernelName: Log1p,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => div(dy, add(x, 1))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Log} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {Tensor} from '../tensor';\n\nexport const logGradConfig: GradConfig = {\n  kernelName: Log,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => div(dy, cast(x, 'float32'))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {LogSoftmax, LogSoftmaxAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {exp} from '../ops/exp';\nimport {mul} from '../ops/mul';\nimport {sub} from '../ops/sub';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const logSoftmaxGradConfig: GradConfig = {\n  kernelName: LogSoftmax,\n  inputsToSave: [],\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [value] = saved;\n    const {axis} = attrs as {} as LogSoftmaxAttrs;\n    return {\n      logits: () => {\n        const keepDims = true;\n        const softmax = exp(value);\n        return sub(dy, mul(sum(dy, axis, keepDims), softmax));\n      }\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {LRNBackprop, LRNBackpropAttrs, LRNBackpropInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nimport {op} from './operation';\n\nfunction localResponseNormalizationBackprop_<T extends Tensor4D>(\n    x: T, y: T, dy: T, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n  const forward: ForwardFunc<Tensor> = backend =>\n      backend.LRNGrad(dy, x, y, depthRadius, bias, alpha, beta);\n\n  const inputs: LRNBackpropInputs = {x, y, dy};\n\n  const attrs: LRNBackpropAttrs = {depthRadius, bias, alpha, beta};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null /* grad */,\n             LRNBackprop, attrs as {} as NamedAttrMap) as T;\n}\n\nexport const localResponseNormalizationBackprop =\n    op({localResponseNormalizationBackprop_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {LRN, LRNAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {localResponseNormalizationBackprop} from '../ops/local_response_normalization_backprop';\nimport {Tensor, Tensor4D} from '../tensor';\n\nexport const lrnGradConfig: GradConfig = {\n  kernelName: LRN,\n  inputsToSave: ['x'],\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x, y] = saved as [Tensor4D, Tensor4D];\n    const {depthRadius, bias, alpha, beta} = attrs as {} as LRNAttrs;\n\n    return {\n      x: () => localResponseNormalizationBackprop(\n          x, y, dy, depthRadius, bias, alpha, beta)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as axis_util from '../ops/axis_util';\nimport {cast} from '../ops/cast';\nimport {equal} from '../ops/equal';\nimport {mul} from '../ops/mul';\nimport {reshape} from '../ops/reshape';\nimport {Tensor} from '../tensor';\n\n/**\n * Gradient helper function for the min and max operations.\n */\nexport function gradForMinAndMax<T extends Tensor>(\n    dy: T, y: T, xOrig: Tensor, origAxes: number[]) {\n  if (y.rank < xOrig.rank) {\n    y = reshape(y, axis_util.expandShapeToKeepDim(y.shape, origAxes)) as T;\n  }\n  if (dy.rank < xOrig.rank) {\n    dy = reshape(dy, axis_util.expandShapeToKeepDim(dy.shape, origAxes)) as T;\n  }\n  return {\n    x: () => {\n      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n      return dx;\n    }\n  };\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Max, MaxAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport * as util from '../util';\n\nimport {gradForMinAndMax} from './min_max_grad_util';\n\nexport const maxGradConfig: GradConfig = {\n  kernelName: Max,\n  inputsToSave: ['x'],\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const maxAttrs: MaxAttrs = attrs as {} as MaxAttrs;\n    const {reductionIndices} = maxAttrs;\n    const x = saved[0];\n    const y = saved[1];\n    const origAxes = util.parseAxisParam(reductionIndices, x.shape);\n    const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n    return {\n      x: () => {\n        return maxGrad['x']();\n      }\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Maximum} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {greaterEqual} from '../ops/greater_equal';\nimport {less} from '../ops/less';\nimport {mul} from '../ops/mul';\nimport {Tensor} from '../tensor';\n\nexport const maximumGradConfig: GradConfig = {\n  kernelName: Maximum,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const derA = () => mul(dy, cast(greaterEqual(a, b), 'float32'));\n    const derB = () => mul(dy, cast(less(a, b), 'float32'));\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {MaxPool3DBackprop, MaxPool3DBackpropAttrs, MaxPool3DBackpropInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D, Tensor5D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\nimport {reshape} from './reshape';\n\n/**\n * Computes the backprop of a 3d max pool.\n *\n * @param dy The dy error, of rank 5 of shape\n *     [batchSize, depth, height, width, channels].\n * assumed.\n * @param input The original input image, of rank 5 or rank 4 of shape\n *     [batchSize, depth, height, width, channels].\n * @param output The original output image, of rank 5 of shape\n *     [batchSize, outDepth, outHeight, outWidth, channels].\n * @param filterSize The filter size:\n *     `[filterDepth, filterHeight, filterWidth]`.\n *     `filterSize` is a single number,\n *     then `filterDepth == filterHeight == filterWidth`.\n * @param strides The strides of the pooling:\n *     `[strideDepth, strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations Deprecated, this field will be gone in v3.0.0.\n *     The dilation rates: `[dilationDepth, dilationHeight, dilationWidth]`\n *     in which we sample input values across the depth, height and width\n *     dimensions in dilated pooling.\n *     Defaults to `[1, 1, 1]`. If `dilations` is a single number,\n *     then `dilationDepth == dilationHeight == dilationWidth`.\n *     If it is greater than 1, then all values of `strides` must be 1.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPool3dBackprop_<T extends Tensor4D|Tensor5D>(\n    dy: T|TensorLike, input: T|TensorLike, output: T|TensorLike,\n    filterSize: [number, number, number]|number,\n    strides: [number, number, number]|number,\n    dilations: [number, number, number]|number = [1, 1, 1],\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $dy = convertToTensor(dy, 'dy', 'maxPool3dBackprop');\n  const $input = convertToTensor(input, 'input', 'maxPool3dBackprop');\n  const $output = convertToTensor(output, 'output', 'maxPool3dBackprop');\n\n  let dy5D = $dy as Tensor5D;\n  let input5D = $input as Tensor5D;\n  let output5D = $output as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($input.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape(\n        $dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n    input5D = reshape($input, [\n      1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]\n    ]);\n    output5D = reshape($output, [\n      1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]\n    ]);\n  }\n\n  util.assert(\n      dy5D.rank === 5,\n      () => `Error in maxPool3dBackprop: dy must be rank 5 but got rank ` +\n          `${dy5D.rank}.`);\n  util.assert(\n      input5D.rank === 5,\n      () => `Error in maxPool3dBackprop: input must be rank 5 but got rank ` +\n          `${input5D.rank}.`);\n  util.assert(\n      output5D.rank === 5,\n      () => `Error in maxPool3dBackprop: output must be rank 5 but got rank ` +\n          `${output5D.rank}.`);\n\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in maxPool3dBackprop: Either strides or dilations ' +\n          `must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in maxPool3dBackprop: pad must be an integer when ` +\n            `using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const convInfo = conv_util.computePool3DInfo(\n        input5D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n\n    return backend.maxPool3dBackprop(dy5D, input5D, output5D, convInfo);\n  };\n\n  const inputs:\n      MaxPool3DBackpropInputs = {dy: dy5D, input: input5D, output: output5D};\n\n  const attrs: MaxPool3DBackpropAttrs =\n      {filterSize, strides, dilations, pad, dimRoundingMode};\n\n  const res = ENGINE.runKernelFunc(\n      forward, inputs as {} as NamedTensorMap, null /* grad */,\n      MaxPool3DBackprop, attrs as {} as NamedAttrMap);\n\n  if (reshapedTo5D) {\n    return reshape(\n               res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as\n        T;\n  }\n\n  return res as T;\n}\n\nexport const maxPool3dBackprop = op({maxPool3dBackprop_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {MaxPool3D, MaxPool3DAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {maxPool3dBackprop} from '../ops/max_pool_3d_backprop';\nimport {Tensor, Tensor5D} from '../tensor';\n\nexport const maxPool3DGradConfig: GradConfig = {\n  kernelName: MaxPool3D,\n  inputsToSave: ['x'],\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x, y] = saved as [Tensor5D, Tensor5D];\n    const {filterSize, strides, dilations, pad, dimRoundingMode} =\n        attrs as {} as MaxPool3DAttrs;\n\n    const $dilations =\n        dilations == null ? [1, 1, 1] as [number, number, number] : dilations;\n\n    return {\n      x: () => maxPool3dBackprop(\n          dy as Tensor5D, x, y, filterSize, strides, $dilations, pad,\n          dimRoundingMode)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {MaxPoolBackprop, MaxPoolBackpropAttrs, MaxPoolBackpropInputs} from '../kernel_names';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\n\n/**\n * Computes the backprop of a 2D max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop_(\n    dy: Tensor4D|TensorLike, input: Tensor4D|TensorLike,\n    output: Tensor4D|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n  const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n  const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n\n  util.assert(\n      $input.rank === $dy.rank,\n      () => `Rank of input (${$input.rank}) does not match rank of dy ` +\n          `(${$dy.rank})`);\n\n  util.assert(\n      $dy.rank === 4,\n      () => `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n          `${$dy.rank}.`);\n  util.assert(\n      $input.rank === 4,\n      () => `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n          `${$input.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        () => `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const forward: ForwardFunc<Tensor> = backend => {\n    const convInfo = conv_util.computePool2DInfo(\n        $input.shape, filterSize, strides, 1 /* dilations */, pad,\n        dimRoundingMode);\n\n    return backend.maxPoolBackprop($dy, $input, $output, convInfo);\n  };\n\n  const inputs:\n      MaxPoolBackpropInputs = {dy: $dy, input: $input, output: $output};\n\n  const attrs:\n      MaxPoolBackpropAttrs = {filterSize, strides, pad, dimRoundingMode};\n\n  return ENGINE.runKernelFunc(\n             forward, inputs as {} as NamedTensorMap, null, MaxPoolBackprop,\n             attrs as {} as NamedAttrMap) as Tensor4D;\n}\n\nexport const maxPoolBackprop = op({maxPoolBackprop_});\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {MaxPool, MaxPoolAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {maxPoolBackprop} from '../ops/max_pool_backprop';\nimport {Tensor, Tensor4D} from '../tensor';\n\nexport const maxPoolGradConfig: GradConfig = {\n  kernelName: MaxPool,\n  inputsToSave: ['x'],\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x, y] = saved as [Tensor4D, Tensor4D];\n    const {filterSize, strides, pad} = attrs as {} as MaxPoolAttrs;\n\n    return {\n      x: () => maxPoolBackprop(dy as Tensor4D, x, y, filterSize, strides, pad)\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Min, MinAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {Tensor} from '../tensor';\nimport * as util from '../util';\n\nimport {gradForMinAndMax} from './min_max_grad_util';\n\nexport const minGradConfig: GradConfig = {\n  kernelName: Min,\n  inputsToSave: ['x'],\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const minAttrs: MinAttrs = attrs as {} as MinAttrs;\n    const {axis} = minAttrs;\n    const [x, y] = saved;\n    const origAxes = util.parseAxisParam(axis, x.shape);\n    const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n    return {\n      x: () => {\n        return minGrad['x']();\n      }\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Minimum} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {greater} from '../ops/greater';\nimport {lessEqual} from '../ops/less_equal';\nimport {mul} from '../ops/mul';\nimport {Tensor} from '../tensor';\n\nexport const minimumGradConfig: GradConfig = {\n  kernelName: Minimum,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const derA = () => mul(dy, cast(lessEqual(a, b), 'float32'));\n    const derB = () => mul(dy, cast(greater(a, b), 'float32'));\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {MirrorPad, MirrorPadAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {slice} from '../ops/slice';\nimport {Tensor} from '../tensor';\n\nexport const mirrorPadGradConfig: GradConfig = {\n  kernelName: MirrorPad,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    // Pad introduces values around the original tensor, so the gradient\n    // slices the original shape out of the gradient.\n    const x = saved[0];\n    const {paddings} = attrs as unknown as MirrorPadAttrs;\n    const begin = paddings.map(p => p[0]);\n    return {x: () => slice(dy, begin, x.shape)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Mod} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {assertAndGetBroadcastShape, getReductionAxes} from '../ops/broadcast_util';\nimport {div} from '../ops/div';\nimport {floor} from '../ops/floor';\nimport {mul} from '../ops/mul';\nimport {neg} from '../ops/neg';\nimport {reshape} from '../ops/reshape';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const modGradConfig: GradConfig = {\n  kernelName: Mod,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n\n    const derA = () => {\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum(dy, reduceAxes), a.shape);\n      }\n      return dy;\n    };\n    const derB = () => {\n      const res = mul(dy, neg(floor(div(a, b))));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum(res, reduceAxes), b.shape);\n      }\n      return res;\n    };\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Multiply} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {assertAndGetBroadcastShape, getReductionAxes} from '../ops/broadcast_util';\nimport {cast} from '../ops/cast';\nimport {mul} from '../ops/mul';\nimport {reshape} from '../ops/reshape';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const multiplyGradConfig: GradConfig = {\n  kernelName: Multiply,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n\n    const derA = () => {\n      const res = mul(dy, cast(b, 'float32'));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      const res = mul(dy, cast(a, 'float32'));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum(res, reduceAxes), b.shape);\n      }\n      return res;\n    };\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Negate} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {neg} from '../ops/neg';\nimport {Tensor} from '../tensor';\n\nexport const negateGradConfig: GradConfig = {\n  kernelName: Negate,\n  gradFunc: (dy: Tensor) => {\n    return {x: () => neg(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OneHot} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zeros} from '../ops/zeros';\nimport {Tensor} from '../tensor';\n\nexport const oneHotGradConfig: GradConfig = {\n  kernelName: OneHot,\n  inputsToSave: ['indices'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const indices = saved[0];\n    return {indices: () => zeros(indices.shape, 'float32')};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {OnesLike} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const onesLikeGradConfig: GradConfig = {\n  kernelName: OnesLike,\n  gradFunc: (dy: Tensor) => {\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {PadV2, PadV2Attrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {slice} from '../ops/slice';\nimport {Tensor} from '../tensor';\n\nexport const padV2GradConfig: GradConfig = {\n  kernelName: PadV2,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    // Pad introduces values around the original tensor, so the gradient\n    // slices the original shape out of the gradient.\n    const x = saved[0];\n    const {paddings} = attrs as unknown as PadV2Attrs;\n    const begin = paddings.map(p => p[0]);\n    return {x: () => slice(dy, begin, x.shape)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Pow} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport {cast} from '../ops/cast';\nimport {greater} from '../ops/greater';\nimport {log} from '../ops/log';\nimport {mul} from '../ops/mul';\nimport {pow} from '../ops/pow';\nimport {reshape} from '../ops/reshape';\nimport {scalar} from '../ops/scalar';\nimport {sub} from '../ops/sub';\nimport {sum} from '../ops/sum';\nimport {where} from '../ops/where';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const powGradConfig: GradConfig = {\n  kernelName: Pow,\n  inputsToSave: ['a', 'b'],\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b, y] = saved;\n    const base = a;\n    const exp = b;\n    const outShape =\n        broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);\n\n    const derBase = () => {\n      const expFloat = cast(exp, 'float32');\n      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n      const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n      }\n      return reshape(res, base.shape);\n    };\n    const derExp = () => {\n      const condition = greater(base, 0);\n      const logBase = where(condition, log(base), zerosLike(base));\n      let res = mul(dy, mul(y, logBase));\n      const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n      }\n      return reshape(res, exp.shape);\n    };\n    return {a: derBase, b: derExp};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Prelu} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {getReductionAxes} from '../ops/broadcast_util';\nimport {greater} from '../ops/greater';\nimport {mul} from '../ops/mul';\nimport {reshape} from '../ops/reshape';\nimport {sum} from '../ops/sum';\nimport {where} from '../ops/where';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const preluGradConfig: GradConfig = {\n  kernelName: Prelu,\n  inputsToSave: ['x', 'alpha'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x, alpha] = saved;\n    const mask = greater(x, 0);\n\n    return {\n      x: () => where(mask, dy, mul(dy, alpha)),\n      alpha: () => {\n        let res = where(mask, zerosLike(dy), mul(dy, x));\n        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n        if (reduceAxes.length > 0) {\n          res = sum(res, reduceAxes);\n        }\n        return reshape(res, alpha.shape);\n      }\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Reciprocal} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {div} from '../ops/div';\nimport {neg} from '../ops/neg';\nimport {square} from '../ops/square';\nimport {Tensor} from '../tensor';\n\nexport const reciprocalGradConfig: GradConfig = {\n  kernelName: Reciprocal,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => div(dy, neg(square(x)))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Relu6} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {lessEqual} from '../ops/less_equal';\nimport {mul} from '../ops/mul';\nimport {step} from '../ops/step';\nimport {Tensor} from '../tensor';\n\nexport const relu6GradConfig: GradConfig = {\n  kernelName: Relu6,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    const mask = mul(lessEqual(x, 6), step(x));\n\n    return {x: () => mul(dy, cast(mask, 'float32'))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Relu} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {mul} from '../ops/mul';\nimport {step} from '../ops/step';\nimport {Tensor} from '../tensor';\n\nexport const reluGradConfig: GradConfig = {\n  kernelName: Relu,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => mul(dy, cast(step(x), 'float32'))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Reshape} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {reshape} from '../ops/reshape';\nimport {Tensor} from '../tensor';\n\nexport const reshapeGradConfig: GradConfig = {\n  kernelName: Reshape,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => reshape(dy, x.shape)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {ResizeBilinear, ResizeBilinearAttrs, ResizeBilinearGrad, ResizeBilinearGradInputs} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nexport const resizeBilinearGradConfig: GradConfig = {\n  kernelName: ResizeBilinear,\n  inputsToSave: ['images'],\n  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [images] = saved;\n\n    const backPropKernelFunc: ForwardFunc<Tensor> = (backend) => {\n      const {alignCorners} = attrs as {} as ResizeBilinearAttrs;\n      return backend.resizeBilinearBackprop(\n          dy, images as Tensor4D, alignCorners);\n    };\n\n    const inputs: ResizeBilinearGradInputs = {images};\n    const imagesDer = () => ENGINE.runKernelFunc(\n        backPropKernelFunc, inputs as {} as NamedTensorMap, null /* gradient */,\n        ResizeBilinearGrad, attrs);\n\n    return {images: imagesDer};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENGINE, ForwardFunc} from '../engine';\nimport {ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborGrad, ResizeNearestNeighborGradInputs} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {NamedAttrMap} from '../kernel_registry';\nimport {Tensor, Tensor4D} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\nexport const resizeNearestNeighborGradConfig: GradConfig = {\n  kernelName: ResizeNearestNeighbor,\n  inputsToSave: ['images'],\n  gradFunc: (dy: Tensor4D, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [images] = saved;\n\n    const backPropKernelFunc: ForwardFunc<Tensor> = (backend) => {\n      const {alignCorners} = attrs as {} as ResizeNearestNeighborAttrs;\n      return backend.resizeNearestNeighborBackprop(\n          dy, images as Tensor4D, alignCorners);\n    };\n\n    const inputs: ResizeNearestNeighborGradInputs = {images};\n    const imagesDer = () => ENGINE.runKernelFunc(\n        backPropKernelFunc, inputs as {} as NamedTensorMap, null /* gradient */,\n        ResizeNearestNeighborGrad, attrs);\n\n    return {images: imagesDer};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Reverse, ReverseAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {reverse} from '../ops/reverse';\nimport {Tensor} from '../tensor';\nimport {parseAxisParam} from '../util';\n\nexport const reverseGradConfig: GradConfig = {\n  kernelName: Reverse,\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const {dims} = attrs as {} as ReverseAttrs;\n    const axes = parseAxisParam(dims, dy.shape);\n    return {x: () => reverse(dy, axes)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Round} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const roundGradConfig: GradConfig = {\n  kernelName: Round,\n  gradFunc: (dy: Tensor) => {\n    // TODO(nsthorat): Let gradients be null for cases where we want to stop\n    // backpropgation.\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Rsqrt} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {neg} from '../ops/neg';\nimport {pow} from '../ops/pow';\nimport {Tensor} from '../tensor';\n\nexport const rsqrtGradConfig: GradConfig = {\n  kernelName: Rsqrt,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => neg(div(dy, mul(pow(x, 1.5), 2)))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {SelectV2} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {logicalNot} from '../ops/logical_not';\nimport {mul} from '../ops/mul';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const selectV2PoolGradConfig: GradConfig = {\n  kernelName: SelectV2,\n  inputsToSave: ['condition'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [condition] = saved;\n    return {\n      // TODO(julianoks): Return null for condition gradient\n      // when backprop supports it.\n      condition: () => cast(zerosLike(condition), 'float32'),\n      t: () => mul(dy, cast(condition, dy.dtype)),\n      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Selu} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {exp} from '../ops/exp';\nimport {greater} from '../ops/greater';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {SELU_SCALE, SELU_SCALEALPHA} from '../ops/selu_util';\nimport {where} from '../ops/where';\nimport {Tensor} from '../tensor';\n\nexport const seluGradConfig: GradConfig = {\n  kernelName: Selu,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const mask = greater(x, scalar(0));\n\n        const scaleAlpha = scalar(SELU_SCALEALPHA);\n        const scale = scalar(SELU_SCALE);\n\n        const greaterThanZeroDer = mul(dy, scale);\n        const lessEqualZeroDer =\n            mul(mul(dy, scaleAlpha), exp(cast(x, 'float32')));\n\n        return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n      }\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Sigmoid} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {sub} from '../ops/sub';\nimport {Tensor} from '../tensor';\n\nexport const sigmoidGradConfig: GradConfig = {\n  kernelName: Sigmoid,\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [y] = saved;\n\n    return {x: () => mul(dy, mul(y, sub(scalar(1), y)))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Sign} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const signGradConfig: GradConfig = {\n  kernelName: Sign,\n  gradFunc: (dy: Tensor) => {\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Sin} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {cos} from '../ops/cos';\nimport {mul} from '../ops/mul';\nimport {Tensor} from '../tensor';\n\nexport const sinGradConfig: GradConfig = {\n  kernelName: Sin,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => mul(cos(cast(x, 'float32')), dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Sinh} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {cosh} from '../ops/cosh';\nimport {mul} from '../ops/mul';\nimport {Tensor} from '../tensor';\n\nexport const sinhGradConfig: GradConfig = {\n  kernelName: Sinh,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => mul(cosh(cast(x, 'float32')), dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Slice, SliceAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {pad} from '../ops/pad';\nimport {parseSliceParams} from '../ops/slice_util';\nimport {Tensor} from '../tensor';\n\nexport const sliceGradConfig: GradConfig = {\n  kernelName: Slice,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x] = saved;\n    const {begin, size} = attrs as {} as SliceAttrs;\n\n    const inputShape = x.shape;\n    const [begin_, size_] = parseSliceParams(x, begin, size);\n\n    // Create an Nx2 padding where the first column represents how many\n    // zeros are prepended (at start) for each dimension, and the second\n    // column indicates how many zeros are appended (at end).\n\n    // The number of zeros to append is the shape of the input\n    // elementwise-subtracted by both the begin vector and sizes vector.\n    const paddings: Array<[number, number]> = [];\n    for (let i = 0; i < dy.rank; i++) {\n      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n    }\n    return {x: () => pad(dy, paddings)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Softmax, SoftmaxAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {mul} from '../ops/mul';\nimport {sub} from '../ops/sub';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const softmaxGradConfig: GradConfig = {\n  kernelName: Softmax,\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [y] = saved;\n    const {dim} = attrs as {} as SoftmaxAttrs;\n    const keepDims = true;\n\n    const dyTimesY = mul(dy, y);\n    return {\n      logits: () => sub(dyTimesY, mul(sum(dyTimesY, [dim], keepDims), y))\n    };\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Softplus} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {mul} from '../ops/mul';\nimport {sigmoid} from '../ops/sigmoid';\nimport {Tensor} from '../tensor';\n\nexport const softplusGradConfig: GradConfig = {\n  kernelName: Softplus,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => mul(dy, sigmoid(x))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {SpaceToBatchND, SpaceToBatchNDAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {batchToSpaceND} from '../ops/batch_to_space_nd';\nimport {Tensor} from '../tensor';\n\nexport const spaceToBatchNDGradConfig: GradConfig = {\n  kernelName: SpaceToBatchND,\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const {blockShape, paddings} = attrs as {} as SpaceToBatchNDAttrs;\n    return {x: () => batchToSpaceND(dy, blockShape, paddings)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {SplitV, SplitVAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {concat} from '../ops/concat';\nimport {Tensor} from '../tensor';\n\nexport const splitVGradConfig: GradConfig = {\n  kernelName: SplitV,\n  gradFunc: (dy: Tensor[], saved: Tensor[], attrs: NamedAttrMap) => {\n    const {axis} = attrs as {} as SplitVAttrs;\n\n    return {x: () => concat(dy, axis)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Sqrt} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/sqrt';\nimport {Tensor} from '../tensor';\n\nexport const sqrtGradConfig: GradConfig = {\n  kernelName: Sqrt,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => div(dy, mul(sqrt(cast(x, 'float32')), 2))};\n  }\n};\n", "/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Square} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cast} from '../ops/cast';\nimport {mul} from '../ops/mul';\nimport {Tensor} from '../tensor';\n\nexport const squareGradConfig: GradConfig = {\n  kernelName: Square,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n    return {x: () => mul(dy, mul(cast(x, 'float32'), 2))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {SquaredDifference} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {sub} from '../ops/sub';\nimport {Tensor} from '../tensor';\n\nexport const squaredDifferenceGradConfig: GradConfig = {\n  kernelName: SquaredDifference,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const two = scalar(2);\n    const derA = () => mul(dy, mul(two, sub(a, b)));\n    const derB = () => mul(dy, mul(two, sub(b, a)));\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Step} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const stepGradConfig: GradConfig = {\n  kernelName: Step,\n  gradFunc: (dy: Tensor) => {\n    // TODO(manrajgrover): Return null for gradients when backprop supports\n    // it.\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Sub} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport {neg} from '../ops/neg';\nimport {reshape} from '../ops/reshape';\nimport {sum} from '../ops/sum';\nimport {Tensor} from '../tensor';\n\nexport const subGradConfig: GradConfig = {\n  kernelName: Sub,\n  inputsToSave: ['a', 'b'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [a, b] = saved;\n    const outShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum(res, reduceAxes);\n      }\n      return reshape(neg(res), b.shape);\n    };\n\n    return {a: derA, b: derB};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Sum, SumAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {mul} from '../ops/mul';\nimport {ones} from '../ops/ones';\nimport {reshape} from '../ops/reshape';\nimport {Tensor} from '../tensor';\nimport {parseAxisParam} from '../util';\n\nexport const sumGradConfig: GradConfig = {\n  kernelName: Sum,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x] = saved;\n    const expandedDyShape = x.shape.slice();\n    const {axis} = attrs as {} as SumAttrs;\n\n    const axes = parseAxisParam(axis, x.shape);\n    axes.forEach(axis => {\n      expandedDyShape[axis] = 1;\n    });\n    const expandedDy = reshape(dy, expandedDyShape);\n    const derX = mul(expandedDy, ones(x.shape, 'float32'));\n\n    return {x: () => derX};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tan} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {cos} from '../ops/cos';\nimport {div} from '../ops/div';\nimport {square} from '../ops/square';\nimport {Tensor} from '../tensor';\n\nexport const tanGradConfig: GradConfig = {\n  kernelName: Tan,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [x] = saved;\n\n    return {x: () => div(dy, square(cos(x)))};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tanh} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {square} from '../ops/square';\nimport {sub} from '../ops/sub';\nimport {Tensor} from '../tensor';\n\nexport const tanhGradConfig: GradConfig = {\n  kernelName: Tanh,\n  outputsToSave: [true],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [y] = saved;\n\n    return {x: () => mul(sub(scalar(1), square(y)), dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tile, TileAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {add} from '../ops/add';\nimport {slice} from '../ops/slice';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const tileGradConfig: GradConfig = {\n  kernelName: Tile,\n  inputsToSave: ['x'],\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const [x] = saved;\n    const {reps} = attrs as unknown as TileAttrs;\n\n    const derX = () => {\n      let xGrad = zerosLike(x);\n      // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n      // slicing.\n      if (x.rank === 1) {\n        for (let i = 0; i < reps[0]; ++i) {\n          xGrad = add(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n        }\n      } else if (x.rank === 2) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            xGrad = add(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n                          x.shape[0], x.shape[1]\n                        ]));\n          }\n        }\n      } else if (x.rank === 3) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              xGrad =\n                  add(xGrad,\n                      slice(\n                          dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]],\n                          [x.shape[0], x.shape[1], x.shape[2]]));\n            }\n          }\n        }\n      } else if (x.rank === 4) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              for (let l = 0; l < reps[3]; ++l) {\n                xGrad =\n                    add(xGrad,\n                        slice(\n                            dy,\n                            [\n                              i * x.shape[0], j * x.shape[1], k * x.shape[2],\n                              l * x.shape[3]\n                            ],\n                            [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n              }\n            }\n          }\n        }\n      } else {\n        throw new Error(\n            `Gradient for tile operation is not implemented for rank-` +\n            `${x.rank} tensors yet.`);\n      }\n      return xGrad;\n    };\n    return {x: derX};\n  },\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Transpose, TransposeAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport * as axis_util from '../ops/axis_util';\nimport {transpose} from '../ops/transpose';\nimport {Tensor} from '../tensor';\n\nexport const transposeGradConfig: GradConfig = {\n  kernelName: Transpose,\n  gradFunc: (dy: Tensor, saved: Tensor[], attrs: NamedAttrMap) => {\n    const transposeAttrs: TransposeAttrs = attrs as {} as TransposeAttrs;\n    const {perm} = transposeAttrs;\n    const undoPerm = axis_util.getUndoAxesPermutation(perm);\n    return {x: () => transpose(dy, undoPerm)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Unpack, UnpackAttrs} from '../kernel_names';\nimport {GradConfig, NamedAttrMap} from '../kernel_registry';\nimport {stack} from '../ops/stack';\nimport {Tensor} from '../tensor';\n\nexport const unpackGradConfig: GradConfig = {\n  kernelName: Unpack,\n  gradFunc: (dy: Tensor[], saved: Tensor[], attrs: NamedAttrMap) => {\n    const unpackAttrs: UnpackAttrs = attrs as {} as UnpackAttrs;\n    const {axis} = unpackAttrs;\n    return {value: () => stack(dy, axis)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {UnsortedSegmentSum} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {expandDims} from '../ops/expand_dims';\nimport {gather} from '../ops/gather';\nimport {greaterEqual} from '../ops/greater_equal';\nimport {logicalAnd} from '../ops/logical_and';\nimport {maximum} from '../ops/maximum';\nimport {ones} from '../ops/ones';\nimport {scalar} from '../ops/scalar';\nimport {where} from '../ops/where';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor, Tensor1D} from '../tensor';\n\nexport const unsortedSegmentSumGradConfig: GradConfig = {\n  kernelName: UnsortedSegmentSum,\n  inputsToSave: ['segmentIds'],\n  gradFunc: (dy: Tensor, saved: Tensor[]) => {\n    const [segmentIds] = saved;\n\n    const derX = () => {\n      return gatherDropNegatives(dy, segmentIds as Tensor1D);\n    };\n    return {x: derX};\n  }\n};\n\nfunction gatherDropNegatives<T extends Tensor>(x: T, indices: Tensor1D) {\n  // Helper function for unsorted segment ops. Gathers params for\n  // positive segment ids and gathers 0 for inputs with negative segment id.\n  // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n  const zeroClippedIndices = maximum(indices, zerosLike(indices));\n  const gathered = gather(x, zeroClippedIndices as Tensor1D);\n  let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n  const numIters = gathered.rank - isPositive.rank;\n  for (let i = 0; i < numIters; ++i) {\n    isPositive = expandDims(isPositive, i + 1);\n  }\n  isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n  const zeroSlice = zerosLike(gathered);\n  return where(isPositive, gathered, zeroSlice);\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ZerosLike} from '../kernel_names';\nimport {GradConfig} from '../kernel_registry';\nimport {zerosLike} from '../ops/zeros_like';\nimport {Tensor} from '../tensor';\n\nexport const zerosLikeGradConfig: GradConfig = {\n  kernelName: ZerosLike,\n  gradFunc: (dy: Tensor) => {\n    return {x: () => zerosLike(dy)};\n  }\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {absGradConfig} from './gradients/Abs_grad';\nimport {acosGradConfig} from './gradients/Acos_grad';\nimport {acoshGradConfig} from './gradients/Acosh_grad';\nimport {addGradConfig} from './gradients/Add_grad';\nimport {addNGradConfig} from './gradients/AddN_grad';\nimport {argMaxGradConfig} from './gradients/ArgMax_grad';\nimport {argMinGradConfig} from './gradients/ArgMin_grad';\nimport {asinGradConfig} from './gradients/Asin_grad';\nimport {asinhGradConfig} from './gradients/Asinh_grad';\nimport {atan2GradConfig} from './gradients/Atan2_grad';\nimport {atanGradConfig} from './gradients/Atan_grad';\nimport {atanhGradConfig} from './gradients/Atanh_grad';\nimport {avgPool3DGradConfig} from './gradients/AvgPool3D_grad';\nimport {avgPoolGradConfig} from './gradients/AvgPool_grad';\nimport {batchMatMulGradConfig} from './gradients/BatchMatMul_grad';\nimport {batchToSpaceNDGradConfig} from './gradients/BatchToSpaceND_grad';\nimport {broadcastToGradConfig} from './gradients/BroadcastTo_grad';\nimport {castGradConfig} from './gradients/Cast_grad';\nimport {ceilGradConfig} from './gradients/Ceil_grad';\nimport {clipByValueGradConfig} from './gradients/ClipByValue_grad';\nimport {concatGradConfig} from './gradients/Concat_grad';\nimport {conv2DGradConfig} from './gradients/Conv2D_grad';\nimport {conv2DBackpropInputGradConfig} from './gradients/Conv2DBackpropInput_grad';\nimport {conv3DGradConfig} from './gradients/Conv3D_grad';\nimport {cosGradConfig} from './gradients/Cos_grad';\nimport {coshGradConfig} from './gradients/Cosh_grad';\nimport {cumsumGradConfig} from './gradients/Cumsum_grad';\nimport {depthwiseConv2dNativeGradConfig} from './gradients/DepthwiseConv2dNative_grad';\nimport {dilation2dGradConfig} from './gradients/Dilation2D_grad';\nimport {divGradConfig} from './gradients/Div_grad';\nimport {eluGradConfig} from './gradients/Elu_grad';\nimport {erfGradConfig} from './gradients/Erf_grad';\nimport {expGradConfig} from './gradients/Exp_grad';\nimport {expm1GradConfig} from './gradients/Expm1_grad';\nimport {floorGradConfig} from './gradients/Floor_grad';\nimport {floorDivGradConfig} from './gradients/FloorDiv_grad';\nimport {fusedBatchNormGradConfig} from './gradients/FusedBatchNorm_grad';\nimport {gatherGradConfig} from './gradients/GatherV2_grad';\nimport {greaterEqualGradConfig} from './gradients/GreaterEqual_grad';\nimport {identityGradConfig} from './gradients/Identity_grad';\nimport {isFiniteGradConfig} from './gradients/IsFinite_grad';\nimport {isInfGradConfig} from './gradients/IsInf_grad';\nimport {isNanGradConfig} from './gradients/IsNan_grad';\nimport {log1pGradConfig} from './gradients/Log1p_grad';\nimport {logGradConfig} from './gradients/Log_grad';\nimport {logSoftmaxGradConfig} from './gradients/LogSoftmax_grad';\nimport {lrnGradConfig} from './gradients/LRN_grad';\nimport {maxGradConfig} from './gradients/Max_grad';\nimport {maximumGradConfig} from './gradients/Maximum_grad';\nimport {maxPool3DGradConfig} from './gradients/MaxPool3D_grad';\nimport {maxPoolGradConfig} from './gradients/MaxPool_grad';\nimport {minGradConfig} from './gradients/Min_grad';\nimport {minimumGradConfig} from './gradients/Minimum_grad';\nimport {mirrorPadGradConfig} from './gradients/MirrorPad_grad';\nimport {modGradConfig} from './gradients/Mod_grad';\nimport {multiplyGradConfig} from './gradients/Multiply_grad';\nimport {negateGradConfig} from './gradients/Negate_grad';\nimport {oneHotGradConfig} from './gradients/OneHot_grad';\nimport {onesLikeGradConfig} from './gradients/OnesLike_grad';\nimport {padV2GradConfig} from './gradients/PadV2_grad';\nimport {powGradConfig} from './gradients/Pow_grad';\nimport {preluGradConfig} from './gradients/Prelu_grad';\nimport {reciprocalGradConfig} from './gradients/Reciprocal_grad';\nimport {relu6GradConfig} from './gradients/Relu6_grad';\nimport {reluGradConfig} from './gradients/Relu_grad';\nimport {reshapeGradConfig} from './gradients/Reshape_grad';\nimport {resizeBilinearGradConfig} from './gradients/ResizeBilinear_grad';\nimport {resizeNearestNeighborGradConfig} from './gradients/ResizeNearestNeighbor_grad';\nimport {reverseGradConfig} from './gradients/Reverse_grad';\nimport {roundGradConfig} from './gradients/Round_grad';\nimport {rsqrtGradConfig} from './gradients/Rsqrt_grad';\nimport {selectV2PoolGradConfig} from './gradients/SelectV2_grad';\nimport {seluGradConfig} from './gradients/Selu_grad';\nimport {sigmoidGradConfig} from './gradients/Sigmoid_grad';\nimport {signGradConfig} from './gradients/Sign_grad';\nimport {sinGradConfig} from './gradients/Sin_grad';\nimport {sinhGradConfig} from './gradients/Sinh_grad';\nimport {sliceGradConfig} from './gradients/Slice_grad';\nimport {softmaxGradConfig} from './gradients/Softmax_grad';\nimport {softplusGradConfig} from './gradients/Softplus_grad';\nimport {spaceToBatchNDGradConfig} from './gradients/SpaceToBatchND_grad';\nimport {splitVGradConfig} from './gradients/SplitV_grad';\nimport {sqrtGradConfig} from './gradients/Sqrt_grad';\nimport {squareGradConfig} from './gradients/Square_grad';\nimport {squaredDifferenceGradConfig} from './gradients/SquaredDifference_grad';\nimport {stepGradConfig} from './gradients/Step_grad';\nimport {subGradConfig} from './gradients/Sub_grad';\nimport {sumGradConfig} from './gradients/Sum_grad';\nimport {tanGradConfig} from './gradients/Tan_grad';\nimport {tanhGradConfig} from './gradients/Tanh_grad';\nimport {tileGradConfig} from './gradients/Tile_grad';\nimport {transposeGradConfig} from './gradients/Transpose_grad';\nimport {unpackGradConfig} from './gradients/Unpack_grad';\nimport {unsortedSegmentSumGradConfig} from './gradients/UnsortedSegmentSum_grad';\nimport {zerosLikeGradConfig} from './gradients/ZerosLike_grad';\nimport {GradConfig} from './kernel_registry';\nimport {registerGradient} from './kernel_registry';\n\n// Export all kernel configs here so that the package can auto register them\nconst gradConfigs: GradConfig[] = [\n  absGradConfig,\n  acosGradConfig,\n  acoshGradConfig,\n  addGradConfig,\n  addNGradConfig,\n  argMaxGradConfig,\n  argMinGradConfig,\n  asinGradConfig,\n  asinhGradConfig,\n  atan2GradConfig,\n  atanGradConfig,\n  atanhGradConfig,\n  avgPool3DGradConfig,\n  avgPoolGradConfig,\n  batchMatMulGradConfig,\n  batchToSpaceNDGradConfig,\n  broadcastToGradConfig,\n  castGradConfig,\n  ceilGradConfig,\n  clipByValueGradConfig,\n  concatGradConfig,\n  conv2DBackpropInputGradConfig,\n  conv2DGradConfig,\n  conv3DGradConfig,\n  cosGradConfig,\n  coshGradConfig,\n  cumsumGradConfig,\n  depthwiseConv2dNativeGradConfig,\n  dilation2dGradConfig,\n  divGradConfig,\n  eluGradConfig,\n  erfGradConfig,\n  expGradConfig,\n  expm1GradConfig,\n  floorDivGradConfig,\n  floorGradConfig,\n  fusedBatchNormGradConfig,\n  gatherGradConfig,\n  greaterEqualGradConfig,\n  identityGradConfig,\n  isFiniteGradConfig,\n  isInfGradConfig,\n  isNanGradConfig,\n  log1pGradConfig,\n  logGradConfig,\n  logSoftmaxGradConfig,\n  lrnGradConfig,\n  maxGradConfig,\n  maxGradConfig,\n  maximumGradConfig,\n  maxPool3DGradConfig,\n  maxPoolGradConfig,\n  minGradConfig,\n  minimumGradConfig,\n  mirrorPadGradConfig,\n  modGradConfig,\n  multiplyGradConfig,\n  negateGradConfig,\n  oneHotGradConfig,\n  onesLikeGradConfig,\n  padV2GradConfig,\n  padV2GradConfig,\n  powGradConfig,\n  preluGradConfig,\n  reciprocalGradConfig,\n  relu6GradConfig,\n  reluGradConfig,\n  reshapeGradConfig,\n  resizeBilinearGradConfig,\n  resizeNearestNeighborGradConfig,\n  reverseGradConfig,\n  roundGradConfig,\n  rsqrtGradConfig,\n  selectV2PoolGradConfig,\n  seluGradConfig,\n  sigmoidGradConfig,\n  signGradConfig,\n  sinGradConfig,\n  sinhGradConfig,\n  sliceGradConfig,\n  softmaxGradConfig,\n  softplusGradConfig,\n  spaceToBatchNDGradConfig,\n  spaceToBatchNDGradConfig,\n  splitVGradConfig,\n  splitVGradConfig,\n  sqrtGradConfig,\n  squaredDifferenceGradConfig,\n  squareGradConfig,\n  stepGradConfig,\n  subGradConfig,\n  sumGradConfig,\n  tanGradConfig,\n  tanhGradConfig,\n  tileGradConfig,\n  transposeGradConfig,\n  unpackGradConfig,\n  unsortedSegmentSumGradConfig,\n  zerosLikeGradConfig\n];\n\nfor (const gradientConfig of gradConfigs) {\n  registerGradient(gradientConfig);\n}\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {abs} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    abs<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.abs = function<T extends Tensor>(this: T) {\n  this.throwIfDisposed();\n  return abs(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {acos} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    acos<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.acos = function<T extends Tensor>(this: T) {\n  this.throwIfDisposed();\n  return acos(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {acosh} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    acosh<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.acosh = function<T extends Tensor>(this: T) {\n  this.throwIfDisposed();\n  return acosh(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {addStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    addStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.addStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike) {\n  this.throwIfDisposed();\n  return addStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {add} from '../../ops/add';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    add<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.add = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return add(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {all} from '../../ops/all';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    all<T extends Tensor>(this: T, axis?: number|number[], keepDims?: boolean):\n        T;\n  }\n}\n\nTensor.prototype.all = function<T extends Tensor>(\n    this: T, axis?: number|number[], keepDims?: boolean): T {\n  this.throwIfDisposed();\n  return all(this, axis, keepDims);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {any} from '../../ops/any';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    any<T extends Tensor>(this: T, axis?: number|number[], keepDims?: boolean):\n        T;\n  }\n}\n\nTensor.prototype.any = function<T extends Tensor>(\n    this: T, axis?: number|number[], keepDims?: boolean): T {\n  this.throwIfDisposed();\n  return any(this, axis, keepDims);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {argMax} from '../../ops/arg_max';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    argMax<T extends Tensor>(axis?: number): T;\n  }\n}\n\nTensor.prototype.argMax = function<T extends Tensor>(axis?: number): T {\n  this.throwIfDisposed();\n  return argMax(this, axis);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {argMin} from '../../ops/arg_min';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    argMin<T extends Tensor>(axis?: number): T;\n  }\n}\n\nTensor.prototype.argMin = function<T extends Tensor>(axis: number): T {\n  this.throwIfDisposed();\n  return argMin(this, axis);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {reshape} from '../../ops/reshape';\nimport {Scalar, Tensor} from '../../tensor';\nimport {Rank} from '../../types';\nimport {assert} from '../../util';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    asScalar<T extends Tensor>(): Scalar;\n  }\n}\n\n/** Converts a size-1 `tf.Tensor` to a `tf.Scalar`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asScalar = function<T extends Tensor>(this: T): Scalar {\n  this.throwIfDisposed();\n  assert(this.size === 1, () => 'The array must have only 1 element.');\n  return reshape(this, []);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {cast} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {DataType, Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    asType<T extends Tensor>(this: T, dtype: DataType): T;\n  }\n}\n\n/**\n * Casts a `tf.Tensor` to a specified dtype.\n *\n * @param dtype Data-type to cast the tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.asType = function<T extends Tensor>(\n    this: T, dtype: DataType): T {\n  this.throwIfDisposed();\n  return cast<T>(this, dtype);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {reshape} from '../../ops/reshape';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    as1D<T extends Tensor>(): Tensor1D;\n  }\n}\n\n/** Converts a `tf.Tensor` to a `tf.Tensor1D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as1D = function<T extends Tensor>(): T {\n  this.throwIfDisposed();\n  return reshape(this, [this.size]) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {reshape} from '../../ops/reshape';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    as2D<T extends Tensor>(rows: number, columns: number): Tensor2D;\n  }\n}\n\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n *\n * @param rows Number of rows in `tf.Tensor2D`.\n * @param columns Number of columns in `tf.Tensor2D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as2D = function<T extends Tensor>(\n    rows: number, columns: number): T {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns]) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {reshape} from '../../ops/reshape';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    as3D<T extends Tensor>(rows: number, columns: number, depth: number):\n        Tensor3D;\n  }\n}\n\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n *\n * @param rows Number of rows in `tf.Tensor3D`.\n * @param columns Number of columns in `tf.Tensor3D`.\n * @param depth Depth of `tf.Tensor3D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as3D = function<T extends Tensor>(\n    rows: number, columns: number, depth: number): T {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth]) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {reshape} from '../../ops/reshape';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    as4D<T extends Tensor>(\n        rows: number, columns: number, depth: number, depth2: number): Tensor4D;\n  }\n}\n\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n *\n * @param rows Number of rows in `tf.Tensor4D`.\n * @param columns Number of columns in `tf.Tensor4D`.\n * @param depth Depth of `tf.Tensor4D`.\n * @param depth2 4th dimension of `tf.Tensor4D`.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as4D = function<T extends Tensor>(\n    rows: number, columns: number, depth: number, depth2: number): T {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth, depth2]) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {reshape} from '../../ops/reshape';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    as5D<T extends Tensor>(\n        rows: number, columns: number, depth: number, depth2: number,\n        depth3: number): Tensor5D;\n  }\n}\n\n/**\n * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n *\n * @param rows Number of rows in `tf.Tensor5D`.\n * @param columns Number of columns in `tf.Tensor5D`.\n * @param depth Depth of `tf.Tensor5D`.\n * @param depth2 4th dimension of `tf.Tensor5D`.\n * @param depth3 5th dimension of 'tf.Tensor5D'\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.as5D = function<T extends Tensor>(\n    rows: number, columns: number, depth: number, depth2: number,\n    depth3: number): T {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth, depth2, depth3]) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {asin} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    asin<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.asin = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return asin(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {asinh} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    asinh<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.asinh = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return asinh(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {atan} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    atan<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.atan = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return atan(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {atan2} from '../../ops/atan2';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    atan2<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.atan2 = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return atan2(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {atanh} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    atanh<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.atanh = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return atanh(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {avgPool} from '../../ops/avg_pool';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    avgPool<T extends Tensor3D|Tensor4D>(\n        filterSize: [number, number]|number, strides: [number, number]|number,\n        pad: 'valid'|'same'|number,\n        dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  }\n}\n\nTensor.prototype.avgPool = function<T extends Tensor3D|Tensor4D>(\n    this: T, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  this.throwIfDisposed();\n  return avgPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {batchToSpaceND} from '../../ops/batch_to_space_nd';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    batchToSpaceND<R extends Rank>(blockShape: number[], crops: number[][]):\n        Tensor<R>;\n  }\n}\n\nTensor.prototype.batchToSpaceND = function<R extends Rank>(\n    blockShape: number[], crops: number[][]): Tensor<R> {\n  this.throwIfDisposed();\n  return batchToSpaceND(this, blockShape, crops);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {batchNorm} from '../../ops/batchnorm';\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    batchNorm<T extends Tensor>(\n        mean: Tensor<R>|Tensor1D|TensorLike,\n        variance: Tensor<R>|Tensor1D|TensorLike,\n        offset?: Tensor<R>|Tensor1D|TensorLike,\n        scale?: Tensor<R>|Tensor1D|TensorLike,\n        varianceEpsilon?: number): Tensor<R>;\n  }\n}\n\nTensor.prototype.batchNorm = function<R extends Rank>(\n    mean: Tensor<R>|Tensor1D|TensorLike,\n    variance: Tensor<R>|Tensor1D|TensorLike,\n    offset?: Tensor<R>|Tensor1D|TensorLike,\n    scale?: Tensor<R>|Tensor1D|TensorLike,\n    varianceEpsilon?: number): Tensor<R> {\n  this.throwIfDisposed();\n  return batchNorm(this, mean, variance, offset, scale, varianceEpsilon);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {broadcastTo} from '../../ops/broadcast_to';\nimport {Tensor} from '../../tensor';\nimport {Rank, ShapeMap} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    broadcastTo<R extends Rank>(shape: ShapeMap[R]): Tensor<R>;\n  }\n}\n\nTensor.prototype.broadcastTo = function<R extends Rank>(shape: ShapeMap[R]):\n    Tensor<R> {\n  this.throwIfDisposed();\n  return broadcastTo(this, shape);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {cast} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {DataType, Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    cast<T extends Tensor>(dtype: DataType): T;\n  }\n}\n\nTensor.prototype.cast = function<T extends Tensor>(dtype: DataType): T {\n  this.throwIfDisposed();\n  return cast(this, dtype) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {ceil} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    ceil<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.ceil = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return ceil(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {clipByValue} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    clipByValue<T extends Tensor>(min: number, max: number): Tensor;\n  }\n}\n\nTensor.prototype.clipByValue = function<T extends Tensor>(\n    min: number, max: number): T {\n  this.throwIfDisposed();\n  return clipByValue(this, min, max) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {concat} from '../../ops/concat';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    concat<T extends Tensor>(tensors: T|Array<T|TensorLike>, axis?: number): T;\n  }\n}\n\nTensor.prototype.concat = function<T extends Tensor>(\n    x: T|Array<T|TensorLike>, axis?: number): T {\n  this.throwIfDisposed();\n  if (x instanceof Tensor) {\n    x = [x];\n  }\n  return concat([this, ...x], axis) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {conv1d} from '../../ops/conv1d';\nimport {ExplicitPadding} from '../../ops/conv_util';\nimport {Tensor, Tensor2D, Tensor3D} from '../../tensor';\nimport {Rank, TensorLike3D} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    conv1d<T extends Tensor2D|Tensor3D>(\n        filter: Tensor3D|TensorLike3D, stride: number,\n        pad: 'valid'|'same'|number|ExplicitPadding, dataFormat?: 'NWC'|'NCW',\n        dilation?: number, dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  }\n}\n\nTensor.prototype.conv1d = function<T extends Tensor2D|Tensor3D>(\n    filter: Tensor3D|TensorLike3D, stride: number,\n    pad: 'valid'|'same'|number|ExplicitPadding, dataFormat?: 'NWC'|'NCW',\n    dilation?: number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  this.throwIfDisposed();\n  return conv1d(\n             this, filter, stride, pad, dataFormat, dilation,\n             dimRoundingMode) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {conv2dTranspose} from '../../ops/conv2d_transpose';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank, TensorLike4D} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    conv2dTranspose<T extends Tensor3D|Tensor4D>(\n        filter: Tensor4D|TensorLike4D,\n        outputShape: [number, number, number, number]|[number, number, number],\n        strides: [number, number]|number, pad: 'valid'|'same'|number,\n        dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  }\n}\n\nTensor.prototype.conv2dTranspose = function<T extends Tensor3D|Tensor4D>(\n    filter: Tensor4D|TensorLike4D,\n    outputShape: [number, number, number, number]|[number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  this.throwIfDisposed();\n  return conv2dTranspose(\n             this, filter, outputShape, strides, pad, dimRoundingMode) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {conv2d} from '../../ops/conv2d';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank, TensorLike4D} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    conv2d<T extends Tensor3D|Tensor4D>(\n        filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n        pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',\n        dilations?: [number, number]|number,\n        dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  }\n}\n\nTensor.prototype.conv2d = function<T extends Tensor3D|Tensor4D>(\n    filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',\n    dilations?: [number, number]|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  this.throwIfDisposed();\n  return conv2d(\n             this, filter, strides, pad, dataFormat, dilations,\n             dimRoundingMode) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {cos} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    cos<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.cos = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return cos(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {cosh} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    cosh<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.cosh = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return cosh(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {cumsum} from '../../ops/cumsum';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    cumsum<R extends Rank>(\n        axis?: number, exclusive?: boolean, reverse?: boolean): Tensor<R>;\n  }\n}\n\nTensor.prototype.cumsum = function<R extends Rank>(\n    axis?: number, exclusive?: boolean, reverse?: boolean): Tensor<R> {\n  this.throwIfDisposed();\n  return cumsum(this, axis, exclusive, reverse);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {depthToSpace} from '../../ops/depth_to_space';\nimport {Tensor, Tensor4D} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    depthToSpace<T extends Tensor4D>(\n        blockSize: number, dataFormat: 'NHWC'|'NCHW'): T;\n  }\n}\n\nTensor.prototype.depthToSpace = function<T extends Tensor4D>(\n    blockSize: number, dataFormat: 'NHWC'|'NCHW'): T {\n  this.throwIfDisposed();\n  return depthToSpace(this, blockSize, dataFormat) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {deprecationWarn} from '../../globals';\nimport {depthwiseConv2d} from '../../ops/depthwise_conv2d';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank, TensorLike4D} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    depthwiseConv2D<T extends Tensor3D|Tensor4D>(\n        filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n        pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',\n        dilations?: [number, number]|number,\n        dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  }\n}\n\n/**\n * @deprecated Use `depthwiseConv2d` instead.\n */\nTensor.prototype.depthwiseConv2D = function<T extends Tensor3D|Tensor4D>(\n    filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',\n    dilations?: [number, number]|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  deprecationWarn('depthwiseConv2D is deprecated, use depthwiseConv2d instead');\n  this.throwIfDisposed();\n  return depthwiseConv2d(\n             this, filter, strides, pad, dataFormat, dilations,\n             dimRoundingMode) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {depthwiseConv2d} from '../../ops/depthwise_conv2d';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank, TensorLike4D} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    depthwiseConv2d<T extends Tensor3D|Tensor4D>(\n        filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n        pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',\n        dilations?: [number, number]|number,\n        dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  }\n}\n\nTensor.prototype.depthwiseConv2d = function<T extends Tensor3D|Tensor4D>(\n    filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number, dataFormat?: 'NHWC'|'NCHW',\n    dilations?: [number, number]|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  this.throwIfDisposed();\n  return depthwiseConv2d(\n             this, filter, strides, pad, dataFormat, dilations,\n             dimRoundingMode) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {dilation2d} from '../../ops/dilation2d';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank, TensorLike3D} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    dilation2d<T extends Tensor3D|Tensor4D>(\n        filter: Tensor3D|TensorLike3D, strides: [number, number]|number,\n        pad: 'valid'|'same', dilations?: [number, number]|number,\n        dataFormat?: 'NHWC'): T;\n  }\n}\n\nTensor.prototype.dilation2d = function<T extends Tensor3D|Tensor4D>(\n    filter: Tensor3D|TensorLike3D, strides: [number, number]|number,\n    pad: 'valid'|'same', dilations?: [number, number]|number,\n    dataFormat?: 'NHWC'): T {\n  this.throwIfDisposed();\n  return dilation2d(this, filter, strides, pad, dilations, dataFormat) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {divNoNan} from '../../ops/div_no_nan';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    divNoNan<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.divNoNan = function<T extends Tensor>(b: Tensor|\n                                                       TensorLike): T {\n  this.throwIfDisposed();\n  return divNoNan(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {divStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    divStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\nTensor.prototype.divStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return divStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {div} from '../../ops/div';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    div<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.div = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return div(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {dot} from '../../ops/dot';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    dot<T extends Tensor>(b: Tensor|TensorLike): Tensor;\n  }\n}\n\nTensor.prototype.dot = function<T extends Tensor>(b: T|TensorLike): Tensor {\n  this.throwIfDisposed();\n  return dot(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {elu} from '../../ops/elu';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    elu<T extends Tensor>(): T;\n  }\n}\n\nTensor.prototype.elu = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return elu(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {equalStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    equalStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.equalStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return equalStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {equal} from '../../ops/equal';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    equal<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.equal = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return equal(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {erf} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    erf<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.erf = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return erf(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {exp} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    exp<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.exp = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return exp(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {expandDims} from '../../ops/expand_dims';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    expandDims<T extends Tensor>(axis?: number): T;\n  }\n}\n\nTensor.prototype.expandDims = function<T extends Tensor>(axis?: number): T {\n  this.throwIfDisposed();\n  return expandDims(this, axis);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {expm1} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    expm1<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.expm1 = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return expm1(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {fft} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    fft<T extends Tensor>(this: Tensor): Tensor;\n  }\n}\n\nTensor.prototype.fft = function<T extends Tensor>(this: Tensor): T {\n  this.throwIfDisposed();\n  return fft(this) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {reshape} from '../../ops/reshape';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    flatten<T extends Tensor>(): Tensor1D;\n  }\n}\n\n/** Flatten a Tensor to a 1D array.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.flatten = function<T extends Tensor>(): T {\n  this.throwIfDisposed();\n  return reshape(this, [this.size]) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {floor} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    floor<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.floor = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return floor(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {floorDiv} from '../../ops/floorDiv';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    floorDiv<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.floorDiv = function<T extends Tensor>(b: Tensor|\n                                                       TensorLike): T {\n  this.throwIfDisposed();\n  return floorDiv(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {gather} from '../../ops/gather';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    gather<T extends Tensor>(\n        this: T, indices: Tensor|TensorLike, axis?: number): T;\n  }\n}\n\nTensor.prototype.gather = function<T extends Tensor>(\n    this: T, indices: Tensor|TensorLike, axis?: number): T {\n  this.throwIfDisposed();\n  return gather(this, indices, axis);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {greaterEqualStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    greaterEqualStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterEqualStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike) {\n  this.throwIfDisposed();\n  return greaterEqualStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {greaterEqual} from '../../ops/greater_equal';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    greaterEqual<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.greaterEqual = function<T extends Tensor>(b: Tensor|\n                                                           TensorLike): T {\n  this.throwIfDisposed();\n  return greaterEqual(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {greaterStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    greaterStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.greaterStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return greaterStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {greater} from '../../ops/greater';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    greater<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.greater = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return greater(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {ifft} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    ifft<T extends Tensor>(this: Tensor): Tensor;\n  }\n}\n\nTensor.prototype.ifft = function<T extends Tensor>(this: Tensor): T {\n  this.throwIfDisposed();\n  return ifft(this) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {irfft} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    irfft<T extends Tensor>(this: Tensor): Tensor;\n  }\n}\n\nTensor.prototype.irfft = function<T extends Tensor>(this: Tensor): T {\n  this.throwIfDisposed();\n  return irfft(this) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {isFinite} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    isFinite<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.isFinite = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return isFinite(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {isInf} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    isInf<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.isInf = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return isInf(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {isNaN} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    isNaN<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.isNaN = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return isNaN(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {leakyRelu} from '../../ops/leaky_relu';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    leakyRelu<T extends Tensor>(alpha: number): T;\n  }\n}\n\nTensor.prototype.leakyRelu = function<T extends Tensor>(\n    this: T, alpha: number): T {\n  this.throwIfDisposed();\n  return leakyRelu(this, alpha);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {lessEqualStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    lessEqualStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.lessEqualStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike) {\n  this.throwIfDisposed();\n  return lessEqualStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {lessEqual} from '../../ops/less_equal';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    lessEqual<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.lessEqual = function<T extends Tensor>(b: Tensor|\n                                                        TensorLike): T {\n  this.throwIfDisposed();\n  return lessEqual(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {lessStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    lessStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\nTensor.prototype.lessStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike) {\n  this.throwIfDisposed();\n  return lessStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {less} from '../../ops/less';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    less<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.less = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return less(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {localResponseNormalization} from '../../ops/local_response_normalization';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    localResponseNormalization<T extends Tensor>(\n        depthRadius?: number, bias?: number, alpha?: number, beta?: number): T;\n  }\n}\n\nTensor.prototype.localResponseNormalization = function<T extends Tensor>(\n    depthRadius?: number, bias?: number, alpha?: number, beta?: number): T {\n  this.throwIfDisposed();\n  return localResponseNormalization(this, depthRadius, bias, alpha, beta) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {logSigmoid} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    logSigmoid<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.logSigmoid = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return logSigmoid(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {logSoftmax} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    logSoftmax<T extends Tensor>(this: T, axis?: number): T;\n  }\n}\n\nTensor.prototype.logSoftmax = function<T extends Tensor>(\n    this: T, axis?: number): T {\n  this.throwIfDisposed();\n  return logSoftmax(this, axis);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {logSumExp} from '../../ops/log_sum_exp';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    logSumExp<T extends Tensor>(\n        this: T, axis?: number|number[], keepDims?: boolean): T;\n  }\n}\n\nTensor.prototype.logSumExp = function<T extends Tensor>(\n    this: T, axis?: number|number[], keepDims?: boolean): T {\n  this.throwIfDisposed();\n  return logSumExp(this, axis, keepDims);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {log} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    log<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.log = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return log(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {log1p} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    log1p<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.log1p = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return log1p(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {logicalAnd} from '../../ops/logical_and';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    logicalAnd<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.logicalAnd = function<T extends Tensor>(b: Tensor|\n                                                         TensorLike): T {\n  this.throwIfDisposed();\n  return logicalAnd(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {logicalNot} from '../../ops/logical_not';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    logicalNot<T extends Tensor>(): T;\n  }\n}\n\nTensor.prototype.logicalNot = function<T extends Tensor>(): T {\n  this.throwIfDisposed();\n  return logicalNot(this) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {logicalOr} from '../../ops/logical_or';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    logicalOr<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.logicalOr = function<T extends Tensor>(b: Tensor|\n                                                        TensorLike): T {\n  this.throwIfDisposed();\n  return logicalOr(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {logicalXor} from '../../ops/logical_xor';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    logicalXor<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.logicalXor = function<T extends Tensor>(b: Tensor|\n                                                         TensorLike): T {\n  this.throwIfDisposed();\n  return logicalXor(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {matMul} from '../../ops/mat_mul';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    matMul<T extends Tensor>(\n        b: Tensor|TensorLike, transposeA?: boolean,\n        transposeB?: boolean): Tensor;\n  }\n}\n\nTensor.prototype.matMul = function<T extends Tensor>(\n    this: T, b: Tensor|TensorLike, transposeA?: boolean,\n    transposeB?: boolean): Tensor {\n  this.throwIfDisposed();\n  return matMul(this, b, transposeA, transposeB);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {maxPool} from '../../ops/max_pool';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    maxPool<T extends Tensor3D|Tensor4D>(\n        filterSize: [number, number]|number, strides: [number, number]|number,\n        pad: 'valid'|'same'|number,\n        dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  }\n}\n\nTensor.prototype.maxPool = function<T extends Tensor3D|Tensor4D>(\n    this: T, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  this.throwIfDisposed();\n  return maxPool(this, filterSize, strides, pad, dimRoundingMode);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {max} from '../../ops/max';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    max<T extends Tensor>(axis?: number|number[], keepDims?: boolean): T;\n  }\n}\n\nTensor.prototype.max = function<T extends Tensor>(\n    axis?: number|number[], keepDims?: boolean): T {\n  this.throwIfDisposed();\n  return max(this, axis, keepDims);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {maximumStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    maximumStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.maximumStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return maximumStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {maximum} from '../../ops/maximum';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    maximum<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.maximum = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return maximum(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {mean} from '../../ops/mean';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    mean<T extends Tensor>(axis?: number|number[], keepDims?: boolean): T;\n  }\n}\n\nTensor.prototype.mean = function<T extends Tensor>(\n    axis?: number|number[], keepDims?: boolean): T {\n  this.throwIfDisposed();\n  return mean(this, axis, keepDims);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {min} from '../../ops/min';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    min<T extends Tensor>(axis?: number|number[], keepDims?: boolean): T;\n  }\n}\n\nTensor.prototype.min = function<T extends Tensor>(\n    axis?: number|number[], keepDims?: boolean): T {\n  this.throwIfDisposed();\n  return min(this, axis, keepDims);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {minimumStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    minimumStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.minimumStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return minimumStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {minimum} from '../../ops/minimum';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    minimum<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.minimum = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return minimum(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {mirrorPad} from '../../ops/mirror_pad';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    mirrorPad<T extends Tensor>(\n        paddings: Array<[number, number]>, mode: 'reflect'|'symmetric'): T;\n  }\n}\n\nTensor.prototype.mirrorPad = function<T extends Tensor>(\n    this: T, paddings: Array<[number, number]>,\n    mode: 'reflect'|'symmetric'): T {\n  this.throwIfDisposed();\n  return mirrorPad(this, paddings, mode);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {modStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    modStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.modStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return modStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {mod} from '../../ops/mod';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    mod<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.mod = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return mod(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {mulStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    mulStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.mulStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return mulStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {mul} from '../../ops/mul';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    mul<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.mul = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return mul(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {neg} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    neg<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.neg = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return neg(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {norm} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    norm<T extends Tensor>(\n        ord?: number|'euclidean'|'fro', axis?: number|number[],\n        keepDims?: boolean): Tensor;\n  }\n}\n\nTensor.prototype.norm = function<T extends Tensor>(\n    ord?: number|'euclidean'|'fro', axis?: number|number[],\n    keepDims?: boolean) {\n  this.throwIfDisposed();\n  return norm(this, ord, axis, keepDims) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {notEqualStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    notEqualStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.notEqualStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return notEqualStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {notEqual} from '../../ops/not_equal';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    notEqual<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.notEqual = function<T extends Tensor>(b: Tensor|\n                                                       TensorLike): T {\n  this.throwIfDisposed();\n  return notEqual(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {oneHot} from '../../ops/one_hot';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    oneHot(depth: number, onValue: number, offValue: number): Tensor;\n  }\n}\n\nTensor.prototype.oneHot = function(\n    depth: number, onValue = 1, offValue = 0): Tensor {\n  this.throwIfDisposed();\n  return oneHot(this, depth, onValue, offValue);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {onesLike} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    onesLike<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.onesLike = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return onesLike(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {pad} from '../../ops/pad';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    pad<T extends Tensor>(\n        paddings: Array<[number, number]>, constantValue?: number): T;\n  }\n}\n\nTensor.prototype.pad = function<T extends Tensor>(\n    this: T, paddings: Array<[number, number]>, constantValue: number): T {\n  this.throwIfDisposed();\n  return pad(this, paddings, constantValue);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {pool} from '../../ops/pool';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    pool<T extends Tensor3D|Tensor4D>(\n        windowShape: [number, number]|number, poolingType: 'avg'|'max',\n        padding: 'valid'|'same'|number, diationRate?: [number, number]|number,\n        strides?: [number, number]|number): T;\n  }\n}\n\nTensor.prototype.pool = function<T extends Tensor3D|Tensor4D>(\n    this: T, windowShape: [number, number]|number, poolingType: 'max'|'avg',\n    padding: 'valid'|'same'|number, dilationRate?: [number, number]|number,\n    strides?: [number, number]|number): T {\n  this.throwIfDisposed();\n  return pool(this, windowShape, poolingType, padding, dilationRate, strides);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {powStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    powStrict<T extends Tensor>(exp: Tensor): Tensor;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.powStrict = function<T extends Tensor>(exp: Tensor): T {\n  this.throwIfDisposed();\n  return powStrict(this, exp) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {pow} from '../../ops/pow';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    pow<T extends Tensor>(exp: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.pow = function<T extends Tensor>(exp: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return pow(this, exp);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {prelu} from '../../ops/prelu';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    prelu<T extends Tensor>(alpha: T|TensorLike): T;\n  }\n}\n\nTensor.prototype.prelu = function<T extends Tensor>(\n    this: T, alpha: T|TensorLike): T {\n  this.throwIfDisposed();\n  return prelu(this, alpha);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {prod} from '../../ops/prod';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    prod<T extends Tensor>(this: T, axis?: number|number[], keepDims?: boolean):\n        T;\n  }\n}\n\nTensor.prototype.prod = function<T extends Tensor>(\n    this: T, axis?: number|number[], keepDims?: boolean): T {\n  this.throwIfDisposed();\n  return prod(this, axis, keepDims);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {reciprocal} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    reciprocal<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.reciprocal = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return reciprocal(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {relu} from '../../ops/relu';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    relu<T extends Tensor>(): T;\n  }\n}\n\nTensor.prototype.relu = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return relu(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {relu6} from '../../ops/relu6';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    relu6<T extends Tensor>(): T;\n  }\n}\n\nTensor.prototype.relu6 = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return relu6(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {reshape} from '../../ops/reshape';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    reshapeAs<T extends Tensor>(x: T): T;\n  }\n}\n\n/**\n * Reshapes the tensor into the shape of the provided tensor.\n *\n * @param x The tensor of required shape.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.reshapeAs = function<T extends Tensor>(x: T): T {\n  this.throwIfDisposed();\n  return reshape(this, x.shape) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {reshape} from '../../ops/reshape';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    reshape<T extends Tensor>(shape: number[]): T;\n  }\n}\n\nTensor.prototype.reshape = function<T extends Tensor>(shape: number[]): T {\n  this.throwIfDisposed();\n  return reshape(this, shape) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {resizeBilinear} from '../../ops/image/resize_bilinear';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    resizeBilinear<T extends Tensor3D|Tensor4D>(\n        newShape2D: [number, number], alignCorners?: boolean): T;\n  }\n}\n\nTensor.prototype.resizeBilinear = function<T extends Tensor3D|Tensor4D>(\n    this: T, newShape2D: [number, number], alignCorners?: boolean): T {\n  this.throwIfDisposed();\n  return resizeBilinear(this, newShape2D, alignCorners);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {resizeNearestNeighbor} from '../../ops/image/resize_nearest_neighbor';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    resizeNearestNeighbor<T extends Tensor3D|Tensor4D>(\n        newShape2D: [number, number], alignCorners?: boolean): T;\n  }\n}\n\nTensor.prototype.resizeNearestNeighbor = function<T extends Tensor3D|Tensor4D>(\n    this: T, newShape2D: [number, number], alignCorners?: boolean): T {\n  this.throwIfDisposed();\n  return resizeNearestNeighbor(this, newShape2D, alignCorners);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {reverse} from '../../ops/reverse';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    reverse<T extends Tensor>(this: T, axis?: number|number[]): T;\n  }\n}\n\nTensor.prototype.reverse = function<T extends Tensor>(\n    this: T, axis?: number|number[]): T {\n  this.throwIfDisposed();\n  return reverse(this, axis);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {rfft} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    rfft<T extends Tensor>(this: Tensor): Tensor;\n  }\n}\n\nTensor.prototype.rfft = function<T extends Tensor>(this: Tensor): T {\n  this.throwIfDisposed();\n  return rfft(this) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {round} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    round<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.round = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return round(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {rsqrt} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    rsqrt<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.rsqrt = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return rsqrt(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {selu} from '../../ops/selu';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    selu<T extends Tensor>(): T;\n  }\n}\n\nTensor.prototype.selu = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return selu(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {separableConv2d} from '../../ops/separable_conv2d';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {Rank, TensorLike, TensorLike4D} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    separableConv2d<T extends Tensor3D|Tensor4D>(\n        depthwiseFilter: Tensor4D|TensorLike4D,\n        pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n        pad: 'valid'|'same', dilation?: [number, number]|number,\n        dataFormat?: 'NHWC'|'NCHW'): T;\n  }\n}\n\nTensor.prototype.separableConv2d = function<T extends Tensor3D|Tensor4D>(\n    depthwiseFilter: Tensor4D|TensorLike4D,\n    pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n    pad: 'valid'|'same', dilation?: [number, number]|number,\n    dataFormat?: 'NHWC'|'NCHW'): T {\n  this.throwIfDisposed();\n  return separableConv2d(\n             this, depthwiseFilter, pointwiseFilter, strides, pad, dilation,\n             dataFormat) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {sigmoid} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    sigmoid<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.sigmoid = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return sigmoid(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {sign} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    sign<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.sign = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return sign(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {sin} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    sin<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.sin = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return sin(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {sinh} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    sinh<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.sinh = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return sinh(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {slice} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    slice<T extends Tensor>(\n        this: T, begin: number|number[], size?: number|number[]): T;\n  }\n}\n\nTensor.prototype.slice = function<T extends Tensor>(\n    this: T, begin: number|number[], size?: number|number[]): T {\n  this.throwIfDisposed();\n  return slice(this, begin, size);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {softmax} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    softmax<T extends Tensor>(this: T, dim?: number): T;\n  }\n}\n\nTensor.prototype.softmax = function<T extends Tensor>(this: T, dim: number): T {\n  this.throwIfDisposed();\n  return softmax(this, dim);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {softplus} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    softplus<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.softplus = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return softplus(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {spaceToBatchND} from '../../ops/space_to_batch_nd';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    spaceToBatchND<R extends Rank>(blockShape: number[], paddings: number[][]):\n        Tensor<R>;\n  }\n}\n\nTensor.prototype.spaceToBatchND = function<R extends Rank>(\n    blockShape: number[], paddings: number[][]): Tensor<R> {\n  this.throwIfDisposed();\n  return spaceToBatchND(this, blockShape, paddings);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {split} from '../../ops/split';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    split<T extends Tensor>(numOrSizeSplits: number[]|number, axis?: number):\n        T[];\n  }\n}\n\nTensor.prototype.split = function<T extends Tensor>(\n    numOrSizeSplits: number[]|number, axis?: number): T[] {\n  this.throwIfDisposed();\n  return split(this, numOrSizeSplits, axis);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {sqrt} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    sqrt<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.sqrt = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return sqrt(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {square} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    square<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.square = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return square(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {squaredDifference} from '../../ops/squared_difference';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    squaredDifference<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.squaredDifference = function<T extends Tensor>(b: Tensor|\n                                                                TensorLike): T {\n  this.throwIfDisposed();\n  return squaredDifference(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {squaredDifferenceStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    squaredDifferenceStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.squaredDifferenceStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return squaredDifferenceStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {squeeze} from '../../ops/squeeze';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    squeeze<T extends Tensor>(axis?: number[]): T;\n  }\n}\n\nTensor.prototype.squeeze = function<T extends Tensor>(axis?: number[]): T {\n  this.throwIfDisposed();\n  return squeeze(this, axis);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {stack} from '../../ops/stack';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    stack<T extends Tensor>(x: Tensor|Tensor[], axis?: number): T;\n  }\n}\n\nTensor.prototype.stack = function<T extends Tensor>(\n    x: Tensor|Tensor[], axis?: number): T {\n  this.throwIfDisposed();\n  const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n  return stack(tensorsToBeStacked, axis) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {step} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    step<T extends Tensor>(this: T, alpha?: number): T;\n  }\n}\n\nTensor.prototype.step = function<T extends Tensor>(this: T, alpha?: number) {\n  this.throwIfDisposed();\n  return step(this, alpha);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {stridedSlice} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    stridedSlice<T extends Tensor>(\n        this: Tensor, begin: number[], end: number[], strides: number[],\n        beginMask?: number, endMask?: number, ellipsisMask?: number,\n        newAxisMask?: number, shrinkAxisMask?: number): Tensor;\n  }\n}\n\nTensor.prototype.stridedSlice = function<T extends Tensor>(\n    this: Tensor, begin: number[], end: number[], strides: number[],\n    beginMask?: number, endMask?: number, ellipsisMask?: number,\n    newAxisMask?: number, shrinkAxisMask?: number): T {\n  this.throwIfDisposed();\n  return stridedSlice(\n             this, begin, end, strides, beginMask, endMask, ellipsisMask,\n             newAxisMask, shrinkAxisMask) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {subStrict} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    subStrict<T extends Tensor>(this: T, x: T|TensorLike): T;\n  }\n}\n\n/**\n * @deprecated strict variants of ops have been deprecated\n */\nTensor.prototype.subStrict = function<T extends Tensor>(\n    this: T, x: T|TensorLike): T {\n  this.throwIfDisposed();\n  return subStrict(this, x);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {sub} from '../../ops/sub';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    sub<T extends Tensor>(b: Tensor|TensorLike): T;\n  }\n}\n\nTensor.prototype.sub = function<T extends Tensor>(b: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return sub(this, b);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {sum} from '../../ops/sum';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    sum<T extends Tensor>(axis?: number|number[], keepDims?: boolean): T;\n  }\n}\n\nTensor.prototype.sum = function<T extends Tensor>(\n    axis?: number|number[], keepDims?: boolean): T {\n  this.throwIfDisposed();\n  return sum(this, axis, keepDims);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {tan} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    tan<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.tan = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return tan(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {tanh} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    tanh<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.tanh = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return tanh(this);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tile} from '../../ops/tile';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    tile<T extends Tensor>(b: number[]): T;\n  }\n}\n\nTensor.prototype.tile = function<T extends Tensor>(reps: number[]): T {\n  this.throwIfDisposed();\n  return tile(this, reps) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {cast} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    toBool<T extends Tensor>(this: T): T;\n  }\n}\n\n/** Casts the array to type `bool`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toBool = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return cast<T>(this, 'bool');\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {cast} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    toFloat<T extends Tensor>(this: T): T;\n  }\n}\n\n/** Casts the array to type `float32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toFloat = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return cast<T>(this, 'float32');\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {cast} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    toInt<T extends Tensor>(this: T): T;\n  }\n}\n\n/** Casts the array to type `int32`\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nTensor.prototype.toInt = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return cast<T>(this, 'int32');\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {topk} from '../../ops/topk';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    topk<T extends Tensor>(this: T, k?: number, sorted?: boolean):\n        {values: T, indices: T};\n  }\n}\n\nTensor.prototype.topk = function<T extends Tensor>(\n    this: T, k?: number, sorted?: boolean): {values: T, indices: T} {\n  this.throwIfDisposed();\n  return topk(this, k, sorted) as {values: T, indices: T};\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {transpose} from '../../ops/transpose';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    transpose<T extends Tensor>(perm?: number[]): T;\n  }\n}\n\nTensor.prototype.transpose = function<T extends Tensor>(\n    this: T, perm?: number[]): T {\n  this.throwIfDisposed();\n  return transpose(this, perm);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {unique} from '../../ops/unique';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    unique<T extends Tensor>(this: T, axis?: number): {values: T, indices: T};\n  }\n}\n\nTensor.prototype.unique = function<T extends Tensor>(\n    this: T, axis?: number): {values: T, indices: T} {\n  this.throwIfDisposed();\n  return unique(this, axis) as {values: T, indices: T};\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {unsortedSegmentSum} from '../../ops/unsorted_segment_sum';\nimport {Tensor, Tensor1D} from '../../tensor';\nimport {Rank, TensorLike1D} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    unsortedSegmentSum<T extends Tensor>(\n        this: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T;\n  }\n}\n\nTensor.prototype.unsortedSegmentSum = function<T extends Tensor>(\n    this: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T {\n  this.throwIfDisposed();\n  return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {unstack} from '../../ops/unstack';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    unstack<T extends Tensor>(axis?: number): T[];\n  }\n}\n\nTensor.prototype.unstack = function<T extends Tensor>(axis?: number): T[] {\n  this.throwIfDisposed();\n  return unstack(this, axis) as T[];\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {where} from '../../ops/where';\nimport {Tensor} from '../../tensor';\nimport {Rank, TensorLike} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    where<T extends Tensor>(condition: Tensor|TensorLike, x: Tensor|TensorLike):\n        T;\n  }\n}\n\nTensor.prototype.where = function<T extends Tensor>(\n    condition: Tensor|TensorLike, x: Tensor|TensorLike): T {\n  this.throwIfDisposed();\n  return where(condition, this, x) as T;\n};\n", "/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// TODO update import path once op is modularized.\nimport {zerosLike} from '../../ops/ops';\nimport {Tensor} from '../../tensor';\nimport {Rank} from '../../types';\n\ndeclare module '../../tensor' {\n  interface Tensor<R extends Rank = Rank> {\n    zerosLike<T extends Tensor>(this: T): T;\n  }\n}\n\nTensor.prototype.zerosLike = function<T extends Tensor>(this: T): T {\n  this.throwIfDisposed();\n  return zerosLike(this);\n};\n", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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"../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
+  "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../dist/tfjs.esm.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '../dist/tfjs.esm.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '../../dist/tfjs.esm.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "import * as tf from '../dist/tfjs.esm.js';\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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   "names": []
 }
diff --git a/dist/face-api.node.json b/dist/face-api.node.json
new file mode 100644
index 0000000..ece8f39
--- /dev/null
+++ b/dist/face-api.node.json
@@ -0,0 +1,2614 @@
+{
+  "inputs": {
+    "dist/tfjs.esm.js": {
+      "bytes": 1042,
+      "imports": []
+    },
+    "src/draw/drawContour.ts": {
+      "bytes": 520,
+      "imports": []
+    },
+    "src/classes/Dimensions.ts": {
+      "bytes": 731,
+      "imports": [
+        {
+          "path": "src/utils/index.ts"
+        }
+      ]
+    },
+    "src/utils/index.ts": {
+      "bytes": 1770,
+      "imports": [
+        {
+          "path": "dist/tfjs.esm.js"
+        },
+        {
+          "path": "src/classes/index.ts"
+        },
+        {
+          "path": "src/classes/Dimensions.ts"
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+        "src/tinyYolov2/extractParamsFromWeigthMap.ts": {
+          "bytesInOutput": 2042
+        },
+        "src/tinyYolov2/TinyYolov2Options.ts": {
+          "bytesInOutput": 901
+        },
+        "src/tinyYolov2/TinyYolov2.ts": {
+          "bytesInOutput": 833
+        },
+        "src/tinyYolov2/index.ts": {
+          "bytesInOutput": 139
+        },
+        "src/tinyFaceDetector/TinyFaceDetectorOptions.ts": {
+          "bytesInOutput": 132
+        },
+        "src/globalApi/ComposableTask.ts": {
+          "bytesInOutput": 154
+        },
+        "src/globalApi/DetectFaceLandmarksTasks.ts": {
+          "bytesInOutput": 1916
+        },
+        "src/globalApi/extractFacesAndComputeResults.ts": {
+          "bytesInOutput": 812
+        },
+        "src/tinyFaceDetector/const.ts": {
+          "bytesInOutput": 215
+        },
+        "src/tinyFaceDetector/TinyFaceDetector.ts": {
+          "bytesInOutput": 663
+        },
+        "src/globalApi/nets.ts": {
+          "bytesInOutput": 1516
+        },
+        "src/globalApi/PredictFaceExpressionsTask.ts": {
+          "bytesInOutput": 1701
+        },
+        "src/globalApi/PredictAgeAndGenderTask.ts": {
+          "bytesInOutput": 1795
+        },
+        "src/globalApi/ComputeFaceDescriptorsTasks.ts": {
+          "bytesInOutput": 1417
+        },
+        "src/globalApi/DetectFacesTasks.ts": {
+          "bytesInOutput": 2219
+        },
+        "src/globalApi/detectFaces.ts": {
+          "bytesInOutput": 224
+        },
+        "src/globalApi/allFaces.ts": {
+          "bytesInOutput": 620
+        },
+        "src/globalApi/index.ts": {
+          "bytesInOutput": 0
+        },
+        "src/euclideanDistance.ts": {
+          "bytesInOutput": 277
+        },
+        "src/globalApi/FaceMatcher.ts": {
+          "bytesInOutput": 1844
+        },
+        "src/tinyFaceDetector/index.ts": {
+          "bytesInOutput": 109
+        },
+        "src/resizeResults.ts": {
+          "bytesInOutput": 824
+        },
+        "package.json": {
+          "bytesInOutput": 19
+        }
+      },
+      "bytes": 134190
+    }
+  }
+}
diff --git a/dist/face-api.node.nobundle.js b/dist/face-api.node.nobundle.js
deleted file mode 100644
index 559db68..0000000
--- a/dist/face-api.node.nobundle.js
+++ /dev/null
@@ -1,4443 +0,0 @@
-var __create = Object.create;
-var __defProp = Object.defineProperty;
-var __getProtoOf = Object.getPrototypeOf;
-var __hasOwnProp = Object.prototype.hasOwnProperty;
-var __getOwnPropNames = Object.getOwnPropertyNames;
-var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
-var __markAsModule = (target) => __defProp(target, "__esModule", {value: true});
-var __commonJS = (callback, module2) => () => {
-  if (!module2) {
-    module2 = {exports: {}};
-    callback(module2.exports, module2);
-  }
-  return module2.exports;
-};
-var __export = (target, all) => {
-  __markAsModule(target);
-  for (var name in all)
-    __defProp(target, name, {get: all[name], enumerable: true});
-};
-var __exportStar = (target, module2, desc) => {
-  __markAsModule(target);
-  if (module2 && typeof module2 === "object" || typeof module2 === "function") {
-    for (let key of __getOwnPropNames(module2))
-      if (!__hasOwnProp.call(target, key) && key !== "default")
-        __defProp(target, key, {get: () => module2[key], enumerable: !(desc = __getOwnPropDesc(module2, key)) || desc.enumerable});
-  }
-  return target;
-};
-var __toModule = (module2) => {
-  if (module2 && module2.__esModule)
-    return module2;
-  return __exportStar(__defProp(module2 != null ? __create(__getProtoOf(module2)) : {}, "default", {value: module2, enumerable: true}), module2);
-};
-
-// src/env/isNodejs.ts
-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;
-  }
-});
-
-// src/index.ts
-__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
-});
-var tf42 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/draw/index.ts
-var 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
-});
-
-// src/draw/drawContour.ts
-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();
-}
-
-// src/utils/index.ts
-var 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
-});
-var tf = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/classes/Dimensions.ts
-var Dimensions = class {
-  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);
-  }
-};
-
-// src/utils/index.ts
-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;
-}
-
-// src/classes/Point.ts
-var Point = class {
-  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));
-  }
-};
-
-// src/classes/Box.ts
-var Box = class {
-  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`);
-    }
-  }
-  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;
-  }
-  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();
-  }
-};
-
-// src/classes/BoundingBox.ts
-var BoundingBox = class extends Box {
-  constructor(left, top, right, bottom, allowNegativeDimensions = false) {
-    super({left, top, right, bottom}, allowNegativeDimensions);
-  }
-};
-
-// src/classes/ObjectDetection.ts
-var ObjectDetection = class {
-  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});
-  }
-};
-
-// src/classes/FaceDetection.ts
-var FaceDetection = class 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);
-  }
-};
-
-// src/ops/iou.ts
-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);
-}
-
-// src/ops/minBbox.ts
-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);
-}
-
-// src/ops/nonMaxSuppression.ts
-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;
-}
-
-// src/ops/normalize.ts
-var tf2 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-function normalize(x, meanRgb) {
-  return tf2.tidy(() => {
-    const [r, g, b] = meanRgb;
-    const avg_r = tf2.fill([...x.shape.slice(0, 3), 1], r, "float32");
-    const avg_g = tf2.fill([...x.shape.slice(0, 3), 1], g, "float32");
-    const avg_b = tf2.fill([...x.shape.slice(0, 3), 1], b, "float32");
-    const avg_rgb = tf2.concat([avg_r, avg_g, avg_b], 3);
-    return tf2.sub(x, avg_rgb);
-  });
-}
-
-// src/ops/padToSquare.ts
-var tf3 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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, "float32");
-    };
-    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);
-  });
-}
-
-// src/ops/shuffleArray.ts
-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;
-}
-
-// src/ops/index.ts
-function sigmoid(x) {
-  return 1 / (1 + Math.exp(-x));
-}
-function inverseSigmoid(x) {
-  return Math.log(x / (1 - x));
-}
-
-// src/classes/Rect.ts
-var Rect = class extends Box {
-  constructor(x, y, width, height, allowNegativeDimensions = false) {
-    super({x, y, width, height}, allowNegativeDimensions);
-  }
-};
-
-// src/classes/FaceLandmarks.ts
-var relX = 0.5;
-var relY = 0.43;
-var relScale = 0.45;
-var FaceLandmarks = class {
-  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");
-  }
-};
-
-// src/classes/FaceLandmarks5.ts
-var FaceLandmarks5 = class extends FaceLandmarks {
-  getRefPointsForAlignment() {
-    const pts = this.positions;
-    return [
-      pts[0],
-      pts[1],
-      getCenterPoint([pts[3], pts[4]])
-    ];
-  }
-};
-
-// src/classes/FaceLandmarks68.ts
-var FaceLandmarks68 = class 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);
-  }
-};
-
-// src/classes/FaceMatch.ts
-var FaceMatch = class {
-  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)})` : ""}`;
-  }
-};
-
-// src/classes/LabeledBox.ts
-var LabeledBox = class extends Box {
-  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`);
-    }
-  }
-  constructor(box, label) {
-    super(box);
-    this._label = label;
-  }
-  get label() {
-    return this._label;
-  }
-};
-
-// src/classes/LabeledFaceDescriptors.ts
-var LabeledFaceDescriptors = class {
-  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);
-  }
-};
-
-// src/classes/PredictedBox.ts
-var PredictedBox = class extends LabeledBox {
-  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]`);
-    }
-  }
-  constructor(box, label, score, classScore) {
-    super(box, label);
-    this._score = score;
-    this._classScore = classScore;
-  }
-  get score() {
-    return this._score;
-  }
-  get classScore() {
-    return this._classScore;
-  }
-};
-
-// src/factories/WithFaceDetection.ts
-function isWithFaceDetection(obj) {
-  return obj["detection"] instanceof FaceDetection;
-}
-function extendWithFaceDetection(sourceObj, detection) {
-  const extension = {detection};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// src/env/createBrowserEnv.ts
-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
-  };
-}
-
-// src/env/createFileSystem.ts
-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
-  };
-}
-
-// src/env/createNodejsEnv.ts
-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
-  };
-}
-
-// src/env/isBrowser.ts
-function isBrowser() {
-  return typeof window === "object" && typeof document !== "undefined" && typeof HTMLImageElement !== "undefined" && typeof HTMLCanvasElement !== "undefined" && typeof HTMLVideoElement !== "undefined" && typeof ImageData !== "undefined" && typeof CanvasRenderingContext2D !== "undefined";
-}
-
-// src/env/index.ts
-var isNodejs = __toModule(require_isNodejs());
-var 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;
-}
-var env = {
-  getEnv,
-  setEnv,
-  initialize,
-  createBrowserEnv,
-  createFileSystem,
-  createNodejsEnv,
-  monkeyPatch,
-  isBrowser,
-  isNodejs: isNodejs.isNodejs
-};
-initialize();
-
-// src/dom/resolveInput.ts
-function resolveInput(arg) {
-  if (!env.isNodejs() && typeof arg === "string") {
-    return document.getElementById(arg);
-  }
-  return arg;
-}
-
-// src/dom/getContext2dOrThrow.ts
-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;
-}
-
-// src/draw/DrawTextField.ts
-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 = {}));
-var DrawTextFieldOptions = class {
-  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;
-  }
-};
-var DrawTextField = class {
-  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);
-    });
-  }
-};
-
-// src/draw/DrawBox.ts
-var DrawBoxOptions = class {
-  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));
-  }
-};
-var DrawBox = class {
-  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);
-    }
-  }
-};
-
-// src/draw/drawDetections.ts
-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);
-  });
-}
-
-// src/faceExpressionNet/FaceExpressionNet.ts
-var tf18 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/dom/isMediaLoaded.ts
-function isMediaLoaded(media) {
-  const {Image, Video} = env.getEnv();
-  return media instanceof Image && media.complete || media instanceof Video && media.readyState >= 3;
-}
-
-// src/dom/awaitMediaLoaded.ts
-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);
-  });
-}
-
-// src/dom/bufferToImage.ts
-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);
-  });
-}
-
-// src/dom/getMediaDimensions.ts
-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);
-}
-
-// src/dom/createCanvas.ts
-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;
-}
-
-// src/dom/imageTensorToCanvas.ts
-var tf4 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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;
-}
-
-// src/dom/isMediaElement.ts
-function isMediaElement(input) {
-  const {Image, Canvas, Video} = env.getEnv();
-  return input instanceof Image || input instanceof Canvas || input instanceof Video;
-}
-
-// src/dom/NetInput.ts
-var tf5 = __toModule(require("@tensorflow/tfjs-core"));
-
-// src/dom/imageToSquare.ts
-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;
-}
-
-// src/dom/NetInput.ts
-var NetInput = class {
-  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;
-    });
-  }
-};
-
-// src/dom/toNetInput.ts
-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));
-}
-
-// src/dom/extractFaces.ts
-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;
-  });
-}
-
-// src/dom/extractFaceTensors.ts
-var tf6 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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;
-  });
-}
-
-// src/dom/fetchOrThrow.ts
-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;
-}
-
-// src/dom/fetchImage.ts
-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: ${res.url}`);
-  }
-  return bufferToImage(blob);
-}
-
-// src/dom/fetchJson.ts
-async function fetchJson(uri) {
-  return (await fetchOrThrow(uri)).json();
-}
-
-// src/dom/fetchNetWeights.ts
-async function fetchNetWeights(uri) {
-  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer());
-}
-
-// src/dom/loadWeightMap.ts
-var tf7 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/common/getModelUris.ts
-function getModelUris(uri, defaultModelName) {
-  const defaultManifestFilename = `${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}`
-  };
-}
-
-// src/dom/loadWeightMap.ts
-async function loadWeightMap(uri, defaultModelName) {
-  const {manifestUri, modelBaseUri} = getModelUris(uri, defaultModelName);
-  let manifest = await fetchJson(manifestUri);
-  return tf7.io.loadWeights(manifest, modelBaseUri);
-}
-
-// src/dom/matchDimensions.ts
-function matchDimensions(input, reference, useMediaDimensions = false) {
-  const {width, height} = useMediaDimensions ? getMediaDimensions(reference) : reference;
-  input.width = width;
-  input.height = height;
-  return {width, height};
-}
-
-// src/faceFeatureExtractor/FaceFeatureExtractor.ts
-var tf15 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/NeuralNetwork.ts
-var tf8 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-var NeuralNetwork = class {
-  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};
-  }
-};
-
-// src/faceFeatureExtractor/denseBlock.ts
-var tf10 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/common/depthwiseSeparableConv.ts
-var tf9 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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;
-  });
-}
-
-// src/faceFeatureExtractor/denseBlock.ts
-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))));
-  });
-}
-
-// src/common/convLayer.ts
-var tf11 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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;
-  });
-}
-
-// src/common/disposeUnusedWeightTensors.ts
-function disposeUnusedWeightTensors(weightMap, paramMappings) {
-  Object.keys(weightMap).forEach((path) => {
-    if (!paramMappings.some((pm) => pm.originalPath === path)) {
-      weightMap[path].dispose();
-    }
-  });
-}
-
-// src/common/extractConvParamsFactory.ts
-var tf12 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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: `${mappedPrefix}/bias`});
-    return {filters, bias};
-  };
-}
-
-// src/common/extractFCParamsFactory.ts
-var tf13 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-function extractFCParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, mappedPrefix) {
-    const fc_weights = tf13.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
-    const fc_bias = tf13.tensor1d(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${mappedPrefix}/weights`}, {paramPath: `${mappedPrefix}/bias`});
-    return {
-      weights: fc_weights,
-      bias: fc_bias
-    };
-  };
-}
-
-// src/common/extractSeparableConvParamsFactory.ts
-var tf14 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/common/types.ts
-var SeparableConvParams = class {
-  constructor(depthwise_filter, pointwise_filter, bias) {
-    this.depthwise_filter = depthwise_filter;
-    this.pointwise_filter = pointwise_filter;
-    this.bias = bias;
-  }
-};
-
-// src/common/extractSeparableConvParamsFactory.ts
-function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
-  return function(channelsIn, channelsOut, mappedPrefix) {
-    const depthwise_filter = tf14.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
-    const pointwise_filter = tf14.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
-    const bias = tf14.tensor1d(extractWeights(channelsOut));
-    paramMappings.push({paramPath: `${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(`${prefix}/bias`, 1);
-    return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
-  };
-}
-
-// src/common/extractWeightEntryFactory.ts
-function extractWeightEntryFactory(weightMap, paramMappings) {
-  return function(originalPath, paramRank, mappedPath) {
-    const tensor2 = weightMap[originalPath];
-    if (!isTensor(tensor2, paramRank)) {
-      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor2}`);
-    }
-    paramMappings.push({originalPath, paramPath: mappedPath || originalPath});
-    return tensor2;
-  };
-}
-
-// src/common/extractWeightsFactory.ts
-function extractWeightsFactory(weights) {
-  let remainingWeights = weights;
-  function extractWeights(numWeights) {
-    const ret = remainingWeights.slice(0, numWeights);
-    remainingWeights = remainingWeights.slice(numWeights);
-    return ret;
-  }
-  function getRemainingWeights() {
-    return remainingWeights;
-  }
-  return {
-    extractWeights,
-    getRemainingWeights
-  };
-}
-
-// src/faceFeatureExtractor/extractorsFactory.ts
-function extractorsFactory(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
-  function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(channelsIn, channelsOut, 3, `${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
-  };
-}
-
-// src/faceFeatureExtractor/extractParams.ts
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {dense0, dense1, dense2, dense3}
-  };
-}
-
-// src/common/loadConvParamsFactory.ts
-function loadConvParamsFactory(extractWeightEntry) {
-  return function(prefix) {
-    const filters = extractWeightEntry(`${prefix}/filters`, 4);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {filters, bias};
-  };
-}
-
-// src/faceFeatureExtractor/loadParamsFactory.ts
-function loadParamsFactory(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  function extractDenseBlock3Params(prefix, isFirstLayer = false) {
-    const conv0 = isFirstLayer ? extractConvParams(`${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
-  };
-}
-
-// src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
-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};
-}
-
-// src/faceFeatureExtractor/FaceFeatureExtractor.ts
-var FaceFeatureExtractor = class 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 = tf15.cast(input.toBatchTensor(112, true), "float32");
-      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);
-  }
-};
-
-// src/faceProcessor/FaceProcessor.ts
-var tf17 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/common/fullyConnectedLayer.ts
-var tf16 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-function fullyConnectedLayer(x, params) {
-  return tf16.tidy(() => tf16.add(tf16.matMul(x, params.weights), params.bias));
-}
-
-// src/faceProcessor/extractParams.ts
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {fc}
-  };
-}
-
-// src/faceProcessor/extractParamsFromWeigthMap.ts
-function extractParamsFromWeigthMap3(weightMap) {
-  const paramMappings = [];
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractFcParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/weights`, 2);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {weights, bias};
-  }
-  const params = {
-    fc: extractFcParams("fc")
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// src/faceProcessor/util.ts
-function seperateWeightMaps(weightMap) {
-  const featureExtractorMap = {};
-  const classifierMap = {};
-  Object.keys(weightMap).forEach((key) => {
-    const map = key.startsWith("fc") ? classifierMap : featureExtractorMap;
-    map[key] = weightMap[key];
-  });
-  return {featureExtractorMap, classifierMap};
-}
-
-// src/faceProcessor/FaceProcessor.ts
-var FaceProcessor = class extends NeuralNetwork {
-  constructor(_name, faceFeatureExtractor) {
-    super(_name);
-    this._faceFeatureExtractor = faceFeatureExtractor;
-  }
-  get faceFeatureExtractor() {
-    return this._faceFeatureExtractor;
-  }
-  runNet(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error(`${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);
-  }
-};
-
-// src/faceExpressionNet/FaceExpressions.ts
-var FACE_EXPRESSION_LABELS = ["neutral", "happy", "sad", "angry", "fearful", "disgusted", "surprised"];
-var FaceExpressions = class {
-  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);
-  }
-};
-
-// src/faceExpressionNet/FaceExpressionNet.ts
-var FaceExpressionNet = class 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;
-  }
-};
-
-// src/factories/WithFaceExpressions.ts
-function isWithFaceExpressions(obj) {
-  return obj["expressions"] instanceof FaceExpressions;
-}
-function extendWithFaceExpressions(sourceObj, expressions) {
-  const extension = {expressions};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// src/draw/drawFaceExpressions.ts
-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);
-  });
-}
-
-// src/factories/WithFaceLandmarks.ts
-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);
-}
-
-// src/draw/DrawFaceLandmarks.ts
-var DrawFaceLandmarksOptions = class {
-  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)";
-  }
-};
-var DrawFaceLandmarks = class {
-  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);
-  });
-}
-
-// src/ageGenderNet/AgeGenderNet.ts
-var tf20 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/xception/TinyXception.ts
-var tf19 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/xception/extractParams.ts
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {entry_flow, middle_flow, exit_flow}
-  };
-}
-
-// src/xception/extractParamsFromWeigthMap.ts
-function loadParamsFactory3(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  const extractConvParams = loadConvParamsFactory(extractWeightEntry);
-  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
-  function extractReductionBlockParams(mappedPrefix) {
-    const separable_conv0 = extractSeparableConvParams(`${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};
-}
-
-// src/xception/TinyXception.ts
-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;
-}
-var TinyXception = class 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 = tf19.cast(input.toBatchTensor(112, true), "float32");
-      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);
-  }
-};
-
-// src/ageGenderNet/extractParams.ts
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    paramMappings,
-    params: {fc: {age, gender}}
-  };
-}
-
-// src/ageGenderNet/extractParamsFromWeigthMap.ts
-function extractParamsFromWeigthMap7(weightMap) {
-  const paramMappings = [];
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractFcParams(prefix) {
-    const weights = extractWeightEntry(`${prefix}/weights`, 2);
-    const bias = extractWeightEntry(`${prefix}/bias`, 1);
-    return {weights, bias};
-  }
-  const params = {
-    fc: {
-      age: extractFcParams("fc/age"),
-      gender: extractFcParams("fc/gender")
-    }
-  };
-  disposeUnusedWeightTensors(weightMap, paramMappings);
-  return {params, paramMappings};
-}
-
-// src/ageGenderNet/types.ts
-var Gender;
-(function(Gender2) {
-  Gender2["FEMALE"] = "female";
-  Gender2["MALE"] = "male";
-})(Gender || (Gender = {}));
-
-// src/ageGenderNet/AgeGenderNet.ts
-var AgeGenderNet = class extends NeuralNetwork {
-  constructor(faceFeatureExtractor = new TinyXception(2)) {
-    super("AgeGenderNet");
-    this._faceFeatureExtractor = faceFeatureExtractor;
-  }
-  get faceFeatureExtractor() {
-    return this._faceFeatureExtractor;
-  }
-  runNet(input) {
-    const {params} = this;
-    if (!params) {
-      throw new Error(`${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);
-  }
-};
-
-// src/faceLandmarkNet/FaceLandmark68NetBase.ts
-var tf21 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-var FaceLandmark68NetBase = class 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, "float32"), tf21.fill([68], fillY, "float32")], 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, "float32")).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;
-  }
-};
-
-// src/faceLandmarkNet/FaceLandmark68Net.ts
-var FaceLandmark68Net = class extends FaceLandmark68NetBase {
-  constructor(faceFeatureExtractor = new FaceFeatureExtractor()) {
-    super("FaceLandmark68Net", faceFeatureExtractor);
-  }
-  getDefaultModelName() {
-    return "face_landmark_68_model";
-  }
-  getClassifierChannelsIn() {
-    return 256;
-  }
-};
-
-// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
-var tf22 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
-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};
-}
-
-// src/faceFeatureExtractor/extractParamsTiny.ts
-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}
-  };
-}
-
-// src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
-var TinyFaceFeatureExtractor = class 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 = tf22.cast(input.toBatchTensor(112, true), "float32");
-      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);
-  }
-};
-
-// src/faceLandmarkNet/FaceLandmark68TinyNet.ts
-var FaceLandmark68TinyNet = class extends FaceLandmark68NetBase {
-  constructor(faceFeatureExtractor = new TinyFaceFeatureExtractor()) {
-    super("FaceLandmark68TinyNet", faceFeatureExtractor);
-  }
-  getDefaultModelName() {
-    return "face_landmark_68_tiny_model";
-  }
-  getClassifierChannelsIn() {
-    return 128;
-  }
-};
-
-// src/faceLandmarkNet/index.ts
-var FaceLandmarkNet = class extends FaceLandmark68Net {
-};
-
-// src/faceRecognitionNet/FaceRecognitionNet.ts
-var tf27 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/faceRecognitionNet/convLayer.ts
-var tf24 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/faceRecognitionNet/scaleLayer.ts
-var tf23 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-function scale(x, params) {
-  return tf23.add(tf23.mul(x, params.weights), params.biases);
-}
-
-// src/faceRecognitionNet/convLayer.ts
-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");
-}
-
-// src/faceRecognitionNet/extractParams.ts
-var tf25 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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: ${getRemainingWeights().length}`);
-  }
-  const params = {
-    conv32_down,
-    conv32_1,
-    conv32_2,
-    conv32_3,
-    conv64_down,
-    conv64_1,
-    conv64_2,
-    conv64_3,
-    conv128_down,
-    conv128_1,
-    conv128_2,
-    conv256_down,
-    conv256_1,
-    conv256_2,
-    conv256_down_out,
-    fc
-  };
-  return {params, paramMappings};
-}
-
-// src/faceRecognitionNet/extractParamsFromWeigthMap.ts
-function extractorsFactory6(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractScaleLayerParams(prefix) {
-    const weights = extractWeightEntry(`${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};
-}
-
-// src/faceRecognitionNet/residualLayer.ts
-var tf26 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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;
-}
-
-// src/faceRecognitionNet/FaceRecognitionNet.ts
-var FaceRecognitionNet = class 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);
-  }
-};
-
-// src/faceRecognitionNet/index.ts
-function createFaceRecognitionNet(weights) {
-  const net = new FaceRecognitionNet();
-  net.extractWeights(weights);
-  return net;
-}
-
-// src/factories/WithFaceDescriptor.ts
-function extendWithFaceDescriptor(sourceObj, descriptor) {
-  const extension = {descriptor};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// src/factories/WithAge.ts
-function isWithAge(obj) {
-  return typeof obj["age"] === "number";
-}
-function extendWithAge(sourceObj, age) {
-  const extension = {age};
-  return Object.assign({}, sourceObj, extension);
-}
-
-// src/factories/WithGender.ts
-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);
-}
-
-// src/ssdMobilenetv1/SsdMobilenetv1.ts
-var tf34 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/ssdMobilenetv1/extractParams.ts
-var tf28 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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: ${getRemainingWeights().length}`);
-  }
-  return {
-    params: {
-      mobilenetv1,
-      prediction_layer,
-      output_layer
-    },
-    paramMappings
-  };
-}
-
-// src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
-function extractorsFactory8(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
-    const filters = extractWeightEntry(`${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};
-}
-
-// src/ssdMobilenetv1/mobileNetV1.ts
-var tf30 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/ssdMobilenetv1/pointwiseConvLayer.ts
-var tf29 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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);
-  });
-}
-
-// src/ssdMobilenetv1/mobileNetV1.ts
-var 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
-    };
-  });
-}
-
-// src/ssdMobilenetv1/nonMaxSuppression.ts
-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);
-}
-
-// src/ssdMobilenetv1/outputLayer.ts
-var tf31 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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
-    };
-  });
-}
-
-// src/ssdMobilenetv1/predictionLayer.ts
-var tf33 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/ssdMobilenetv1/boxPredictionLayer.ts
-var tf32 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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
-    };
-  });
-}
-
-// src/ssdMobilenetv1/predictionLayer.ts
-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
-    };
-  });
-}
-
-// src/ssdMobilenetv1/SsdMobilenetv1Options.ts
-var SsdMobilenetv1Options = class {
-  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;
-  }
-};
-
-// src/ssdMobilenetv1/SsdMobilenetv1.ts
-var SsdMobilenetv1 = class 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);
-  }
-};
-
-// src/ssdMobilenetv1/index.ts
-function createSsdMobilenetv1(weights) {
-  const net = new SsdMobilenetv1();
-  net.extractWeights(weights);
-  return net;
-}
-function createFaceDetectionNet(weights) {
-  return createSsdMobilenetv1(weights);
-}
-var FaceDetectionNet = class extends SsdMobilenetv1 {
-};
-
-// src/tinyYolov2/const.ts
-var IOU_THRESHOLD = 0.4;
-var 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)
-];
-var 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)
-];
-var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
-var DEFAULT_MODEL_NAME = "tiny_yolov2_model";
-var DEFAULT_MODEL_NAME_SEPARABLE_CONV = "tiny_yolov2_separable_conv_model";
-
-// src/tinyYolov2/TinyYolov2Base.ts
-var tf39 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/tinyYolov2/config.ts
-var 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: ${JSON.stringify(config2.meanRgb)}`);
-  }
-}
-
-// src/tinyYolov2/convWithBatchNorm.ts
-var tf36 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/tinyYolov2/leaky.ts
-var tf35 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-function leaky(x) {
-  return tf35.tidy(() => {
-    const min = tf35.mul(x, tf35.scalar(0.10000000149011612));
-    return tf35.add(tf35.relu(tf35.sub(x, min)), min);
-  });
-}
-
-// src/tinyYolov2/convWithBatchNorm.ts
-function convWithBatchNorm(x, params) {
-  return tf36.tidy(() => {
-    let out = tf36.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = tf36.conv2d(out, params.conv.filters, [1, 1], "valid");
-    out = tf36.sub(out, params.bn.sub);
-    out = tf36.mul(out, params.bn.truediv);
-    out = tf36.add(out, params.conv.bias);
-    return leaky(out);
-  });
-}
-
-// src/tinyYolov2/depthwiseSeparableConv.ts
-var tf37 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-function depthwiseSeparableConv3(x, params) {
-  return tf37.tidy(() => {
-    let out = tf37.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
-    out = tf37.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], "valid");
-    out = tf37.add(out, params.bias);
-    return leaky(out);
-  });
-}
-
-// src/tinyYolov2/extractParams.ts
-var tf38 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-function extractorsFactory9(extractWeights, paramMappings) {
-  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
-  function extractBatchNormParams(size, mappedPrefix) {
-    const sub6 = tf38.tensor1d(extractWeights(size));
-    const truediv = tf38.tensor1d(extractWeights(size));
-    paramMappings.push({paramPath: `${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: ${getRemainingWeights().length}`);
-  }
-  return {params, paramMappings};
-}
-
-// src/tinyYolov2/extractParamsFromWeigthMap.ts
-function extractorsFactory10(weightMap, paramMappings) {
-  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
-  function extractBatchNormParams(prefix) {
-    const sub6 = extractWeightEntry(`${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};
-}
-
-// src/tinyYolov2/TinyYolov2Options.ts
-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 = {}));
-var TinyYolov2Options = class {
-  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;
-  }
-};
-
-// src/tinyYolov2/TinyYolov2Base.ts
-var TinyYolov2Base2 = class 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 || TinyYolov2Base2.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);
-  }
-};
-var TinyYolov2Base = TinyYolov2Base2;
-TinyYolov2Base.DEFAULT_FILTER_SIZES = [
-  3,
-  16,
-  32,
-  64,
-  128,
-  256,
-  512,
-  1024,
-  1024
-];
-
-// src/tinyYolov2/TinyYolov2.ts
-var TinyYolov2 = class 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);
-  }
-};
-
-// src/tinyYolov2/index.ts
-function createTinyYolov2(weights, withSeparableConvs = true) {
-  const net = new TinyYolov2(withSeparableConvs);
-  net.extractWeights(weights);
-  return net;
-}
-
-// src/tinyFaceDetector/TinyFaceDetectorOptions.ts
-var TinyFaceDetectorOptions = class extends TinyYolov2Options {
-  constructor() {
-    super(...arguments);
-    this._name = "TinyFaceDetectorOptions";
-  }
-};
-
-// src/globalApi/ComposableTask.ts
-var ComposableTask = class {
-  async then(onfulfilled) {
-    return onfulfilled(await this.run());
-  }
-  async run() {
-    throw new Error("ComposableTask - run is not implemented");
-  }
-};
-
-// src/globalApi/DetectFaceLandmarksTasks.ts
-var tf41 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-
-// src/globalApi/extractFacesAndComputeResults.ts
-var tf40 = __toModule(require("@tensorflow/tfjs/dist/tf.es2017.js"));
-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);
-}
-
-// src/tinyFaceDetector/const.ts
-var IOU_THRESHOLD2 = 0.4;
-var 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)
-];
-var MEAN_RGB = [117.001, 114.697, 97.404];
-
-// src/tinyFaceDetector/TinyFaceDetector.ts
-var TinyFaceDetector = class 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);
-  }
-};
-
-// src/globalApi/nets.ts
-var 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()
-};
-var ssdMobilenetv1 = (input, options) => nets.ssdMobilenetv1.locateFaces(input, options);
-var tinyFaceDetector = (input, options) => nets.tinyFaceDetector.locateFaces(input, options);
-var tinyYolov23 = (input, options) => nets.tinyYolov2.locateFaces(input, options);
-var detectFaceLandmarks = (input) => nets.faceLandmark68Net.detectLandmarks(input);
-var detectFaceLandmarksTiny = (input) => nets.faceLandmark68TinyNet.detectLandmarks(input);
-var computeFaceDescriptor = (input) => nets.faceRecognitionNet.computeFaceDescriptor(input);
-var recognizeFaceExpressions = (input) => nets.faceExpressionNet.predictExpressions(input);
-var predictAgeAndGender = (input) => nets.ageGenderNet.predictAgeAndGender(input);
-var loadSsdMobilenetv1Model = (url) => nets.ssdMobilenetv1.load(url);
-var loadTinyFaceDetectorModel = (url) => nets.tinyFaceDetector.load(url);
-var loadTinyYolov2Model = (url) => nets.tinyYolov2.load(url);
-var loadFaceLandmarkModel = (url) => nets.faceLandmark68Net.load(url);
-var loadFaceLandmarkTinyModel = (url) => nets.faceLandmark68TinyNet.load(url);
-var loadFaceRecognitionModel = (url) => nets.faceRecognitionNet.load(url);
-var loadFaceExpressionModel = (url) => nets.faceExpressionNet.load(url);
-var loadAgeGenderModel = (url) => nets.ageGenderNet.load(url);
-var loadFaceDetectionModel = loadSsdMobilenetv1Model;
-var locateFaces = ssdMobilenetv1;
-var detectLandmarks = detectFaceLandmarks;
-
-// src/globalApi/PredictFaceExpressionsTask.ts
-var PredictFaceExpressionsTaskBase = class extends ComposableTask {
-  constructor(parentTask, input, extractedFaces) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.extractedFaces = extractedFaces;
-  }
-};
-var PredictAllFaceExpressionsTask = class 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);
-  }
-};
-var PredictSingleFaceExpressionsTask = class 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);
-  }
-};
-var PredictAllFaceExpressionsWithFaceAlignmentTask = class extends PredictAllFaceExpressionsTask {
-  withAgeAndGender() {
-    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-};
-var PredictSingleFaceExpressionsWithFaceAlignmentTask = class extends PredictSingleFaceExpressionsTask {
-  withAgeAndGender() {
-    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-};
-
-// src/globalApi/PredictAgeAndGenderTask.ts
-var PredictAgeAndGenderTaskBase = class extends ComposableTask {
-  constructor(parentTask, input, extractedFaces) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-    this.extractedFaces = extractedFaces;
-  }
-};
-var PredictAllAgeAndGenderTask = class 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);
-  }
-};
-var PredictSingleAgeAndGenderTask = class 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);
-  }
-};
-var PredictAllAgeAndGenderWithFaceAlignmentTask = class extends PredictAllAgeAndGenderTask {
-  withFaceExpressions() {
-    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptors() {
-    return new ComputeAllFaceDescriptorsTask(this, this.input);
-  }
-};
-var PredictSingleAgeAndGenderWithFaceAlignmentTask = class extends PredictSingleAgeAndGenderTask {
-  withFaceExpressions() {
-    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);
-  }
-  withFaceDescriptor() {
-    return new ComputeSingleFaceDescriptorTask(this, this.input);
-  }
-};
-
-// src/globalApi/ComputeFaceDescriptorsTasks.ts
-var ComputeFaceDescriptorsTaskBase = class extends ComposableTask {
-  constructor(parentTask, input) {
-    super();
-    this.parentTask = parentTask;
-    this.input = input;
-  }
-};
-var ComputeAllFaceDescriptorsTask = class 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);
-  }
-};
-var ComputeSingleFaceDescriptorTask = class 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);
-  }
-};
-
-// src/globalApi/DetectFaceLandmarksTasks.ts
-var DetectFaceLandmarksTaskBase = class 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;
-  }
-};
-var DetectAllFaceLandmarksTask = class 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);
-  }
-};
-var DetectSingleFaceLandmarksTask = class 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);
-  }
-};
-
-// src/globalApi/DetectFacesTasks.ts
-var DetectFacesTaskBase = class extends ComposableTask {
-  constructor(input, options = new SsdMobilenetv1Options()) {
-    super();
-    this.input = input;
-    this.options = options;
-  }
-};
-var DetectAllFacesTask = class 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);
-  }
-};
-var DetectSingleFaceTask = class 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);
-  }
-};
-
-// src/globalApi/detectFaces.ts
-function detectSingleFace(input, options = new SsdMobilenetv1Options()) {
-  return new DetectSingleFaceTask(input, options);
-}
-function detectAllFaces(input, options = new SsdMobilenetv1Options()) {
-  return new DetectAllFacesTask(input, options);
-}
-
-// src/globalApi/allFaces.ts
-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();
-}
-var allFaces = allFacesSsdMobilenetv1;
-
-// src/euclideanDistance.ts
-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));
-}
-
-// src/globalApi/FaceMatcher.ts
-var FaceMatcher = class {
-  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);
-  }
-};
-
-// src/tinyFaceDetector/index.ts
-function createTinyFaceDetector(weights) {
-  const net = new TinyFaceDetector();
-  net.extractWeights(weights);
-  return net;
-}
-
-// src/resizeResults.ts
-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;
-}
-
-// package.json
-var version = "0.8.8";
-
-// src/index.ts
-var node = typeof process !== "undefined";
-var browser3 = typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined";
-var version2 = {faceapi: version, node, browser: browser3};
-//# sourceMappingURL=face-api.node.nobundle.js.map
diff --git a/dist/face-api.node.nobundle.js.map b/dist/face-api.node.nobundle.js.map
deleted file mode 100644
index 9583b20..0000000
--- a/dist/face-api.node.nobundle.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["../src/env/isNodejs.ts", "../src/index.ts", "../src/index.ts", "../src/draw/index.ts", "../src/draw/drawContour.ts", "../src/utils/index.ts", "../src/utils/index.ts", "../src/classes/Dimensions.ts", "../src/utils/index.ts", "../src/classes/Point.ts", "../src/classes/Box.ts", "../src/classes/BoundingBox.ts", "../src/classes/ObjectDetection.ts", "../src/classes/FaceDetection.ts", "../src/ops/iou.ts", "../src/ops/minBbox.ts", "../src/ops/nonMaxSuppression.ts", "../src/ops/normalize.ts", "../src/ops/padToSquare.ts", "../src/ops/shuffleArray.ts", "../src/ops/index.ts", "../src/classes/Rect.ts", "../src/classes/FaceLandmarks.ts", "../src/classes/FaceLandmarks5.ts", "../src/classes/FaceLandmarks68.ts", "../src/classes/FaceMatch.ts", "../src/classes/LabeledBox.ts", "../src/classes/LabeledFaceDescriptors.ts", "../src/classes/PredictedBox.ts", "../src/factories/WithFaceDetection.ts", "../src/env/createBrowserEnv.ts", "../src/env/createFileSystem.ts", "../src/env/createNodejsEnv.ts", "../src/env/isBrowser.ts", "../src/env/index.ts", "../src/dom/resolveInput.ts", "../src/dom/getContext2dOrThrow.ts", "../src/draw/DrawTextField.ts", "../src/draw/DrawBox.ts", "../src/draw/drawDetections.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/dom/isMediaLoaded.ts", "../src/dom/awaitMediaLoaded.ts", "../src/dom/bufferToImage.ts", "../src/dom/getMediaDimensions.ts", "../src/dom/createCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/imageTensorToCanvas.ts", "../src/dom/isMediaElement.ts", "../src/dom/NetInput.ts", "../src/dom/imageToSquare.ts", "../src/dom/NetInput.ts", "../src/dom/toNetInput.ts", "../src/dom/extractFaces.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/extractFaceTensors.ts", "../src/dom/fetchOrThrow.ts", "../src/dom/fetchImage.ts", "../src/dom/fetchJson.ts", "../src/dom/fetchNetWeights.ts", "../src/dom/loadWeightMap.ts", "../src/common/getModelUris.ts", "../src/dom/loadWeightMap.ts", "../src/dom/matchDimensions.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/NeuralNetwork.ts", "../src/NeuralNetwork.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/depthwiseSeparableConv.ts", "../src/faceFeatureExtractor/denseBlock.ts", "../src/common/convLayer.ts", "../src/common/disposeUnusedWeightTensors.ts", "../src/common/extractConvParamsFactory.ts", "../src/common/extractFCParamsFactory.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/types.ts", "../src/common/extractSeparableConvParamsFactory.ts", "../src/common/extractWeightEntryFactory.ts", "../src/common/extractWeightsFactory.ts", "../src/faceFeatureExtractor/extractorsFactory.ts", "../src/faceFeatureExtractor/extractParams.ts", "../src/common/loadConvParamsFactory.ts", "../src/faceFeatureExtractor/loadParamsFactory.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMap.ts", "../src/faceFeatureExtractor/FaceFeatureExtractor.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/common/fullyConnectedLayer.ts", "../src/faceProcessor/extractParams.ts", "../src/faceProcessor/extractParamsFromWeigthMap.ts", "../src/faceProcessor/util.ts", "../src/faceProcessor/FaceProcessor.ts", "../src/faceExpressionNet/FaceExpressions.ts", "../src/faceExpressionNet/FaceExpressionNet.ts", "../src/factories/WithFaceExpressions.ts", "../src/draw/drawFaceExpressions.ts", "../src/factories/WithFaceLandmarks.ts", "../src/draw/DrawFaceLandmarks.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/xception/TinyXception.ts", "../src/xception/extractParams.ts", "../src/xception/extractParamsFromWeigthMap.ts", "../src/xception/TinyXception.ts", "../src/ageGenderNet/extractParams.ts", "../src/ageGenderNet/extractParamsFromWeigthMap.ts", "../src/ageGenderNet/types.ts", "../src/ageGenderNet/AgeGenderNet.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68NetBase.ts", "../src/faceLandmarkNet/FaceLandmark68Net.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts", "../src/faceFeatureExtractor/extractParamsTiny.ts", "../src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts", "../src/faceLandmarkNet/FaceLandmark68TinyNet.ts", "../src/faceLandmarkNet/index.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/scaleLayer.ts", "../src/faceRecognitionNet/convLayer.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParams.ts", "../src/faceRecognitionNet/extractParamsFromWeigthMap.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/residualLayer.ts", "../src/faceRecognitionNet/FaceRecognitionNet.ts", "../src/faceRecognitionNet/index.ts", "../src/factories/WithFaceDescriptor.ts", "../src/factories/WithAge.ts", "../src/factories/WithGender.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParams.ts", "../src/ssdMobilenetv1/extractParamsFromWeigthMap.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/pointwiseConvLayer.ts", "../src/ssdMobilenetv1/mobileNetV1.ts", "../src/ssdMobilenetv1/nonMaxSuppression.ts", "../src/ssdMobilenetv1/outputLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/boxPredictionLayer.ts", "../src/ssdMobilenetv1/predictionLayer.ts", "../src/ssdMobilenetv1/SsdMobilenetv1Options.ts", "../src/ssdMobilenetv1/SsdMobilenetv1.ts", "../src/ssdMobilenetv1/index.ts", "../src/tinyYolov2/const.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/config.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/leaky.ts", "../src/tinyYolov2/convWithBatchNorm.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/depthwiseSeparableConv.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParams.ts", "../src/tinyYolov2/extractParamsFromWeigthMap.ts", "../src/tinyYolov2/TinyYolov2Options.ts", "../src/tinyYolov2/TinyYolov2Base.ts", "../src/tinyYolov2/TinyYolov2.ts", "../src/tinyYolov2/index.ts", "../src/tinyFaceDetector/TinyFaceDetectorOptions.ts", "../src/globalApi/ComposableTask.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/globalApi/extractFacesAndComputeResults.ts", "../src/tinyFaceDetector/const.ts", "../src/tinyFaceDetector/TinyFaceDetector.ts", "../src/globalApi/nets.ts", "../src/globalApi/PredictFaceExpressionsTask.ts", "../src/globalApi/PredictAgeAndGenderTask.ts", "../src/globalApi/ComputeFaceDescriptorsTasks.ts", "../src/globalApi/DetectFaceLandmarksTasks.ts", "../src/globalApi/DetectFacesTasks.ts", "../src/globalApi/detectFaces.ts", "../src/globalApi/allFaces.ts", "../src/euclideanDistance.ts", "../src/globalApi/FaceMatcher.ts", "../src/tinyFaceDetector/index.ts", "../src/resizeResults.ts", "../src/index.ts"],
-  "sourcesContent": ["export function isNodejs(): boolean {\n  return typeof global === 'object'\n    && typeof require === 'function'\n    && typeof module !== 'undefined'\n    // issues with gatsby.js: module.exports is undefined\n    // && !!module.exports\n    && typeof process !== 'undefined' && !!process.version\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n", "export * from './drawContour'\nexport * from './drawDetections'\nexport * from './drawFaceExpressions'\nexport * from './DrawBox'\nexport * from './DrawFaceLandmarks'\nexport * from './DrawTextField'", "import { Point } from '../classes';\n\nexport function drawContour(\n  ctx: CanvasRenderingContext2D,\n  points: Point[],\n  isClosed: boolean = false\n) {\n  ctx.beginPath()\n\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\n    const from = points[prevIdx]\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(x, y)\n  })\n\n  if (isClosed) {\n    const from = points[points.length - 1]\n    const to = points[0]\n    if (!from || !to) {\n      return\n    }\n\n    ctx.moveTo(from.x, from.y)\n    ctx.lineTo(to.x, to.y)\n  }\n\n  ctx.stroke()\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "import { isValidNumber } from '../utils';\n\nexport interface IDimensions {\n  width: number\n  height: number\n}\n\nexport class Dimensions implements IDimensions {\n\n  private _width: number\n  private _height: number\n\n  constructor(width: number, height: number) {\n    if (!isValidNumber(width) || !isValidNumber(height)) {\n      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\n    }\n\n    this._width = width\n    this._height = height\n  }\n\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n\n  public reverse(): Dimensions {\n    return new Dimensions(1 / this.width, 1 / this.height)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Point } from '../classes';\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\n\nexport function isTensor(tensor: any, dim: number) {\n  return tensor instanceof tf.Tensor && tensor.shape.length === dim\n}\n\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\n  return isTensor(tensor, 1)\n}\n\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\n  return isTensor(tensor, 2)\n}\n\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\n  return isTensor(tensor, 3)\n}\n\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\n  return isTensor(tensor, 4)\n}\n\nexport function isFloat(num: number) {\n  return num % 1 !== 0\n}\n\nexport function isEven(num: number) {\n  return num % 2 === 0\n}\n\nexport function round(num: number, prec: number = 2) {\n  const f = Math.pow(10, prec)\n  return Math.floor(num * f) / f\n}\n\nexport function isDimensions(obj: any): boolean {\n  return obj && obj.width && obj.height\n}\n\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\n  const scale = inputSize / Math.max(height, width)\n  return new Dimensions(Math.round(width * scale), Math.round(height * scale))\n}\n\nexport function getCenterPoint(pts: Point[]): Point {\n  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\n    .div(new Point(pts.length, pts.length))\n}\n\nexport function range(num: number, start: number, step: number): number[] {\n  return Array(num).fill(0).map((_, i) => start + (i * step))\n}\n\nexport function isValidNumber(num: any) {\n  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\n}\n\nexport function isValidProbablitiy(num: any) {\n  return isValidNumber(num) && 0 <= num && num <= 1.0\n}", "export interface IPoint {\n  x: number\n  y: number\n}\n\nexport class Point implements IPoint {\n  private _x: number\n  private _y: number\n\n  constructor(x: number, y: number) {\n    this._x = x\n    this._y = y\n  }\n\n  get x(): number { return this._x }\n  get y(): number { return this._y }\n\n  public add(pt: IPoint): Point {\n    return new Point(this.x + pt.x, this.y + pt.y)\n  }\n\n  public sub(pt: IPoint): Point {\n    return new Point(this.x - pt.x, this.y - pt.y)\n  }\n\n  public mul(pt: IPoint): Point {\n    return new Point(this.x * pt.x, this.y * pt.y)\n  }\n\n  public div(pt: IPoint): Point {\n    return new Point(this.x / pt.x, this.y / pt.y)\n  }\n\n  public abs(): Point {\n    return new Point(Math.abs(this.x), Math.abs(this.y))\n  }\n\n  public magnitude(): number {\n    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\n  }\n\n  public floor(): Point {\n    return new Point(Math.floor(this.x), Math.floor(this.y))\n  }\n}", "import { isDimensions, isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { IDimensions } from './Dimensions';\nimport { Point } from './Point';\nimport { IRect } from './Rect';\n\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\n\n  public static isRect(rect: any): boolean {\n    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\n  }\n\n  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\n    if (!Box.isRect(box)) {\n      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\n    }\n\n    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\n      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\n    }\n  }\n\n  private _x: number\n  private _y: number\n  private _width: number\n  private _height: number\n\n  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\n    const box = (_box || {}) as any\n\n    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\n    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\n\n    if (!isRect && !isBbox) {\n      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\n    }\n\n    const [x, y, width, height] = isRect\n      ? [box.x, box.y, box.width, box.height]\n      : [box.left, box.top, box.right - box.left, box.bottom - box.top]\n\n    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\n\n    this._x = x\n    this._y = y\n    this._width = width\n    this._height = height\n  }\n\n  public get x(): number { return this._x }\n  public get y(): number { return this._y }\n  public get width(): number { return this._width }\n  public get height(): number { return this._height }\n  public get left(): number { return this.x }\n  public get top(): number { return this.y }\n  public get right(): number { return this.x + this.width }\n  public get bottom(): number { return this.y + this.height }\n  public get area(): number { return this.width * this.height }\n  public get topLeft(): Point { return new Point(this.left, this.top) }\n  public get topRight(): Point { return new Point(this.right, this.top) }\n  public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\n  public get bottomRight(): Point { return new Point(this.right, this.bottom) }\n\n  public round(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.round(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public floor(): Box<BoxType> {\n    const [x, y, width, height] = [this.x, this.y, this.width, this.height]\n      .map(val => Math.floor(val))\n    return new Box({ x, y, width, height })\n  }\n\n  public toSquare(): Box<BoxType> {\n    let { x, y, width, height } = this\n    const diff = Math.abs(width - height)\n    if (width < height) {\n      x -= (diff / 2)\n      width += diff\n    }\n    if (height < width) {\n      y -= (diff / 2)\n      height += diff\n    }\n\n    return new Box({ x, y, width, height })\n  }\n\n  public rescale(s: IDimensions | number): Box<BoxType> {\n    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\n    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\n    return new Box({\n      x: this.x * scaleX,\n      y: this.y * scaleY,\n      width: this.width * scaleX,\n      height: this.height * scaleY\n    })\n  }\n\n  public pad(padX: number, padY: number): Box<BoxType> {\n    let [x, y, width, height] = [\n      this.x - (padX / 2),\n      this.y - (padY / 2),\n      this.width + padX,\n      this.height + padY\n    ]\n    return new Box({ x, y, width, height })\n  }\n\n  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\n    const { x, y, right, bottom } = this\n    const clippedX = Math.max(x, 0)\n    const clippedY = Math.max(y, 0)\n\n    const newWidth = right - clippedX\n    const newHeight = bottom - clippedY\n    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\n    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\n\n    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\n  }\n\n  public shift(sx: number, sy: number): Box<BoxType> {\n    const { width, height } = this\n    const x = this.x + sx\n    const y = this.y + sy\n\n    return new Box({ x, y, width, height })\n  }\n\n  public padAtBorders(imageHeight: number, imageWidth: number) {\n    const w = this.width + 1\n    const h = this.height + 1\n\n    let dx = 1\n    let dy = 1\n    let edx = w\n    let edy = h\n\n    let x = this.left\n    let y = this.top\n    let ex = this.right\n    let ey = this.bottom\n\n    if (ex > imageWidth) {\n      edx = -ex + imageWidth + w\n      ex = imageWidth\n    }\n    if (ey > imageHeight) {\n      edy = -ey + imageHeight + h\n      ey = imageHeight\n    }\n    if (x < 1) {\n      edy = 2 - x\n      x = 1\n    }\n    if (y < 1) {\n      edy = 2 - y\n      y = 1\n    }\n\n    return { dy, edy, dx, edx, y, ey, x, ex, w, h }\n  }\n\n  public calibrate(region: Box) {\n    return new Box({\n      left: this.left + (region.left * this.width),\n      top: this.top + (region.top * this.height),\n      right: this.right + (region.right * this.width),\n      bottom: this.bottom + (region.bottom * this.height)\n    }).toSquare().round()\n  }\n}", "import { Box } from './Box';\n\nexport interface IBoundingBox {\n  left: number\n  top: number\n  right: number\n  bottom: number\n}\n\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\n  constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\n    super({ left, top, right, bottom }, allowNegativeDimensions)\n  }\n}", "import { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { IRect, Rect } from './Rect';\n\nexport class ObjectDetection {\n  private _score: number\n  private _classScore: number\n  private _className: string\n  private _box: Rect\n  private _imageDims: Dimensions\n\n  constructor(\n    score: number,\n    classScore: number,\n    className: string,\n    relativeBox: IRect,\n    imageDims: IDimensions\n  ) {\n    this._imageDims = new Dimensions(imageDims.width, imageDims.height)\n    this._score = score\n    this._classScore = classScore\n    this._className = className\n    this._box = new Box(relativeBox).rescale(this._imageDims)\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n  public get className(): string { return this._className }\n  public get box(): Box { return this._box }\n  public get imageDims(): Dimensions { return this._imageDims }\n  public get imageWidth(): number { return this.imageDims.width }\n  public get imageHeight(): number { return this.imageDims.height }\n  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\n\n  public forSize(width: number, height: number): ObjectDetection {\n    return new ObjectDetection(\n      this.score,\n      this.classScore,\n      this.className,\n      this.relativeBox,\n      { width, height}\n    )\n  }\n}", "import { Box } from './Box';\nimport { IDimensions } from './Dimensions';\nimport { ObjectDetection } from './ObjectDetection';\nimport { Rect } from './Rect';\n\nexport interface IFaceDetecion {\n  score: number\n  box: Box\n}\n\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\n  constructor(\n    score: number,\n    relativeBox: Rect,\n    imageDims: IDimensions\n  ) {\n    super(score, score, '', relativeBox, imageDims)\n  }\n\n  public forSize(width: number, height: number): FaceDetection {\n    const { score, relativeBox, imageDims } = super.forSize(width, height)\n    return new FaceDetection(score, relativeBox, imageDims)\n  }\n}", "import { Box } from '../classes/Box';\n\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\n  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\n  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\n  const interSection = width * height\n\n  return isIOU\n    ? interSection / (box1.area + box2.area - interSection)\n    : interSection / Math.min(box1.area, box2.area)\n}", "import { BoundingBox, IPoint } from '../classes';\n\nexport function minBbox(pts: IPoint[]): BoundingBox {\n  const xs = pts.map(pt => pt.x)\n  const ys = pts.map(pt => pt.y)\n  const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\n  const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\n  const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\n  const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\n\n  return new BoundingBox(minX, minY, maxX, maxY)\n}\n", "import { Box } from '../classes/Box';\nimport { iou } from './iou';\n\nexport function nonMaxSuppression(\n  boxes: Box[],\n  scores: number[],\n  iouThreshold: number,\n  isIOU: boolean = true\n): number[] {\n\n  let indicesSortedByScore = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .sort((c1, c2) => c1.score - c2.score)\n    .map(c => c.boxIndex)\n\n  const pick: number[] = []\n\n  while(indicesSortedByScore.length > 0) {\n    const curr = indicesSortedByScore.pop() as number\n    pick.push(curr)\n\n    const indices = indicesSortedByScore\n\n    const outputs: number[] = []\n    for (let i = 0; i < indices.length; i++) {\n      const idx = indices[i]\n\n      const currBox = boxes[curr]\n      const idxBox = boxes[idx]\n\n      outputs.push(iou(currBox, idxBox, isIOU))\n    }\n\n    indicesSortedByScore = indicesSortedByScore.filter(\n      (_, j) => outputs[j] <= iouThreshold\n    )\n  }\n\n  return pick\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\n  return tf.tidy(() => {\n    const [r, g, b] = meanRgb\n    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r, 'float32')\n    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g, 'float32')\n    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b, 'float32')\n    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\n\n    return tf.sub(x, avg_rgb)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\n/**\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\n *\n * @param imgTensor The image tensor.\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n * both sides of the minor dimension oof the image.\n * @returns The padded tensor with width === height.\n */\nexport function padToSquare(\n  imgTensor: tf.Tensor4D,\n  isCenterImage: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n\n    const [height, width] = imgTensor.shape.slice(1)\n    if (height === width) {\n      return imgTensor\n    }\n\n    const dimDiff = Math.abs(height - width)\n    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\n    const paddingAxis = height > width ? 2 : 1\n\n    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\n      const paddingTensorShape = imgTensor.shape.slice()\n      paddingTensorShape[paddingAxis] = paddingAmount\n      return tf.fill(paddingTensorShape, 0, 'float32')\n    }\n\n    const paddingTensorAppend = createPaddingTensor(paddingAmount)\n    const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\n\n    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\n      ? createPaddingTensor(remainingPaddingAmount)\n      : null\n\n    const tensorsToStack = [\n      paddingTensorPrepend,\n      imgTensor,\n      paddingTensorAppend\n    ]\n      .filter(t => !!t)\n      // .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\n      .map((t: tf.Tensor) => tf.cast(t, 'float32')) as tf.Tensor4D[]\n    return tf.concat(tensorsToStack, paddingAxis)\n\n  })\n}", "export function shuffleArray(inputArray: any[]) {\n  const array = inputArray.slice()\n  for (let i = array.length - 1; i > 0; i--) {\n      const j = Math.floor(Math.random() * (i + 1))\n      const x = array[i]\n      array[i] = array[j]\n      array[j] = x\n  }\n  return array\n}", "export * from './iou'\nexport * from './minBbox'\nexport * from './nonMaxSuppression'\nexport * from './normalize'\nexport * from './padToSquare'\nexport * from './shuffleArray'\n\nexport function sigmoid(x: number) {\n  return 1 / (1 + Math.exp(-x))\n}\n\nexport function inverseSigmoid(x: number) {\n  return Math.log(x / (1 - x))\n}", "import { Box } from './Box';\n\nexport interface IRect {\n  x: number\n  y: number\n  width: number\n  height: number\n}\n\nexport class Rect extends Box<Rect> implements IRect {\n  constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\n    super({ x, y, width, height }, allowNegativeDimensions)\n  }\n}", "import { minBbox } from '../ops';\nimport { getCenterPoint } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { Dimensions, IDimensions } from './Dimensions';\nimport { FaceDetection } from './FaceDetection';\nimport { Point } from './Point';\nimport { IRect, Rect } from './Rect';\n\n// face alignment constants\nconst relX = 0.5\nconst relY = 0.43\nconst relScale = 0.45\n\nexport interface IFaceLandmarks {\n  positions: Point[]\n  shift: Point\n}\n\nexport class FaceLandmarks implements IFaceLandmarks {\n  protected _shift: Point\n  protected _positions: Point[]\n  protected _imgDims: Dimensions\n\n  constructor(\n    relativeFaceLandmarkPositions: Point[],\n    imgDims: IDimensions,\n    shift: Point = new Point(0, 0)\n  ) {\n    const { width, height } = imgDims\n    this._imgDims = new Dimensions(width, height)\n    this._shift = shift\n    this._positions = relativeFaceLandmarkPositions.map(\n      pt => pt.mul(new Point(width, height)).add(shift)\n    )\n  }\n\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\n  public get imageWidth(): number { return this._imgDims.width }\n  public get imageHeight(): number { return this._imgDims.height }\n  public get positions(): Point[] { return this._positions }\n  public get relativePositions(): Point[] {\n    return this._positions.map(\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\n    )\n  }\n\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      { width, height }\n    )\n  }\n\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\n    return new (this.constructor as any)(\n      this.relativePositions,\n      this._imgDims,\n      new Point(x, y)\n    )\n  }\n\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\n    return this.shiftBy(pt.x, pt.y)\n  }\n\n  /**\n   * Aligns the face landmarks after face detection from the relative positions of the faces\n   * bounding box, or it's current shift. This function should be used to align the face images\n   * after face detection has been performed, before they are passed to the face recognition net.\n   * This will make the computed face descriptor more accurate.\n   *\n   * @param detection (optional) The bounding box of the face or the face detection result. If\n   * no argument was passed the position of the face landmarks are assumed to be relative to\n   * it's current shift.\n   * @returns The bounding box of the aligned face.\n   */\n  public align(\n    detection?: FaceDetection | IRect | IBoundingBox | null,\n    options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\n  ): Box {\n    if (detection) {\n      const box = detection instanceof FaceDetection\n        ? detection.box.floor()\n        : new Box(detection)\n\n      return this.shiftBy(box.x, box.y).align(null, options)\n    }\n\n    const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\n\n    if (useDlibAlignment) {\n      return this.alignDlib()\n    }\n\n    return this.alignMinBbox(minBoxPadding)\n  }\n\n  private alignDlib(): Box {\n\n    const centers = this.getRefPointsForAlignment()\n\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\n\n    const size = Math.floor(eyeToMouthDist / relScale)\n\n    const refPoint = getCenterPoint(centers)\n    // TODO: pad in case rectangle is out of image bounds\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\n\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\n  }\n\n  private alignMinBbox(padding: number): Box {\n    const box = minBbox(this.positions)\n    return box.pad(box.width * padding, box.height * padding)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    throw new Error('getRefPointsForAlignment not implemented by base class')\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\n\nexport class FaceLandmarks5 extends FaceLandmarks {\n\n  protected getRefPointsForAlignment(): Point[] {\n    const pts = this.positions\n    return [\n      pts[0],\n      pts[1],\n      getCenterPoint([pts[3], pts[4]])\n    ]\n  }\n}", "import { getCenterPoint } from '../utils';\nimport { FaceLandmarks } from './FaceLandmarks';\nimport { Point } from './Point';\n\nexport class FaceLandmarks68 extends FaceLandmarks {\n  public getJawOutline(): Point[] {\n    return this.positions.slice(0, 17)\n  }\n\n  public getLeftEyeBrow(): Point[] {\n    return this.positions.slice(17, 22)\n  }\n\n  public getRightEyeBrow(): Point[] {\n    return this.positions.slice(22, 27)\n  }\n\n  public getNose(): Point[] {\n    return this.positions.slice(27, 36)\n  }\n\n  public getLeftEye(): Point[] {\n    return this.positions.slice(36, 42)\n  }\n\n  public getRightEye(): Point[] {\n    return this.positions.slice(42, 48)\n  }\n\n  public getMouth(): Point[] {\n    return this.positions.slice(48, 68)\n  }\n\n  protected getRefPointsForAlignment(): Point[] {\n    return [\n      this.getLeftEye(),\n      this.getRightEye(),\n      this.getMouth()\n    ].map(getCenterPoint)\n  }\n}", "import { round } from '../utils';\n\nexport interface IFaceMatch {\n  label: string\n  distance: number\n}\n\nexport class FaceMatch implements IFaceMatch {\n  private _label: string\n  private _distance: number\n\n  constructor(label: string, distance: number) {\n    this._label = label\n    this._distance = distance\n  }\n\n  public get label(): string { return this._label }\n  public get distance(): number { return this._distance }\n\n  public toString(withDistance: boolean = true): string {\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\n  }\n}", "import { isValidNumber } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { Box } from './Box';\nimport { IRect } from './Rect';\n\nexport class LabeledBox extends Box<LabeledBox> {\n\n  public static assertIsValidLabeledBox(box: any, callee: string) {\n    Box.assertIsValidBox(box, callee)\n\n    if (!isValidNumber(box.label)) {\n      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\n    }\n  }\n\n  private _label: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number) {\n    super(box)\n    this._label = label\n  }\n\n  public get label(): number { return this._label }\n\n}", "export class LabeledFaceDescriptors {\n  private _label: string\n  private _descriptors: Float32Array[]\n\n  constructor(label: string, descriptors: Float32Array[]) {\n    if (!(typeof label === 'string')) {\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\n    }\n\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\n    }\n\n    this._label = label\n    this._descriptors = descriptors\n  }\n\n  public get label(): string { return this._label }\n  public get descriptors(): Float32Array[] { return this._descriptors }\n\n  public toJSON(): any {\n    return {\n      label: this.label,\n      descriptors: this.descriptors.map((d) => Array.from(d))\n    };\n  }\n\n  public static fromJSON(json: any): LabeledFaceDescriptors {\n    const descriptors = json.descriptors.map((d: any) => {\n      return new Float32Array(d);\n    });\n    return new LabeledFaceDescriptors(json.label, descriptors);\n  }\n\n}", "import { isValidProbablitiy } from '../utils';\nimport { IBoundingBox } from './BoundingBox';\nimport { LabeledBox } from './LabeledBox';\nimport { IRect } from './Rect';\n\nexport class PredictedBox extends LabeledBox {\n\n  public static assertIsValidPredictedBox(box: any, callee: string) {\n    LabeledBox.assertIsValidLabeledBox(box, callee)\n\n    if (\n      !isValidProbablitiy(box.score)\n      || !isValidProbablitiy(box.classScore)\n    ) {\n      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\n    }\n  }\n\n  private _score: number\n  private _classScore: number\n\n  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\n    super(box, label)\n    this._score = score\n    this._classScore = classScore\n  }\n\n  public get score(): number { return this._score }\n  public get classScore(): number { return this._classScore }\n\n}", "import { FaceDetection } from '../classes/FaceDetection';\n\nexport type WithFaceDetection<TSource> = TSource & {\n  detection: FaceDetection\n}\n\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\n  return obj['detection'] instanceof FaceDetection\n}\n\nexport function extendWithFaceDetection<\n  TSource\n> (\n  sourceObj: TSource,\n  detection: FaceDetection\n): WithFaceDetection<TSource> {\n\n  const extension = { detection }\n  return Object.assign({}, sourceObj, extension)\n}\n", "import { Environment } from './types';\n\nexport function createBrowserEnv(): Environment {\n\n  const fetch = window['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for browser environment')\n  }\n\n  const readFile = function() {\n    throw new Error('readFile - filesystem not available for browser environment')\n  }\n\n  return {\n    Canvas: HTMLCanvasElement,\n    CanvasRenderingContext2D: CanvasRenderingContext2D,\n    Image: HTMLImageElement,\n    ImageData: ImageData,\n    Video: HTMLVideoElement,\n    createCanvasElement: () => document.createElement('canvas'),\n    createImageElement: () => document.createElement('img'),\n    fetch,\n    readFile\n  }\n}", "import { FileSystem } from './types';\n\nexport function createFileSystem(fs?: any): FileSystem {\n\n  let requireFsError = ''\n\n  if (!fs) {\n    try {\n      fs = require('fs')\n    } catch (err) {\n      requireFsError = err.toString()\n    }\n  }\n\n  const readFile = fs\n    ? function(filePath: string) {\n      return new Promise<Buffer>((res, rej) => {\n        fs.readFile(filePath, function(err: any, buffer: Buffer) {\n          return err ? rej(err) : res(buffer)\n        })\n      })\n    }\n    : function() {\n      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\n    }\n\n  return {\n    readFile\n  }\n}", "import { createFileSystem } from './createFileSystem';\nimport { Environment } from './types';\n\nexport function createNodejsEnv(): Environment {\n\n  const Canvas = global['Canvas'] || global['HTMLCanvasElement']\n  const Image = global['Image'] || global['HTMLImageElement']\n\n  const createCanvasElement = function() {\n    if (Canvas) {\n      return new Canvas()\n    }\n    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\n  }\n\n  const createImageElement = function() {\n    if (Image) {\n      return new Image()\n    }\n    throw new Error('createImageElement - missing Image implementation for nodejs environment')\n  }\n\n  const fetch = global['fetch'] || function() {\n    throw new Error('fetch - missing fetch implementation for nodejs environment')\n  }\n\n  const fileSystem = createFileSystem()\n\n  return {\n    Canvas: Canvas || class {},\n    CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\n    Image: Image || class {},\n    ImageData: global['ImageData'] || class {},\n    Video: global['HTMLVideoElement'] || class {},\n    createCanvasElement,\n    createImageElement,\n    fetch,\n    ...fileSystem\n  }\n}", "export function isBrowser(): boolean {\n  return typeof window === 'object'\n    && typeof document !== 'undefined'\n    && typeof HTMLImageElement !== 'undefined'\n    && typeof HTMLCanvasElement !== 'undefined'\n    && typeof HTMLVideoElement !== 'undefined'\n    && typeof ImageData !== 'undefined'\n    && typeof CanvasRenderingContext2D !== 'undefined'\n}", "import { createBrowserEnv } from './createBrowserEnv';\nimport { createFileSystem } from './createFileSystem';\nimport { createNodejsEnv } from './createNodejsEnv';\nimport { isBrowser } from './isBrowser';\nimport { isNodejs } from './isNodejs';\nimport { Environment } from './types';\n\nlet environment: Environment | null\n\nfunction getEnv(): Environment {\n  if (!environment) {\n    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\n  }\n  return environment\n}\n\nfunction setEnv(env: Environment) {\n  environment = env\n}\n\nfunction initialize() {\n  // check for isBrowser() first to prevent electron renderer process\n  // to be initialized with wrong environment due to isNodejs() returning true\n  if (isBrowser()) {\n    return setEnv(createBrowserEnv())\n  }\n  if (isNodejs()) {\n    return setEnv(createNodejsEnv())\n  }\n}\n\nfunction monkeyPatch(env: Partial<Environment>) {\n  if (!environment) {\n    initialize()\n  }\n\n  if (!environment) {\n    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\n  }\n\n  const { Canvas = environment.Canvas, Image = environment.Image } = env\n  environment.Canvas = Canvas\n  environment.Image = Image\n  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\n  environment.createImageElement = env.createImageElement || (() => new Image())\n\n  environment.ImageData = env.ImageData || environment.ImageData\n  environment.Video = env.Video || environment.Video\n  environment.fetch = env.fetch || environment.fetch\n  environment.readFile = env.readFile || environment.readFile\n}\n\nexport const env = {\n  getEnv,\n  setEnv,\n  initialize,\n  createBrowserEnv,\n  createFileSystem,\n  createNodejsEnv,\n  monkeyPatch,\n  isBrowser,\n  isNodejs\n}\n\ninitialize()\n\nexport * from './types'\n", "import { env } from '../env';\n\nexport function resolveInput(arg: string | any) {\n  if (!env.isNodejs() && typeof arg === 'string') {\n    return document.getElementById(arg)\n  }\n  return arg\n}", "import { env } from '../env';\nimport { resolveInput } from './resolveInput';\n\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\n\n  const { Canvas, CanvasRenderingContext2D } = env.getEnv()\n\n  if (canvasArg instanceof CanvasRenderingContext2D) {\n    return canvasArg\n  }\n\n  const canvas = resolveInput(canvasArg)\n\n  if (!(canvas instanceof Canvas)) {\n    throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\n  }\n\n  const ctx = canvas.getContext('2d')\n  if (!ctx) {\n    throw new Error('resolveContext2d - canvas 2d context is null')\n  }\n\n  return ctx\n}", "import { IDimensions, IPoint } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { resolveInput } from '../dom/resolveInput';\n\nexport enum AnchorPosition {\n  TOP_LEFT = 'TOP_LEFT',\n  TOP_RIGHT = 'TOP_RIGHT',\n  BOTTOM_LEFT = 'BOTTOM_LEFT',\n  BOTTOM_RIGHT = 'BOTTOM_RIGHT'\n}\n\nexport interface IDrawTextFieldOptions {\n  anchorPosition?: AnchorPosition\n  backgroundColor?: string\n  fontColor?: string\n  fontSize?: number\n  fontStyle?: string\n  padding?: number\n}\n\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\n  public anchorPosition: AnchorPosition\n  public backgroundColor: string\n  public fontColor: string\n  public fontSize: number\n  public fontStyle: string\n  public padding: number\n\n  constructor(options: IDrawTextFieldOptions = {}) {\n    const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\n    this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\n    this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\n    this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\n    this.fontSize = fontSize || 14\n    this.fontStyle = fontStyle || 'Georgia'\n    this.padding = padding || 4\n  }\n}\n\nexport class DrawTextField {\n  public text: string[]\n  public anchor : IPoint\n  public options: DrawTextFieldOptions\n\n  constructor(\n    text: string | string[] | DrawTextField,\n    anchor: IPoint,\n    options: IDrawTextFieldOptions = {}\n  ) {\n    this.text = typeof text === 'string'\n      ? [text]\n      : (text instanceof DrawTextField ? text.text : text)\n    this.anchor = anchor\n    this.options = new DrawTextFieldOptions(options)\n  }\n\n  measureWidth(ctx: CanvasRenderingContext2D): number {\n    const { padding } = this.options\n    return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\n  }\n\n  measureHeight(): number {\n    const { fontSize, padding } = this.options\n    return this.text.length * fontSize + (2 * padding)\n  }\n\n  getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\n    const { anchorPosition } = this.options\n    const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\n    const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\n\n    const textFieldWidth = this.measureWidth(ctx)\n    const textFieldHeight = this.measureHeight()\n    const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\n    const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\n\n    // adjust anchor if text box exceeds canvas borders\n    if (canvasDims) {\n      const { width, height } = canvasDims\n      const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\n      const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\n      return { x: newX, y: newY }\n    }\n    return { x, y }\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const canvas = resolveInput(canvasArg)\n    const ctx = getContext2dOrThrow(canvas)\n\n    const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\n\n    ctx.font = `${fontSize}px ${fontStyle}`\n    const maxTextWidth = this.measureWidth(ctx)\n    const textHeight = this.measureHeight()\n\n    ctx.fillStyle = backgroundColor\n    const upperLeft = this.getUpperLeft(ctx, canvas)\n    ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\n\n    ctx.fillStyle = fontColor;\n    this.text.forEach((textLine, i) => {\n      const x = padding + upperLeft.x\n      const y = padding + upperLeft.y + ((i + 1) * fontSize)\n      ctx.fillText(textLine, x, y)\n    })\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\n\nexport interface IDrawBoxOptions {\n  boxColor?: string\n  lineWidth?: number\n  drawLabelOptions?: IDrawTextFieldOptions\n  label?: string\n}\n\nexport class DrawBoxOptions {\n  public boxColor: string\n  public lineWidth: number\n  public drawLabelOptions: DrawTextFieldOptions\n  public label?: string\n\n  constructor(options: IDrawBoxOptions = {}) {\n    const { boxColor, lineWidth, label, drawLabelOptions } = options\n    this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\n    this.lineWidth = lineWidth || 2\n    this.label = label\n\n    const defaultDrawLabelOptions = {\n      anchorPosition: AnchorPosition.BOTTOM_LEFT,\n      backgroundColor: this.boxColor\n    }\n    this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\n  }\n}\n\nexport class DrawBox {\n  public box: Box\n  public options: DrawBoxOptions\n\n  constructor(\n    box: IBoundingBox | IRect,\n    options: IDrawBoxOptions = {}\n  ) {\n    this.box = new Box(box)\n    this.options = new DrawBoxOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { boxColor, lineWidth } = this.options\n\n    const { x, y, width, height } = this.box\n    ctx.strokeStyle = boxColor\n    ctx.lineWidth = lineWidth\n    ctx.strokeRect(x, y, width, height)\n\n    const { label } = this.options\n    if (label) {\n      new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\n    }\n  }\n}", "import { Box, IBoundingBox, IRect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { round } from '../utils';\nimport { DrawBox } from './DrawBox';\n\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\n\nexport function drawDetections(\n  canvasArg: string | HTMLCanvasElement,\n  detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\n) {\n  const detectionsArray = Array.isArray(detections) ? detections : [detections]\n\n  detectionsArray.forEach(det => {\n    const score = det instanceof FaceDetection\n      ? det.score\n      : (isWithFaceDetection(det) ? det.detection.score : undefined)\n\n    const box = det instanceof FaceDetection\n      ? det.box\n      : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\n\n    const label = score ? `${round(score)}` : undefined\n    new DrawBox(box, { label }).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { env } from '../env';\n\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\n\n  const { Image, Video } = env.getEnv()\n\n  return (media instanceof Image && media.complete)\n    || (media instanceof Video && media.readyState >= 3)\n}\n", "import { env } from '../env';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\n\n  return new Promise((resolve, reject) => {\n    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\n      return resolve(null)\n    }\n\n    function onLoad(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      resolve(e)\n    }\n\n    function onError(e: Event) {\n      if (!e.currentTarget) return\n      e.currentTarget.removeEventListener('load', onLoad)\n      e.currentTarget.removeEventListener('error', onError)\n      reject(e)\n    }\n\n    media.addEventListener('load', onLoad)\n    media.addEventListener('error', onError)\n  })\n}", "import { env } from '../env';\n\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\n  return new Promise((resolve, reject) => {\n    if (!(buf instanceof Blob)) {\n      return reject('bufferToImage - expected buf to be of type: Blob')\n    }\n\n    const reader = new FileReader()\n    reader.onload = () => {\n      if (typeof reader.result !== 'string') {\n        return reject('bufferToImage - expected reader.result to be a string, in onload')\n      }\n\n      const img = env.getEnv().createImageElement()\n      img.onload = () => resolve(img)\n      img.onerror = reject\n      img.src = reader.result\n    }\n    reader.onerror = reject\n    reader.readAsDataURL(buf)\n  })\n}", "import { Dimensions, IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\n\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\n\n  const { Image, Video } = env.getEnv()\n\n  if (input instanceof Image) {\n    return new Dimensions(input.naturalWidth, input.naturalHeight)\n  }\n  if (input instanceof Video) {\n    return new Dimensions(input.videoWidth, input.videoHeight)\n  }\n  return new Dimensions(input.width, input.height)\n}\n", "import { IDimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\nimport { isMediaLoaded } from './isMediaLoaded';\n\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\n\n  const { createCanvasElement } = env.getEnv()\n  const canvas = createCanvasElement()\n  canvas.width = width\n  canvas.height = height\n  return canvas\n}\n\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\n\n  const { ImageData } = env.getEnv()\n\n  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\n    throw new Error('createCanvasFromMedia - media has not finished loading yet')\n  }\n\n  const { width, height } = dims || getMediaDimensions(media)\n  const canvas = createCanvas({ width, height })\n\n  if (media instanceof ImageData) {\n    getContext2dOrThrow(canvas).putImageData(media, 0, 0)\n  } else {\n    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\n  }\n  return canvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { env } from '../env';\nimport { isTensor4D } from '../utils';\n\nexport async function imageTensorToCanvas(\n  imgTensor: tf.Tensor,\n  canvas?: HTMLCanvasElement\n): Promise<HTMLCanvasElement> {\n\n  const targetCanvas = canvas || env.getEnv().createCanvasElement()\n\n  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\n  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\n  await tf.browser.toPixels(imgTensor3D, targetCanvas)\n\n  imgTensor3D.dispose()\n\n  return targetCanvas\n}", "import { env } from '../env';\n\nexport function isMediaElement(input: any) {\n\n  const { Image, Canvas, Video } = env.getEnv()\n\n  return input instanceof Image\n    || input instanceof Canvas\n    || input instanceof Video\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { env } from '../env';\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\n\n  const { Image, Canvas } = env.getEnv()\n\n  if (!(input instanceof Image || input instanceof Canvas)) {\n    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\n  }\n\n  const dims = getMediaDimensions(input)\n  const scale = inputSize / Math.max(dims.height, dims.width)\n  const width = scale * dims.width\n  const height = scale * dims.height\n\n  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\n  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\n\n  const offset = Math.abs(width - height) / 2\n  const dx = centerImage && width < height ? offset : 0\n  const dy = centerImage && height < width ? offset : 0\n  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\n\n  return targetCanvas\n}", "import * as tf from '@tensorflow/tfjs-core';\n\nimport { Dimensions } from '../classes/Dimensions';\nimport { env } from '../env';\nimport { padToSquare } from '../ops/padToSquare';\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\nimport { createCanvasFromMedia } from './createCanvas';\nimport { imageToSquare } from './imageToSquare';\nimport { TResolvedNetInput } from './types';\n\nexport class NetInput {\n  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\n  private _canvases: HTMLCanvasElement[] = []\n  private _batchSize: number\n  private _treatAsBatchInput: boolean = false\n\n  private _inputDimensions: number[][] = []\n  private _inputSize: number\n\n  constructor(\n    inputs: Array<TResolvedNetInput>,\n    treatAsBatchInput: boolean = false\n  ) {\n    if (!Array.isArray(inputs)) {\n      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\n    }\n\n    this._treatAsBatchInput = treatAsBatchInput\n    this._batchSize = inputs.length\n\n    inputs.forEach((input, idx) => {\n\n      if (isTensor3D(input)) {\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = input.shape\n        return\n      }\n\n      if (isTensor4D(input)) {\n        const batchSize = (input as any).shape[0]\n        if (batchSize !== 1) {\n          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n        }\n\n        this._imageTensors[idx] = input\n        this._inputDimensions[idx] = (input as any).shape.slice(1)\n        return\n      }\n\n      const canvas = (input as any) instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\n      this._canvases[idx] = canvas\n      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\n    })\n  }\n\n  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\n    return this._imageTensors\n  }\n\n  public get canvases(): HTMLCanvasElement[] {\n    return this._canvases\n  }\n\n  public get isBatchInput(): boolean {\n    return this.batchSize > 1 || this._treatAsBatchInput\n  }\n\n  public get batchSize(): number {\n    return this._batchSize\n  }\n\n  public get inputDimensions(): number[][] {\n    return this._inputDimensions\n  }\n\n  public get inputSize(): number | undefined {\n    return this._inputSize\n  }\n\n  public get reshapedInputDimensions(): Dimensions[] {\n    return range(this.batchSize, 0, 1).map(\n      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\n    )\n  }\n\n  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {\n    return this.canvases[batchIdx] || this.imageTensors[batchIdx]\n  }\n\n  public getInputDimensions(batchIdx: number): number[] {\n    return this._inputDimensions[batchIdx]\n  }\n\n  public getInputHeight(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][0]\n  }\n\n  public getInputWidth(batchIdx: number): number {\n    return this._inputDimensions[batchIdx][1]\n  }\n\n  public getReshapedInputDimensions(batchIdx: number): Dimensions {\n    if (typeof this.inputSize !== 'number') {\n      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\n    }\n\n    const width = this.getInputWidth(batchIdx)\n    const height = this.getInputHeight(batchIdx)\n    return computeReshapedDimensions({ width, height }, this.inputSize)\n  }\n\n  /**\n   * Create a batch tensor from all input canvases and tensors\n   * with size [batchSize, inputSize, inputSize, 3].\n   *\n   * @param inputSize Height and width of the tensor.\n   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\n   * both sides of the minor dimension oof the image.\n   * @returns The batch tensor.\n   */\n  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\n\n    this._inputSize = inputSize\n\n    return tf.tidy(() => {\n\n      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\n        const input = this.getInput(batchIdx)\n\n        if (input instanceof tf.Tensor) {\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\n          // @ts-ignore: error TS2344: Type 'Rank.R4' does not satisfy the constraint 'Tensor<Rank>'.\n          imgTensor = padToSquare(imgTensor, isCenterInputs)\n\n          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\n            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\n          }\n\n          return imgTensor.as3D(inputSize, inputSize, 3)\n        }\n\n        if (input instanceof env.getEnv().Canvas) {\n          return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\n        }\n\n        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\n      })\n\n      // const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\n      const batchTensor = tf.stack(inputTensors.map(t => tf.cast(t, 'float32'))).as4D(this.batchSize, inputSize, inputSize, 3)\n      // const batchTensor = tf.stack(inputTensors.map(t => tf.Tensor.as4D(tf.cast(t, 'float32'))), this.batchSize, inputSize, inputSize, 3);\n      \n      return batchTensor\n    })\n  }\n}", "import { isTensor3D, isTensor4D } from '../utils';\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\nimport { isMediaElement } from './isMediaElement';\nimport { NetInput } from './NetInput';\nimport { resolveInput } from './resolveInput';\nimport { TNetInput } from './types';\n\n/**\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\n * to be finished loading.\n *\n * @param input The input, which can be a media element or an array of different media elements.\n * @returns A NetInput instance, which can be passed into one of the neural networks.\n */\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\n  if (inputs instanceof NetInput) {\n    return inputs\n  }\n\n  let inputArgArray = Array.isArray(inputs)\n      ? inputs\n      : [inputs]\n\n  if (!inputArgArray.length) {\n    throw new Error('toNetInput - empty array passed as input')\n  }\n\n  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\n\n  const inputArray = inputArgArray.map(resolveInput)\n\n  inputArray.forEach((input, i) => {\n    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\n\n      if (typeof inputArgArray[i] === 'string') {\n        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\n      }\n\n      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\n    }\n\n    if (isTensor4D(input)) {\n      // if tf.Tensor4D is passed in the input array, the batch size has to be 1\n      const batchSize = input.shape[0]\n      if (batchSize !== 1) {\n        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\n      }\n    }\n  })\n\n  // wait for all media elements being loaded\n  await Promise.all(\n    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\n  )\n\n  return new NetInput(inputArray, Array.isArray(inputs))\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { Rect } from '../classes/Rect';\nimport { env } from '../env';\nimport { createCanvas } from './createCanvas';\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\nimport { toNetInput } from './toNetInput';\nimport { TNetInput } from './types';\n\n/**\n * Extracts the image regions containing the detected faces.\n *\n * @param input The image that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns The Canvases of the corresponding image region for each detected face.\n */\nexport async function extractFaces(\n  input: TNetInput,\n  detections: Array<FaceDetection | Rect>\n): Promise<HTMLCanvasElement[]> {\n\n  const { Canvas } = env.getEnv()\n\n  let canvas = input as HTMLCanvasElement\n\n  if (!(input instanceof Canvas)) {\n    const netInput = await toNetInput(input)\n\n    if (netInput.batchSize > 1) {\n      throw new Error('extractFaces - batchSize > 1 not supported')\n    }\n\n    const tensorOrCanvas = netInput.getInput(0)\n    canvas = tensorOrCanvas instanceof Canvas\n      ? tensorOrCanvas\n      : await imageTensorToCanvas(tensorOrCanvas)\n  }\n\n  const ctx = getContext2dOrThrow(canvas)\n  const boxes = detections.map(\n    det => det instanceof FaceDetection\n      ? det.forSize(canvas.width, canvas.height).box.floor()\n      : det\n  )\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\n\n  return boxes.map(({ x, y, width, height }) => {\n    const faceImg = createCanvas({ width, height })\n    getContext2dOrThrow(faceImg)\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\n    return faceImg\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { isTensor3D, isTensor4D } from '../utils';\n\n/**\n * Extracts the tensors of the image regions containing the detected faces.\n * Useful if you want to compute the face descriptors for the face images.\n * Using this method is faster then extracting a canvas for each face and\n * converting them to tensors individually.\n *\n * @param imageTensor The image tensor that face detection has been performed on.\n * @param detections The face detection results or face bounding boxes for that image.\n * @returns Tensors of the corresponding image region for each detected face.\n */\nexport async function extractFaceTensors(\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\n  detections: Array<FaceDetection | Rect>\n): Promise<tf.Tensor3D[]> {\n\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\n  }\n\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\n  }\n\n  return tf.tidy(() => {\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\n\n    const boxes = detections.map(\n      det => det instanceof FaceDetection\n        ? det.forSize(imgWidth, imgHeight).box\n        : det\n    )\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\n\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\n    )\n\n    return faceTensors\n  })\n}", "import { env } from '../env';\n\nexport async function fetchOrThrow(\n  url: string,\n  init?: RequestInit\n): Promise<Response> {\n\n  const fetch = env.getEnv().fetch\n  const res = await fetch(url, init)\n  if (!(res.status < 400)) {\n    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\n  }\n  return res\n}", "import { bufferToImage } from './bufferToImage';\nimport { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\n  const res = await fetchOrThrow(uri)\n  const blob = await (res).blob()\n\n  if (!blob.type.startsWith('image/')) {\n    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\n  }\n  return bufferToImage(blob)\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchJson<T>(uri: string): Promise<T> {\n  return (await fetchOrThrow(uri)).json()\n}\n", "import { fetchOrThrow } from './fetchOrThrow';\n\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\n  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "export function getModelUris(uri: string | undefined, defaultModelName: string) {\n  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\n\n  if (!uri) {\n    return {\n      modelBaseUri: '',\n      manifestUri: defaultManifestFilename\n    }\n  }\n\n  if (uri === '/') {\n    return {\n      modelBaseUri: '/',\n      manifestUri: `/${defaultManifestFilename}`\n    }\n  }\n  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\n  uri = uri.replace(protocol, '');\n\n  const parts = uri.split('/').filter(s => s)\n\n  const manifestFile = uri.endsWith('.json')\n    ? parts[parts.length - 1]\n    : defaultManifestFilename\n\n  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\n  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\n\n  return {\n    modelBaseUri,\n    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { getModelUris } from '../common/getModelUris';\nimport { fetchJson } from './fetchJson';\n\nexport async function loadWeightMap(\n  uri: string | undefined,\n  defaultModelName: string,\n): Promise<tf.NamedTensorMap> {\n  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\n  let manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\n  // if (manifest['weightsManifest']) manifest = manifest['weightsManifest'];\n  return tf.io.loadWeights(manifest, modelBaseUri)\n}", "import { IDimensions } from '../classes';\nimport { getMediaDimensions } from './getMediaDimensions';\n\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\n  const { width, height } = useMediaDimensions\n    ? getMediaDimensions(reference)\n    : reference\n  input.width = width\n  input.height = height\n  return { width, height }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ParamMapping } from './common';\nimport { getModelUris } from './common/getModelUris';\nimport { loadWeightMap } from './dom';\nimport { env } from './env';\n\nexport abstract class NeuralNetwork<TNetParams> {\n\n  protected _params: TNetParams | undefined = undefined\n  protected _paramMappings: ParamMapping[] = []\n\n  constructor(protected _name: string) {\n    /*\n    try {\n      const testTensor = tf.tensor([0]);\n      testTensor.toFloat();\n    } catch (err) {\n      throw new Error(`tfjs module not loaded: load '@tensorflow/tfjs' or '@tensorflow/tfjs-core' with appropriate backend explicitly`);\n    }\n    */\n  }\n\n  public get params(): TNetParams | undefined { return this._params }\n  public get paramMappings(): ParamMapping[] { return this._paramMappings }\n  public get isLoaded(): boolean { return !!this.params }\n\n  public getParamFromPath(paramPath: string): tf.Tensor {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    return obj[objProp]\n  }\n\n  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\n    const { obj, objProp } = this.traversePropertyPath(paramPath)\n    obj[objProp].dispose()\n    obj[objProp] = tensor\n  }\n\n  public getParamList() {\n    return this._paramMappings.map(({ paramPath }) => ({\n      path: paramPath,\n      tensor: this.getParamFromPath(paramPath)\n    }))\n  }\n\n  public getTrainableParams() {\n    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\n  }\n\n  public getFrozenParams() {\n    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\n  }\n\n  public variable() {\n    this.getFrozenParams().forEach(({ path, tensor }) => {\n      this.reassignParamFromPath(path, tensor.variable())\n    })\n  }\n\n  public freeze() {\n    this.getTrainableParams().forEach(({ path, tensor: variable }) => {\n      const tensor = tf.tensor(variable.dataSync())\n      variable.dispose()\n      this.reassignParamFromPath(path, tensor)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.getParamList().forEach(param => {\n      if (throwOnRedispose && param.tensor.isDisposed) {\n        throw new Error(`param tensor has already been disposed for path ${param.path}`)\n      }\n      param.tensor.dispose()\n    })\n    this._params = undefined\n  }\n\n  public serializeParams(): Float32Array {\n    return new Float32Array(\n      this.getParamList()\n        .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\n        .reduce((flat, arr) => flat.concat(arr))\n    )\n  }\n\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\n    if (weightsOrUrl instanceof Float32Array) {\n      this.extractWeights(weightsOrUrl)\n      return\n    }\n\n    await this.loadFromUri(weightsOrUrl)\n  }\n\n  public async loadFromUri(uri: string | undefined) {\n    if (uri && typeof uri !== 'string') {\n      throw new Error(`${this._name}.loadFromUri - expected model uri`)\n    }\n\n    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public async loadFromDisk(filePath: string | undefined) {\n    if (filePath && typeof filePath !== 'string') {\n      throw new Error(`${this._name}.loadFromDisk - expected model file path`)\n    }\n\n    const { readFile } = env.getEnv()\n\n    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\n\n    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\n      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\n    )\n    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n    const manifest = JSON.parse((await readFile(manifestUri)).toString())\n    const weightMap = await loadWeights(manifest, modelBaseUri)\n\n    this.loadFromWeightMap(weightMap)\n  }\n\n  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParamsFromWeigthMap(weightMap)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  public extractWeights(weights: Float32Array) {\n    const {\n      paramMappings,\n      params\n    } = this.extractParams(weights)\n\n    this._paramMappings = paramMappings\n    this._params = params\n  }\n\n  private traversePropertyPath(paramPath: string) {\n    if (!this.params) {\n      throw new Error(`traversePropertyPath - model has no loaded params`)\n    }\n\n    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\n      if (!res.nextObj.hasOwnProperty(objProp)) {\n        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\n      }\n\n      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\n    }, { nextObj: this.params })\n\n    const { obj, objProp } = result\n    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\n      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\n    }\n\n    return { obj, objProp }\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\n  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from './types';\n\nexport function depthwiseSeparableConv(\n  x: tf.Tensor4D,\n  params: SeparableConvParams,\n  stride: [number, number]\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\n    out = tf.add(out, params.bias)\n    return out\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, SeparableConvParams } from '../common';\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function denseBlock3(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock3Params,\n  isFirstLayer: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n  })\n}\n\nexport function denseBlock4(\n  x: tf.Tensor4D,\n  denseBlockParams: DenseBlock4Params,\n  isFirstLayer: boolean = false,\n  isScaleDown: boolean = true\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out1 = tf.relu(\n      isFirstLayer\n        ? tf.add(\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\n          denseBlockParams.conv0.bias\n        )\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\n    ) as tf.Tensor4D\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\n\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\n\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\n\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function convLayer(\n  x: tf.Tensor4D,\n  params: ConvParams,\n  padding: 'valid' | 'same' = 'same',\n  withRelu: boolean = false\n): tf.Tensor4D {\n  return tf.tidy(() => {\n    const out = tf.add(\n      tf.conv2d(x, params.filters, [1, 1], padding),\n      params.bias\n    ) as tf.Tensor4D\n\n    return withRelu ? tf.relu(out) : out\n  })\n}", "import { ParamMapping } from './types';\n\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\n  Object.keys(weightMap).forEach(path => {\n    if (!paramMappings.some(pm => pm.originalPath === path)) {\n      weightMap[path].dispose()\n    }\n  })\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\n\nexport function extractConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\n\n\nexport function extractFCParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): FCParams {\n\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return {\n      weights: fc_weights,\n      bias: fc_bias\n    }\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\n\nexport type ParamMapping = {\n  originalPath?: string\n  paramPath: string\n}\n\nexport type ConvParams = {\n  filters: tf.Tensor4D\n  bias: tf.Tensor1D\n}\n\nexport type FCParams = {\n  weights: tf.Tensor2D\n  bias: tf.Tensor1D\n}\n\nexport class SeparableConvParams {\n  constructor(\n    public depthwise_filter: tf.Tensor4D,\n    public pointwise_filter: tf.Tensor4D,\n    public bias: tf.Tensor1D\n  ) {}\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\n\nexport function extractSeparableConvParamsFactory(\n  extractWeights: ExtractWeightsFunction,\n  paramMappings: ParamMapping[]\n) {\n\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n\nexport function loadSeparableConvParamsFactory(\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\n) {\n\n  return function (prefix: string): SeparableConvParams {\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return new SeparableConvParams(\n      depthwise_filter,\n      pointwise_filter,\n      bias\n    )\n  }\n\n}\n", "import { isTensor } from '../utils';\nimport { ParamMapping } from './types';\n\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\n    const tensor = weightMap[originalPath]\n\n    if (!isTensor(tensor, paramRank)) {\n      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\n    }\n\n    paramMappings.push(\n      { originalPath, paramPath: mappedPath || originalPath }\n    )\n\n    return tensor\n  }\n\n}\n", "export function extractWeightsFactory(weights: Float32Array) {\n  let remainingWeights = weights\n\n  function extractWeights(numWeights: number): Float32Array {\n    const ret = remainingWeights.slice(0, numWeights)\n    remainingWeights = remainingWeights.slice(numWeights)\n    return ret\n  }\n\n  function getRemainingWeights(): Float32Array {\n    return remainingWeights\n  }\n\n  return {\n    extractWeights,\n    getRemainingWeights\n  }\n}", "import {\n  extractConvParamsFactory,\n  extractSeparableConvParamsFactory,\n  ExtractWeightsFunction,\n  ParamMapping,\n} from '../common';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n\n    const conv0 = isFirstLayer\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\n\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock4Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2, dense3 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from './types';\n\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\n  return function(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n\n    return { filters, bias }\n  }\n}", "import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\n\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n\n    return { conv0, conv1, conv2 }\n  }\n\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\n    const conv0 = isFirstLayer\n      ? extractConvParams(`${prefix}/conv0`)\n      : extractSeparableConvParams(`${prefix}/conv0`)\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\n\n    return { conv0, conv1, conv2, conv3 }\n  }\n\n  return {\n    extractDenseBlock3Params,\n    extractDenseBlock4Params\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { FaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock4Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock4Params('dense0', true),\n    dense1: extractDenseBlock4Params('dense1'),\n    dense2: extractDenseBlock4Params('dense2'),\n    dense3: extractDenseBlock4Params('dense3')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock4 } from './denseBlock';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\n\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\n\n  constructor() {\n    super('FaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock4(normalized, params.dense0, true)\n      out = denseBlock4(out, params.dense1)\n      out = denseBlock4(out, params.dense2)\n      out = denseBlock4(out, params.dense3)\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from './types';\n\nexport function fullyConnectedLayer(\n  x: tf.Tensor2D,\n  params: FCParams\n): tf.Tensor2D {\n  return tf.tidy(() =>\n    tf.add(\n      tf.matMul(x, params.weights),\n      params.bias\n    )\n  )\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: extractFcParams('fc')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\n\n  const featureExtractorMap: tf.NamedTensorMap = {}\n  const classifierMap: tf.NamedTensorMap = {}\n\n  Object.keys(weightMap).forEach(key => {\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\n    map[key] = weightMap[key]\n  })\n\n  return { featureExtractorMap, classifierMap }\n\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { NetInput } from '../dom';\nimport {\n  FaceFeatureExtractorParams,\n  IFaceFeatureExtractor,\n  TinyFaceFeatureExtractorParams,\n} from '../faceFeatureExtractor/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { NetParams } from './types';\nimport { seperateWeightMaps } from './util';\n\nexport abstract class FaceProcessor<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends NeuralNetwork<NetParams> {\n\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\n\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\n    super(_name)\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\n    return this._faceFeatureExtractor\n  }\n\n  protected abstract getDefaultModelName(): string\n  protected abstract getClassifierChannelsIn(): number\n  protected abstract getClassifierChannelsOut(): number\n\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\n    })\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const cIn = this.getClassifierChannelsIn()\n    const cOut = this.getClassifierChannelsOut()\n    const classifierWeightSize = (cOut * cIn ) + cOut\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\n\nexport class FaceExpressions {\n  public neutral: number\n  public happy: number\n  public sad: number\n  public angry: number\n  public fearful: number\n  public disgusted: number\n  public surprised: number\n\n  constructor(probabilities: number[] | Float32Array) {\n    if (probabilities.length !== 7) {\n      throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\n    }\n\n    FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\n      this[expression] = probabilities[idx]\n    })\n  }\n\n  asSortedArray() {\n    return FACE_EXPRESSION_LABELS\n      .map(expression => ({ expression, probability: this[expression] as number }))\n      .sort((e0, e1) => e1.probability - e0.probability)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { FaceExpressions } from './FaceExpressions';\n\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceExpressionNet', faceFeatureExtractor)\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictExpressions(input: TNetInput) {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\n      const data = await t.data()\n      t.dispose()\n      return data\n    }))\n    out.dispose()\n\n    const predictionsByBatch = probabilitesByBatch\n      .map(probabilites => new FaceExpressions(probabilites as Float32Array))\n\n    return netInput.isBatchInput\n      ? predictionsByBatch\n      : predictionsByBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_expression_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 7\n  }\n}", "import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\n\nexport type WithFaceExpressions<TSource> = TSource & {\n  expressions: FaceExpressions\n}\n\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\n  return obj['expressions'] instanceof FaceExpressions\n}\n\nexport function extendWithFaceExpressions<\n  TSource\n> (\n  sourceObj: TSource,\n  expressions: FaceExpressions\n): WithFaceExpressions<TSource> {\n\n  const extension = { expressions }\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint, Point } from '../classes';\nimport { FaceExpressions } from '../faceExpressionNet';\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { round } from '../utils';\nimport { DrawTextField } from './DrawTextField';\n\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\n\nexport function drawFaceExpressions(\n  canvasArg: string | HTMLCanvasElement,\n  faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\n  minConfidence = 0.1,\n  textFieldAnchor?: IPoint\n) {\n  const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\n\n  faceExpressionsArray.forEach(e => {\n    const expr = e instanceof FaceExpressions\n      ? e\n      : (isWithFaceExpressions(e) ? e.expressions : undefined)\n    if (!expr) {\n      throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\n    }\n\n    const sorted = expr.asSortedArray()\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\n\n    const anchor = isWithFaceDetection(e)\n      ? e.detection.box.bottomLeft\n      : (textFieldAnchor || new Point(0, 0))\n\n    const drawTextField = new DrawTextField(\n      resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\n      anchor\n    )\n    drawTextField.draw(canvasArg)\n  })\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\n\nexport type WithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> = TSource & {\n  landmarks: TFaceLandmarks\n  unshiftedLandmarks: TFaceLandmarks\n  alignedRect: FaceDetection\n}\n\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\n  return isWithFaceDetection(obj)\n    && obj['landmarks'] instanceof FaceLandmarks\n    && obj['unshiftedLandmarks'] instanceof FaceLandmarks\n    && obj['alignedRect'] instanceof FaceDetection\n}\n\nexport function extendWithFaceLandmarks<\n  TSource extends WithFaceDetection<{}>,\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\n> (\n  sourceObj: TSource,\n  unshiftedLandmarks: TFaceLandmarks\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\n\n  const { box: shift } = sourceObj.detection\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\n\n  const rect = landmarks.align()\n  const { imageDims } = sourceObj.detection\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\n\n  const extension = {\n    landmarks,\n    unshiftedLandmarks,\n    alignedRect\n  }\n\n  return Object.assign({}, sourceObj, extension)\n}", "import { IPoint } from '../classes';\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { drawContour } from './drawContour';\n\nexport interface IDrawFaceLandmarksOptions {\n  drawLines?: boolean\n  drawPoints?: boolean\n  lineWidth?: number\n  pointSize?: number\n  lineColor?: string\n  pointColor?: string\n}\n\nexport class DrawFaceLandmarksOptions {\n  public drawLines: boolean\n  public drawPoints: boolean\n  public lineWidth: number\n  public pointSize: number\n  public lineColor: string\n  public pointColor: string\n\n  constructor(options: IDrawFaceLandmarksOptions = {}) {\n    const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\n    this.drawLines = drawLines\n    this.drawPoints = drawPoints\n    this.lineWidth = lineWidth || 1\n    this.pointSize = pointSize || 2\n    this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\n    this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\n  }\n}\n\nexport class DrawFaceLandmarks {\n  public faceLandmarks: FaceLandmarks\n  public options: DrawFaceLandmarksOptions\n\n  constructor(\n    faceLandmarks: FaceLandmarks,\n    options: IDrawFaceLandmarksOptions = {}\n  ) {\n    this.faceLandmarks = faceLandmarks\n    this.options = new DrawFaceLandmarksOptions(options)\n  }\n\n  draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\n    const ctx = getContext2dOrThrow(canvasArg)\n\n    const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\n\n    if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\n      ctx.strokeStyle = lineColor\n      ctx.lineWidth = lineWidth\n      drawContour(ctx, this.faceLandmarks.getJawOutline())\n      drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\n      drawContour(ctx, this.faceLandmarks.getNose())\n      drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\n      drawContour(ctx, this.faceLandmarks.getRightEye(), true)\n      drawContour(ctx, this.faceLandmarks.getMouth(), true)\n    }\n\n    if (drawPoints) {\n      ctx.strokeStyle = pointColor\n      ctx.fillStyle = pointColor\n\n      const drawPoint = (pt: IPoint) => {\n        ctx.beginPath()\n        ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\n        ctx.fill()\n      }\n      this.faceLandmarks.positions.forEach(drawPoint)\n    }\n  }\n}\n\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\n\nexport function drawFaceLandmarks(\n  canvasArg: string | HTMLCanvasElement,\n  faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\n) {\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\n  faceLandmarksArray.forEach(f => {\n    const landmarks = f instanceof FaceLandmarks\n      ? f\n      : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\n    if (!landmarks) {\n      throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\n    }\n\n    new DrawFaceLandmarks(landmarks).draw(canvasArg)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { entry_flow, middle_flow, exit_flow }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport {\n  disposeUnusedWeightTensors,\n  extractWeightEntryFactory,\n  loadSeparableConvParamsFactory,\n  ParamMapping,\n} from '../common';\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\nimport { range } from '../utils';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  const extractConvParams = loadConvParamsFactory(extractWeightEntry)\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\n\n    return { separable_conv0, separable_conv1, expansion_conv }\n  }\n\n  function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\n\n    const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\n    const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\n    const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\n\n    return { separable_conv0, separable_conv1, separable_conv2 }\n  }\n\n  return {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  numMainBlocks: number\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractSeparableConvParams,\n    extractReductionBlockParams,\n    extractMainBlockParams\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\n  const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\n  const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\n\n  const entry_flow = {\n    conv_in: entry_flow_conv_in,\n    reduction_block_0: entry_flow_reduction_block_0,\n    reduction_block_1: entry_flow_reduction_block_1\n  }\n\n  const middle_flow = {}\n  range(numMainBlocks, 0, 1).forEach((idx) => {\n    middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\n  })\n\n  const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\n  const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\n\n  const exit_flow = {\n    reduction_block: exit_flow_reduction_block,\n    separable_conv: exit_flow_separable_conv\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, depthwiseSeparableConv } from '../common';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { range } from '../utils';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\n\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\n  return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\n}\n\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\n  let out = isActivateInput ? tf.relu(x) : x\n  out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out),  params.separable_conv1, [1, 1])\n  out = tf.maxPool(out, [3, 3], [2, 2], 'same')\n  out = tf.add(out, conv(x,  params.expansion_conv, [2, 2]))\n  return out\n}\n\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\n  let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\n  out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\n  out = tf.add(out, x)\n  return out\n}\n\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\n\n  private _numMainBlocks: number\n\n  constructor(numMainBlocks: number) {\n    super('TinyXception')\n    this._numMainBlocks = numMainBlocks\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyXception - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\n      out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\n      out = reductionBlock(out, params.entry_flow.reduction_block_1)\n\n      range(this._numMainBlocks, 0, 1).forEach((idx) => {\n        out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\n      })\n\n      out = reductionBlock(out, params.exit_flow.reduction_block)\n      out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_xception_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights, this._numMainBlocks)\n  }\n}", "import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\n\n  const age = extractFCParams(512, 1, 'fc/age')\n  const gender = extractFCParams(512, 2, 'fc/gender')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { fc: { age, gender } }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\nimport { NetParams } from './types';\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractFcParams(prefix: string): FCParams {\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { weights, bias }\n  }\n\n  const params = {\n    fc: {\n      age: extractFcParams('fc/age'),\n      gender: extractFcParams('fc/gender')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FCParams } from '../common';\n\nexport type AgeAndGenderPrediction = {\n  age: number\n  gender: Gender\n  genderProbability: number\n}\n\nexport enum Gender {\n  FEMALE = 'female',\n  MALE = 'male'\n}\n\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\n\nexport type NetParams = {\n  fc: {\n    age: FCParams\n    gender: FCParams\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\nimport { seperateWeightMaps } from '../faceProcessor/util';\nimport { TinyXception } from '../xception/TinyXception';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\n\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\n\n  private _faceFeatureExtractor: TinyXception\n\n  constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\n    super('AgeGenderNet')\n    this._faceFeatureExtractor = faceFeatureExtractor\n  }\n\n  public get faceFeatureExtractor(): TinyXception {\n    return this._faceFeatureExtractor\n  }\n\n  public runNet(input: NetInput | tf.Tensor4D): NetOutput {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error(`${this._name} - load model before inference`)\n    }\n\n    return tf.tidy(() => {\n      const bottleneckFeatures = input instanceof NetInput\n        ? this.faceFeatureExtractor.forwardInput(input)\n        : input\n\n      const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\n      const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\n      const gender = fullyConnectedLayer(pooled, params.fc.gender)\n      return { age, gender }\n    })\n  }\n\n  public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\n    return tf.tidy(() => {\n      const { age, gender } = this.runNet(input)\n      return { age, gender: tf.softmax(gender) }\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<NetOutput> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput)\n\n    const ages = tf.unstack(out.age)\n    const genders = tf.unstack(out.gender)\n    const ageAndGenderTensors = ages.map((ageTensor, i) => ({\n      ageTensor,\n      genderTensor: genders[i]\n    }))\n\n    const predictionsByBatch = await Promise.all(\n      ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\n        const age = (await ageTensor.data())[0]\n        const probMale = (await genderTensor.data())[0]\n        const isMale = probMale > 0.5\n        const gender = isMale ? Gender.MALE : Gender.FEMALE\n        const genderProbability = isMale ? probMale : (1 - probMale)\n\n        ageTensor.dispose()\n        genderTensor.dispose()\n        return { age, gender, genderProbability }\n      })\n    )\n    out.age.dispose()\n    out.gender.dispose()\n\n    return netInput.isBatchInput ? predictionsByBatch as AgeAndGenderPrediction[] : predictionsByBatch[0] as AgeAndGenderPrediction\n  }\n\n  protected getDefaultModelName(): string {\n    return 'age_gender_model'\n  }\n\n  public dispose(throwOnRedispose: boolean = true) {\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\n    super.dispose(throwOnRedispose)\n  }\n\n  public loadClassifierParams(weights: Float32Array) {\n    const { params, paramMappings } = this.extractClassifierParams(weights)\n    this._params = params\n    this._paramMappings = paramMappings\n  }\n\n  public extractClassifierParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\n\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\n\n    return extractParamsFromWeigthMap(classifierMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n\n    const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\n\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\n\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\n    return this.extractClassifierParams(classifierWeights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { IDimensions, Point } from '../classes';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\nimport { isEven } from '../utils';\n\nexport abstract class FaceLandmark68NetBase<\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\n>\n  extends FaceProcessor<TExtractorParams> {\n\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\n\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\n      const scale = inputSize / Math.max(height, width)\n      return {\n        width: width * scale,\n        height: height * scale\n      }\n    })\n\n    const batchSize = inputDimensions.length\n\n    return tf.tidy(() => {\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\n        tf.stack([tf.fill([68], fillX, 'float32'), tf.fill([68], fillY, 'float32')], 1).as2D(1, 136).as1D()\n\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\n        const { width, height } = inputDimensions[batchIdx]\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\n      }\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\n\n      const landmarkTensors = output\n        .mul(tf.fill([batchSize, 136], inputSize, 'float32'))\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            getPaddingX(batchIdx),\n            getPaddingY(batchIdx)\n          )\n        )))\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\n          createInterleavedTensor(\n            inputDimensions[batchIdx].width,\n            inputDimensions[batchIdx].height\n          )\n        )))\n\n      return landmarkTensors as tf.Tensor2D\n    })\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n    return tf.tidy(() => {\n      const out = this.runNet(input)\n      return this.postProcess(\n        out,\n        input.inputSize as number,\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\n      )\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\n    const netInput = await toNetInput(input)\n    const landmarkTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\n      async (landmarkTensor, batchIdx) => {\n        const landmarksArray = Array.from(await landmarkTensor.data())\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\n\n        return new FaceLandmarks68(\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i] as number, yCoords[i] as number)),\n          {\n            height: netInput.getInputHeight(batchIdx),\n            width : netInput.getInputWidth(batchIdx),\n          }\n        )\n      }\n    ))\n\n    landmarkTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput ? landmarksForBatch as FaceLandmarks68[] : landmarksForBatch[0] as FaceLandmarks68;\n  }\n\n  protected getClassifierChannelsOut(): number {\n    return 136\n  }\n}", "import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\n    super('FaceLandmark68Net', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 256\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\nimport { loadParamsFactory } from './loadParamsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\nexport function extractParamsFromWeigthMapTiny(\n  weightMap: tf.NamedTensorMap\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractDenseBlock3Params\n  } = loadParamsFactory(weightMap, paramMappings)\n\n  const params = {\n    dense0: extractDenseBlock3Params('dense0', true),\n    dense1: extractDenseBlock3Params('dense1'),\n    dense2: extractDenseBlock3Params('dense2')\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import { extractWeightsFactory, ParamMapping } from '../common';\nimport { extractorsFactory } from './extractorsFactory';\nimport { TinyFaceFeatureExtractorParams } from './types';\n\n\n\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractDenseBlock3Params\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    paramMappings,\n    params: { dense0, dense1, dense2 }\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { denseBlock3 } from './denseBlock';\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\nimport { extractParamsTiny } from './extractParamsTiny';\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\n\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\n\n  constructor() {\n    super('TinyFaceFeatureExtractor')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      const batchTensor = tf.cast(input.toBatchTensor(112, true), 'float32');\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\n\n      let out = denseBlock3(normalized, params.dense0, true)\n      out = denseBlock3(out, params.dense1)\n      out = denseBlock3(out, params.dense2)\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\n\n      return out\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_feature_extractor_tiny_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMapTiny(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParamsTiny(weights)\n  }\n}", "import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\n\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\n\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_landmark_68_tiny_model'\n  }\n\n  protected getClassifierChannelsIn(): number {\n    return 128\n  }\n}", "import { FaceLandmark68Net } from './FaceLandmark68Net';\n\nexport * from './FaceLandmark68Net';\nexport * from './FaceLandmark68TinyNet';\n\nexport class FaceLandmarkNet extends FaceLandmark68Net {}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ScaleLayerParams } from './types';\n\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\n  return tf.add(tf.mul(x, params.weights), params.biases)\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { scale } from './scaleLayer';\nimport { ConvLayerParams } from './types';\n\n\nfunction convLayer(\n  x: tf.Tensor4D,\n  params: ConvLayerParams,\n  strides: [number, number],\n  withRelu: boolean,\n  padding: 'valid' | 'same' = 'same'\n): tf.Tensor4D {\n  const { filters, bias } = params.conv\n\n  let out = tf.conv2d(x, filters, strides, padding)\n  out = tf.add(out, bias)\n  out = scale(out, params.scale)\n  return withRelu ? tf.relu(out) : out\n}\n\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], true)\n}\n\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [1, 1], false)\n}\n\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\n  return convLayer(x, params, [2, 2], true, 'valid')\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\nimport { isFloat } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\n    const weights = extractWeights(numFilterValues)\n    const depth = weights.length / (numFilters * filterSize * filterSize)\n\n    if (isFloat(depth)) {\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\n    }\n\n    return tf.tidy(\n      () => tf.transpose(\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\n        [2, 3, 1, 0]\n      )\n    )\n  }\n\n  function extractConvParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvParams {\n\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\n    const bias = tf.tensor1d(extractWeights(numFilters))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/bias` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\n\n    const weights = tf.tensor1d(extractWeights(numWeights))\n    const biases = tf.tensor1d(extractWeights(numWeights))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/weights` },\n      { paramPath: `${mappedPrefix}/biases` }\n    )\n\n    return {\n      weights,\n      biases\n    }\n  }\n\n  function extractConvLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): ConvLayerParams {\n\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\n\n    return { conv, scale }\n  }\n\n  function extractResidualLayerParams(\n    numFilterValues: number,\n    numFilters: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isDown: boolean = false\n  ): ResidualLayerParams {\n\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\n\n    return { conv1, conv2 }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\n\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\n\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\n\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\n\n  const fc = tf.tidy(\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\n  )\n  paramMappings.push({ paramPath: `fc` })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor2D } from '../utils';\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\n\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\n\n    return { weights, biases }\n  }\n\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\n    const scale = extractScaleLayerParams(prefix)\n\n    return { conv: { filters, bias }, scale }\n  }\n\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\n    return {\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\n    }\n  }\n\n  return {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvLayerParams,\n    extractResidualLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const conv32_down = extractConvLayerParams('conv32_down')\n  const conv32_1 = extractResidualLayerParams('conv32_1')\n  const conv32_2 = extractResidualLayerParams('conv32_2')\n  const conv32_3 = extractResidualLayerParams('conv32_3')\n\n  const conv64_down = extractResidualLayerParams('conv64_down')\n  const conv64_1 = extractResidualLayerParams('conv64_1')\n  const conv64_2 = extractResidualLayerParams('conv64_2')\n  const conv64_3 = extractResidualLayerParams('conv64_3')\n\n  const conv128_down = extractResidualLayerParams('conv128_down')\n  const conv128_1 = extractResidualLayerParams('conv128_1')\n  const conv128_2 = extractResidualLayerParams('conv128_2')\n\n  const conv256_down = extractResidualLayerParams('conv256_down')\n  const conv256_1 = extractResidualLayerParams('conv256_1')\n  const conv256_2 = extractResidualLayerParams('conv256_2')\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\n\n  const fc = weightMap['fc']\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\n\n  if (!isTensor2D(fc)) {\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\n  }\n\n  const params = {\n    conv32_down,\n    conv32_1,\n    conv32_2,\n    conv32_3,\n    conv64_down,\n    conv64_1,\n    conv64_2,\n    conv64_3,\n    conv128_down,\n    conv128_1,\n    conv128_2,\n    conv256_down,\n    conv256_1,\n    conv256_2,\n    conv256_down_out,\n    fc\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { conv, convDown, convNoRelu } from './convLayer';\nimport { ResidualLayerParams } from './types';\n\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = conv(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n  out = tf.add(out, x)\n  out = tf.relu(out)\n  return out\n}\n\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\n  let out = convDown(x, params.conv1)\n  out = convNoRelu(out, params.conv2)\n\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\n  const isPad = pooled.shape[3] !== out.shape[3]\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\n\n  if (isAdjustShape) {\n    const padShapeX = [...out.shape] as [number, number, number, number]\n    padShapeX[1] = 1\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\n    out = tf.concat([out, zerosW], 1)\n\n    const padShapeY = [...out.shape] as [number, number, number, number]\n    padShapeY[2] = 1\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\n    out = tf.concat([out, zerosH], 2)\n  }\n\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\n  out = tf.add(pooled, out) as tf.Tensor4D\n\n  out = tf.relu(out)\n  return out\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { normalize } from '../ops';\nimport { convDown } from './convLayer';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { residual, residualDown } from './residualLayer';\nimport { NetParams } from './types';\n\n\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('FaceRecognitionNet')\n  }\n\n  public forwardInput(input: NetInput): tf.Tensor2D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('FaceRecognitionNet - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(150, true).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(150, true), 'float32');\n\n      const meanRgb = [122.782, 117.001, 104.298]\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\n\n      let out = convDown(normalized, params.conv32_down)\n      out = tf.maxPool(out, 3, 2, 'valid')\n\n      out = residual(out, params.conv32_1)\n      out = residual(out, params.conv32_2)\n      out = residual(out, params.conv32_3)\n\n      out = residualDown(out, params.conv64_down)\n      out = residual(out, params.conv64_1)\n      out = residual(out, params.conv64_2)\n      out = residual(out, params.conv64_3)\n\n      out = residualDown(out, params.conv128_down)\n      out = residual(out, params.conv128_1)\n      out = residual(out, params.conv128_2)\n\n      out = residualDown(out, params.conv256_down)\n      out = residual(out, params.conv256_1)\n      out = residual(out, params.conv256_2)\n      out = residualDown(out, params.conv256_down_out)\n\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\n\n      return fullyConnected\n    })\n  }\n\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\n    const netInput = await toNetInput(input)\n\n    const faceDescriptorTensors = tf.tidy(\n      () => tf.unstack(this.forwardInput(netInput))\n    )\n\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\n      t => t.data()\n    )) as Float32Array[]\n\n    faceDescriptorTensors.forEach(t => t.dispose())\n\n    return netInput.isBatchInput\n      ? faceDescriptorsForBatch\n      : faceDescriptorsForBatch[0]\n  }\n\n  protected getDefaultModelName(): string {\n    return 'face_recognition_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { FaceRecognitionNet } from './FaceRecognitionNet';\n\nexport * from './FaceRecognitionNet';\n\nexport function createFaceRecognitionNet(weights: Float32Array) {\n  const net = new FaceRecognitionNet()\n  net.extractWeights(weights)\n  return net\n}", "export type WithFaceDescriptor<TSource> = TSource & {\n  descriptor: Float32Array\n}\n\nexport function extendWithFaceDescriptor<\n  TSource\n> (\n  sourceObj: TSource,\n  descriptor: Float32Array\n): WithFaceDescriptor<TSource> {\n\n  const extension = { descriptor }\n  return Object.assign({}, sourceObj, extension)\n}\n\n", "export type WithAge<TSource> = TSource & {\n  age: number\n}\n\nexport function isWithAge(obj: any): obj is WithAge<{}> {\n  return typeof obj['age'] === 'number'\n}\n\nexport function extendWithAge<\n  TSource\n> (\n  sourceObj: TSource,\n  age: number\n): WithAge<TSource> {\n\n  const extension = { age }\n  return Object.assign({}, sourceObj, extension)\n}", "import { Gender } from '../ageGenderNet/types';\nimport { isValidProbablitiy } from '../utils';\n\nexport type WithGender<TSource> = TSource & {\n  gender: Gender\n  genderProbability: number\n}\n\nexport function isWithGender(obj: any): obj is WithGender<{}> {\n  return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\n    && isValidProbablitiy(obj['genderProbability'])\n}\n\nexport function extendWithGender<\n  TSource\n> (\n  sourceObj: TSource,\n  gender: Gender,\n  genderProbability: number\n): WithGender<TSource> {\n\n  const extension = { gender, genderProbability }\n  return Object.assign({}, sourceObj, extension)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\n\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\n    )\n\n    return {\n      filters,\n      batch_norm_scale,\n      batch_norm_offset,\n      batch_norm_mean,\n      batch_norm_variance\n    }\n  }\n\n  function extractConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string,\n    isPointwiseConv?: boolean\n  ): ConvParams {\n\n    const filters = tf.tensor4d(\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\n      [filterSize, filterSize, channelsIn, channelsOut]\n    )\n    const bias = tf.tensor1d(extractWeights(channelsOut))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/filters` },\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\n    )\n\n    return { filters, bias }\n  }\n\n  function extractPointwiseConvParams(\n    channelsIn: number,\n    channelsOut: number,\n    filterSize: number,\n    mappedPrefix: string\n  ): PointwiseConvParams {\n\n    const {\n      filters,\n      bias\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\n\n    return {\n      filters,\n      batch_norm_offset: bias\n    }\n  }\n\n  function extractConvPairParams(\n    channelsIn: number,\n    channelsOut: number,\n    mappedPrefix: string\n  ): MobileNetV1.ConvPairParams {\n\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\n\n    return { depthwise_conv, pointwise_conv }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\n\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      conv_8,\n      conv_9,\n      conv_10,\n      conv_11,\n      conv_12,\n      conv_13\n    }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\n\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\n\n    const box_predictor_0 = {\n      box_encoding_predictor: box_encoding_0_predictor,\n      class_predictor: class_predictor_0\n    }\n    const box_predictor_1 = {\n      box_encoding_predictor: box_encoding_1_predictor,\n      class_predictor: class_predictor_1\n    }\n    const box_predictor_2 = {\n      box_encoding_predictor: box_encoding_2_predictor,\n      class_predictor: class_predictor_2\n    }\n    const box_predictor_3 = {\n      box_encoding_predictor: box_encoding_3_predictor,\n      class_predictor: class_predictor_3\n    }\n    const box_predictor_4 = {\n      box_encoding_predictor: box_encoding_4_predictor,\n      class_predictor: class_predictor_4\n    }\n    const box_predictor_5 = {\n      box_encoding_predictor: box_encoding_5_predictor,\n      class_predictor: class_predictor_5\n    }\n\n    return {\n      conv_0,\n      conv_1,\n      conv_2,\n      conv_3,\n      conv_4,\n      conv_5,\n      conv_6,\n      conv_7,\n      box_predictor_0,\n      box_predictor_1,\n      box_predictor_2,\n      box_predictor_3,\n      box_predictor_4,\n      box_predictor_5\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n\n}\n\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  const mobilenetv1 = extractMobilenetV1Params()\n  const prediction_layer = extractPredictionLayerParams()\n  const extra_dim = tf.tensor3d(\n    extractWeights(5118 * 4),\n    [1, 5118, 4]\n  )\n  const output_layer = {\n    extra_dim\n  }\n\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n  return {\n    params: {\n      mobilenetv1,\n      prediction_layer,\n      output_layer\n    },\n    paramMappings\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\nimport { isTensor3D } from '../utils';\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\n\n    return { filters, batch_norm_offset }\n  }\n\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\n\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\n\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\n\n    return {\n      depthwise_conv: {\n        filters,\n        batch_norm_scale,\n        batch_norm_offset,\n        batch_norm_mean,\n        batch_norm_variance\n      },\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\n    }\n  }\n\n  function extractMobilenetV1Params(): MobileNetV1.Params {\n    return {\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\n      conv_1: extractConvPairParams(1),\n      conv_2: extractConvPairParams(2),\n      conv_3: extractConvPairParams(3),\n      conv_4: extractConvPairParams(4),\n      conv_5: extractConvPairParams(5),\n      conv_6: extractConvPairParams(6),\n      conv_7: extractConvPairParams(7),\n      conv_8: extractConvPairParams(8),\n      conv_9: extractConvPairParams(9),\n      conv_10: extractConvPairParams(10),\n      conv_11: extractConvPairParams(11),\n      conv_12: extractConvPairParams(12),\n      conv_13: extractConvPairParams(13)\n    }\n  }\n\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\n\n    return { filters, bias }\n  }\n\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\n\n    const box_encoding_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\n    )\n    const class_predictor = extractConvParams(\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\n      `prediction_layer/box_predictor_${idx}/class_predictor`\n    )\n\n    return { box_encoding_predictor, class_predictor }\n  }\n\n  function extractPredictionLayerParams(): PredictionLayerParams {\n    return {\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\n      box_predictor_0: extractBoxPredictorParams(0),\n      box_predictor_1: extractBoxPredictorParams(1),\n      box_predictor_2: extractBoxPredictorParams(2),\n      box_predictor_3: extractBoxPredictorParams(3),\n      box_predictor_4: extractBoxPredictorParams(4),\n      box_predictor_5: extractBoxPredictorParams(5)\n    }\n  }\n\n  return {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  }\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap\n): { params: NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractMobilenetV1Params,\n    extractPredictionLayerParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  const extra_dim = weightMap['Output/extra_dim']\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\n\n  if (!isTensor3D(extra_dim)) {\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\n  }\n\n  const params = {\n    mobilenetv1: extractMobilenetV1Params(),\n    prediction_layer: extractPredictionLayerParams(),\n    output_layer: {\n      extra_dim\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { PointwiseConvParams } from './types';\n\nexport function pointwiseConvLayer(\n  x: tf.Tensor4D,\n  params: PointwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.conv2d(x, params.filters, strides, 'same')\n    out = tf.add(out, params.batch_norm_offset)\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { MobileNetV1 } from './types';\n\nconst epsilon = 0.0010000000474974513\n\nfunction depthwiseConvLayer(\n  x: tf.Tensor4D,\n  params: MobileNetV1.DepthwiseConvParams,\n  strides: [number, number]\n) {\n  return tf.tidy(() => {\n\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\n    out = tf.batchNorm<tf.Rank.R4>(\n      out,\n      params.batch_norm_mean,\n      params.batch_norm_variance,\n      params.batch_norm_offset,\n      params.batch_norm_scale,\n      epsilon\n    )\n    return tf.clipByValue(out, 0, 6)\n\n  })\n}\n\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\n}\n\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\n  return tf.tidy(() => {\n\n    let conv11;\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\n\n    const convPairParams = [\n      params.conv_1,\n      params.conv_2,\n      params.conv_3,\n      params.conv_4,\n      params.conv_5,\n      params.conv_6,\n      params.conv_7,\n      params.conv_8,\n      params.conv_9,\n      params.conv_10,\n      params.conv_11,\n      params.conv_12,\n      params.conv_13\n    ]\n\n    convPairParams.forEach((param, i) => {\n      const layerIdx = i + 1\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\n      if (layerIdx === 11) {\n        conv11 = out\n      }\n    })\n\n    if (conv11 === null) {\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\n    }\n\n    return {\n      out,\n      conv11: conv11 as any\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs'\n\nexport function nonMaxSuppression(\n  boxes: tf.Tensor2D,\n  scores: number[],\n  maxOutputSize: number,\n  iouThreshold: number,\n  scoreThreshold: number\n): number[] {\n\n  const numBoxes = boxes.shape[0]\n  const outputSize = Math.min(\n    maxOutputSize,\n    numBoxes\n  )\n\n  const candidates = scores\n    .map((score, boxIndex) => ({ score, boxIndex }))\n    .filter(c => c.score > scoreThreshold)\n    .sort((c1, c2) => c2.score - c1.score)\n\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\n\n  const selected: number[] = []\n\n  candidates.forEach(c => {\n    if (selected.length >= outputSize) {\n      return\n    }\n    const originalScore = c.score\n\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = IOU(boxes, c.boxIndex, selected[j])\n      if (iou === 0.0) {\n        continue\n      }\n      c.score *= suppressFunc(iou)\n      if (c.score <= scoreThreshold) {\n        break\n      }\n    }\n\n    if (originalScore === c.score) {\n      selected.push(c.boxIndex)\n    }\n  })\n\n  return selected\n}\n\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\n  const boxesData = boxes.arraySync()\n  const yminI = Math.min(boxesData[i][0], boxesData[i][2])\n  const xminI = Math.min(boxesData[i][1], boxesData[i][3])\n  const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\n  const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\n  const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\n  const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\n  const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\n  const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0\n  }\n  const intersectionYmin = Math.max(yminI, yminJ)\n  const intersectionXmin = Math.max(xminI, xminJ)\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\n  const intersectionArea =\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\n  return intersectionArea / (areaI + areaJ - intersectionArea)\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { OutputLayerParams } from './types';\n\n\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\n\n  const sizes = [\n    tf.sub(vec[2], vec[0]),\n    tf.sub(vec[3], vec[1])\n  ]\n\n  const centers = [\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\n  ]\n\n  return {\n    sizes,\n    centers\n  }\n}\n\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\n  const {\n    sizes,\n    centers\n  } = getCenterCoordinatesAndSizesLayer(x0)\n\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\n\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\n\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\n\n  return tf.transpose(\n    tf.stack([\n      tf.sub(add0_out, div0_out),\n      tf.sub(add1_out, div1_out),\n      tf.add(add0_out, div0_out),\n      tf.add(add1_out, div1_out)\n    ]),\n    [1, 0]\n  )\n}\n\nexport function outputLayer(\n  boxPredictions: tf.Tensor4D,\n  classPredictions: tf.Tensor4D,\n  params: OutputLayerParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = boxPredictions.shape[0]\n\n    let boxes = decodeBoxesLayer(\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\n    )\n    boxes = tf.reshape(\n      boxes,\n      [batchSize, (boxes.shape[0] / batchSize), 4]\n    )\n\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\n\n    scores = tf.reshape(\n      scores,\n      [batchSize, scores.shape[1] as number]\n    )\n\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\n\n    return {\n      boxes: boxesByBatch,\n      scores: scoresByBatch\n    }\n\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { convLayer } from '../common';\nimport { BoxPredictionParams } from './types';\n\n\nexport function boxPredictionLayer(\n  x: tf.Tensor4D,\n  params: BoxPredictionParams\n) {\n  return tf.tidy(() => {\n\n    const batchSize = x.shape[0]\n\n    const boxPredictionEncoding = tf.reshape(\n      convLayer(x, params.box_encoding_predictor),\n      [batchSize, -1, 1, 4]\n    )\n    const classPrediction = tf.reshape(\n      convLayer(x, params.class_predictor),\n      [batchSize, -1, 3]\n    )\n\n    return {\n      boxPredictionEncoding,\n      classPrediction\n    }\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { boxPredictionLayer } from './boxPredictionLayer';\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\nimport { PredictionLayerParams } from './types';\n\nexport function predictionLayer(\n  x: tf.Tensor4D,\n  conv11: tf.Tensor4D,\n  params: PredictionLayerParams\n) {\n  return tf.tidy(() => {\n\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\n\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\n\n    const boxPredictions = tf.concat([\n      boxPrediction0.boxPredictionEncoding,\n      boxPrediction1.boxPredictionEncoding,\n      boxPrediction2.boxPredictionEncoding,\n      boxPrediction3.boxPredictionEncoding,\n      boxPrediction4.boxPredictionEncoding,\n      boxPrediction5.boxPredictionEncoding\n    ], 1) as tf.Tensor4D\n\n    const classPredictions = tf.concat([\n      boxPrediction0.classPrediction,\n      boxPrediction1.classPrediction,\n      boxPrediction2.classPrediction,\n      boxPrediction3.classPrediction,\n      boxPrediction4.classPrediction,\n      boxPrediction5.classPrediction\n    ], 1) as tf.Tensor4D\n\n    return {\n      boxPredictions,\n      classPredictions\n    }\n  })\n}", "export interface ISsdMobilenetv1Options {\n  minConfidence?: number\n  maxResults?: number\n}\n\nexport class SsdMobilenetv1Options {\n  protected _name: string = 'SsdMobilenetv1Options'\n\n  private _minConfidence: number\n  private _maxResults: number\n\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\n    this._minConfidence = minConfidence || 0.5\n    this._maxResults = maxResults || 100\n\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\n    }\n\n    if (typeof this._maxResults !== 'number') {\n      throw new Error(`${this._name} - expected maxResults to be a number`)\n    }\n  }\n\n  get minConfidence(): number { return this._minConfidence }\n  get maxResults(): number { return this._maxResults }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { Rect } from '../classes';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { NetInput, TNetInput, toNetInput } from '../dom';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { mobileNetV1 } from './mobileNetV1';\nimport { nonMaxSuppression } from './nonMaxSuppression';\nimport { outputLayer } from './outputLayer';\nimport { predictionLayer } from './predictionLayer';\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\nimport { NetParams } from './types';\n\n\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\n\n  constructor() {\n    super('SsdMobilenetv1')\n  }\n\n  public forwardInput(input: NetInput) {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('SsdMobilenetv1 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n      // const batchTensor = input.toBatchTensor(512, false).toFloat()\n      const batchTensor = tf.cast(input.toBatchTensor(512, false), 'float32');\n\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\n      const features = mobileNetV1(x, params.mobilenetv1)\n\n      const {\n        boxPredictions,\n        classPredictions\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\n\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\n    })\n  }\n\n  public async forward(input: TNetInput) {\n    return this.forwardInput(await toNetInput(input))\n  }\n\n  public async locateFaces(\n    input: TNetInput,\n    options: ISsdMobilenetv1Options = {}\n  ): Promise<FaceDetection[]> {\n\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\n\n    const netInput = await toNetInput(input)\n\n    const {\n      boxes: _boxes,\n      scores: _scores\n    } = this.forwardInput(netInput)\n\n\n    // TODO batches\n    const boxes = _boxes[0]\n    const scores = _scores[0]\n    for (let i = 1; i < _boxes.length; i++) {\n      _boxes[i].dispose()\n      _scores[i].dispose()\n    }\n\n    // TODO find a better way to filter by minConfidence\n    const scoresData = Array.from(await scores.data())\n\n    const iouThreshold = 0.5\n    const indices = nonMaxSuppression(\n      boxes,\n      scoresData as number[],\n      maxResults,\n      iouThreshold,\n      minConfidence\n    )\n\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\n    const inputSize = netInput.inputSize as number\n    const padX = inputSize / reshapedDims.width\n    const padY = inputSize / reshapedDims.height\n\n    const boxesData = boxes.arraySync()\n    const results = indices\n      .map(idx => {\n        const [top, bottom] = [\n          Math.max(0, boxesData[idx][0]),\n          Math.min(1.0, boxesData[idx][2])\n        ].map(val => val * padY)\n        const [left, right] = [\n          Math.max(0, boxesData[idx][1]),\n          Math.min(1.0, boxesData[idx][3])\n        ].map(val => val * padX)\n        return new FaceDetection(\n          scoresData[idx] as number,\n          new Rect(\n            left,\n            top,\n            right - left,\n            bottom - top\n          ),\n          {\n            height: netInput.getInputHeight(0),\n            width: netInput.getInputWidth(0)\n          }\n        )\n      })\n\n    boxes.dispose()\n    scores.dispose()\n\n    return results\n  }\n\n  protected getDefaultModelName(): string {\n    return 'ssd_mobilenetv1_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    return extractParams(weights)\n  }\n}", "import { SsdMobilenetv1 } from './SsdMobilenetv1';\n\nexport * from './SsdMobilenetv1';\nexport * from './SsdMobilenetv1Options';\n\nexport function createSsdMobilenetv1(weights: Float32Array) {\n  const net = new SsdMobilenetv1()\n  net.extractWeights(weights)\n  return net\n}\n\nexport function createFaceDetectionNet(weights: Float32Array) {\n  return createSsdMobilenetv1(weights)\n}\n\n// alias for backward compatibily\nexport class FaceDetectionNet extends SsdMobilenetv1 {}", "import { Point } from '../classes';\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(0.738768, 0.874946),\n  new Point(2.42204, 2.65704),\n  new Point(4.30971, 7.04493),\n  new Point(10.246, 4.59428),\n  new Point(12.6868, 11.8741)\n]\n\nexport const BOX_ANCHORS_SEPARABLE = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\n\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import { Point } from '../classes/Point';\n\nexport type TinyYolov2Config = {\n  withSeparableConvs: boolean\n  iouThreshold: number\n  anchors: Point[]\n  classes: string[]\n  meanRgb?: [number, number, number]\n  withClassScores?: boolean,\n  filterSizes?: number[]\n  isFirstLayerConv2d?: boolean\n}\n\nconst isNumber = (arg: any) => typeof arg === 'number'\n\nexport function validateConfig(config: any) {\n  if (!config) {\n    throw new Error(`invalid config: ${config}`)\n  }\n\n  if (typeof config.withSeparableConvs !== 'boolean') {\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\n  }\n\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\n  }\n\n  if (\n    !Array.isArray(config.classes)\n    || !config.classes.length\n    || !config.classes.every((c: any) => typeof c === 'string')\n  ) {\n\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\n  }\n\n  if (\n    !Array.isArray(config.anchors)\n    || !config.anchors.length\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\n  ) {\n\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\n  }\n\n  if (config.meanRgb && (\n    !Array.isArray(config.meanRgb)\n    || config.meanRgb.length !== 3\n    || !config.meanRgb.every(isNumber)\n  )) {\n\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\n  return tf.tidy(() => {\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\n    return tf.add(tf.relu(tf.sub(x, min)), min)\n    //return tf.maximum(x, min)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { leaky } from './leaky';\nimport { ConvWithBatchNorm } from './types';\n\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\n    out = tf.sub(out, params.bn.sub)\n    out = tf.mul(out, params.bn.truediv)\n    out = tf.add(out, params.conv.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { SeparableConvParams } from '../common/types';\nimport { leaky } from './leaky';\n\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\n  return tf.tidy(() => {\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\n\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\n    out = tf.add(out, params.bias)\n\n    return leaky(out)\n  })\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { extractConvParamsFactory } from '../common';\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\n\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\n\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\n\n    const sub = tf.tensor1d(extractWeights(size))\n    const truediv = tf.tensor1d(extractWeights(size))\n\n    paramMappings.push(\n      { paramPath: `${mappedPrefix}/sub` },\n      { paramPath: `${mappedPrefix}/truediv` }\n    )\n\n    return { sub, truediv }\n  }\n\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\n\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\n\n    return { conv, bn }\n  }\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParams(\n  weights: Float32Array,\n  config: TinyYolov2Config,\n  boxEncodingSize: number,\n  filterSizes: number[]\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const {\n    extractWeights,\n    getRemainingWeights\n  } = extractWeightsFactory(weights)\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(extractWeights, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n\n    const conv0 = config.isFirstLayerConv2d\n      ? extractConvParams(s0, s1, 3, 'conv0')\n      : extractSeparableConvParams(s0, s1, 'conv0')\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  } else {\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\n  }\n\n  if (getRemainingWeights().length !== 0) {\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\n  }\n\n\n  return { params, paramMappings }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { ConvParams } from '../common';\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\nimport { ParamMapping } from '../common/types';\nimport { TinyYolov2Config } from './config';\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\n\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\n\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\n\n  function extractBatchNormParams(prefix: string): BatchNorm {\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\n    return { sub, truediv }\n  }\n\n  function extractConvParams(prefix: string): ConvParams {\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\n    return { filters, bias }\n  }\n\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\n    const conv = extractConvParams(`${prefix}/conv`)\n    const bn = extractBatchNormParams(`${prefix}/bn`)\n    return { conv, bn }\n  }\n\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\n\n  return {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  }\n\n}\n\nexport function extractParamsFromWeigthMap(\n  weightMap: tf.NamedTensorMap,\n  config: TinyYolov2Config\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n\n  const paramMappings: ParamMapping[] = []\n\n  const {\n    extractConvParams,\n    extractConvWithBatchNormParams,\n    extractSeparableConvParams\n  } = extractorsFactory(weightMap, paramMappings)\n\n  let params: TinyYolov2NetParams\n\n  if (config.withSeparableConvs) {\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\n    params = {\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\n      conv1: extractSeparableConvParams('conv1'),\n      conv2: extractSeparableConvParams('conv2'),\n      conv3: extractSeparableConvParams('conv3'),\n      conv4: extractSeparableConvParams('conv4'),\n      conv5: extractSeparableConvParams('conv5'),\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\n      conv8: extractConvParams('conv8')\n    }\n  } else {\n    params = {\n      conv0: extractConvWithBatchNormParams('conv0'),\n      conv1: extractConvWithBatchNormParams('conv1'),\n      conv2: extractConvWithBatchNormParams('conv2'),\n      conv3: extractConvWithBatchNormParams('conv3'),\n      conv4: extractConvWithBatchNormParams('conv4'),\n      conv5: extractConvWithBatchNormParams('conv5'),\n      conv6: extractConvWithBatchNormParams('conv6'),\n      conv7: extractConvWithBatchNormParams('conv7'),\n      conv8: extractConvParams('conv8')\n    }\n  }\n\n  disposeUnusedWeightTensors(weightMap, paramMappings)\n\n  return { params, paramMappings }\n}", "export enum TinyYolov2SizeType {\n  XS = 224,\n  SM = 320,\n  MD = 416,\n  LG = 608\n}\n\nexport interface ITinyYolov2Options {\n  inputSize?: number\n  scoreThreshold?: number\n}\n\nexport class TinyYolov2Options {\n  protected _name: string = 'TinyYolov2Options'\n\n  private _inputSize: number\n  private _scoreThreshold: number\n\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\n    this._inputSize = inputSize || 416\n    this._scoreThreshold = scoreThreshold || 0.5\n\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\n    }\n\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\n    }\n  }\n\n  get inputSize(): number { return this._inputSize }\n  get scoreThreshold(): number { return this._scoreThreshold }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { BoundingBox } from '../classes/BoundingBox';\nimport { Dimensions } from '../classes/Dimensions';\nimport { ObjectDetection } from '../classes/ObjectDetection';\nimport { convLayer } from '../common';\nimport { ConvParams, SeparableConvParams } from '../common/types';\nimport { toNetInput } from '../dom';\nimport { NetInput } from '../dom/NetInput';\nimport { TNetInput } from '../dom/types';\nimport { NeuralNetwork } from '../NeuralNetwork';\nimport { sigmoid } from '../ops';\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\nimport { normalize } from '../ops/normalize';\nimport { TinyYolov2Config, validateConfig } from './config';\nimport { convWithBatchNorm } from './convWithBatchNorm';\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\nimport { extractParams } from './extractParams';\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\nimport { leaky } from './leaky';\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\n\n  public static DEFAULT_FILTER_SIZES = [\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\n  ]\n\n  private _config: TinyYolov2Config\n\n  constructor(config: TinyYolov2Config) {\n    super('TinyYolov2')\n    validateConfig(config)\n    this._config = config\n  }\n\n  public get config(): TinyYolov2Config {\n    return this._config\n  }\n\n  public get withClassScores(): boolean {\n    return this.config.withClassScores || this.config.classes.length > 1\n  }\n\n  public get boxEncodingSize(): number {\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\n  }\n\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\n\n    let out = convWithBatchNorm(x, params.conv0)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = convWithBatchNorm(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = convWithBatchNorm(out, params.conv6)\n    out = convWithBatchNorm(out, params.conv7)\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\n\n    let out = this.config.isFirstLayerConv2d\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv1)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv2)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv3)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv4)\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\n    out = depthwiseSeparableConv(out, params.conv5)\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\n\n    return convLayer(out, params.conv8, 'valid', false)\n  }\n\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\n\n    const { params } = this\n\n    if (!params) {\n      throw new Error('TinyYolov2 - load model before inference')\n    }\n\n    return tf.tidy(() => {\n\n      // let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\n      let batchTensor = tf.cast(input.toBatchTensor(inputSize, false), 'float32');\n      batchTensor = this.config.meanRgb\n        ? normalize(batchTensor, this.config.meanRgb)\n        : batchTensor\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\n\n      return this.config.withSeparableConvs\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\n    })\n  }\n\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\n    return await this.forwardInput(await toNetInput(input), inputSize)\n  }\n\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\n\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\n\n    const netInput = await toNetInput(input)\n    const out = await this.forwardInput(netInput, inputSize)\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\n\n    const inputDimensions = {\n      width: netInput.getInputWidth(0),\n      height: netInput.getInputHeight(0)\n    }\n\n    const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\n    out.dispose()\n    out0.dispose()\n\n    const boxes = results.map(res => res.box)\n    const scores = results.map(res => res.score)\n    const classScores = results.map(res => res.classScore)\n    const classNames = results.map(res => this.config.classes[res.label])\n\n    const indices = nonMaxSuppression(\n      boxes.map(box => box.rescale(inputSize)),\n      scores,\n      this.config.iouThreshold,\n      true\n    )\n\n    const detections = indices.map(idx =>\n      new ObjectDetection(\n        scores[idx],\n        classScores[idx],\n        classNames[idx],\n        boxes[idx],\n        inputDimensions\n      )\n    )\n\n    return detections\n  }\n\n  protected getDefaultModelName(): string {\n    return ''\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\n    return extractParamsFromWeigthMap(weightMap, this.config)\n  }\n\n  protected extractParams(weights: Float32Array) {\n    const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\n\n    const numFilters = filterSizes ? filterSizes.length : undefined\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\n    }\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\n  }\n\n  protected async extractBoxes(\n    outputTensor: tf.Tensor4D,\n    inputBlobDimensions: Dimensions,\n    scoreThreshold?: number\n  ) {\n\n    const { width, height } = inputBlobDimensions\n    const inputSize = Math.max(width, height)\n    const correctionFactorX = inputSize / width\n    const correctionFactorY = inputSize / height\n\n    const numCells = outputTensor.shape[1]\n    const numBoxes = this.config.anchors.length\n\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\n\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\n      const classScores = this.withClassScores\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\n        : tf.scalar(0)\n      return [boxes, scores, classScores]\n    })\n\n    const results = [] as any;\n\n    const scoresData = await scoresTensor.array()\n    const boxesData = await boxesTensor.array()\n    for (let row = 0; row < numCells; row ++) {\n      for (let col = 0; col < numCells; col ++) {\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\n\n          const score = sigmoid(scoresData[row][col][anchor][0]);\n          if (!scoreThreshold || score > scoreThreshold) {\n            const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\n            const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\n            const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\n            const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\n\n            const x = (ctX - (width / 2))\n            const y = (ctY - (height / 2))\n\n            const pos = { row, col, anchor }\n            const { classScore, label } = this.withClassScores\n              ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\n              : { classScore: 1, label: 0 }\n\n            results.push({\n              box: new BoundingBox(x, y, x + width, y + height),\n              score: score,\n              classScore: score * classScore,\n              label,\n              ...pos\n            })\n          }\n        }\n      }\n    }\n\n    boxesTensor.dispose()\n    scoresTensor.dispose()\n    classScoresTensor.dispose()\n\n    return results\n  }\n\n  private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\n    const { row, col, anchor } = pos\n    const classesData = await classesTensor.array()\n    return Array(this.config.classes.length).fill(0)\n      .map((_, i) => classesData[row][col][anchor][i])\n      .map((classScore, label) => ({\n        classScore,\n        label\n      }))\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common/types';\nimport { TNetInput } from '../dom/types';\nimport {\n  BOX_ANCHORS,\n  BOX_ANCHORS_SEPARABLE,\n  DEFAULT_MODEL_NAME,\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\n  IOU_THRESHOLD,\n  MEAN_RGB_SEPARABLE,\n} from './const';\nimport { TinyYolov2Base } from './TinyYolov2Base';\nimport { ITinyYolov2Options } from './TinyYolov2Options';\nimport { TinyYolov2NetParams } from './types';\n\nexport class TinyYolov2 extends TinyYolov2Base {\n\n  constructor(withSeparableConvs: boolean = true) {\n    const config = Object.assign({}, {\n      withSeparableConvs,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face']\n    },\n    withSeparableConvs\n      ? {\n        anchors: BOX_ANCHORS_SEPARABLE,\n        meanRgb: MEAN_RGB_SEPARABLE\n      }\n      : {\n        anchors: BOX_ANCHORS,\n        withClassScores: true\n      }\n    )\n\n    super(config)\n  }\n\n  public get withSeparableConvs(): boolean {\n    return this.config.withSeparableConvs\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { TinyYolov2 } from './TinyYolov2';\n\nexport * from './TinyYolov2Options';\n\nexport * from './config'\nexport * from './types'\n\nexport { TinyYolov2 }\n\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\n  const net = new TinyYolov2(withSeparableConvs)\n  net.extractWeights(weights)\n  return net\n}", "import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\n\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\n\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\n  protected _name: string = 'TinyFaceDetectorOptions'\n}", "export class ComposableTask<T> {\n\n  public async then(\n    onfulfilled: (value: T) => T | PromiseLike<T>\n  ): Promise<T> {\n    return onfulfilled(await this.run())\n  }\n\n  public async run(): Promise<T> {\n    throw new Error('ComposableTask - run is not implemented')\n  }\n}\n", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\n\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResults: TSource[],\n  input: TNetInput,\n  computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\n) {\n  const faceBoxes = parentResults.map(parentResult =>\n    isWithFaceLandmarks(parentResult)\n      ? getRectForAlignment(parentResult)\n      : parentResult.detection\n  )\n  const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\n    input instanceof tf.Tensor\n      ? await extractFaceTensors(input, faceBoxes)\n      : await extractFaces(input, faceBoxes)\n  )\n\n  const results = await computeResults(faces)\n\n  faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n  return results\n}\n\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\n  parentResult: TSource,\n  input: TNetInput,\n  computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\n  extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\n  getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\n) {\n  return extractAllFacesAndComputeResults<TSource, TResult>(\n    [parentResult],\n    input,\n    async faces => computeResult(faces[0]),\n    extractedFaces,\n    getRectForAlignment\n  )\n}", "import { Point } from '../classes'\n\nexport const IOU_THRESHOLD = 0.4\n\nexport const BOX_ANCHORS = [\n  new Point(1.603231, 2.094468),\n  new Point(6.041143, 7.080126),\n  new Point(2.882459, 3.518061),\n  new Point(4.266906, 5.178857),\n  new Point(9.041765, 10.66308)\n]\n\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceDetection, Point } from '../classes';\nimport { ParamMapping } from '../common';\nimport { TNetInput } from '../dom';\nimport { ITinyYolov2Options } from '../tinyYolov2';\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\n\nexport class TinyFaceDetector extends TinyYolov2Base {\n\n  constructor() {\n    const config = {\n      withSeparableConvs: true,\n      iouThreshold: IOU_THRESHOLD,\n      classes: ['face'],\n      anchors: BOX_ANCHORS,\n      meanRgb: MEAN_RGB,\n      isFirstLayerConv2d: true,\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\n    }\n\n    super(config)\n  }\n\n  public get anchors(): Point[] {\n    return this.config.anchors\n  }\n\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\n    const objectDetections = await this.detect(input, forwardParams)\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\n  }\n\n  protected getDefaultModelName(): string {\n    return 'tiny_face_detector_model'\n  }\n\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\n    return super.extractParamsFromWeigthMap(weightMap)\n  }\n}", "import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { FaceDetection } from '../classes/FaceDetection';\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { TNetInput } from '../dom';\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\n\nexport const nets = {\n  ssdMobilenetv1: new SsdMobilenetv1(),\n  tinyFaceDetector: new TinyFaceDetector(),\n  tinyYolov2: new TinyYolov2(),\n  faceLandmark68Net: new FaceLandmark68Net(),\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\n  faceRecognitionNet: new FaceRecognitionNet(),\n  faceExpressionNet: new FaceExpressionNet(),\n  ageGenderNet: new AgeGenderNet()\n}\n\n/**\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\n  nets.ssdMobilenetv1.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Face Detector.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\n  nets.tinyFaceDetector.locateFaces(input, options)\n\n/**\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\n *\n * @param input The input image.\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\n * @returns Bounding box of each face with score.\n */\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\n  nets.tinyYolov2.locateFaces(input, options)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\n  nets.faceLandmark68Net.detectLandmarks(input)\n\n/**\n * Detects the 68 point face landmark positions of the face shown in an image\n * using a tinier version of the 68 point face landmark model, which is slightly\n * faster at inference, but also slightly less accurate.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns 68 point face landmarks or array thereof in case of batch input.\n */\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\n\n/**\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\n * which uniquely represents the features of that persons face. The computed face descriptor can\n * be used to measure the similarity between faces, by computing the euclidean distance of two\n * face descriptors.\n *\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\n */\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\n\n\n/**\n * Recognizes the facial expressions from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\n */\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\n  nets.faceExpressionNet.predictExpressions(input)\n\n/**\n * Predicts age and gender from a face image.\n *\n * @param inputs The face image extracted from the bounding box of a face. Can\n * also be an array of input images, which will be batch processed.\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\n */\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\n  nets.ageGenderNet.predictAgeAndGender(input)\n\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\n\n// backward compatibility\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\nexport const locateFaces = ssdMobilenetv1\nexport const detectLandmarks = detectFaceLandmarks", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { TNetInput } from '../dom';\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderTask,\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\n\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map(\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\n      parentResult,\n      this.input,\n      face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\n      this.extractedFaces\n    )\n\n    return extendWithFaceExpressions(parentResult, faceExpressions)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(this, this.input)\n  }\n}\n\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleFaceExpressionsTask<TSource> {\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\nimport { TNetInput } from '../dom';\nimport { extendWithAge, WithAge } from '../factories/WithAge';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { extendWithGender, WithGender } from '../factories/WithGender';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllFaceExpressionsTask,\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\n  ) {\n    super()\n  }\n}\n\nexport class PredictAllAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>>[]> {\n\n    const parentResults = await this.parentTask\n\n    const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\n      parentResults,\n      this.input,\n      async faces => await Promise.all(faces.map(\n        face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\n      )),\n      this.extractedFaces\n    )\n\n    return parentResults.map((parentResult, i) => {\n      const { age, gender, genderProbability } = ageAndGenderByFace[i]\n      return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n    })\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderTask<\n  TSource extends WithFaceDetection<{}>\n>  extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\n      parentResult,\n      this.input,\n      face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\n      this.extractedFaces\n    )\n\n    return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(this, this.input)\n  }\n}\n\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictAllAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends PredictSingleAgeAndGenderTask<TSource> {\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { TNetInput } from '../dom';\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput\n  ) {\n    super()\n  }\n}\n\nexport class ComputeAllFaceDescriptorsTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\n\n    const parentResults = await this.parentTask\n\n    const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\n      parentResults,\n      this.input,\n      faces => Promise.all(faces.map(face =>\n        nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\n      )),\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}\n\nexport class ComputeSingleFaceDescriptorTask<\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n    const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\n      parentResult,\n      this.input,\n      face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\n      null,\n      parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\n    )\n\n    return extendWithFaceDescriptor(parentResult, descriptor)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n}", "import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\nimport { ComposableTask } from './ComposableTask';\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\nimport { nets } from './nets';\nimport {\n  PredictAllAgeAndGenderWithFaceAlignmentTask,\n  PredictSingleAgeAndGenderWithFaceAlignmentTask,\n} from './PredictAgeAndGenderTask';\nimport {\n  PredictAllFaceExpressionsWithFaceAlignmentTask,\n  PredictSingleFaceExpressionsWithFaceAlignmentTask,\n} from './PredictFaceExpressionsTask';\n\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\n    protected input: TNetInput,\n    protected useTinyLandmarkNet: boolean\n  ) {\n    super()\n  }\n\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\n    return this.useTinyLandmarkNet\n      ? nets.faceLandmark68TinyNet\n      : nets.faceLandmark68Net\n  }\n}\n\nexport class DetectAllFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\n\n    const parentResults = await this.parentTask\n    const detections = parentResults.map(res => res.detection)\n\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, detections)\n      : await extractFaces(this.input, detections)\n\n    const faceLandmarksByFace = await Promise.all(faces.map(\n      face => this.landmarkNet.detectLandmarks(face)\n    )) as FaceLandmarks68[]\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return parentResults.map((parentResult, i) =>\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptors() {\n    return new ComputeAllFaceDescriptorsTask(this, this.input)\n  }\n}\n\nexport class DetectSingleFaceLandmarksTask<\n  TSource extends WithFaceDetection<{}>\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\n\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\n\n    const parentResult = await this.parentTask\n    if (!parentResult) {\n      return\n    }\n\n    const { detection } = parentResult\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\n      ? await extractFaceTensors(this.input, [detection])\n      : await extractFaces(this.input, [detection])\n\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\n\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\n\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\n  }\n\n  withFaceDescriptor() {\n    return new ComputeSingleFaceDescriptorTask(this, this.input)\n  }\n}", "import { FaceDetection } from '../classes/FaceDetection';\nimport { TNetInput } from '../dom';\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\nimport { TinyYolov2Options } from '../tinyYolov2';\nimport { ComposableTask } from './ComposableTask';\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\nimport { nets } from './nets';\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\nimport { FaceDetectionOptions } from './types';\n\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\n  constructor(\n    protected input: TNetInput,\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\n  ) {\n    super()\n  }\n}\n\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\n\n  public async run(): Promise<FaceDetection[]> {\n\n    const { input, options } = this\n\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\n      : (\n        options instanceof SsdMobilenetv1Options\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\n          : (\n            options instanceof TinyYolov2Options\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\n              : null\n          )\n      )\n\n    if (!faceDetectionFunction) {\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\n    }\n\n    return faceDetectionFunction(input)\n  }\n\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\n      const detections = await this.run()\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectAllFaceLandmarksTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictAllFaceExpressionsTask (\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictAllAgeAndGenderTask(\n      this.runAndExtendWithFaceDetections(),\n      this.input\n    )\n  }\n}\n\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\n\n  public async run(): Promise<FaceDetection | undefined> {\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\n    let faceDetectionWithHighestScore = faceDetections[0];\n    faceDetections.forEach(faceDetection => {\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\n        faceDetectionWithHighestScore = faceDetection;\n      }\n    });\n    return faceDetectionWithHighestScore;\n  }\n\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}> | undefined> {\n    return new Promise<WithFaceDetection<{}> | undefined>(async res => {\n      const detection = await this.run()\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\n    })\n  }\n\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\n    return new DetectSingleFaceLandmarksTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input,\n      useTinyLandmarkNet\n    )\n  }\n\n  withFaceExpressions() {\n    return new PredictSingleFaceExpressionsTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n\n  withAgeAndGender() {\n    return new PredictSingleAgeAndGenderTask(\n      this.runAndExtendWithFaceDetection(),\n      this.input\n    )\n  }\n}", "import { TNetInput } from '../dom';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\nimport { FaceDetectionOptions } from './types';\n\nexport function detectSingleFace(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectSingleFaceTask {\n  return new DetectSingleFaceTask(input, options)\n}\n\nexport function detectAllFaces(\n  input: TNetInput,\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\n): DetectAllFacesTask {\n  return new DetectAllFacesTask(input, options)\n}", "import { TNetInput } from '../dom';\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\nimport { detectAllFaces } from './detectFaces';\n\n// export allFaces API for backward compatibility\n\nexport async function allFacesSsdMobilenetv1(\n  input: TNetInput,\n  minConfidence?: number\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport async function allFacesTinyYolov2(\n  input: TNetInput,\n  forwardParams: ITinyYolov2Options = {}\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\n  console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\n    .withFaceLandmarks()\n    .withFaceDescriptors()\n}\n\nexport const allFaces = allFacesSsdMobilenetv1\n", "export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\n  if (arr1.length !== arr2.length)\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\n\n  const desc1 = Array.from(arr1)\n  const desc2 = Array.from(arr2)\n\n  return Math.sqrt(\n    desc1\n      .map((val, i) => val - desc2[i])\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\n  )\n}", "import { FaceMatch } from '../classes/FaceMatch';\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\nimport { euclideanDistance } from '../euclideanDistance';\nimport { WithFaceDescriptor } from '../factories';\n\nexport class FaceMatcher {\n\n  private _labeledDescriptors: LabeledFaceDescriptors[]\n  private _distanceThreshold: number\n\n  constructor(\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\n    distanceThreshold: number = 0.6\n  ) {\n\n    this._distanceThreshold = distanceThreshold\n\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\n\n    if (!inputArray.length) {\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\n    }\n\n    let count = 1\n    const createUniqueLabel = () => `person ${count++}`\n\n    this._labeledDescriptors = inputArray.map((desc) => {\n      if (desc instanceof LabeledFaceDescriptors) {\n        return desc\n      }\n\n      if (desc instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\n      }\n\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\n      }\n\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\n    })\n  }\n\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\n  public get distanceThreshold(): number { return this._distanceThreshold }\n\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\n    return descriptors\n      .map(d => euclideanDistance(d, queryDescriptor))\n      .reduce((d1, d2) => d1 + d2, 0)\n        / (descriptors.length || 1)\n  }\n\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\n    return this.labeledDescriptors\n      .map(({ descriptors, label }) => new FaceMatch(\n          label,\n          this.computeMeanDistance(queryDescriptor, descriptors)\n      ))\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\n  }\n\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\n    const bestMatch = this.matchDescriptor(queryDescriptor)\n    return bestMatch.distance < this.distanceThreshold\n      ? bestMatch\n      : new FaceMatch('unknown', bestMatch.distance)\n  }\n\n  public toJSON(): any {\n    return {\n      distanceThreshold: this.distanceThreshold,\n      labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\n    };\n  }\n\n  public static fromJSON(json: any): FaceMatcher {\n    const labeledDescriptors = json.labeledDescriptors\n      .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\n    return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\n  }\n\n}", "import { TinyFaceDetector } from './TinyFaceDetector';\n\nexport * from './TinyFaceDetector';\nexport * from './TinyFaceDetectorOptions';\n\nexport function createTinyFaceDetector(weights: Float32Array) {\n  const net = new TinyFaceDetector()\n  net.extractWeights(weights)\n  return net\n}", "import { Dimensions, IDimensions } from './classes';\nimport { FaceDetection } from './classes/FaceDetection';\nimport { FaceLandmarks } from './classes/FaceLandmarks';\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\n\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\n\n  const { width, height } = new Dimensions(dimensions.width, dimensions.height)\n\n  if (width <= 0 || height <= 0) {\n    throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\n  }\n\n  if (Array.isArray(results)) {\n    // return results.map(obj => resizeResults(obj, { width, height })) as any as T\n    return (results as Array<any>).map(obj => resizeResults(obj, { width, height } as IDimensions)) as any as T\n  }\n\n  if (isWithFaceLandmarks(results)) {\n    const resizedDetection = results.detection.forSize(width, height)\n    const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\n    return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\n  }\n\n  if (isWithFaceDetection(results)) {\n    return extendWithFaceDetection(results, results.detection.forSize(width, height))\n  }\n\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\n    return (results as any).forSize(width, height)\n  }\n\n  return results\n}", "// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\nimport * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';\n// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');\n// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');\n// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');\nimport * as draw from './draw';\nimport * as utils from './utils';\nexport { tf, draw, utils };\n\nexport * from './ageGenderNet/index';\nexport * from './classes/index';\nexport * from './dom/index'\nexport * from './env/index';\nexport * from './faceExpressionNet/index';\nexport * from './faceLandmarkNet/index';\nexport * from './faceRecognitionNet/index';\nexport * from './factories/index';\nexport * from './globalApi/index';\nexport * from './ops/index';\nexport * from './ssdMobilenetv1/index';\nexport * from './tinyFaceDetector/index';\nexport * from './tinyYolov2/index';\nexport * from './euclideanDistance';\nexport * from './NeuralNetwork';\nexport * from './resizeResults';\n\nimport * as pkg from '../package.json';\nconst node = (typeof process !== 'undefined');\nconst browser = (typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined');\nexport const version = { faceapi: pkg.version, node, browser };\n\n// import { PlatformBrowser } from './Platform';\n// if (!tf.env().platform && tf.env().get('IS_BROWSER')) tf.env().setPlatform('browser', new PlatformBrowser);\n// tf.setBackend('cpu');\n\n// import {MathBackendCPU} from '@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js';\n// export {MathBackendCPU};\n// tf.registerBackend('cpu', () => new MathBackendCPU(), 1 /* priority */);\n"],
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-}
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
new file mode 100644
index 0000000..08650c9
--- /dev/null
+++ b/dist/tfjs.esm.js
@@ -0,0 +1,4121 @@
+
+  /*
+  Face-API
+  homepage: <https://github.com/vladmandic/face-api>
+  author: <https://github.com/vladmandic>'
+  */
+
+var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all5)=>{__markAsModule(target);for(var name in all5)__defProp(target,name,{get:all5[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module),require_browser=__commonJS(()=>{}),require_alea=__commonJS((exports3,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state6=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state6&&(typeof 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XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow=__commonJS((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,k==strseed.length&&(me.d=me.x<<10^me.x>>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift7=__commonJS((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(state6.x&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor4096=__commonJS((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(state6.X&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS(()=>{}),require_seedrandom=__commonJS((exports3,module)=>{(function(pool6,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten5(options.entropy?[seed,tostring(pool6)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool6),(options.pass||callback||function(prng2,seed2,is_math_call,state6){return state6&&(state6.S&&copy(state6,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten5(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten5(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser=global2.navigator,plugins=browser&&browser.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool6)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool6),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS((exports3,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5;sr.xor128=xor128;sr.xorwow=xorwow;sr.xorshift7=xorshift7;sr.xor4096=xor4096;sr.tychei=tychei;module.exports=sr}),require_string_decoder=__commonJS(()=>{}),require_path=__commonJS(()=>{}),require_worker_threads=__commonJS(()=>{}),require_perf_hooks=__commonJS(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS((exports3,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer11=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data=read(f,"binary"),assert3(typeof data=="object"),data)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir&&(scriptDirectory=_scriptDir),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&(typeof performance=="undefined"&&(performance=require_perf_hooks().performance));var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:165,maximum:165+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack9=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack9===0&&(stack9=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack9!==0&&stackRestore(stack9),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer12){GROWABLE_HEAP_I8().set(array2,buffer12)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer11,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer11=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer11=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer11=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer11.byteLength,updateGlobalBufferAndViews(buffer11),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks3){for(;callbacks3.length>0;){var callback=callbacks3.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports5=instance.exports;if(Module.asm=exports5,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports4=Module.instantiateWasm(info,receiveInstance);return exports4}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=12976,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data){worker.onmessage({data})}),worker.on("error",function(data){worker.onerror(data)}),worker.on("exit",function(data){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack9=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack9),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer11.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests())return;for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),context.initExtensionsDone)return;context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer12=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer12,0)),buffer12.length=0):buffer12.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer12){HEAP8.set(array2,buffer12)}var buffer11,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer11=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks3){for(;callbacks3.length>0;){var callback=callbacks3.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). 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Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolBackprop="AvgPoolBackprop",AvgPool3D="AvgPool3D",AvgPool3DBackprop="AvgPool3DBackprop",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",Div="Div",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNBackprop="LRNBackprop",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolBackprop="MaxPoolBackprop",MaxPool3D="MaxPool3D",MaxPool3DBackprop="MaxPool3DBackprop",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multiply="Multiply",Negate="Negate",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",SelectV2="SelectV2",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D";var kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config]=value,[backend3]=key.split("_");backend3===backendName&&result.push(config)}return result}function registerKernel(config){let{kernelName,backendName}=config,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config)}function registerGradient(config){let{kernelName}=config;gradRegistry.has(kernelName)&&(env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`)),gradRegistry.set(kernelName,config)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){let kernels=getKernelsForBackend(registeredBackendName);kernels.forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}var Profiler=class{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs,holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);for(let i=0;i<outputs.length;i++){let output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}let kernelProfile={kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")};return kernelProfile}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){let num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}var Logger=class{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){let time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14),inputShapesDescription="";for(let name2 in inputs){let input2=inputs[name2];if(input2!=null){let inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape2,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape2.length;i++){let node=tape2[i],nodeInputs=node.inputs;for(let inputName in nodeInputs){let input2=nodeInputs[inputName],anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape2.length-1;i>=0;i--){let node=tape2[i],nodeInputs=node.inputs;for(let j=0;j<node.outputs.length;j++)if(tensorsLeadToY[node.outputs[j].id]){for(let inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node.id]=!0;break}}let filteredTape=[];for(let i=0;i<tape2.length;i++){let node=tape2[i];if(nodesFromX[node.id]&&nodesToY[node.id]){let prunedInputs={};for(let inputName in node.inputs){let nodeInput=node.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}let prunedNode=Object.assign({},node);prunedNode.inputs=prunedInputs,prunedNode.outputs=node.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy2,add33){for(let i=filteredTape.length-1;i>=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add33(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
+`)),lines.join(`
+`)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){let offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString(val,pad11,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`:isString(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),rightPad(valStr,pad11)}function boolNumToString(v){return v===0?"false":"true"}function subTensorToString(vals,shape,dtype,strides,padPerCol,isLast=!0){let storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){let complexTuple=createComplexTuples(vals);return[valToString(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){let start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}let sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
+`;for(let i=2;i<rank;i++)newLineSep+=`
+`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){let complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}var TensorBuffer=class{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){let n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];let locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}},trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}var Tensor=class{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){let vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let data=trackerFn().read(this.dataId);if(this.dtype==="string"){let bytes=await data;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data}dataSync(){this.throwIfDisposed();let data=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data}async bytes(){this.throwIfDisposed();let data=await trackerFn().read(this.dataId);return this.dtype==="string"?data:new Uint8Array(data.buffer)}dispose(){if(this.isDisposed)return;trackerFn().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor168,tensorList){return tensorList.some(x=>x.id===tensor168.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container2,list,seen){if(container2==null)return;if(container2 instanceof Tensor){list.push(container2);return}if(!isIterable(container2))return;let iterable=container2;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],success=await this.initializeBackend(backendName).success;if(success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){let{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;let{success,asyncInit}=this.initializeBackend(backendName),result=asyncInit?await success:success;if(!result)return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){let kernels=getKernelsForBackend(this.backendName);kernels.forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){let kernels=getKernelsForBackend(backendName);kernels.forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend3=registryFactoryEntry.factory();if(backend3&&!(backend3 instanceof KernelBackend)&&typeof backend3.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend3.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend3,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){let backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend3,dataId){let info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend3,backend3.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend3=>backend3.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3,kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(({dataId,shape,dtype})=>this.makeTensorFromDataId(dataId,shape,dtype));if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{let saveFunc=tensors=>{if(!isTapeOn)return;saved=tensors.map(tensor168=>this.keep(this.clone(tensor168)))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){let saved=tensors.map(tensor168=>this.keep(this.clone(tensor168)));return saved}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend3){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend3=backend3||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend3.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend3),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend3){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend3),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend3){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend3||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId),refCount=info.refCount;refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){let tensor168=this.state.activeScope.track[i];!tensor168.kept&&!tensorsToTrackInParentSet.has(tensor168.id)&&tensor168.dispose()}let oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor168=>{!tensor168.kept&&tensor168.scopeId===oldScope.id&&this.track(tensor168)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node=>{for(let tensor168 of node.saved)tensor168.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.readSync(dataId)}read(dataId){let info=this.state.tensorInfo.get(dataId);return info.backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment15=new Environment(ns);ns._tfengine=new Engine(environment15)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.add(a,b);return save([a,b]),res},inputs,null,Add)}var device_util_exports={};__export(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype==null)return;if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let skipTypedArray=!0,values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],skipTypedArray);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);let tensors=arg;return tensors.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName),parseAsDtype)}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real8,imag8){let $real=convertToTensor(real8,"real","complex"),$imag=convertToTensor(imag8,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let forward=backend3=>backend3.complex($real,$imag),inputs={real:$real,imag:$imag};return ENGINE.runKernelFunc(forward,inputs,null,Complex)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){let inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor4(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};var NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor168=>tensor168.name):Object.keys(tensors);for(let i=0;i<names.length;++i){let name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);let spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){let utf8bytes=new Promise(async resolve=>{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2<vals.length;i2++){let val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}let tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer11,specs){let out={},float16Decode,offset=0;for(let spec of specs){let name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape),values;if("quantization"in spec){let quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer11.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){let v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){let size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){let byteLength=new Uint32Array(buffer11.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;let bytes=new Uint8Array(buffer11.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{let dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer11.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);let real8=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real8.length;i++)real8[i]=values[i*2],image3[i]=values[i*2+1];let realTensor=tensor4(real8,shape,"float32"),imageTensor=tensor4(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor4(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0,normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer11){if(useNodeBuffer)return Buffer.from(buffer11).toString("base64");let buf=new Uint8Array(buffer11),s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){let buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}let s=atob(str),buffer11=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer11.set([s.charCodeAt(i)],i);return buffer11.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer11=>{totalByteLength+=buffer11.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer11=>{temp.set(new Uint8Array(buffer11),offset),offset+=buffer11.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)===0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer11=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer11);for(let index=0;index<quantizedArray.length;index++){let float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer11)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[],routers=handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters;return routers.forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions);var DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),modelStore=modelTx.objectStore(MODEL_STORE_NAME),getRequest=modelStore.get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME),putModelRequest=modelStore.put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME))?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),store=tx.objectStore(INFO_STORE_NAME),getAllInfoRequest=store.getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let modelStore=modelTx.objectStore(MODEL_STORE_NAME),deleteModelRequest=modelStore.delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}};var PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology21=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{return this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology21),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata})),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology21=JSON.parse(this.LS.getItem(this.keys.topology));if(topology21==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology21;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,out.userDefinedMetadata=metadata.userDefinedMetadata}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME))?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){let key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){let modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);let keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);let info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}};var URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=class{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url),manager=ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);return manager.removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){let deleteSource=!1;return cloneModelInternal(sourceURL,destURL,deleteSource)}async function moveModel(sourceURL,destURL){let deleteSource=!0;return cloneModelInternal(sourceURL,destURL,deleteSource)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}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}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernelFunc(backend3=>backend3.cast($x,dtype),inputs,null,Cast,attrs)}var cast=op({cast_});function clone_(x){let $x=convertToTensor(x,"x","clone",null),forward=()=>ENGINE.makeTensorFromDataId($x.dataId,$x.shape,$x.dtype),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Identity)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest},modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1&&resolve({modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy,userDefinedMetadata:modelJSON.userDefinedMetadata})},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&(!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME))?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,responses=loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction),bufferPromises=responses.map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1,buffers=loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction);return buffers}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){let fetchWeights=fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}),loadWeights2=weightsLoaderFactory(fetchWeights);return loadWeights2(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
+Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;let groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer),groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){let buffer11=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer11,groupBufferOffset),groupBufferOffset+=buffer11.byteLength}let weightsEntries=groupWeightsToFetch[i];weightsEntries.forEach(weightsEntry=>{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata,weightsManifest};init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;if(weightsManifest!=null){let results=await this.loadWeights(weightsManifest);[weightSpecs,weightData]=results}let artifacts={modelTopology,weightSpecs,weightData,userDefinedMetadata,generatedBy,convertedBy,format},initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){if(arguments.length===1){let isModelArtifacts=modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null;return isModelArtifacts?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts}))}else return console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig})}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export(math_exports,{confusionMatrix:()=>confusionMatrix});function reshape_(x,shape){let $x=convertToTensor(x,"x","reshape",null),inputs={x:$x},attrs={shape},forward=(backend3,save)=>(shape=inferFromImplicitShape(shape,$x.size),assert($x.size===sizeFromShape(shape),()=>"new shape and old shape must have the same number of elements."),save([$x]),backend3.reshape($x,shape));return ENGINE.runKernelFunc(forward,inputs,null,Reshape,attrs)}var reshape=op({reshape_});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{save([$a,$b]);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;assert($a.rank>=2&&$b.rank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`),assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShapeOuterDims=batchDimA>batchDimB?outerDimsA:outerDimsB,outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),res3d=backend3.batchMatMul(a3D,b3D,transposeA,transposeB);return reshape(res3d,outShape)},inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernelFunc(forward,inputs,null,BatchMatMul,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let $indices=convertToTensor(indices,"indices","oneHot","int32"),outShape=[...$indices.shape,depth],forward=(backend3,save)=>(save([$indices]),reshape(backend3.oneHot(reshape($indices,[$indices.size]),depth,onValue,offValue),outShape)),inputs={indices:$indices},attrs={depth,onValue,offValue};return ENGINE.runKernelFunc(forward,inputs,null,OneHot,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernelFunc(backend3=>backend3.transpose($x,perm),inputs,null,Transpose,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_});var browser_exports={};__export(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}let kernel=getKernel(FromPixels,ENGINE.backendName);if(kernel!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}let outShape=[height,width,numChannels];return tensor3d(values,outShape,"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){let originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);let[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){let rgba=[0,0,0,255];for(let d=0;d<depth;d++){let value=data[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor168,indices){if(tensor168.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor168.rank}.`);if(indices.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indices.rank-1]>tensor168.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank-1]} vs. ${tensor168.rank}`);if(tensor168.size===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor168.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];let inputShape=tensor168.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensor168.rank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);let strides=[...computeStrides(tensor168.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){let indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];let safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}var slice_util_exports={};__export(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){let newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){let elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){let inputRank=inputShape.length,normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){let newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){let axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis],stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_,xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}var serialization_exports={};__export(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config){return new cls(config)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`);for(let i=0;i<expectedFlat.length;++i){let a=actualFlat[i],e=expectedFlat[i];if(!predicate(a,e))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){let exp13=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp13,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon3){if(epsilon3==null&&(epsilon3=testEpsilon()),!areClose(a,e,epsilon3))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon3){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon3)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}var version="2.7.0";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine15(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container2){let tensors=getTensorsInContainer(container2);tensors.forEach(tensor168=>tensor168.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend2(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.add($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Add)}var add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.floorDiv($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,FloorDiv)}var floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);let forward=(backend3,save)=>{let res=backend3.realDivide($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE.runKernelFunc(forward,inputs,null,Div,attrs)}var div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.multiply($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Multiply)}var mul=op({mul_});function abs_(x){let $x=convertToTensor(x,"x","abs"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>(save([$x]),$x.dtype==="complex64"?backend3.complexAbs($x):backend3.abs($x)),inputs,null,Abs)}var abs=op({abs_});function acos_(x){let $x=convertToTensor(x,"x","acos"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.acos($x);return save([$x]),res},inputs,null,Acos)}var acos=op({acos_});function acosh_(x){let $x=convertToTensor(x,"x","acosh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.acosh($x);return save([$x]),res},inputs,null,Acosh)}var acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);let $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let forward=(backend3,save)=>{let res=backend3.addN($tensors);return save($tensors),res},inputs=$tensors;return ENGINE.runKernelFunc(forward,inputs,null,AddN)}var addN=op({addN_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){let rank=outputLoc.length+reduceLoc.length,loc=[],outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);let reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function all_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","all","bool"),forward=backend3=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank);permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let res=backend3.all($x,axes);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,origAxes);return reshape(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,All,attrs)}var all=op({all_});function any_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","any","bool"),forward=backend3=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank);permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let res=backend3.any($x,axes);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,origAxes);return reshape(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Any,attrs)}var any=op({any_});function argMax_(x,axis=0){let $x=convertToTensor(x,"x","argMax"),forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam(axis,$x.shape),permutedAxes=getAxesPermutation(axes,$x.rank);return permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank)),backend3.argMax($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,ArgMax,attrs)}var argMax=op({argMax_});function argMin_(x,axis=0){let $x=convertToTensor(x,"x","argMin"),forward=(backend3,save)=>{save([$x]),axis==null&&(axis=0);let axes=parseAxisParam(axis,$x.shape),permutedAxes=getAxesPermutation(axes,$x.rank);return permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank)),backend3.argMin($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,ArgMin,attrs)}var argMin=op({argMin_});function asin_(x){let $x=convertToTensor(x,"x","asin"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.asin($x);return save([$x]),res},inputs,null,Asin)}var asin=op({asin_});function asinh_(x){let $x=convertToTensor(x,"x","asinh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.asinh($x);return save([$x]),res},inputs,null,Asinh)}var asinh=op({asinh_});function atan_(x){let $x=convertToTensor(x,"x","atan"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.atan($x);return save([$x]),res},inputs,null,Atan)}var atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.atan2($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Atan2)}var atan2=op({atan2_});function atanh_(x){let $x=convertToTensor(x,"x","atanh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.atanh($x);return save([$x]),res},inputs,null,Atanh)}var atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad11,dataFormat="NHWC",dilations){let inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad11,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad11,roundingMode,dataFormat="channelsLast"){let[filterHeight,filterWidth]=parseTupleParam(filterSize),filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad11,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad11,roundingMode,dataFormat="NDHWC"){let[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize),filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad11,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad11,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad11,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad11,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);let[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad11,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels,outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputRows=inShape[0],inputCols=inShape[1],outputRows=conditionalRound((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert(isInt(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));let inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=conditionalRound((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputDepths),()=>`The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);let outputRows=conditionalRound((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);let outputCols=conditionalRound((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert(isInt(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){let effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad11,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad11=="number"){let padType=pad11===0?"VALID":"NUMBER";padInfo={top:pad11,bottom:pad11,left:pad11,right:pad11,type:padType};let outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad11,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad11==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad11==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad11=="object"){let top=dataFormat==="channelsLast"?pad11[1][0]:pad11[2][0],bottom=dataFormat==="channelsLast"?pad11[1][1]:pad11[2][1],left=dataFormat==="channelsLast"?pad11[2][0]:pad11[3][0],right=dataFormat==="channelsLast"?pad11[2][1]:pad11[3][1],padType=top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT";padInfo={top,bottom,left,right,type:padType},outHeight=conditionalRound((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=conditionalRound((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad11}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad11,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad11=="number"){let padType=pad11===0?"VALID":"NUMBER";padInfo={top:pad11,bottom:pad11,left:pad11,right:pad11,front:pad11,back:pad11,type:padType};let outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad11,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad11==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);let padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad11==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad11}`);return{padInfo,outDepth,outHeight,outWidth}}function conditionalRound(value,roundingMode){if(!roundingMode)return value;switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){let[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function avgPool_(x,filterSize,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo(x4D.shape,filterSize,strides,1,pad11,dimRoundingMode);return save([x4D]),convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual(convInfo.inShape,convInfo.outShape)?x4D.clone():backend3.avgPool(x4D,convInfo)},inputs={x:x4D},attrs={filterSize,strides,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad11,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","avgPool3d","float32"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1,1]);let convInfo=computePool3DInfo(x5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode,dataFormat);return save([x5D]),backend3.avgPool3d(x5D,convInfo)},inputs={x:x5D},attrs={filterSize,strides,pad:pad11,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool3D,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3d=op({avgPool3d_});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat");$tensors[0].dtype==="complex64"&&$tensors.forEach(tensor168=>{if(tensor168.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor168.dtype}. `)});let forward=(backend3,save)=>{let $axis=parseAxisParam(axis,$tensors[0].shape)[0],outShape=computeOutShape2($tensors.map(t=>t.shape),$axis);if(sizeFromShape(outShape)===0)return tensor4([],outShape);if($tensors=$tensors.filter(t=>t.size>0),$tensors.length===1)return $tensors[0];let shapes=$tensors.map(t=>t.shape);assertParamsConsistent(shapes,$axis);let res=backend3.concat($tensors,$axis);return save($tensors),res},inputs=$tensors,attr={axis};return ENGINE.runKernelFunc(forward,inputs,null,Concat,attr)}var concat=op({concat_});function sigmoid_(x){let $x=convertToTensor(x,"x","sigmoid"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sigmoid($x);return save([res]),res},inputs,null,Sigmoid)}var sigmoid=op({sigmoid_});function slice_(x,begin,size){let $x=convertToTensor(x,"x","slice");if($x.rank===0)throw new Error("Slicing scalar is not possible");let forward=(backend3,save)=>{let[begin_,size_]=parseSliceParams($x,begin,size);return assertParamsValid($x,begin_,size_),save([$x]),backend3.slice($x,begin_,size_)},inputs={x:$x},attrs={begin,size};return ENGINE.runKernelFunc(forward,inputs,null,Slice,attrs)}var slice=op({slice_});function tanh_(x){let $x=convertToTensor(x,"x","tanh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let y=backend3.tanh($x);return save([y]),y},inputs,null,Tanh)}var tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data,c,h){let $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}var basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){let $x=convertToTensor(x,"x","batchToSpaceND"),prod5=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod5===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);let forward=backend3=>backend3.batchToSpaceND($x,blockShape,crops),inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernelFunc(forward,inputs,null,BatchToSpaceND,attrs)}var batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean7,variance,offset,scale2,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let x4D=xAs4D($x),forward=(backend3,save)=>(save([x4D,$mean,$variance,$scale]),backend3.batchNorm(x4D,as1DOr4D($mean),as1DOr4D($variance),as1DOr4D($offset),as1DOr4D($scale),varianceEpsilon)),inputs={x:x4D,scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernelFunc(forward,inputs,null,FusedBatchNorm,attrs);return reshape(res,$x.shape)}function as1DOr4D(x){return x==null?null:x.rank===0?reshape(x,[x.size]):x.rank===1?x:x.rank===2?reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x}var batchNorm=op({batchNorm_});function batchNorm2d_(x,mean7,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean7,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean7,variance,offset,scale2,varianceEpsilon){let $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm"),$scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}var batchNorm4d=op({batchNorm4d_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x"),xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){let newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}let inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);let axes=reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0);if(axes.length===0)return clone(input2);let forward=backend3=>backend3.tile(input2,reps),inputs={x:input2},attrs={shape,inputShape};return ENGINE.runKernelFunc(forward,inputs,null,BroadcastTo,attrs)}var broadcastTo=op({broadcastTo_});function ceil_(x){let $x=convertToTensor(x,"x","ceil"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.ceil($x),inputs,null,Ceil)}var ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){let $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);let inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.clip($x,clipValueMin,clipValueMax);return save([$x]),res},inputs,null,ClipByValue,attrs)}var clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}var concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}var concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}var concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}var concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let forward=(backend3,save)=>{let $dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2d(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,Conv2D,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad11,dataFormat="NWC",dilation=1,dimRoundingMode){let $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d"),x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);let filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),strides=[1,stride],dilations=[1,dilation],conv2dDataFormat="NHWC",res=conv2d(input4D,filter4D,strides,pad11,conv2dDataFormat,dilations,dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}var conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad11,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);let inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let dilations=1,$dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(xShape4D,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2dDerInput(dy4D,filter,convInfo);return save([dy4D,filter]),res2},inputs={dy:dy4D,filter},attrs={strides,pad:pad11,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernelFunc(forward,inputs,null,Conv2DBackpropInput,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad11,"NHWC",dimRoundingMode)}var conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad11,dataFormat="NDHWC",dilations=[1,1,1]){let $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);let forward=(backend3,save)=>{let convInfo=computeConv3DInfo(x5D.shape,$filter.shape,strides,dilations,pad11),res2=backend3.conv3d(x5D,$filter,convInfo);return save([x5D,$filter]),res2},inputs={x:x5D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,Conv3D,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad11){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);let inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo(xShape5D,filter.shape,strides,dilations,pad11);return backend3.conv3dDerInput(dy5D,filter,convInfo)},inputs={dy:dy5D,filter},attrs={pad:pad11,strides,inputShape:xShape5D},res=ENGINE.runKernelFunc(forward,inputs,null,Conv3DBackpropInputV2,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad11){let $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad11)}var conv3dTranspose=op({conv3dTranspose_});function cos_(x){let $x=convertToTensor(x,"x","cos"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.cos($x);return save([$x]),res},inputs,null,Cos)}var cos=op({cos_});function cosh_(x){let $x=convertToTensor(x,"x","cosh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.cosh($x);return save([$x]),res},inputs,null,Cosh)}var cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse12=!1){let $x=convertToTensor(x,"x","cumsum"),forward=(backend3,save)=>{let permutation=getAxesPermutation([axis],$x.rank),permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation));let permutedAxis=getInnerMostAxes(1,$x.rank)[0],value=backend3.cumsum(permutedX,permutedAxis,exclusive,reverse12);if(save([$x]),permutation!=null){let reversePermutation=getUndoAxesPermutation(permutation);value=transpose(value,reversePermutation)}return value},inputs={x:$x},attrs={axis,exclusive,reverse:reverse12};return ENGINE.runKernelFunc(forward,inputs,null,Cumsum,attrs)}var cumsum=op({cumsum_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){let $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
+    ${$x.shape}`),assert(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
+    ${inputWidth} and ${blockSize} for depthToSpace with input shape
+        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let forward=backend3=>backend3.depthToSpace($x,blockSize,dataFormat),inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernelFunc(forward,inputs,null,DepthToSpace,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!0),res2=backend3.depthwiseConv2D(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNative,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let $x=convertToTensor(x,"x","diag"),forward=backend3=>{let flat=reshape($x,[$x.size]),result=backend3.diag(flat),outShape=[...x.shape,...x.shape];return reshape(result,outShape)},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Diag)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad11,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){let dim=inRank-1-i,a=inShape[dim]||1,b=outShape[outShape.length-1-i]||1;b>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i<outShape.length;i++){let inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){let errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.equal($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Equal)}var equal=op({equal_});function where_(condition,a,b){let $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let forward=(backend3,save)=>{let res=backend3.select($condition,$broadcastedA,$broadcastedB);return save([$condition]),res},inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernelFunc(forward,inputs,null,SelectV2)}var where=op({where_});function zerosLike_(x){let $x=convertToTensor(x,"x","zerosLike"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.zerosLike($x),inputs,null,ZerosLike)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros10=zerosLike(divResult),bEqualsZero=equal($b,zeros10);return where(bEqualsZero,zeros10,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul($t1,t22D);return t1t2}}var dot=op({dot_});function elu_(x){let $x=convertToTensor(x,"x","elu"),forward=(backend3,save)=>{let y=backend3.elu($x);return save([y]),y},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Elu)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.erf($x);return save([$x]),res},inputs,null,Erf)}var erf=op({erf_});function exp_(x){let $x=convertToTensor(x,"x","exp"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.exp($x);return save([res]),res},inputs,null,Exp)}var exp=op({exp_});function expandDims_(x,axis=0){let parseAs=null,$x=convertToTensor(x,"x","expandDims",parseAs);assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let newShape=$x.shape.slice();return axis<0&&(assert(-($x.rank+1)<=axis,()=>`Axis must be in the interval [${-($x.rank+1)}, ${$x.rank}]`),axis=$x.rank+axis+1),newShape.splice(axis,0,1),reshape($x,newShape)}var expandDims=op({expandDims_});function expm1_(x){let $x=convertToTensor(x,"x","expm1"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.expm1($x);return save([$x]),res},inputs,null,Expm1)}var expm1=op({expm1_});function tile_(x,reps){let parseAs=null,$x=convertToTensor(x,"x","tile",parseAs);assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let forward=(backend3,save)=>{let res=backend3.tile($x,reps);return save([$x]),res},inputsToSave=[$x],inputs={x:$x},attrs={reps};return ENGINE.runKernelFunc(forward,inputs,null,Tile,attrs,inputsToSave)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);let out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}var eye=op({eye_});function fill(shape,value,dtype){let attrs={shape,value,dtype};return ENGINE.runKernelFunc(backend3=>backend3.fill(shape,value,dtype),{},null,Fill,attrs)}function floor_(x){let $x=convertToTensor(x,"x","floor"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.floor($x),inputs,null,Floor)}var floor=op({floor_});var segment_util_exports={};__export(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis){let dimSize=x.shape[axis],outputShape=[],batchSize=1,sliceSize=1;for(let i=0;i<axis;i++)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=0;i<indices.rank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<x.rank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,dimSize,outputShape}}function gather_(x,indices,axis=0){let $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis},forward=(backend3,save)=>{let parsedAxis=parseAxisParam(axis,$x.shape)[0],shapeInfo=collectGatherOpShapeInfo($x,$indices,parsedAxis),res=backend3.gather($x,reshape($indices,[$indices.size]),parsedAxis);return save([$x,$indices]),reshape(res,shapeInfo.outputShape)};return ENGINE.runKernelFunc(forward,inputs,null,GatherV2,attrs)}var gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.greater($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Greater)}var greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.greaterEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,GreaterEqual)}var greaterEqual=op({greaterEqual_});function imag_(input2){let $input=convertToTensor(input2,"input","imag"),forward=backend3=>backend3.imag($input),inputs={input:$input};return ENGINE.runKernelFunc(forward,inputs,null,Imag)}var imag=op({imag_});function isFinite_(x){let $x=convertToTensor(x,"x","isFinite"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isFinite($x),inputs,null,IsFinite)}var isFinite2=op({isFinite_});function isInf_(x){let $x=convertToTensor(x,"x","isInf"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isInf($x),inputs,null,IsInf)}var isInf=op({isInf_});function isNaN_(x){let $x=convertToTensor(x,"x","isNaN"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isNaN($x),inputs,null,IsNan)}var isNaN2=op({isNaN_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.maximum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Maximum)}var maximum=op({maximum_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");let shape=[],inferredShape=[];return makeTensor(value,shape,inferredShape,dtype)}function leakyRelu_(x,alpha=.2){let $x=convertToTensor(x,"x","leakyRelu");return maximum(mul(scalar(alpha),$x),$x)}var leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.less($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Less)}var less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.lessEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,LessEqual)}var lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");let attrs={start,stop,num};return ENGINE.runKernelFunc(backend3=>backend3.linspace(start,stop,num),{},null,LinSpace,attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){let $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
+               rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let forward=(backend3,save)=>{let y=backend3.localResponseNormalization4D(x4D,depthRadius,bias,alpha,beta);return save([x4D,y]),y},inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernelFunc(forward,inputs,null,LRN,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let $x=convertToTensor(x,"x","log"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.log($x);return save([$x]),res},inputs,null,Log)}var log=op({log_});function log1p_(x){let $x=convertToTensor(x,"x","log1p"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.log1p($x);return save([$x]),res},inputs,null,Log1p)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad",null),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads",null),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable3=>!variable3.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable3=>variable3.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){let numNullGradients=grads2.filter(g=>g==null).length;if(numNullGradients>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
+    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let $x=convertToTensor(x,"x","neg"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.neg($x),inputs,null,Negate)}var neg=op({neg_});function softplus_(x){let $x=convertToTensor(x,"x","softplus"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.softplus($x);return save([$x]),res},inputs,null,Softplus)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid"),customOp=customGrad(x2=>{let value=neg(softplus(neg(x2))),gradFunc=dy=>{let derX=mul(dy,sigmoid(neg(x2)));return derX};return{value,gradFunc}});return customOp($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","max"),forward=(backend3,save)=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank),maxInput=$x;permutedAxes!=null&&(maxInput=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,maxInput.rank));let y=backend3.max(maxInput,axes);permutedAxes!=null&&maxInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim(res.shape,parseAxisParam(axis,$x.shape));res=reshape(res,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Max,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.subtract($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Sub)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam(axis,$x.shape),permutation=getAxesPermutation(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation),reductionAxes=getInnerMostAxes(reductionAxes.length,$x.rank));let value=backend3.sum(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim(value.shape,axes);value=reshape(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Sum,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);let forward=(backend3,save)=>{let keepDims=!0,xMax=max(logits,axis,!0),shifted=sub(logits,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),value},inputs={logits:$logits},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,LogSoftmax,attrs)}var logSoftmax=op({logSoftmax_});function logSumExp_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){let newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}var logSumExp=op({logSumExp_});function logicalAnd_(a,b){let $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernelFunc(backend3=>backend3.logicalAnd($a,$b),inputs,null,LogicalAnd)}var logicalAnd=op({logicalAnd_});function logicalNot_(x){let $x=convertToTensor(x,"x","logicalNot","bool"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.logicalNot($x),inputs,null,LogicalNot)}var logicalNot=op({logicalNot_});function logicalOr_(a,b){let $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernelFunc(backend3=>backend3.logicalOr($a,$b),inputs,null,LogicalOr)}var logicalOr=op({logicalOr_});function logicalXor_(a,b){let $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}var logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad11,dimRoundingMode){let $x=convertToTensor(x,"x","maxPool"),dilations=1,x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{let convInfo=computePool2DInfo(x4D.shape,filterSize,strides,1,pad11,dimRoundingMode),y;return convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual(convInfo.inShape,convInfo.outShape)?y=x4D.clone():y=backend3.maxPool(x4D,convInfo),save([x4D,y]),y},inputs={x:x4D},attrs={filterSize,strides,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad11,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=(backend3,save)=>{dilations==null&&(dilations=[1,1,1]);let convInfo=computePool3DInfo(x5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode,dataFormat),y=backend3.maxPool3d(x5D,convInfo);return save([x5D,y]),y},inputs={x:x5D},attrs={filterSize,strides,pad:pad11,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool3D,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad11,includeBatchInIndex=!1){let $x=convertToTensor(x,"x","maxPoolWithArgmax"),inputs={x:$x},attrs={filterSize,strides,pad:pad11,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real8=zeros(shape,"float32"),imag8=zeros(shape,"float32");return complex(real8,imag8)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real8=ones2(shape,"float32"),imag8=zeros(shape,"float32");return complex(real8,imag8)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function mean_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","mean"),axes=parseAxisParam(axis,$x.shape),shapes=computeOutAndReduceShapes($x.shape,axes),reduceShape=shapes[1],reduceSize=sizeFromShape(reduceShape),inputs={x:$x},attrs={axis,keepDims},forward=()=>{let reduceSizeScalar=scalar(reduceSize),xReduce=reduceSizeScalar.dtype===$x.dtype?$x:cast($x,reduceSizeScalar.dtype),res=div(xReduce,reduceSizeScalar);return sum2(res,axis,keepDims)},customOp=customGrad(x2=>{let value=ENGINE.runKernelFunc(forward,inputs,null,Mean,attrs),gradFunc=dy=>{let expandedDyShape=x2.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=div(mul(expandedDy,ones2(x2.shape,"float32")),reduceSize);return derX};return{value,gradFunc}});return customOp($x)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","min"),forward=(backend3,save)=>{let origAxes=parseAxisParam(axis,$x.shape),axes=origAxes,permutedAxes=getAxesPermutation(axes,$x.rank),minInput=$x;permutedAxes!=null&&(minInput=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));let y=backend3.min(minInput,axes);permutedAxes!=null&&minInput.dispose();let res=y;if(keepDims){let expandedShape=expandShapeToKeepDim(res.shape,origAxes);res=reshape(y,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Min,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.minimum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Minimum)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let forward=(backend3,save)=>{let res=backend3.mod($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Mod)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={},inputsToSave=[$x],outputsToSave=[];return ENGINE.runKernelFunc((backend3,save)=>(save([$x]),backend3.square($x)),{x:$x},null,"Square",attrs,inputsToSave,outputsToSave)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data,c,h){let $data=convertToTensor(data,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i<lstmCells.length;i++){let output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}let newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}var multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){let $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let logits2D=origRank===1?reshape($logits,[1,-1]):$logits,res=ENGINE.runKernelFunc(backend3=>backend3.multinomial(logits2D,normalized,numSamples,seed),{logits2D});return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=backend3=>backend3.notEqual($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,NotEqual)}var notEqual=op({notEqual_});function real_(input2){let $input=convertToTensor(input2,"input","real"),forward=backend3=>backend3.real($input),inputs={input:$input};return ENGINE.runKernelFunc(forward,inputs,null,Real)}var real=op({real_});function onesLike_(x){let $x=convertToTensor(x,"x","onesLike"),forward=(backend3,save)=>{if($x.dtype==="complex64"){let r=onesLike(real($x)),i=zerosLike(imag($x));return complex(r,i)}return backend3.onesLike($x)},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,OnesLike)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let forward=(backend3,save)=>(save([$x]),backend3.pad($x,paddings,constantValue)),attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,PadV2,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let forward=backend3=>backend3.spaceToBatchND($x,blockShape,paddings),inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernelFunc(forward,inputs,null,SpaceToBatchND,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad11,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad11===0&&(pad11="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad11),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad11==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad11:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),forwardOp=poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad),y=forwardOp(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let dilatedFilterShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)),padExtraShape=dilatedFilterShape.map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp13){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp13,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp},forward=(backend3,save)=>{let y=backend3.pow($base,$exp);return save([$base,$exp,y]),y};return ENGINE.runKernelFunc(forward,inputs,null,Pow)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),forward=(backend3,save)=>{let res=backend3.prelu($x,$alpha);return save([$x,$alpha]),res},inputs={x:$x,alpha:$alpha};return ENGINE.runKernelFunc(forward,inputs,null,Prelu)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let forward=backend3=>{let axes=parseAxisParam(axis,$x.shape),permutation=getAxesPermutation(axes,$x.rank),reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation),reductionAxes=getInnerMostAxes(reductionAxes.length,$x.rank));let value=backend3.prod(permutedX,reductionAxes);if(keepDims){let newShape=expandShapeToKeepDim(value.shape,axes);value=reshape(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Prod,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}var rand=op({rand_});var seedrandom=__toModule(require_seedrandom2());var MPRandGauss=class{constructor(mean7,stdDeviation,dtype,truncated,seed){this.mean=mean7,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){let value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);let mul64=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul64,resultY=this.mean+this.stdDev*v2*mul64,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}},UniformRandom=class{constructor(min8=0,max10=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min8,this.range=max10-min8,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min8} - ${max10} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}var randomGamma=op({randomGamma_});function randomNormal_(shape,mean7=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);let randGauss=new MPRandGauss(mean7,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){let res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}var randomUniform=op({randomUniform_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");let shape=null;return makeTensor(values,shape,inferredShape,dtype)}function range(start,stop,step9=1,dtype="float32"){if(step9===0)throw new Error("Cannot have a step of zero");let forward=()=>{let sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step9<0,decreasingRangePositiveStep=stop<start&&step9>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return zeros([0],dtype);let numElements=Math.abs(Math.ceil((stop-start)/step9)),values=makeZerosTypedArray(numElements,dtype);stop<start&&step9===1&&(step9=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d(values,dtype)},attrs={start,stop,step:step9,dtype};return ENGINE.runKernelFunc(forward,{},null,Range,attrs)}function reciprocal_(x){let $x=convertToTensor(x,"x","reciprocal"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.reciprocal($x);return save([$x]),res},inputs,null,Reciprocal)}var reciprocal=op({reciprocal_});function relu_(x){let $x=convertToTensor(x,"x","relu"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast($x,"int32"):backend3.relu($x)),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Relu)}var relu=op({relu_});function relu6_(x){let $x=convertToTensor(x,"x","relu6"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast($x,"int32"):backend3.relu6($x)),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Relu6)}var relu6=op({relu6_});function reverse_(x,axis){let $x=convertToTensor(x,"x","reverse"),forward=backend3=>{let axes=parseAxisParam(axis,$x.shape);if($x.rank===0)return clone($x);let res=backend3.reverse($x,axes);return reshape(res,$x.shape)},inputs={x:$x},attrs={dims:axis};return ENGINE.runKernelFunc(forward,inputs,null,Reverse,attrs)}var reverse=op({reverse_});function reverse1d_(x){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let $x=convertToTensor(x,"x","round"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.round($x),inputs,null,Round)}var round=op({round_});function rsqrt_(x){let $x=convertToTensor(x,"x","rsqrt"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.rsqrt($x);return save([$x]),res},inputs,null,Rsqrt)}var rsqrt=op({rsqrt_});function selu_(x){let $x=convertToTensor(x,"x","selu"),forward=(backend3,save)=>{let res=backend3.selu($x);return save([$x]),res},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Selu)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad11,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad11,dataFormat,dilation),pointwiseStride=1,res=conv2d(depthwise,$pointwiseFilter,pointwiseStride,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;let buffer11=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer11.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer11.toTensor(),indices.toTensor()]}var setdiff1dAsync=setdiff1dAsync_;function sign_(x){let $x=convertToTensor(x,"x","sign"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.sign($x),inputs,null,Sign)}var sign=op({sign_});function sin_(x){let $x=convertToTensor(x,"x","sin"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sin($x);return save([$x]),res},inputs,null,Sin)}var sin=op({sin_});function sinh_(x){let $x=convertToTensor(x,"x","sinh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sinh($x);return save([$x]),res},inputs,null,Sinh)}var sinh=op({sinh_});function slice1d_(x,begin,size){let $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernelFunc((backend3,save)=>{let y=backend3.softmax($logits,dim);return save([y]),y},inputs,null,Softmax,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=input2.as2D(batch,innerDimensionSize),result=backend3.fft(input2D);return result.reshape(input2.shape)},inputs,null,FFT)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernelFunc(backend3=>{let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=reshape(input2,[batch,innerDimensionSize]),result=backend3.ifft(input2D);return reshape(result,input2.shape)},inputs,null,IFFT)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}function split_(x,numOrSizeSplits,axis=0){let $x=convertToTensor(x,"x","split"),forward=(backend3,_)=>{let $axis=parseAxisParam(axis,$x.shape)[0],splitSizes=prepareSplitSize($x,numOrSizeSplits,$axis);return backend3.split($x,splitSizes,$axis)},inputs={x:$x},attr={numOrSizeSplits,axis};return ENGINE.runKernelFunc(forward,inputs,null,SplitV,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){let begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let $x=convertToTensor(x,"x","sqrt"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.sqrt($x);return save([$x]),res},inputs,null,Sqrt)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let forward=(backend3,save)=>{let res=backend3.squaredDifference($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE.runKernelFunc(forward,inputs,null,SquaredDifference,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack");if(assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length===1)return expandDims($tensors[0],axis);let rank=$tensors[0].rank,shape=$tensors[0].shape,dtype=$tensors[0].dtype;assert(axis<=rank,()=>"Axis must be <= rank of the tensor"),$tensors.forEach(t=>{assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=$tensors.map(t=>expandDims(t,axis));return concat(expandedTensors,axis)}var stack=op({stack_});function step_(x,alpha=0){let $x=convertToTensor(x,"x","step"),inputs={x:$x},attrs={alpha};return ENGINE.runKernelFunc(backend3=>backend3.step($x,alpha),inputs,null,Step,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let $x=convertToTensor(x,"x","stridedSlice"),forward=backend3=>{strides==null&&(strides=new Array(begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=$x.rank-begin.length,expandAxes=maskToAxes(newAxisMask),newShape=$x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)}),$x=reshape($x,newShape);let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes($x.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided)return reshape(slice($x,begin,size),outShape);let res=backend3.stridedSlice($x,begin,end,strides);return reshape(res,outShape)},inputs={x:$x},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernelFunc(forward,inputs,null,StridedSlice,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let $x=convertToTensor(x,"x","tan"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{let res=backend3.tan($x);return save([$x]),res},inputs,null,Tan)}var tan=op({tan_});function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernelFunc(b=>b.topk($x,k,sorted),inputs,null,TopK,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean7=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean7,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}var truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){let $x=convertToTensor(x,"x","unique",null);assert($x.rank>0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments},forward=(backend3,save)=>{let res=backend3.unsortedSegmentSum($x,$segmentIds,numSegments);return save([$segmentIds]),res};return ENGINE.runKernelFunc(forward,inputs,null,UnsortedSegmentSum,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`),axis<0&&(axis+=$x.shape.length);let inputs={value:$x},attrs={axis},forward=backend3=>backend3.unstack($x,axis);return ENGINE.runKernelFunc(forward,inputs,null,Unpack,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);let inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){let loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){let $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}var whereAsync=whereAsync_;async function booleanMaskAsync_(tensor168,mask,axis){let $tensor=convertToTensor(tensor168,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];let targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor168!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}var booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}var equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp13){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp13.shape,"Error in powStrict: "),pow(base2,exp13)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");let $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}var addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");let norm5=normImpl(x,ord,axis),keepDimsShape=norm5.shape;if(keepDims){let axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm5.shape,axes)}return reshape(norm5,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}var norm=op({norm_});function movingAverage_(v,x,decay,step9,zeroDebias=!0){let $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step9!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step9,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let forward=backend3=>backend3.scatterND($indices,$updates,shape),inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernelFunc(forward,inputs,null,ScatterNd,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernelFunc(backend3=>backend3.sparseToDense($sparseIndices,$sparseValues,outputShape,$defaultValue),inputs,null,SparseToDense,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),$x=convertToTensor(x,"x","gatherND"),forward=backend3=>backend3.gatherND($x,$indices),inputs={params:$x,indices:$indices};return ENGINE.runKernelFunc(forward,inputs,null,GatherNd)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){let $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){let cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){let $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){let offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor4(precision3,$targets.shape,"bool")}var inTopKAsync=inTopKAsync_;var fused_ops_exports={};__export(fused_ops_exports,{conv2d:()=>conv2d5,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad11,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let dilations=1,$dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(x4D.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat);return backend3.conv2dDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad11,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,Conv2DBackpropFilter,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>{let gradientMode=gradientDepth>0;return!gradientMode||activation2==="linear"};function fusedConv2d_({x,filter,strides,pad:pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad11,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad11),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad11),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},forward=backend3=>{let res=backend3.fusedConv2d({input:x4D,filter:$filter,convInfo,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return res},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2};if(bias==null){let customOp=customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedConv2D,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOp(x4D,$filter)}else{let customOpWithBias=customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedConv2D,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOpWithBias(x4D,$filter,$bias)}}var conv2d5=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad11,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo(x.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!0);return backend3.depthwiseConv2DDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad11,dimRoundingMode,dilations,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropFilter,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad11,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let forward=backend3=>{let convInfo=computeConv2DInfo(xShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0);return backend3.depthwiseConv2DDerInput(dy4D,filter,convInfo)},inputs={dy:dy4D,filter},attrs={strides,pad:pad11,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropInput,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad11,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad11,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad11,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},forward=backend3=>{let res=backend3.fusedDepthwiseConv2D({input:x4D,filter:$filter,convInfo,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return res},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2};if(bias==null){let customOp=customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedDepthwiseConv2D,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOp(x4D,$filter)}else{let customOpWithBias=customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedDepthwiseConv2D,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOpWithBias(x4D,$filter,$bias)}}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},forward=backend3=>{let y=backend3.fusedBatchMatMul({a:a3D,b:b3D,transposeA,transposeB,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return y},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2};if(bias==null){let customOp=customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,_FusedMatMul,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}});return customOp(a3D,b3D)}else{let customOpWithBias=customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,_FusedMatMul,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}});return customOpWithBias(a3D,b3D,$bias)}}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal2.size;){let padLen=start+frameLength-signal2.size,pad11=concat([slice(signal2,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad11),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}var frame=op({frame_});function stft_(signal2,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));let framedSignal=frame(signal2,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}var stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method,extrapolationValue){let $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32");method=method||"bilinear",extrapolationValue=extrapolationValue||0;let numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let forward=backend3=>backend3.cropAndResize($image,$boxes,$boxInd,cropSize,method,extrapolationValue),inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize},res=ENGINE.runKernelFunc(forward,inputs,null,CropAndResize,attrs);return res}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},res=ENGINE.runKernel(FlipLeftRight,inputs,{});return res}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center},res=ENGINE.runKernel(RotateWithOffset,inputs,attrs);return res}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernelFunc(b=>b.nonMaxSuppression($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold),{boxes:$boxes,scores:$scores},null,NonMaxSuppressionV3,attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0).selectedIndices}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices:tensor1d(selectedIndices,"int32")};return returnScoresTensor&&(result.selectedScores=tensor1d(selectedScores,"float32")),returnValidOutputs&&(result.validOutputs=scalar(validOutputs,"int32")),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),res=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,forward=(backend3,save)=>(save([batchImages]),backend3.resizeBilinear(batchImages,newHeight,newWidth,alignCorners)),inputs={images:batchImages},attrs={alignCorners,size},res=ENGINE.runKernelFunc(forward,inputs,null,ResizeBilinear,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[newHeight,newWidth]=size,inputs={images:batchImages},attrs={alignCorners,size},forward=(backend3,save)=>(save([batchImages]),backend3.resizeNearestNeighbor(batchImages,newHeight,newWidth,alignCorners)),res=ENGINE.runKernelFunc(forward,inputs,null,ResizeNearestNeighbor,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){let proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}var gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j<iters;++j){let rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_});var Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses8,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses8,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses8=abs(sub($labels,$predictions));return computeWeightedLoss(losses8,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses8=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses8,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses8=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses8,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses8=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses8,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses8=sub(l13,l23);return computeWeightedLoss(losses8,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses8=squaredDifference($labels,$predictions);return computeWeightedLoss(losses8,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses8=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses8,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);let customOp=customGrad((labels2,logits2,save)=>{let keepDims=!0,lse=logSumExp(logits2,[dim],keepDims),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2)),value=sum2(costVector,[dim]),gradFunc=(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]};return{value,gradFunc}});return customOp(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses8=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses8,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_});var spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy};var Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables5=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.rho,config.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config){return new cls(config.learningRate,config.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables5=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config){return new cls(config.learningRate,config.beta1,config.beta2,config.epsilon,config.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);varNames.forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config){return new cls(config.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config){return new cls(config.learningRate,config.momentum,config.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables5=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables5.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config){return new cls(config.learningRate,config.decay,config.momentum,config.epsilon,config.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}};MomentumOptimizer,SGDOptimizer,AdadeltaOptimizer,AdagradOptimizer,RMSPropOptimizer,AdamaxOptimizer,AdamOptimizer;var train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam};var delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,linspaceImpl:()=>linspaceImpl,log:()=>log6,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function getImageCenter(center,imageHeight,imageWidth){let centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod5,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod5),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);let spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){let permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{let permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2===1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){let sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){let sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}var SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805;var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log6(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real8,imag8){if(real8.length!==imag8.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real8.length}, imag: ${imag8.length}.`);let result=new Float32Array(real8.length*2);for(let i=0;i<result.length;i+=2)result[i]=real8[i/2],result[i+1]=imag8[i/2];return result}function splitRealAndImagArrays(complex11){let real8=new Float32Array(complex11.length/2),imag8=new Float32Array(complex11.length/2);for(let i=0;i<complex11.length;i+=2)real8[i/2]=complex11[i],imag8[i/2]=complex11[i+1];return{real:real8,imag:imag8}}function complexWithEvenIndex(complex11){let len=Math.ceil(complex11.length/4),real8=new Float32Array(len),imag8=new Float32Array(len);for(let i=0;i<complex11.length;i+=4)real8[Math.floor(i/4)]=complex11[i],imag8[Math.floor(i/4)]=complex11[i+1];return{real:real8,imag:imag8}}function complexWithOddIndex(complex11){let len=Math.floor(complex11.length/4),real8=new Float32Array(len),imag8=new Float32Array(len);for(let i=2;i<complex11.length;i+=4)real8[Math.floor(i/4)]=complex11[i],imag8[Math.floor(i/4)]=complex11[i+1];return{real:real8,imag:imag8}}function getComplexWithIndex(complex11,index){let real8=complex11[index*2],imag8=complex11[index*2+1];return{real:real8,imag:imag8}}function assignToTypedArray(data,real8,imag8,index){data[index*2]=real8,data[index*2+1]=imag8}function exponents(n,inverse){let real8=new Float32Array(n/2),imag8=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){let x=(inverse?2:-2)*Math.PI*(i/n);real8[i]=Math.cos(x),imag8[i]=Math.sin(x)}return{real:real8,imag:imag8}}function exponent(k,n,inverse){let x=(inverse?2:-2)*Math.PI*(k/n),real8=Math.cos(x),imag8=Math.sin(x);return{real:real8,imag:imag8}}function castTensor(x,dtype,backend3){if(dtype==="complex64"){if(x.dtype==="complex64")return x.clone();let zerosTensor=zeros(x.shape),floatX=cast(x,"float32"),result=backend3.complex(floatX,zerosTensor);return zerosTensor.dispose(),floatX.dispose(),result}if(!hasEncodingLoss(x.dtype,dtype))return ENGINE.makeTensorFromDataId(x.dataId,x.shape,dtype);if(x.dtype==="complex64"){let real8=backend3.real(x),result=cast(real8,dtype);return real8.dispose(),result}if(dtype==="int32")return backend3.int(x);if(dtype==="bool"){let zero=scalar(0,x.dtype),result=backend3.notEqual(x,zero);return zero.dispose(),result}else throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}function reshapeTensor(x,shape){return ENGINE.makeTensorFromDataId(x.dataId,shape,x.dtype)}function linspaceImpl(start,stop,num){let step9=(stop-start)/(num-1),values=makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d(values,"float32")}var kernel_impls_exports={};__export(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,split:()=>split5,tile:()=>tile4,topkImpl:()=>topkImpl,whereImpl:()=>whereImpl});function split5(x,sizeSplits,axis){let begin=new Array(x.rank).fill(0),size=x.shape.slice();return sizeSplits.map(s=>{let sliceSize=[...size];sliceSize[axis]=s;let sliceT=slice(x,begin,sliceSize);return begin[axis]+=s,sliceT})}function tile4(xBuf,reps){let newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];let result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){let newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result.toTensor()}function topkImpl(x,xShape,xDtype,k,sorted){let lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){let offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);let outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}let outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[tensor4(allTopKVals,outputShape,xDtype),tensor4(allTopKIndices,outputShape,"int32")]}var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}};var acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}};var acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}};var addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)};return{a:derA,b:derB}}};var addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}};var argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}};var argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}};var asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}};var asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}};var atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)};return{a:derA,b:derB}}};var atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}};var atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dBackprop_(dy,input2,filterSize,strides,dilations=[1,1,1],pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dBackprop"),$input=convertToTensor(input2,"input","avgPool3dBackprop"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool3DInfo(input5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode);return backend3.avgPool3dBackprop(dy5D,input5D,convInfo)},inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool3DBackprop,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dBackprop=op({avgPool3dBackprop_});var avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dBackprop(dy,x,filterSize,strides,$dilations,pad11,dimRoundingMode)}}};function avgPoolBackprop_(dy,input2,filterSize,strides,pad11){let $dy=convertToTensor(dy,"dy","avgPoolBackprop"),$input=convertToTensor(input2,"input","avgPoolBackprop");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);let forward=backend3=>{let convInfo=computePool2DInfo(input4D.shape,filterSize,strides,1,pad11);return backend3.avgPoolBackprop(dy4D,input4D,convInfo)},inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad11},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPoolBackprop,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolBackprop=op({avgPoolBackprop_});var avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad11}=attrs;return{x:()=>avgPoolBackprop(dy,x,filterSize,strides,pad11)}}};var batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}};var batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}};var broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}};var castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})};var ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})};var clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}};var concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]),derTensors=split(dy,sizeSplits,$axis);return derTensors.map(t=>()=>t)}};var conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad11,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad11,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad11,dataFormat)}}};var conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad11,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad11,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad11,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad11){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let forward=backend3=>{let dilations=1,convInfo=computeConv3DInfo(x5D.shape,filterShape,strides,dilations,pad11);return backend3.conv3dDerFilter(x5D,dy5D,convInfo)},inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad11,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,Conv3DBackpropFilterV2,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_});var conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad11}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad11),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad11)}}};var cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}};var coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}};var cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse12}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse12);return permutation!=null&&(out=transpose(out,permutation)),out}}}};var depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad11,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad11,dilations,dimRoundingMode)}}};var dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}};var divGradConfig={kernelName:Div,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))};return{a:derA,b:derB}}};var eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,backPropKernelFunc=backend3=>backend3.eluDer(dy,y),inputs={dy,y};return{x:()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,EluGrad)}}};var erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}};var expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}};var expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}};var floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})};var floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))};return{a:derA,b:derB}}};var fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean7,variance,scale2]=saved,scaleValue=scale2==null?scalar(1):scale2,reductionAxes=getReductionAxes(mean7.shape,x.shape),tileShape=[];if(mean7.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}let xMinusMean=sub(x,mean7),dyTimesScaleValue=mul(dy,scaleValue),oneOverSqrtVariance=rsqrt(add2(variance,scalar(varianceEpsilon))),minusHalfRCube=mul(mul(mul(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar(-.5)),derX=()=>mean7.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean7.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),derMean=()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean7.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean7.shape)},derVariance=()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean7.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean7.shape)},derScale=()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean7.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean7.shape)},derOffset=()=>{let offsetDer=dy;return mean7.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean7.shape)};return{x:derX,mean:derMean,variance:derVariance,scale:derScale,offset:derOffset}}};var gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0],derX=()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad};return{x:derX,indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat(arrays){let result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}var greaterEqualGradConfig={kernelName:GreaterEqual,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}};var identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})};var isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})};var isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})};var isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})};var log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}};var logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}};var logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax6=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax6))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let forward=backend3=>backend3.LRNGrad(dy,x,y,depthRadius,bias,alpha,beta),inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernelFunc(forward,inputs,null,LRNBackprop,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_});var lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape(y,expandShapeToKeepDim(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape(dy,expandShapeToKeepDim(dy.shape,origAxes))),{x:()=>{let dx=mul(dy,cast(equal(xOrig,y),dy.dtype));return dx}}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}};var maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul(dy,cast(greaterEqual(a,b),"float32")),derB=()=>mul(dy,cast(less(a,b),"float32"));return{a:derA,b:derB}}};function maxPool3dBackprop_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dBackprop"),$input=convertToTensor(input2,"input","maxPool3dBackprop"),$output=convertToTensor(output,"output","maxPool3dBackprop"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool3DInfo(input5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode);return backend3.maxPool3dBackprop(dy5D,input5D,output5D,convInfo)},inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool3DBackprop,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dBackprop=op({maxPool3dBackprop_});var maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dBackprop(dy,x,y,filterSize,strides,$dilations,pad11,dimRoundingMode)}}};function maxPoolBackprop_(dy,input2,output,filterSize,strides,pad11,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolBackprop"),$input=convertToTensor(input2,"input","maxPoolBackprop"),$output=convertToTensor(output,"output","maxPoolBackprop");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);let forward=backend3=>{let convInfo=computePool2DInfo($input.shape,filterSize,strides,1,pad11,dimRoundingMode);return backend3.maxPoolBackprop($dy,$input,$output,convInfo)},inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad11,dimRoundingMode};return ENGINE.runKernelFunc(forward,inputs,null,MaxPoolBackprop,attrs)}var maxPoolBackprop=op({maxPoolBackprop_});var maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad11}=attrs;return{x:()=>maxPoolBackprop(dy,x,y,filterSize,strides,pad11)}}};var minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}};var minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,derA=()=>mul(dy,cast(lessEqual(a,b),"float32")),derB=()=>mul(dy,cast(greater(a,b),"float32"));return{a:derA,b:derB}}};var mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}};var modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},derB=()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}};var multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}};var negateGradConfig={kernelName:Negate,gradFunc:dy=>({x:()=>neg(dy)})};var oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}};var onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})};var padV2GradConfig={kernelName:PadV2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}};var powGradConfig={kernelName:Pow,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp13=b,outShape=assertAndGetBroadcastShape(base2.shape,exp13.shape),derBase=()=>{let expFloat=cast(exp13,"float32"),res=mul(dy,mul(expFloat,pow(base2,sub(expFloat,scalar(1))))),reduceAxes=getReductionAxes(base2.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,base2.shape)},derExp=()=>{let condition=greater(base2,0),logBase=where(condition,log(base2),zerosLike(base2)),res=mul(dy,mul(y,logBase)),reduceAxes=getReductionAxes(exp13.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,exp13.shape)};return{a:derBase,b:derExp}}};var preluGradConfig={kernelName:Prelu,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha)),alpha:()=>{let res=where(mask,zerosLike(dy),mul(dy,x)),reduceAxes=getReductionAxes(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,alpha.shape)}}}};var reciprocalGradConfig={kernelName:Reciprocal,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,neg(square(x)))}}};var relu6GradConfig={kernelName:Relu6,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul(lessEqual(x,6),step(x));return{x:()=>mul(dy,cast(mask,"float32"))}}};var reluGradConfig={kernelName:Relu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,cast(step(x),"float32"))}}};var reshapeGradConfig={kernelName:Reshape,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape(dy,x.shape)}}};var resizeBilinearGradConfig={kernelName:ResizeBilinear,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeBilinearBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,ResizeBilinearGrad,attrs);return{images:imagesDer}}};var resizeNearestNeighborGradConfig={kernelName:ResizeNearestNeighbor,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,backPropKernelFunc=backend3=>{let{alignCorners}=attrs;return backend3.resizeNearestNeighborBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,ResizeNearestNeighborGrad,attrs);return{images:imagesDer}}};var reverseGradConfig={kernelName:Reverse,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam(dims,dy.shape);return{x:()=>reverse(dy,axes)}}};var roundGradConfig={kernelName:Round,gradFunc:dy=>({x:()=>zerosLike(dy)})};var rsqrtGradConfig={kernelName:Rsqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg(div(dy,mul(pow(x,1.5),2)))}}};var selectV2PoolGradConfig={kernelName:SelectV2,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast(zerosLike(condition),"float32"),t:()=>mul(dy,cast(condition,dy.dtype)),e:()=>mul(dy,cast(logicalNot(condition),dy.dtype))}}};var seluGradConfig={kernelName:Selu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale2=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale2),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}};var sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}};var signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})};var sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}};var sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}};var sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let i=0;i<dy.rank;i++)paddings.push([begin_[i],inputShape[i]-begin_[i]-size_[i]]);return{x:()=>pad(dy,paddings)}}};var softmaxGradConfig={kernelName:Softmax,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul(dy,y);return{logits:()=>sub(dyTimesY,mul(sum2(dyTimesY,[dim],keepDims),y))}}};var softplusGradConfig={kernelName:Softplus,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,sigmoid(x))}}};var spaceToBatchNDGradConfig={kernelName:SpaceToBatchND,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND(dy,blockShape,paddings)}}};var splitVGradConfig={kernelName:SplitV,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat(dy,axis)}}};var sqrtGradConfig={kernelName:Sqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,mul(sqrt(cast(x,"float32")),2))}}};var squareGradConfig={kernelName:Square,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,mul(cast(x,"float32"),2))}}};var squaredDifferenceGradConfig={kernelName:SquaredDifference,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar(2),derA=()=>mul(dy,mul(two,sub(a,b))),derB=()=>mul(dy,mul(two,sub(b,a)));return{a:derA,b:derB}}};var stepGradConfig={kernelName:Step,gradFunc:dy=>({x:()=>zerosLike(dy)})};var subGradConfig={kernelName:Sub,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)};return{a:derA,b:derB}}};var sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs,axes=parseAxisParam(axis,x.shape);axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}};var tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}};var tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}};var tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs,derX=()=>{let xGrad=zerosLike(x);if(x.rank===1)for(let i=0;i<reps[0];++i)xGrad=add2(xGrad,slice(dy,[i*x.shape[0]],[x.shape[0]]));else if(x.rank===2)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1]],[x.shape[0],x.shape[1]]));else if(x.rank===3)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2]],[x.shape[0],x.shape[1],x.shape[2]]));else if(x.rank===4)for(let i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)for(let l=0;l<reps[3];++l)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2],l*x.shape[3]],[x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);return xGrad};return{x:derX}}};var transposeGradConfig={kernelName:Transpose,gradFunc:(dy,saved,attrs)=>{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}};var unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}};var unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved,derX=()=>gatherDropNegatives(dy,segmentIds);return{x:derX}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims(isPositive,i+1);isPositive=logicalAnd(isPositive,ones2(gathered.shape,"bool"));let zeroSlice=zerosLike(gathered);return where(isPositive,gathered,zeroSlice)}var zerosLikeGradConfig={kernelName:ZerosLike,gradFunc:dy=>({x:()=>zerosLike(dy)})};var gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negateGradConfig,oneHotGradConfig,onesLikeGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectV2PoolGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad11,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean7,variance,offset,scale2,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean7,variance,offset,scale2,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min8,max10){return this.throwIfDisposed(),clipByValue(this,min8,max10)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad11,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad11,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad11,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse12){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse12)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad11,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad11,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad11,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding2,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding2,dilationRate,strides)};Tensor.prototype.powStrict=function(exp13){return this.throwIfDisposed(),powStrict(this,exp13)};Tensor.prototype.pow=function(exp13){return this.throwIfDisposed(),pow(this,exp13)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad11,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad11,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};var exports_constraints_exports={};__export(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon(){return _epsilon==null&&(_epsilon=backend2().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;i<numValues;i++)newArray=newArray.concat(value);return newArray}else{let newArray=new Array(numValues);return newArray.fill(value),newArray}}function assert2(val,message){if(!val)throw new AssertionError(message)}function count(array2,refernce){let counter=0;for(let item of array2)item===refernce&&counter++;return counter}function singletonOrArray(xs){return xs.length===1?xs[0]:xs}function toList(x){return Array.isArray(x)?x:[x]}function toSnakeCase(name){let intermediate=name.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2"),insecure=intermediate.replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return insecure[0]!=="_"?insecure:"private"+insecure}function toCamelCase(identifier){return identifier.length<=1||identifier.indexOf("_")===-1?identifier:identifier.replace(/[_]+(\w|$)/g,(m,p1)=>p1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config){if(config==null||typeof config!="object")return;if(Array.isArray(config))config.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config);for(let field of fields){let value=config[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config=identifier;if(config.className==null||config.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.
+'className' and 'config' must set.`);let className=config.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config.config);let returnObj=fromConfig(cls,config.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique5(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value==null)return;if(values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult,f2=(...args)=>{let now2=util_exports.now();return now2-lastTime<waitMs||(lastTime=now2,lastResult=f(...args)),lastResult};return f2}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let className=identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config={className,config:{}};return deserializeConstraint(config)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config){return new MinMaxNorm(config)}var exports_initializers_exports={};__export(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones8,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros9});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"];var nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i<end;++i)prod5*=array2[i];return prod5}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min6(array2){return min(toArray1D(array2)).dataSync()[0]}function max8(array2){return max(toArray1D(array2)).dataSync()[0]}function range4(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);let out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast48(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){let outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims2(x,1);return tile8(y,[1,n,1])})}function flatten3(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile8(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean7=0,stddev=1,dtype,seed){return randomNormal(shape,mean7,stddev,dtype,seed)}function dot5(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather7(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square24(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu6(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training5=!1){return training5?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={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 deserializeInitializer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config={};return config.className=className,config.config={},deserializeInitializer(config)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros9(){return new Zeros}function ones8(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config){return new VarianceScaling(config)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add31,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d5,conv2d:()=>conv2d10,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d3,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot6,dropout:()=>dropout3,elu:()=>elu7,embedding:()=>embedding,flatten:()=>flatten4,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum9,minimum:()=>minimum7,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu6,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape87,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax4,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),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(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{let variable3=variableAndValue[0];variable3.write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,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 name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`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 AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}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(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){let x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec==null)continue;let ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){let xShape=x.shape;for(let key in spec.axes){let axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){let specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();let inputsList=toList(inputs),allAreSymbolic=!0;for(let input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(let input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape==null)return;if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) 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 AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`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 RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){let pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));let initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses8){if(losses8==null||Array.isArray(losses8)&&losses8.length===0)return;losses8=toList(losses8),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses8)}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){let config={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config.batchInputShape=this.batchInputShape),this.dtype!=null&&(config.dtype=this.dtype),config}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount===0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor168,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor168.sourceLayer,nodeIndex=tensor168.nodeIndex),layer.inboundNodes.length===0)return[tensor168];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i<node.inboundLayers.length;i++){let x=node.inputTensors[i],layer2=node.inboundLayers[i],nodeIndex2=node.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(let x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}var InputLayer=class extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];let inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config){if(config.batchShape==null&&config.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config.batchShape!=null&&config.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config.batchShape;config.shape!=null&&batchShape==null&&(batchShape=[null].concat(config.shape));let dtype=config.dtype;dtype==null&&(dtype="float32");let inputLayer2=new InputLayer({batchInputShape:batchShape,name:config.name,dtype,sparse:config.sparse}),outputs=inputLayer2.inboundNodes[0].outputTensors;return outputs[0]}async function resolveScalarsInLogs(logs5){if(logs5==null)return;let promises=[],keys=[],scalarsToDispose=[];for(let key in logs5){let value=logs5[key];if(typeof value!="number"){let valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){let values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs5[keys[i]]=values[i][0];dispose(scalarsToDispose)}}function disposeTensorsInLogs(logs5){if(logs5==null)return;for(let key in logs5){let value=logs5[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));var DEFAULT_YIELD_EVERY_MS=125,BaseCallback=class{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs5){}async onEpochEnd(epoch,logs5){}async onBatchBegin(batch,logs5){}async onBatchEnd(batch,logs5){}async onTrainBegin(logs5){}async onTrainEnd(logs5){}setModel(model2){}},CallbackList=class{constructor(callbacks3,queueLength=10){callbacks3==null&&(callbacks3=[]),this.callbacks=callbacks3,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(let callback of this.callbacks)callback.setParams(params)}setModel(model2){for(let callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onEpochBegin(epoch,logs5)}async onEpochEnd(epoch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onEpochEnd(epoch,logs5)}async onBatchBegin(batch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onBatchBegin(batch,logs5)}async onBatchEnd(batch,logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onBatchEnd(batch,logs5)}async onTrainBegin(logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onTrainBegin(logs5)}async onTrainEnd(logs5){logs5==null&&(logs5={});for(let callback of this.callbacks)await callback.onTrainEnd(logs5)}},BaseLogger=class extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs5){logs5==null&&(logs5={});let batchSize=logs5.size==null?0:logs5.size;this.seen+=batchSize;for(let key in logs5){let value=logs5[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;let total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs5){if(logs5!=null)for(let key of this.params.metrics){if(this.totals[key]==null)continue;typeof this.totals[key]=="number"?logs5[key]=this.totals[key]/this.seen:tidy(()=>{let log10=mul(div(1,this.seen),this.totals[key]);logs5[key]=log10,this.totals[key].dispose(),keep(logs5[key])})}}},History=class extends BaseCallback{async onTrainBegin(logs5){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs5){logs5==null&&(logs5={}),this.epoch.push(epoch);for(let key in logs5)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs5[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){let valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}let values=await Promise.all(promises);for(let n=0;n<values.length;++n){let tensorToDispose=this.history[keys[n]][indices[n]];tensorToDispose.dispose(),this.history[keys[n]][indices[n]]=values[n][0]}}},CustomCallback=class extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs5){let ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.yield(epoch,batch,logs5))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs5){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs5),await this.epochBegin(epoch,logs5))}async onEpochEnd(epoch,logs5){let ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.epochEnd(epoch,logs5))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs5){this.batchBegin!=null&&(await resolveScalarsInLogs(logs5),await this.batchBegin(batch,logs5))}async onBatchEnd(batch,logs5){let ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.batchEnd(batch,logs5))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs5)),await Promise.all(ps)}async onTrainBegin(logs5){this.trainBegin!=null&&(await resolveScalarsInLogs(logs5),await this.trainBegin(logs5))}async onTrainEnd(logs5){this.trainEnd!=null&&(await resolveScalarsInLogs(logs5),await this.trainEnd(logs5))}};function standardizeCallbacks(callbacks3,yieldEvery){if(callbacks3==null&&(callbacks3={}),callbacks3 instanceof BaseCallback)return[callbacks3];if(Array.isArray(callbacks3)&&callbacks3[0]instanceof BaseCallback)return callbacks3;let callbackConfigs=toList(callbacks3);return callbackConfigs.map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors){let constructors=CallbackConstructorRegistry.constructors[+levelName];constructors.forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks3,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks3!=null&&actualCallbacks.push(...callbacks3),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square24(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm5=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm5)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square24(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square24(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square24(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg20=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg20,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten3(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast48(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast48(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys){if(typeof key!="string")return!1;if(!plainObjectCheck(x[key]))return!1}return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();let trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0,nodesByDepth=[],nodes=[];for(let depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(let depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes){if(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1)continue;for(let i=0;i<node.inboundLayers.length;++i){let inboundLayer=node.inboundLayers[i].name,inboundLayerIndex=node.nodeIndices[i],inboundTensorIndex=node.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}}let name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){let tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){let item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{let tsDict={};for(let pythonicKey of Object.keys(pythonicConfig)){let pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{let tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){let pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){let item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{let pyDict={};for(let tsKey of Object.keys(tsConfig)){let tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}var version2="2.7.0";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}var FeedDict=class{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(let id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(let feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{let id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}},cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){let training5=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training5||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){let numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}let symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(srcLayer instanceof InputLayer)continue;let inputValues=[],inputMasks=[],tensorsToDispose=[],maskExists=!1;for(let input2 of symbolic.inputs){let value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training5||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);let outputTensors=toList(srcLayer.apply(inputValues,kwargs)),outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));let layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);let index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training5||dispose(tensorsToDispose)}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack9=[],marks=[];for(stack9.push(fetch3);stack9.length>0;){let top=stack9[stack9.length-1];if(visited.has(top.name)){stack9.pop();continue}let topIsMarked=marks[marks.length-1]===stack9.length-1;if(top.inputs.length===0||topIsMarked)stack9.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack9.length-1);for(let input2 of top.inputs){if(recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),visited.has(input2.name))continue;stack9.push(input2)}}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(let outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}var Container=class extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){let prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique5(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique5(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(let layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor168,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor168.sourceLayer,nodeIndex=tensor168.nodeIndex,tensorIndex=tensor168.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor168.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){let x=node.inputTensors[i],layer2=node.inboundLayers[i],nodeIndex2=node.nodeIndices[i],tensorIndex2=node.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node);nodesInProgress2.indexOf(node)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;i<node.inboundLayers.length;i++){let inboundLayer=node.inboundLayers[i],nodeIndex=node.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}let nodesByDepth={};for(let nodeID in nodesDepths){let depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}let layersByDepth={};for(let layerID in layersDepths){let depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container2 of this.internalContainerRefs)result.numDisposedVariables+=container2.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("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 weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/"),shortenNameArray=tokens.slice(0,-2).concat([tokens[tokens.length-1]]);validatedName=shortenNameArray.join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){let layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;j<node.inboundLayers.length;j++){let inboundLayer=node.inboundLayers[j],nodeIndex2=node.nodeIndices[j],tensorIndex=node.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}let outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node);for(let j=0;j<outputShapes2.length;j++){let shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}let outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){let key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));let tensorMap={};for(let i=0;i<this.inputs.length;++i){let x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}let depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){let x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}let outputTensors=[],outputMasks=[],outputShapes=[];for(let x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);let[tensor168,mask]=tensorMap[x.id];outputShapes.push(tensor168.shape),outputTensors.push(tensor168),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){let nodeConversionMap={},keptNodes;for(let layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(let layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{let losses8=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){let nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses8.push(...layer.calculateLosses())}return losses8})}getConfig(){let config={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(let layer of this.layers){let layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){let node=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex),kwargs={};if(this.containerNodes.has(nodeKey)){if(node.callArgs)try{JSON.stringify(node.callArgs),kwargs=node.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node.inboundLayers.length>0){let nodeData=[];for(let i=0;i<node.inboundLayers.length;i++){let inboundLayer=node.inboundLayers[i],nodeIndex=node.nodeIndices[i],tensorIndex=node.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex),newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}let dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config.layers=layerConfigs;let modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){let layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}config.inputLayers=modelInputs;let modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){let layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);let tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}return config.outputLayers=modelOutputs,config}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){let inputTensors2=[],kwargs;for(let inputData of nodeData){let inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}let inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}let inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config.customObjects!=null?config.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer;let inboundNodesData=layerData.inboundNodes;inboundNodesData.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config.name,layersFromConfig=config.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layer=createdLayers[layerName],layerOutputTensors=layer.inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layer=createdLayers[layerName],layerOutputTensors=layer.inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("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(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses8,sampleWeights){return mul(losses8,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=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 ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data){if(data.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data[0],ys:data[1]}}async function fitDataset(model2,dataset5,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks3=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks3,verbose,args.epochs,null,null,getStepsPerEpoch(dataset5,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset5.iterator();for(;epoch<args.epochs;){let epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset5.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){let iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);let sampleWeights=[];if(args.classWeight!=null){let standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}let ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset5,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset5.size)&&(stepsPerEpoch=dataset5.size),stepsPerEpoch}function isDatasetObject(dataset5){return typeof dataset5.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset5,args){args=args||{};let hasBatches=args.batches!=null,f=model2.testFunction,outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset5)?dataset5:await dataset5.iterator(),numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){let iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));let batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){let oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather7(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks3,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range4(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks3,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);let epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);let epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){let valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){let label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks3=standardizeCallbacks(args.callbacks,args.yieldEvery),out=await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks3,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null);return out}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){let tensor168=tensors[i];if(tensor168.rank===1)outs.push(expandDims2(tensor168,1));else{if(tensor168.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor168)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors==null)return;let oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor168=tensors[name];oldTensorIds.indexOf(tensor168.id)===-1&&tensorsToDispose.push(tensor168)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data!=null){let gotUnexpectedData=!1;if(isDataArray(data)&&data.length>0)gotUnexpectedData=!0;else if(isDataDict(data)){for(let key in data)if(data.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`)}return[]}if(data==null)return names.map(name=>null);let arrays;if(isDataDict(data)){data=data,arrays=[];for(let name of names){if(data[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data[name])}}else if(isDataArray(data)){if(data=data,data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);arrays=data}else{if(data=data,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);arrays=[data]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique5(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique5(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){let y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss==null)continue;if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){let slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){let targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}function checkInputData(data,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data)){if(data.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);arrays=data}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);arrays=[data]}if(shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;let array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;let dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);let theLosses=args.loss;lossFunctions=theLosses.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}let skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;let outputMetrics=nestedMetrics[i],handleMetrics=metrics2=>{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else{let metricFn=get2(metric);weightedMetricFn=metricFn,metricName=metricNamePrefix+getLossOrMetricName(metric)}let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}};handleMetrics(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){if(this.collectedTrainableWeights==null)return;this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset5,args){return this.makeTestFunction(),evaluateDataset(this,dataset5,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(let input2 of this.inputs){let tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}let executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){let outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length),outputsRemaining=symbolicTensorNames.length;for(let layer of this.layers){let layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){let index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor168,i)=>{tensor168==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchOuts=tidy(()=>{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});let feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)});batchOuts.forEach((batchOut,i)=>outsBatches[i].push(batchOut))}return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){let outputShape=this.feedOutputShapes[i],lossFn=this.feedLossFns[i];lossFn===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!==0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range4(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){let batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){let outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){let label=outLabels[i],newLabel=label;if(count(outLabels,label)>1){let dupIndex=count(outLabels.slice(0,i),label);newLabel+=`_${dupIndex}`}dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data=>{let lossValues=[],inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0}),totalLoss;for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=lossFunction(targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));let meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables5=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0,totalLossValue=this.optimizer_.minimize(totalLossFunction,returnCost,variables5);return[totalLossValue].concat(metricsValues)}}makeTestFunction(){this.testFunction=data=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data.slice(0,this.inputs.length),targets=data.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});let feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){let lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){let metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset5,args){return fitDataset(this,dataset5,args)}async trainOnBatch(x,y){let standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],trainFunction=this.makeTrainFunction(),losses8=trainFunction(inputs.concat(targets)),lossValues=[];for(let loss of losses8){let v=await loss.data();lossValues.push(v[0])}return dispose(losses8),singletonOrArray(lossValues)}getNamedWeights(config){let namedWeights=[],trainableOnly=config!=null&&config.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i){if(trainableOnly&&!weights[i].trainable)continue;namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]})}return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer7){this.optimizer_!==optimizer7&&(this.optimizer_=optimizer7,this.isOptimizerOwned=!1)}dispose(){let result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(let loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses8=this.loss;for(let outputName of outputNames)if(typeof losses8[outputName]=="string")lossNames[outputName]=toSnakeCase(losses8[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer7=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer:optimizer7})}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config)),returnString=!1,unusedArg=null,modelConfig=this.toJSON(unusedArg,returnString),modelArtifacts={modelTopology:modelConfig,format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null},includeOptimizer=config==null?!1:config.includeOptimizer;if(includeOptimizer&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer11,specs){let name2Tensor=io_exports.decodeWeights(buffer11,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){let shape=layer.inboundNodes[0].outputTensors[0].shape;if(shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("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(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))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(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),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{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),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 LayersModel({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(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset5,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset5,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),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(optimizer7){this.model.optimizer=optimizer7}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset5,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset5,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config instanceof Array){if(!(config[0].className!=null)||config[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config}else util_exports.assert(config.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config.layers,delete config.layers,extraModelConfig=config;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let customObjects2=void 0,layer=deserialize(conf,customObjects2,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config){return new Sequential(config)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config){return Input(config)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu6(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config={};return config.className="linear",config.config={},deserializeActivation(config)}if(typeof identifier=="string"){let config={};return config.className=identifier,config.config={},deserializeActivation(config)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square24(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config){return new cls({l1:config.l1,l2:config.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config,customObjects={}){return deserializeKerasObject(config,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let className=identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config={className,config:{}};return deserializeRegularizer(config)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){let config={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};PReLU.className="PReLU";serialization_exports.registerClass(PReLU);var ELU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ELU.className="ELU";serialization_exports.registerClass(ELU);var ThresholdedReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return x.mul(cast48(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){let config={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);var Softmax3=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){let singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding2,stride,dilation=1){if(inputLength==null)return inputLength;let dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1),outputLength;return padding2==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding2){if(dimSize==null)return null;if(padding2==="valid")dimSize=dimSize*strideSize+max8([kernelSize-strideSize,0]);else if(padding2==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding2}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding2="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding2==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding2="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding2==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding2="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding2==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`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 ValueError(`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 ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,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:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){let newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){let config={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config=super.getConfig();return delete config.rank,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config=super.getConfig();return delete config.dilationRate,config}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config){super(rank,config);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config.kernelInitializer!=null||config.kernelRegularizer!=null||config.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config.padding!=null&&config.padding!=="same"&&config.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);this.depthMultiplier=config.depthMultiplier==null?1:config.depthMultiplier,this.depthwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);let trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config=super.getConfig();return delete config.rank,delete config.kernelInitializer,delete config.kernelRegularizer,delete config.kernelConstraint,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config=super.getConfig();return delete config.rank,delete config.dataFormat,config}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=input2.resizeNearestNeighbor([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return input2.resizeNearestNeighbor([height,width])}})}getConfig(){let config={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding2="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding2==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config=super.getConfig();return config.depthMultiplier=this.depthMultiplier,config.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range4(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){let currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state6,i)=>stepOutputs[1][i].mul(stepMask).add(state6.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;if(needPerStepOutputs){let axis=1;outputs=stack(perStepOutputs,axis)}return[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range4(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){let constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});let stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`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=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training5=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("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(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training5===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state6=>keep(state6.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state6 of initialState)this.stateSpec.push(new InputSpec({shape:state6.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length);let isTensor=additionalInputs[0]instanceof SymbolicTensor;if(isTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training:training5},step9=(inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},rnnOutputs=rnn(step9,inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training5);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile8(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile8(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cellConfig=config.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.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(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training5=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training:training5}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot5(mul(inputs,dpMask),this.kernel.read()):h=dot5(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot5(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config){return new cls(config)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);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",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,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(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training5=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot5(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));let recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot5(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));let recurrentH=dot5(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));let h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config)}};GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);var GRU=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);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=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,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 biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training5=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot5(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot5(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));let[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);let h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){let baseConfig=super.getConfig(),config={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config)}};LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);var LSTM=class extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config){return config.implmentation===0&&(config.implementation=1),new cls(config)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i<this.cells.length;++i){let cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(let cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),cellConfigs=this.cells.map(getCellConfig),config={cells:cellConfigs};return Object.assign({},baseConfig,config)}static fromConfig(cls,config,customObjects={}){let cells=[];for(let cellConfig of config.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}};StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){let{ones:ones9,rate,training:training5=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones9(),rate),createMask=()=>inTrainPhase(droppedInputs,ones9,training5);if(!count2||count2<=1)return keep(createMask().clone());let masks=Array(count2).fill(void 0).map(createMask);return masks.map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t},ConvRNN2DCell=class extends RNNCell{},ConvRNN2D=class extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training5=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],batchSize=inputShape[0];if(batchSize==null)throw new ValueError("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(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training5?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){let value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state6=>keep(state6.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding:padding2,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding2,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding2,strides[1],dilationRate[1]),outShape=[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]];return outShape}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding:padding2,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding2||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training5=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training:training5,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config)}inputConv(x,w,b,padding2){let out=conv2d(x,w,this.strides,padding2||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){let strides=1;return conv2d(x,w,strides,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config){return new cls(config)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){let training5=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2),output=inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training5);return output}return inputs})}getConfig(){let config={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,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]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot5(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot5(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){let config={};this.dataFormat!=null&&(config.dataFormat=this.dataFormat);let baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Flatten.className="Flatten";serialization_exports.registerClass(Flatten);var Activation2=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config={n:this.n},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){let errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice(),known=1,unknown=null;for(let i=0;i<finalShape.length;++i){let dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}let originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!==0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range4(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={maskValue:this.maskValue};return Object.assign(config,baseConfig),config}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims),output=input2.mul(booleanMask.asType(input2.dtype));return output})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){let s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);input2.dtype!=="int32"&&(input2=cast48(input2,"int32"));let output=gather7(this.embeddings.read(),input2.as1D());return output.reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;let outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){let i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique5(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique5(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max8(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(let x of inputs){let xNDim=x.rank;if(xNDim==null){let xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]),xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){let dims=range4(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range4(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){let shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(let shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique5(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}},Add2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}};Add2.className="Add";serialization_exports.registerClass(Add2);var Multiply2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}};Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);var Average=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}};Average.className="Average";serialization_exports.registerClass(Average);var Maximum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}};Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);var Minimum2=class extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}};Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);var Concatenate=class extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(let shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(allNoneShape)return;let shapeSet=[];for(let i=0;i<inputShape.length;++i){let shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(let shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);let concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){let config={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{let adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}var Dot=class extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={stddev:this.stddev};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),noised=()=>randomNormal2(input2.shape,0,this.stddev).add(input2),output=inTrainPhase(noised,()=>input2,kwargs.training||!1);return output})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.rate>0&&this.rate<1){let noised=()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))};return inTrainPhase(noised,()=>input2,kwargs.training||!1)}return input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config={rate:this.rate};return Object.assign(config,baseConfig),config}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs),droppedInputs=()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast48(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate,x=input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));return x.mul(a).add(b)};return inTrainPhase(droppedInputs,()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean7,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean7,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean7,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean7,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean7=meanAndVariance.mean,variance=meanAndVariance.variance,normed=batchNormalization(x,mean7,variance,beta,gamma,epsilon3);return[normed,mean7,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean7=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range4(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean7.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape),normed=batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3);return[normed,mean7,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range4(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training5=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range4(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range4(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training5)return normalizeInference();let[normedTraining,mean7,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable3,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable3.read(),updateDelta=origValue.sub(value).mul(decay);variable3.write(origValue.sub(updateDelta))})},updateMovingMeanAndVariance=()=>{doMovingAverage(this.movingMean,mean7,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)};return updateMovingMeanAndVariance(),normedTraining})}getConfig(){let config={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.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(let axis of this.axis)if(!Number.isInteger(axis))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=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.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]+=nDims);for(let axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique5(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean7,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean7=mean7.tile(momentsTiling),variance=variance.tile(momentsTiling),scale2=scale2.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean7,variance,offset,scale2,this.epsilon)})}getConfig(){let config={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding2,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding2==null&&(padding2=[[1,1],[1,1]]),padding2.length!==2||padding2[0].length!==2||padding2[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding2[0],padding2[1]]:pattern=[[0,0],padding2[0],padding2[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding2,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding2),strides==null&&(strides=[1,1]),padding2==null&&(padding2="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding2==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding2,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding2),strides==null&&(strides=[1,1,1]),padding2==null&&(padding2="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding2==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool3d(inputs,poolSize,strides,padding2,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool3d(inputs,poolSize,strides,padding2,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}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(weights){this.layer.setWeights(weights)}getConfig(){let config={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config,customObjects={}){let layerConfig=config.layer,layer=deserialize(layerConfig,customObjects);delete config.layer;let newConfig={layer};return Object.assign(newConfig,config),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let step9=(inputs2,states)=>{let output=getExactlyOneTensor(this.layer.call(inputs2,kwargs));return[output,[]]},rnnOutputs=rnn(step9,inputs,[],!1,null,null,!1,!0),y=rnnOutputs[1];return y})}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=!(layerConfig.goBackwards===!0);let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state6=>new InputSpec({shape:state6.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor168 of additionalInputs)if(tensor168 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let states=this.forwardLayer.states,stateMask=states.map(state6=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config,baseConfig),config}static fromConfig(cls,config){let rnnLayer=deserialize(config.layer);if(delete config.layer,config.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu7(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu6(args){return new PReLU(args)}function softmax4(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d5(args){return new Conv1D(args)}function conv2d10(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d3(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten4(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape87(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add31(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum9(args){return new Maximum2(args)}function minimum7(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot6(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config){return new L1L2(config)}function l12(config){return l1(config)}function l22(config){return l2(config)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less7(currVal,prevVal){return currVal<prevVal}function greater11(currVal,prevVal){return currVal>prevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less7:this.mode==="max"?this.monitorFunc=greater11:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater11:this.monitorFunc=less7,this.monitorFunc===less7&&(this.minDelta*=-1)}async onTrainBegin(logs5){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less7?Infinity:-Infinity}async onEpochEnd(epoch,logs5){await resolveScalarsInLogs(logs5);let current=this.getMonitorValue(logs5);if(current==null)return;this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0))}async onTrainEnd(logs5){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs5){logs5==null&&(logs5={});let monitorValue=logs5[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs5)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping};var DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors"){let inputs=node.inputNames.slice(start,end);return inputs.map(name=>getTensor(name,tensorMap,context,resourceManager))}let tensor168=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data=tensor168.dataSync();return inputParam.type==="number"?data[0]:util_exports.toNestedArray(tensor168.shape,data)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor168=resourceManager.getHashTableHandleByName(nodeName);if(tensor168!=null)return tensor168}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");if(parts.length===1)return[name,0];let nodeName=parts[0];return[nodeName,Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad11=getParamValue("pad",node,tensorMap,context);if(pad11==="explicit"){pad11=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad11[i*2],explicitPadding[i][1]=pad11[i*2+1];return explicitPadding}return pad11}function cloneTensor(tensor168){return tensor168.kept?tensor168:clone(tensor168)}var arithmetic_exports={};__export(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"clip_value_min",name:"clipValueMin",type:"number"},{tfName:"clip_value_max",name:"clipValueMax",type:"number"}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"clipValueMin",name:"clipValueMin",type:"number",defaultValue:0},{tfName:"clipValueMax",name:"clipValueMax",type:"number",defaultValue:6}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export(control_exports,{json:()=>json3});var 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json4=[{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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json12=[{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}]}],normalization_exports={};__export(normalization_exports,{json:()=>json13});var json13=[{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}]}],reduction_exports={};__export(reduction_exports,{json:()=>json14});var json14=[{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"}]}],slice_join_exports={};__export(slice_join_exports,{json:()=>json15});var json15=[{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}]}],spectral_exports={};__export(spectral_exports,{json:()=>json16});var json16=[{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}]}],transformation_exports={};__export(transformation_exports,{json:()=>json17});var json17=[{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:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{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:[]}];var OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops69=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops69.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node});let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){typeof value=="string"&&(value=DataType[value]);switch(value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){return shape.unknownRank?void 0:shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}};var executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"Relu6":case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}var TensorArray=class{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor168=>{(keepIds==null||!keepIds.has(tensor168.tensor.id))&&tensor168.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor168){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
+          because the value dtype is ${tensor168.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor168.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor168.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor168,keep(tensor168),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i<this.size();i++)indices.push(i)}if(indices.length===0)return tensor4([],[0].concat(this.elementShape));let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,"TensorArray shape mismatch: "),stack(tensors,0)}concat(dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);if(this.size()===0)return tensor4([],[0].concat(this.elementShape));let indices=[];for(let i=0;i<this.size();i++)indices.push(i);let tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`),concat(tensors,0)}scatter(indices,tensor168){if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor168.dtype}`);if(indices.length!==tensor168.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor168.shape[0]}`);let maxIndex=Math.max(...indices);if(!this.dynamicSize&&maxIndex>=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor168,0))}split(length,tensor168){if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor168.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor168.shape[0])throw new Error(`Expected sum of lengths to be equal to
+          tensor.shape[0], but sum of lengths is
+        ${totalLength}, and tensor's shape is: ${tensor168.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor168.size/totalLength,tensors=[];tidy(()=>{tensor168=reshape(tensor168,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices2=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors[i]=reshape(slice(tensor168,indices2,sizes),this.elementShape)}return tensors});let indices=[];for(let i=0;i<length.length;i++)indices[i]=i;this.writeMany(indices,tensors)}};var TensorList=class{constructor(tensors,elementShape,elementDtype,maxNumElements=-1){this.tensors=tensors,this.elementShape=elementShape,this.elementDtype=elementDtype,tensors!=null&&tensors.forEach(tensor168=>{if(elementDtype!==tensor168.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor168.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor168.shape,"TensorList shape mismatch: "),keep(tensor168)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor168=>{(keepIds==null||!keepIds.has(tensor168.id))&&tensor168.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor168=>reshape(tensor168,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor168=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor168.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor168,elementShape)}pushBack(tensor168){if(tensor168.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor168.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor168),this.tensors.push(tensor168)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor168){if(tensor168.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor168.shape,"TensorList shape mismatch: "),keep(tensor168),this.tensors[elementIndex]=tensor168}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor4([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor4([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor168,elementShape,elementDtype){let dtype=tensor168.dtype;if(tensor168.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor168.shape}`);if(tensor168.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${elementDtype}`);let outputShape=tensor168.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor168);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor168,indices,elementShape,numElements){if(indices.length!==tensor168.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor168.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);let list=new TensorList([],elementShape,tensor168.dtype,numElements),tensors=unstack(tensor168,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split9(tensor168,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor168.shape[0])throw new Error(`Expected sum of lengths to be equal to
+          tensor.shape[0], but sum of lengths is
+        ${totalLength}, and tensor's shape is: ${tensor168.shape}`);let elementPerRow=totalLength===0?0:tensor168.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor168=reshape(tensor168,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){let previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor168,indices,sizes),elementShape)}return tensor168.dispose(),tensors2}),list=new TensorList([],elementShape,tensor168.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}var executeOp3=async(node,tensorMap,context)=>{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condValue=await cond.data();return condValue[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor168=>tensor168.id),condValue=await condResult[0].data();condResult.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&tensor168.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor168=>tensor168.id);origResult.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&resultIds.indexOf(tensor168.id)===-1&&tensor168.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&resultIds.indexOf(tensor168.id)===-1&&tensor168.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data=getParamValue("data",node,tensorMap,context);return data.kept||(data=cloneTensor(data)),(await pred.data())[0]?[void 0,data]:[data,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data=getTensor(inputName,tensorMap,context);return[cloneTensor(data)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data)]}case"Exit":{let data=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data)]}case"NextIteration":{let data=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),readTensorArray=context.getTensorArray(readId.id);return[readTensorArray.read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context),gatherTensorArray=context.getTensorArray(gatherId.id);return[gatherTensorArray.gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=context.getTensorList(gatherId.id);return[tensorList.gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor168=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor168,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split9(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad11=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad11,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad11=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad11,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad11,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad11,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad11=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad11)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad11=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad11,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad11,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad11)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad11)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad11,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step9=getParamValue("step",node,tensorMap,context);return[range(start,stop,step9,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean7=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean7,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data2=getParamValue("x",node,tensorMap,context);return[cloneTensor(data2)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data.length;i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var HashTable=class{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){let key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);let $keys=await keys.data();return tidy(()=>{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}};var executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),hashTable2=resourceManager.getHashTableById(handle.id);return[await hashTable2.import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context),hashTable2=resourceManager.getHashTableById(handle.id);return[await hashTable2.find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse12=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse12)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"GatherV2":case"Gather":{let axis=getParamValue("axis",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis)]}case"ReverseV2":case"Reverse":{let axis=getParamValue("axis",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{let begin=getParamValue("begin",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[slice(getParamValue("x",node,tensorMap,context),begin,size)]}case"StridedSlice":{let begin=getParamValue("begin",node,tensorMap,context),end=getParamValue("end",node,tensorMap,context),strides=getParamValue("strides",node,tensorMap,context),beginMask=getParamValue("beginMask",node,tensorMap,context),endMask=getParamValue("endMask",node,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node,tensorMap,context),tensor168=getParamValue("x",node,tensorMap,context);return[stridedSlice(tensor168,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor168=>{let sameShape=util_exports.arraysEqual(tensor168.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor168).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor168:reshape(tensor168,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor168=getParamValue("tensor",node,tensorMap,context);return unstack(tensor168,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor168=getParamValue("x",node,tensorMap,context);return split(tensor168,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};var executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data=>[].concat(data)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;i<this.contexts.length-1;i++){let contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),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++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&(dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name)))),usedNodes.add(node.name),weightMap[node.name]!=null)continue;if(inputNodeNames.indexOf(node.name)!==-1)continue;if(initNodeNames.indexOf(node.name)!==-1)continue;if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{if(seen.has(input2.name))return;seen.add(input2.name),frontier.push(input2)})}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],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(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor168=>tensor168.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){let node=orderedNodes[i];if(!tensorsMap[node.name]){let tensors=executeOp18(node,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node.name]=tensors,this.checkTensorForDisposal(node.name,node,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor168=>tensor168.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){if(node.category==="control"||outputNames.indexOf(nodeName)!==-1)return;tensorMap[nodeName].forEach(tensor168=>{tensor168!=null&&(intermediateTensorConsumerCount[tensor168.id]=(intermediateTensorConsumerCount[tensor168.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor168=>{if(tensor168&&!tensorsToKeep.has(tensor168.id)){let count2=intermediateTensorConsumerCount[tensor168.id];count2===1?(tensor168.dispose(),delete intermediateTensorConsumerCount[tensor168.id]):count2!=null&&intermediateTensorConsumerCount[tensor168.id]--}})}})}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{let tensorArray=tensorMap[key];tensorArray.forEach(tensor168=>{tensor168&&!tensor168.isDisposed&&!keepIds.has(tensor168.id)&&tensor168.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor168,index)=>(map[this.inputs[index].name]=tensor168,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack9=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack9.length>0;){let promises=this.processStack(inputNodes,stack9,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack9,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack9.length>0;){let item=stack9.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack9,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack9,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack9,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack9,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);if(added[nodeName]||!usedNodes.has(childNode.name))return;childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack9.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack9.push({contexts:context.currentContext,node:childNode}))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor168=>tensor168.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor168=this._signature.inputs[inputName];result[tensor168.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null){let tensor168=this._signature.outputs[name];return tensor168.name}return name},{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}};var TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature={};this.artifacts.userDefinedMetadata!=null&&(signature=this.artifacts.userDefinedMetadata.signature),this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&(modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`));let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version6="2.7.0";var dist_exports={};__export(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version8,webcam:()=>webcam,zip:()=>zip});var seedrandom3=__toModule(require_seedrandom2()),seedrandom2=__toModule(require_seedrandom2());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}let result=deepMapInternal(input2,mapFn,seen);return result}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container2){return deepMap(container2,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}};var GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}};GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}var LazyIterator=class{async toArray(){let result=[],x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){let stream=this.prefetch(100),result=[],x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){let rowBatches=this.rowMajorBatch(batchSize,smallLastBatch);return rowBatches.map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}},TakeIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length<this.batchSize;){let item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){let upstreamSummaries="TODO: fill in upstream of chained summaries";return`${upstreamSummaries} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){let upstreamSummaries="TODO: fill in upstream of zip summaries";return`{${upstreamSummaries}} -> Zip`}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container2){if(container2 instanceof LazyIterator){let result=container2.next();return{value:result.then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}}else return{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max10){return Math.floor(this.random()*max10)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}};var Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
+      ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset5){let base2=this,size;return this.size===Infinity||dataset5.size===Infinity?size=Infinity:this.size!=null&&dataset5.size!=null?size=this.size+dataset5.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset5.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size<count2||count2===void 0||count2<0)?size=0:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<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)`);let base2=this,random=seedrandom3.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}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()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i<datasets.length;i++)size=size==null?datasets[i].size:Math.min(size,datasets[i].size);else if(datasets instanceof Object)for(let ds in datasets)size=size==null?datasets[ds].size:Math.min(size,datasets[ds].size);return datasetFromIteratorFn(async()=>{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];if(canTensorify(exampleRow)){let value=batchConcat(rows);return{value,recurse:!1}}return{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor4(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){let inputIterator=await this.input.iterator(),utf8Iterator=inputIterator.decodeUTF8(),lineIterator=utf8Iterator.split(`
+`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line));return lineIterator}};var CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=!(csvConfig.hasHeader===!1),this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.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 ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs)for(let key of Object.keys(this.columnConfigs)){let index=this.fullColumnNames.indexOf(key);if(index===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let iter=await this.base.iterator(),firstElement=await iter.next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value,headers=this.parseRow(firstLine,!1);return headers}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){let key=this.fullColumnNames[i],config=this.columnConfigs?this.columnConfigs[key]:null;if(this.configuredColumnsOnly&&!config)continue;{let value=values[i],parsedValue=null;if(value==="")if(config&&config.default!==void 0)parsedValue=config.default;else{if(config&&(config.required||config.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{let valueAsNum=Number(value);if(isNaN(valueAsNum))config&&config.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config||!config.dtype)parsedValue=valueAsNum;else switch(config.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config&&config.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){let result=[],readOffset=0,readLength=line.length,currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}};var MicrophoneIterator=class extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;let fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))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=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=!(microphoneConfig.includeSpectrogram===!1),this.includeWaveform=microphoneConfig.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(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!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}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.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 spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},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(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data,i)=>freqData.set(data,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor4(vals,shape)}};var WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.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(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){let tracks=this.stream.getTracks();tracks.forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var DataSource=class{};var StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}};var ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}};var FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.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};let chunk=new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data=fileReader.result;if(data instanceof ArrayBuffer&&(data=new Uint8Array(data)),!(data instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice21=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice21)}this.offset=end});return{value:await chunk,done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>{let init2={method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity};return init2};function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}};var URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version8="2.7.0";var seedrandom4=__toModule(require_seedrandom2());function assertNotComplex(tensor168,opName){Array.isArray(tensor168)||(tensor168=[tensor168]),tensor168.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl,split10=kernel_impls_exports.split,tile9=kernel_impls_exports.tile,topkImpl2=kernel_impls_exports.topkImpl,whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine15())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
+============================
+Hi there \u{1F44B}. 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.
+============================`));let dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){let encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){let tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){let data=this.readSync(t.dataId),decodedData=data;if(t.dtype==="string")try{decodedData=data.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){let dataId=this.write(values,shape,dtype);return engine15().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){let{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.data.has(dataId)){let tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){let start=util_exports.now();f();let kernelMs=util_exports.now()-start;return{kernelMs}}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(x,begin,end,strides){assertNotComplex(x,"stridedSlice");let outShape=slice_util_exports.computeOutShape(begin,end,strides);if(outShape.some(axis=>axis===0))return tensor4([],outShape);let buffer11=buffer(outShape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<buffer11.size;i++){let loc=buffer11.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];buffer11.set(xBuf.get(...newLoc),...loc)}return buffer11.toTensor()}diag(x){let xVals=this.readSync(x.dataId),buffer11=buffer([x.size,x.size],x.dtype),vals=buffer11.values;for(let i=0;i<xVals.length;i++)vals[i*x.size+i]=xVals[i];return buffer11.toTensor()}unstack(x,axis){let num=x.shape[axis],outShape=new Array(x.rank-1),outIndex=0;for(let i=0;i<x.rank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let begin=new Array(x.rank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++)begin[axis]=i,res[i]=slice(x,begin,size).reshape(outShape);return res}reverse(x,axis){assertNotComplex(x,"reverse");let buffer11=buffer(x.shape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<buffer11.size;i++){let outLoc=buffer11.indexToLoc(i),inLoc=outLoc.slice();axis.forEach(ax=>inLoc[ax]=x.shape[ax]-1-inLoc[ax]),buffer11.set(xBuf.get(...inLoc),...outLoc)}return buffer11.toTensor()}neg(x){return assertNotComplex(x,"neg"),mul(scalar(-1),x)}addN(tensors){assertNotComplex(tensors,"addN");let vals=tensors.map(t=>this.readSync(t.dataId)),result=buffer(tensors[0].shape,tensors[0].dtype),resultVals=result.values;for(let i=0;i<tensors.length;i++){let currVals=vals[i];for(let j=0;j<resultVals.length;j++)resultVals[j]+=currVals[j]}return result.toTensor()}softmax(logits,dim){let axes=util_exports.parseAxisParam([dim],logits.shape),maxLogit=max(logits,axes),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),a=sub(logits,maxLogit.reshape(expandedShape)),b=exp(a),sumExp=this.sum(b,axes).reshape(expandedShape);return div(b,sumExp)}pow(a,b){return assertNotComplex([a,b],"pow"),this.broadcastedBinaryOp(a,b,a.dtype,(aValue,bValue)=>Math.pow(aValue,bValue))}floorDiv(a,b){assertNotComplex([a,b],"floorDiv");let op2=(a6,b2)=>Math.floor(a6/b2),outputDtype="int32";return this.broadcastedBinaryOp(a,b,outputDtype,op2)}sum(x,axes){assertNotComplex(x,"sum"),backend_util_exports.assertAxesAreInnerMostDims("sum",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),resultDtype=upcastType(x.dtype,"int32"),result=zeros(outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,sum29=0;for(let j=0;j<reduceSize;++j)sum29+=aVals[offset+j];vals[i]=sum29}return result}prod(x,axes){assertNotComplex(x,"sum");let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),resultDtype=upcastType(x.dtype,"int32"),result=zeros(outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,prod5=1;for(let j=0;j<reduceSize;++j)prod5*=aVals[offset+j];vals[i]=prod5}return result}unsortedSegmentSum(x,segmentIds,numSegments){assertNotComplex(x,"unsortedSegmentSum");let res=[],numIters=x.rank-segmentIds.rank;for(let i=0;i<numIters;++i)segmentIds=segmentIds.expandDims(i+1);for(let i=0;i<numSegments;++i){let segmentId=scalar(i,"int32"),mask=equal(segmentId,segmentIds).asType("float32"),sum29=mask.mul(x).sum(0);res.push(sum29)}return stack(res)}argMin(x,axis){assertNotComplex(x,"argMin");let axes=[axis];backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min8=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min8&&(min8=value,minIndex=j)}vals[i]=minIndex}return result}argMax(x,axis){assertNotComplex(x,"argMax");let axes=[axis];backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max10=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max10&&(max10=value,maxIndex=j)}vals[i]=maxIndex}return result}cumsum(x,axis,exclusive,reverse12){if(assertNotComplex(x,"cumsum"),axis!==x.rank-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank-1} but got axis=${axis}`);let resultDtype=upcastType(x.dtype,"int32"),result=zeros(x.shape,resultDtype),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId),finalDim=x.shape[x.rank-1],indexAdjuster=reverse12?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){let idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{let prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}return result}equal(a,b){return assertNotComplex([a,b],"equal"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal===bVal?1:0)}notEqual(a,b){return assertNotComplex([a,b],"notEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal!==bVal?1:0)}less(a,b){return assertNotComplex([a,b],"less"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal<bVal?1:0)}lessEqual(a,b){return assertNotComplex([a,b],"lessEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal<=bVal?1:0)}greater(a,b){return assertNotComplex([a,b],"greater"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal>bVal?1:0)}greaterEqual(a,b){return assertNotComplex([a,b],"greaterEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal>=bVal?1:0)}logicalAnd(a,b){return assertNotComplex([a,b],"logicalAnd"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal&&bVal)}logicalOr(a,b){return assertNotComplex([a,b],"logicalOr"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal||bVal)}select(condition,a,b){assertNotComplex([condition,a,b],"select");let values=this.readSync(condition.dataId),aValues=this.readSync(a.dataId),bValues=this.readSync(b.dataId),result=zeros(a.shape,upcastType(a.dtype,b.dtype)),newValues=this.readSync(result.dataId),index=0,offset=condition.rank===0||condition.rank>1||a.rank===1?1:util_exports.sizeFromShape(a.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=aValues[i]:newValues[index++]=bValues[i];return result}where(condition){assertNotComplex([condition],"where");let condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}topk(x,k,sorted){assertNotComplex(x,"topk");let xVals=this.readSync(x.dataId);return topkImpl2(xVals,x.shape,x.dtype,k,sorted)}min(x,axes){assertNotComplex(x,"min"),backend_util_exports.assertAxesAreInnerMostDims("min",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,min8=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value<min8&&(min8=value)}vals[i]=min8}return result}minimum(a,b){return assertNotComplex([a,b],"minimum"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>Math.min(aVal,bVal))}mod(a,b){return assertNotComplex([a,b],"mod"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>{let rem=aVal%bVal;return aVal<0&&bVal<0||aVal>=0&&bVal>=0?rem:(rem+bVal)%bVal})}maximum(a,b){return assertNotComplex([a,b],"maximum"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>Math.max(aVal,bVal))}all(x,axes){assertNotComplex(x,"all"),backend_util_exports.assertAxesAreInnerMostDims("all",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,all5=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];all5=all5&&value}vals[i]=all5}return result}any(x,axes){assertNotComplex(x,"any"),backend_util_exports.assertAxesAreInnerMostDims("any",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){let offset=i*reduceSize,anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}return result}squaredDifference(a,b){return assertNotComplex([a,b],"squaredDifference"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>{let diff=aVal-bVal;return diff*diff})}eluDer(dy,y){assertNotComplex([dy,y],"eluDer");let resultValues=new Float32Array(y.size),values=this.readSync(y.dataId),dyValues=this.readSync(dy.dataId);for(let i=0;i<values.length;++i){let v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return this.makeOutput(resultValues,y.shape,"float32")}atan2(a,b){return assertNotComplex([a,b],"atan2"),this.broadcastedBinaryOp(a,b,a.dtype,(aValue,bValue)=>Math.atan2(aValue,bValue))}tile(x,reps){return assertNotComplex(x,"tile"),tile9(this.bufferSync(x),reps)}gather(x,indices,axis){assertNotComplex([x,indices],"gather");let newShape=x.shape.slice(),indicesValues=this.readSync(indices.dataId);newShape[axis]=indicesValues.length;let result=buffer(newShape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<result.size;++i){let newLoc=result.indexToLoc(i),originalLoc=newLoc.slice();originalLoc[axis]=indicesValues[newLoc[axis]];let originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result.toTensor()}batchToSpaceND(x,blockShape,crops){assertNotComplex([x],"batchToSpaceND");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length);return transpose(x.reshape(reshaped),permuted).reshape(reshapedPermuted).slice(sliceBeginCoords,sliceSize)}pool3d(x,convInfo,poolType){assertNotComplex(x,"pool3d");let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,xValues=this.readSync(x.dataId),output=buffer(convInfo.outShape,x.dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){let outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*x.strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides,minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let xDepthOffset=inputBatchOffset+xDepth*x.strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let xRowOffset=xDepthOffset+xRow*x.strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let xColOffset=xRowOffset+xCol*x.strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}let outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output.toTensor()}avgPool3d(x,convInfo){return assertNotComplex(x,"avgPool3d"),this.pool3d(x,convInfo,"avg").toFloat()}avgPool3dBackprop(dy,x,convInfo){assertNotComplex([dy,x],"avgPool3dBackprop");let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=this.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth)continue;for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow)continue;for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let pixel=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel);dotProd+=pixel}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return dx.toTensor()}maxPool3d(x,convInfo){return assertNotComplex(x,"maxPool3d"),this.pool3d(x,convInfo,"max").toFloat()}maxPool3dPositions(x,convInfo){let maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=this.bufferSync(x);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){let xDepthCorner=yDepth*strideDepth-padFront,xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;let xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){let xRowCorner=yRow*strideHeight-padTop,xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;let xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){let xColCorner=yCol*strideWidth-padLeft,xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;let xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){let wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){let wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){let wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions.toTensor()}maxPool3dBackprop(dy,x,y,convInfo){assertNotComplex([x,y],"maxPool3dBackprop");let maxPositions=this.maxPool3dPositions(x,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),maxPosBuf=this.bufferSync(maxPositions),dyBuf=this.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){let dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft,dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){let dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth)continue;for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){let dyRow=(dyRowCorner+wRow)/strideHeight;if(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow)continue;for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){let dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;let maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;if(mask===0)continue;let pixel=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel);dotProd+=pixel*mask}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return dx.toTensor()}resizeBilinear(x,newHeight,newWidth,alignCorners){assertNotComplex(x,"resizeBilinear");let[batch,oldHeight,oldWidth,numChannels]=x.shape,xValues=this.readSync(x.dataId),result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],outputIdx=0,effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){let sourceFracRow=effectiveRowSizeRatio*r,sourceRowFloor=Math.floor(sourceFracRow),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*x.strides[0]+sourceRowFloor*x.strides[1],botRowOffset=b*x.strides[0]+sourceRowCeil*x.strides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=effectiveColSizeRatio*c,sourceColFloor=Math.floor(sourceFracCol),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*x.strides[2],botLeftOffset=botRowOffset+sourceColFloor*x.strides[2],topRightOffset=topRowOffset+sourceColCeil*x.strides[2],botRightOffest=botRowOffset+sourceColCeil*x.strides[2];for(let d=0;d<numChannels;d++){let topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return tensor4(result,[batch,newHeight,newWidth,numChannels])}resizeBilinearBackprop(dy,x,alignCorners){assertNotComplex([dy,x],"resizeBilinearBackprop");let[batch,xHeight,xWidth,depth]=x.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=this.readSync(dy.dataId),offset=0;for(let b=0;b<batch;b++){let bOffset=b*x.strides[0];for(let r=0;r<yHeight;r++){let dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*x.strides[1],bottomDxROffset=bOffset+bottomDxRIndex*x.strides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){let dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*x.strides[2],topRightRCOffset=topDxROffset+rightDxCIndex*x.strides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*x.strides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*x.strides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){let dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return tensor4d(output,[batch,xWidth,xHeight,depth],x.dtype)}resizeNearestNeighbor(x,newHeight,newWidth,alignCorners){assertNotComplex(x,"resizeNearestNeighbor");let[batch,oldHeight,oldWidth,numChannels]=x.shape,xValues=this.readSync(x.dataId),output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1],outputOffset=0;for(let b=0;b<batch;b++){let batchOffset=b*x.strides[0];for(let r=0;r<newHeight;r++){let sourceFracRow=effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow)),rowOffset=batchOffset+sourceNearestRow*x.strides[1];for(let c=0;c<newWidth;c++){let sourceFracCol=effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol)),colOffset=rowOffset+sourceNearestCol*x.strides[2];for(let d=0;d<numChannels;d++){let newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return tensor4(output,[batch,newHeight,newWidth,numChannels],x.dtype)}resizeNearestNeighborBackprop(dy,x,alignCorners){assertNotComplex([dy,x],"resizeNearestNeighborBackprop");let[batch,xHeight,xWidth,depth]=x.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=this.readSync(dy.dataId),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){let batchOffset=b*x.strides[0];for(let r=0;r<xHeight;r++){let rowOffset=batchOffset+r*x.strides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){let colOffset=rowOffset+c*x.strides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){let dyR=dyRIndex+startDyR;if(dyR<0||dyR>=yHeight)continue;let dyROffset=batchOffset+dyR*dy.strides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r!==sourceNearestRow)continue;for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){let dyC=dyCIndex+startDyC;if(dyC<0||dyC>=yWidth)continue;let dyCOffset=dyROffset+dyC*dy.strides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}output[colOffset+d]=accum}}}}return tensor4d(output,x.shape,x.dtype)}localResponseNormalization4D(x,depthRadius,bias,alpha,beta){assertNotComplex(x,"localResponseNormalization4D");let channels=x.shape[3],maxD=channels-1,xValues=this.readSync(x.dataId),size=x.size,result=new Float32Array(size);function sumAcrossChannels(offset){let currentChannel=offset%channels,beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius),endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD),sum29=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){let z=xValues[beginSumOffset];sum29+=z*z}return sum29}for(let offset=0;offset<size;offset++){let sum29=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum29,-beta);result[offset]=val}return tensor4d(result,x.shape)}LRNGrad(dy,inputImage,outputImage,depthRadius,bias,alpha,beta){assertNotComplex(dy,"LRNGrad");let channels=dy.shape[3],dyValues=this.readSync(dy.dataId),inputImageValues=this.readSync(inputImage.dataId),outputImageValues=this.readSync(outputImage.dataId),result=new Float32Array(dy.size),size=dy.size;for(let offset=0;offset<size;offset++){let currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1),norm5=0;for(let k=depthBegin;k<depthEnd;k++)norm5+=Math.pow(inputImageValues[k],2);norm5=alpha*norm5+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*inputImageValues[k]*outputImageValues[offset]/norm5;offset===k&&(dyi+=Math.pow(norm5,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return tensor4d(result,dy.shape)}multinomial(logits,normalized,numSamples,seed){assertNotComplex(logits,"multinomial");let probabilities=normalized?logits:softmax(logits),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],res=zeros([batchSize,numSamples],"int32"),resVals=this.readSync(res.dataId),probVals=this.readSync(probabilities.dataId);for(let b=0;b<batchSize;++b){let offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];let random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){let r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return res}oneHot(indices,depth,onValue,offValue){assertNotComplex(indices,"oneHot");let res=new Float32Array(indices.size*depth);res.fill(offValue);let indicesVal=this.readSync(indices.dataId);for(let event=0;event<indices.size;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return tensor2d(res,[indices.size,depth],"int32")}nonMaxSuppression(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){assertNotComplex(boxes,"nonMaxSuppression");let boxesVals=this.readSync(boxes.dataId),scoresVals=this.readSync(scores.dataId);return nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold)}depthToSpace(x,blockSize,dataFormat){util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=this.readSync(x.dataId),result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth),outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){let inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){let inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){let inD=d+offsetD,inputIdx=inD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return tensor4d(result,[batchSize,outputHeight,outputWidth,outputDepth])}broadcastedBinaryOp(a,b,dtype,op2){let newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),result=buffer(newShape,dtype),aVals=this.readSync(a.dataId),bVals=this.readSync(b.dataId),aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),resVals=result.values;if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resVals.length;++i)resVals[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else{let aBuf=this.bufferSync(a),bBuf=this.bufferSync(b);for(let i=0;i<resVals.length;++i){let loc=result.indexToLoc(i),aLoc=loc.slice(-a.rank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=aBuf.locToIndex(aLoc),bLoc=loc.slice(-b.rank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=bBuf.locToIndex(bLoc);resVals[i]=op2(aVals[aIndex],bVals[bIndex])}}return result.toTensor()}split(x,sizeSplits,axis){return split10(x,sizeSplits,axis)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(images,boxes,boxIndex,cropSize,method,extrapolationValue){let[batch,imageHeight,imageWidth,numChannels]=images.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=this.readSync(boxes.dataId),boxIndVals=this.readSync(boxIndex.dataId),imageVals=this.readSync(images.dataId),inStride=images.strides,outStride=output.strides;for(let b=0;b<numBoxes;b++){let startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(bInd>=batch)continue;let heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){let yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){let topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0],topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];let topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];let bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){let xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){let ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}let closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){let inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}return output.toTensor()}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1;return this.scatter(sparseIndices,sparseValues,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices)}gatherND(x,indices){let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(x,indices);if(numSlices===0)return tensor4([],resultShape,x.dtype);let buffer11=new TensorBuffer([numSlices,sliceSize],x.dtype),indicesData=this.readSync(indices.dataId),xData=this.readSync(x.dataId);for(let i=0;i<numSlices;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=x.size/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);for(let k=0;k<sliceSize;k++)buffer11.values[i*sliceSize+k]=xData[flattenIndex*sliceSize+k]}return buffer11.toTensor().reshape(resultShape)}scatterND(indices,updates,shape){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),defaultValue=scalar(0),sumDupeIndices=!0;return this.scatter(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices)}onesLike(x){if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");return fill(x.shape,1,x.dtype)}zerosLike(x){let values=util_exports.getArrayFromDType(x.dtype,util_exports.sizeFromShape(x.shape));return this.makeOutput(values,x.shape,x.dtype)}linspace(start,stop,num){return backend_util_exports.linspaceImpl(start,stop,num)}scatter(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){let flattenShape=[outputSize/sliceSize,sliceSize],indicesData=this.readSync(indices.dataId),updatesData=this.readSync(updates.dataId);if(outputSize===0)return tensor4([],shape,updates.dtype);let buffer11=new TensorBuffer(flattenShape,updates.dtype);buffer11.values.fill(this.readSync(defaultValue.dataId)[0]);for(let i=0;i<numUpdates;i++){let index=[],flattenIndex=0;for(let j=0;j<sliceRank;j++){let dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?buffer11.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:buffer11.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return buffer11.toTensor().reshape(shape)}},shared_exports={};__export(shared_exports,{addImpl:()=>addImpl,ceilImpl:()=>ceilImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,multiplyImpl:()=>multiplyImpl,notEqualImpl:()=>notEqualImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,subImpl:()=>subImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){let resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}var abs9=args=>{let{x}=args.inputs,cpuBackend=args.backend,resultValues=new Float32Array(util_exports.sizeFromShape(x.shape));if(x.dtype!=="complex64"){let values=cpuBackend.data.get(x.dataId).values;resultValues=simpleAbsImpl(values)}else{let complexVals=cpuBackend.data.get(x.dataId),real8=complexVals.complexTensorInfos.real,imag8=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real8.dataId).values,imagVals=cpuBackend.data.get(imag8.dataId).values;for(let i=0;i<realVals.length;i++){let real9=realVals[i],imag9=imagVals[i];resultValues[i]=Math.hypot(real9,imag9)}}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs9};function createSimpleBinaryKernelImpl(op2){return(aShape,bShape,aVals,bVals,dtype)=>{let newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op2(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex9(args){let{inputs,backend:backend3}=args,{real:real8,imag:imag8}=inputs,realVals=backend3.data.get(real8.dataId).values,imagVals=backend3.data.get(imag8.dataId).values,complexInfo=backend3.makeTensorInfo(real8.shape,"complex64"),complex11=backend3.data.get(complexInfo.dataId);return complex11.complexTensorInfos={real:backend3.makeTensorInfo(real8.shape,"float32",realVals),imag:backend3.makeTensorInfo(imag8.shape,"float32",imagVals)},complexInfo}var complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex9};function identity2(args){let{inputs,backend:backend3}=args,{x}=inputs;return backend3.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,real8=backend3.data.get(input2.dataId).complexTensorInfos.real,realVal=backend3.data.get(real8.dataId).values;return backend3.makeTensorInfo(real8.shape,real8.dtype,realVal)}var realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real6};function cast49(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend3});let zerosTensor=zeros(x.shape),floatX=cast49({inputs:{x},backend:backend3,attrs:{dtype:"float32"}}),result=complex9({inputs:{real:floatX,imag:zerosTensor},backend:backend3});return zerosTensor.dispose(),backend3.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real6({inputs:{input:x},backend:backend3}),result=cast49({inputs:{x:realPart},backend:backend3,attrs:{dtype}});return backend3.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity2({inputs:{x},backend:backend3});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){let values=backend3.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend3.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){let xVals=backend3.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend3.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast49};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend3})=>{let{a,b}=inputs,cpuBackend=backend3;assertNotComplex([a,b],name);let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend3})=>{let{a,b}=inputs,cpuBackend=backend3;if(a.dtype==="complex64"||b.dtype==="complex64"){let $aComplex=cast49({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast49({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex9({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{let aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op2){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{let resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){let aIdx=i%aVals.length,bIdx=i%bVals.length,result=op2(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){let loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);let aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);let bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op2(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}var addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add32=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add32};function createSimpleUnaryImpl(op2){return(values,dtype,attrs)=>{let newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op2(values[i],attrs);return newValues}}function unaryKernelFunc(name,op2,dtype){return({inputs,attrs,backend:backend3})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend3,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op2(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend3})=>{let{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");let cpuBackend=backend3,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}var ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil4=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil4};var expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp12=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp12};var expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm14=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm14};var floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor6=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor6};var logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log9=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log9};function maxImpl(aVals,reduceSize,outShape,dtype){let vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){let offset=i*reduceSize,max10=aVals[offset];for(let j=0;j<reduceSize;++j){let value=aVals[offset+j];value>max10&&(max10=value)}vals[i]=max10}return vals}var multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2};var notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2};var rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt5=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt5};function sliceImpl(vals,begin,size,shape,dtype){let isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return vals.subarray(flatOffset,flatOffset+length)}let outVals=util_exports.getTypedArrayFromDType(dtype,length);for(let i=0;i<length;++i){let rank=size.length,strides=util_exports.computeStrides(size),loc=util_exports.indexToLoc(i,rank,strides),xLoc=loc.map((idx,j)=>idx+begin[j]),xIndex=util_exports.locToIndex(xLoc,shape.length,xStrides);outVals[i]=vals[xIndex]}return outVals}function slice19(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");let[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);let vals=backend3.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend3.makeTensorInfo($size,x.dtype,outVals)}var sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice19};var squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{let diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2};var subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub34=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub34};function transposeImpl(xVals,xShape,dtype,perm,newShape){let xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){let loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];let newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function uniqueImpl(values,axis,shape,dtype){let $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];let uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{let axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{let uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}let outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;let outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});let outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}var version10="2.7.0";registerBackend("cpu",()=>new MathBackendCPU,1);var elu8=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu8};var preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu7(args){let{inputs,backend:backend3}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");let aVals=backend3.data.get(x.dataId).values,bVals=backend3.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend3.makeTensorInfo(resultShape,x.dtype,resultData)}var preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu7};var relu9=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu9};var relu66=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu66};function applyActivation2(backend3,x,activation2,preluActivationWeights){if(activation2==="linear")return identity2({inputs:{x},backend:backend3});if(activation2==="relu")return relu9({inputs:{x},backend:backend3});if(activation2==="elu")return elu8({inputs:{x},backend:backend3});if(activation2==="relu6")return relu66({inputs:{x},backend:backend3});if(activation2==="prelu")return prelu7({inputs:{x,alpha:preluActivationWeights},backend:backend3});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape88(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend3.incRef(x.dataId);let xData=backend3.data.get(x.dataId);if(xData.complexTensorInfos!=null){let real8=xData.complexTensorInfos.real,imag8=xData.complexTensorInfos.imag;real8.shape=$shape,imag8.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape88};function batchMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShapeOuterDims=batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2),outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape88({inputs:{x:a},backend:backend3,attrs:{shape:a3dShape}}),b3d=reshape88({inputs:{x:b},backend:backend3,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend3.data.get(a3d.dataId).values,b3dValues=backend3.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend3.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){let iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum29=0;for(let k=k0;k<kBlock;k++){let batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum29+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum29}}return backend3.disposeIntermediateTensorInfo(a3d),backend3.disposeIntermediateTensorInfo(b3d),backend3.makeTensorInfo(outShape,result.dtype,result.values)}var batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2}=attrs,current,addRes,activationRes,intermediates=[],matMulRes=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend3});current=matMulRes,bias&&(addRes=add32({inputs:{a:current,b:bias},backend:backend3}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend3,current,activation2,preluActivationWeights),intermediates.push(current),current=activationRes);for(let i of intermediates)backend3.disposeIntermediateTensorInfo(i);return current}var _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul};var acos4=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos4};var acosh4=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh4};var asin4=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin4};var asinh4=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh4};var atan5=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan5};var atanh4=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh4};function pool5(xValues,xShape,dtype,strides,convInfo,poolType){let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){let outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}let outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){let maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){let xRCorner=yR*strideHeight-padTop,xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;let xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){let xCCorner=yC*strideWidth-padLeft,xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;let xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner),maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){let wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){let wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function avgPool2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend3});else{let xValues=backend3.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer11=pool5(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend3.makeTensorInfo(convInfo.outShape,x.dtype,buffer11.values)}return res}var avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPoolBackprop2(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolBackprop");let{filterSize,strides,pad:pad11}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend3.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR)continue;for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let pixel=dyBuf.get(b,dyR,dyC,d);dotProd+=pixel}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var avgPoolBackpropConfig={kernelName:AvgPoolBackprop,backendName:"cpu",kernelFunc:avgPoolBackprop2};function batchNorm2(args){let{inputs,backend:backend3,attrs}=args,{x,scale:scale2,offset,mean:mean7,variance}=inputs;util_exports.assert(mean7.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean7.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale2==null||mean7.shape.length===scale2.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean7,variance,scale2,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let xVals=backend3.data.get(x.dataId).values,mVals=backend3.data.get(mean7.dataId).values,varVals=backend3.data.get(variance.dataId).values,sVals=scale2?backend3.data.get(scale2.dataId).values:new Float32Array([1]),offVals=offset?backend3.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length,offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend3.makeTensorInfo(x.shape,x.dtype,outVals)}var batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2};var clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{let clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip};function imag6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,imag8=backend3.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend3.data.get(imag8.dataId).values;return backend3.makeTensorInfo(imag8.shape,imag8.dtype,imagVal)}var imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag6};function concat17(args){let{inputs,backend:backend3,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend3.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){let reals=$inputs.map(t=>real6({inputs:{input:t},backend:backend3})),imags=$inputs.map(t=>imag6({inputs:{input:t},backend:backend3})),realConcated=concat17({inputs:reals,backend:backend3,attrs:{axis:$axis}}),imagConcated=concat17({inputs:imags,backend:backend3,attrs:{axis:$axis}}),result=complex9({inputs:{real:realConcated,imag:imagConcated},backend:backend3});return reals.forEach(r=>backend3.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend3.disposeIntermediateTensorInfo(i)),backend3.disposeIntermediateTensorInfo(realConcated),backend3.disposeIntermediateTensorInfo(imagConcated),result}let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice($axis)),shape=[-1,innerSize];return reshape88({inputs:{x:t},backend:backend3,attrs:{shape}})});outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let outVals=util_exports.getTypedArrayFromDType($inputs[0].dtype,util_exports.sizeFromShape(outShape));if(inputs2D[0].shape[0]===1){let offset=0;inputs2D.forEach(t=>{let val=backend3.data.get(t.dataId).values,size=util_exports.sizeFromShape(t.shape);outVals.set(val,offset),offset+=size})}else{let colOffset=0;inputs2D.forEach(t=>{let tVals=backend3.data.get(t.dataId).values,tIdx=0;for(let row=0;row<t.shape[0];++row){let resIdx=row*outShape[1]+colOffset;for(let col=0;col<t.shape[1];++col)outVals[resIdx+col]=tVals[tIdx++]}colOffset+=t.shape[1]})}let finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend3.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend3.disposeIntermediateTensorInfo(t)),outInfo}var concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat17};function conv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,yVals)}var conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,pad:pad11,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad11,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend3.data.get(x.dataId).values,dyVals=backend3.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend3.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad11,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");let filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape),$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad11,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend3.data.get(dy.dataId).values,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;let topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}let dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dilations}=attrs;assertNotComplex([x,filter],"conv3d");let convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad11),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){let yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){let xF=xFCorner+wF*dilationDepth;if(xF<0||xF>=convInfo.inDepth)continue;let wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels,wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,y.values)}var conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,pad:pad11,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");let xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad11),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend3.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){let yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){let xF=wF+yF*strideDepth-frontPad,xOffset2=xF*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad,xOffset3=xR*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad,xOffset4=xC*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend3.makeTensorInfo(dw.shape,dw.dtype,dw.values)}var conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{pad:pad11,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad11),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){let xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){let wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){let pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2};var cos6=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos6};var cosh5=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh5};function depthwiseConv2dNative(args){let{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");let xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),$dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad11,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){let xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){let yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){let xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;let wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){let yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){let xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;let wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels,yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){let xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,y.values)}var depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){let{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad11,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");let convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend3.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend3.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){let yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){let yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){let d1=Math.trunc(d2/chMul),dm=d2%chMul,dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){let xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){let xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend3.makeTensorInfo(dW.shape,dW.dtype,dW.values)}var depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){let{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad11,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");let dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){let xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){let xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth),dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){let wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){let wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){let d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2};var dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}let outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}let dataId=cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype);return{dataId,shape:outShape,dtype:x.dtype}}};var dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients8=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients8[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}let dataId=cpuBackend.write(util_exports.toTypedArray(gradients8,x.dtype),filter.shape,filter.dtype);return{dataId,shape:filter.shape,dtype:filter.dtype}}};var dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{x,filter,dy}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);let $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients8=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){let hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){let wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){let hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){let wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){let val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients8[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}let dataId=cpuBackend.write(util_exports.toTypedArray(gradients8,x.dtype),x.shape,x.dtype);return{dataId,shape:x.shape,dtype:x.dtype}}};var divImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div35=binaryKernelFunc(Div,divImpl),divConfig={kernelName:Div,backendName:"cpu",kernelFunc:div35};var p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf4=unaryKernelFunc(Erf,xi=>{let sign5=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign5*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf4};function fftBatch(input2,inverse,cpuBackend){let inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){let r=slice19({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice19({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex9({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real8,imag:imag8}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real8,imag8);for(let d=0;d<innerDim;d++){let c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}let $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex9({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){let inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){let result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){let realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=divConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=divConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)===0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};let data=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex9({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex9({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex9({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex9({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex9({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add32({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub34({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real6({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real6({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag6({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag6({inputs:{input:subPart},backend:cpuBackend}),$real=concat17({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat17({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data,size,inverse){let ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real8=0,imag8=0;for(let c=0;c<size;c++){let e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data,c);real8+=term.real*e.real-term.imag*e.imag,imag8+=term.real*e.imag+term.imag*e.real}inverse&&(real8/=size,imag8/=size),backend_util_exports.assignToTypedArray(ret,real8,imag8,r)}return ret}function fft6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape88({inputs:{x:input2},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend3),resultReshaped=reshape88({inputs:{x:result},backend:backend3,attrs:{shape:input2.shape}});return backend3.disposeIntermediateTensorInfo(input2D),backend3.disposeIntermediateTensorInfo(result),resultReshaped}var fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft6};function fill5(args){let{backend:backend3,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend3.makeTensorInfo(shape,$dtype,values)}var fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill5};function fillValues(values,value,dtype){dtype==="string",values.fill(value)}var flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,cpuBackend=backend3,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel,outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){let rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}let dataId=cpuBackend.write(output,image3.shape,image3.dtype);return{dataId,shape:image3.shape,dtype:image3.dtype}}};function fusedConv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2}=attrs,result=conv2D({inputs:{x,filter},backend:backend3,attrs:{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}});if(bias){let resultOld=result;result=add32({inputs:{a:result,b:bias},backend:backend3}),backend3.disposeIntermediateTensorInfo(resultOld)}if(activation2){let resultOld=result;result=applyActivation2(backend3,result,activation2,preluActivationWeights),backend3.disposeIntermediateTensorInfo(resultOld)}return result}var fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){let{inputs,backend:backend3,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2}=attrs,result=depthwiseConv2dNative({inputs:{x,filter},backend:backend3,attrs:{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}});if(bias){let oldResult=result;result=add32({inputs:{a:result,b:bias},backend:backend3}),backend3.disposeIntermediateTensorInfo(oldResult)}if(activation2){let oldResult=result;result=applyActivation2(backend3,result,activation2,preluActivationWeights),backend3.disposeIntermediateTensorInfo(oldResult)}return result}var fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function ifft6(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape88({inputs:{x:input2},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend3),resultReshaped=reshape88({inputs:{x:result},backend:backend3,attrs:{shape:input2.shape}});return backend3.disposeIntermediateTensorInfo(input2D),backend3.disposeIntermediateTensorInfo(result),resultReshaped}var ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft6};var isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3};var isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2};var isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3};var log1p5=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p5};var logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2};var maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend3,xShape=x.shape,xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype),outShape=maxOutShape;if(keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes);outShape=newShape}return{dataId,shape:outShape,dtype:x.dtype}}};function maxPool2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode),res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend3});else{let xValues=backend3.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer11=pool5(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend3.makeTensorInfo(convInfo.outShape,x.dtype,buffer11.values)}return res}var maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPoolBackprop2(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolBackprop");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),xValues=backend3.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend3.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){let dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft,dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){let dyR=(dyRCorner+wR)/strideHeight;if(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR)continue;for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){let dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;let maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;if(mask===0)continue;let pixel=dyBuf.get(b,dyR,dyC,d);dotProd+=pixel*mask}}dx.set(dotProd,b,dxR,dxC,d)}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}var maxPoolBackpropConfig={kernelName:MaxPoolBackprop,backendName:"cpu",kernelFunc:maxPoolBackprop2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){let strides=util_exports.computeStrides(xShape),maxPools=pool5(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}var maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{filterSize,strides,pad:pad11,includeBatchInIndex}=attrs,cpuBackend=backend3;assertNotComplex(x,"MaxPoolWithArgmax");let values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad11),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function mirrorPad2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend3.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);let inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}let outId=backend3.write(resVals,outShape,x.dtype);return{dataId:outId,shape:outShape,dtype:x.dtype}}var mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2};var nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl,nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs,cpuBackend=backend3;assertNotComplex(boxes,"NonMaxSuppressionPadded");let boxesVals=cpuBackend.data.get(boxes.dataId).values,scoresVals=cpuBackend.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[selectedIndices,validOutputs]}};var nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl,nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs,cpuBackend=backend3;assertNotComplex(boxes,"NonMaxSuppressionWithScore");let boxesVals=cpuBackend.data.get(boxes.dataId).values,scoresVals=cpuBackend.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[selectedIndices,selectedScores]}};function padV2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");let outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend3.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){let coords2=util_exports.indexToLoc(i,xRank,xStrides),outCoords=coords2.map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}let outId=backend3.write(resVals,outShape,x.dtype);return{dataId:outId,shape:outShape,dtype:x.dtype}}var padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2};var reciprocal4=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal4};var rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend3,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){let batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){let rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){let colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){let coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1],coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){let rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}let outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}let dataId=cpuBackend.write(output,image3.shape,image3.dtype);return{dataId,shape:image3.shape,dtype:image3.dtype}}};var round4=unaryKernelFunc(Round,xi=>{let base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2===0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round4};var scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu5=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu5};var sigmoid7=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid7};var sign4=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign4};var sin5=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin5};var sinh5=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh5};var epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus5=unaryKernelFunc(Softplus,xi=>{let tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi),result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus5};function transpose18(args){let{inputs,attrs,backend:backend3}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");let xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let values=backend3.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape),dataId=backend3.write(result,newShape,x.dtype);return{dataId,shape:newShape,dtype:x.dtype}}var transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose18};function spaceToBatchND2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");let prod5=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend3,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),reshapeInputs={x:paddedX},reshapeAttrs={shape:reshapedPaddedShape},paddedXReshaped=reshape88({inputs:reshapeInputs,backend:backend3,attrs:reshapeAttrs}),transposeInputs={x:paddedXReshaped},transposeAttrs={perm:permutedReshapedPaddedPermutation},paddedXT=transpose18({inputs:transposeInputs,backend:backend3,attrs:transposeAttrs}),resultReshapeInputs={x:paddedXT},resultReshapeAttrs={shape:flattenShape},result=reshape88({inputs:resultReshapeInputs,backend:backend3,attrs:resultReshapeAttrs});return backend3.disposeIntermediateTensorInfo(paddedX),backend3.disposeIntermediateTensorInfo(paddedXReshaped),backend3.disposeIntermediateTensorInfo(paddedXT),result}var spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2};var sqrt13=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt13};var squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend3})=>{let{x}=inputs,cpuBackend=backend3;assertNotComplex(x,"square");let values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){let value=values[i];newValues[i]=value*value}let dataId=cpuBackend.write(newValues,x.shape,x.dtype);return{dataId,shape:x.shape,dtype:x.dtype}}};var step8=unaryKernelFunc(Step,(xi,attrs)=>{let stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step8};var tan4=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan4};var tanh6=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh6};function unique6(args){let{inputs,attrs,backend:backend3}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");let values=backend3.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend3.makeTensorInfo(outputShape,x.dtype,outputValues),backend3.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique6};var kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,asinConfig,asinhConfig,atanConfig,atanhConfig,avgPoolConfig,avgPoolBackpropConfig,batchMatMulConfig,batchNormConfig,castConfig,ceilConfig,clipConfig,complexConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,divConfig,eluConfig,erfConfig,expConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,logConfig,log1pConfig,logicalNotConfig,maxPoolConfig,maxPoolBackpropConfig,maxPoolWithArgmaxConfig,maxConfig,mirrorPadConfig,multiplyConfig,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,padV2Config,preluConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softplusConfig,spaceToBatchNDConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,subConfig,tanConfig,tanhConfig,transposeConfig,uniqueConfig];for(let kernelConfig of kernelConfigs)registerKernel(kernelConfig);var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){let newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}let gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){let size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){let[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){let glany=gl,internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){let returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){let error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}var MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){let vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){let fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){let lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}let lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
+`),pad11=shaderLines.length.toString().length+2,linesWithLineNumbers=shaderLines.map((line,lineNumber2)=>util_exports.rightPad((lineNumber2+1).toString(),pad11)+line),maxLineLength=0;for(let i=0;i<linesWithLineNumbers.length;i++)maxLineLength=Math.max(linesWithLineNumbers[i].length,maxLineLength);let beforeErrorLines=linesWithLineNumbers.slice(0,lineNumber-1),errorLine=linesWithLineNumbers.slice(lineNumber-1,lineNumber),afterErrorLines=linesWithLineNumbers.slice(lineNumber);console.log(beforeErrorLines.join(`
+`)),console.log(shaderInfoLog.split(`
+`)[0]),console.log(`%c ${util_exports.rightPad(errorLine[0],maxLineLength)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(afterErrorLines.join(`
+`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data){let buffer11=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer11}function createStaticIndexBuffer(gl,data){let buffer11=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data,gl.STATIC_DRAW)),buffer11}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){let maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){let requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){let requested=`[${width}x${height}]`,max10=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max10+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer11,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){let loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){let status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){let tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){let maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){let textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1],isScalar=shape.length===0||shape.length===1&&shape[0]===1;return isScalar||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2){let squeezeResult=util_exports.squeezeShape(logShape);logShape=squeezeResult.newShape}let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){let batchDim=getBatchDim(logShape),rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2===0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2))return!0;if(!shape1.length||!shape2.length)return!0;if(shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){let shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols)return!0;if(isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}var MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){let gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion,gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){let ext=gl.getExtension(extensionName);return ext!=null}function isWebGLVersionEnabled(webGLVersion){try{let gl=getWebGLContext(webGLVersion);if(gl!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;let isFrameBufferComplete=createFloatTextureAndBindToFramebuffer(gl);return isFrameBufferComplete}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;let gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1;if(!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);let COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){let textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}let isFrameBufferComplete=createFloatTextureAndBindToFramebuffer(gl);return isFrameBufferComplete}function createFloatTextureAndBindToFramebuffer(gl){let texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){let texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);let width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);let frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);let isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){if(webGLVersion!==2)return!1;let gl=getWebGLContext(webGLVersion),isEnabled=gl.fenceSync!=null;return isEnabled}function assertNotComplex2(tensor168,opName){Array.isArray(tensor168)||(tensor168=[tensor168]),tensor168.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}var ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{let useUniforms=ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return useUniforms?4:0});ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});var{simpleAbsImpl:simpleAbsImplCPU,addImpl:addImplCPU,ceilImpl:ceilImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,multiplyImpl:multiplyImplCPU,rsqrtImpl:rsqrtImplCPU,sliceImpl:sliceImplCPU,subImpl:subImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports;var AddNProgram=class{constructor(outputShape,shapes){this.outputShape=[],this.outputShape=outputShape,this.variableNames=shapes.map((_,i)=>`T${i}`);let snippets=[];this.variableNames.forEach(variable3=>{snippets.push(`float v${variable3} = get${variable3}AtOutCoords();`)});let operation211=this.variableNames.map(variable3=>`v${variable3}`).join(" + ");this.userCode=`
+      void main() {
+        ${snippets.join(`
+        `)}
+
+        float result = ${operation211};
+        setOutput(result);
+      }
+    `}};var AddNPackedProgram=class{constructor(outputShape,shapes){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape,this.variableNames=shapes.map((_,i)=>`T${i}`);let snippets=[];this.variableNames.forEach(variable3=>{snippets.push(`vec4 v${variable3} = get${variable3}AtOutCoords();`)});let operation211=this.variableNames.map(variable3=>`v${variable3}`).join(" + ");this.userCode=`
+      void main() {
+        ${snippets.join(`
+        `)}
+
+        vec4 result = ${operation211};
+        setOutput(result);
+      }
+    `}};var ArgMinMaxProgram=class{constructor(reduceInfo,op2,firstPass){this.variableNames=["A"];let{windowSize,batchSize,outSize}=reduceInfo;firstPass||this.variableNames.push("bestIndicesA"),this.outputShape=[batchSize,outSize];let compOp=op2==="max"?">":"<",indexSnippet=firstPass?"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 * ${windowSize};
+
+        int bestIndex = inOffset;
+        float bestValue = getA(batch, bestIndex);
+
+        for (int i = 0; i < ${windowSize}; i++) {
+          int inIdx = ${indexSnippet};
+          float candidate = getA(batch, inIdx);
+          if (candidate ${compOp} bestValue) {
+            bestValue = candidate;
+            bestIndex = inIdx;
+          }
+        }
+        setOutput(float(bestIndex));
+      }
+    `}};function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}function getGlslDifferences(){let version19,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version19="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
+      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)
+    `,defineSpecialInf="",defineRound=`
+      #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)));
+      }
+    `):(version19="",attribute="attribute",varyingVs="varying",varyingFs="varying",texture2D="texture2D",output="gl_FragColor",defineOutput="",defineSpecialNaN=`
+      #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));
+      }
+    `,defineSpecialInf=`
+      uniform float INFINITY;
+
+      bool isinf(float val) {
+        return abs(val) == INFINITY;
+      }
+      bvec4 isinf(vec4 val) {
+        return equal(abs(val), vec4(INFINITY));
+      }
+    `,defineRound=`
+      int round(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 round(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `),{version:version19,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound}}function getLogicalCoordinatesFromFlatIndex(coords2,shape,index="index"){let strides=util_exports.computeStrides(shape);return strides.map((stride,i)=>{let line1=`int ${coords2[i]} = ${index} / ${stride}`,line2=i===strides.length-1?`int ${coords2[i+1]} = ${index} - ${coords2[i]} * ${stride}`:`index -= ${coords2[i]} * ${stride}`;return`${line1}; ${line2};`}).join("")}function getFlatIndexFrom3D(shape){let strides=util_exports.computeStrides(shape).map(d=>d.toString());return`
+  int getFlatIndex(ivec3 coords) {
+    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
+  }
+`}var ENCODE_FLOAT_SNIPPET=`
+  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;
+  }
+`;var{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){let prefixSnippets=[];inputsInfo.forEach(x=>{let size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});let inputPrefixSnippet=prefixSnippets.join(`
+`),inputSamplingSnippet=inputsInfo.map(x=>getInputSamplingSnippet(x,outputShape,usesPackedTextures)).join(`
+`),outTexShape=outputShape.texShape,glsl=getGlslDifferences(),floatTextureSampleSnippet=getFloatTextureSampleSnippet(glsl),outputSamplingSnippet,floatTextureSetOutputSnippet,shaderPrefix=getShaderPrefix(glsl);outputShape.isPacked?(outputSamplingSnippet=getPackedOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRGBASnippet(glsl)):(outputSamplingSnippet=getOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRSnippet(glsl)),usesPackedTextures&&(shaderPrefix+=SHADER_PACKED_PREFIX);let source=[shaderPrefix,floatTextureSampleSnippet,floatTextureSetOutputSnippet,inputPrefixSnippet,outputSamplingSnippet,inputSamplingSnippet,userCode].join(`
+`);return source}function getSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getSamplerScalar(inInfo);case 1:return getSampler1D(inInfo);case 2:return getSampler2D(inInfo);case 3:return getSampler3D(inInfo);case 4:return getSampler4D(inInfo);case 5:return getSampler5D(inInfo);case 6:return getSampler6D(inInfo);default:throw new Error(`${shape.length}-D input sampling is not yet supported`)}}function getPackedSamplerFromInInfo(inInfo){let shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getPackedSamplerScalar(inInfo);case 1:return getPackedSampler1D(inInfo);case 2:return getPackedSampler2D(inInfo);case 3:return getPackedSampler3D(inInfo);default:return getPackedSamplerND(inInfo)}}function getInputSamplingSnippet(inInfo,outShapeInfo,usesPackedTextures=!1){let res="";usesPackedTextures?res+=getPackedSamplerFromInInfo(inInfo):res+=getSamplerFromInInfo(inInfo);let inShape=inInfo.shapeInfo.logicalShape,outShape=outShapeInfo.logicalShape;return inShape.length<=outShape.length&&(usesPackedTextures?res+=getPackedSamplerAtOutputCoords(inInfo,outShapeInfo):res+=getSamplerAtOutputCoords(inInfo,outShapeInfo)),res}function getPackedOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(outShape,outTexShape);case 2:return getOutputPacked2DCoords(outShape,outTexShape);case 3:return getOutputPacked3DCoords(outShape,outTexShape);default:return getOutputPackedNDCoords(outShape,outTexShape)}}function getOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(outShape,outTexShape);case 2:return getOutput2DCoords(outShape,outTexShape);case 3:return getOutput3DCoords(outShape,outTexShape);case 4:return getOutput4DCoords(outShape,outTexShape);case 5:return getOutput5DCoords(outShape,outTexShape);case 6:return getOutput6DCoords(outShape,outTexShape);default:throw new Error(`${outShape.length}-D output sampling is not yet supported`)}}function getFloatTextureSampleSnippet(glsl){return`
+    float sampleTexture(sampler2D textureSampler, vec2 uv) {
+      return ${glsl.texture2D}(textureSampler, uv).r;
+    }
+  `}function getFloatTextureSetRSnippet(glsl){return`
+    void setOutput(float val) {
+      ${glsl.output} = vec4(val, 0, 0, 0);
+    }
+  `}function getFloatTextureSetRGBASnippet(glsl){return`
+    void setOutput(vec4 val) {
+      ${glsl.output} = val;
+    }
+  `}function getShaderPrefix(glsl){let SHADER_PREFIX=`${glsl.version}
+    precision highp float;
+    precision highp int;
+    precision highp sampler2D;
+    ${glsl.varyingFs} vec2 resultUV;
+    ${glsl.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;
+    ${glsl.defineSpecialNaN}
+    ${glsl.defineSpecialInf}
+    ${glsl.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);
+    }
+
+    ${SAMPLE_1D_SNIPPET}
+    ${SAMPLE_2D_SNIPPET}
+    ${SAMPLE_3D_SNIPPET}
+  `;return SHADER_PREFIX}var SAMPLE_1D_SNIPPET=`
+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);
+}
+`,SAMPLE_2D_SNIPPET=`
+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);
+}
+`,SAMPLE_3D_SNIPPET=`
+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);
+}
+`,SHADER_PACKED_PREFIX=`
+  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 getOutputScalarCoords(){return`
+    int getOutputCoords() {
+      return 0;
+    }
+  `}function getOutputPacked1DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];return packedTexShape[0]===1?`
+      int getOutputCoords() {
+        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
+      }
+    `:packedTexShape[1]===1?`
+      int getOutputCoords() {
+        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
+      }
+    `:`
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
+    }
+  `}function getOutput1DCoords(shape,texShape){return texShape[0]===1?`
+      int getOutputCoords() {
+        return int(resultUV.x * ${texShape[1]}.0);
+      }
+    `:texShape[1]===1?`
+      int getOutputCoords() {
+        return int(resultUV.y * ${texShape[0]}.0);
+      }
+    `:`
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      return resTexRC.x * ${texShape[1]} + resTexRC.y;
+    }
+  `}function getOutputPacked3DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texelsInLogicalRow=Math.ceil(shape[2]/2),texelsInBatch=texelsInLogicalRow*Math.ceil(shape[1]/2);return`
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
+
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec3(b, r, c);
+    }
+  `}function getOutput3DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape);return`
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
+      return ivec3(r, c, d);
+    }
+  `}function getOutputPackedNDCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texelsInLogicalRow=Math.ceil(shape[shape.length-1]/2),texelsInBatch=texelsInLogicalRow*Math.ceil(shape[shape.length-2]/2),texelsInBatchN=texelsInBatch,batches="",coords2="b, r, c";for(let b=2;b<shape.length-1;b++)texelsInBatchN*=shape[shape.length-b-1],batches=`
+      int b${b} = index / ${texelsInBatchN};
+      index -= b${b} * ${texelsInBatchN};
+    `+batches,coords2=`b${b}, `+coords2;return`
+    ivec${shape.length} getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+
+      ${batches}
+
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
+
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec${shape.length}(${coords2});
+    }
+  `}function getOutput4DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2"],shape);return`
+    ivec4 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
+      return ivec4(r, c, d, d2);
+    }
+  `}function getOutput5DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],shape);return`
+    ivec5 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
+                             ${texShape[1]}));
+
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+
+      ${coordsFromIndexSnippet}
+
+      ivec5 outShape = ivec5(r, c, d, d2, d3);
+      return outShape;
+    }
+  `}function getOutput6DCoords(shape,texShape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],shape);return`
+    ivec6 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+
+      ${coordsFromIndexSnippet}
+
+      ivec6 result = ivec6(r, c, d, d2, d3, d4);
+      return result;
+    }
+  `}function getOutputPacked2DCoords(shape,texShape){let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];if(util_exports.arraysEqual(shape,texShape))return`
+      ivec2 getOutputCoords() {
+        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      }
+    `;let texelsInLogicalRow=Math.ceil(shape[1]/2);return`
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec2(r, c);
+    }
+  `}function getOutput2DCoords(shape,texShape){return util_exports.arraysEqual(shape,texShape)?`
+      ivec2 getOutputCoords() {
+        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
+      }
+    `:shape[1]===1?`
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+        return ivec2(index, 0);
+      }
+    `:shape[0]===1?`
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+        return ivec2(0, index);
+      }
+    `:`
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      int r = index / ${shape[1]};
+      int c = index - r * ${shape[1]};
+      return ivec2(r, c);
+    }
+  `}function getFlatOffsetUniformName(texName){return`offset${texName}`}function getPackedSamplerScalar(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),glsl=getGlslDifferences();return`
+    vec4 ${funcName}() {
+      return ${glsl.texture2D}(${texName}, halfCR);
+    }
+  `}function getSamplerScalar(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1);if(inputInfo.shapeInfo.isUniform)return`float ${funcName}() {return ${texName};}`;let[texNumR,texNumC]=inputInfo.shapeInfo.texShape;if(texNumR===1&&texNumC===1)return`
+      float ${funcName}() {
+        return sampleTexture(${texName}, halfCR);
+      }
+    `;let[tNumR,tNumC]=inputInfo.shapeInfo.texShape,offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}() {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `}function getPackedSampler1D(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],glsl=getGlslDifferences();return`
+    vec4 ${funcName}(int index) {
+      vec2 uv = packedUVfrom1D(
+        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `}function getSampler1D(inputInfo){let texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1);if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int index) {
+        ${getUniformSampler(inputInfo)}
+      }
+    `;let texShape=inputInfo.shapeInfo.texShape,tNumR=texShape[0],tNumC=texShape[1];if(tNumC===1&&tNumR===1)return`
+      float ${funcName}(int index) {
+        return sampleTexture(${texName}, halfCR);
+      }
+    `;let offset=getFlatOffsetUniformName(texName);return tNumC===1?`
+      float ${funcName}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `:tNumR===1?`
+      float ${funcName}(int index) {
+        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
+        return sampleTexture(${texName}, uv);
+      }
+    `:`
+    float ${funcName}(int index) {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `}function getPackedSampler2D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1],glsl=getGlslDifferences();if(texShape!=null&&util_exports.arraysEqual(shape,texShape))return`
+      vec4 ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
+
+        return ${glsl.texture2D}(${texName}, uv);
+      }
+    `;let packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],valuesPerRow=Math.ceil(shape[1]/2);return`
+    vec4 ${funcName}(int row, int col) {
+      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `}function getSampler2D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape;if(texShape!=null&&util_exports.arraysEqual(shape,texShape)){let texNumR2=texShape[0],texNumC2=texShape[1];return`
+    float ${funcName}(int row, int col) {
+      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `}let{newShape,keptDims}=util_exports.squeezeShape(shape),squeezedShape=newShape;if(squeezedShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["row","col"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
+      }
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col) {
+        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;let texNumR=texShape[0],texNumC=texShape[1],offset=getFlatOffsetUniformName(texName);return texNumC===1?`
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `:texNumR===1?`
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
+      return sampleTexture(${texName}, uv);
+    }
+  `:`
+  float ${funcName}(int row, int col) {
+    // Explicitly use integer operations as dot() only works on floats.
+    int index = row * ${shape[1]} + col + ${offset};
+    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+    return sampleTexture(${texName}, uv);
+  }
+`}function getPackedSampler3D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];if(shape[0]===1){let squeezedShape=shape.slice(1),keptDims=[1,2],newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["b","row","col"];return`
+        ${getPackedSamplerFromInInfo(newInputInfo)}
+        vec4 ${funcName}(int b, int row, int col) {
+          return ${funcName}(${getSqueezedParams(params,keptDims)});
+        }
+      `}let texNumR=packedTexShape[0],texNumC=packedTexShape[1],valuesPerRow=Math.ceil(shape[2]/2),texelsInBatch=valuesPerRow*Math.ceil(shape[1]/2),glsl=getGlslDifferences();return`
+    vec4 ${funcName}(int b, int row, int col) {
+      vec2 uv = packedUVfrom3D(
+        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `}function getSampler3D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride0=shape[1]*shape[2],stride1=shape[2],{newShape,keptDims}=util_exports.squeezeShape(shape),squeezedShape=newShape;if(squeezedShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["row","col","depth"];return`
+        ${getSamplerFromInInfo(newInputInfo)}
+        float ${funcName}(int row, int col, int depth) {
+          return ${funcName}(${getSqueezedParams(params,keptDims)});
+        }
+      `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth) {
+        int index = round(dot(vec3(row, col, depth),
+                          vec3(${stride0}, ${stride1}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;let texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1],flatOffset=inputInfo.shapeInfo.flatOffset;if(texNumC===stride0&&flatOffset==null)return`
+        float ${funcName}(int row, int col, int depth) {
+          float texR = float(row);
+          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
+          vec2 uv = (vec2(texC, texR) + halfCR) /
+                     vec2(${texNumC}.0, ${texNumR}.0);
+          return sampleTexture(${texName}, uv);
+        }
+      `;if(texNumC===stride1&&flatOffset==null)return`
+    float ${funcName}(int row, int col, int depth) {
+      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
+      float texC = float(depth);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `;let offset=getFlatOffsetUniformName(texName);return`
+      float ${funcName}(int row, int col, int depth) {
+        // Explicitly use integer operations as dot() only works on floats.
+        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
+        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+        return sampleTexture(${texName}, uv);
+      }
+  `}function getPackedSamplerND(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,rank=shape.length,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texNumR=packedTexShape[0],texNumC=packedTexShape[1],valuesPerRow=Math.ceil(shape[rank-1]/2),texelsInBatch=valuesPerRow*Math.ceil(shape[rank-2]/2),params="int b, int row, int col",index=`b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;for(let b=2;b<rank-1;b++)params=`int b${b}, `+params,texelsInBatch*=shape[rank-b-1],index=`b${b} * ${texelsInBatch} + `+index;let glsl=getGlslDifferences();return`
+    vec4 ${funcName}(${params}) {
+      int index = ${index};
+      int texR = index / ${texNumC};
+      int texC = index - texR * ${texNumC};
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `}function getSampler4D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride2=shape[3],stride1=shape[2]*stride2,stride0=shape[1]*stride1,{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
+      }
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        int index = round(dot(vec4(row, col, depth, depth2),
+                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        float texR = float(row);
+        float texC =
+            dot(vec3(col, depth, depth2),
+                vec3(${stride1}, ${stride2}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;if(texNumC===stride2&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        float texR = dot(vec3(row, col, depth),
+                         vec3(${shape[1]*shape[2]}, ${shape[2]}, 1));
+        float texC = float(depth2);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(int row, int col, int depth, int depth2) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${stride0} + col * ${stride1} +
+          depth * ${stride2} + depth2;
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `}function getSampler5D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride3=shape[4],stride2=shape[3]*stride3,stride1=shape[2]*stride2,stride0=shape[1]*stride1,{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2","depth3"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
+      }
+    `}if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        float index = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
+          depth3;
+        ${getUniformSampler(inputInfo)}
+      }
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;if(texNumC===stride3&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        float texR = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${shape[1]*shape[2]*shape[3]},
+               ${shape[2]*shape[3]}, ${shape[3]}, 1));
+        int texC = depth3;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
+    }
+  `}function getSampler6D(inputInfo){let shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){let newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2","depth3","depth4"];return`
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        return ${funcName}(${getSqueezedParams(params,keptDims)});
+      }
+    `}let stride4=shape[5],stride3=shape[4]*stride4,stride2=shape[3]*stride3,stride1=shape[2]*stride2,stride0=shape[1]*stride1;if(inputInfo.shapeInfo.isUniform)return`
+      float ${funcName}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+        int index = round(dot(
+          vec4(row, col, depth, depth2),
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
+          dot(
+            vec2(depth3, depth4),
+            vec2(${stride4}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;let flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
+               float(depth4);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;if(texNumC===stride4&&flatOffset==null)return`
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        float texR = dot(vec4(row, col, depth, depth2),
+          vec4(${shape[1]*shape[2]*shape[3]*shape[4]},
+               ${shape[2]*shape[3]*shape[4]},
+               ${shape[3]*shape[4]},
+               ${shape[4]})) + float(depth3);
+        int texC = depth4;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;let offset=getFlatOffsetUniformName(texName);return`
+    float ${funcName}(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 * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
+    }
+  `}function getUniformSampler(inputInfo){let texName=inputInfo.name,inSize=util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);return inSize<2?`return ${texName};`:`
+    for (int i = 0; i < ${inSize}; i++) {
+      if (i == index) {
+        return ${texName}[i];
+      }
+    }
+  `}function getPackedSamplerAtOutputCoords(inputInfo,outShapeInfo){let texName=inputInfo.name,texFuncSnippet=texName.charAt(0).toUpperCase()+texName.slice(1),funcName="get"+texFuncSnippet+"AtOutCoords",inRank=inputInfo.shapeInfo.logicalShape.length,outRank=outShapeInfo.logicalShape.length,broadcastDims=getBroadcastDims2(inputInfo.shapeInfo.logicalShape,outShapeInfo.logicalShape),type=getCoordsDataType(outRank),rankDiff=outRank-inRank,coordsSnippet,fields=["x","y","z","w","u","v"];inRank===0?coordsSnippet="":outRank<2&&broadcastDims.length>=1?coordsSnippet="coords = 0;":coordsSnippet=broadcastDims.map(d=>`coords.${fields[d+rankDiff]} = 0;`).join(`
+`);let unpackedCoordsSnippet="";outRank<2&&inRank>0?unpackedCoordsSnippet="coords":unpackedCoordsSnippet=inputInfo.shapeInfo.logicalShape.map((s,i)=>`coords.${fields[i+rankDiff]}`).join(", ");let output="return outputValue;",inSize=util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape),isInputScalar=inSize===1,outSize=util_exports.sizeFromShape(outShapeInfo.logicalShape),isOutputScalar=outSize===1;if(inRank===1&&!isInputScalar&&!isOutputScalar)output=`
+      return vec4(outputValue.xy, outputValue.xy);
+    `;else if(isInputScalar&&!isOutputScalar)outRank===1?output=`
+        return vec4(outputValue.x, outputValue.x, 0., 0.);
+      `:output=`
+        return vec4(outputValue.x);
+      `;else if(broadcastDims.length){let rows=inRank-2,cols=inRank-1;broadcastDims.indexOf(rows)>-1&&broadcastDims.indexOf(cols)>-1?output="return vec4(outputValue.x);":broadcastDims.indexOf(rows)>-1?output="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":broadcastDims.indexOf(cols)>-1&&(output="return vec4(outputValue.xx, outputValue.zz);")}return`
+    vec4 ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
+      ${output}
+    }
+  `}function getSamplerAtOutputCoords(inputInfo,outShapeInfo){let texName=inputInfo.name,texFuncSnippet=texName.charAt(0).toUpperCase()+texName.slice(1),funcName="get"+texFuncSnippet+"AtOutCoords",outTexShape=outShapeInfo.texShape,inTexShape=inputInfo.shapeInfo.texShape,inRank=inputInfo.shapeInfo.logicalShape.length,outRank=outShapeInfo.logicalShape.length;if(!inputInfo.shapeInfo.isUniform&&inRank===outRank&&inputInfo.shapeInfo.flatOffset==null&&util_exports.arraysEqual(inTexShape,outTexShape))return`
+      float ${funcName}() {
+        return sampleTexture(${texName}, resultUV);
+      }
+    `;let type=getCoordsDataType(outRank),broadcastDims=getBroadcastDims2(inputInfo.shapeInfo.logicalShape,outShapeInfo.logicalShape),rankDiff=outRank-inRank,coordsSnippet,fields=["x","y","z","w","u","v"];inRank===0?coordsSnippet="":outRank<2&&broadcastDims.length>=1?coordsSnippet="coords = 0;":coordsSnippet=broadcastDims.map(d=>`coords.${fields[d+rankDiff]} = 0;`).join(`
+`);let unpackedCoordsSnippet="";return outRank<2&&inRank>0?unpackedCoordsSnippet="coords":unpackedCoordsSnippet=inputInfo.shapeInfo.logicalShape.map((s,i)=>`coords.${fields[i+rankDiff]}`).join(", "),`
+    float ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      return get${texFuncSnippet}(${unpackedCoordsSnippet});
+    }
+  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){let newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}var ArgMinMaxPackedProgram=class{constructor(shape,windowSize,op2,firstPass){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,util_exports.assert(shape.length>2,()=>`Packed arg${op2.charAt(0).toUpperCase()+op2.slice(1)} supports only inputs with rank above 2.`);let inSize=shape[shape.length-1],outSize=Math.ceil(inSize/windowSize);this.outputShape=shape.slice(0,-1),outSize>1&&this.outputShape.push(outSize),firstPass||this.variableNames.push("bestIndicesA");let outShape=this.outputShape,rank=outShape.length,dtype=getCoordsDataType(rank),coords2=getChannels("coords",rank),sourceLocSetup,sourceRank;if(outSize===1){sourceRank=rank+1;let sourceLocDType=getCoordsDataType(sourceRank);sourceLocSetup=`
+        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0);
+        ++${coords2[rank-1]};
+        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0);
+        ++${coords2[rank-2]};
+        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0);
+        --${coords2[rank-1]};
+        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0);
+        --${coords2[rank-2]};`}else sourceRank=rank,sourceLocSetup=`
+        ${dtype} sourceLocR = coords;
+        ++${coords2[rank-1]};
+        ${dtype} sourceLocG = coords;
+        ++${coords2[rank-2]};
+        ${dtype} sourceLocA = coords;
+        --${coords2[rank-1]};
+        ${dtype} sourceLocB = coords;
+        --${coords2[rank-2]};`;let channels=["x","y","z","w","u","v"].slice(0,sourceRank),inChannel="."+channels[sourceRank-1],intChannels=channels.map(x=>"int "+x),srcRCoords=getChannels("sourceLocR",sourceRank-1).concat("inIdx.r"),srcGCoords=getChannels("sourceLocG",sourceRank-1).concat("inIdx.g"),srcBCoords=getChannels("sourceLocB",sourceRank-1).concat("inIdx.b"),srcACoords=getChannels("sourceLocA",sourceRank-1).concat("inIdx.a"),compOp=op2==="max"?"greaterThan":"lessThan",fetchCandidateIdx=firstPass?"":`
+          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
+                             getBestIndicesAChannel(${srcGCoords.join()}),
+                             getBestIndicesAChannel(${srcBCoords.join()}),
+                             getBestIndicesAChannel(${srcACoords.join()})));`,fetchValue=`vec4(
+            getAChannel(${srcRCoords.join()}),
+            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
+            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`,getBestIndicesAChannelSnippet=firstPass?"":`
+      float getBestIndicesAChannel(${intChannels.join()}) {
+        return getChannel(getBestIndicesA(${channels.join()}),
+                                          vec2(${channels.slice(-2).join()}));
+      }`;this.userCode=`
+      float getAChannel(${intChannels.join()}) {
+        return getChannel(getA(${channels.join()}),
+                               vec2(${channels.slice(-2).join()}));
+      }
+      ${getBestIndicesAChannelSnippet}
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        bool hasNextCol = ${coords2[rank-1]} < ${outShape[rank-1]-1};
+        bool hasNextRow = ${coords2[rank-2]} < ${outShape[rank-2]-1};
+        ${sourceLocSetup}
+        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
+          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
+        ivec4 inIdx = srcIdx;
+        vec4 bestIndex = vec4(inIdx);
+        vec4 bestValue = ${fetchValue};
+
+        for (int i = 0; i < ${windowSize}; i++) {
+          inIdx = srcIdx;
+          ${fetchCandidateIdx}
+          vec4 candidate = ${fetchValue};
+          bvec4 nan = isnan(candidate);
+          bvec4 replace = bvec4(
+            vec4(${compOp}(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);
+      }
+    `}};var AvgPool2DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,avgMultiplier=1/(filterHeight*filterWidth);this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
+
+      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 < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${effectiveFilterWidth};
+            wC+= ${dilationWidth}) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+
+            dotProd += dyValue * avgMultiplier;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},AvgPool3DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy"],this.outputShape=convInfo.inShape;let filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,avgMultiplier=1/(filterDepth*filterHeight*filterWidth);this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
+
+      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 < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
+
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+
+              dotProd += dyValue * avgMultiplier;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};var CHECK_NAN_SNIPPET=`
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`,INT_DIV=`
+  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;
+  }
+`,POW=`
+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);
+`,EQUAL="return float(a == b);",LESS="return float(a < b);",LESS_EQUAL="return float(a <= b);",GREATER="return float(a > b);",GREATER_EQUAL="return float(a >= b);",LOGICAL_AND="return float(a >= 1.0 && b >= 1.0);",LOGICAL_OR="return float(a >= 1.0 || b >= 1.0);",MAX=CHECK_NAN_SNIPPET+`
+  return max(a, b);
+`,MIN=CHECK_NAN_SNIPPET+`
+  return min(a, b);
+`,MOD=`if (b == 0.0) return NAN;
+  return mod(a, b);`,ELU_DER="return (b >= 1.0) ? a : a * (b + 1.0);",PRELU="return (a < 0.) ? b * a : a;",BinaryOpProgram=class{constructor(op2,aShape,bShape){this.variableNames=["A","B"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
+      float binaryOperation(float a, float b) {
+        ${op2}
+      }
+
+      void main() {
+        float a = getAAtOutCoords();
+        float b = getBAtOutCoords();
+        setOutput(binaryOperation(a, b));
+      }
+    `}};var CHECK_NAN_SNIPPET2=`
+  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;
+`,INT_DIV2=`
+  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);
+`,POW2=`
+  // 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));
+  `+CHECK_NAN_SNIPPET2+`
+  return result;
+`,PRELU2=`
+  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
+  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
+`,ELU_DER2=`
+  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
+  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
+`,EQUAL2=`
+  return vec4(equal(a, b));
+`,LESS2=`
+  return vec4(lessThan(a, b));
+`,LESS_EQUAL2=`
+  return vec4(lessThanEqual(a, b));
+`,GREATER2=`
+  return vec4(greaterThan(a, b));
+`,GREATER_EQUAL2=`
+  return vec4(greaterThanEqual(a, b));
+`,LOGICAL_AND2=`
+  return vec4(
+    vec4(greaterThanEqual(a, vec4(1.0))) *
+    vec4(greaterThanEqual(b, vec4(1.0))));
+`,LOGICAL_OR2=`
+  return min(
+    vec4(greaterThanEqual(a, vec4(1.0))) +
+    vec4(greaterThanEqual(b, vec4(1.0))),
+    vec4(1.0));
+`,MAX2=`
+  vec4 result = vec4(max(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+CHECK_NAN_SNIPPET2+`
+  return result;
+`,MIN2=`
+  vec4 result = vec4(min(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+CHECK_NAN_SNIPPET2+`
+  return result;
+`,MOD2=`
+  vec4 result = mod(a, b);
+  vec4 isNaN = vec4(equal(b, vec4(0.0)));
+  `+CHECK_NAN_SNIPPET2+`
+  return result;
+`,BinaryOpPackedProgram=class{constructor(op2,aShape,bShape,checkOutOfBounds=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape);let rank=this.outputShape.length,checkOutOfBoundsString="";if(checkOutOfBounds)if(rank===0||util_exports.sizeFromShape(this.outputShape)===1)checkOutOfBoundsString=`
+          result.y = 0.;
+          result.z = 0.;
+          result.w = 0.;
+        `;else{let dtype=getCoordsDataType(rank);if(checkOutOfBoundsString=`
+          ${dtype} coords = getOutputCoords();
+        `,rank===1)checkOutOfBoundsString+=`
+            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
+            result.z = 0.;
+            result.w = 0.;
+          `;else{let channels=getChannels("coords",rank);checkOutOfBoundsString+=`
+            bool nextRowOutOfBounds =
+              (${channels[rank-2]} + 1) >= ${this.outputShape[rank-2]};
+            bool nextColOutOfBounds =
+              (${channels[rank-1]} + 1) >= ${this.outputShape[rank-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) {
+        ${op2}
+      }
+
+      void main() {
+        vec4 a = getAAtOutCoords();
+        vec4 b = getBAtOutCoords();
+
+        vec4 result = binaryOperation(a, b);
+        ${checkOutOfBoundsString}
+
+        setOutput(result);
+      }
+    `}};var ClipProgram=class{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,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(min8,max10){return(gpgpu,webGLProgram)=>{this.minLoc==null&&(this.minLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"minVal"),this.maxLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"maxVal")),gpgpu.gl.uniform1f(this.minLoc,min8),gpgpu.gl.uniform1f(this.maxLoc,max10)}}};var ClipPackedProgram=class{constructor(aShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=aShape,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(min8,max10){return(gpgpu,webGLProgram)=>{this.minLoc==null&&(this.minLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"minVal"),this.maxLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"maxVal")),gpgpu.gl.uniform1f(this.minLoc,min8),gpgpu.gl.uniform1f(this.maxLoc,max10)}}};var ComplexAbsProgram=class{constructor(shape){this.variableNames=["real","imag"],this.outputShape=shape,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))
+        );
+      }
+    `}};var Conv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.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 < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              if (${isChannelsLast}) {
+                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);
+      }
+    `}},Conv2DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,isChannelsLast=convInfo.dataFormat==="channelsLast",padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left,rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[${channelDim}];
+
+        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - 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 < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${filterHeight} - 1 - wR;
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${filterWidth} - 1 - wC;
+
+            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
+
+              if (${isChannelsLast}) {
+                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);
+      }
+    `}},Conv3DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.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 < ${convInfo.batchSize}; b++) {
+          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
+            int xF = wF + yF * ${strideDepth} - ${padFront};
+
+            if (xF < 0 || xF >= ${convInfo.inDepth}) {
+              continue;
+            }
+
+            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+              int xR = wR + yR * ${strideHeight} - ${padTop};
+
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
+                continue;
+              }
+
+              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+                int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                  continue;
+                }
+
+                float dyValue = getDy(b, yF, yR, yC, d2);
+                float xValue = getX(b, xF, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},Conv3DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padFront=filterDepth-1-convInfo.padInfo.front,padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      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 < ${filterDepth}; wF++) {
+          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;
+
+          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
+            continue;
+          }
+          int idyF = int(dyF);
+
+          int wFPerm = ${filterDepth} - 1 - wF;
+
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+              fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            int wRPerm = ${filterHeight} - 1 - wR;
+
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              int wCPerm = ${filterWidth} - 1 - wC;
+
+              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
+                float xValue = getDy(batch, idyF, idyR, idyC, d2);
+                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};var DepthwiseConv2DDerFilterProgram=class{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.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 * ${channelMul} + dm;
+
+        float dotProd = 0.0;
+
+        // TO DO: Vec4 over the batch size
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              float dyValue = getDy(b, yR, yC, d2);
+              float xValue = getX(b, xR, xC, d1);
+              dotProd += (xValue * dyValue);
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},DepthwiseConv2DDerInputProgram=class{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      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 < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${filterHeight} - 1 - wR;
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${filterWidth} - 1 - wC;
+
+            // TO DO: Vec4 over the channelMul
+            for (int dm = 0; dm < ${channelMul}; dm++) {
+              int d2 = d1 * ${channelMul} + dm;
+              float xValue = getDy(batch, idyR, idyC, d2);
+              float wValue = getW(wRPerm, wCPerm, d1, dm);
+              dotProd += xValue * wValue;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};var Conv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`:activationSnippet=`
+          float activation(float x) {
+            ${activation2}
+          }
+        `,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
+
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d2 = coords[${channelDim}];
+
+        ivec2 xRCCorner =
+            ivec2(coords[${rowDim}], coords[${colDim}]) * 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 < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * ${dilationHeight};
+
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * ${dilationWidth};
+
+            if (xC < 0 || xC >= ${convInfo.inWidth}) {
+              continue;
+            }
+
+            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
+              vec4 wValues = vec4(
+                getW(wR, wC, d1, d2),
+                getW(wR, wC, d1 + 1, d2),
+                getW(wR, wC, d1 + 2, d2),
+                getW(wR, wC, d1 + 3, d2)
+              );
+
+              if (${isChannelsLast}) {
+                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 (${inputDepthVec4Remainder===1}) {
+
+              if (${isChannelsLast}) {
+                dotProd +=
+                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
+              } else {
+                dotProd +=
+                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
+              }
+
+            } else if (${inputDepthVec4Remainder===2}) {
+              vec2 wValues = vec2(
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
+              );
+
+              if (${isChannelsLast}) {
+                vec2 xValues = vec2(
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec2 xValues = vec2(
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            } else if (${inputDepthVec4Remainder===3}) {
+              vec3 wValues = vec3(
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
+              );
+
+              if (${isChannelsLast}) {
+                vec3 xValues = vec3(
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec3 xValues = vec3(
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            }
+          }
+        }
+
+        float result = dotProd;
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `}},Conv3DProgram=class{constructor(convInfo){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4;this.userCode=`
+      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      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 < ${filterDepth}; wF++) {
+          int xF = xFCorner + wF * ${dilationDepth};
+
+          if (xF < 0 || xF >= ${convInfo.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            int xR = xRCorner + wR * ${dilationHeight};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              int xC = xCCorner + wC * ${dilationWidth};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
+                vec4 xValues = vec4(
+                  getX(batch, xF, xR, xC, d1),
+                  getX(batch, xF, xR, xC, d1 + 1),
+                  getX(batch, xF, xR, xC, d1 + 2),
+                  getX(batch, xF, xR, xC, d1 + 3)
+                );
+                vec4 wValues = vec4(
+                  getW(wF, wR, wC, d1, d2),
+                  getW(wF, wR, wC, d1 + 1, d2),
+                  getW(wF, wR, wC, d1 + 2, d2),
+                  getW(wF, wR, wC, d1 + 3, d2)
+                );
+
+                dotProd += dot(xValues, wValues);
+              }
+
+              if (${inputDepthVec4Remainder===1}) {
+                dotProd +=
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
+              } else if (${inputDepthVec4Remainder===2}) {
+                vec2 xValues = vec2(
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
+                );
+                vec2 wValues = vec2(
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else if (${inputDepthVec4Remainder===3}) {
+                vec3 xValues = vec3(
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
+                );
+                vec3 wValues = vec3(
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};var DepthwiseConv2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;let xNumRows=convInfo.inHeight,xNumCols=convInfo.inWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,channelMul=convInfo.outChannels/convInfo.inChannels,activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`:activationSnippet=`
+          float activation(float x) {
+            ${activation2}
+          }
+        `,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
+
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2 / ${channelMul};
+        int q = d2 - d1 * ${channelMul};
+
+        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 < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * ${dilationHeight};
+
+          if (xR < 0 || xR >= ${xNumRows}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * ${dilationWidth};
+
+            if (xC < 0 || xC >= ${xNumCols}) {
+              continue;
+            }
+
+            float xVal = getX(batch, xR, xC, d1);
+            float wVal = getW(wR, wC, d1, q);
+            dotProd += xVal * wVal;
+          }
+        }
+
+        float result = dotProd;
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `}};var DepthwiseConvPacked2DProgram=class{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=convInfo.outShape;let xNumRows=convInfo.inHeight,xNumCols=convInfo.inWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,texelsAcross=filterWidth,mainLoop="int xR; int xC; int xCOffset;";for(let r=0;r<filterHeight;r++)for(let c=0;c<filterWidth;c++)mainLoop+=`
+          vec4 xTexelR${r}C${c*2} = vec4(0.);
+          vec4 wR${r}C${c} = vec4(0.);
+          vec4 xR${r}C${c} = vec4(0.);`;for(let r=0;r<filterHeight;r++)for(let texelC=0;texelC<texelsAcross;texelC++){let c=texelC*2;if(mainLoop+=`
+          xR = xRCorner + ${r*dilationHeight};
+          xC = xCCorner + ${c*dilationWidth};
+        `,strideWidth===1){if(c<filterWidth&&(padLeft%2===1?mainLoop+=`
+                xCOffset = xC + 1;
+                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
+
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if(xCOffset + 1 >= ${xNumCols}) {
+                    xTexelR${r}C${c}.zw = vec2(0.);
+                  }
+                } else {
+                  xTexelR${r}C${c} = vec4(0.);
+                }
+
+                xCOffset = xC + 1 - 2;
+                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  vec4 previous = getX(batch, xR, xCOffset, d1);
+
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if(xCOffset + 1 >= ${xNumCols}) {
+                    previous.zw = vec2(0.);
+                  }
+
+                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);
+                } else {
+                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);
+                }
+              `:mainLoop+=`
+                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
+                } else {
+                  xTexelR${r}C${c} = vec4(0.);
+                }
+
+                xR${r}C${c} = xTexelR${r}C${c};
+              `,c+1<filterWidth)){let nextTexelOffset=padLeft%2===0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2===0&&padLeft%2===1||dilationWidth%2!==0&&padLeft%2!==1?(mainLoop+=`
+                  xCOffset = xC + ${padLeft%2} + ${nextTexelOffset};
+
+                  if(xR >= 0 && xR < ${xNumRows} &&
+                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                    xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
+                  }
+                `,dilationWidth>1&&(mainLoop+=`
+                    xCOffset -= 2;
+                    if(xR >= 0 && xR < ${xNumRows} &&
+                      xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
+                    } else {
+                      xTexelR${r}C${c} = vec4(0.);
+                    }
+                  `),mainLoop+=`
+                  xR${r}C${c+1} = vec4(
+                    xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.xy);
+                `):mainLoop+=`
+                  xCOffset = xC + ${nextTexelOffset};
+
+                  if(xR >= 0 && xR < ${xNumRows} &&
+                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                    xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
+                  }
+
+                  xR${r}C${c+1} = xTexelR${r}C${c+2};
+                `}}else c<filterWidth&&(mainLoop+=`
+              if(xR >= 0 && xR < ${xNumRows}) {
+            `,padLeft%2===1?(mainLoop+=`
+                xCOffset = xC + 1 - ${strideWidth};
+                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
+                } else {
+                  xTexelR${r}C${c} = vec4(0.);
+                }
+
+                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {
+                  xTexelR${r}C${c+2} = getX(batch, xR, xC + 1, d1);
+                } else {
+                  xTexelR${r}C${c+2} = vec4(0.);
+                }
+
+                xR${r}C${c} = vec4(
+                  xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.zw);
+              `,c+1<filterWidth&&(mainLoop+=`
+                  vec4 final = vec4(0.);
+                  xCOffset = xC + 1 + ${strideWidth};
+                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                    final = getX(batch, xR, xCOffset, d1);
+                  }
+                  xR${r}C${c+1} = vec4(xTexelR${r}C${c+2}.xy, final.xy);
+                `)):(mainLoop+=`
+                if(xC >= 0 && xC < ${xNumCols}) {
+                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
+                } else {
+                  xTexelR${r}C${c} = vec4(0.);
+                }
+
+                xCOffset = xC + ${strideWidth};
+                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
+                  xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
+                } else {
+                  xTexelR${r}C${c+2} = vec4(0.);
+                }
+
+                xR${r}C${c} = vec4(
+                  xTexelR${r}C${c}.xy, xTexelR${r}C${c+2}.xy);
+              `,c+1<filterWidth&&(mainLoop+=`
+                  xR${r}C${c+1} = vec4(
+                    xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.zw);
+                `)),mainLoop+="}");c<filterWidth&&(mainLoop+=`
+            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);
+            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);
+          `,c+1<filterWidth&&(mainLoop+=`
+              vec4 wTexelR${r}C${c+1} = getW(${r}, ${c+1}, d1, q);
+              wR${r}C${c+1} =
+                vec4(wTexelR${r}C${c+1}.xz, wTexelR${r}C${c+1}.xz);`))}for(let r=0;r<filterHeight;r++)for(let c=0;c<filterWidth;c++)mainLoop+=`dotProd += xR${r}C${c} * wR${r}C${c};`;let activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`:activationSnippet=`vec4 activation(vec4 x) {
+          ${activation2}
+        }`,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights"),this.userCode=`
+      ${activationSnippet}
+
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      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.);
+
+        ${mainLoop}
+
+        vec4 result = dotProd;
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `}};var CropAndResizeProgram=class{constructor(imageShape,boxShape,cropSize,method,extrapolationValue){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[batch,imageHeight,imageWidth,depth]=imageShape,[numBoxes]=boxShape,[cropHeight,cropWidth]=cropSize;this.outputShape=[numBoxes,cropHeight,cropWidth,depth];let methodId=method==="bilinear"?1:0,[inputHeightFloat,inputWidthFloat]=[`${imageHeight-1}.0`,`${imageWidth-1}.0`],[heightRatio,heightScale,inY]=cropHeight>1?[`${(imageHeight-1)/(cropHeight-1)}`,"(y2-y1) * height_ratio",`y1*${inputHeightFloat} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${inputHeightFloat}`],[widthRatio,widthScale,inX]=cropWidth>1?[`${(imageWidth-1)/(cropWidth-1)}`,"(x2-x1) * width_ratio",`x1*${inputWidthFloat} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${inputWidthFloat}`];this.userCode=`
+      const float height_ratio = float(${heightRatio});
+      const float width_ratio = float(${widthRatio});
+      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 >= ${batch}) {
+          return;
+        }
+
+        float height_scale = ${heightScale};
+        float width_scale = ${widthScale};
+
+        float in_y = ${inY};
+        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
+          setOutput(float(${extrapolationValue}));
+          return;
+        }
+        float in_x = ${inX};
+        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
+          setOutput(float(${extrapolationValue}));
+          return;
+        }
+
+        vec2 sourceFracIndexCR = vec2(in_x,in_y);
+        if(${methodId} == 1) {
+          // Compute the four integer indices.
+          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
+          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
+
+          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
+          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
+          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
+          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
+
+          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
+
+          float top = topLeft + (topRight - topLeft) * fracCR.x;
+          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
+          float newValue = top + (bottom - top) * fracCR.y;
+          setOutput(newValue);
+        } else {
+          // Compute the coordinators of nearest neighbor point.
+          ivec2 sourceNearestCR = ivec2(floor(
+            sourceFracIndexCR + vec2(0.5,0.5)));
+          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
+          setOutput(newValue);
+        }
+      }
+    `}},CumSumProgram=class{constructor(shape,exclusive,reverse12){this.variableNames=["x"],this.outputShape=shape;let rank=shape.length,val=exclusive?"0.0":`getX(${getCoords(rank,"coords")})`,length=shape[shape.length-1],condition="",idxString="";exclusive?(condition=reverse12?`end != ${length-1}`:"end != 0",idxString=reverse12?"end + 1":"end - 1"):(condition=reverse12?`end + pow2 < ${length}`:"end >= pow2",idxString=reverse12?"end + pow2":"end - pow2"),this.userCode=`
+      uniform float index;
+      void main() {
+        ${getCoordsDataType(rank)} coords = getOutputCoords();
+        int end = ${getFinalCoord(rank,"coords")};
+        float val = ${val};
+        int pow2 = int(pow(2.0, index));
+        if (${condition}) {
+          int idx = ${idxString};
+          ${getFinalCoord(rank,"coords")} = idx;
+          val += getX(${getCoords(rank,"coords")});
+        }
+        setOutput(val);
+      }
+    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}};function getCoords(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}var DecodeMatrixProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=PackingScheme.DENSE;let texShape=getDenseTexShape(outputShape),glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],outputShape)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+          vec2(${texShape[0]}, ${texShape[1]}));
+        int index = 4 * (resTexRC.x * ${texShape[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);
+        }
+
+        ${glsl.output} = result;
+      }
+    `}};var DecodeMatrixPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=PackingScheme.DENSE;let texShape=getDenseTexShape(outputShape),glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],outputShape)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+          vec2(${texShape[0]}, ${texShape[1]}));
+        int index = 4 * (resTexRC.x * ${texShape[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));
+        }
+
+        ${glsl.output} = result;
+      }
+    `}};var DepthToSpaceProgram=class{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,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 / ${blockSize};
+      int offset_h = imod(h, ${blockSize});
+      int in_w = w / ${blockSize};
+      int offset_w = imod(w, ${blockSize});
+      int offset_d = (offset_h * ${blockSize} + 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)"}};var DiagProgram=class{constructor(size){this.variableNames=["X"],this.outputShape=[size,size],this.userCode=`
+      void main() {
+          ivec2 coords = getOutputCoords();
+          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
+          setOutput(val);
+      }
+    `}};var EncodeFloatProgram=class{constructor(outputShape){this.variableNames=["A"],this.outTexUsage=TextureUsage.DOWNLOAD;let glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
+      ${ENCODE_FLOAT_SNIPPET}
+
+      void main() {
+        float x = getAAtOutCoords();
+        ${glsl.output} = encode_float(x);
+      }
+    `}};var EncodeFloatPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=TextureUsage.DOWNLOAD;let glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
+      ${ENCODE_FLOAT_SNIPPET}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
+        ${glsl.output} = encode_float(x);
+      }
+    `}};var EncodeMatrixProgram=class{constructor(outputShape,texShape,inputIsUnsignedByte=!1){this.variableNames=["A"];let glsl=getGlslDifferences(),[height,width]=texShape;this.outputShape=outputShape;let output="result";inputIsUnsignedByte&&(output="floor(result * 255. + 0.5)"),this.userCode=`
+      ${getFlatIndexFrom3D(outputShape)}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+
+        int flatIndex = getFlatIndex(coords);
+        int offset = imod(flatIndex, 4);
+
+        flatIndex = idiv(flatIndex, 4, 1.);
+
+        int r = flatIndex / ${width};
+        int c = imod(flatIndex, ${width});
+        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
+        vec4 values = ${glsl.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];
+        }
+
+        ${glsl.output} = vec4(${output}, 0., 0., 0.);
+      }
+    `}};var EncodeMatrixPackedProgram=class{constructor(outputShape,texShape,inputIsUnsignedByte=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let glsl=getGlslDifferences(),[height,width]=texShape;this.outputShape=outputShape;let mainLoop="",output="result";inputIsUnsignedByte&&(output="floor(result * 255. + 0.5)");for(let row=0;row<=1;row++)for(let col=0;col<=1;col++){let channel=row*2+col;mainLoop+=`
+          localCoords = coords;
+          if(localCoords[2] + ${col} < ${outputShape[2]}) {
+            localCoords[2] += ${col};
+            if(localCoords[1] + ${row} < ${outputShape[1]}) {
+              localCoords[1] += ${row};
+
+              flatIndex = getFlatIndex(localCoords);
+              offset = imod(flatIndex, 4);
+
+              flatIndex = idiv(flatIndex, 4, 1.);
+
+              r = flatIndex / ${width};
+              c = imod(flatIndex, ${width});
+              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
+              values = ${glsl.texture2D}(A, uv);
+
+              if(offset == 0) {
+                result[${channel}] = values[0];
+              } else if(offset == 1) {
+                result[${channel}] = values[1];
+              } else if(offset == 2) {
+                result[${channel}] = values[2];
+              } else {
+                result[${channel}] = values[3];
+              }
+            }
+          }
+        `}this.userCode=`
+      ${getFlatIndexFrom3D(outputShape)}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+
+        vec4 result = vec4(0.);
+        int flatIndex, r, c, offset;
+        ivec3 localCoords;
+        vec2 uv;
+        vec4 values;
+
+        ${mainLoop}
+
+        ${glsl.output} = ${output};
+      }
+    `}};var FillProgram=class{constructor(shape,value){this.outputShape=[],this.variableNames=["x"],this.outputShape=shape,this.userCode=`
+      uniform float value;
+      void main() {
+        // Input can be obtained from uniform value.
+        setOutput(value);
+      }
+    `}getCustomSetupFunc(value){return(gpgpu,webGLProgram)=>{this.valueLoc==null&&(this.valueLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"value")),gpgpu.gl.uniform1f(this.valueLoc,value)}}};var GatherProgram=class{constructor(aShape,indicesLength,axis){this.variableNames=["A","indices"];let outputShape=aShape.slice();outputShape[axis]=indicesLength,this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),sourceCoords=getSourceCoords2(aShape,axis);this.userCode=`
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        setOutput(getA(${sourceCoords}));
+      }
+    `}};function getSourceCoords2(aShape,axis){let rank=aShape.length;if(rank>4)throw Error(`Gather for rank ${rank} is not yet supported`);if(rank===1)return"int(getIndices(resRC))";let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w"],sourceCoords=[];for(let i=0;i<aShape.length;i++)i===axis?sourceCoords.push(`int(getIndices(${currentCoords[i]}))`):sourceCoords.push(`${currentCoords[i]}`);return sourceCoords.join()}var GatherNDProgram=class{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${stridesType} strides = ${stridesType}(${this.strides});
+         void main() {
+          ${dtype} coords = getOutputCoords();
+          int flattenIndex = 0;
+          for (int j = 0; j < ${this.sliceDim}; j++) {
+            int index = round(getIndices(coords[0], j));
+            flattenIndex += index * ${strideString};
+          }
+          setOutput(getX(flattenIndex, coords[1]));
+        }
+      `}};function createVertexShader2(gl){let glsl=getGlslDifferences(),vertexShaderSource=`${glsl.version}
+    precision highp float;
+    ${glsl.attribute} vec3 clipSpacePos;
+    ${glsl.attribute} vec2 uv;
+    ${glsl.varyingVs} vec2 resultUV;
+
+    void main() {
+      gl_Position = vec4(clipSpacePos, 1);
+      resultUV = uv;
+    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){let vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){let triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);let texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){let posOffset=0,uvOffset=3*4,stride=3*4+2*4;callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer));let success=bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset);return success&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){let buffer11=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11));let bytesPerFloat=4,valuesPerTexel=4,bufferSizeBytes=bytesPerFloat*valuesPerTexel*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer11}function downloadFloat32MatrixFromBuffer(gl,buffer11,size){let gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){let[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer11,batch,rows,cols,physicalRows,physicalCols,textureConfig){let gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){let packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}var GPGPUContext=class{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float",COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){let TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().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(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().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(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().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.");let gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer11,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer11,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer11,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer11,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);let result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){let fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){let gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{let status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();let gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();let[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().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(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}let ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}let ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){let gl2=this.gl,timeElapsedNanos=gl2.getQueryParameter(query,gl2.QUERY_RESULT);return timeElapsedNanos/1e6}else{let ext=this.getQueryTimerExtensionWebGL1(),timeElapsedNanos=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT);return timeElapsedNanos/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){let gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{let ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){let index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){let{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){if(this.itemsToPoll.push({isDoneFn,resolveFn}),this.itemsToPoll.length>1)return;util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);let result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();let gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}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 linearSearchLastTrue(arr){let i=0;for(;i<arr.length;++i){let isDone=arr[i]();if(!isDone)break}return i-1}function compileProgram(gpgpu,program,inputs,output){let userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{let shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source),infLoc=null,nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));let uniformLocations={};for(let i=0;i<program.variableNames.length;i++){let varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{let shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(s.isUniform&&input2.isUniform)return;let texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);let outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&(binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity)),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{let varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc==null)return;if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{let hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});let keyUserCode=program.userCode,key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}var Im2ColPackedProgram=class{constructor(outputShape,inputShape,convInfo){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let{filterWidth,inChannels,strideWidth,strideHeight,padInfo,outWidth,dilationWidth,dilationHeight,dataFormat}=convInfo,{left,top}=padInfo,itemsPerBlockRow=inChannels*filterWidth,glsl=getGlslDifferences(),isChannelsLast=dataFormat==="channelsLast",rowDim=isChannelsLast?0:1,colDim=isChannelsLast?1:2,unrolled="";for(let row=0;row<=1;row++)for(let col=0;col<=1;col++)unrolled+=`
+          blockIndex = rc.y + ${col};
+          pos = rc.x + ${row};
+
+          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {
+            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};
+            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});
+
+            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {
+
+              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);
+              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));
+
+              if(d1 < ${inputShape[colDim]} && d1 >= 0) {
+
+                ch = int(mod(float(pos), ${inChannels}.));
+
+                if (${isChannelsLast}) {
+                  innerDims = vec2(d1, ch);
+                  result[${row*2+col}] = getChannel(
+                    getA(d0, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                } else {
+                  innerDims = vec2(d0, d1);
+                  result[${row*2+col}] = 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;
+
+        ${unrolled}
+
+        ${glsl.output} = result;
+      }
+    `}};var LRNProgram=class{constructor(xShape,radius,bias,alpha,beta){this.variableNames=["x"],this.outputShape=[];let rad=radius,maxD=xShape[3]-1;this.outputShape=xShape;let powOperator,basis=`float(${bias}) + float(${alpha}) * sum`;beta===.5?powOperator=`inversesqrt(${basis})`:beta===1?powOperator=`1.0/(${basis})`:powOperator=`exp(log(${basis}) * float(-${beta}));`,this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+        int d = coords[3];
+        float x = getX(b, r, c, d);
+        float sum = 0.0;
+        for (int j = -${rad}; j <= ${rad}; j++) {
+          int idx = d + j;
+          if (idx >= 0 && idx <=  ${maxD}) {
+            float z = getX(b, r, c, idx);
+            sum += z * z;
+          }
+        }
+        float val = x * ${powOperator};
+        setOutput(val);
+      }
+    `}};var LRNGradProgram=class{constructor(inputShape,depthRadius,bias,alpha,beta){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=inputShape,this.depth=inputShape[3],this.depthRadius=depthRadius,this.bias=bias,this.alpha=alpha,this.beta=beta,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 - ${depthRadius})));
+          int depthEnd = int(min(float(${this.depth}),
+              float(d + ${depthRadius} + 1)));
+
+          const int MIN_DEPTH_BEGIN = 0;
+          const int MAX_DEPTH_END = ${this.depth};
+
+          float norm = 0.0;
+          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd) {
+              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
+            }
+            else {
+              break;
+            }
+          }
+
+          norm = float(${alpha}) * norm + float(${bias});
+
+          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd){
+              float dyi = -2.0 * float(${alpha})
+                * float(${beta})
+                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
+                / norm;
+              if (k == d) {
+                dyi += pow(norm, -1.0 * ${beta});
+              }
+              if (k == coords[3]) {
+                dyi *= getDy(b, r, c, d);
+                result += dyi;
+              }
+            }
+            else {
+              break;
+            }
+          }
+      }
+      setOutput(result);
+      }
+    `}};var LRNPackedProgram=class{constructor(xShape,radius,bias,alpha,beta){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let rad=radius,maxD=xShape[3]-1;this.outputShape=xShape;let powOperator,basis=`float(${bias}) + float(${alpha}) * sum`;beta===.5?powOperator=`inversesqrt(${basis})`:beta===1?powOperator=`1.0/(${basis})`:powOperator=`exp(log(${basis}) * float(-${beta}));`,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 - ${rad};
+        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 = - ${rad}; j <= ${rad}; j++) {
+          ivec2 idx = depth + j;
+          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));
+
+          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 * ${powOperator};
+        setOutput(result);
+      }
+    `}};var MaxPool2DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy","maxPos"],this.outputShape=convInfo.inShape;let strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,lastIndex=effectiveFilterHeight*effectiveFilterWidth-1;this.userCode=`
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      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 < ${effectiveFilterHeight};
+          wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));
+
+            // Get the current value, check it against the value from the
+            // position matrix.
+            int curPosValue = wR * ${effectiveFilterWidth} + wC;
+            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+            dotProd += dyValue * mask;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},MaxPool3DBackpropProgram=class{constructor(convInfo){this.variableNames=["dy","maxPos"],this.outputShape=convInfo.inShape;let strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,lastIndex=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1;this.userCode=`
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      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 < ${effectiveFilterDepth};
+           wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
+
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+              int maxPosValue = ${lastIndex} -
+                  int(getMaxPos(batch, idyD, idyR, idyC, ch));
+
+              // Get the current value, check it against the value from the
+              // position matrix.
+              int curPosValue =
+                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                  wR * ${effectiveFilterWidth} + wC;
+              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+              dotProd += dyValue * mask;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};var MatMulPackedProgram=class{constructor(aShape,bShape,outputShape,transposeA=!1,transposeB=!1,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let sharedDim=transposeA?aShape[1]:aShape[2],sharedDimensionPacked=Math.ceil(sharedDim/2),aSample=transposeA?"i * 2, rc.y":"rc.y, i * 2",bSample=transposeB?"rc.z, i * 2":"i * 2, rc.z",aSwizzle=transposeA?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],bSwizzle=transposeB?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`:activationSnippet=`vec4 activation(vec4 x) {
+          ${activation2}
+        }`,applyActivationSnippet="result = activation(result);");let addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights");let batchASnippet="rc.x",batchBSnippet="rc.x";aShape[0]<bShape[0]?batchASnippet=`int(min(float(rc.x), ${aShape[0]-1}.))`:bShape[0]<aShape[0]&&(batchBSnippet=`int(min(float(rc.x), ${bShape[0]-1}.))`),this.userCode=`
+      ${activationSnippet}
+
+      const float sharedDimension = ${sharedDimensionPacked}.0;
+
+      vec4 dot2x2ARowBCol(ivec3 rc) {
+        vec4 result = vec4(0);
+        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
+          int batchA = ${batchASnippet};
+          int batchB = ${batchBSnippet};
+          vec4 a = getMatrixA(batchA, ${aSample});
+          vec4 b = getMatrixB(batchB, ${bSample});
+
+          // These swizzled products need to be separately added.
+          // See: https://github.com/tensorflow/tfjs/issues/1735
+          result += (${aSwizzle[0]} * ${bSwizzle[0]});
+          result += (${aSwizzle[1]} * ${bSwizzle[1]});
+        }
+        return result;
+      }
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+        vec4 result = dot2x2ARowBCol(rc);
+
+        ${addBiasSnippet}
+
+        ${applyActivationSnippet}
+
+        setOutput(result);
+      }
+    `}};var MultinomialProgram=class{constructor(batchSize,numOutcomes,numSamples){this.variableNames=["probs"],this.outputShape=[batchSize,numSamples],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 < ${numOutcomes-1}; i++) {
+          cdf += getProbs(batch, i);
+
+          if (r < cdf) {
+            setOutput(float(i));
+            return;
+          }
+        }
+
+        // If no other event happened, last event happened.
+        setOutput(float(${numOutcomes-1}));
+      }
+    `}getCustomSetupFunc(seed){return(gpgpu,webGLProgram)=>{this.seedLoc==null&&(this.seedLoc=gpgpu.getUniformLocation(webGLProgram,"seed")),gpgpu.gl.uniform1f(this.seedLoc,seed)}}};var OneHotProgram=class{constructor(numIndices,depth,onValue,offValue){this.variableNames=["indices"],this.outputShape=[numIndices,depth],this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int index = round(getIndices(coords.x));
+        setOutput(mix(float(${offValue}), float(${onValue}),
+                      float(index == coords.y)));
+      }
+    `}};var PackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;let rank=outputShape.length;if(rank===0)this.userCode=`
+        void main() {
+          setOutput(vec4(getA(), 0., 0., 0.));
+        }
+      `;else{let channels=getChannels("rc",rank),dtype=getCoordsDataType(rank),outOfBoundsCondition=getOutOfBoundsCondition(rank,outputShape,channels),setup38=getSetup(rank,outputShape[outputShape.length-1],outputShape[outputShape.length-2],channels),output=getOutput(outputShape,channels);this.userCode=`
+        void main() {
+          ${dtype} rc = getOutputCoords();
+
+          if(${outOfBoundsCondition}) {
+            setOutput(vec4(0));
+          } else {
+            ${setup38}
+
+            setOutput(vec4(${output}));
+          }
+        }
+      `}}};function getSourceCoordsArr(rank,dims){let coords2=[];for(let row=0;row<=1;row++)for(let col=0;col<=1;col++){let coord=`${row===0?"r":"rp1"}, ${col===0?"c":"cp1"}`;for(let d=2;d<rank;d++)coord=`${dims[dims.length-1-d]},`+coord;coords2.push(coord)}return coords2}function getOutOfBoundsCondition(rank,shape,dims){if(rank===1)return`rc > ${shape[0]}`;let cond="";for(let i=rank-2;i<rank;i++)cond+=`${dims[i]} >= ${shape[i]}`,i<rank-1&&(cond+="||");return cond}function getSetup(rank,cols,rows,dims){if(rank===1)return"";let innerDims=dims.slice(-2);return`
+    int r = ${innerDims[0]};
+    int c = ${innerDims[1]};
+    int rp1 = r + 1;
+    int cp1 = c + 1;
+
+    bool cEdge = cp1 >= ${cols};
+    bool rEdge = rp1 >= ${rows};
+  `}function getOutput(shape,dims){let rank=shape.length,sourceCoords=getSourceCoordsArr(rank,dims);return rank===1?`getA(rc),
+            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),
+            0, 0`:`getA(${sourceCoords[0]}),
+          cEdge ? 0. : getA(${sourceCoords[1]}),
+          rEdge ? 0. : getA(${sourceCoords[2]}),
+          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}var PadProgram=class{constructor(xShape,paddings,constantValue){this.variableNames=["x"],this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,type=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),unpackedCoords=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,rank);if(rank===1){this.userCode=`
+        int start = ${start};
+        int end = ${end};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start || outC >= end) {
+            setOutput(float(${constantValue}));
+          } else {
+            setOutput(getX(outC - start));
+          }
+        }
+      `;return}this.userCode=`
+      ${type} start = ${type}(${start});
+      ${type} end = ${type}(${end});
+
+      void main() {
+        ${type} outC = getOutputCoords();
+        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
+          setOutput(float(${constantValue}));
+        } else {
+          ${type} coords = outC - start;
+          setOutput(getX(${unpackedCoords}));
+        }
+      }
+    `}};var PadPackedProgram=class{constructor(xShape,paddings,constantValue){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),coords2=getChannels("rc",rank),source=getChannels("source",rank),cLimit=`${coords2[rank-1]} < ${this.outputShape[rank-1]}`,innerDims=rank===1?"source":`vec2(${source.slice(-2).join()})`,componentSetup=[`${dtype} rc = outputLoc;`,`${coords2[rank-1]} += 1;
+       if(${cLimit}) {
+      `,rank===1?"":`}
+       rc = outputLoc;
+       ${coords2[rank-2]} += 1;
+       if(${coords2[rank-2]} < ${this.outputShape[rank-2]}) {`,rank===1?"":`  ${coords2[rank-1]} += 1;
+         if(${cLimit}) {`],paddingArea=rank===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",mainLoop="";for(let i=0,j=rank===1?2:4;i<j;i++)mainLoop+=`
+        ${componentSetup[i]}
+        if (${paddingArea}) {
+          result[${i}] = float(${constantValue});
+        } else {
+          ${dtype} source = rc - start;
+          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+      `;mainLoop+=rank===1?"} ":"}}",this.userCode=`
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${mainLoop}
+        setOutput(result);
+      }
+    `}};var Pool2DProgram=class{constructor(convInfo,poolType,computePositions,flattenPositions=!1,includeBatchInIndex=!1){if(this.variableNames=["x"],poolType==="avg"&&computePositions)throw new Error("Cannot compute positions for average pool.");let filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.outputShape=convInfo.outShape;let isAvgPool=poolType==="avg",batchFlattenPositionStr=`((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`,flattenPositionStr=`(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`,initializationValue="0.0";if(isAvgPool||(initializationValue="-1.0 / 1e-20"),computePositions){let compareOp2=">=";this.userCode=`
+        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+        const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+        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 < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              int xC = xCCorner + wC;
+
+              if (xC < 0 || xC >= ${convInfo.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 ${compareOp2} currMinMaxValue) {
+                minMaxValue = value;
+                minMaxValueFound = 1.0;
+                minMaxPosition = ${flattenPositions?includeBatchInIndex?batchFlattenPositionStr:flattenPositionStr:`wR * ${effectiveFilterWidth} + wC`};
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;return}let compareOp="max",returnValue=`${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;poolType==="avg"&&(returnValue="avgValue / count");let filterWidthNearestVec4=Math.floor(filterWidth/4)*4,filterWidthVec4Remainder=filterWidth%4,updateSnippet=`
+      if (${isAvgPool}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+      }
+    `;this.userCode=`
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
+      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 >= ${convInfo.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xR, xC, d);
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d = coords[3];
+
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // max/min x(?, ?, d) to get y(yR, yC, d).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${initializationValue});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+          int xR = xRCorner + wR;
+
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+            int xC = xCCorner + wC * ${dilationWidth};
+
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
+              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
+            );
+
+            ${updateSnippet}
+          }
+
+          int xC = xCCorner + ${filterWidthNearestVec4};
+          if (${filterWidthVec4Remainder===1}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              initializationValue,
+              initializationValue,
+              initializationValue
+            );
+
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder===2}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              initializationValue,
+              initializationValue
+            );
+
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder===3}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
+              initializationValue
+            );
+
+            ${updateSnippet}
+          }
+        }
+        setOutput(${returnValue});
+      }
+    `}},Pool3DProgram=class{constructor(convInfo,poolType,computePositions,flattenPositions=!1,includeBatchInIndex=!1){if(this.variableNames=["x"],poolType==="avg"&&computePositions)throw new Error("Cannot compute positions for average pool.");let filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.outputShape=convInfo.outShape;let isAvgPool=poolType==="avg",initializationValue="0.0";if(isAvgPool||(initializationValue="-1.0 / 1e-20"),computePositions){let compareOp2=">=";this.userCode=`
+        const ivec3 strides =
+            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+        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 < ${effectiveFilterDepth};
+              wD += ${dilationDepth}) {
+            int xD = xDCorner + wD;
+
+            if (xD < 0 || xD >= ${convInfo.inDepth}) {
+              continue;
+            }
+
+            for (int wR = 0; wR < ${effectiveFilterHeight};
+                wR += ${dilationHeight}) {
+              int xR = xRCorner + wR;
+
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
+                continue;
+              }
+
+              for (int wC = 0; wC < ${effectiveFilterWidth};
+                  wC += ${dilationWidth}) {
+                int xC = xCCorner + wC;
+
+                if (xC < 0 || xC >= ${convInfo.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 ${compareOp2} currMinMaxValue) {
+                  minMaxValue = value;
+                  minMaxValueFound = 1.0;
+                  minMaxPosition = ${flattenPositions?includeBatchInIndex?`(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`:`((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`:`wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                      wR * ${effectiveFilterWidth} + wC`};
+                }
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;return}let compareOp="max",returnValue=`${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;poolType==="avg"&&(returnValue="avgValue / count");let filterWidthNearestVec4=Math.floor(filterWidth/4)*4,filterWidthVec4Remainder=filterWidth%4,updateSnippet=`
+      if (${isAvgPool}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+      }
+    `;this.userCode=`
+      const ivec3 strides =
+        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
+      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 >= ${convInfo.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xD, xR, xC, ch);
+      }
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xDCorner = xCorner.x;
+        int xRCorner = xCorner.y;
+        int xCCorner = xCorner.z;
+
+        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${initializationValue});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
+          int xD = xDCorner + wD;
+
+          if (xD < 0 || xD >= ${convInfo.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+              int xC = xCCorner + wC * ${dilationWidth};
+
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
+              );
+
+              ${updateSnippet}
+            }
+
+            int xC = xCCorner + ${filterWidthNearestVec4};
+            if (${filterWidthVec4Remainder===1}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                initializationValue,
+                initializationValue,
+                initializationValue
+              );
+
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder===2}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                initializationValue,
+                initializationValue
+              );
+
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder===3}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
+                initializationValue
+              );
+
+              ${updateSnippet}
+            }
+          }
+          setOutput(${returnValue});
+        }
+      }
+    `}};var ReduceProgram=class{constructor(reduceInfo,reduceType){this.variableNames=["x"];let{windowSize,batchSize,inSize,outSize}=reduceInfo;this.outputShape=[batchSize,outSize];let initializationValue="0.0",compareOp="";reduceType==="prod"?initializationValue="1.0":reduceType==="min"?(initializationValue="1.0 / 1e-20",compareOp="min"):reduceType==="max"&&(initializationValue="-1.0 / 1e-20",compareOp="max");let returnValue=`${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;reduceType==="sum"?returnValue="sumValue":reduceType==="prod"?returnValue="prodValue":reduceType==="all"?returnValue="allValue":reduceType==="any"&&(returnValue="anyValue");let windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
+      if (${reduceType==="sum"}) {
+        sumValue += dot(values, ones);
+      } else if (${reduceType==="prod"}) {
+        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
+        prodValue *= tmp[0] * tmp[1];
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+      }
+    `,vecType="vec4";reduceType==="all"?(initializationValue="1.0",updateSnippet=`
+        bool reducedAllValue = all(values);
+        float floatedReducedAllValue = float(reducedAllValue);
+        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
+      `,vecType="bvec4"):reduceType==="any"&&(initializationValue="0.0",updateSnippet=`
+        bool reducedAnyValue = any(values);
+        float floatedReducedAnyValue = float(reducedAnyValue);
+        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
+      `,vecType="bvec4");let checkOutOfBounds="";inSize%windowSize>0&&(checkOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return initializationValue;
+        }
+      `),this.userCode=`
+      const float initializationValue = ${initializationValue};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${checkOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${windowSize};
+
+        vec4 minMaxValue = vec4(${initializationValue});
+        float prodValue = 1.0;
+        float sumValue = 0.0;
+        float allValue = 1.0;
+        float anyValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
+          int inIdx = inOffset + i;
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===1}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===2}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===3}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          ${updateSnippet}
+        }
+        setOutput(${returnValue});
+      }
+    `}};var ReshapePackedProgram=class{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2===1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
+        ${thisRC}
+        ${i>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
+          int flatIndex = getFlatIndex(thisRC);
+
+          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
+          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
+
+          result[${i}] =
+            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
+        ${i>0?"}":""}
+      `}this.userCode=`
+      ${getReshapedInputCoords(inputShape)}
+      ${getFlatIndexFrom3D(outputShape)}
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+
+        vec4 result = vec4(0.);
+
+        ivec3 thisRC;
+        int rows = ${outputShape[1]};
+        int cols = ${outputShape[2]};
+
+        ${mainLoop}
+
+        setOutput(result);
+      }
+    `}};function getReshapedInputCoords(shape){let coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape);return`
+    ivec3 inputCoordsFromReshapedOutCoords(int index) {
+      ${coordsFromIndexSnippet}
+      return ivec3(r, c, d);
+    }
+  `}var ResizeBilinearBackpropProgram=class{constructor(dy,x,alignCorners){this.variableNames=["dy"],this.outputShape=[],this.outputShape=x.shape;let[,xHeight,xWidth]=x.shape,[,yHeight,yWidth]=dy.shape,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
+
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
+
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
+
+        // 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 >= ${yHeight}) {
+            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 >= ${yWidth}) {
+              continue;
+            }
+
+            float dxR = float(dyR) * heightScale;
+            int topDxRIndex = int(floor(dxR));
+            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight-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), ${xWidth-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);
+      }
+    `}};var ResizeBilinearProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];this.userCode=`
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.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);
+      }
+    `}};var ResizeBilinearPackedProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];this.userCode=`
+      const vec3 effectiveInputOverOutputRatioRC = vec3(
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
+                                     ${oldWidth}.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 < ${depth-1};
+        bool hasNextRow = coords.z < ${newWidth-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);
+      }
+    `}};var ResizeNearestNeigborBackpropProgram=class{constructor(dy,x,alignCorners){this.variableNames=["dy"],this.outputShape=[],this.outputShape=x.shape;let[,xHeight,xWidth]=x.shape,[,yHeight,yWidth]=dy.shape,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*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(${heightScale});
+        const float widthScale = float(${widthScale});
+
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
+
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
+
+        // 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 >= ${yHeight}) {
+            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 >= ${yWidth}) {
+              continue;
+            }
+
+            float sourceFracRow =
+              float(${effectiveXSize[0]}) *
+                (float(dyR) / float(${effectiveYSize[0]}));
+
+            float sourceFracCol =
+                float(${effectiveXSize[1]}) *
+                  (float(dyC) / float(${effectiveYSize[1]}));
+
+            int sourceNearestRow = int(min(
+                float(int(${xHeight}) - 1),
+                ${alignCorners} ? float(round(sourceFracRow)) :
+                                  float(floor(sourceFracRow))));
+
+            int sourceNearestCol = int(min(
+                float(int(${xWidth}) - 1),
+                ${alignCorners} ? float(round(sourceFracCol)) :
+                                  float(floor(sourceFracCol))));
+
+            if (r == sourceNearestRow && c == sourceNearestCol) {
+              accumulator += getDy(b, dyR, dyC, d);
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `}};var ResizeNearestNeighborProgram=class{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.outputShape=[];let[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];let effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],roundBase=alignCorners?"0.5":"0.0";this.userCode=`
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${effectiveInSize[0]/effectiveOutSize[0]},
+          ${effectiveInSize[1]/effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.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 + ${roundBase})));
+
+        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
+
+        setOutput(newValue);
+      }
+    `}};var ReverseProgram=class{constructor(xShape,axis){this.variableNames=["x"];let rank=xShape.length;if(rank>4)throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);if(this.outputShape=xShape,rank===1){this.userCode=`
+        void main() {
+          int coord = getOutputCoords();
+          setOutput(getX(${xShape[0]} - coord - 1));
+        }
+      `;return}let getInCoord=i=>axis.indexOf(i)!==-1&&xShape[i]!==1?`${xShape[i]} - coords[${i}] - 1`:`coords[${i}]`,inCoords=xShape.map((_,i)=>getInCoord(i)).join(","),type=getCoordsDataType(rank);this.userCode=`
+      void main() {
+        ${type} coords = getOutputCoords();
+        setOutput(getX(${inCoords}));
+      }
+    `}};var ReversePackedProgram=class{constructor(xShape,axis){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let rank=xShape.length;if(rank>4)throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);this.outputShape=xShape;let channels=getChannels("rc",rank),nextColumn=`${channels[rank-1]} + 1 < ${this.outputShape[rank-1]}`,nextRow=`${channels[rank-2]} + 1 < ${this.outputShape[rank-2]}`,type=getCoordsDataType(rank);rank===1?this.userCode=`
+        void main(){
+          int rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = getChannel(getX(${xShape[0]} - rc - 1),
+            ${xShape[0]} - rc - 1);
+          if(${nextColumn}){
+              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
+                ${xShape[0]} - (rc  + 1) - 1);
+          }
+          setOutput(result);
+        }
+      `:this.userCode=`
+        void main() {
+          ${type} rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = ${getR(channels.slice())};
+          if(${nextColumn}){
+            result.g = ${getG(channels.slice())};
+          }
+          if(${nextRow}) {
+            result.b = ${getB(channels.slice())};
+            if(${nextColumn}) {
+              result.a = ${getA(channels.slice())};
+            }
+          }
+          setOutput(result);
+        }
+    `;function getR(channels2){return getChannel(channels2)}function getG(channels2){return channels2[rank-1]="("+channels2[rank-1]+" + 1)",getChannel(channels2)}function getB(channels2){return channels2[rank-2]="("+channels2[rank-2]+" + 1)",getChannel(channels2)}function getA(channels2){return channels2[rank-1]="("+channels2[rank-1]+" + 1)",channels2[rank-2]="("+channels2[rank-2]+" + 1)",getChannel(channels2)}function getChannel(channels2){let inCoordsArray=xShape.map((_,i)=>getInCoord(i,channels2)),inCoords=inCoordsArray.join(","),innerDims=inCoordsArray.slice(-2).join(",");return`getChannel(getX(${inCoords}), vec2(${innerDims}))`}function getInCoord(i,channels1){return axis.indexOf(i)!==-1&&xShape[i]!==1?`${xShape[i]} - ${channels1[i]} - 1`:`${channels1[i]}`}}};var ScatterProgram=class{constructor(updateSize,sliceDim,indicesRank,updatesRank,strides,shape,summingDupeIndex=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=shape;let stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),indicesString="";indicesRank===1?indicesString="i":indicesRank===2&&(indicesString="i, j");let indicesSnippet=`getIndices(${indicesString})`,updatesString="";updatesRank===1?updatesString="i":updatesRank===2&&(updatesString="i, coords[1]");let updatesSnippet=`getUpdates(${updatesString})`,strideString=sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${stridesType} strides = ${stridesType}(${strides});
+
+        void main() {
+          ${dtype} coords = getOutputCoords();
+          float sum = 0.0;
+          bool found = false;
+          for (int i = 0; i < ${updateSize}; i++) {
+            int flattenedIndex = 0;
+            for (int j = 0; j < ${sliceDim}; j++) {
+              int index = round(${indicesSnippet});
+              flattenedIndex += index * ${strideString};
+            }
+            if (flattenedIndex == coords[0]) {
+              sum += ${updatesSnippet};
+              found = true;
+            }
+          }
+          setOutput(mix(getDefaultValue(), sum, float(found)));
+        }
+      `}};var SegmentOpProgram=class{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];let windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];let initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
+        sumValue += dot(values, segFilter);
+    `,checkValueOutOfBounds="";inSize%windowSize>0&&(checkValueOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return initializationValue;
+        }
+      `);let checkSegmentIdOutOfBounds="";inSize%windowSize>0&&(checkSegmentIdOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return -1.0;
+        }
+      `),this.userCode=`
+      const float initializationValue = ${initializationValue};
+
+      float getValue(int batch, int inIdx) {
+        ${checkValueOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      float getSegmentIdAtIndex(int inIdx) {
+        ${checkSegmentIdOutOfBounds}
+        return getSegmentIds(inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = int(floor(float(outIdx) / float(
+          ${numSegments})) * float(${windowSize}));
+        int currentSeg = int(mod(float(outIdx), float(${numSegments})));
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; 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
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===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
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===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
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===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
+          );
+
+          ${updateSnippet}
+        }
+        setOutput(${returnValue});
+      }
+    `}};var SelectProgram=class{constructor(cRank,shape,rank){this.variableNames=["c","a","b"],this.outputShape=shape;let cCoords,abCoords;if(rank>4)throw Error(`Where for rank ${rank} is not yet supported`);if(rank===1)abCoords="resRC",cCoords="resRC";else{let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w"],cCoordVars=[],abCoordVars=[];for(let i=0;i<shape.length;i++)abCoordVars.push(`${currentCoords[i]}`),i<cRank&&cCoordVars.push(`${currentCoords[i]}`);cCoords=cCoordVars.join(),abCoords=abCoordVars.join()}let dtype=getCoordsDataType(rank);this.userCode=`
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        float cVal = getC(${cCoords});
+        if (cVal >= 1.0) {
+          setOutput(getA(${abCoords}));
+        } else {
+          setOutput(getB(${abCoords}));
+        }
+      }
+    `}};var SliceProgram=class{constructor(destSize){this.variableNames=["source"],this.outputShape=destSize,this.rank=destSize.length;let dtype=getCoordsDataType(this.rank),uniformPart=`uniform int start[${this.rank}];`,sourceCoords=getCoords2(this.rank),body,coordSum=destSize.map((_,i)=>`sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`);body=`
+        ${dtype} sourceLoc;
+        ${dtype} coords = getOutputCoords();
+        ${coordSum.join(`
+`)}
+      `,this.userCode=`
+      ${uniformPart}
+      void main() {
+        ${body}
+        setOutput(getSource(${sourceCoords}));
+      }
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{if(this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null))return;gpgpu.gl.uniform1iv(this.startLoc,start)}}},coords=["x","y","z","w","u","v"];function getCoords2(rank){if(rank===1)return"sourceLoc";if(rank<=6)return coords.slice(0,rank).map(x=>"sourceLoc."+x).join(",");throw Error(`Slicing for rank ${rank} is not yet supported`)}var SlicePackedProgram=class{constructor(destSize){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=destSize,this.rank=destSize.length;let dtype=getCoordsDataType(this.rank),coords2=getChannels("coords",this.rank),sourceLoc=getChannels("sourceLoc",this.rank),innerDims=this.rank===1?"sourceLoc":`vec2(${sourceLoc.slice(-2).join()})`,getChannel=`getChannel(getSource(${sourceLoc.join()}), ${innerDims})`,upperRow=`
+      result.x = ${getChannel};
+      if (++${coords2[this.rank-1]} < ${destSize[this.rank-1]}) {
+        ++${sourceLoc[this.rank-1]};
+        result.y = ${getChannel};
+        --${sourceLoc[this.rank-1]};
+      }
+    `,lowerRow=this.rank===1?"":`
+      --${coords2[this.rank-1]};
+      if (++${coords2[this.rank-2]} < ${destSize[this.rank-2]}) {
+        ++${sourceLoc[this.rank-2]};
+        result.z = ${getChannel};
+        if (++${coords2[this.rank-1]} < ${destSize[this.rank-1]}) {
+          ++${sourceLoc[this.rank-1]};
+          result.w = ${getChannel};
+        }
+      }
+    `,sourceLocSetup=this.rank<=4?`sourceLoc = coords +
+            ${dtype}(${destSize.map((_,i)=>`start[${i}]`).join()});`:destSize.map((_,i)=>`${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join(`
+`);this.userCode=`
+      uniform int start[${this.rank}];
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        ${dtype} sourceLoc;
+        ${sourceLocSetup}
+        vec4 result = vec4(0.);
+        ${upperRow}
+        ${lowerRow}
+        setOutput(result);
+      }
+    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{if(this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null))return;gpgpu.gl.uniform1iv(this.startLoc,start)}}};var StridedSliceProgram=class{constructor(begin,strides,size){this.variableNames=["x"],this.outputShape=size;let rank=size.length,inputDtype=getCoordsDataType(size.length),dtype=getCoordsDataType(size.length),newCoords="";if(rank===1)newCoords="coords * strides + begin";else{let outputAxis=0;newCoords=size.map((_,i)=>(outputAxis++,size.length===1?`coords * strides[${i}] + begin[${i}]`:`coords[${outputAxis-1}] * strides[${i}] + begin[${i}]`)).join(",")}this.userCode=`
+      ${inputDtype} begin = ${inputDtype}(${begin});
+      ${inputDtype} strides = ${inputDtype}(${strides});
+
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        setOutput(getX(${newCoords}));
+      }
+    `}};var TextureManager=class{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){let physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);let texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();let newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures==null)return;let physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);let texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;let texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}log(){if(!this.logEnabled)return;let total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);let freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}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(let texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(let texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}};function numBytesForInternalFormat(gl,internalFormat){let glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F)return 16;if(internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){let internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig),numElements;if(isPacked){let[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{let[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}let bytesPerElement2=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement2}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}var TileProgram=class{constructor(aShape,reps){this.variableNames=["A"];let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[i]*reps[i];this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),sourceCoords=getSourceCoords3(aShape);this.userCode=`
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        setOutput(getA(${sourceCoords}));
+      }
+    `}};function getSourceCoords3(aShape){let rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;let currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}var UnaryOpProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
+      float unaryOperation(float x) {
+        ${opSnippet}
+      }
+
+      void main() {
+        float x = getAAtOutCoords();
+        float y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `}},CHECK_NAN_SNIPPET3="if (isnan(x)) return x;",LINEAR="return x;",ABS="return abs(x);",RELU=CHECK_NAN_SNIPPET3+`
+  return (x < 0.0) ? 0.0 : x;
+`,RELU6=CHECK_NAN_SNIPPET3+`
+  return (x < 0.0) ? 0.0 : min(6.0, x);
+`,ELU2="return (x >= 0.0) ? x : (exp(x) - 1.0);",SELU=`
+  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
+  // see: https://arxiv.org/abs/1706.02515
+  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};
+  float scale = ${backend_util_exports.SELU_SCALE};
+  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
+`;function STEP(alpha=0){return CHECK_NAN_SNIPPET3+`
+    return x > 0.0 ? 1.0 : float(${alpha});
+  `}var NEG="return -x;",CEIL="return ceil(x);",FLOOR="return floor(x);",SIGN=`
+  if (isnan(x)) { return 0.0; }
+  return sign(x);
+`,IS_NAN="return float(isnan(x));",IS_INF="return float(isinf(x));",IS_FINITE="return float(!isnan(x) && !isinf(x));",ROUND=`
+  // 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;
+    }
+  }
+`,EXP="return exp(x);",EXPM1="return exp(x) - 1.0;",LOG=`if (x < 0.0) return NAN;
+  return log(x);`,LOG1P="return log(1.0 + x);",SQRT="return sqrt(x);",RSQRT="return inversesqrt(x);",SIGMOID="return 1.0 / (1.0 + exp(-1.0 * x));",SOFTPLUS=`
+  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;
+`,ASIN=CHECK_NAN_SNIPPET3+`
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return asin(x);
+`,ACOS=CHECK_NAN_SNIPPET3+`
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return acos(x);
+`,ATAN=CHECK_NAN_SNIPPET3+`
+  return atan(x);
+`,SINH=`
+  float e2x = exp(x);
+  return (e2x - 1.0 / e2x) / 2.0;
+`,COSH=`
+  float e2x = exp(-x);
+  return (e2x + 1.0 / e2x) / 2.0;
+`,TANH=`
+  float e2x = exp(-2.0 * abs(x));
+  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
+`,ASINH=CHECK_NAN_SNIPPET3+"return log(x + sqrt(x * x + 1.0));",ACOSH=CHECK_NAN_SNIPPET3+`
+  if (x < 1.0) return NAN;
+  return log(x + sqrt(x * x - 1.0));`,ATANH=CHECK_NAN_SNIPPET3+`
+  if ((x < -1.0) || (x > 1.0)) return NAN;
+  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ERF=`
+  // Error function is calculated approximately with elementary function.
+  // See "Handbook of Mathematical Functions with Formulas,
+  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
+  float p = ${backend_util_exports.ERF_P};
+  float a1 = ${backend_util_exports.ERF_A1};
+  float a2 = ${backend_util_exports.ERF_A2};
+  float a3 = ${backend_util_exports.ERF_A3};
+  float a4 = ${backend_util_exports.ERF_A4};
+  float a5 = ${backend_util_exports.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));
+`,RECIPROCAL="return 1.0 / x;",LOGICAL_NOT="return float(!(x >= 1.0));",CLONE="return x;";var LINEAR2="return x;",LOG2=`
+  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;
+`,RELU2=`
+  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;
+`,RELU62=`
+  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;
+`,ELU3=`
+  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;
+`,UnaryOpPackedProgram=class{constructor(aShape,opSnippet){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=aShape,this.userCode=`
+      vec4 unaryOperation(vec4 x) {
+        ${opSnippet}
+      }
+
+      void main() {
+        vec4 x = getAAtOutCoords();
+        vec4 y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `}};var UnpackProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=outputShape;let rank=outputShape.length,channels=getChannels("rc",rank),dtype=getCoordsDataType(rank),sourceCoords=getSourceCoords(rank,channels),innerDims=channels.slice(-2),coords2=rank<=1?"rc":`vec2(${innerDims.join(",")})`;this.userCode=`
+      void main() {
+        ${dtype} rc = getOutputCoords();
+        vec4 packedInput = getA(${sourceCoords});
+
+        setOutput(getChannel(packedInput, ${coords2}));
+      }
+    `}};var{segment_util:segment_util2}=backend_util_exports,split11=kernel_impls_exports.split,tile10=kernel_impls_exports.tile,topkImpl3=kernel_impls_exports.topkImpl,whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}function mapActivationToShaderProgram(activation2,packed=!1){if(activation2==="linear")return packed?LINEAR2:LINEAR;if(activation2==="relu")return packed?RELU2:RELU;if(activation2==="elu")return packed?ELU3:ELU2;if(activation2==="relu6")return packed?RELU62:RELU6;if(activation2==="prelu")return packed?PRELU2:PRELU;throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`)}var CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}var MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=class extends KernelBackend{constructor(gpgpu){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,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){let gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine15())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){let texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){let texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){let dataId=tensorInfo.dataId;if(this.texData.has(dataId)){let textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){let texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice21,shape,isPacked}=texData;if(slice21!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){let realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){let subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}let texData=this.texData.get(dataId),{values,shape,slice:slice21,dtype,complexTensorInfos,isPacked}=texData;if(slice21!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);let res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer11=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);let tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer11=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){let ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer11==null)vals=this.getValuesFromTexture(dataId);else{let size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer11,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);let dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}checkNumericalProblems(values){if(values==null)return;for(let i=0;i<values.length;i++){let num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){let{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}let shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){let oldActiveTimers=this.activeTimers,newActiveTimers=[],outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();let flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);let res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);let timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(this.pendingDisposal.has(dataId))return;if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(!this.texData.has(dataId))return;if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);let{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}releaseGPUData(dataId){let{texture,dtype,texShape,usage,isPacked,slice:slice21}=this.texData.get(dataId),key=slice21&&slice21.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));let texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine15().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){let cpuBackend=this.getCPUBackend();return!this.warnedAboutCPUBackend&&cpuBackend==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),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}slice(x,begin,size){if(this.shouldExecuteOnCPU([x])){let outValues=sliceImplCPU(this.texData.get(x.dataId).values,begin,size,x.shape,x.dtype);return this.makeOutput(size,x.dtype,outValues)}if(util_exports.sizeFromShape(size)===0)return tensor4([],size,x.dtype);let{isPacked}=this.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,begin,size);if(isPacked||!isContinous){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram(size):new SliceProgram(size),customSetup=program.getCustomSetupFunc(begin);return this.compileAndRun(program,[x],null,customSetup)}return this.uploadToGPU(x.dataId),this.shallowSlice(x,begin,size)}shallowSlice(x,begin,size){let xTexData=this.texData.get(x.dataId),t=this.makeOutput(size,x.dtype),newTexData=this.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,x.strides);xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};let refCount=this.dataRefCount.get(newTexData.slice.origDataId)||1;return this.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}stridedSlice(x,begin,end,strides){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.stridedSlice(x,begin,end,strides));if(cpuRes)return cpuRes;let outShape=slice_util_exports.computeOutShape(begin,end,strides);if(outShape.some(axis=>axis===0))return tensor4([],outShape);let program=new StridedSliceProgram(begin,strides,outShape);return this.compileAndRun(program,[x])}reverse(x,axis){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ReversePackedProgram(x.shape,axis):new ReverseProgram(x.shape,axis);return this.compileAndRun(program,[x])}neg(x){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.neg(x));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,NEG,x.dtype);let program=new UnaryOpProgram(x.shape,NEG);return this.compileAndRun(program,[x])}batchMatMul(a,b,transposeA,transposeB){let outerShapeA=transposeA?a.shape[2]:a.shape[1],outerShapeB=transposeB?b.shape[1]:b.shape[2],sharedDim=transposeA?a.shape[1]:a.shape[2],batch=Math.max(a.shape[0],b.shape[0]);if((outerShapeA===1||outerShapeB===1)&&sharedDim>MATMUL_SHARED_DIM_THRESHOLD){transposeA&&(a=transpose(a,[0,2,1])),transposeB&&(b=transpose(b,[0,2,1]));let a3D=outerShapeB===1?a:a.as3D(batch,sharedDim,1),axis=outerShapeB===1?2:1,b3D=outerShapeB===1?b.as3D(batch,1,sharedDim):b,product=mul(a3D,b3D);return product.sum(axis,!0)}let dtype=upcastType(a.dtype,b.dtype),program=new MatMulPackedProgram(a.shape,b.shape,[batch,outerShapeA,outerShapeB],transposeA,transposeB);return this.compileAndRun(program,[a,b],dtype)}fusedBatchMatMul({a,b,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}){let outerShapeA=transposeA?a.shape[2]:a.shape[1],outerShapeB=transposeB?b.shape[1]:b.shape[2],batch=Math.max(a.shape[0],b.shape[0]),dtype=upcastType(a.dtype,b.dtype),hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,program=new MatMulPackedProgram(a.shape,b.shape,[batch,outerShapeA,outerShapeB],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[a,b];return bias&&inputs.push(bias),preluActivationWeights&&inputs.push(preluActivationWeights),this.compileAndRun(program,inputs,dtype)}localResponseNormalization4D(x,radius,bias,alpha,beta){let program=env().getBool("WEBGL_PACK_NORMALIZATION")?new LRNPackedProgram(x.shape,radius,bias,alpha,beta):new LRNProgram(x.shape,radius,bias,alpha,beta);return this.compileAndRun(program,[x])}LRNGrad(dy,inputImage,outputImage,depthRadius,bias,alpha,beta){let program=new LRNGradProgram(inputImage.shape,depthRadius,bias,alpha,beta);return this.compileAndRun(program,[inputImage,outputImage,dy])}tile(x,reps){if(x.dtype==="string"){let data=this.readSync(x.dataId),decodedData=data.map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData);return tile10(buf,reps)}let program=new TileProgram(x.shape,reps);return this.compileAndRun(program,[x])}pad(x,paddings,constantValue){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PadPackedProgram(x.shape,paddings,constantValue):new PadProgram(x.shape,paddings,constantValue);return this.compileAndRun(program,[x])}gather(x,indices,axis){let cpuRes=this.tryRunOnCpuOrThrow([x,indices],()=>this.cpuBackend.gather(x,indices,axis));if(cpuRes)return cpuRes;let program=new GatherProgram(x.shape,indices.size,axis);return this.compileAndRun(program,[x,indices])}batchToSpaceND(x,blockShape,crops){util_exports.assert(x.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length);return transpose(x.reshape(reshaped),permuted).reshape(reshapedPermuted).slice(sliceBeginCoords,sliceSize)}spaceToBatchND(x,blockShape,paddings){util_exports.assert(x.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let prod5=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);let paddedX=x.pad(completePaddings),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),paddedXT=transpose(paddedX.reshape(reshapedPaddedShape),permutedReshapedPaddedPermutation);return reshape(paddedXT,flattenShape)}reduce(x,reduceType,dtype){let batchSize=x.shape[0],inSize=x.shape[1],windowSize=backend_util_exports.computeOptimalWindowSize(inSize),outSize=Math.ceil(inSize/windowSize),reduceInfo={windowSize,inSize,batchSize,outSize},program=new ReduceProgram(reduceInfo,reduceType),output=this.compileAndRun(program,[x],dtype);return output.shape[1]===1?output:this.reduce(output,reduceType,dtype)}argReduce(x,reduceType,bestIndicesA=null){let batchSize=x.shape[0],inSize=x.shape[1];bestIndicesA!=null&&(batchSize=bestIndicesA.shape[0],inSize=bestIndicesA.shape[1]);let windowSize=backend_util_exports.computeOptimalWindowSize(inSize),reduceInfo={windowSize,inSize,batchSize,outSize:Math.ceil(inSize/windowSize)},program=new ArgMinMaxProgram(reduceInfo,reduceType,bestIndicesA==null),inputs=[x];bestIndicesA!=null&&inputs.push(bestIndicesA);let output=this.compileAndRun(program,inputs,"int32");return output.shape[1]===1?output:this.argReduce(x,reduceType,output)}argReducePacked(x,reduceType,bestIndicesA=null){let inShape=bestIndicesA!=null?bestIndicesA.shape:x.shape,inSize=inShape[inShape.length-1],windowSize=backend_util_exports.computeOptimalWindowSize(inSize),program=new ArgMinMaxPackedProgram(inShape,windowSize,reduceType,bestIndicesA==null),inputs=bestIndicesA==null?[x]:[x,bestIndicesA],output=this.compileAndRun(program,inputs,"int32");return output.rank===x.rank?this.argReducePacked(x,reduceType,output):output}sum(x,axes){backend_util_exports.assertAxesAreInnerMostDims("sum",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize),outputDType=sumOutType(x.dtype);return this.reduce(a2D,"sum",outputDType).reshape(outShape)}prod(x,axes){let cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.prod(x,axes));if(cpuRes)return cpuRes;let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize),outputDType=sumOutType(x.dtype);return this.reduce(a2D,"prod",outputDType).reshape(outShape)}unsortedSegmentSum(x,segmentIds,numSegments){let axis=0,permutation=backend_util_exports.getAxesPermutation([axis],x.rank),permutedX=x;permutation!=null&&(permutedX=transpose(x,permutation),axis=backend_util_exports.getInnerMostAxes(1,x.rank)[0]);let outShape=segment_util2.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=permutedX.as2D(-1,inSize),outputDType=sumOutType(x.dtype),result=this.segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments).reshape(outShape);return permutation!=null&&(result=transpose(result,backend_util_exports.getUndoAxesPermutation(permutation))),result}segOpCompute(x,segOpType,segmentIds,dtype,numSegments){let batchSize=x.shape[0],inSize=x.shape[1],windowSize=segment_util2.segOpComputeOptimalWindowSize(inSize,numSegments),segOpInfo={windowSize,inSize,batchSize,numSegments},program=new SegmentOpProgram(segOpInfo,segOpType),output=this.compileAndRun(program,[x,segmentIds],dtype);return output.shape[1]===numSegments?output:(segmentIds=range(0,numSegments).tile([inSize/windowSize]),this.segOpCompute(output,segOpType,segmentIds,dtype,numSegments))}argMinMaxReduce(x,axis,reduceType){let axes=[axis];if(backend_util_exports.assertAxesAreInnerMostDims("arg"+reduceType.charAt(0).toUpperCase()+reduceType.slice(1),axes,x.rank),!env().getBool("WEBGL_PACK_REDUCE")||x.rank<=2){let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.argReduce(a2D,reduceType).reshape(outShape)}return this.argReducePacked(x,reduceType)}argMin(x,axis){return this.argMinMaxReduce(x,axis,"min")}argMax(x,axis){return this.argMinMaxReduce(x,axis,"max")}cumsum(x,axis,exclusive,reverse12){if(axis!==x.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank-1} but got axis=${axis}`);let size=x.shape[axis],result=x;for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){let program=new CumSumProgram(x.shape,!1,reverse12),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=this.compileAndRun(program,[result],result.dtype,customSetup),prevResult.dispose()}if(exclusive){let program=new CumSumProgram(x.shape,exclusive,reverse12),prevResult=result;result=this.compileAndRun(program,[result]),prevResult.dispose()}return result}equal(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,EQUAL2,"bool");let program=new BinaryOpProgram(EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}less(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.less(a,b));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LESS2,"bool");let program=new BinaryOpProgram(LESS,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}lessEqual(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LESS_EQUAL2,"bool");let program=new BinaryOpProgram(LESS_EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}greater(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.greater(a,b));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,GREATER2,"bool");let program=new BinaryOpProgram(GREATER,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}greaterEqual(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,GREATER_EQUAL2,"bool");let program=new BinaryOpProgram(GREATER_EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}logicalNot(x){let program=new UnaryOpProgram(x.shape,LOGICAL_NOT);return this.compileAndRun(program,[x])}logicalAnd(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LOGICAL_AND2,"bool");let program=new BinaryOpProgram(LOGICAL_AND,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}logicalOr(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LOGICAL_OR2,"bool");let program=new BinaryOpProgram(LOGICAL_OR,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}select(condition,a,b){let program=new SelectProgram(condition.rank,a.shape,a.rank);return this.compileAndRun(program,[condition,a,b],upcastType(a.dtype,b.dtype))}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}topk(x,k,sorted){let xVals=x.dataSync();return topkImpl3(xVals,x.shape,x.dtype,k,sorted)}min(x,axes){backend_util_exports.assertAxesAreInnerMostDims("min",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"min",a2D.dtype).reshape(outShape)}minimum(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.minimum(a,b));if(cpuRes)return cpuRes;let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MIN2,a.shape,b.shape):new BinaryOpProgram(MIN,a.shape,b.shape);return this.compileAndRun(program,[a,b])}mod(a,b){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MOD2,a.shape,b.shape):new BinaryOpProgram(MOD,a.shape,b.shape);return this.compileAndRun(program,[a,b])}maximum(a,b){let cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.maximum(a,b));if(cpuRes)return cpuRes;let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MAX2,a.shape,b.shape):new BinaryOpProgram(MAX,a.shape,b.shape);return this.compileAndRun(program,[a,b])}all(x,axes){backend_util_exports.assertAxesAreInnerMostDims("all",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"all",a2D.dtype).reshape(outShape)}any(x,axes){backend_util_exports.assertAxesAreInnerMostDims("any",axes,x.rank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"any",a2D.dtype).reshape(outShape)}floorDiv(a,b){let op2=INT_DIV,outputDtype="int32";if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,INT_DIV2,outputDtype);let program=new BinaryOpProgram(op2,a.shape,b.shape);return this.compileAndRun(program,[a,b],outputDtype)}packedUnaryOp(x,op2,dtype){let program=new UnaryOpPackedProgram(x.shape,op2);return this.compileAndRun(program,[x],dtype)}packedBinaryOp(a,b,op2,dtype,checkOutOfBounds=!1){let program=new BinaryOpPackedProgram(op2,a.shape,b.shape,checkOutOfBounds);return this.compileAndRun(program,[a,b],dtype)}makeComplexComponentTensorInfo(complexTensor,complexPart){return{dataId:complexPart.dataId,dtype:complexPart.dtype,shape:complexTensor.shape}}addN(tensors){if(tensors.length===1)return tensors[0];if(tensors.length>env().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(tensors.length/2),leftSide=this.addN(tensors.slice(0,midIndex)),rightSide=this.addN(tensors.slice(midIndex));return this.addN([leftSide,rightSide])}let dtype=tensors.map(t=>t.dtype).reduce((d1,d2)=>upcastType(d1,d2)),shapes=tensors.map(t=>t.shape),usePackedOp=env().getBool("WEBGL_PACK"),program=usePackedOp?new AddNPackedProgram(tensors[0].shape,shapes):new AddNProgram(tensors[0].shape,shapes);return this.compileAndRun(program,tensors,dtype)}pow(a,b){let usePackedOp=env().getBool("WEBGL_PACK_BINARY_OPERATIONS"),program=usePackedOp?new BinaryOpPackedProgram(POW2,a.shape,b.shape):new BinaryOpProgram(POW,a.shape,b.shape),dtype=upcastType(a.dtype,b.dtype);return this.compileAndRun(program,[a,b],dtype)}ceil(x){if(this.shouldExecuteOnCPU([x])){let outValues=ceilImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,CEIL,x.dtype);let program=new UnaryOpProgram(x.shape,CEIL);return this.compileAndRun(program,[x])}floor(x){if(this.shouldExecuteOnCPU([x])){let outValues=floorImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,FLOOR,x.dtype);let program=new UnaryOpProgram(x.shape,FLOOR);return this.compileAndRun(program,[x])}sign(x){let program=new UnaryOpProgram(x.shape,SIGN);return this.compileAndRun(program,[x])}isNaN(x){let program=new UnaryOpProgram(x.shape,IS_NAN);return this.compileAndRun(program,[x],"bool")}isInf(x){let program=new UnaryOpProgram(x.shape,IS_INF);return this.compileAndRun(program,[x],"bool")}isFinite(x){let program=new UnaryOpProgram(x.shape,IS_FINITE);return this.compileAndRun(program,[x],"bool")}round(x){let program=new UnaryOpProgram(x.shape,ROUND);return this.compileAndRun(program,[x])}exp(x){if(this.shouldExecuteOnCPU([x])){let outValues=expImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,EXP,x.dtype);let program=new UnaryOpProgram(x.shape,EXP);return this.compileAndRun(program,[x])}expm1(x){if(this.shouldExecuteOnCPU([x])){let outValues=expm1ImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,EXPM1,x.dtype);let program=new UnaryOpProgram(x.shape,EXPM1);return this.compileAndRun(program,[x])}softmax(logits,dim){let axes=util_exports.parseAxisParam([dim],logits.shape),maxLogit=max(logits,axes),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),a=sub(logits,maxLogit.reshape(expandedShape)),b=this.exp(a),sumExp=this.sum(b,axes).reshape(expandedShape);return div(b,sumExp)}log(x){if(this.shouldExecuteOnCPU([x])){let outValues=logImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,LOG2,x.dtype);let program=new UnaryOpProgram(x.shape,LOG);return this.compileAndRun(program,[x])}log1p(x){let program=new UnaryOpProgram(x.shape,LOG1P);return this.compileAndRun(program,[x])}sqrt(x){let program=new UnaryOpProgram(x.shape,SQRT);return this.compileAndRun(program,[x])}rsqrt(x){if(this.shouldExecuteOnCPU([x])){let outValues=rsqrtImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}let program=new UnaryOpProgram(x.shape,RSQRT);return this.compileAndRun(program,[x])}reciprocal(x){let program=new UnaryOpProgram(x.shape,RECIPROCAL);return this.compileAndRun(program,[x])}relu(x){let program;return env().getBool("WEBGL_PACK")?program=new UnaryOpPackedProgram(x.shape,RELU2):program=new UnaryOpProgram(x.shape,RELU),this.compileAndRun(program,[x])}relu6(x){let program;return env().getBool("WEBGL_PACK")?program=new UnaryOpPackedProgram(x.shape,RELU62):program=new UnaryOpProgram(x.shape,RELU6),this.compileAndRun(program,[x])}prelu(x,alpha){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(PRELU2,x.shape,alpha.shape):new BinaryOpProgram(PRELU,x.shape,alpha.shape);return this.compileAndRun(program,[x,alpha])}elu(x){if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ELU3,x.dtype);let program=new UnaryOpProgram(x.shape,ELU2);return this.compileAndRun(program,[x])}eluDer(dy,y){let program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(ELU_DER2,dy.shape,y.shape):new BinaryOpProgram(ELU_DER,dy.shape,y.shape);return this.compileAndRun(program,[dy,y])}selu(x){let program=new UnaryOpProgram(x.shape,SELU);return this.compileAndRun(program,[x])}clip(x,min8,max10){let program;env().getBool("WEBGL_PACK_CLIP")?program=new ClipPackedProgram(x.shape):program=new ClipProgram(x.shape);let customSetup=program.getCustomSetupFunc(min8,max10);return this.compileAndRun(program,[x],null,customSetup)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){let outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);let program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}complexAbs(x){let xData=this.texData.get(x.dataId),program=new ComplexAbsProgram(x.shape),inputs=[this.makeComplexComponentTensorInfo(x,xData.complexTensorInfos.real),this.makeComplexComponentTensorInfo(x,xData.complexTensorInfos.imag)];return this.compileAndRun(program,inputs)}sigmoid(x){let program=new UnaryOpProgram(x.shape,SIGMOID);return this.compileAndRun(program,[x])}softplus(x){let program=new UnaryOpProgram(x.shape,SOFTPLUS);return this.compileAndRun(program,[x])}asin(x){let program=new UnaryOpProgram(x.shape,ASIN);return this.compileAndRun(program,[x])}acos(x){let program=new UnaryOpProgram(x.shape,ACOS);return this.compileAndRun(program,[x])}atan(x){let program=new UnaryOpProgram(x.shape,ATAN);return this.compileAndRun(program,[x])}sinh(x){let program=new UnaryOpProgram(x.shape,SINH);return this.compileAndRun(program,[x])}cosh(x){let program=new UnaryOpProgram(x.shape,COSH);return this.compileAndRun(program,[x])}tanh(x){let program=new UnaryOpProgram(x.shape,TANH);return this.compileAndRun(program,[x])}asinh(x){let program=new UnaryOpProgram(x.shape,ASINH);return this.compileAndRun(program,[x])}acosh(x){let program=new UnaryOpProgram(x.shape,ACOSH);return this.compileAndRun(program,[x])}atanh(x){let program=new UnaryOpProgram(x.shape,ATANH);return this.compileAndRun(program,[x])}erf(x){let program=new UnaryOpProgram(x.shape,ERF);return this.compileAndRun(program,[x])}step(x,alpha){let program=new UnaryOpProgram(x.shape,STEP(alpha));return this.compileAndRun(program,[x])}conv2dByMatMul(x,filter,convInfo,bias,activation2,preluActivationWeights){let xShape=x.shape,xTexData=this.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!==0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){let targetShape2=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped2=reshape(x,[1,targetShape2,convInfo.inChannels]),filterReshaped2=reshape(filter,[1,convInfo.inChannels,convInfo.outChannels]),result=this.fusedBatchMatMul({a:xReshaped2,b:filterReshaped2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights});return reshape(result,convInfo.outShape)}let targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);let filterReshaped=reshape(filter,[1,convInfo.inChannels,convInfo.outChannels]),pointwiseConv=this.fusedBatchMatMul({a:xReshaped,b:filterReshaped,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}),pointwiseConvTexData=this.texData.get(pointwiseConv.dataId);return util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,engine15().makeTensorFromDataId(pointwiseConv.dataId,convInfo.outShape,pointwiseConv.dtype)}conv2dWithIm2Row(x,filter,convInfo,bias,activation2,preluActivationWeights){let{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,xSqueezed=x.squeeze([0]),w2Row=filter.reshape([1,sharedDim,-1]),im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=this.compileAndRun(im2ColProgram,[xSqueezed]).reshape([1,x2ColShape[0],x2ColShape[1]]),hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2Col.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[im2Col,w2Row];bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights);let product=this.compileAndRun(matmulProgram,inputs);return isChannelsLast?product.reshape([1,outHeight,outWidth,convInfo.outChannels]):product.reshape([1,convInfo.outChannels,outHeight,outWidth])}fusedConv2d({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))return this.conv2dByMatMul(input2,filter,convInfo,bias,activation2,preluActivationWeights);if(env().getBool("WEBGL_CONV_IM2COL")&&input2.shape[0]===1)return this.conv2dWithIm2Row(input2,filter,convInfo,bias,activation2,preluActivationWeights);let hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[input2,filter];return bias&&inputs.push(bias),preluActivationWeights&&inputs.push(preluActivationWeights),this.compileAndRun(program,inputs)}conv2d(x,filter,convInfo){if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))return this.conv2dByMatMul(x,filter,convInfo);if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)return this.conv2dWithIm2Row(x,filter,convInfo);let program=new Conv2DProgram(convInfo);return this.compileAndRun(program,[x,filter])}conv2dDerInput(dy,filter,convInfo){let program=new Conv2DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}conv2dDerFilter(x,dy,convInfo){let program=new Conv2DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}fusedDepthwiseConv2D({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){let shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,inputs=[input2,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null;hasBias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights);let program;return shouldPackDepthwiseConv?(program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),this.compileAndRun(program,inputs)):(program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),this.compileAndRun(program,inputs))}depthwiseConv2D(x,filter,convInfo){let program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1?(program=new DepthwiseConvPacked2DProgram(convInfo),this.compileAndRun(program,[x,filter])):(program=new DepthwiseConv2DProgram(convInfo),this.compileAndRun(program,[x,filter]))}depthwiseConv2DDerInput(dy,filter,convInfo){let program=new DepthwiseConv2DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}depthwiseConv2DDerFilter(x,dy,convInfo){let program=new DepthwiseConv2DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}conv3d(x,filter,convInfo){let program=new Conv3DProgram(convInfo);return this.compileAndRun(program,[x,filter])}conv3dDerInput(dy,filter,convInfo){let program=new Conv3DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}conv3dDerFilter(x,dy,convInfo){let program=new Conv3DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}unstack(x,axis){let num=x.shape[axis],outShape=new Array(x.rank-1),outIndex=0;for(let i=0;i<x.rank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);let begin=new Array(x.rank).fill(0),size=x.shape.slice();size[axis]=1;let res=new Array(num);for(let i=0;i<res.length;i++)begin[axis]=i,res[i]=this.slice(x,begin,size).reshape(outShape);return res}avgPool3d(x,convInfo){let program=new Pool3DProgram(convInfo,"avg",!1);return this.compileAndRun(program,[x],"float32")}avgPool3dBackprop(dy,x,convInfo){let avgPool3dBackpropProgram=new AvgPool3DBackpropProgram(convInfo);return this.compileAndRun(avgPool3dBackpropProgram,[dy],x.dtype)}maxPool3d(x,convInfo){let program=new Pool3DProgram(convInfo,"max",!1);return this.compileAndRun(program,[x],"float32")}maxPool3dBackprop(dy,x,y,convInfo){let getPositions=!0,maxPool3dPositionsProgram=new Pool3DProgram(convInfo,"max",getPositions),maxPool3dPositions=this.compileAndRun(maxPool3dPositionsProgram,[x]),maxPool3dBackPropProgram=new MaxPool3DBackpropProgram(convInfo),result=this.compileAndRun(maxPool3dBackPropProgram,[dy,maxPool3dPositions],x.dtype);return maxPool3dPositions.dispose(),result}resizeBilinear(x,newHeight,newWidth,alignCorners){let program=env().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ResizeBilinearPackedProgram(x.shape,newHeight,newWidth,alignCorners):new ResizeBilinearProgram(x.shape,newHeight,newWidth,alignCorners);return this.compileAndRun(program,[x],"float32")}resizeBilinearBackprop(dy,x,alignCorners){let program=new ResizeBilinearBackpropProgram(dy,x,alignCorners);return this.compileAndRun(program,[dy])}resizeNearestNeighbor(x,newHeight,newWidth,alignCorners){let program=new ResizeNearestNeighborProgram(x.shape,newHeight,newWidth,alignCorners);return this.compileAndRun(program,[x])}resizeNearestNeighborBackprop(dy,x,alignCorners){let program=new ResizeNearestNeigborBackpropProgram(dy,x,alignCorners);return this.compileAndRun(program,[dy])}multinomial(logits,normalized,numSamples,seed){let probs=normalized?logits:softmax(logits),batchSize=probs.shape[0],numOutcomes=probs.shape[1],program=new MultinomialProgram(batchSize,numOutcomes,numSamples),customSetup=program.getCustomSetupFunc(seed);return this.compileAndRun(program,[probs],"int32",customSetup)}oneHot(indices,depth,onValue,offValue){let program=new OneHotProgram(indices.size,depth,onValue,offValue);return this.compileAndRun(program,[indices])}diag(x){let program=new DiagProgram(x.size);return this.compileAndRun(program,[x])}cropAndResize(image3,boxes,boxIndex,cropSize,method,extrapolationValue){let program=new CropAndResizeProgram(image3.shape,boxes.shape,cropSize,method,extrapolationValue);return this.compileAndRun(program,[image3,boxes,boxIndex],"float32")}depthToSpace(x,blockSize,dataFormat){util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],program=new DepthToSpaceProgram(outputShape,blockSize,dataFormat);return this.compileAndRun(program,[x])}split(x,sizeSplits,axis){return split11(x,sizeSplits,axis)}scatterND(indices,updates,shape){let{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),flattenShape=[outputSize/sliceSize,sliceSize],flattenIndices=indices.reshape([numUpdates,sliceRank]),flattenX=updates.reshape([numUpdates,sliceSize]);if(outputSize===0)return backend_util_exports.reshapeTensor(tensor4([]),shape);let defaultValue=scalar(0),program=new ScatterProgram(numUpdates,sliceRank,flattenIndices.rank,flattenX.rank,strides,flattenShape),res=this.compileAndRun(program,[flattenX,flattenIndices,defaultValue]);return res.reshape(shape)}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){let{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.rank,sparseValues.rank,strides,[outputSize,1],sumDupeIndices),res=this.compileAndRun(program,[sparseValues,sparseIndices,defaultValue]);return res.reshape(outputShape)}gatherND(x,indices){let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(x,indices),flattenIndices=indices.reshape([numSlices,sliceRank]),flattenX=x.reshape([x.size/sliceSize,sliceSize]),program=new GatherNDProgram(sliceRank,strides,[numSlices,sliceSize]),res=this.compileAndRun(program,[flattenX,flattenIndices]);return res.reshape(resultShape)}fill(shape,value,dtype){if(dtype=dtype||util_exports.inferDtype(value),dtype==="string"){let values=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(shape));return values.fill(value),engine15().makeTensor(values,shape,dtype,this)}else{let program=new FillProgram(shape,value),customSetup=program.getCustomSetupFunc(value);return this.compileAndRun(program,[],dtype,customSetup)}}onesLike(x){if(x.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(x.shape,1,x.dtype)}zerosLike(x){return this.fill(x.shape,x.dtype==="string"?"":0,x.dtype)}linspace(start,stop,num){return backend_util_exports.linspaceImpl(start,stop,num)}makeTensorInfo(shape,dtype,values){let dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){let{dataId}=this.makeTensorInfo(shape,dtype,values);return engine15().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){let program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){let program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){let texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape),program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);let preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){let output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){let texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;let dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!==!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){let savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);let outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null,query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){let unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;let outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine15().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!env().getBool("IS_TEST")){let allKeys=Object.keys(this.binaryCache);allKeys.forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]})}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=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){let debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);let underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){let texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture!=null)return;let shouldTimeProgram=this.activeTimers!=null,start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){let shapeAs3D=getShapeAs3D(shape),program,width=texShape[1],height=texShape[0],isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);let tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);let preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{let newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}convertAndCacheOnCPU(dataId,float32Values){let texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}tryRunOnCpuOrThrow(inputs,fn){if(this.shouldExecuteOnCPU(inputs))try{return fn()}catch(e){if(env().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}};function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){let result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}var version12="2.7.0";function forceHalfFloat(){env().set("WEBGL_FORCE_F16_TEXTURES",!0)}device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);var webgl2={forceHalfFloat};function identity3(args){let{inputs,backend:backend3}=args,{x}=inputs;return backend3.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}var identityConfig2={kernelName:Identity,backendName:"webgl",kernelFunc:identity3};function complex10(args){let{inputs,backend:backend3}=args,{real:real8,imag:imag8}=inputs,complexInfo=backend3.makeTensorInfo(real8.shape,"complex64"),complex11=backend3.texData.get(complexInfo.dataId),realTensorInfo=identity3({inputs:{x:real8},backend:backend3}),realData=backend3.texData.get(realTensorInfo.dataId);realData.complexParentRefCount++;let imagTensorInfo=identity3({inputs:{x:imag8},backend:backend3}),imagData=backend3.texData.get(imagTensorInfo.dataId);return imagData.complexParentRefCount++,complex11.complexTensorInfos={real:realTensorInfo,imag:imagTensorInfo},complexInfo}var complexConfig2={kernelName:Complex,backendName:"webgl",kernelFunc:complex10};var CHECK_NAN_SNIPPET_UNARY="if (isnan(x)) return x;",CHECK_NAN_SNIPPET_BINARY=`
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`,CHECK_NAN_SNIPPET_BINARY_PACKED=`
+  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 unaryKernelFunc2(opSnippet){return({inputs,backend:backend3})=>{let{x}=inputs,webglBackend=backend3,program=new UnaryOpProgram(x.shape,opSnippet);return webglBackend.runWebGLProgram(program,[x],x.dtype)}}function binaryKernelFunc2({opSnippet,packedOpSnippet,checkOutOfBounds=!1,supportsComplex=!1,cpuKernelImpl,dtype}){return({inputs,backend:backend3})=>{let{a,b}=inputs,webglBackend=backend3;if(supportsComplex&&a.dtype==="complex64"){let aData=webglBackend.texData.get(a.dataId),bData=webglBackend.texData.get(b.dataId),[real8,imag8]=[[aData.complexTensorInfos.real,bData.complexTensorInfos.real],[aData.complexTensorInfos.imag,bData.complexTensorInfos.imag]].map(complexParts=>{let[aPart,bPart]=complexParts,aHandle={dataId:aPart.dataId,dtype:aPart.dtype,shape:a.shape},bHandle={dataId:bPart.dataId,dtype:bPart.dtype,shape:b.shape},program2=new BinaryOpProgram(opSnippet,a.shape,b.shape);return webglBackend.runWebGLProgram(program2,[aHandle,bHandle],upcastType(aPart.dtype,bPart.dtype))}),complexOutput=complex10({inputs:{real:real8,imag:imag8},backend:webglBackend});return webglBackend.disposeIntermediateTensorInfo(real8),webglBackend.disposeIntermediateTensorInfo(imag8),complexOutput}let $dtype=dtype||upcastType(a.dtype,b.dtype);if(webglBackend.shouldExecuteOnCPU([a,b])&&cpuKernelImpl!=null){let aData=webglBackend.texData.get(a.dataId),bData=webglBackend.texData.get(b.dataId),[outValues,outShape]=cpuKernelImpl(a.shape,b.shape,aData.values,bData.values,$dtype),out=webglBackend.makeTensorInfo(outShape,$dtype),outData=webglBackend.texData.get(out.dataId);return outData.values=outValues,out}let shouldUsePackedProgram=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&packedOpSnippet!=null,program;return shouldUsePackedProgram?program=new BinaryOpPackedProgram(packedOpSnippet,a.shape,b.shape,checkOutOfBounds):program=new BinaryOpProgram(opSnippet,a.shape,b.shape),webglBackend.runWebGLProgram(program,[a,b],$dtype)}}var ADD="return a + b;",addKernelFunc=binaryKernelFunc2({opSnippet:ADD,packedOpSnippet:ADD,supportsComplex:!0,cpuKernelImpl:addImplCPU}),addConfig2={kernelName:Add,backendName:"webgl",kernelFunc:addKernelFunc};var ATAN2=CHECK_NAN_SNIPPET_BINARY+`
+  return atan(a, b);
+`,ATAN2_PACKED=`
+  vec4 result = atan(a, b);
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+CHECK_NAN_SNIPPET_BINARY_PACKED+`
+  return result;
+`,atan25=binaryKernelFunc2({opSnippet:ATAN2,packedOpSnippet:ATAN2_PACKED}),atan2Config={kernelName:Atan2,backendName:"webgl",kernelFunc:atan25};function avgPool3(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex2(x,"avgPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))return identity3({inputs:{x},backend:backend3});let avgPoolProgram=new Pool2DProgram(convInfo,"avg",!1);return backend3.runWebGLProgram(avgPoolProgram,[x],"float32")}var avgPoolConfig2={kernelName:AvgPool,backendName:"webgl",kernelFunc:avgPool3};function avgPoolBackprop3(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex2([dy,input2],"avgPoolBackprop");let{filterSize,strides,pad:pad11}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11),avgPoolBackpropProgram=new AvgPool2DBackpropProgram(convInfo);return backend3.runWebGLProgram(avgPoolBackpropProgram,[dy],x.dtype)}var avgPoolBackpropConfig2={kernelName:AvgPoolBackprop,backendName:"webgl",kernelFunc:avgPoolBackprop3};var BatchNormProgram=class{constructor(xShape,meanShape,varianceShape,offsetShape,scaleShape,varianceEpsilon){this.outputShape=[],this.variableNames=["x","mean","variance"],backend_util_exports.assertAndGetBroadcastShape(xShape,meanShape),backend_util_exports.assertAndGetBroadcastShape(xShape,varianceShape);let offsetSnippet="0.0";offsetShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,offsetShape),this.variableNames.push("offset"),offsetSnippet="getOffsetAtOutCoords()");let scaleSnippet="1.0";scaleShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,scaleShape),this.variableNames.push("scale"),scaleSnippet="getScaleAtOutCoords()"),this.outputShape=xShape,this.userCode=`
+      void main() {
+        float x = getXAtOutCoords();
+        float mean = getMeanAtOutCoords();
+        float variance = getVarianceAtOutCoords();
+        float offset = ${offsetSnippet};
+        float scale = ${scaleSnippet};
+        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
+        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
+      }
+    `}};var BatchNormPackedProgram=class{constructor(xShape,meanShape,varianceShape,offsetShape,scaleShape,varianceEpsilon){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],backend_util_exports.assertAndGetBroadcastShape(xShape,meanShape),backend_util_exports.assertAndGetBroadcastShape(xShape,varianceShape);let offsetSnippet="vec4(0.0)";offsetShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,offsetShape),this.variableNames.push("offset"),offsetSnippet="getOffsetAtOutCoords()");let scaleSnippet="vec4(1.0)";scaleShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,scaleShape),this.variableNames.push("scale"),scaleSnippet="getScaleAtOutCoords()"),this.outputShape=xShape,this.userCode=`
+      void main() {
+        vec4 offset = ${offsetSnippet};
+        vec4 scale = ${scaleSnippet};
+
+        vec4 x = getXAtOutCoords();
+        vec4 mean = getMeanAtOutCoords();
+        vec4 variance = getVarianceAtOutCoords();
+
+        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));
+
+        setOutput((x - mean) * inv + offset);
+      }
+    `}};var batchNorm3=({inputs,backend:backend3,attrs})=>{let{x,mean:mean7,variance,offset,scale:scale2}=inputs;util_exports.assert(mean7.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean7.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale2==null||mean7.shape.length===scale2.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);let finalInputs=[x,mean7,variance],offsetShape=null;offset!=null&&(offsetShape=offset.shape,finalInputs.push(offset));let scaleShape=null;scale2!=null&&(scaleShape=scale2.shape,finalInputs.push(scale2));let program=env().getBool("WEBGL_PACK_NORMALIZATION")?new BatchNormPackedProgram(x.shape,mean7.shape,variance.shape,offsetShape,scaleShape,varianceEpsilon):new BatchNormProgram(x.shape,mean7.shape,variance.shape,offsetShape,scaleShape,varianceEpsilon),output=backend3.runWebGLProgram(program,finalInputs,finalInputs[0].dtype);return output},batchNormConfig2={kernelName:FusedBatchNorm,backendName:"webgl",kernelFunc:batchNorm3};var NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputData=backend3.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend3})}var realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real7};var TO_INT="return float(int(x));";function int(input2,backend3){let program=new UnaryOpProgram(input2.shape,TO_INT),output=backend3.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast50(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend3});let zerosTensor=zeros(x.shape),floatX=cast50({inputs:{x},backend:backend3,attrs:{dtype:"float32"}}),result=complex10({inputs:{real:floatX,imag:zerosTensor},backend:backend3});return zerosTensor.dispose(),backend3.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){let realPart=real7({inputs:{input:x},backend:backend3}),result=cast50({inputs:{x:realPart},backend:backend3,attrs:{dtype}});return backend3.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){let result=identity3({inputs:{x},backend:backend3});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend3);if(dtype==="bool"){let zerosTensorInfo=backend3.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),binaryInputs={a:x,b:zerosTensorInfo},result=notEqual3({inputs:binaryInputs,backend:backend3});return backend3.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}var castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast50};var ConcatProgram=class{constructor(shapes){this.outputShape=[],this.outputShape=backend_util_exports.computeOutShape(shapes,1),this.variableNames=shapes.map((_,i)=>`T${i}`);let offsets=new Array(shapes.length-1);offsets[0]=shapes[0][1];for(let i=1;i<offsets.length;i++)offsets[i]=offsets[i-1]+shapes[i][1];let snippets=[`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<offsets.length;i++){let shift=offsets[i-1];snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`)}let lastIndex=offsets.length,lastShift=offsets[offsets.length-1];snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`),this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int yR = coords.x;
+        int yC = coords.y;
+
+        ${snippets.join(`
+        `)}
+      }
+    `}};var ConcatPackedProgram=class{constructor(shapes,axis){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=backend_util_exports.computeOutShape(shapes,axis);let shape=this.outputShape,rank=shape.length,dtype=getCoordsDataType(rank),coords2=getChannels("coords",rank),channels=["x","y","z","w","u","v"].slice(0,rank);this.variableNames=shapes.map((_,i)=>`T${i}`);let offsets=new Array(shapes.length-1);offsets[0]=shapes[0][axis];for(let i=1;i<offsets.length;i++)offsets[i]=offsets[i-1]+shapes[i][axis];let channel=channels[axis],lastChannels=channels.slice(-2),allChannels=channels.join(),getValueSnippet=`if (${channel} < ${offsets[0]}) {
+        return getChannel(
+            getT0(${allChannels}), vec2(${lastChannels.join()}));
+        }`;for(let i=1;i<offsets.length;i++){let shift2=offsets[i-1];getValueSnippet+=`
+        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i-1]}) {
+          return getChannel(
+            getT${i}(${shiftedChannels(channels,channel,shift2)}),
+            vec2(${shiftedChannels(lastChannels,channel,shift2)}));
+        }`}let lastIndex=offsets.length,shift=offsets[offsets.length-1];getValueSnippet+=`
+        return getChannel(
+          getT${lastIndex}(${shiftedChannels(channels,channel,shift)}),
+          vec2(${shiftedChannels(lastChannels,channel,shift)}));`,this.userCode=`
+      float getValue(${channels.map(x=>"int "+x)}) {
+        ${getValueSnippet}
+      }
+
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        vec4 result = vec4(getValue(${coords2}), 0., 0., 0.);
+
+        ${coords2[rank-1]} = ${coords2[rank-1]} + 1;
+        if (${coords2[rank-1]} < ${shape[rank-1]}) {
+          result.g = getValue(${coords2});
+        }
+
+        ${coords2[rank-2]} = ${coords2[rank-2]} + 1;
+        if (${coords2[rank-2]} < ${shape[rank-2]}) {
+          result.a = getValue(${coords2});
+        }
+
+        ${coords2[rank-1]} = ${coords2[rank-1]} - 1;
+        if (${coords2[rank-2]} < ${shape[rank-2]} &&
+            ${coords2[rank-1]} < ${shape[rank-1]}) {
+          result.b = getValue(${coords2});
+        }
+        setOutput(result);
+      }
+    `}};function shiftedChannels(channels,channel,shift){let channelIdx=channels.indexOf(channel),res=channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c);return res.join()}function imag7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs,inputData=backend3.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend3})}var imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag7};function packedReshape(input2,afterShape,backend3){let input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=backend3.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}function reshape90(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{shape}=attrs,webglBackend=backend3,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);let xTexData=webglBackend.texData.get(x.dataId);return xTexData.isPacked&&!isReshapeFree(x.shape,$shape)&&!(xTexData.texture!==null&&isReshapeFree(xTexData.shape,$shape))?packedReshape(x,$shape,webglBackend):(webglBackend.incRef(x.dataId),{dataId:x.dataId,shape:$shape,dtype:x.dtype})}var reshapeConfig2={kernelName:Reshape,backendName:"webgl",kernelFunc:reshape90};function concatImpl(inputs,axis,backend3){let dtype=inputs[0].dtype;if(dtype==="complex64"){let reals=inputs.map(t=>real7({inputs:{input:t},backend:backend3})),imags=inputs.map(t=>imag7({inputs:{input:t},backend:backend3})),realConcated=concatImpl(reals,axis,backend3),imagConcated=concatImpl(imags,axis,backend3),result2=complex10({inputs:{real:realConcated,imag:imagConcated},backend:backend3});return reals.forEach(r=>backend3.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend3.disposeIntermediateTensorInfo(i)),backend3.disposeIntermediateTensorInfo(realConcated),backend3.disposeIntermediateTensorInfo(imagConcated),result2}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let midIndex=Math.floor(inputs.length/2),leftSide=concatImpl(inputs.slice(0,midIndex),axis,backend3),rightSide=concatImpl(inputs.slice(midIndex),axis,backend3),result2=concatImpl([leftSide,rightSide],axis,backend3);return backend3.disposeIntermediateTensorInfo(leftSide),backend3.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){let program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend3.runWebGLProgram(program2,inputs,dtype)}let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),tensors2D=inputs.map(x=>reshape90({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend3})),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend3.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend3.disposeIntermediateTensorInfo(r));let reshapedResult=reshape90({inputs:{x:result},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(result),reshapedResult}function concat18(args){let{inputs,backend:backend3,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend3.makeTensorInfo(outShape,inputs[0].dtype,[]);let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl($inputs,$axis,backend3)}var concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat18};var COS=CHECK_NAN_SNIPPET_UNARY+`
+  return cos(x);
+`,cos7=unaryKernelFunc2(COS),cosConfig2={kernelName:Cos,backendName:"webgl",kernelFunc:cos7};var DIV=`
+if (a == b) {
+  return 1.0;
+};
+return a / b;`,DIV_PACKED=`
+  // 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;
+`,div36=binaryKernelFunc2({opSnippet:DIV,packedOpSnippet:DIV_PACKED,checkOutOfBounds:!0}),divConfig2={kernelName:Div,backendName:"webgl",kernelFunc:div36};var FFTProgram=class{constructor(component,inputShape,inverse){this.variableNames=["real","imag"];let innerDim=inputShape[1];this.outputShape=inputShape;let exponentMultiplierSnippet=inverse?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,resultDenominator=inverse?`${innerDim}.0`:"1.0",opString;if(component==="real")opString="return real * expR - imag * expI;";else if(component==="imag")opString="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);this.userCode=`
+      const float exponentMultiplier = ${exponentMultiplierSnippet};
+
+      float unaryOpComplex(float real, float expR, float imag, float expI) {
+        ${opString}
+      }
+
+      float mulMatDFT(int batch, int index) {
+        float indexRatio = float(index) / float(${innerDim});
+        float exponentMultiplierTimesIndexRatio =
+            exponentMultiplier * indexRatio;
+
+        float result = 0.0;
+
+        for (int i = 0; i < ${innerDim}; 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) / ${resultDenominator};
+        }
+
+        return result;
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        setOutput(mulMatDFT(coords[0], coords[1]));
+      }
+    `}};function fftImpl2(x,inverse,backend3){let xData=backend3.texData.get(x.dataId),inputSize=util_exports.sizeFromShape(x.shape),innerDimensionSize=x.shape[x.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape90({inputs:{x},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),xShape=input2D.shape,realProgram=new FFTProgram("real",xShape,inverse),imagProgram=new FFTProgram("imag",xShape,inverse),inputs=[{dataId:xData.complexTensorInfos.real.dataId,dtype:xData.complexTensorInfos.real.dtype,shape:xShape},{dataId:xData.complexTensorInfos.imag.dataId,dtype:xData.complexTensorInfos.imag.dtype,shape:xShape}],realPart=backend3.runWebGLProgram(realProgram,inputs,"float32"),imagPart=backend3.runWebGLProgram(imagProgram,inputs,"float32"),complexOutput=complex10({inputs:{real:realPart,imag:imagPart},backend:backend3});backend3.disposeIntermediateTensorInfo(realPart),backend3.disposeIntermediateTensorInfo(imagPart);let complexOutputReshaped=reshape90({inputs:{x:complexOutput},backend:backend3,attrs:{shape:x.shape}});return backend3.disposeIntermediateTensorInfo(complexOutputReshaped),complexOutputReshaped}function fft7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs;return fftImpl2(input2,!1,backend3)}var fftConfig2={kernelName:FFT,backendName:"webgl",kernelFunc:fft7};var FlipLeftRightProgram=class{constructor(imageShape){this.variableNames=["Image"],this.outputShape=[];let imageWidth=imageShape[2];this.outputShape=imageShape,this.userCode=`
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+
+          int coordX = ${imageWidth} - x;
+          float outputValue;
+          if(coordX >= 0 && coordX < ${imageWidth}) {
+            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
+          } else {
+            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `}};var flipLeftRightConfig2={kernelName:FlipLeftRight,backendName:"webgl",kernelFunc:({inputs,backend:backend3})=>{let{image:image3}=inputs,webglBackend=backend3,program=new FlipLeftRightProgram(image3.shape),output=webglBackend.runWebGLProgram(program,[image3],image3.dtype);return output}};var FromPixelsProgram=class{constructor(outputShape){this.variableNames=["A"];let glsl=getGlslDifferences(),[height,width]=outputShape;this.outputShape=outputShape,this.userCode=`
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);
+
+        vec4 values = ${glsl.texture2D}(A, uv);
+        float value;
+        if (depth == 0) {
+          value = values.r;
+        } else if (depth == 1) {
+          value = values.g;
+        } else if (depth == 2) {
+          value = values.b;
+        } else if (depth == 3) {
+          value = values.a;
+        }
+
+        setOutput(floor(value * 255.0 + 0.5));
+      }
+    `}};var FromPixelsPackedProgram=class{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let glsl=getGlslDifferences(),[height,width]=outputShape;this.outputShape=outputShape,this.userCode=`
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+
+        vec4 result = vec4(0.);
+
+        for(int row=0; row<=1; row++) {
+          for(int col=0; col<=1; col++) {
+            texC = coords[1] + row;
+            depth = coords[2] + col;
+
+            vec2 uv = (vec2(texC, texR) + halfCR) /
+                       vec2(${width}.0, ${height}.0);
+            vec4 values = ${glsl.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);
+          }
+        }
+
+        ${glsl.output} = result;
+      }
+    `}};var fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2},fromPixels2DContext2;function fromPixels2(args){let{inputs,backend:backend3,attrs}=args,{pixels}=inputs,{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);let tempPixelHandle=backend3.makeTensorInfo(texShape,"int32");backend3.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend3.gpgpu.uploadPixelDataToTexture(backend3.getTexture(tempPixelHandle.dataId),pixels);let program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend3.runWebGLProgram(program,[tempPixelHandle],"int32");return backend3.disposeData(tempPixelHandle.dataId),res}function ifft7(args){let{inputs,backend:backend3}=args,{input:input2}=inputs;return fftImpl2(input2,!0,backend3)}var ifftConfig2={kernelName:IFFT,backendName:"webgl",kernelFunc:ifft7};var MeanProgram=class{constructor(reduceInfo,divisor){this.variableNames=["x"];let{windowSize,batchSize,inSize,outSize}=reduceInfo;this.outputShape=[batchSize,outSize];let windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet="sumValue += dot(values, ones);";if(divisor!=null){let denominator=1/divisor;updateSnippet=`sumValue += dot(values * ${util_exports.isInt(denominator)?denominator.toPrecision(2):denominator}, ones);`}let checkOutOfBounds="";inSize%windowSize>0&&(checkOutOfBounds=`
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return 0.0;
+        }
+      `),this.userCode=`
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${checkOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${windowSize};
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
+          int inIdx = inOffset + i;
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder===1}) {
+          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===2}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1), 0.0, 0.0);
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder===3}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2), 0.0);
+
+          ${updateSnippet}
+        }
+        setOutput(sumValue);
+      }
+    `}};function getReductionStages(inShape){let stages=[];for(;stages.length===0||stages[stages.length-1].outSize!==1;){let outSize=stages.length?stages[stages.length-1].outSize:inShape[1],windowSize=backend_util_exports.computeOptimalWindowSize(outSize);stages.push({inSize:outSize,windowSize,outSize:Math.ceil(outSize/windowSize)})}return stages}function reduce(x,dtype,reductionType,backend3){let reductionStages=getReductionStages(x.shape),result=x;for(let i=0;i<reductionStages.length;i++){let{inSize,windowSize,outSize}=reductionStages[i],program,previousResult;reductionType==="mean"?program=i===0?new MeanProgram({windowSize,inSize,batchSize:x.shape[0],outSize},inSize):new MeanProgram({windowSize,inSize,batchSize:x.shape[0],outSize}):program=new ReduceProgram({windowSize,inSize,batchSize:x.shape[0],outSize},reductionType),previousResult=result,result=backend3.runWebGLProgram(program,[result],dtype),previousResult.dataId!==x.dataId&&backend3.disposeIntermediateTensorInfo(previousResult)}return result}function maxImpl2(x,reduceShape,outShape,backend3){let inSize=util_exports.sizeFromShape(reduceShape),xSize=util_exports.sizeFromShape(x.shape),batchSize=xSize/inSize,reshapedInput=reshape90({inputs:{x},attrs:{shape:[batchSize,inSize]},backend:backend3}),reduced=reduce(reshapedInput,x.dtype,"max",backend3),reshapedOutput=reshape90({inputs:{x:reduced},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(reshapedInput),backend3.disposeIntermediateTensorInfo(reduced),reshapedOutput}var TransposeProgram=class{constructor(aShape,newDim){this.variableNames=["A"];let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[newDim[i]];this.outputShape=outputShape,this.rank=outputShape.length;let dtype=getCoordsDataType(this.rank),switched=getSwitchedCoords(newDim);this.userCode=`
+    void main() {
+      ${dtype} resRC = getOutputCoords();
+      setOutput(getA(${switched}));
+    }
+    `}};function getSwitchedCoords(newDim){let rank=newDim.length;if(rank>6)throw Error(`Transpose for rank ${rank} is not yet supported`);let originalOrder=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],switchedCoords=new Array(rank);for(let i=0;i<newDim.length;i++)switchedCoords[newDim[i]]=originalOrder[i];return switchedCoords.join()}var TransposePackedProgram=class{constructor(aShape,newDim){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[newDim[i]];if(this.outputShape=outputShape,this.rank=outputShape.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let dtype=getCoordsDataType(this.rank),outputOrder=getVecChannels("rc",this.rank),switchedOrder=new Array(this.rank);for(let i=0;i<newDim.length;i++)switchedOrder[newDim[i]]=outputOrder[i];let innerDims=`vec2(${switchedOrder.slice(-2).join()})`,nextColumn=`++${outputOrder[this.rank-1]} < ${outputShape[this.rank-1]}`,getc=`getChannel(getA(${switchedOrder.join()}), ${innerDims})`;this.userCode=`
+    void main() {
+      ${dtype} rc = getOutputCoords();
+      vec4 result = vec4(0.);
+      result[0] = ${getc};
+      if(${nextColumn}) {
+        result[1] = ${getc};
+      }
+      --${outputOrder[this.rank-1]};
+      if(++${outputOrder[this.rank-2]} < ${outputShape[this.rank-2]}) {
+        result[2] = ${getc};
+        if(${nextColumn}) {
+          result[3] = ${getc};
+        }
+      }
+      setOutput(result);
+    }
+    `}};function transposeImpl2(x,perm,backend3){let program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new TransposePackedProgram(x.shape,perm):new TransposeProgram(x.shape,perm);return backend3.runWebGLProgram(program,[x],x.dtype)}var maxConfig2={kernelName:Max,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{reductionIndices,keepDims}=attrs,webglBackend=backend3,xRank=x.shape.length,origAxes=util_exports.parseAxisParam(reductionIndices,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),maxInputIsTransposed=permutedAxes!=null,shouldExecuteOnCPU=webglBackend.shouldExecuteOnCPU([x]),maxInput=x;if(maxInputIsTransposed){if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(maxInput.dataId),values=xTexData.values,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[permutedAxes[i]];let maxInputValues=transposeImplCPU(values,x.shape,x.dtype,permutedAxes,newShape);maxInput=webglBackend.makeTensorInfo(newShape,x.dtype);let maxInputData=webglBackend.texData.get(maxInput.dataId);maxInputData.values=maxInputValues}else maxInput=transposeImpl2(x,permutedAxes,webglBackend);axes=backend_util_exports.getInnerMostAxes(axes.length,xRank)}backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);let[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(maxInput.shape,axes),outShape=maxOutShape;keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes));let out;if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(maxInput.dataId),values=xTexData.values,outValues=maxImplCPU(values,util_exports.sizeFromShape(reduceShape),outShape,x.dtype);out=webglBackend.makeTensorInfo(outShape,x.dtype);let outData=webglBackend.texData.get(out.dataId);outData.values=outValues}else out=maxImpl2(maxInput,reduceShape,outShape,webglBackend);return maxInputIsTransposed&&webglBackend.disposeIntermediateTensorInfo(maxInput),out}};function maxPool3(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex2(x,"maxPool");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))return identity3({inputs:{x},backend:backend3});let maxPoolProgram=new Pool2DProgram(convInfo,"max",!1);return backend3.runWebGLProgram(maxPoolProgram,[x],x.dtype)}var maxPoolConfig2={kernelName:MaxPool,backendName:"webgl",kernelFunc:maxPool3};function maxPoolBackprop3(args){let{inputs,backend:backend3,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex2([input2,output],"maxPoolBackprop");let{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),getPositions=!0,maxPoolPositionsProgram=new Pool2DProgram(convInfo,"max",getPositions),maxPoolPositions2=backend3.runWebGLProgram(maxPoolPositionsProgram,[x],x.dtype),maxPoolBackPropProgram=new MaxPool2DBackpropProgram(convInfo),result=backend3.runWebGLProgram(maxPoolBackPropProgram,[dy,maxPoolPositions2],x.dtype);return backend3.disposeIntermediateTensorInfo(maxPoolPositions2),result}var maxPoolBackpropConfig2={kernelName:MaxPoolBackprop,backendName:"webgl",kernelFunc:maxPoolBackprop3};function maxPoolWithArgmaxImpl2(x,includeBatchInIndex,convInfo,backend3){let program=new Pool2DProgram(convInfo,"max",!1),poolOutput=backend3.runWebGLProgram(program,[x],"float32");program=new Pool2DProgram(convInfo,"max",!0,!0,includeBatchInIndex);let indexOutput=backend3.runWebGLProgram(program,[x],"float32");return[poolOutput,indexOutput]}var maxPoolWithArgmaxConfig2={kernelName:MaxPoolWithArgmax,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{filterSize,strides,pad:pad11,includeBatchInIndex}=attrs,webglBackend=backend3;util_exports.assert(x.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);let dilations=[1,1];util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11),[result,indexes]=maxPoolWithArgmaxImpl2(x,includeBatchInIndex,convInfo,webglBackend);return[result,indexes]}};function meanImpl(x,reduceShape,outShape,backend3){let inSize=util_exports.sizeFromShape(reduceShape),xSize=util_exports.sizeFromShape(x.shape),batchSize=xSize/inSize,reshapedInput=reshape90({inputs:{x},attrs:{shape:[batchSize,inSize]},backend:backend3}),reduced=reduce(reshapedInput,"float32","mean",backend3),reshapedOutput=reshape90({inputs:{x:reduced},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(reshapedInput),backend3.disposeIntermediateTensorInfo(reduced),reshapedOutput}var meanConfig={kernelName:Mean,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{keepDims,axis}=attrs,webglBackend=backend3,xRank=x.shape.length,origAxes=util_exports.parseAxisParam(axis,x.shape),axes=origAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),meanInputIsTransposed=permutedAxes!=null,shouldExecuteOnCPU=webglBackend.shouldExecuteOnCPU([x]),intermediates=[],meanInput=x;if(meanInputIsTransposed){if(shouldExecuteOnCPU){let xTexData=webglBackend.texData.get(meanInput.dataId),values=xTexData.values,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[permutedAxes[i]];let meanInputValues=transposeImplCPU(values,x.shape,x.dtype,permutedAxes,newShape);meanInput=webglBackend.makeTensorInfo(newShape,x.dtype);let meanInputData=webglBackend.texData.get(meanInput.dataId);meanInputData.values=meanInputValues}else meanInput=transposeImpl2(x,permutedAxes,webglBackend);intermediates.push(meanInput),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank)}backend_util_exports.assertAxesAreInnerMostDims("sum",axes,xRank);let[meanOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(meanInput.shape,axes),outShape=meanOutShape;keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(meanOutShape,origAxes));let out=meanImpl(meanInput,reduceShape,outShape,webglBackend);for(let i of intermediates)webglBackend.disposeIntermediateTensorInfo(i);return out}};var MirrorPadProgram=class{constructor(xShape,paddings,mode){this.variableNames=["x"],this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),unpackedCoords=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,rank),offset=mode==="reflect"?0:1;if(rank===1){this.userCode=`
+        int start = ${start};
+        int end = ${end};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start) {
+            outC = start * 2 - outC - ${offset};
+          } else if(outC >= end) {
+            outC = (end - 1) * 2 - outC + ${offset};
+          }
+          setOutput(getX(outC - start));
+        }
+      `;return}this.userCode=`
+      ${dtype} start = ${dtype}(${start});
+      ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outC = getOutputCoords();
+        for (int i = 0; i < ${rank}; i++) {
+          if (outC[i] < start[i]) {
+            outC[i] = start[i] * 2 - outC[i] - ${offset};
+          } else if(outC[i] >= end[i]) {
+            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
+          }
+        }
+        ${dtype} coords = outC - start;
+        setOutput(getX(${unpackedCoords}));
+      }
+    `}};var MirrorPadPackedProgram=class{constructor(xShape,paddings,mode){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);let rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),coords2=getChannels("rc",rank),source=getChannels("source",rank),cLimit=`${coords2[rank-1]} < ${this.outputShape[rank-1]}`,innerDims=rank===1?"source":`vec2(${source.slice(-2).join()})`,offset=mode==="reflect"?0:1,mainLoop="";if(rank===1){let padSetup=`
+        ${dtype} source = rc;
+        if (source < start) {
+          source = start * 2 - source - ${offset};
+        } else if (source >= end) {
+          source = (end - 1) * 2 - source + ${offset};
+        }
+        source -= start;
+      `;mainLoop=`
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords2[rank-1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+      `}else{let padSetup=`
+        ${dtype} source = rc;
+        ${dtype} lt = ${dtype}(lessThan(source, start));
+        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
+        ${dtype} orig = 1 - (lt + gte);
+        source = orig * source +
+                lt * (start * 2 - source - ${offset}) +
+                gte * ((end - 1) * 2 - source + ${offset});
+        source -= start;
+      `;mainLoop=`
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords2[rank-1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+        rc = outputLoc;
+        ${coords2[rank-2]} += 1;
+        if(${coords2[rank-2]} < ${this.outputShape[rank-2]}) {
+          ${padSetup}
+          result[2] = getChannel(getX(${source.join()}), ${innerDims});
+          ${coords2[rank-1]} += 1;
+          if(${cLimit}) {
+            ${padSetup}
+            result[3] = getChannel(getX(${source.join()}), ${innerDims});
+          }
+        }
+      `}this.userCode=`
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${mainLoop}
+        setOutput(result);
+      }
+    `}};var mirrorPadKernelFunc=({inputs,backend:backend3,attrs})=>{let{x}=inputs,{paddings,mode}=attrs,program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MirrorPadPackedProgram(x.shape,paddings,mode):new MirrorPadProgram(x.shape,paddings,mode),output=backend3.runWebGLProgram(program,[x],x.dtype);return output},mirrorPadConfig2={kernelName:MirrorPad,backendName:"webgl",kernelFunc:mirrorPadKernelFunc};var COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},BinaryOpComplexProgram=class{constructor(op2,aShape,bShape){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
+      float binaryOpComplex(
+          float areal, float aimag, float breal, float bimag) {
+        ${op2}
+      }
+
+      void main() {
+        float areal = getARealAtOutCoords();
+        float aimag = getAImagAtOutCoords();
+        float breal = getBRealAtOutCoords();
+        float bimag = getBImagAtOutCoords();
+        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
+      }
+    `}};var MUL="return a * b;";function multiply3(args){let{inputs,backend:backend3}=args,{a,b}=inputs,dtype=backend_util_exports.upcastType(a.dtype,b.dtype);if(a.dtype==="complex64"){let aData=backend3.texData.get(a.dataId),bData=backend3.texData.get(b.dataId),realProgram=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,a.shape,b.shape),imagProgram=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,a.shape,b.shape),inputs2=[{dataId:aData.complexTensorInfos.real.dataId,dtype:aData.complexTensorInfos.real.dtype,shape:a.shape},{dataId:aData.complexTensorInfos.imag.dataId,dtype:aData.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:bData.complexTensorInfos.real.dataId,dtype:bData.complexTensorInfos.real.dtype,shape:b.shape},{dataId:bData.complexTensorInfos.imag.dataId,dtype:bData.complexTensorInfos.imag.dtype,shape:b.shape}],realPart=backend3.runWebGLProgram(realProgram,inputs2,"float32"),imagPart=backend3.runWebGLProgram(imagProgram,inputs2,"float32"),complexOutput=complex10({inputs:{real:realPart,imag:imagPart},backend:backend3});return backend3.disposeIntermediateTensorInfo(realPart),backend3.disposeIntermediateTensorInfo(imagPart),complexOutput}if(backend3.shouldExecuteOnCPU([a,b])){let aData=backend3.texData.get(a.dataId),bData=backend3.texData.get(b.dataId),[outValues,outShape]=multiplyImplCPU(a.shape,b.shape,aData.values,bData.values,dtype),out=backend3.makeTensorInfo(outShape,dtype),outData=backend3.texData.get(out.dataId);return outData.values=outValues,out}let program;return env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?program=new BinaryOpPackedProgram(MUL,a.shape,b.shape):program=new BinaryOpProgram(MUL,a.shape,b.shape),backend3.runWebGLProgram(program,[a,b],dtype)}var multiplyConfig2={kernelName:Multiply,backendName:"webgl",kernelFunc:multiply3};var nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold;return kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal)}};var nonMaxSuppressionV4Impl3=kernel_impls_exports.nonMaxSuppressionV4Impl,nonMaxSuppressionV4Config2={kernelName:NonMaxSuppressionV4,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl3(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[selectedIndices,validOutputs]}};var nonMaxSuppressionV5Impl3=kernel_impls_exports.nonMaxSuppressionV5Impl,nonMaxSuppressionV5Config2={kernelName:NonMaxSuppressionV5,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl3(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[selectedIndices,selectedScores]}};var RotateProgram=class{constructor(imageShape,radians,fillValue,center){this.variableNames=["Image"],this.outputShape=[];let imageHeight=imageShape[1],imageWidth=imageShape[2],sinFactor=Math.sin(radians).toFixed(3),cosFactor=Math.cos(radians).toFixed(3);this.outputShape=imageShape;let[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),centerXString=centerX.toFixed(3),centerYString=centerY.toFixed(3),fillSnippet="";typeof fillValue=="number"?fillSnippet=`float outputValue = ${fillValue.toFixed(2)};`:fillSnippet=`
+        vec3 fill = vec3(${fillValue.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) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};
+          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};
+          int coordX = int(round(coordXFloat + ${centerXString}));
+          int coordY = int(round(coordYFloat + ${centerYString}));
+          ${fillSnippet}
+          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
+            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `}};var rotateWithOffsetConfig2={kernelName:RotateWithOffset,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{image:image3}=inputs,{radians,fillValue,center}=attrs,webglBackend=backend3,program=new RotateProgram(image3.shape,radians,fillValue,center),output=webglBackend.runWebGLProgram(program,[image3],image3.dtype);return output}};var SIN=CHECK_NAN_SNIPPET_UNARY+`
+  return sin(x);
+`,sin6=unaryKernelFunc2(SIN),sinConfig2={kernelName:Sin,backendName:"webgl",kernelFunc:sin6};var SQUARE="return x * x;",square25=unaryKernelFunc2(SQUARE),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square25};var SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3};var SUB="return a - b;",subKernelFunc=binaryKernelFunc2({opSnippet:SUB,packedOpSnippet:SUB,supportsComplex:!0,cpuKernelImpl:subImplCPU}),subConfig2={kernelName:Sub,backendName:"webgl",kernelFunc:subKernelFunc};var TAN="return tan(x);",tan5=unaryKernelFunc2(TAN),tanConfig2={kernelName:Tan,backendName:"webgl",kernelFunc:tan5};var transposeConfig2={kernelName:Transpose,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{let{x}=inputs,{perm}=attrs,webglBackend=backend3,xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let out;if(webglBackend.shouldExecuteOnCPU([x])){let xTexData=webglBackend.texData.get(x.dataId),values=xTexData.values,outValues=transposeImplCPU(values,x.shape,x.dtype,perm,newShape);out=webglBackend.makeTensorInfo(newShape,x.dtype);let outData=webglBackend.texData.get(out.dataId);outData.values=outValues}else out=transposeImpl2(x,perm,webglBackend);return out}};function unique7(args){let{inputs,attrs,backend:backend3}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let values=backend3.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend3.makeTensorInfo(outputShape,x.dtype,outputValues),backend3.makeTensorInfo([indices.length],"int32",indices)]}var uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique7};var kernelConfigs2=[addConfig2,atan2Config,avgPoolConfig2,avgPoolBackpropConfig2,batchNormConfig2,castConfig2,complexConfig2,concatConfig2,cosConfig2,divConfig2,fftConfig2,flipLeftRightConfig2,fromPixelsConfig,identityConfig2,ifftConfig2,imagConfig2,maxConfig2,maxPoolConfig2,maxPoolBackpropConfig2,maxPoolWithArgmaxConfig2,meanConfig,mirrorPadConfig2,multiplyConfig2,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,realConfig2,reshapeConfig2,rotateWithOffsetConfig2,sinConfig2,squareConfig2,subConfig2,squaredDifferenceConfig2,tanConfig2,transposeConfig2,uniqueConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var version14="2.7.0";var version16={"tfjs-core":version,"tfjs-backend-cpu":version10,"tfjs-backend-webgl":version12,"tfjs-data":version8,"tfjs-layers":version2,"tfjs-converter":version6,tfjs:version14};var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend3){wasmFusedMatMul=backend3.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend3,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2}=attrs,aId=backend3.dataIdMap.get(a.dataId).id,bId=backend3.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend3.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend3.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc8;function setupFunc2(backend3){wasmFunc8=backend3.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend3,inputs:{x}}=args,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:kernelFunc3}}var absConfig2=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc8;function setupFunc2(backend3){wasmFunc8=backend3.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend3,inputs}=args,{a,b}=inputs,aId=backend3.dataIdMap.get(a.dataId).id,bId=backend3.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend3.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend3.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc8(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast);var wasmFunc;function setupFunc(backend3){wasmFunc=backend3.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend3}=args,out=backend3.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend3.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),inVals=backend3.typedArrayFromHeap(x),outVals=backend3.typedArrayFromHeap(out);return outVals.set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4};var wasmTranspose;function setup2(backend3){wasmTranspose=backend3.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose19(args){let{inputs,backend:backend3,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);let outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){let cloned=identity4({inputs,backend:backend3});return cloned.shape=outShape,cloned}let out=backend3.makeOutput(outShape,x.dtype),xId=backend3.dataIdMap.get(x.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){let outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){let newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose19,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend3){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose19({inputs:{x},attrs:{perm:permutedAxes},backend:backend3});let xId=backend3.dataIdMap.get(x.dataId).id,transposedId=backend3.dataIdMap.get(xTransposed.dataId).id;transposedId!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}var wasmFunc2;function setup3(backend3){wasmFunc2=backend3.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){let{backend:backend3,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let outShape=input2.shape.slice(0,-1),out=backend3.makeOutput(outShape,"int32"),outId=backend3.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend3.disposeData(transposed.dataId),out}var argMaxConfig={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3};var wasmAvgPool;function setup4(backend3){wasmAvgPool=backend3.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){let{inputs,attrs,backend:backend3}=args,x=inputs.x,xId=backend3.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}var avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape91(args){let{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape91};var wasmBatchMatMul;function setup5(backend3){wasmBatchMatMul=backend3.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul2(args){let{inputs,backend:backend3,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShapeOuterDims=batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2),outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape91({inputs:{x:a},backend:backend3,attrs:{shape:a3dShape}}),b3d=reshape91({inputs:{x:b},backend:backend3,attrs:{shape:b3dShape}}),a3dId=backend3.dataIdMap.get(a3d.dataId).id,b3dId=backend3.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend3.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend3.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig2={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul2};function cast51(args){let{inputs:{x},attrs:{dtype},backend:backend3}=args,out=backend3.makeOutput(x.shape,dtype),inVals=backend3.typedArrayFromHeap(x),outVals=backend3.typedArrayFromHeap(out);return outVals.set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast51};var wasmClip;function setup6(backend3){wasmClip=backend3.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat19(args){let{inputs,backend:backend3}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),out=backend3.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];let shapes=$inputs.map(t=>t.shape);backend_util_exports.assertParamsConsistent(shapes,axis);let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend3.typedArrayFromHeap(input2)),outVals=backend3.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){let innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}var concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat19};var wasmConv2d;function setup7(backend3){wasmConv2d=backend3.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d11(args){let{inputs,attrs,backend:backend3}=args,{x,filter}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad11,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var conv2DConfig2={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d11};var wasmConv2DBackpropInput;function setup8(backend3){wasmConv2DBackpropInput=backend3.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput3(args){let{backend:backend3,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad11,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend3.makeOutput(convInfo.inShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,dyId=backend3.dataIdMap.get(dy.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}var conv2DBackpropInputConfig2={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput3};var cosConfig3=createUnaryKernelConfig(Cos);var InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));var wasmCropAndResize;function setup9(backend3){wasmCropAndResize=backend3.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize2(args){let{backend:backend3,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]],imagesData=backend3.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast51({backend:backend3,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend3.dataIdMap.get(castedData.dataId));let imagesId=imagesData.id,boxesId=backend3.dataIdMap.get(boxes.dataId).id,boxIndId=backend3.dataIdMap.get(boxInd.dataId).id,out=backend3.makeOutput(outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend3.disposeData(castedData.dataId),out}var cropAndResizeConfig={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize2};var wasmCumsum;function setup10(backend3){wasmCumsum=backend3.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum6(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse12}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose19({inputs:{x},attrs:{perm:permutation},backend:backend3}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend3.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend3.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend3.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse12?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose19({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend3}),backend3.disposeData(permutedX.dataId),backend3.disposeData(permutedOut.dataId)}return out}var cumsumConfig={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum6};var wasmDepthToSpace;function setup11(backend3){wasmDepthToSpace=backend3.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace2(args){let{backend:backend3,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend3.makeOutput(outputShape,"float32"),xData=backend3.dataIdMap.get(x.dataId),xId=xData.id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend3.dataIdMap.get(out.dataId).id,channelsLast=dataFormat==="NHWC"?1:0;return wasmDepthToSpace(xId,blockSize,channelsLast,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace2};var wasmDepthwiseConv2d;function setup12(backend3){wasmDepthwiseConv2d=backend3.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){let{inputs,attrs,backend:backend3}=args,{x,filter}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad11,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5};var supportsFullBroadcast2=!0,divConfig3=createBinaryKernelConfig(Div,supportsFullBroadcast2);var supportsFullBroadcast3=!1,equalConfig=createBinaryKernelConfig(Equal,supportsFullBroadcast3,"bool");var expConfig2=createUnaryKernelConfig(Exp);function fill6(args){let{attrs:{shape,value,dtype},backend:backend3}=args,out=backend3.makeOutput(shape,dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(value),out}var fillConfig2={kernelName:Fill,backendName:"wasm",kernelFunc:fill6};var wasmFlipLeftRight;function setup13(backend3){wasmFlipLeftRight=backend3.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend3}=args,{image:image3}=inputs,out=backend3.makeOutput(image3.shape,image3.dtype),imageId=backend3.dataIdMap.get(image3.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13};var supportsFullBroadcast4=!1,floorDivConfig=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast4);var wasmBatchNorm;function setup14(backend3){wasmBatchNorm=backend3.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend3,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean7,variance,offset,scale:scale2}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,meanId=backend3.dataIdMap.get(mean7.dataId).id,varianceId=backend3.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend3.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend3.dataIdMap.get(scale2.dataId).id:0,out=backend3.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend3.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm};var wasmFusedConv2d;function setup15(backend3){wasmFusedConv2d=backend3.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d(args){let{inputs,attrs,backend:backend3}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dilations,dataFormat,dimRoundingMode,activation:activation2}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,outId),out}var fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d};var wasmFusedDepthwiseConv2d;function setup16(backend3){wasmFusedDepthwiseConv2d=backend3.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend3}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dilations,dataFormat,dimRoundingMode,activation:activation2}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,outId),out}var fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d};var wasmGatherNd;function setup17(backend3){wasmGatherNd=backend3.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd(args){let{backend:backend3,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend3.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xData=backend3.dataIdMap.get(params.dataId),xId=xData.id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd};var wasmGather;function setup18(backend3){wasmGather=backend3.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV2(args){let{backend:backend3,inputs,attrs}=args,{x,indices}=inputs,{axis}=attrs,newShape=x.shape.slice();newShape[axis]=util_exports.sizeFromShape(indices.shape);let stridesSize=x.shape.length-1,out=backend3.makeOutput(newShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let xData=backend3.dataIdMap.get(x.dataId),xId=xData.id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,outId=backend3.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(newShape)).buffer);wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,axis,outStridesBytes,outId);let parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis);return out.shape=shapeInfo.outputShape,out}var gatherV2Config={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV2};var supportsFullBroadcast5=!1,greaterConfig=createBinaryKernelConfig(Greater,supportsFullBroadcast5,"bool");var supportsFullBroadcast6=!1,greaterEqualConfig=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast6,"bool");var supportsFullBroadcast7=!1,lessConfig=createBinaryKernelConfig(Less,supportsFullBroadcast7,"bool");var supportsFullBroadcast8=!1,lessEqualConfig=createBinaryKernelConfig(LessEqual,supportsFullBroadcast8,"bool");var logConfig2=createUnaryKernelConfig(Log);var supportsFullBroadcast9=!1,logicalAndConfig=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast9,"bool");var wasmMax;function setup19(backend3){wasmMax=backend3.wasm.cwrap(Max,null,["number, number, number"])}function max9(args){let{backend:backend3,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max9};var supportsFullBroadcast10=!1,maximumConfig=createBinaryKernelConfig(Maximum,supportsFullBroadcast10);var wasmMaxPool;function setup20(backend3){wasmMaxPool=backend3.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend3}=args,x=inputs.x,xId=backend3.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4};var wasmMin;function setup21(backend3){wasmMin=backend3.wasm.cwrap(Min,null,["number, number, number"])}function min7(args){let{backend:backend3,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig={kernelName:Min,backendName:"wasm",setupFunc:setup21,kernelFunc:min7};var supportsFullBroadcast11=!1,minimumConfig=createBinaryKernelConfig(Minimum,supportsFullBroadcast11);var supportsFullBroadcast12=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast12);var negateConfig=createUnaryKernelConfig(Negate);function parseResultStruct(backend3,resOffset){let result=new Int32Array(backend3.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend3.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc3;function setup22(backend3){wasmFunc3=backend3.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc3(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pSelectedScores),backend3.wasm._free(pValidOutputs);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices);return selectedIndicesTensor}var nonMaxSuppressionV3Config2={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup22,kernelFunc};var wasmFunc4;function setup23(backend3){wasmFunc4=backend3.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV4(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pSelectedScores);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend3.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup23,kernelFunc:nonMaxSuppressionV4};var wasmFunc5;function setup24(backend3){wasmFunc5=backend3.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pValidOutputs);let selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend3.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup24,kernelFunc:kernelFunc2};var supportsFullBroadcast13=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast13,"bool");var wasmOneHot;function setup25(backend3){wasmOneHot=backend3.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot2(args){let{inputs,backend:backend3,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend3.makeOutput([...indices.shape,depth],"int32"),outId=backend3.dataIdMap.get(out.dataId).id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig={kernelName:OneHot,backendName:"wasm",setupFunc:setup25,kernelFunc:oneHot2};function onesLike2(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(1),out}var onesLikeConfig={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike2};var wasmPadV2;function setup26(backend3){wasmPadV2=backend3.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad10(args){let{inputs:{x},backend:backend3,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(outShape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config2={kernelName:PadV2,backendName:"wasm",kernelFunc:pad10,setupFunc:setup26};var supportsFullBroadcast14=!1,powConfig=createBinaryKernelConfig(Pow,supportsFullBroadcast14);var wasmPrelu;function setup27(backend3){wasmPrelu=backend3.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu8(args){let{inputs,backend:backend3}=args,{x,alpha}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,weightsId=backend3.dataIdMap.get(alpha.dataId).id,out=backend3.makeOutput(x.shape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig2={kernelName:Prelu,backendName:"wasm",setupFunc:setup27,kernelFunc:prelu8};var reluConfig2=createUnaryKernelConfig(Relu);var relu6Config2=createUnaryKernelConfig(Relu6);var wasmResizeBilinear;function setup28(backend3){wasmResizeBilinear=backend3.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number"])}function resizeBilinear2(args){let{backend:backend3,inputs,attrs}=args,{images}=inputs,{alignCorners,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend3.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast51({backend:backend3,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend3.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend3.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend3.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,outId),castedData!=null&&backend3.disposeData(castedData.dataId),out}var resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup28,kernelFunc:resizeBilinear2};var wasmReverse;function setup29(backend3){wasmReverse=backend3.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse11(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend3});let out=backend3.makeOutput(x.shape,x.dtype),xId=backend3.dataIdMap.get(x.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape91({inputs:{x:out},attrs:{shape:x.shape},backend:backend3})}var reverseConfig={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse11,setupFunc:setup29};var wasmRotate;function setup30(backend3){wasmRotate=backend3.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend3,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend3.makeOutput(image3.shape,image3.dtype),imageId=backend3.dataIdMap.get(image3.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup30};var rsqrtConfig2=createUnaryKernelConfig(Rsqrt);var wasmScatterNd;function setup31(backend3){wasmScatterNd=backend3.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd(args){let{backend:backend3,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend3.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,updatesData=backend3.dataIdMap.get(updates.dataId),updatesId=updatesData.id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup31,kernelFunc:scatterNd};var wasmSelect;function setup32(backend3){wasmSelect=backend3.wasm.cwrap(SelectV2,null,["number","number","number","number","number"])}function select(args){let{inputs,backend:backend3}=args,{condition,t,e}=inputs,conditionId=backend3.dataIdMap.get(condition.dataId).id,tId=backend3.dataIdMap.get(t.dataId).id,eId=backend3.dataIdMap.get(e.dataId).id,out=backend3.makeOutput(t.shape,t.dtype),outId=backend3.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectV2Config={kernelName:SelectV2,backendName:"wasm",kernelFunc:select,setupFunc:setup32};var wasmFunc6;function setup33(backend3){wasmFunc6=backend3.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid8(args){let{backend:backend3,inputs:{x}}=args,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc6(xId,outId),out}var sigmoidConfig2={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup33,kernelFunc:sigmoid8};var sinConfig3=createUnaryKernelConfig(Sin);function slice20(args){let{inputs:{x},attrs:{begin,size},backend:backend3}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend3.typedArrayFromHeap(x),out=backend3.makeOutput(size_,x.dtype),outVals=backend3.typedArrayFromHeap(out),xStrides=util_exports.computeStrides(x.shape);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return outVals.set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}let rank=x.shape.length;return rank===2?slice2d3(xVals,xStrides[0],outVals,begin_,size_):rank===3?slice3d3(xVals,xStrides[0],xStrides[1],outVals,begin_,size_):rank===4?slice4d3(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_):genericSliceSlow(xVals,x,outVals,begin_,size_),out}function slice2d3(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){let xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d3(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){let xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d3(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){let xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}function genericSliceSlow(xVals,xInfo,outVals,begin,size){let outBuf=buffer(size,xInfo.dtype,outVals),xBuf=buffer(xInfo.shape,xInfo.dtype,xVals);for(let i=0;i<outBuf.size;++i){let loc=outBuf.indexToLoc(i),xLoc=loc.map((idx,j)=>idx+begin[j]);outVals[i]=xBuf.get(...xLoc)}}var sliceConfig2={kernelName:Slice,backendName:"wasm",kernelFunc:slice20};var wasmFunc7;function setup34(backend3){wasmFunc7=backend3.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){let{backend:backend3,inputs:{logits},attrs:{dim}}=args,xId=backend3.dataIdMap.get(logits.dataId).id,out=backend3.makeOutput(logits.shape,logits.dtype),outId=backend3.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId,channels,batch),out}var softmaxConfig={kernelName:Softmax,backendName:"wasm",setupFunc:setup34,kernelFunc:softmax5};function split12(args){let{inputs,attrs,backend:backend3}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice20({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend3});return begin[$axis]+=s,xSlice})}var splitVConfig={kernelName:SplitV,backendName:"wasm",kernelFunc:split12};var sqrtConfig2=createUnaryKernelConfig(Sqrt);var squareConfig3=createUnaryKernelConfig(Square);var supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15);var wasmStridedSlice;function setup35(backend3){wasmStridedSlice=backend3.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice2(args){let{backend:backend3,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape91({inputs:{x},attrs:{shape:newShape},backend:backend3}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided){let xSliced=slice20({inputs:{x},attrs:{begin,size},backend:backend3});return reshape91({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend3})}let out=backend3.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend3.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend3.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape91({inputs:{x:out},attrs:{shape:outShape},backend:backend3})}var stridedSliceConfig={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup35,kernelFunc:stridedSlice2};var supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16);var wasmSum;function setup36(backend3){wasmSum=backend3.wasm.cwrap(Sum,null,["number, number, number"])}function sum28(args){let{backend:backend3,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend3.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig={kernelName:Sum,backendName:"wasm",setupFunc:setup36,kernelFunc:sum28};var tanhConfig2=createUnaryKernelConfig(Tanh);var wasmTile;function setup37(backend3){wasmTile=backend3.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile11(args){let{inputs,backend:backend3,attrs}=args,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];let xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend3.makeOutput(newShape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}var tileConfig={kernelName:Tile,backendName:"wasm",setupFunc:setup37,kernelFunc:tile11};function unpack(args){let{inputs,backend:backend3,attrs}=args,{value}=inputs,{axis}=attrs,numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);let outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice20({inputs:{x:value},attrs:{begin,size},backend:backend3});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}var unpackConfig={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack};function zerosLike2(args){let{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(0),out}var zerosLikeConfig={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike2};var kernelConfigs3=[absConfig2,addConfig3,addNConfig,argMaxConfig,avgPoolConfig3,batchMatMulConfig2,castConfig3,clipByValueConfig,concatConfig3,conv2DConfig2,conv2DBackpropInputConfig2,cosConfig3,cropAndResizeConfig,cumsumConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig2,divConfig3,equalConfig,expConfig2,fillConfig2,flipLeftRightConfig3,floorDivConfig,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig3,lessConfig,lessEqualConfig,logConfig2,logicalAndConfig,maxConfig3,maximumConfig,maxPoolConfig3,minConfig,minimumConfig,multiplyConfig3,negateConfig,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig,onesLikeConfig,padV2Config2,powConfig,preluConfig2,reluConfig2,relu6Config2,reshapeConfig3,resizeBilinearConfig,reverseConfig,rotateWithOffsetConfig3,rsqrtConfig2,scatterNdConfig,selectV2Config,sigmoidConfig2,sinConfig3,sliceConfig2,softmaxConfig,splitVConfig,sqrtConfig2,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig,subConfig3,sumConfig,tanhConfig2,tileConfig,transposeConfig3,unpackConfig,zerosLikeConfig];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var tfjs_backend_wasm_threaded_simd=__toModule(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',tfjs_backend_wasm=__toModule(require_tfjs_backend_wasm());var WASM_PRIORITY=2,BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine15())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();f();let kernelMs=util_exports.now()-start;return{kernelMs}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data=this.dataIdMap.get(dataId);this.wasm._free(data.memoryOffset),this.wasm.tfjs.disposeData(data.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer11=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer11,memoryOffset,size);case"int32":return new Int32Array(buffer11,memoryOffset,size);case"bool":return new Uint8Array(buffer11,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var _scriptDir = undefined; var WasmBackendModuleThreadedSimd = "+tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{if(initialized)return;if(initAborted)return;initAborted=!0;let rejectMsg="Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers";reject({message:rejectMsg})}})}function typedArrayFromBuffer(buffer11,dtype){switch(dtype){case"float32":return new Float32Array(buffer11);case"int32":return new Int32Array(buffer11);case"bool":return new Uint8Array(buffer11);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version17="2.7.0";export{Abs,Acos,Acosh,AdadeltaOptimizer,AdagradOptimizer,AdamOptimizer,AdamaxOptimizer,Add,AddN,All,Any,ArgMax,ArgMin,Asin,Asinh,Atan,Atan2,Atanh,AvgPool,AvgPool3D,AvgPool3DBackprop,AvgPoolBackprop,BackendWasm,BatchMatMul,BatchToSpaceND,BroadcastTo,Callback,CallbackList,Cast,Ceil,ClipByValue,Complex,Concat,Conv2D,Conv2DBackpropFilter,Conv2DBackpropInput,Conv3D,Conv3DBackpropFilterV2,Conv3DBackpropInputV2,Cos,Cosh,CropAndResize,Cumsum,CustomCallback,DataStorage,DepthToSpace,DepthwiseConv2dNative,DepthwiseConv2dNativeBackpropFilter,DepthwiseConv2dNativeBackpropInput,Diag,Dilation2D,Dilation2DBackpropFilter,Dilation2DBackpropInput,Div,ENV,EarlyStopping,Elu,EluGrad,Environment,Equal,Erf,Exp,Expm1,FFT,Fill,FlipLeftRight,Floor,FloorDiv,FromPixels,FusedBatchNorm,FusedConv2D,FusedDepthwiseConv2D,GatherNd,GatherV2,GraphModel,Greater,GreaterEqual,History,IFFT,Identity,Imag,InputSpec,IsFinite,IsInf,IsNan,KernelBackend,LRN,LRNBackprop,LayerVariable,LayersModel,Less,LessEqual,LinSpace,Log,Log1p,LogSoftmax,LogicalAnd,LogicalNot,LogicalOr,Max,MaxPool,MaxPool3D,MaxPool3DBackprop,MaxPoolBackprop,MaxPoolWithArgmax,Maximum,Mean,Min,Minimum,MirrorPad,Mod,MomentumOptimizer,Multiply,Negate,NonMaxSuppressionV3,NonMaxSuppressionV4,NonMaxSuppressionV5,NotEqual,OP_SCOPE_SUFFIX,OneHot,OnesLike,Optimizer,PadV2,Pool,Pow,Prelu,Prod,RMSPropOptimizer,RNN,Range,Rank,Real,Reciprocal,Reduction,Relu,Relu6,Reshape,ResizeBilinear,ResizeBilinearGrad,ResizeNearestNeighbor,ResizeNearestNeighborGrad,Reverse,RotateWithOffset,Round,Rsqrt,SGDOptimizer,ScatterNd,SelectV2,Selu,Sequential,Sigmoid,Sign,Sin,Sinh,Slice,Softmax,Softplus,SpaceToBatchND,SparseToDense,SplitV,Sqrt,Square,SquaredDifference,Step,StridedSlice,Sub,Sum,SymbolicTensor,Tan,Tanh,Tensor,TensorBuffer,Tile,TopK,Transpose,Unique,Unpack,UnsortedSegmentSum,Variable,ZerosLike,_FusedMatMul,abs,acos,acosh,add2 as add,addN,addStrict,all,any,argMax,argMin,asin,asinh,atan,atan2,atanh,avgPool,avgPool3d,backend2 as backend,backend_util_exports as backend_util,basicLSTMCell,batchNorm,batchNorm2d,batchNorm3d,batchNorm4d,batchToSpaceND,booleanMaskAsync,broadcastTo,browser_exports as browser,buffer,callbacks,cast,ceil,clipByValue,clone,complex,concat,concat1d,concat2d,concat3d,concat4d,exports_constraints_exports as constraints,conv1d,conv2d,conv2dTranspose,conv3d,conv3dTranspose,copyRegisteredKernels,cos,cosh,cosineWindow,cumsum,customGrad,dist_exports as data,deprecationWarn,depthToSpace,depthwiseConv2d,deregisterOp,device_util_exports as device_util,diag,dilation2d,disableDeprecationWarnings,dispose,disposeVariables,div,divNoNan,divStrict,dot,dropout,elu,enableDebugMode,enableProdMode,enclosingPowerOfTwo,engine15 as engine,env,equal,equalStrict,erf,exp,expandDims,expm1,eye,fft,fill,findBackend,findBackendFactory,floor,floorDiv,fused_ops_exports as fused,gather,gatherND,gather_nd_util_exports as gather_util,getBackend,getGradient,getKernel,getKernelsForBackend,grad,grads,greater,greaterEqual,greaterEqualStrict,greaterStrict,ifft,imag,image,inTopKAsync,exports_initializers_exports as initializers,input,io_exports as io,irfft,isFinite2 as isFinite,isInf,isNaN2 as isNaN,keep,kernel_impls_exports as kernel_impls,exports_layers_exports as layers,leakyRelu,less,lessEqual,lessEqualStrict,lessStrict,linalg,linspace,loadGraphModel,loadLayersModel,localResponseNormalization,log,log1p,logSigmoid,logSoftmax,logSumExp,logicalAnd,logicalNot,logicalOr,logicalXor,losses,matMul,math_exports as math,max,maxPool,maxPool3d,maxPoolWithArgmax,maximum,maximumStrict,mean,memory,exports_metrics_exports as metrics,min,minimum,minimumStrict,mirrorPad,mod,modStrict,model,exports_models_exports as models,moments,movingAverage,mul,mulStrict,multiRNNCell,multinomial,neg,nextFrame,norm,notEqual,notEqualStrict,oneHot,ones2 as ones,onesLike,op,outerProduct,pad,pad1d,pad2d,pad3d,pad4d,pool,pow,powStrict,prelu,print2 as print,prod,profile,rand,randomGamma,randomNormal,randomUniform,range,ready,real,reciprocal,registerBackend,registerCallbackConstructor,registerGradient,registerKernel,registerOp,exports_regularizers_exports as regularizers,relu,relu6,removeBackend,reshape,reverse,reverse1d,reverse2d,reverse3d,reverse4d,rfft,round,rsqrt,scalar,scatterND,scatter_nd_util_exports as scatter_util,selu,separableConv2d,sequential,serialization_exports as serialization,setBackend,setPlatform,setWasmPath,setWasmPaths,setdiff1dAsync,sigmoid,sign,signal,sin,sinh,slice,slice1d,slice2d,slice3d,slice4d,slice_util_exports as slice_util,softmax,softplus,spaceToBatchND,sparseToDense,spectral,split,sqrt,square,squaredDifference,squaredDifferenceStrict,squeeze,stack,step,stridedSlice,sub,subStrict,sum2 as sum,sumOutType,tan,tanh2 as tanh,tensor4 as tensor,tensor1d,tensor2d,tensor3d,tensor4d,tensor5d,tensor6d,tensor_util_exports as tensor_util,test_util_exports as test_util,tidy,tile,time,topk,train,transpose,truncatedNormal,unique,unregisterGradient,unregisterKernel,unsortedSegmentSum,unstack,upcastType,util_exports as util,valueAndGrad,valueAndGrads,variable,variableGrads,version16 as version,version6 as version_converter,version as version_core,version2 as version_layers,version17 as version_wasm,where,whereAsync,zeros,zerosLike};
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @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=tfjs.esm.js.map
diff --git a/dist/tfjs.esm.js.map b/dist/tfjs.esm.js.map
new file mode 100644
index 0000000..7b334e6
--- /dev/null
+++ b/dist/tfjs.esm.js.map
@@ -0,0 +1,7 @@
+{
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+  "sourcesContent": ["", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":165,\"maximum\":165+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=12976;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop;STACK_MAX=stackMax;stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){GROWABLE_HEAP_F64()[b+i]=arguments[2+i]}var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){if(!readAsmConstArgs.array){readAsmConstArgs.array=[]}var args=readAsmConstArgs.array;args.length=0;var ch;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){if(ch===100||ch===102){buf=buf+7&~7;args.push(GROWABLE_HEAP_F64()[buf>>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1];var varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2];var constArgs=readAsmConstArgs(sigPtr,varargPtr);return func.apply(null,constArgs)}return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1)}var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++){GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)}},recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={};var a=attributes>>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var 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thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function 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data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":147,\"maximum\":147+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Div=Module[\"_Div\"]=function(){return(_Div=Module[\"_Div\"]=Module[\"asm\"][\"Div\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"MaxPool\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Maximum\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"Min\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Minimum\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Multiply\"]).apply(null,arguments)};var _Negate=Module[\"_Negate\"]=function(){return(_Negate=Module[\"_Negate\"]=Module[\"asm\"][\"Negate\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"NotEqual\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"OneHot\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"PadV2\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"Pow\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"Prelu\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"Relu\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"Relu6\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"ResizeBilinear\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"Reverse\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"RotateWithOffset\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"Rsqrt\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"ScatterNd\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"SelectV2\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"Sigmoid\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"Sin\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"Softmax\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Sqrt\"]).apply(null,arguments)};var _Square=Module[\"_Square\"]=function(){return(_Square=Module[\"_Square\"]=Module[\"asm\"][\"Square\"]).apply(null,arguments)};var _SquaredDifference=Module[\"_SquaredDifference\"]=function(){return(_SquaredDifference=Module[\"_SquaredDifference\"]=Module[\"asm\"][\"SquaredDifference\"]).apply(null,arguments)};var _StridedSlice=Module[\"_StridedSlice\"]=function(){return(_StridedSlice=Module[\"_StridedSlice\"]=Module[\"asm\"][\"StridedSlice\"]).apply(null,arguments)};var _Sub=Module[\"_Sub\"]=function(){return(_Sub=Module[\"_Sub\"]=Module[\"asm\"][\"Sub\"]).apply(null,arguments)};var _Sum=Module[\"_Sum\"]=function(){return(_Sum=Module[\"_Sum\"]=Module[\"asm\"][\"Sum\"]).apply(null,arguments)};var _Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Tanh\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Tile\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Transpose\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"_FusedMatMul\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"malloc\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"free\"]).apply(null,arguments)};var __start=Module[\"__start\"]=function(){return(__start=Module[\"__start\"]=Module[\"asm\"][\"_start\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"stackSave\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"stackAlloc\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"stackRestore\"]).apply(null,arguments)};Module[\"asm\"]=asm;Module[\"cwrap\"]=cwrap;var calledRun;Module[\"then\"]=function(func){if(calledRun){func(Module)}else{var old=Module[\"onRuntimeInitialized\"];Module[\"onRuntimeInitialized\"]=function(){if(old)old();func(Module)}}return Module};function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n  return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n      module.exports = WasmBackendModule;\n    else if (typeof define === 'function' && define['amd'])\n      define([], function() { return WasmBackendModule; });\n    else if (typeof exports === 'object')\n      exports[\"WasmBackendModule\"] = WasmBackendModule;\n    ", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 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null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof 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+}
diff --git a/dist/tfjs.esm.json b/dist/tfjs.esm.json
new file mode 100644
index 0000000..2d3722e
--- /dev/null
+++ b/dist/tfjs.esm.json
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+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js": {
+          "bytesInOutput": 96
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js": {
+          "bytesInOutput": 1295
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js": {
+          "bytesInOutput": 46
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js": {
+          "bytesInOutput": 767
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js": {
+          "bytesInOutput": 608
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js": {
+          "bytesInOutput": 257
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js": {
+          "bytesInOutput": 1271
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js": {
+          "bytesInOutput": 494
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js": {
+          "bytesInOutput": 6610
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js": {
+          "bytesInOutput": 3425
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js": {
+          "bytesInOutput": 0
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js": {
+          "bytesInOutput": 22
+        },
+        "node_modules/@tensorflow/tfjs-backend-wasm/dist/index.js": {
+          "bytesInOutput": 0
+        }
+      },
+      "bytes": 1501677
+    }
+  }
+}
diff --git a/package-lock.json b/package-lock.json
index a0df30d..c5fe59e 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -33,6 +33,15 @@
         "seedrandom": "2.4.3"
       }
     },
+    "@tensorflow/tfjs-backend-wasm": {
+      "version": "2.7.0",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-wasm/-/tfjs-backend-wasm-2.7.0.tgz",
+      "integrity": "sha512-q1p6vR99kvxNjPMH4SMzg5R43g2ACmp3GRMoS2JVCruMyBWroD3KrmgXX2f94t0bl8BysW0JBkGXihajifjd6Q==",
+      "dev": true,
+      "requires": {
+        "@types/emscripten": "~0.0.34"
+      }
+    },
     "@tensorflow/tfjs-backend-webgl": {
       "version": "2.7.0",
       "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-2.7.0.tgz",
@@ -110,6 +119,40 @@
         }
       }
     },
+    "@tensorflow/tfjs-node-gpu": {
+      "version": "2.7.0",
+      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs-node-gpu/-/tfjs-node-gpu-2.7.0.tgz",
+      "integrity": "sha512-aL49B/0R7sBJpgeQ3sOeH38UFf78Hr0mi3CLLswGFgt+DALd+1sc4jBpSORycoHjZLiPysW5YsFz9t5BNIw8ig==",
+      "dev": true,
+      "requires": {
+        "@tensorflow/tfjs": "2.7.0",
+        "@tensorflow/tfjs-core": "2.7.0",
+        "adm-zip": "^0.4.11",
+        "google-protobuf": "^3.9.2",
+        "https-proxy-agent": "^2.2.1",
+        "node-pre-gyp": "0.14.0",
+        "progress": "^2.0.0",
+        "rimraf": "^2.6.2",
+        "tar": "^4.4.6"
+      },
+      "dependencies": {
+        "rimraf": {
+          "version": "2.7.1",
+          "resolved": "https://registry.npmjs.org/rimraf/-/rimraf-2.7.1.tgz",
+          "integrity": "sha512-uWjbaKIK3T1OSVptzX7Nl6PvQ3qAGtKEtVRjRuazjfL3Bx5eI409VZSqgND+4UNnmzLVdPj9FqFJNPqBZFve4w==",
+          "dev": true,
+          "requires": {
+            "glob": "^7.1.3"
+          }
+        }
+      }
+    },
+    "@types/emscripten": {
+      "version": "0.0.34",
+      "resolved": "https://registry.npmjs.org/@types/emscripten/-/emscripten-0.0.34.tgz",
+      "integrity": "sha512-QSb9ojDincskc+uKMI0KXp8e1NALFINCrMlp8VGKGcTSxeEyRTTKyjWw75NYrCZHUsVEEEpr1tYHpbtaC++/sQ==",
+      "dev": true
+    },
     "@types/node": {
       "version": "14.14.10",
       "resolved": "https://registry.npmjs.org/@types/node/-/node-14.14.10.tgz",
diff --git a/package.json b/package.json
index 6e0bdb3..3fa9244 100644
--- a/package.json
+++ b/package.json
@@ -11,12 +11,7 @@
   },
   "scripts": {
     "start": "node --trace-warnings example/node.js",
-    "build-esm": "esbuild --bundle --format=esm --target=es2018 --platform=browser --minify --sourcemap --outfile=./dist/face-api.esm.js --log-level=error --tsconfig=./tsconfig.json --external:util --external:string_decoder --external:fs src/index.ts",
-    "build-esm-nobundle": "esbuild --bundle --format=esm --target=es2018 --platform=browser --sourcemap --outfile=./dist/face-api.esm.nobundle.js --log-level=error --tsconfig=./tsconfig.json --external:@tensorflow --external:util --external:string_decoder --external:fs --global-name=faceapi src/index.ts",
-    "build-iife": "esbuild --bundle --format=iife --target=es2018 --platform=browser --minify --sourcemap --outfile=./dist/face-api.js --log-level=error --tsconfig=./tsconfig.json --external:util --external:string_decoder --external:fs --global-name=faceapi src/index.ts",
-    "build-node": "esbuild --bundle --format=cjs --target=es2018 --platform=node --minify --sourcemap --outfile=./dist/face-api.node.js --log-level=error --tsconfig=./tsconfig.json src/index.ts",
-    "build-node-nobundle": "esbuild --bundle --format=cjs --target=es2018 --platform=node --sourcemap --outfile=./dist/face-api.node.nobundle.js --external:@tensorflow --log-level=error --tsconfig=./tsconfig.json src/index.ts",
-    "build": "rimraf build/* dist/* && tsc && npm run build-iife && npm run build-esm && npm run build-esm-nobundle && npm run build-node && npm run build-node-nobundle && ls -l dist/"
+    "build": "rimraf dist/* && node ./build.js"
   },
   "keywords": [
     "tensorflow",
@@ -43,7 +38,9 @@
   "Dependencies": {},
   "devDependencies": {
     "@tensorflow/tfjs": "^2.7.0",
+    "@tensorflow/tfjs-backend-wasm": "^2.7.0",
     "@tensorflow/tfjs-node": "^2.7.0",
+    "@tensorflow/tfjs-node-gpu": "^2.7.0",
     "@types/node": "^14.14.10",
     "@vladmandic/pilogger": "^0.2.9",
     "esbuild": "^0.8.17",
diff --git a/src/NeuralNetwork.ts b/src/NeuralNetwork.ts
index 8fb1e68..99f6e57 100644
--- a/src/NeuralNetwork.ts
+++ b/src/NeuralNetwork.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../dist/tfjs.esm.js';
 
 import { ParamMapping } from './common';
 import { getModelUris } from './common/getModelUris';
diff --git a/src/ageGenderNet/AgeGenderNet.ts b/src/ageGenderNet/AgeGenderNet.ts
index 5a49aef..f995772 100644
--- a/src/ageGenderNet/AgeGenderNet.ts
+++ b/src/ageGenderNet/AgeGenderNet.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
 import { seperateWeightMaps } from '../faceProcessor/util';
diff --git a/src/ageGenderNet/extractParamsFromWeigthMap.ts b/src/ageGenderNet/extractParamsFromWeigthMap.ts
index e08aac0..f9c7f38 100644
--- a/src/ageGenderNet/extractParamsFromWeigthMap.ts
+++ b/src/ageGenderNet/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';
 import { NetParams } from './types';
diff --git a/src/ageGenderNet/types.ts b/src/ageGenderNet/types.ts
index 80847f3..8592c31 100644
--- a/src/ageGenderNet/types.ts
+++ b/src/ageGenderNet/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { FCParams } from '../common';
 
diff --git a/src/common/convLayer.ts b/src/common/convLayer.ts
index c81b7dc..0cd1ba8 100644
--- a/src/common/convLayer.ts
+++ b/src/common/convLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams } from './types';
 
diff --git a/src/common/depthwiseSeparableConv.ts b/src/common/depthwiseSeparableConv.ts
index f4b9ed8..5cc1a56 100644
--- a/src/common/depthwiseSeparableConv.ts
+++ b/src/common/depthwiseSeparableConv.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { SeparableConvParams } from './types';
 
diff --git a/src/common/extractConvParamsFactory.ts b/src/common/extractConvParamsFactory.ts
index cb16055..1507d6d 100644
--- a/src/common/extractConvParamsFactory.ts
+++ b/src/common/extractConvParamsFactory.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';
 
diff --git a/src/common/extractFCParamsFactory.ts b/src/common/extractFCParamsFactory.ts
index d92558a..55b14d0 100644
--- a/src/common/extractFCParamsFactory.ts
+++ b/src/common/extractFCParamsFactory.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ExtractWeightsFunction, FCParams, ParamMapping } from './types';
 
diff --git a/src/common/extractSeparableConvParamsFactory.ts b/src/common/extractSeparableConvParamsFactory.ts
index 5fda65f..1513468 100644
--- a/src/common/extractSeparableConvParamsFactory.ts
+++ b/src/common/extractSeparableConvParamsFactory.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';
 
diff --git a/src/common/fullyConnectedLayer.ts b/src/common/fullyConnectedLayer.ts
index 97b2b1e..df3b150 100644
--- a/src/common/fullyConnectedLayer.ts
+++ b/src/common/fullyConnectedLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { FCParams } from './types';
 
diff --git a/src/common/loadConvParamsFactory.ts b/src/common/loadConvParamsFactory.ts
index bebe61d..8910412 100644
--- a/src/common/loadConvParamsFactory.ts
+++ b/src/common/loadConvParamsFactory.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams } from './types';
 
diff --git a/src/common/types.ts b/src/common/types.ts
index 388a7d8..de6dab4 100644
--- a/src/common/types.ts
+++ b/src/common/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 export type ExtractWeightsFunction = (numWeights: number) => Float32Array
 
diff --git a/src/dom/extractFaceTensors.ts b/src/dom/extractFaceTensors.ts
index 711f6f9..9c62b7d 100644
--- a/src/dom/extractFaceTensors.ts
+++ b/src/dom/extractFaceTensors.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { Rect } from '../classes';
 import { FaceDetection } from '../classes/FaceDetection';
diff --git a/src/dom/imageTensorToCanvas.ts b/src/dom/imageTensorToCanvas.ts
index fb316d2..a7cef97 100644
--- a/src/dom/imageTensorToCanvas.ts
+++ b/src/dom/imageTensorToCanvas.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { env } from '../env';
 import { isTensor4D } from '../utils';
diff --git a/src/dom/loadWeightMap.ts b/src/dom/loadWeightMap.ts
index ae845be..1245c06 100644
--- a/src/dom/loadWeightMap.ts
+++ b/src/dom/loadWeightMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { getModelUris } from '../common/getModelUris';
 import { fetchJson } from './fetchJson';
diff --git a/src/dom/types.ts b/src/dom/types.ts
index a8dc061..ace427b 100644
--- a/src/dom/types.ts
+++ b/src/dom/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { NetInput } from './NetInput';
 
diff --git a/src/faceExpressionNet/FaceExpressionNet.ts b/src/faceExpressionNet/FaceExpressionNet.ts
index 91f1568..7220534 100644
--- a/src/faceExpressionNet/FaceExpressionNet.ts
+++ b/src/faceExpressionNet/FaceExpressionNet.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { NetInput, TNetInput, toNetInput } from '../dom';
 import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
diff --git a/src/faceFeatureExtractor/FaceFeatureExtractor.ts b/src/faceFeatureExtractor/FaceFeatureExtractor.ts
index 21f6d04..59b48e8 100644
--- a/src/faceFeatureExtractor/FaceFeatureExtractor.ts
+++ b/src/faceFeatureExtractor/FaceFeatureExtractor.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { NetInput, TNetInput, toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts b/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
index 9b47314..5e7eb9b 100644
--- a/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
+++ b/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { NetInput, TNetInput, toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/src/faceFeatureExtractor/denseBlock.ts b/src/faceFeatureExtractor/denseBlock.ts
index 8d20b31..f724977 100644
--- a/src/faceFeatureExtractor/denseBlock.ts
+++ b/src/faceFeatureExtractor/denseBlock.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams, SeparableConvParams } from '../common';
 import { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';
diff --git a/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts b/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
index 1d6a623..31b8943 100644
--- a/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
+++ b/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { disposeUnusedWeightTensors, ParamMapping } from '../common';
 import { loadParamsFactory } from './loadParamsFactory';
diff --git a/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts b/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
index ceb7e9d..02962c2 100644
--- a/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
+++ b/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { disposeUnusedWeightTensors, ParamMapping } from '../common';
 import { loadParamsFactory } from './loadParamsFactory';
diff --git a/src/faceFeatureExtractor/types.ts b/src/faceFeatureExtractor/types.ts
index 298a883..da17579 100644
--- a/src/faceFeatureExtractor/types.ts
+++ b/src/faceFeatureExtractor/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { NetInput, TNetInput } from '..';
 import { ConvParams, SeparableConvParams } from '../common';
diff --git a/src/faceLandmarkNet/FaceLandmark68NetBase.ts b/src/faceLandmarkNet/FaceLandmark68NetBase.ts
index b264a6d..b1a2601 100644
--- a/src/faceLandmarkNet/FaceLandmark68NetBase.ts
+++ b/src/faceLandmarkNet/FaceLandmark68NetBase.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { IDimensions, Point } from '../classes';
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
diff --git a/src/faceProcessor/FaceProcessor.ts b/src/faceProcessor/FaceProcessor.ts
index 5d1e49f..31f380d 100644
--- a/src/faceProcessor/FaceProcessor.ts
+++ b/src/faceProcessor/FaceProcessor.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
 import { NetInput } from '../dom';
diff --git a/src/faceProcessor/extractParamsFromWeigthMap.ts b/src/faceProcessor/extractParamsFromWeigthMap.ts
index 388decd..1a2bd7f 100644
--- a/src/faceProcessor/extractParamsFromWeigthMap.ts
+++ b/src/faceProcessor/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';
 import { NetParams } from './types';
diff --git a/src/faceProcessor/util.ts b/src/faceProcessor/util.ts
index 0b8924e..2f47ac2 100644
--- a/src/faceProcessor/util.ts
+++ b/src/faceProcessor/util.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 export function seperateWeightMaps(weightMap: tf.NamedTensorMap) {
 
diff --git a/src/faceRecognitionNet/FaceRecognitionNet.ts b/src/faceRecognitionNet/FaceRecognitionNet.ts
index f81923b..4bea31b 100644
--- a/src/faceRecognitionNet/FaceRecognitionNet.ts
+++ b/src/faceRecognitionNet/FaceRecognitionNet.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { NetInput, TNetInput, toNetInput } from '../dom';
 import { NeuralNetwork } from '../NeuralNetwork';
diff --git a/src/faceRecognitionNet/convLayer.ts b/src/faceRecognitionNet/convLayer.ts
index 7a797f9..a9934a2 100644
--- a/src/faceRecognitionNet/convLayer.ts
+++ b/src/faceRecognitionNet/convLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { scale } from './scaleLayer';
 import { ConvLayerParams } from './types';
diff --git a/src/faceRecognitionNet/extractParams.ts b/src/faceRecognitionNet/extractParams.ts
index 0e5d992..acf0108 100644
--- a/src/faceRecognitionNet/extractParams.ts
+++ b/src/faceRecognitionNet/extractParams.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';
 import { isFloat } from '../utils';
diff --git a/src/faceRecognitionNet/extractParamsFromWeigthMap.ts b/src/faceRecognitionNet/extractParamsFromWeigthMap.ts
index dbd904d..445adf0 100644
--- a/src/faceRecognitionNet/extractParamsFromWeigthMap.ts
+++ b/src/faceRecognitionNet/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';
 import { isTensor2D } from '../utils';
diff --git a/src/faceRecognitionNet/residualLayer.ts b/src/faceRecognitionNet/residualLayer.ts
index 3f94548..1c0f2ca 100644
--- a/src/faceRecognitionNet/residualLayer.ts
+++ b/src/faceRecognitionNet/residualLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { conv, convDown, convNoRelu } from './convLayer';
 import { ResidualLayerParams } from './types';
diff --git a/src/faceRecognitionNet/scaleLayer.ts b/src/faceRecognitionNet/scaleLayer.ts
index cbd0b13..551d373 100644
--- a/src/faceRecognitionNet/scaleLayer.ts
+++ b/src/faceRecognitionNet/scaleLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ScaleLayerParams } from './types';
 
diff --git a/src/faceRecognitionNet/types.ts b/src/faceRecognitionNet/types.ts
index 8d2a8d5..ff101b1 100644
--- a/src/faceRecognitionNet/types.ts
+++ b/src/faceRecognitionNet/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams } from '../common';
 
diff --git a/src/globalApi/DetectFaceLandmarksTasks.ts b/src/globalApi/DetectFaceLandmarksTasks.ts
index f424f24..53fcd4c 100644
--- a/src/globalApi/DetectFaceLandmarksTasks.ts
+++ b/src/globalApi/DetectFaceLandmarksTasks.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
 import { extractFaces, extractFaceTensors, TNetInput } from '../dom';
diff --git a/src/globalApi/PredictAgeAndGenderTask.ts b/src/globalApi/PredictAgeAndGenderTask.ts
index 8bc1898..94eafe7 100644
--- a/src/globalApi/PredictAgeAndGenderTask.ts
+++ b/src/globalApi/PredictAgeAndGenderTask.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { AgeAndGenderPrediction } from '../ageGenderNet/types';
 import { TNetInput } from '../dom';
diff --git a/src/globalApi/PredictFaceExpressionsTask.ts b/src/globalApi/PredictFaceExpressionsTask.ts
index 8f9d08a..9d26d6a 100644
--- a/src/globalApi/PredictFaceExpressionsTask.ts
+++ b/src/globalApi/PredictFaceExpressionsTask.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { TNetInput } from '../dom';
 import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';
diff --git a/src/globalApi/extractFacesAndComputeResults.ts b/src/globalApi/extractFacesAndComputeResults.ts
index 11e00bb..c16fb91 100644
--- a/src/globalApi/extractFacesAndComputeResults.ts
+++ b/src/globalApi/extractFacesAndComputeResults.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { FaceDetection } from '../classes/FaceDetection';
 import { extractFaces, extractFaceTensors, TNetInput } from '../dom';
diff --git a/src/index.ts b/src/index.ts
index 74285f2..5b7787f 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -1,8 +1,4 @@
-// import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
-// const tf = require('@tensorflow/tfjs-core/dist/tf-core.es2017.js');
-// require('@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.es2017.js');
-// require('@tensorflow/tfjs-backend-webgl/dist/tf-backend-webgl.es2017.js');
+import * as tf from '../dist/tfjs.esm.js';
 import * as draw from './draw';
 import * as utils from './utils';
 export { tf, draw, utils };
diff --git a/src/ops/normalize.ts b/src/ops/normalize.ts
index 36a4be0..85e0658 100644
--- a/src/ops/normalize.ts
+++ b/src/ops/normalize.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 export function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {
   return tf.tidy(() => {
diff --git a/src/ops/padToSquare.ts b/src/ops/padToSquare.ts
index 40cae19..e8b4717 100644
--- a/src/ops/padToSquare.ts
+++ b/src/ops/padToSquare.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 /**
  * Pads the smaller dimension of an image tensor with zeros, such that width === height.
diff --git a/src/ssdMobilenetv1/SsdMobilenetv1.ts b/src/ssdMobilenetv1/SsdMobilenetv1.ts
index 0b306b9..5bea321 100644
--- a/src/ssdMobilenetv1/SsdMobilenetv1.ts
+++ b/src/ssdMobilenetv1/SsdMobilenetv1.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { Rect } from '../classes';
 import { FaceDetection } from '../classes/FaceDetection';
diff --git a/src/ssdMobilenetv1/boxPredictionLayer.ts b/src/ssdMobilenetv1/boxPredictionLayer.ts
index 1e11da5..5c3c860 100644
--- a/src/ssdMobilenetv1/boxPredictionLayer.ts
+++ b/src/ssdMobilenetv1/boxPredictionLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { convLayer } from '../common';
 import { BoxPredictionParams } from './types';
diff --git a/src/ssdMobilenetv1/extractParams.ts b/src/ssdMobilenetv1/extractParams.ts
index 37fbd6b..6aff04d 100644
--- a/src/ssdMobilenetv1/extractParams.ts
+++ b/src/ssdMobilenetv1/extractParams.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';
 import { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
diff --git a/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts b/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
index e48f7cd..0c47aff 100644
--- a/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
+++ b/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';
 import { isTensor3D } from '../utils';
diff --git a/src/ssdMobilenetv1/mobileNetV1.ts b/src/ssdMobilenetv1/mobileNetV1.ts
index 42590aa..29ea9fa 100644
--- a/src/ssdMobilenetv1/mobileNetV1.ts
+++ b/src/ssdMobilenetv1/mobileNetV1.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { pointwiseConvLayer } from './pointwiseConvLayer';
 import { MobileNetV1 } from './types';
diff --git a/src/ssdMobilenetv1/outputLayer.ts b/src/ssdMobilenetv1/outputLayer.ts
index a575e76..bcbdbb1 100644
--- a/src/ssdMobilenetv1/outputLayer.ts
+++ b/src/ssdMobilenetv1/outputLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { OutputLayerParams } from './types';
 
diff --git a/src/ssdMobilenetv1/pointwiseConvLayer.ts b/src/ssdMobilenetv1/pointwiseConvLayer.ts
index 39b4fd1..e6e8de1 100644
--- a/src/ssdMobilenetv1/pointwiseConvLayer.ts
+++ b/src/ssdMobilenetv1/pointwiseConvLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { PointwiseConvParams } from './types';
 
diff --git a/src/ssdMobilenetv1/predictionLayer.ts b/src/ssdMobilenetv1/predictionLayer.ts
index a188cd1..0582585 100644
--- a/src/ssdMobilenetv1/predictionLayer.ts
+++ b/src/ssdMobilenetv1/predictionLayer.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { boxPredictionLayer } from './boxPredictionLayer';
 import { pointwiseConvLayer } from './pointwiseConvLayer';
diff --git a/src/ssdMobilenetv1/types.ts b/src/ssdMobilenetv1/types.ts
index 9446f1e..a23f028 100644
--- a/src/ssdMobilenetv1/types.ts
+++ b/src/ssdMobilenetv1/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams } from '../common';
 
diff --git a/src/tinyFaceDetector/TinyFaceDetector.ts b/src/tinyFaceDetector/TinyFaceDetector.ts
index 8fe7b5f..515e864 100644
--- a/src/tinyFaceDetector/TinyFaceDetector.ts
+++ b/src/tinyFaceDetector/TinyFaceDetector.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { FaceDetection, Point } from '../classes';
 import { ParamMapping } from '../common';
diff --git a/src/tinyYolov2/TinyYolov2.ts b/src/tinyYolov2/TinyYolov2.ts
index bb27901..a8a9489 100644
--- a/src/tinyYolov2/TinyYolov2.ts
+++ b/src/tinyYolov2/TinyYolov2.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { FaceDetection, Point } from '../classes';
 import { ParamMapping } from '../common/types';
diff --git a/src/tinyYolov2/TinyYolov2Base.ts b/src/tinyYolov2/TinyYolov2Base.ts
index cf7dc4f..dcba0dd 100644
--- a/src/tinyYolov2/TinyYolov2Base.ts
+++ b/src/tinyYolov2/TinyYolov2Base.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { BoundingBox } from '../classes/BoundingBox';
 import { Dimensions } from '../classes/Dimensions';
diff --git a/src/tinyYolov2/convWithBatchNorm.ts b/src/tinyYolov2/convWithBatchNorm.ts
index 3af80ec..bada40f 100644
--- a/src/tinyYolov2/convWithBatchNorm.ts
+++ b/src/tinyYolov2/convWithBatchNorm.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { leaky } from './leaky';
 import { ConvWithBatchNorm } from './types';
diff --git a/src/tinyYolov2/depthwiseSeparableConv.ts b/src/tinyYolov2/depthwiseSeparableConv.ts
index 337da2d..0bc2a3d 100644
--- a/src/tinyYolov2/depthwiseSeparableConv.ts
+++ b/src/tinyYolov2/depthwiseSeparableConv.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { SeparableConvParams } from '../common/types';
 import { leaky } from './leaky';
diff --git a/src/tinyYolov2/extractParams.ts b/src/tinyYolov2/extractParams.ts
index 983430d..ea0fa8f 100644
--- a/src/tinyYolov2/extractParams.ts
+++ b/src/tinyYolov2/extractParams.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { extractConvParamsFactory } from '../common';
 import { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';
diff --git a/src/tinyYolov2/extractParamsFromWeigthMap.ts b/src/tinyYolov2/extractParamsFromWeigthMap.ts
index 35f69c9..149cc8d 100644
--- a/src/tinyYolov2/extractParamsFromWeigthMap.ts
+++ b/src/tinyYolov2/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams } from '../common';
 import { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';
diff --git a/src/tinyYolov2/leaky.ts b/src/tinyYolov2/leaky.ts
index 80e6e99..c05e675 100644
--- a/src/tinyYolov2/leaky.ts
+++ b/src/tinyYolov2/leaky.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 export function leaky(x: tf.Tensor4D): tf.Tensor4D {
   return tf.tidy(() => {
diff --git a/src/tinyYolov2/types.ts b/src/tinyYolov2/types.ts
index 5e5ee76..cbc5f2e 100644
--- a/src/tinyYolov2/types.ts
+++ b/src/tinyYolov2/types.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams } from '../common';
 import { SeparableConvParams } from '../common/types';
diff --git a/src/utils/index.ts b/src/utils/index.ts
index b9a8ca2..d5d51fa 100644
--- a/src/utils/index.ts
+++ b/src/utils/index.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { Point } from '../classes';
 import { Dimensions, IDimensions } from '../classes/Dimensions';
diff --git a/src/xception/TinyXception.ts b/src/xception/TinyXception.ts
index 33280b1..45f1b29 100644
--- a/src/xception/TinyXception.ts
+++ b/src/xception/TinyXception.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import { ConvParams, depthwiseSeparableConv } from '../common';
 import { NetInput, TNetInput, toNetInput } from '../dom';
diff --git a/src/xception/extractParamsFromWeigthMap.ts b/src/xception/extractParamsFromWeigthMap.ts
index f00aff2..82863ed 100644
--- a/src/xception/extractParamsFromWeigthMap.ts
+++ b/src/xception/extractParamsFromWeigthMap.ts
@@ -1,4 +1,4 @@
-import * as tf from '@tensorflow/tfjs/dist/tf.es2017.js';
+import * as tf from '../../dist/tfjs.esm.js';
 
 import {
   disposeUnusedWeightTensors,